Path Planning and Controlled Crash Landing of a Quadcopter in case of a Rotor Failure

This paper presents a framework for controlled emergency landing of a quadcopter, experiencing a rotor failure, away from sensitive areas. A complete mathematical model capturing the dynamics of the system is presented that takes the asymmetrical aerodynamic load on the propellers into account. An equilibrium state of the system is calculated around which a linear time-invariant control strategy is developed to stabilize the system. By utilizing the proposed model, a specific configuration for a quadcopter is introduced that leads to the minimum power consumption during a yaw-rateresolved hovering after a rotor failure. Furthermore, given a 3D representation of the environment, an optimal flight trajectory towards a safe crash landing spot, while avoiding collision with obstacles, is developed using an RRT* approach. The cost function for determining the best landing spot consists of: (i) finding the safest landing spot with the largest clearance from the obstacles; and (ii) finding the most energy-efficient trajectory towards the landing spot. The performance of the proposed framework is tested via simulations.


A Bibliometric Study on Culture Research in International Business

National cultures and cultural differences provide a crucial component of the context of international business (IB) research. We conducted a bibliometric study of the articles published in seven leading IB journals, over a period of three decades, to analyze how “national culture” has been impacting in IB research. Co-citation mappings permit us to identify the ties binding works dealing with culture and cultural issues in IB. We identify two main clusters of research each comprising two sub-clusters, with Hofstede’s (1980) work setting much of the conceptual and empirical approach on culture-related studies. One main cluster entails works on the conceptualization of culture and its dimensions and other cluster on cultural distance. This conceptual framework captures the extant IB research incorporating culture-related concepts and influences.


Ensemble Algorithms in Reinforcement Learning

This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms.


Multiresolution Tree Networks for 3D Point Cloud Processing

We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions. This allows efficient feed-forward processing through 1D convolutions, coarse-to-fine analysis through a multi-grid architecture, and it leads to faster convergence and small memory footprint during training. The proposed treestructured encoders can be used to classify shapes and outperform existing pointbased architectures on shape classification benchmarks, while tree-structured decoders can be used for generating point clouds directly and they outperform existing approaches for image-to-shape inference tasks learned using the ShapeNet dataset. Our model also allows unsupervised learning of point-cloud based shapes by using a variational autoencoder, leading to higher-quality generated shapes.


An application of explicit model predictive control to electric power assisted steering systems

This paper presents explicit model predictive control (MPC) for electric power assisted steering (EPAS) systems. Explicit MPC has the ability to alleviate the computational burdens of MPC, which is an online optimization technique, using multi-parametric quadratic programming (mp-QP). EPAS systems are being used more frequently for passenger cars because of their advantages over hydraulic steering systems in terms of performance and cost. The main objective of EPAS systems is to provide a desired motor torque; therefore, to handle this problem, explicit MPC is employed owing to the ability of MPC to deal with constraints on controls and states and of explicit MPC to do the same work offline. This paper summarizes a formulation of explicit MPC and introduces a straight-line boost curve, which is used to determine desired motor torques. A mechanical model, motion equations and the state-space form of a column-type EPAS system are also described in this paper. An linear-quadratic regulator (LQR) controller is designed so that the explicit MPC controller could be compared against it. The parameter values are respectively varied to demonstrate the robustness of the proposed explicit MPC controller. The proposed control strategy shows better performance than that of the LQR as well as a reduction in the required computational complexity.


Neural Semantic Encoders

We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.


CCDN: Content-Centric Data Center Networks

Data center networks continually seek higher network performance to meet the ever increasing application demand. Recently, researchers are exploring the method to enhance the data center network performance by intelligent caching and increasing the access points for hot data chunks. Motivated by this, we come up with a simple yet useful caching mechanism for generic data centers, i.e., a server caches a data chunk after an application on it reads the chunk from the file system, and then uses the cached chunk to serve subsequent chunk requests from nearby servers. To turn the basic idea above into a practical system and address the challenges behind it, we design content-centric data center networks CCDNs, which exploits an innovative combination of content-based forwarding and location [Internet Protocol IP]-based forwarding in switches, to correctly locate the target server for a data chunk on a fully distributed basis. Furthermore, CCDN enhances traditional content-based forwarding to determine the nearest target server, and enhances traditional location IP-based forwarding to make high utilization of the precious memory space in switches. Extensive simulations based on real-world workloads and experiments on a test bed built with NetFPGA prototypes show that, even with a small portion of the server’s storage as cache e.g., 3% and with a modest content forwarding information base size e.g., 1000 entries in switches, CCDN can improve the average throughput to get data chunks by 43% compared with a pure Hadoop File System HDFS system in a real data center.


Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other machine learning techniques, is vulnerable to adversarial samples: inputs crafted to force a deep neural network (DNN) to provide adversary-selected outputs. Such attacks can seriously undermine the security of the system supported by the DNN, sometimes with devastating consequences. For example, autonomous vehicles can be crashed, illicit or illegal content can bypass content filters, or biometric authentication systems can be manipulated to allow improper access. In this work, we introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs. We analytically investigate the generalizability and robustness properties granted by the use of defensive distillation when training DNNs. We also empirically study the effectiveness of our defense mechanisms on two DNNs placed in adversarial settings. The study shows that defensive distillation can reduce effectiveness of sample creation from 95% to less than 0.5% on a studied DNN. Such dramatic gains can be explained by the fact that distillation leads gradients used in adversarial sample creation to be reduced by a factor of 1030. We also find that distillation increases the average minimum number of features that need to be modified to create adversarial samples by about 800% on one of the DNNs we tested.


The Limitations of Deep Learning in Adversarial Settings

Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the training phase of deep neural networks make them vulnerable to adversarial samples: inputs crafted by adversaries with the intent of causing deep neural networks to misclassify. In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs. In an application to computer vision, we show that our algorithms can reliably produce samples correctly classified by human subjects but misclassified in specific targets by a DNN with a 97% adversarial success rate while only modifying on average 4.02% of the input features per sample. We then evaluate the vulnerability of different sample classes to adversarial perturbations by defining a hardness measure. Finally, we describe preliminary work outlining defenses against adversarial samples by defining a predictive measure of distance between a benign input and a target classification.


TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Martı́n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng Google Research∗ Abstract


Intriguing properties of neural networks

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extent. We can cause the network to misclassify an image by applying a certain hardly perceptible perturbation, which is found by maximizing the network’s prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.


Millimeter wave cavity backed aperture coupled microstrip patch antenna

In this paper, a new single element broad band cavity backed multi-layer microstrip patch antenna is presented. This antenna is designed to be used in 79 GHz MIMO radar applications, and is fabricated with advanced high resolution multi-layer PCB technology. It has a wide bandwidth of 11.43 GHz, which is 14.5% of the center frequency. The beam widths in both E and H-planes are very wide, and are 144 degrees and 80 degrees respectively. Also, the antenna gain and efficiency are respectively 4.7 dB and 87%. Furthermore, measurements have been done, and show a good agreement with simulations.


ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA

This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.


Feature integration analysis of bag-of-features model for image retrieval

One of the biggest challenges in content based image retrieval is to solve the problem of “semantic gaps” between low-level features and high-level semantic concepts. In this paper, we aim to investigate various combinations of mid-level features to build an effective image retrieval system based on the bag-offeatures (BoF) model. Specifically, we study two ways of integrating the SIFT and LBP descriptors, HOG and LBP descriptors, respectively. Based on the qualitative and quantitative evaluations on two benchmark datasets, we show that the integrations of these features yield complementary and substantial improvement on image retrieval even with noisy background and ambiguous objects. Two integration models are proposed: the patch-based integration and image-based integration. By using a weighted K-means clustering algorithm, the image-based SIFT-LBP integration achieves the best performance on the given benchmark problems comparing to the existing algorithms. & 2013 Elsevier B.V. All rights reserved.


Emotion Regulation Ability 1 Running head : EMOTION REGULATION The Ability to Regulate Emotion is Associated with Greater Well-Being , Income , and Socioeconomic Status

Are people who are best able to implement strategies to regulate their emotional expressive behavior happier and more successful than their counterparts? Although past research has examined individual variation in knowledge of the most effective emotion regulation strategies, little is known about how individual differences in the ability to actually implement these strategies, as assessed objectively in the laboratory, is associated with external criteria. In two studies, we examined how individual variation in the ability to modify emotional expressive behavior in response to evocative stimuli is related to well-being and financial success. Study 1 showed that individuals who can best suppress their emotional reaction to an acoustic startle are happiest with their lives. Study 2 showed that individuals who can best amplify their emotional reaction to a disgust-eliciting movie are happiest with their lives and have the highest disposable income and socioeconomic status. Thus, being able to implement emotion regulation strategies in the laboratory is closely linked to well-being and financial success. Emotion Regulation Ability 3 The Ability to Regulate Emotion is Associated with Greater Well-Being, Income, and Socioeconomic Status Individual variation in cognitive abilities, such as language and mathematics, has been shown to relate strongly to a number of important life criteria, including performance at school and at work (Kunzel, Hezlett, & Ones, 2004; Schmidt & Hunter, 1998). Research in recent years has suggested that there is also important variation among individuals in emotional abilities (see Mayer, Roberts, & Barsade, 2008; Mayer, Salovey, & Caruso, 2008, for reviews). In particular, the ability to regulate emotions reflects variation in how well people adjust emotional responses to meet current situational demands (Gross & Thompson, 2007; Salovey & Mayer, 1990). Equipped with this ability, individuals can aptly modify which emotions they have, when they have them, and how they experience and express them (Gross, 1998). This ability is arguably one of the most critical elements of our emotion repertoire, and it is the focus of the present research. Past research has begun to examine whether individual variation in the ability to regulate emotions is associated with various criteria. This research has found that variation in knowledge of how to best regulate emotions – whether people know the rules of emotion regulation – is associated with well-being, close social relationships, high grades in school, and high job performance (e.g., Côté & Miners, 2006; Lopes, Salovey, Côté, & Beers, 2005; MacCann & Roberts, 2008). The measures used in these studies assess the degree to which people know how to best manage emotions. Specifically, they reflect how closely respondents’ judgments of how to best regulate emotion in hypothetical scenarios match the judgments of experts. For instance, the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey, & Caruso, 2002) asks respondents to rate the effectiveness of a series of strategies to manage emotions in several hypothetical scenarios, and their responses are compared to those provided by expert emotion researchers. Emotion Regulation Ability 4 Notwithstanding the importance of knowing how to best manage emotions, knowledge does not fully represent the domain of emotion regulation ability. People who know the best strategies may not implement them well. The distinction between knowledge and the ability to implement is established in the larger literature on intelligence (cf. Ackerman, 1996), and it is also theoretically useful to describe emotional abilities. For example, a customer service agent who knows that cognitively reframing an interaction with a difficult customer is the best strategy may not implement that strategy well during the interaction. Thus, to understand fully how emotion regulation ability is associated with criteria such as well-being and financial success, researchers must also examine the ability to implement strategies to regulate emotions – whether people can actually operate the machinery of emotion regulation. Several of the measures used in studies of the relationship between emotion regulation and other criteria do not assess actual ability to implement emotion regulation strategies. For example, the MSCEIT (Mayer et al., 2002) does not ask respondents to implement the strategy that they believe best addresses the issues depicted in the scenarios. Recent advances in affective science, however, provide tools to objectively assess the ability to implement emotion regulation strategies (Gross & Levenson, 1993; Hagemann, Levenson, & Gross, 2006; Jackson, Malmstadt, Larson, & Davidson, 2000; Kunzmann, Kupperbusch, & Levenson, 2005). In these laboratory paradigms, individuals receive specific instructions about how to regulate their emotions (e.g., reduce the intensity of their emotional expressive behaviors) when encountering emotional stimuli, such as loud noises or emotionally evocative film clips. Success at implementing the emotion regulation strategy can be measured objectively, for example, by coding how much respondents change their emotional expressive behavior when being instructed to do so. Several studies have used this paradigm to examine how regulating emotions is associated with cognitive task performance (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Emotion Regulation Ability 5 Bonanno, Papa, Lalande, Westphal, & Coifman, 2004; Schmeichel, Demaree, Robinson, & Pu, 2006), the activation of neural systems (Beauregard, Levesque, & Bourgouin, 2001; Oschner, Bunge, Gross, & Gabrieli, 2002), and emotion experience, emotional expressive behavior, and autonomic physiology (Demaree, Schmeichel, Robinson, Pu, Everhart, & Berntson, 2006; Giuliani, McCrae, & Gross, 2008; Gross & Levenson, 1993, 1997; Hagemann et al., 2006). This paradigm has also been used as an individual difference measure to test how the ability to implement emotion regulation strategies is associated with age (Kunzmann et al., 2005; Scheibe & Blanchard-Fields, 2009), working memory (Schmeichel, Volokhov, & Demaree, 2008), and executive function (Gyurak, Goodkind, Madan, Kramer, Miller, & Levenson, 2008). In addition, one study employed this paradigm to assess people’s flexibility in using different emotion regulation strategies depending on the situation, showing that flexibility is associated with lower distress after a traumatic event (Bonanno, Papa, Lalande, Westphal, & Coifman, 2004). Thus, this body of research supports the utility of these laboratory paradigms for assessing individual variation in the ability to implement emotion regulation strategies and the correlates of this ability. In this report, we present the results of two studies that examine whether individual variation in the ability to implement strategies to regulate emotions is associated with well-being and financial success and, if so, in what direction. Most people regulate their emotions daily, and more than half the time, they do so by modifying the expression of emotions in their face, voice, and posture (Gross, Richards, & John, 2006). Given the frequency with which we regulate our emotional expressive behavior, it is reasonable to expect that the individual’s ability in this realm would exhibit important associations with other constructs. The regulation of visible expressive behavior encompasses both up-regulation (amplifying emotional expressive behavior) and downEmotion Regulation Ability 6 regulation (reducing emotional expressive behavior). We considered the association of both with our criteria. We now turn to our theoretical development. A review of the existing literature suggests the possibility of both a positive and a negative association between the ability to implement emotion regulation strategies assessed in the laboratory and well-being and financial success. Furthermore, because we do not test the direction of causality in our studies, we consider theoretical arguments for both causal directions of associations, reviewing literatures that suggest that emotion regulation ability has consequences for well-being and financial success (both positive and negative), and also that well-being and financial success have consequences for emotion regulation ability (both positive and negative). The Ability to Regulate Emotional Behavior and Well-Being and Financial Success: Positive Associations In this section, we present theoretical arguments suggesting that the ability to regulate emotion and well-being and financial success are positively associated. We first describe why high emotion regulation ability may help people become happier and garner more financial resources, and then we examine whether happiness and financial resources may help people develop better abilities to regulate their emotions. Why Would Emotion Regulation Ability Increase Well-Being and Financial Success? Philosophers have argued that rational thought and a happy life requires the ability to rein in on emotional impulses (Aristotle, 1884; Solomon, 1993). The ability to modify emotional expressive behavior effectively may help people adapt flexibly to situational demands. Equipped with this ability, individuals might be more successful in communicating attitudes, goals, and intentions that are appropriate in various situations (Keltner & Haidt, 1999) and that might be Emotion Regulation Ability 7 rewarded and fulfilled. The ability to adapt successfully to situational demands then could be associated with various indicators of well-being and success. At a more micro-level, modifying emotional expressive behavior effectively may help people conform to display rules about who can show which emotions to whom and when they can do so (Friesen, 1972). People often attain rewards for conforming to displays rules in various settings. For instance, employees who conform to display rules at wo


Optimizing Space Amplification in RocksDB

RocksDB is an embedded, high-performance, persistent keyvalue storage engine developed at Facebook. Much of our current focus in developing and configuring RocksDB is to give priority to resource efficiency instead of giving priority to the more standard performance metrics, such as response time latency and throughput, as long as the latter remain acceptable. In particular, we optimize space efficiency while ensuring read and write latencies meet service-level requirements for the intended workloads. This choice is motivated by the fact that storage space is most often the primary bottleneck when using Flash SSDs under typical production workloads at Facebook. RocksDB uses log-structured merge trees to obtain significant space efficiency and better write throughput while achieving acceptable read performance. This paper describes methods we used to reduce storage usage in RocksDB. We discuss how we are able to trade off storage efficiency and CPU overhead, as well as read and write amplification. Based on experimental evaluations of MySQL with RocksDB as the embedded storage engine (using TPC-C and LinkBench benchmarks) and based on measurements taken from production databases, we show that RocksDB uses less than half the storage that InnoDB uses, yet performs well and in many cases even better than the B-tree-based InnoDB storage engine. To the best of our knowledge, this is the first time a Log-structured merge treebased storage engine has shown competitive performance when running OLTP workloads at large scale.


Ultra-Reliable and Low-Latency 5 G Communication

Machine-to-machine communication, M2M, will make up a large portion of the new types of services and use cases that the fifth generation (5G) systems will address. On the one hand, 5G will connect a large number of low-cost and low-energy devices in the context of the Internet of things; on the other hand it will enable critical machine type communication use cases, such as smart factory, automotive, energy, and e-health – which require communication with very high reliability and availability, as well as very low end-to-end latency. In this paper, we will discuss the requirements, enablers and challenges to support these emerging mission-critical 5G use cases. Keywords— 5G, NR, M2M, MTC, URLLC, Tactile Internet, Reliability, Latency.


A GLOBAL MEASURE OF PERCEIVED STRESS

Aim: To evaluate validity of the Greek version of a global measure of perceived stress PSS−14 (Perceived Stress Scale – 14 item). Materials and Methods: The original PSS−14 (theoretical range 0−56) was translated into Greek and then back-translated. One hundred men and women (39±10 years old, 40 men) participated in the validation process. Firstly, participants completed the Greek PSS−14 and, then they were interviewed by a psychologist specializing in stress management. Cronbach’s alpha (α) evaluated internal consistency of the measurement, whereas Kendall’s tau-b and Bland & Altman methods assessed consistency with the clinical evaluation. Exploratory and Confirmatory Factor analyses were conducted to reveal hidden factors within the data and to confirm the two-dimensional character of the scale. Results: Mean (SD) PSS−14 score was 25(7.9). Strong internal consistency (Cronbach’s α = 0.847) as well as moderate-to-good concordance between clinical assessment and PSS−14 (Kendall’s tau-b = 0.43, p<0.01) were observed. Two factors were extracted. Factor one explained 34.7% of variability and was heavily laden by positive items, and factor two that explained 10.6% of the variability by negative items. Confirmatory factor analysis revealed that the model with 2 factors had chi-square equal to 241.23 (p<0.001), absolute fix indexes were good (i.e. GFI=0.733, AGFI=0.529), and incremental fix indexes were also adequate (i.e. NFI=0.89 and CFI=0.92). Conclusion: The developed Greek version of PSS−14 seems to be a valid instrument for the assessment of perceived stress in the Greek adult population living in urban areas; a finding that supports its local use in research settings as an evaluation tool measuring perceived stress, mainly as a risk factor but without diagnostic properties.


Polysemy: Theoretical and Computational Approaches Yael Ravin and Claudia Leacock (editors) (IBM T. J. Watson Research Center and Educational Testing Services) New York: Oxford University Press, 2000, xi+227 pp; hardbound, ISBN 0-19-823842-8, $74.00, £45.00; paperbound, ISBN 0-19-925086-3, $21.95, £

As the editors of this volume remind us, polysemy has been a vexing issue for the understanding of language since antiquity. For half a century, it has been a major bottleneck for natural language processing. It contributed to the failure of early machine translation research (remember Bar-Hillel’s famous pen and box example) and is still plaguing most natural language processing and information retrieval applications. A recent issue of this journal described the state of the art in automatic sense disambiguation (Ide and Véronis 1998), and Senseval system competitions have revealed the immense difficulty of the task (http://www.sle.sharp.co.uk/senseval2). However, no significant progress can be made on the computational aspects of polysemy without serious advances in theoretical issues. At the same time, theoretical work can be fostered by computational results and problems, and language-processing applications can provide a unique test bed for theories. It was therefore an excellent idea to gather both theoretical and applied contributions in the same book. Yael Ravin and Claudia Leacock are well-known names to those who work on the theoretical and computational aspects of word meaning. In this volume, they bring together a collection of essays from leading researchers in the field. As far as I can tell, these essays are not reprints or expanded versions of conference papers, as is often the case for edited works; instead, they seem to have been specially commissioned for the purposes of this book, which makes it even more exciting to examine. The book is composed of 11 chapters. It is not formally divided into parts, but chapters dealing more specifically with the computational aspects of polysemy are grouped together at the end (and constitute about one-third of the volume). Chapter 1 is an overview written by the volume editors. Yael Ravin and Claudia Leacock survey the main theories of meaning and their treatment of polysemy. These include the classical Aristotelian approach revived by Katz and Fodor (1963); Rosch’s (1977) prototypical approach, which has its roots in Wittgenstein’s Philosophical Investigations (1953); and the relational approach recently exemplified by WordNet (Fellbaum 1998), which (although the authors do not mention it) can be traced back to Peirce’s (1931–1958) and Selz’s (1913, 1922) graphs and which gained popularity with Quillian’s (1968) semantic networks. In the course of this overview, Ravin and Leacock put the individual chapters into perspective by relating them to the various theories.


Effects of a Dynamic Warm-Up, Static Stretching or Static Stretching with Tendon Vibration on Vertical Jump Performance and EMG Responses

The purpose of this study was to investigate the short-term effects of static stretching, with vibration given directly over Achilles tendon, on electro-myographic (EMG) responses and vertical jump (VJ) performances. Fifteen male, college athletes voluntarily participated in this study (n=15; age: 22±4 years old; body height: 181±10 cm; body mass: 74±11 kg). All stages were completed within 90 minutes for each participant. Tendon vibration bouts lasted 30 seconds at 50 Hz for each volunteer. EMG analysis for peripheral silent period, H-reflex, H-reflex threshold, T-reflex and H/M ratio were completed for each experimental phases. EMG data were obtained from the soleus muscle in response to electro stimulation on the popliteal post tibial nerve. As expected, the dynamic warm-up (DW) increased VJ performances (p=0.004). Increased VJ performances after the DW were not statistically substantiated by the EMG findings. In addition, EMG results did not indicate that either static stretching (SS) or tendon vibration combined with static stretching (TVSS) had any detrimental or facilitation effect on vertical jump performances. In conclusion, using TVSS does not seem to facilitate warm-up effects before explosive performance.


Relation between language experiences in preschool classrooms and children's kindergarten and fourth-grade language and reading abilities.

Indirect effects of preschool classroom indexes of teacher talk were tested on fourth-grade outcomes for 57 students from low-income families in a longitudinal study of classroom and home influences on reading. Detailed observations and audiotaped teacher and child language data were coded to measure content and quantity of verbal interactions in preschool classrooms. Preschool teachers' use of sophisticated vocabulary during free play predicted fourth-grade reading comprehension and word recognition (mean age=9; 7), with effects mediated by kindergarten child language measures (mean age=5; 6). In large group preschool settings, teachers' attention-getting utterances were directly related to later comprehension. Preschool teachers' correcting utterances and analytic talk about books, and early support in the home for literacy predicted fourth-grade vocabulary, as mediated by kindergarten receptive vocabulary.


Static secure page allocation for light-weight dynamic information flow tracking

Dynamic information flow tracking (DIFT) is an effective security countermeasure for both low-level memory corruptions and high-level semantic attacks. However, many software approaches suffer large performance degradation, and hardware approaches have high logic and storage overhead. We propose a flexible and light-weight hardware/software co-design approach to perform DIFT based on secure page allocation. Instead of associating every data with a taint tag, we aggregate data according to their taints, i.e., putting data with different attributes in separate memory pages. Our approach is a compiler-aided process with architecture support. The implementation and analysis show that the memory overhead is little, and our approach can protect critical information, including return address, indirect jump address, and system call IDs, from being overwritten by malicious users.


A concept for automated construction progress monitoring using BIM-based geometric constraints and photogrammetric point clouds

On-site progress monitoring is essential for keeping track of the ongoing work on construction sites. Currently, this task is a manual, time-consuming activity. The research presented here, describes a concept for an automated comparison of the actual state of construction with the planned state for the early detection of deviations in the construction process. The actual state of the construction site is detected by photogrammetric surveys. From these recordings, dense point clouds are generated by the fusion of disparity maps created with semi-global-matching (SGM). These are matched against the target state provided by a 4D Building Information Model (BIM). For matching the point cloud and the BIM, the distances between individual points of the cloud and a component’s surface are aggregated using a regular cell grid. For each cell, the degree of coverage is determined. Based on this, a confidence value is computed which serves as basis for the existence decision concerning the respective component. Additionally, processand dependency-relations are included to further enhance the detection process. Experimental results from a real-world case study are presented and discussed.


Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). GECCO ’18 Companion, July 15--19, 2018, Kyoto, Japan © 2018 Copyright held by the owner/author(s). 978-1-4503-57647/18/07...$15.00 DOI https://doi.org/10.1145/3205651.3207859


Serotonin alterations in anorexia and bulimia nervosa: New insights from imaging studies

Anorexia nervosa (AN) and bulimia nervosa (BN) are related disorders with relatively homogenous presentations such as age of onset and gender distribution. In addition, they share symptoms, such as extremes of food consumption, body image distortion, anxiety and obsessions, and ego-syntonic neglect, raises the possibility that these symptoms reflect disturbed brain function that contributes to the pathophysiology of this illness. Recent brain imaging studies have identified altered activity in frontal, cingulate, temporal, and parietal cortical regions in AN and BN. Importantly, such disturbances are present when subjects are ill and persist after recovery, suggesting that these may be traits that are independent of the state of the illness. Emerging data point to a dysregulation of serotonin pathways in cortical and limbic structures that may be related to anxiety, behavioral inhibition, and body image distortions. In specific, recent studies using PET with serotonin specific radioligands implicate alterations of 5-HT1A and 5-HT2A receptors and the 5-HT transporter. Alterations of these circuits may affect mood and impulse control as well as the motivating and hedonic aspects of feeding behavior. Such imaging studies may offer insights into new pharmacology and psychotherapy approaches.


Thunderstrike: EFI firmware bootkits for Apple MacBooks

There are several flaws in Apple's MacBook firmware security that allows untrusted modifications to be written to the SPI Flash boot ROM of these laptops. This capability represents a new class of persistent firmware rootkits, or 'bootkits', for the popular Apple MacBook product line. Stealthy bootkits can conceal themselves from detection and prevent software attempts to remove them. Malicious modifications to the boot ROM are able to survive re-installation of the operating system and even hard-drive replacement. Additionally, the malware can install a copy of itself onto other Thunderbolt devices' Option ROMs as a means to spread virally across air-gap security perimeters. Apple has fixed some of these flaws as part of CVE 2014-4498, but there is no easy solution to this class of vulnerability, since the MacBook lacks trusted hardware to perform cryptographic validation of the firmware at boot time.


High-Q MEMS Resonators for Laser Beam Scanning Displays

This paper reports on design, fabrication and characterization of high-Q MEMS resonators to be used in optical applications like laser displays and LIDAR range sensors. Stacked vertical comb drives for electrostatic actuation of single-axis scanners and biaxial MEMS mirrors were realized in a dual layer polysilicon SOI process. High Q-factors up to 145,000 have been achieved applying wafer level vacuum packaging technology including deposition of titanium thin film getters. The effective reduction of gas damping allows the MEMS actuator to achieve large amplitudes at high oscillation frequencies while driving voltage and power consumption can be minimized. Exemplarily shown is a micro scanner that achieves a total optical scan angle of 86 degrees at a resonant frequency of 30.8 kHz, which fulfills the requirements for HD720 resolution. Furthermore, results of a new wafer based glass-forming technology for fabrication of three dimensionally shaped glass lids with tilted optical windows are presented.


Supplementary material for the paper : Discriminative Correlation Filter with Channel and Spatial Reliability DCF-CSR

This is the supplementary material for the paper ”Discriminative Correlation Filter with Channel and Spatial Reliability” submitted to the CVPR 2017. Due to spatial constraints, parts not crucial for understanding the DCF-CSR tracker formulation, but helpful for gaining insights, were moved here. 1. Derivation of the augmented Lagrangian minimizer This section provides the complete derivation of the closed-form solutions for the relations (9,10) in the submitted paper [3]. The augmented Lagrangian from Equation (5) in [3] is L(ĥc,h, l̂) = ‖ĥc diag(f̂)− ĝ‖ + λ 2 ‖hm‖ + (1) [̂l(ĥc − ĥm) + l̂(ĥc − ĥm)] + μ‖ĥc − ĥm‖, with hm = (m h). For the purposes of derivation we will rewrite (1) into a fully vectorized form L(ĥc,h, l̂) = ‖ĥc diag(f̂)− ĝ‖ + λ 2 ‖hm‖+ (2) [̂ l(ĥc − √ DFMh) + l̂(ĥc − √ DFMh) ] + μ‖ĥc − √ DFMh‖, where F denotes D × D orthonormal matrix of Fourier coefficients, such that the Fourier transform is defined as x̂ = F(x) = √ DFx and M = diag(m). For clearer representation we denote the four terms in the summation (2) as L(ĥc,h, l̂) = L1 + L2 + L3 + L4, (3) where L1 = ( ĥc diag(f̂)− ĝ )( ĥc diag(f̂)− ĝ )T , (4)


Efficient Energy-Optimal Routing for Electric Vehicles

Traditionally routing has focused on finding shortest paths in networks with positive, static edge costs representing the distance between two nodes. Energy-optimal routing for electric vehicles creates novel algorithmic challenges, as simply understanding edge costs as energy values and applying standard algorithms does not work. First, edge costs can be negative due to recuperation, excluding Dijkstra-like algorithms. Second, edge costs may depend on parameters such as vehicle weight only known at query time, ruling out existing preprocessing techniques. Third, considering battery capacity limitations implies that the cost of a path is no longer just the sum of its edge costs. This paper shows how these challenges can be met within the framework of A* search. We show how the specific domain gives rise to a consistent heuristic function yielding an O(n) routing algorithm. Moreover, we show how battery constraints can be treated by dynamically adapting edge costs and hence can be handled in the same way as parameters given at query time, without increasing run-time complexity. Experimental results with real road networks and vehicle data demonstrate the advantages of our solution.


Matching RGB Images to CAD Models for Object Pose Estimation

We propose a novel method for 3D object pose estimation in RGB images, which does not require pose annotations of objects in images in the training stage. We tackle the pose estimation problem by learning how to establish correspondences between RGB images and rendered depth images of CAD models. During training, our approach only requires textureless CAD models and aligned RGB-D frames of a subset of object instances, without explicitly requiring pose annotations for the RGB images. We employ a deep quadruplet convolutional neural network for joint learning of suitable keypoints and their associated descriptors in pairs of rendered depth images which can be matched across modalities with aligned RGB-D views. During testing, keypoints are extracted from a query RGB image and matched to keypoints extracted from rendered depth images, followed by establishing 2D-3D correspondences. The object’s pose is then estimated using the RANSAC and PnP algorithms. We conduct experiments on the recently introduced Pix3D [33] dataset and demonstrate the efficacy of our proposed approach in object pose estimation as well as generalization to object instances not seen during training.


Field-weakening Control Algorithm for Interior Permanent Magnet Synchronous Motor Based on Space-Vector Modulation Technique

We investigate the implementation of field-weakening control for interior permanent magnet synchronous motor (IPMSM), and analyze the algorithm of field-weakening control in d-q axes. To deal with the problem that the dc-link voltage is not fully utilized when the voltage regulation is employed, we propose a new field-weakening scheme based on the space-vector modulation technique. The duty-time of zero-voltage vector is used as the feedback signal to decide the switching of fieldweakening. To avoid the regulation lag in the q-axis component, we apply the lead-angle control during the field-weakening progress. The proposed scheme is validated with Matlab/Simulink tool. Simulation results show that the scheme is feasible. It not only improves the utilization ratio of the space-vector-pulse-width-modulation (SVPWM) inverter, but also achieves a smooth transition between the constant-torque mode below the base-speed and the constant-power mode above the basespeed.


An Image Matching Algorithm Based on SIFT and Improved LTP

SIFT is one of the most robust and widely used image matching algorithms based on local features. But the key-points descriptor of SIFT algorithm have 128 dimensions. Aiming to the problem of its high dimension and complexity, a novel image matching algorithm is proposed. The descriptors of SIFT key-points are constructed by the rotation invariant LTP, city-block distance is also employed to reduce calculation of key-points matching. The experiment is achieved through different lighting, blur changes and rotation of images, the results show that this method can reduce the processing time and raise image matching efficiency.


An Over-110-GHz-Bandwidth 2:1 Analog Multiplexer in 0.25-μm InP DHBT Technology

This paper presents an over-110-GHz-bandwidth 2:1 analog multiplexer (AMUX) for ultra-broadband digital-to-analog (D/A) conversion subsystems. The AMUX was designed and fabricated by using newly developed $\pmb{0.25-\mu \mathrm{m}}$ -emitter-width InP double heterojunction bipolar transistors (DHBTs), which have a peak $\pmb{f_{\mathrm{T}}}$ and $\pmb{ f\displaystyle \max}$ of 460 and 480 GHz, respectively. The AMUX IC consists of lumped building blocks, including data-input linear buffers, a clock-input limiting buffer, an AMUX core, and an output linear buffer. The measured 3-dB bandwidth for data and clock paths are both over 110 GHz. In addition, it measures and obtains time-domain large-signal sampling operations of up to 180 GS/s. A 224-Gb/s (112-GBaud) four-level pulse-amplitude modulation (PAM4) signal was successfully generated by using this AMUX. To the best of our knowledge, this AMUX IC has the broadest bandwidth and the fastest sampling rate compared with any other previously reported AMUXes.


Field-stop layer optimization for 1200V FS IGBT operating at 200˚C

This paper is concerned with design considerations for enabling the operation of Field-Stop Insulated Gate Bipolar Transistors (FS IGBTs) at 200 C. It is found that through a careful optimization of the Field-Stop layer doping profile the device has a low leakage current and delivers a favorable tradeoff between the on-state voltage (Von) and turn-off loss (Eoff). An investigation of the adverse effects of increasing the junction temperature on the temperature-dependent properties of the FS IGBTs is also discussed herein.


Automatic indexing: an approach using an index term corpus and combining linguistic and statistical methods

This thesis discusses the problems and the methods of finding relevant information in large collections of documents. The contribution of this thesis to this problem is to develop better content analysis methods which can be used to describe document content with


Theory of Outlier Ensembles

Outlier detection is an unsupervised problem, in which labels are not available with data records [2]. As a result, it is generally more challenging to design ensemble analysis algorithms for outlier detection. In particular, methods that require the use of labels in intermediate steps of the algorithm cannot be generalized to outlier detection. For example, in the case of boosting, the classifier algorithm needs to be evaluated in the intermediate steps of the algorithm with the use of training-data labels. Such methods are generally not possible in the case of outlier analysis. As discussed in [1], there are unique reasons why ensemble analysis is generally more difficult in the case of outlier analysis as compared to classification. In spite of the unsupervised nature of outlier ensemble analysis, we show that the theoretical foundations of outlier analysis and classification are surprisingly similar. A number of useful discussions on the theory of classification ensembles may be found in [27, 29, 33]. Further explanations on the use of the bias-variance decomposition in different types of classifiers such as neural networks, support vector machines, and rule-based classifiers are discussed in [17, 30, 31]. It is noteworthy that the biasvariance decomposition is often used in customized ways for different types of base classifiers and combination methods; this general principle is also true in outlier detection. Several arguments have recently been proposed on the theory explaining the accuracy improvements of outlier ensembles. In some cases, incorrect new arguments (such as those in [32]) are proposed to justify experimental results that can be explained by well-known ideas, and an artificial distinction is made between the theory of classification ensembles and outlier ensembles. A recent paper [4]


The Usability of Open Source Software

Twidale Open source communities have successfully developed a great deal of software although most computer users only use proprietary applications. The usability of open source software is often regarded as one reason for this limited distribution. In this paper we review the existing evidence of the usability of open source software and discuss how the characteristics of open source development influence usability. We describe how existing human-computer interaction techniques can be used to leverage distributed networked communities, of developers and users, to address issues of usability.


Detecting adversarial advertisements in the wild

In a large online advertising system, adversaries may attempt to profit from the creation of low quality or harmful advertisements. In this paper, we present a large scale data mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. Because both false positives and false negatives have high cost, our deployed system uses a tiered strategy combining automated and semi-automated methods to ensure reliable classification. We also employ strategies to address the challenges of learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge, and independently assessing the effectiveness of our system.


Social coding in GitHub: transparency and collaboration in an open software repository

Social applications on the web let users track and follow the activities of a large number of others regardless of location or affiliation. There is a potential for this transparency to radically improve collaboration and learning in complex knowledge-based activities. Based on a series of in-depth interviews with central and peripheral GitHub users, we examined the value of transparency for large-scale distributed collaborations and communities of practice. We find that people make a surprisingly rich set of social inferences from the networked activity information in GitHub, such as inferring someone else's technical goals and vision when they edit code, or guessing which of several similar projects has the best chance of thriving in the long term. Users combine these inferences into effective strategies for coordinating work, advancing technical skills and managing their reputation.


Searching for build debt: Experiences managing technical debt at Google

With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators, or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed.


Counterfactual reasoning and learning systems: the example of computational advertising

This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.


A Fully Integrated Reconfigurable Self-Startup RF Energy-Harvesting System With Storage Capability

This paper introduces a fully integrated RF energy-harvesting system. The system can simultaneously deliver the current demanded by external dc loads and store the extra energy in external capacitors, during periods of extra output power. The design is fabricated in 0.18- $\mu \text{m}$  CMOS technology, and the active chip area is 1.08 mm2. The proposed self-startup system is reconfigurable with an integrated LC matching network, an RF rectifier, and a power management/controller unit, which consumes 66–157 nW. The required clock generation and the voltage reference circuit are integrated on the same chip. Duty cycle control is used to operate for the low input power that cannot provide the demanded output power. Moreover, the number of stages of the RF rectifier is reconfigurable to increase the efficiency of the available output power. For high available power, a secondary path is activated to charge an external energy storage element. The measured RF input power sensitivity is −14.8 dBm at a 1-V dc output.


HDTLR: A CNN based Hierarchical Detector for Traffic Lights

Reliable traffic light detection is one crucial key component for autonomous driving in urban areas. This includes the extraction of direction arrows contained within the traffic lights as an autonomous car will need this information for selecting the traffic light corresponding to its current lane. Current state of the art traffic light detection systems are not able to provide such information. Within this work we present a hierarchical traffic light detection algorithm, which is able to detect traffic lights and determine their state and contained direction information within one CNN forward pass. This Hierarchical DeepTLR (HDTLR) outperforms current state of the art traffic light detection algorithms in state aware detection and can detect traffic lights with direction information down to a size of 4 pixel in width at a frequency of 12 frames per second.


Implementation of behavior change techniques in mobile applications for physical activity.

BACKGROUND
Mobile applications (apps) for physical activity are popular and hold promise for promoting behavior change and reducing non-communicable disease risk. App marketing materials describe a limited number of behavior change techniques (BCTs), but apps may include unmarketed BCTs, which are important as well.

PURPOSE
To characterize the extent to which BCTs have been implemented in apps from a systematic user inspection of apps.

METHODS
Top-ranked physical activity apps (N=100) were identified in November 2013 and analyzed in 2014. BCTs were coded using a contemporary taxonomy following a user inspection of apps.

RESULTS
Users identified an average of 6.6 BCTs per app and most BCTs in the taxonomy were not represented in any apps. The most common BCTs involved providing social support, information about others' approval, instructions on how to perform a behavior, demonstrations of the behavior, and feedback on the behavior. A latent class analysis of BCT configurations revealed that apps focused on providing support and feedback as well as support and education.

CONCLUSIONS
Contemporary physical activity apps have implemented a limited number of BCTs and have favored BCTs with a modest evidence base over others with more established evidence of efficacy (e.g., social media integration for providing social support versus active self-monitoring by users). Social support is a ubiquitous feature of contemporary physical activity apps and differences between apps lie primarily in whether the limited BCTs provide education or feedback about physical activity.


Future affective technology for autism and emotion communication.

People on the autism spectrum often experience states of emotional or cognitive overload that pose challenges to their interests in learning and communicating. Measurements taken from home and school environments show that extreme overload experienced internally, measured as autonomic nervous system (ANS) activation, may not be visible externally: a person can have a resting heart rate twice the level of non-autistic peers, while outwardly appearing calm and relaxed. The chasm between what is happening on the inside and what is seen on the outside, coupled with challenges in speaking and being pushed to perform, is a recipe for a meltdown that may seem to come 'out of the blue', but in fact may have been steadily building. Because ANS activation both influences and is influenced by efforts to process sensory information, interact socially, initiate motor activity, produce meaningful speech and more, deciphering the dynamics of ANS states is important for understanding and helping people on the autism spectrum. This paper highlights advances in technology that can comfortably sense and communicate ANS arousal in daily life, allowing new kinds of investigations to inform the science of autism while also providing personalized feedback to help individuals who participate in the research.


Toward Massive Machine Type Cellular Communications

Cellular networks have been engineered and optimized to carrying ever-increasing amounts of mobile data, but over the last few years, a new class of applications based on machine-centric communications has begun to emerge. Automated devices such as sensors, tracking devices, and meters, often referred to as machine-to-machine (M2M) or machine-type communications (MTC), introduce an attractive revenue stream for mobile network operators, if a massive number of them can be efficiently supported. The novel technical challenges posed by MTC applications include increased overhead and control signaling as well as diverse application-specific constraints such as ultra-low complexity, extreme energy efficiency, critical timing, and continuous data intensive uploading. This article explains the new requirements and challenges that large-scale MTC applications introduce, and provides a survey of key techniques for overcoming them. We focus on the potential of 4.5G and 5G networks to serve both the high data rate needs of conventional human-type communication (HTC) subscribers and the forecasted billions of new MTC devices. We also opine on attractive economic models that will enable this new class of cellular subscribers to grow to its full potential.


A class D output stage with zero dead time

An integrated class-D output stage has been realized with zero dead time, thereby removing one of the dominant sources of distortion in class-D amplifiers. Dead time is eliminated through proper dimensioning of the power transistor drivers and accurate matching of switch timing. Open-loop distortion of this output stage stays below 0.1% up to 35 W.


Partially Observable Reinforcement Learning for Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as ‘Partially Detected Intelligent Transportation Systems’. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.


Fuzzy & Datamining based Disease Prediction Using K-NN Algorithm

Disease diagnosis is one of the most important applications of such system as it is one of the leading causes of deaths all over the world. Almost all system predicting disease use inputs from complex tests conducted in labs and none of the system predicts disease based on the risk factors such as tobacco smoking, alcohol intake, age, family history, diabetes, hypertension, high cholesterol, physical inactivity, obesity. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. K-Nearest-Neighbour (KNN) is one of the successful data mining techniques used in classification problems. However, it is less used in the diagnosis of heart disease patients. Recently, researchers are showing that combining different classifiers through voting is outperforming other single classifiers. This paper investigates applying KNN to help healthcare professionals in the diagnosis of disease specially heart disease. It also investigates if integrating voting with KNN can enhance its accuracy in the diagnosis of heart disease patients. The results show that applying KNN could achieve higher accuracy than neural network ensemble in the diagnosis of heart disease patients. The results also show that applying voting could not enhance the KNN accuracy in the diagnosis of heart disease.


ENHANCEMENT STRATEGIES FOR FRAME-TO-FRAME UAS STEREO VISUAL ODOMETRY

Autonomous navigation of indoor unmanned aircraft systems (UAS) requires accurate pose estimations usually obtained from indirect measurements. Navigation based on inertial measurement units (IMU) is known to be affected by high drift rates. The incorporation of cameras provides complementary information due to the different underlying measurement principle. The scale ambiguity problem for monocular cameras is avoided when a light-weight stereo camera setup is used. However, also frame-to-frame stereo visual odometry (VO) approaches are known to accumulate pose estimation errors over time. Several valuable real-time capable techniques for outlier detection and drift reduction in frame-to-frame VO, for example robust relative orientation estimation using random sample consensus (RANSAC) and bundle adjustment, are available. This study addresses the problem of choosing appropriate VO components. We propose a frame-to-frame stereo VO method based on carefully selected components and parameters. This method is evaluated regarding the impact and value of different outlier detection and drift-reduction strategies, for example keyframe selection and sparse bundle adjustment (SBA), using reference benchmark data as well as own real stereo data. The experimental results demonstrate that our VO method is able to estimate quite accurate trajectories. Feature bucketing and keyframe selection are simple but effective strategies which further improve the VO results. Furthermore, introducing the stereo baseline constraint in pose graph optimization (PGO) leads to significant improvements. * Corresponding author


An Empirical Analysis of Deep Network Loss Surfaces

The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find local minima.


Now it's personal: on abusive game design

In this paper, we introduce the concept of abusive game design as an attitude towards creating games -- an aesthetic practice that operates as a critique of certain conventionalisms in popular game design wisdom. We emphasize that abusive game design is, at its core, about spotlighting the dialogic relation between player and designer.


Classification of Multiple-Sentence Questions

Conventional QA systems cannot answer to the questions composed of two or more sentences. Therefore, we aim to construct a QA system that can answer such multiple-sentence questions. As the first stage, we propose a method for classifying multiple-sentence questions into question types. Specifically, we first extract the core sentence from a given question text. We use the core sentence and its question focus in question classification. The result of experiments shows that the proposed method improves F-measure by 8.8% and accuracy by 4.4%.


Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models

Text mining the relations between chemicals and proteins is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical-protein relations in running text (PubMed abstracts). This manuscript describes our submission, which is an ensemble of three systems, including a Support Vector Machine, a Convolutional Neural Network, and a Recurrent Neural Network. Their output is combined using a decision based on majority voting or stacking. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an f-score of 0.6410, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature. Our submission achieved the highest performance in the task during the 2017 challenge. Keywords—relation extraction; deep learning; chemical; protein


EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art

Many Internet of Things (IoT) technologies have been used in applications for money flow, logistics flow, people flow, interactive art design, and so on. To manage these increasing disparate devices and connectivity options, ETSI has specified end-to-end machine-to-machine (M2M) system architecture for IoT applications. Based on this architecture, we develop an IoT EasyConnect system to manage IoT devices. In our approach, an IoT device is characterized by its “features” (e.g., temperature, vibration, and display) that are manipulated by the network applications. If a network application handles the individual device features independently, then we can write a software module for each device feature, and the network application can be simply constructed by including these brick-like device feature modules. Based on the concept of device feature, brick-like software modules can provide simple and efficient mechanism to develop IoT device applications and interactions.


Inherent Resistance of Efficient ECC Designs against SCA Attacks

The Montgomery kP algorithm using Lopez-Dahab projective coordinates is a time and energy efficient way to perform the elliptic curve point multiplication. It even can provide resistance against simple power analysis attacks. Nevertheless, straight forward implementations of this algorithm are not resistant against differential power analysis attacks. But highly-efficient designs i.e. designs in which different functional units are operating in parallel can increase the resistance of ECC implementations against DPA. In this paper we demonstrate this fact on the examples of our 6 implementations of the Montgomery kP algorithm. We perform horizontal DPA attacks to demonstrate the fact that the resistance of the kP algorithm against side channel analysis attacks can be increased through its implementation as an efficient design.


The Theory of Correlation Formulas and Their Application to Discourse Coherence

The Winograd Schema Challenge (WSC) was proposed as a measure of machine intelligence. It boils down to anaphora resolution, a task familiar from computational linguistics. Research in linguistics and AI has coalesced around discourse coherence as the critical factor in solving this task, and the process of establishing discourse coherence relies fundamentally on world and commonsense knowledge. In this thesis, we build on an approach to establishing coherence on the basis of correlation. The utility of this approach lies in its conceptual clarity and ability to flexibly represent commonsense knowledge. We work to fill some conceptual holes with the Correlation Calculus approach. First, understanding the calculus in a vacuum is not straightfoward unless it has a precise semantics. Second, existing demonstrations of the Correlation Calculus on Winograd Schema Challenge problems have not been linguistically credible. We hope to ameliorate some—but by no means all—of the outstanding issues with the Correlation Calculus. We do so first by providing a precise semantics of the calculus, which relates our intuitive understanding of correlation with a precise notion involving probabilities. Second, we formulate the establishment of discourse coherence by correlation formulas within the framework of Discourse Representation Theory. This provides a more complete and linguistically credible account of the relationship between the Correlation Calculus, discourse coherence, and Winograd Schema Challenge problems.


Game-Related Statistics that Discriminated Winning, Drawing and Losing Teams from the Spanish Soccer League.

The aim of the present study was to analyze men's football competitions, trying to identify which game-related statistics allow to discriminate winning, drawing and losing teams. The sample used corresponded to 380 games from the 2008-2009 season of the Spanish Men's Professional League. The game-related statistics gathered were: total shots, shots on goal, effectiveness, assists, crosses, offsides commited and received, corners, ball possession, crosses against, fouls committed and received, corners against, yellow and red cards, and venue. An univariate (t-test) and multivariate (discriminant) analysis of data was done. The results showed that winning teams had averages that were significantly higher for the following game statistics: total shots (p < 0.001), shots on goal (p < 0.01), effectiveness (p < 0.01), assists (p < 0.01), offsides committed (p < 0.01) and crosses against (p < 0.01). Losing teams had significantly higher averages in the variable crosses (p < 0.01), offsides received (p < 0. 01) and red cards (p < 0.01). Discriminant analysis allowed to conclude the following: the variables that discriminate between winning, drawing and losing teams were the total shots, shots on goal, crosses, crosses against, ball possession and venue. Coaches and players should be aware for these different profiles in order to increase knowledge about game cognitive and motor solicitation and, therefore, to evaluate specificity at the time of practice and game planning. Key pointsThis paper increases the knowledge about soccer match analysis.Give normative values to establish practice and match objectives.Give applications ideas to connect research with coaches' practice.


Free radicals and antioxidants in human health: current status and future prospects.

Free radicals and related species have attracted a great deal of attention in recent years. They are mainly derived from oxygen (reactive oxygen species/ROS) and nitrogen (reactive nitrogen species/RNS), and are generated in our body by various endogenous systems, exposure to different physicochemical conditions or pathophysiological states. Free radicals can adversely alter lipids, proteins and DNA and have been implicated in aging and a number of human diseases. Lipids are highly prone to free radical damage resulting in lipid peroxidation that can lead to adverse alterations. Free radical damage to protein can result in loss of enzyme activity. Damage caused to DNA, can result in mutagenesis and carcinogenesis. Redox signaling is a major area of free radical research that is attracting attention. Nature has endowed us with protective antioxidant mechanisms- superoxide dismutase (SOD), catalase, glutathione, glutathione peroxidases and reductase, vitamin E (tocopherols and tocotrienols), vitamin C etc., apart from many dietary components. There are epidemiological evidences correlating higher intake of components/ foods with antioxidant abilities to lower incidence of various human morbidities or mortalities. Current research reveals the different potential applications of antioxidant/free radical manipulations in prevention or control of disease. Natural products from dietary components such as Indian spices and medicinal plants are known to possess antioxidant activity. Newer and future approaches include gene therapy to produce more antioxidants in the body, genetically engineered plant products with higher level of antioxidants, synthetic antioxidant enzymes (SOD mimics), novel biomolecules and the use of functional foods enriched with antioxidants.


Cultivate Self-Efficacy for Personal and Organizational Effectiveness

Bandura, A. (2000). Cultivate self-efficacy for personal and organizational effectiveness. In E.


Power LDMOS with novel STI profile for improved Rsp, BVdss, and reliability

The profile of shallow trench isolation (STI) is designed to improve LDMOS specific on-resistance (Rsp), BVDSS, safe operating area (SOA), and hot carrier lifetimes (HCL) in an integrated BiCMOS power technology. Silicon etch, liner oxidation and CMP processes are tuned to improve the tradeoffs in a power technology showing significant improvement to both p-channel and n-channel Rsp compared to devices fabricated with the STI profile inherited from the original submicron CMOS platform. Extensive TCAD and experiments were carried out to gain insight into the physical mechanisms and further improve device performance after STI process optimization. The final process and device structures yield SOAs that are limited only by thermal constraints up to rated voltages


A Hygroscopic Sensor Electrode for Fast Stabilized Non-Contact ECG Signal Acquisition

A capacitive electrocardiography (cECG) technique using a non-invasive ECG measuring technology that does not require direct contact between the sensor and the skin has attracted much interest. The system encounters several challenges when the sensor electrode and subject's skin are weakly coupled. Because there is no direct physical contact between the subject and any grounding point, there is no discharge path for the built-up electrostatic charge. Subsequently, the electrostatic charge build-up can temporarily contaminate the ECG signal from being clearly visible; a stabilization period (3-15 min) is required for the measurement of a clean, stable ECG signal at low humidity levels (below 55% relative humidity). Therefore, to obtain a clear ECG signal without noise and to reduce the ECG signal stabilization time to within 2 min in a dry ambient environment, we have developed a fabric electrode with embedded polymer (FEEP). The designed hygroscopic FEEP has an embedded superabsorbent polymer layer. The principle of FEEP as a conductive electrode is to provide humidity to the capacitive coupling to ensure strong coupling and to allow for the measurement of a stable, clear biomedical signal. The evaluation results show that hygroscopic FEEP is capable of rapidly measuring high-accuracy ECG signals with a higher SNR ratio.


A 3.9-ps RMS Precision Time-to-Digital Converter Using Ones-Counter Encoding Scheme in a Kintex-7 FPGA

A 3.9-ps time-interval rms precision and 277-M events/second measurement throughput time-to-digital converter (TDC) is implemented in a Xilinx Kintex-7 field programmable gate array (FPGA). Unlike previous work, the TDC is achieved with a multichain tapped-delay line (TDL) followed by an ones-counter encoder. The four normal TDLs merged together make the TDC bins very small, so that the time precision can be significantly improved. The ones-counter encoder naturally applies global bubble error correction to the output of TDL, thus the TDC design is relatively simple even when using FPGAs made with current advanced process technology. The TDC implementation is a generally applicable method that can simultaneously achieve high time precision and high measurement throughput.


Comparative Evaluation of Background Subtraction Algorithms in Remote Scene Videos Captured by MWIR Sensors

Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR.


Can You Spot the Fakes?: On the Limitations of User Feedback in Online Social Networks

Online social networks (OSNs) are appealing platforms for spammers and fraudsters, who typically use fake or compromised accounts to connect with and defraud real users. To combat such abuse, OSNs allow users to report fraudulent profiles or activity. The OSN can then use reporting data to review and/or limit activity of reported accounts. Previous authors have suggested that an OSN can augment its takedown algorithms by identifying a “trusted set” of users whose reports are weighted more heavily in the disposition of flagged accounts. Such identification would allow the OSN to improve both speed and accuracy of fake account detection and thus reduce the impact of spam on users. In this work we provide the first public, data-driven assessment of whether the above assumption is true: are some users better at reporting than others? Specifically, is reporting skill both measurable, i.e., possible to distinguish from random guessing; and repeatable, i.e., persistent over repeated sampling? Our main contributions are to develop a statistical framework that describes these properties and to apply this framework to data from LinkedIn, the professional social network. Our data includes member reports of fake profiles as well as the more voluminous, albeit weaker, signal of member responses to connection requests. We find that members demonstrating measurable, repeatable skill in identifying fake profiles do exist but are rare: at most 2.4% of those reporting fakes and at most 1.3% of those rejecting connection requests. We conclude that any reliable “trusted set” of members will be too small to have noticeable impact on spam metrics.


How to practice person‐centred care: A conceptual framework

BACKGROUND
Globally, health-care systems and organizations are looking to improve health system performance through the implementation of a person-centred care (PCC) model. While numerous conceptual frameworks for PCC exist, a gap remains in practical guidance on PCC implementation.

METHODS
Based on a narrative review of the PCC literature, a generic conceptual framework was developed in collaboration with a patient partner, which synthesizes evidence, recommendations and best practice from existing frameworks and implementation case studies. The Donabedian model for health-care improvement was used to classify PCC domains into the categories of "Structure," "Process" and "Outcome" for health-care quality improvement.

DISCUSSION
The framework emphasizes the structural domain, which relates to the health-care system or context in which care is delivered, providing the foundation for PCC, and influencing the processes and outcomes of care. Structural domains identified include: the creation of a PCC culture across the continuum of care; co-designing educational programs, as well as health promotion and prevention programs with patients; providing a supportive and accommodating environment; and developing and integrating structures to support health information technology and to measure and monitor PCC performance. Process domains describe the importance of cultivating communication and respectful and compassionate care; engaging patients in managing their care; and integration of care. Outcome domains identified include: access to care and Patient-Reported Outcomes.

CONCLUSION
This conceptual framework provides a step-wise roadmap to guide health-care systems and organizations in the provision PCC across various health-care sectors.


Probabilistic topic models

Surveying a suite of algorithms that offer a solution to managing large document archives.


SNOW 2014 Data Challenge: Assessing the Performance of News Topic Detection Methods in Social Media

The SNOW 2014 Data Challenge aimed at creating a public benchmark and evaluation resource for the problem of topic detection in streams of social content. In particular, given a set of tweets spanning a time interval of interest, the Challenge required the extraction of the most significant news topics in short timeslots within the selected interval. Here, we provide details with respect to the Challenge definition, the data collection and evaluation process, and the results achieved by the 11 teams that participated in it, along with a concise retrospective analysis of the main conclusions and arising issues.


Sensing Trending Topics in Twitter

Online social and news media generate rich and timely information about real-world events of all kinds. However, the huge amount of data available, along with the breadth of the user base, requires a substantial effort of information filtering to successfully drill down to relevant topics and events. Trending topic detection is therefore a fundamental building block to monitor and summarize information originating from social sources. There are a wide variety of methods and variables and they greatly affect the quality of results. We compare six topic detection methods on three Twitter datasets related to major events, which differ in their time scale and topic churn rate. We observe how the nature of the event considered, the volume of activity over time, the sampling procedure and the pre-processing of the data all greatly affect the quality of detected topics, which also depends on the type of detection method used. We find that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel. One of the novel topic detection methods we propose, based on -grams cooccurrence and topic ranking, consistently achieves the best performance across all these conditions, thus being more reliable than other state-of-the-art techniques.


Collective entity linking in web text: a graph-based method

Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy.
 This paper proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Specifically, we first propose a graph-based representation, called Referent Graph, which can model the global interdependence between different EL decisions. Then we propose a collective inference algorithm, which can jointly infer the referent entities of all name mentions by exploiting the interdependence captured in Referent Graph. The key benefit of our method comes from: 1) The global interdependence model of EL decisions; 2) The purely collective nature of the inference algorithm, in which evidence for related EL decisions can be reinforced into high-probability decisions. Experimental results show that our method can achieve significant performance improvement over the traditional EL methods.


Structural Architectures for a Deployable Wideband UHF Antenna

This paper explores concepts for a wideband deployable antenna suitable for small satellites. The general approach taken was to closely couple antenna theory and structural mechanics, to produce a deployable antenna that is considered efficient in both fields. Approaches that can be deployed using stored elastic strain energy have been favored over those that require powered actuators or environmental effects to enforce deployment. Two types of concepts were developed: thin shell structure and pantograph. These concepts cover four antenna topologies: crossed log periodic, conical log spiral, helical and quadrifilar helix. Of these, the conical log spiral antenna and the accompanying deployment concepts are determined to be the most promising approaches that warrant further study.


Global climate change and terrestrial net primary production

A process-based model was used to estimate global patterns of net primary production and soil nitrogen cycling for contemporary climate conditions and current atmospheric C02 concentration. Over half of the global annual net primary production was estimated to occur in the tropics, with most of the production attributable to tropical evergreen forest. The effects of C02 doubling and associated climate changes were also explored. The responses in tropical and dry temperate ecosystems were dominated by C02, but those in northern and moist temperate ecosystems reflected the effects of temperature on nitrogen availability.


Low-cost backdrivable motor control based on feed-forward/feed-back friction compensation

A low-cost but accurate and backdrivable motor control system running on a DC motor and a harmonic drive gear is proposed. It compensates internal friction of the gear and the counter-electromotive torque by combining a model-based feed-forward method and a disturbance observer using a cheap torque sensor. A complementary use of those techniques lowers requirements to their performances i.e. precision, bandwidth, etc, while it is equipped with a flexible property against the external torque. A 2-DOF servo controller is also built upon the system in order to simultaneously achieve smooth responses and robust convergences to the reference.


Submaximal exercise testing: clinical application and interpretation.

Compared with maximal exercise testing, submaximal exercise testing appears to have greater applicability to physical therapists in their role as clinical exercise specialists. This review contrasts maximal and submaximal exercise testing. Two major categories of submaximal tests (ie, predictive and performance tests) and their relative merits are described. Predictive tests are submaximal tests that are used to predict maximal aerobic capacity. Performance tests involve measuring the responses to standardized physical activities that are typically encountered in everyday life. To maximize the validity and reliability of data obtained from submaximal tests, physical therapists are cautioned to apply the tests selectively based on their indications; to adhere to methods, including the requisite number of practice sessions; and to use measurements such as heart rate, blood pressure, exertion, and pain to evaluate test performance and to safely monitor patients.


Control of Grid Connected PV Array Using P&O MPPT Algorithm

The renewable energy is becoming moremainstream and accessible. This has been made possible due toan increase in environmental awareness coupled with thepopular demand to cut back on the greenhouse emissions. We inthis project propose a grid connected PV system. The aim of theproject is to implement a complete distributed energy resourcesystem (DER). The project will feature a PV module, which willbe controlled and optimized by means of a maximum powerpoint tracking (MPPT) algorithm. A boost converter along witha single phase grid tie inverter will be used to increase theoutput voltage and to convert it to AC. A phase locked loopcircuit will be used to integrate the single phase inverter withthe grid. A control methodology consisting of PI controllers isemployed for operating the PV at the MPPT point bycontrolling the switching of the boost converter and also for theoperation of the single phase inverter and its integration withthe grid. The parameters of these controllers are tuned to givethe best results. This will be followed by a detailed mathematicaland engineering analysis for the simulated results. The validityof the proposed scheme will be verified by simulation using thePSIM software.


Learning analytics: envisioning a research discipline and a domain of practice

Learning analytics are rapidly being implemented in different educational settings, often without the guidance of a research base. Vendors incorporate analytics practices, models, and algorithms from datamining, business intelligence, and the emerging "big data" fields. Researchers, in contrast, have built up a substantial base of techniques for analyzing discourse, social networks, sentiments, predictive models, and in semantic content (i.e., "intelligent" curriculum). In spite of the currently limited knowledge exchange and dialogue between researchers, vendors, and practitioners, existing learning analytics implementations indicate significant potential for generating novel insight into learning and vital educational practices. This paper presents an integrated and holistic vision for advancing learning analytics as a research discipline and a domain of practices. Potential areas of collaboration and overlap are presented with the intent of increasing the impact of analytics on teaching, learning, and the education system.


[POSTER] Realtime Shape-from-Template: System and Applications

An important yet unsolved problem in computer vision and Augmented Reality (AR) is to compute the 3D shape of nonrigid objects from live 2D videos. When the object's shape is provided in a rest pose, this is the Shape-from-Template (SfT) problem. Previous realtime SfT methods require simple, smooth templates, such as flat sheets of paper that are densely textured, and which deform in simple, smooth ways. We present a realtime SfT framework that handles generic template meshes, complex deformations and most of the difficulties present in real imaging conditions. Achieving this has required new, fast solutions to the two core sub-problems: robust registration and 3D shape inference. Registration is achieved with what we call Deformable Render-based Block Matching (DRBM): a highly-parallel solution which densely matches a time-varying render of the object to each video frame. We then combine matches from DRBM with physical deformation priors and perform shape inference, which is done by quickly solving a sparse linear system with a Geometric Multi-Grid (GMG)-based method. On a standard PC we achieve up to 21fps depending on the object. Source code will be released.


Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop

Existing fine-grained visual categorization methods often suffer from three challenges: lack of training data, large number of fine-grained categories, and high intraclass vs. low inter-class variance. In this work we propose a generic iterative framework for fine-grained categorization and dataset bootstrapping that handles these three challenges. Using deep metric learning with humans in the loop, we learn a low dimensional feature embedding with anchor points on manifolds for each category. These anchor points capture intra-class variances and remain discriminative between classes. In each round, images with high confidence scores from our model are sent to humans for labeling. By comparing with exemplar images, labelers mark each candidate image as either a "true positive" or a "false positive." True positives are added into our current dataset and false positives are regarded as "hard negatives" for our metric learning model. Then the model is retrained with an expanded dataset and hard negatives for the next round. To demonstrate the effectiveness of the proposed framework, we bootstrap a fine-grained flower dataset with 620 categories from Instagram images. The proposed deep metric learning scheme is evaluated on both our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show significant performance gain using dataset bootstrapping and demonstrate state-of-the-art results achieved by the proposed deep metric learning methods.


A 1.2 V Reactive-Feedback 3.1–10.6 GHz Low-Noise Amplifier in 0.13 $\mu{\hbox {m}}$  CMOS

A 15.1 dB gain, 2.1 dB (min.) noise figure low-noise amplifier (LNA) fabricated in 0.13 mum CMOS operates across the entire 3.1-10.6 GHz ultrawideband (UWB). Noise figure variation over the band is limited to 0.43 dB. Reactive (transformer) feedback reduces the noise figure, stabilizes the gain, and sets the terminal impedances over the desired bandwidth. It also provides a means of separating ESD protection circuitry from the RF input path. Bias current-reuse limits power consumption of the 0.87mm2 IC to 9 mW from a 1.2 V supply. Comparable measured results are presented from both packaged and wafer probed test samples


Advances in Reversed Nested Miller Compensation

The use of two frequency compensation schemes for three-stage operational transconductance amplifiers, namely the reversed nested Miller compensation with nulling resistor (RN-MCNR) and reversed active feedback frequency compensation (RAFFC), is presented in this paper. The techniques are based on the basic RNMC and show an inherent advantage over traditional compensation strategies, especially for heavy capacitive loads. Moreover, they are implemented without entailing extra transistors, thus saving circuit complexity and power consumption. A well-defined design procedure, introducing phase margin as main design parameter, is also developed for each solution. To verify the effectiveness of the techniques, two amplifiers have been fabricated in a standard 0.5-mum CMOS process. Experimental measurements are found in good agreement with theoretical analysis and show an improvement in small-signal and large-signal amplifier performances. Finally, an analytical comparison with the nonreversed counterparts topologies, which shows the superiority of the proposed solutions, is also included.