A 3-D Camera With Adaptable Background Light Suppression Using Pixel-Binning and Super-Resolution

This paper presents a CMOS time-of-flight (TOF) 3-D camera employing a column-level background light (BGL) suppression scheme for high-resolution outdoor imaging. The use of the column-level approach minimizes a pixel size for high-density pixel arrays. Pixel-binning and super-resolution can be adaptably applied for an optimal BGL suppression at given spatiotemporal resolutions. A prototype sensor has been fabricated by using 0.11 μm CMOS processes. The sensor achieved a fill factor of 24% in a pixel pitch of 5.9 μm which is the smallest among all the reported TOF cameras up to date. Measurement results showed the temporal noise of 1.47 cm-rms with a 100 ms integration time at a demodulation frequency of 12.5 MHz using a white target at 1 m distance. The non-linearity was measured as 1% over the range of 0.75 m ~ 4 m. The BGL suppression over 100 klx was achieved from indoor and outdoor experiments, while the BGL-induced offset was maintained less than 2.6 cm under 0 ~ 100 klx.


Map Matching with Inverse Reinforcement Learning

We study map-matching, the problem of estimating the route that is traveled by a vehicle, where the points observed with the Global Positioning System are available. A state-of-the-art approach for this problem is a Hidden Markov Model (HMM). We propose a particular transition probability between latent road segments by the use of the number of turns in addition to the travel distance between the latent road segments. We use inverse reinforcement learning to estimate the importance of the number of turns relative to the travel distance. This estimated importance is incorporated in the transition probability of the HMM. We show, through numerical experiments, that the error of map-matching can be reduced substantially with the proposed transition probability.


Market power and efficiency in a computational electricity market with discriminatory double-auction pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth Erev individual reinforcement learning algorithm to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained, and that market microstructure is strongly predictive for the relative market power of buyers and sellers independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms.


Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors.

Statistical power analysis provides the conventional approach to assess error rates when designing a research study. However, power analysis is flawed in that a narrow emphasis on statistical significance is placed as the primary focus of study design. In noisy, small-sample settings, statistically significant results can often be misleading. To help researchers address this problem in the context of their own studies, we recommend design calculations in which (a) the probability of an estimate being in the wrong direction (Type S [sign] error) and (b) the factor by which the magnitude of an effect might be overestimated (Type M [magnitude] error or exaggeration ratio) are estimated. We illustrate with examples from recent published research and discuss the largest challenge in a design calculation: coming up with reasonable estimates of plausible effect sizes based on external information.


Commutation Torque Ripple Reduction of Brushless DC Motor in Braking Operation

In this paper, the commutation torque ripple is studied in the brushless DC motor (BLDCM) braking operation. As the phase currents flow in the reversed directions in the braking operation, the current circuits in the braking operation are different from those in the motor operation. For this reason, it is found that the commutation torque ripple can be reduced in the speed range from the rated speed to zero just by the common braking pulse width modulation strategies, where the three-phase modulation is not needed. However, it is impossible in motor operation. Under the common braking pulse width modulation strategies, the speed ranges where commutation torque ripple can be reduced are derived and compared. Based on the analysis of these speed ranges, a simple braking method is proposed. With this method, the BLDCM can be slowed down without commutation torque ripple in the whole braking process from the rated speed to zero. Experiment results verify the effectiveness of the method and the correctness of the theory.


Fast Screening Algorithm for Rotation and Scale Invariant Template Matching

This paper presents a generic pre-processor for expediting conventional template matching techniques. Instead of locating the best matched patch in the reference image to a query template via exhaustive search, the proposed algorithm rules out regions with no possible matches with minimum computational efforts. While working on simple patch features, such as mean, variance and gradient, the fast prescreening is highly discriminative. Its computational efficiency is gained by using a novel octagonal-star-shaped template and the inclusion-exclusion principle to extract and compare patch features. Moreover, it can handle arbitrary rotation and scaling of reference images effectively. Extensive experiments demonstrate that the proposed algorithm greatly reduces the search space while never missing the best match.


Psychological Stressors : Overview

Psychological stressors are social and physical environmental circumstances that challenge the adaptive capabilities and resources of an organism. These circumstances represent an extremely wide and varied array of different situations that possess both common and specific psychological and physical attributes. The challenge for theory, research, and practice is to abstract and understand the specific qualities and characteristics of environmental exposures that most strongly elicit noxious psychological and biological responses, which in turn can lead to serious mental and physical health problems over the life course. In the present article, historical perspectives and conceptual considerations are addressed first, which provides the context for the subsequent discussion of key issues for defining and assessing psychological stressors. Susceptibility to psychological stressors is subject to individual differences, which can alter the impact and adverse consequences of such environmental exposures, necessitating a discussion of these moderating influences as well. HISTORICAL AND GENERAL CONSIDERATIONS


The Love of Large Numbers: A Popularity Bias in Consumer Choice.

Social learning-the ability to learn from observing the decisions of other people and the outcomes of those decisions-is fundamental to human evolutionary and cultural success. The Internet now provides social evidence on an unprecedented scale. However, properly utilizing this evidence requires a capacity for statistical inference. We examined how people's interpretation of online review scores is influenced by the numbers of reviews-a potential indicator both of an item's popularity and of the precision of the average review score. Our task was designed to pit statistical information against social information. We modeled the behavior of an "intuitive statistician" using empirical prior information from millions of reviews posted on Amazon.com and then compared the model's predictions with the behavior of experimental participants. Under certain conditions, people preferred a product with more reviews to one with fewer reviews even though the statistical model indicated that the latter was likely to be of higher quality than the former. Overall, participants' judgments suggested that they failed to make meaningful statistical inferences.


PubViz: Lightweight Visual Presentation of Publication Data

Publications play a central role in presenting the outcome of scientific research but are typically presented as textual lists, whereas related work in visualization of publication focuses on exploration – not presentation. To bridge this gap, we conducted a design study of an interactive visual representation of publication data in a BibTeX file. This paper reports our domain and problem characterization as well as our visualization design decisions in light of our user-centered design process including interviews, two user studies with a paper prototype and a d3.js prototype, and practical application at our group’s website.


Challenges in Shared-Environment Human-Robot Collaboration

We present and discuss four important yet underserved research questions critical to the future of sharedenvironment human-robot collaboration. We begin with a brief survey of research surrounding individual components required for a complete collaborative robot control system, discussing the current state of the art in Learning from Demonstration, active learning, adaptive planning systems, and intention recognition. We motivate the exploration of the presented research questions by relating them to existing work and representative use cases from the domains of construction and cooking.


Automatic semantic classification of scientific literature according to the hallmarks of cancer

MOTIVATION
The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.

RESULTS
We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.

AVAILABILITY AND IMPLEMENTATION
The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html.

CONTACT
simon.baker@cl.cam.ac.uk.


Grid-connected photovoltaic systems with energy storage

There are different interesting ways that can be followed in order to reduce costs of grid-connected photovoltaic systems, i.e., by maximizing their energy production in every operating conditions, minimizing electrical losses on the plant, utilizing grid-connected photovoltaic systems not only to generate electrical energy to be put into the power system but also to implement some other important auxiliary services normally implemented by properly conceived apparatus (electrical power quality control, load demand peak control, …), making easy the integration of different kind of renewable electrical energy resources (wind plant, fuel-cells, …).


Ontology-based personalized search and browsing

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by ‘watching over the user’s shoulder’ while they browse. We show that these automatically created profiles reflect the user’s interests quite well and they are able to produce moderate improvements when applied to search results.


Learning-to-Rank in Research Paper CBF recommendation: Leveraging Irrelevant Papers

Suggesting relevant literature to researchers has become an active area of study, typically relying on content-based filtering (CBF) over the rich textual features available. Given the high dimensionality and the sparsity of the training samples inherent to this domain, the focus has so far been on heuristic-based methods. In this paper, we argue for the model-based approach and propose a learning-to-rank method that leverages publicly available publications’ metadata to produce an effective prediction model. The proposed method is systematically evaluated on a scholarly paper recommendation dataset and compared against state-of-the-art model-based approaches as well as current, domain-specific heuristic methods. The results show that our approach clearly outperforms state-of-the-art research paper recommendations utilizing only publicly available meta-data.


Upper lateral cartilage fold-in flap: a combined spreader and/or splay graft effect without cartilage grafts.

Dorsal hump reduction almost always breaks the internal nasal valve and nasal obstruction is likely to occur postoperatively, unless reconstructed. One hundred eighty patients were operated using both open and closed rhinoplasty approaches. Upper lateral cartilages were meticulously separated from their junction with septum. Following bony and septal cartilaginous hump removal, upper lateral cartilages were folded inward. Either transcartilaginous horizontal mattress/simple sutures or perichondrial sutures were used depending of the desired width of the middle vault and the necessity for a splay-graft effect. In 7 patients unilateral, and in 1 patient bilateral, nasal synechia occurred and they were all treated under local anesthesia. All patients but 9 stated significantly improved nasal breathing. There was no inverted-V deformity or middle-vault narrowing observed. This technique is simple and physiologic, might be applicable for almost all primary rhinoplasty patients. Although it is possible with closed rhinoplasty approaches, it is easier with an open approach.


Light warping for enhanced surface depiction

Recent research on the human visual system shows that our perception of object shape relies in part on compression and stretching of the reflected lighting environment onto its surface. We use this property to enhance the shape depiction of 3D objects by locally warping the environment lighting around main surface features. Contrary to previous work, which require specific illumination, material characteristics and/or stylization choices, our approach enhances surface shape without impairing the desired appearance.
 Thanks to our novel local shape descriptor, salient surface features are explicitly extracted in a view-dependent fashion at various scales without the need of any pre-process. We demonstrate our system on a variety of rendering settings, using object materials ranging from diffuse to glossy, to mirror or refractive, with direct or global illumination, and providing styles that range from photorealistic to non-photorealistic. The warping itself is very fast to compute on modern graphics hardware, enabling real-time performance in direct illumination scenarios.
 Note: Third-Party Material Attribution  Third-party material used in ACM Transactions on Graphics 28(3), Article 25 - "Light Warping for Enhanced Surface Depiction," by Vergne, Pacanowski, Barla, Granier, and Schlick - was used without proper attribution.
 The 3D model used in Figures 1, 3, and 5, as well as in the cover image of this volume of the journal, was downloaded from the Shape Repository of AIM@SHAPE Project (http://shapes.aimatshape.net) and is the property of CNR-IMATI.
 We regret this oversight.


Fully Distributed Scrum: The Secret Sauce for Hyperproductive Offshored Development Teams

Scrum was designed to achieve a hyperproductive state where productivity increases 5-10 times over industry averages and many collocated teams have achieved this effect. The question for this paper is whether distributed, offshore teams can consistently achieve the hyperproductive state. In particular, can a team establish a localized velocity and then maintain or increase that velocity when distributing teams across continents. Since 2006, Xebia started projects with half Dutch and half Indian team members. After establishing localized hyperproductivity, they move the Indian members of the team to India and show increasing velocity with fully distributed teams. After running XP engineering practices inside many distributed Scrum projects, Xebia has systematically productized a model very similar to the SirsiDynix model (J. Sutherland, 2006) for high performance, distributed, offshore teams with outstanding quality.


A* CCG Parsing with a Supertag-factored Model

We introduce a new CCG parsing model which is factored on lexical category assignments. Parsing is then simply a deterministic search for the most probable category sequence that supports a CCG derivation. The parser is extremely simple, with a tiny feature set, no POS tagger, and no statistical model of the derivation or dependencies. Formulating the model in this way allows a highly effective heuristic for A∗ parsing, which makes parsing extremely fast. Compared to the standard C&C CCG parser, our model is more accurate out-of-domain, is four times faster, has higher coverage, and is greatly simplified. We also show that using our parser improves the performance of a state-ofthe-art question answering system.


Joint A* CCG Parsing and Semantic Role Labelling

Joint models of syntactic and semantic parsing have the potential to improve performance on both tasks—but to date, the best results have been achieved with pipelines. We introduce a joint model using CCG, which is motivated by the close link between CCG syntax and semantics. Semantic roles are recovered by labelling the deep dependency structures produced by the grammar. Furthermore, because CCG is lexicalized, we show it is possible to factor the parsing model over words and introduce a new A∗ parsing algorithm— which we demonstrate is faster and more accurate than adaptive supertagging. Our joint model is the first to substantially improve both syntactic and semantic accuracy over a comparable pipeline, and also achieves state-of-the-art results for a nonensemble semantic role labelling model.


LSTM CCG Parsing

We demonstrate that a state-of-the-art parser can be built using only a lexical tagging model and a deterministic grammar, with no explicit model of bi-lexical dependencies. Instead, all dependencies are implicitly encoded in an LSTM supertagger that assigns CCG lexical categories. The parser significantly outperforms all previously published CCG results, supports efficient and optimal A∗ decoding, and benefits substantially from semisupervised tri-training. We give a detailed analysis, demonstrating that the parser can recover long-range dependencies with high accuracy and that the semi-supervised learning enables significant accuracy gains. By running the LSTM on a GPU, we are able to parse over 2600 sentences per second while improving state-of-the-art accuracy by 1.1 F1 in domain and up to 4.5 F1 out of domain.


Chainer : a Next-Generation Open Source Framework for Deep Learning

Software frameworks for neural networks play key roles in the development and application of deep learning methods. However, as new types of deep learning models are developed, existing frameworks designed for convolutional neural networks are becoming less useful. In this paper, we introduce Chainer, a Pythonbased, standalone open source framework for deep learning models. Chainer provides a flexible, intuitive, and high performance means of implementing a full range of deep learning models, including state-of-the-art models such as recurrent neural networks and variational autoencoders.


Multi-level Lasso for Sparse Multi-task Regression

We present a flexible formulation for variable selection in multi-task regression to allow for discrepancies in the estimated sparsity patterns accross the multiple tasks, while leveraging the common structure among them. Our approach is based on an intuitive decomposition of the regression coefficients into a product between a component that is common to all tasks and another component that captures task-specificity. This decomposition yields the Multi-level Lasso objective that can be solved efficiently via alternating optimization. The analysis of the “orthonormal design” case reveals some interesting insights on the nature of the shrinkage performed by our method, compared to that of related work. Theoretical guarantees are provided on the consistency of Multi-level Lasso. Simulations and empirical study of micro-array data further demonstrate the value of our framework.


How Much Is Enough? Choosing ε for Differential Privacy

Differential privacy is a recent notion, and while it is nice conceptually it has been difficult to apply in practice. The parameters of differential privacy have an intuitive theoretical interpretation, but the implications and impacts on the risk of disclosure in practice have not yet been studied, and choosing appropriate values for them is non-trivial. Although the privacy parameter in differential privacy is used to quantify the privacy risk posed by releasing statistics computed on sensitive data, is not an absolute measure of privacy but rather a relative measure. In effect, even for the same value of , the privacy guarantees enforced by differential privacy are different based on the domain of attribute in question and the query supported. We consider the probability of identifying any particular individual as being in the database, and demonstrate the challenge of setting the proper value of given the goal of protecting individuals in the database with some fixed probability.


Coupling Distributed and Symbolic Execution for Natural Language Queries

Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning. An executor for table querying typically requires multiple steps of execution because queries may have complicated structures. In previous studies, researchers have developed either fully distributed executors or symbolic executors for table querying. A distributed executor can be trained in an end-to-end fashion, but is weak in terms of execution efficiency and explicit interpretability. A symbolic executor is efficient in execution, but is very difficult to train especially at initial stages. In this paper, we propose to couple distributed and symbolic execution for natural language queries, where the symbolic executor is pretrained with the distributed executor’s intermediate execution results in a step-by-step fashion. Experiments show that our approach significantly outperforms both distributed and symbolic executors, exhibiting high accuracy, high learning efficiency, high execution efficiency, and high interpretability.


A High-Speed and Ultra Low-Power Subthreshold Signal Level Shifter

In this paper, we present a novel level shifter circuit converting subthreshold signal levels to super-threshold signal levels at high-speed using ultra low-power and a small silicon area, making it well-suited for low-power applications such as wireless sensor networks and implantable medical devices. The proposed circuit introduces a new voltage level shifter topology employing a level-shifting capacitor contributing to increase the range of conversion voltages, while significantly reducing the conversion delay. Such a level-shifting capacitor is quickly charged, whenever the input signal detects a low-to-high transition, in order to boost internal voltage nodes, and quickly reach a high output voltage level. The proposed circuit achieves a shorter propagation delay and a smaller silicon area for a given operating frequency and power consumption compared to other circuit solutions. Measurement results are presented for the proposed circuit fabricated in a 0.18- $\mu \text {m}$  TSMC technology. The proposed circuit can convert a wide range of the input voltages from 330 mV to 1.8 V, and operate over a frequency range of 100 Hz to 100 MHz. It has a propagation delay of 29 ns and a power consumption of 61.5 nW for input signals 0.4 V, at a frequency of 500-kHz, outperforming previous designs.


Developing Non-goal Dialog System Based on Examples of Drama Television

This paper presents a design and experiments of developing a non-goal dialog system by utilizing human-to-human conversation examples from drama television. The aim is to build a conversational agent that can interact with users in as natural a fashion as possible, while reducing the time requirement for database design and collection. A number of the challenging design issues we faced are described, including (1) filtering and constructing a dialog example database from the drama conversations, and (2) retrieving a proper system response by finding the best dialog example based on the current user query. Subjective evaluation from a small user study is also discussed.


Asynchronous frameless event-based optical flow

This paper introduces a process to compute optical flow using an asynchronous event-based retina at high speed and low computational load. A new generation of artificial vision sensors has now started to rely on biologically inspired designs for light acquisition. Biological retinas, and their artificial counterparts, are totally asynchronous and data driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework for processing visual data using asynchronous event-based acquisition, providing a method for the evaluation of optical flow. The paper shows that current limitations of optical flow computation can be overcome by using event-based visual acquisition, where high data sparseness and high temporal resolution permit the computation of optical flow with micro-second accuracy and at very low computational cost.


Graphene plasmonics for terahertz to mid-infrared applications.

In recent years, we have seen a rapid progress in the field of graphene plasmonics, motivated by graphene's unique electrical and optical properties, tunability, long-lived collective excitation and its extreme light confinement. Here, we review the basic properties of graphene plasmons: their energy dispersion, localization and propagation, plasmon-phonon hybridization, lifetimes and damping pathways. The application space of graphene plasmonics lies in the technologically significant, but relatively unexploited terahertz to mid-infrared regime. We discuss emerging and potential applications, such as modulators, notch filters, polarizers, mid-infrared photodetectors, and mid-infrared vibrational spectroscopy, among many others.


Learning a Probabilistic Topology Discovering Model for Scene Categorization

A recent advance in scene categorization prefers a topological based modeling to capture the existence and relationships among different scene components. To that effect, local features are typically used to handle photographing variances such as occlusions and clutters. However, in many cases, the local features alone cannot well capture the scene semantics since they are extracted from tiny regions (e.g., 4 × 4 patches) within an image. In this paper, we mine a discriminative topology and a low-redundant topology from the local descriptors under a probabilistic perspective, which are further integrated into a boosting framework for scene categorization. In particular, by decomposing a scene image into basic components, a graphlet model is used to describe their spatial interactions. Accordingly, scene categorization is formulated as an intergraphlet matching problem. The above procedure is further accelerated by introducing a probabilistic based representative topology selection scheme that makes the pairwise graphlet comparison trackable despite their exponentially increasing volumes. The selected graphlets are highly discriminative and independent, characterizing the topological characteristics of scene images. A weak learner is subsequently trained for each topology, which are boosted together to jointly describe the scene image. In our experiment, the visualized graphlets demonstrate that the mined topological patterns are representative to scene categories, and our proposed method beats state-of-the-art models on five popular scene data sets.


An open-label study to evaluate sildenafil for the treatment of lymphatic malformations.

BACKGROUND
Lymphatic malformations can be challenging to treat. Mainstay interventions including surgery and sclerotherapy are invasive and can result in local recurrence and complications.

OBJECTIVE
We sought to assess the effect of 20 weeks of oral sildenafil on reducing lymphatic malformation volume and symptoms in children.

METHODS
Seven children (4 boys, 3 girls; ages 13-85 months) with lymphatic malformations were given oral sildenafil for 20 weeks in this open-label study. The volume of the lymphatic malformation was calculated blindly using magnetic resonance imaging performed before and after 20 weeks of sildenafil. Lymphatic malformations were assessed clinically on weeks 4, 12, 20, and 32. Both the physician and parents evaluated the lymphatic malformation in comparison with baseline.

RESULTS
Four subjects had a lymphatic malformation volume decrease (1.0%-31.7%). In 2 subjects, despite a lymphatic malformation volume increase (1.1%-3.7%), clinical improvement was noted while on sildenafil. One subject had a 29.6% increase in lymphatic malformation volume and no therapeutic response. Lymphatic malformations of all 6 subjects who experienced a therapeutic response on sildenafil softened and became easily compressible. Adverse events were minimal.

LIMITATIONS
A randomized controlled trial will be necessary to verify the effects of sildenafil on lymphatic malformations.

CONCLUSIONS
Sildenafil can reduce lymphatic malformation volume and symptoms in some children.


Data mining a diabetic data warehouse

Diabetes is a major health problem in the United States. There is a long history of diabetic registries and databases with systematically collected patient information. We examine one such diabetic data warehouse, showing a method of applying data mining techniques, and some of the data issues, analysis problems, and results. The diabetic data warehouse is from a large integrated health care system in the New Orleans area with 30,383 diabetic patients. Methods for translating a complex relational database with time series and sequencing information to a flat file suitable for data mining are challenging. We discuss two variables in detail, a comorbidity index and the HgbA1c, a measure of glycemic control related to outcomes. We used the classification tree approach in Classification and Regression Trees (CART) with a binary target variable of HgbA1c >9.5 and 10 predictors: age, sex, emergency department visits, office visits, comorbidity index, dyslipidemia, hypertension, cardiovascular disease, retinopathy, end-stage renal disease. Unexpectedly, the most important variable associated with bad glycemic control is younger age, not the comorbiditity index or whether patients have related diseases. If we want to target diabetics with bad HgbA1c values, the odds of finding them is 3.2 times as high in those <65 years of age than those older. Data mining can discover novel associations that are useful to clinicians and administrators [corrected].


Exploring Game Space of Minimal Action Games via Parameter Tuning and Survival Analysis

Game designers can use computer-aided game design methods to model how players may experience the perceived difficulty of a game. We present methods to generate and analyze the difficulty of a wide variety of minimal action game variants throughout game space, where each point in this abstract design space represents a unique game variant. Focusing on a parameterized version of Flappy Bird, we predict hazard rates and difficulty curves using automatic playtesting, Monte Carlo simulation, a player model based on human motor skills (precision and actions per second), and survival analysis of score histograms. We demonstrate our techniques using simulated game play and actual game data from over 106 million play sessions of a popular online Flappy Bird variant, showing quantitative reasons why balancing a game for a wide range of player skill can be difficult. Some applications of our techniques include searching for a specific difficulty, game space visualization, computational creativity to find unique variants, and tuning game balance to adjust the difficulty curve even when game parameters are time varying, score dependent, or changing based on game progress.


Trainable approaches to surface natural language generation and their application to conversational dialog systems


Multi-accent deep neural network acoustic model with accent-specific top layer using the KLD-regularized model adaptation

We propose a multi-accent deep neural network acoustic model with an accent-specific top layer and shared bottom hidden layers. The accent-specific top layer is used to model the distinct accent specific patterns. The shared bottom hidden layers allow maximum knowledge sharing between the native and the accent models. This design is particularly attractive when considering deploying such a system to a live speech service due to its computational efficiency. We applied the KL-divergence (KLD) regularized model adaptation to train the accent-specific top layer. On the mobile short message dictation task (SMD), with 1K, 10K, and 100K British or Indian accent adaptation utterances, the proposed approach achieves 18.1%, 26.0%, and 28.5% or 16.1%, 25.4%, and 30.6% word error rate reduction (WERR) for the British and the Indian accent respectively against a baseline cross entropy (CE) model trained from 400 hour data. On the 100K utterance accent adaptation setup, comparable performance gain can be obtained against a baseline CE model trained with 2000 hour data. We observe smaller yet significant WER reduction on a baseline model trained using the MMI sequence-level criterion.


Vegetable price prediction using data mining classification technique

Each and every sector in this digital world is undergoing a dramatic change due to the influence of IT field. The agricultural sector needs more support for its development in developing countries like India. Price prediction helps the farmers and also Government to make effective decision. Based on the complexity of vegetable price prediction, making use of the characteristics of neural networks such as self-adapt, self-study and high fault tolerance, to build up the model of Back-propagation neural network to predict vegetable price. A prediction model was set up by applying the neural network. Taking tomato as an example, the parameters of the model are analyzed through experiment. At the end of the result of Back-propagation neural network shows absolute error percentage of monthly and weekly vegetable price prediction and analyze the accuracy percentage of the price prediction.


Image processing on multinode hadoop cluster

In the past few years the data produced all over the internet has increased with an exponential rate. The storage costs have been rising immensely. But, in the field of Computer Science, the introduction of new technologies has resulted in reduction of storage costs. This led to a rampant rise in the data generation rates. This huge amount of data that is so vast and complex such that classical methods are insufficient for processing is termed as ‘Big Data’. There are various tools in Hadoop to analyze the textual data such as Pig, HBase, etc. But the data present on the Internet as well as the Social Networking sites comprises of unstructured media. The maximum spectrum of the media files is covered by Image data. The major concern is not about the storage of images, but the processing of images being generated with the speed of light. Every day around 350 million pictures are being uploaded on social network. Until now, over 200 billion photos have been uploaded only on Facebook. This accounts to an average of around 200 photos per user. This whole amount of data generated round the globe can be classified into three formats-Structured, Semi-Structured, and Unstructured. The Image data contains not only the pictures but also the data defining those pictures such as the resolution, source of the image, capture device, etc. To fetch all this information in a structured format HIPI (Hadoop Image Processing Interface) tools are used. In this paper image processing is performed on a MultiNode Hadoop Cluster and its performance is measured.


Exploratory Analysis of the Factors Affecting Consumer Choice in E-Commerce: Conjoint Analysis

According to previous studies of online consumer behaviour, three factors are the most influential on purchasing behavior brand, colour and position of the product on the screen. However, a simultaneous influence of these three factors on the consumer decision making process has not been investigated previously. In this particular work we aim to execute a comprehensive study of the influence of these three factors. In order to answer our main research questions, we conducted an experiment with 96 different combinations of the three attributes, and using statistical analysis, such as conjoint analysis, t-test analysis and Kendall analysis we identified that the most influential factor to the online consumer decision making process is brand, the second most important attribute is the colour, which was estimated half as important as brand, and the least important attribute is the position on the screen. Additionally, we identified the main differences regarding consumers stated and revealed preferences regarding these three attributes.


Research guidelines for the Delphi survey technique.

Consensus methods such as the Delphi survey technique are being employed to help enhance effective decision-making in health and social care. The Delphi survey is a group facilitation technique, which is an iterative multistage process, designed to transform opinion into group consensus. It is a flexible approach, that is used commonly within the health and social sciences, yet little guidance exists to help researchers undertake this method of data collection. This paper aims to provide an understanding of the preparation, action steps and difficulties that are inherent within the Delphi. Used systematically and rigorously, the Delphi can contribute significantly to broadening knowledge within the nursing profession. However, careful thought must be given before using the method; there are key issues surrounding problem identification, researcher skills and data presentation that must be addressed. The paper does not claim to be definitive; it purports to act as a guide for those researchers who wish to exploit the Delphi methodology.


NOMA in 5G Systems: Exciting Possibilities for Enhancing Spectral Efficiency

This article provides an overview of power-domain non-orthogonal multiple access for 5G systems. The basic concepts and benefits are briefly presented, along with current solutions and standardization activities. In addition, limitations and research challenges are discussed.


What Shape Are Dolphins? Building 3D Morphable Models from 2D Images

3D morphable models are low-dimensional parameterizations of 3D object classes which provide a powerful means of associating 3D geometry to 2D images. However, morphable models are currently generated from 3D scans, so for general object classes such as animals they are economically and practically infeasible. We show that, given a small amount of user interaction (little more than that required to build a conventional morphable model), there is enough information in a collection of 2D pictures of certain object classes to generate a full 3D morphable model, even in the absence of surface texture. The key restriction is that the object class should not be strongly articulated, and that a very rough rigid model should be provided as an initial estimate of the “mean shape.” The model representation is a linear combination of subdivision surfaces, which we fit to image silhouettes and any identifiable key points using a novel combined continuous-discrete optimization strategy. Results are demonstrated on several natural object classes, and show that models of rather high quality can be obtained from this limited information.


A novel strong-motion seismic network for community participation in earthquake monitoring

The Quake-Catcher Network (QCN) is breaking new ground in seismology by combining new micro-electro-mechanical systems (MEMS) technology with volunteer seismic station distributed computing. Rather than distributing just computations, the QCN allows volunteers to participate in scientific data collection and computation. Using these innovative tools, QCN will increase the number of strong-motion observations for improved earthquake detection and analysis in California, and throughout the world. QCN's increased density of seismic measurements will revolutionize seismology. The QCN, in concert with current seismic networks, may soon provide advanced alerts when earthquakes occur, estimate the response of a building to earthquakes even before they happen, and generate a greater understanding of earthquakes for scientists and the general public alike. Details of how one can join the QCN are outlined. In addition, we have activities on our website that can be used in K-16 classrooms to teach students basic seismology and physics concepts.


A summary of team MIT's approach to the virtual robotics challenge

The paper describes the system developed by researchers from MIT for the Defense Advanced Research Projects Agency's (DARPA) Virtual Robotics Challenge (VRC), held in June 2013. The VRC was the first competition in the DARPA Robotics Challenge (DRC), a program that aims to “develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments”. The VRC required teams to guide a model of Boston Dynamics' humanoid robot, Atlas, through driving, walking, and manipulation tasks in simulation. Team MIT's user interface, the Viewer, provided the operator with a unified representation of all available information. A 3D rendering of the robot depicted its most recently estimated body state with respect to the surrounding environment, represented by point clouds and texture-mapped meshes as sensed by on-board LIDAR and fused over time.


High Power Outphasing Modulation


A Universal Denoising Framework With a New Impulse Detector and Nonlocal Means

Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). The operation is carried out in two stages, i.e., detection followed by filtering. For detection, first, we propose the robust outlyingness ratio (ROR) for measuring how impulselike each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.


Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).


Fast restoration of natural images corrupted by high-density impulse noise

In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures.


An Efficient Edge-Preserving Algorithm for Removal of Salt-and-Pepper Noise

In this letter, a novel algorithm for removing salt-and-pepper noise from corrupted images is presented. We employ an efficient impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art impulse denoising techniques. Especially, the proposed method can preserve edges very well while removing impulse noise. Since our algorithm is algorithmically simple, it is very suitable to be applied to many real-time applications.


Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction

This letter presents a novel two-stage noise adaptive fuzzy switching median (NAFSM) filter for salt-and-pepper noise detection and removal. Initially, the detection stage will utilize the histogram of the corrupted image to identify noise pixels. These detected ¿noise pixels¿ will then be subjected to the second stage of the filtering action, while ¿noise-free pixels¿ are retained and left unprocessed. Then, the NAFSM filtering mechanism employs fuzzy reasoning to handle uncertainty present in the extracted local information as introduced by noise. Simulation results indicate that the NAFSM is able to outperform some of the salt-and-pepper noise filters existing in literature.


A Communication-Optimal Framework for Contracting Distributed Tensors

Tensor contractions are extremely compute intensive generalized matrix multiplication operations encountered in many computational science fields, such as quantum chemistry and nuclear physics. Unlike distributed matrix multiplication, which has been extensively studied, limited work has been done in understanding distributed tensor contractions. In this paper, we characterize distributed tensor contraction algorithms on torus networks. We develop a framework with three fundamental communication operators to generate communication-efficient contraction algorithms for arbitrary tensor contractions. We show that for a given amount of memory per processor, the framework is communication optimal for all tensor contractions. We demonstrate performance and scalability of the framework on up to 262,144 cores on a Blue Gene/Q supercomputer.


\pi Match: Monocular vSLAM and Piecewise Planar Reconstruction Using Fast Plane Correspondences

This paper proposes πMatch, a monocular SLAM pipeline that, in contrast to current state-of-the-art feature-based methods, provides a dense Piecewise Planar Reconstruction (PPR) of the scene. It builds on recent advances in planar segmentation from affine correspondences (ACs) for generating motion hypotheses that are fed to a PEaRL framework which merges close motions and decides about multiple motion situations. Among the selected motions, the camera motion is identified and refined, allowing the subsequent refinement of the initial plane estimates. The high accuracy of this two-view approach allows a good scale estimation and a small drift in scale is observed, when compared to prior monocular methods. The final discrete optimization step provides an improved PPR of the scene. Experiments on the KITTI dataset show the accuracy of πMatch and that it robustly handles situations of multiple motions and pure rotation of the camera. A Matlab implementation of the pipeline runs in about 0.7 s per frame.


$K$ -Band CMOS Differential and Quadrature Voltage-Controlled Oscillators for Low Phase-Noise and Low-Power Applications

In this paper, modified circuit topologies of a differential voltage-controlled oscillator (VCO) and a quadrature VCO (QVCO) in a standard bulk 90-nm CMOS process are presented for low dc power and low phase-noise applications. By utilizing current-reuse and transformer-feedback techniques, the proposed VCO and QVCO can be operated at reduced dc power consumption while maintaining extraordinary circuit performance in terms of low phase-noise and low amplitude/phase errors. The VCO circuit topology is investigated to obtain the design procedure. The VCO is further applied to the QVCO design with a bottom-series coupling technique. The coupling network between two differential VCOs and device size are properly designed based on our proposed design methodology to achieve low amplitude and phase errors. Moreover, the VCO and the QVCO are fully characterized with amplitude and phase errors via a four-port vector network analyzer. With a dc power of 3 mW, the VCO exhibits a frequency tuning range from 20.3 to 21.3 GHz, a phase noise of - 116.4 dBc/Hz at 1-MHz offset, a figure-of-merit (FOM) of -198 dBc/Hz, a phase error of 3.8° , and an amplitude error of 0.9 dB. With a dc power of 6 mW, the QVCO demonstrates a phase noise of -117.4 dBc/Hz, a FOM of -195.6 dBc/Hz, a phase error of 4° , and an amplitude error of 0.6 dB. The proposed VCO and QVCO can be compared with the previously reported state-of-the-art VCOs and QVCOs in silicon-based technologies.


Abstract syntax graphs for domain specific languages

This paper presents a representation for embedded domain specific languages (EDSLs) using abstract syntax graphs (ASGs). The purpose of this representation is to deal with the important problem of defining operations that require observing or preserving sharing and recursion in EDSLs in an expressive, yet easy-to-use way. In contrast to more conventional representations based on abstract syntax trees, ASGs represent sharing and recursion explicitly as binder constructs. We use a functional representation of ASGs based on structured graphs, where binders are encoded with parametric higher-order abstract syntax. We show how adapt to this representation to well-typed ASGs. This is especially useful for EDSLs, which often reuse the type system of the host language. We also show an alternative class-based encoding of(well-typed) ASGs that enables extensible and modular well-typed EDSLs while allowing the manipulation of sharing and recursion.


An Investigation into the Pedagogical Features of Documents

Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the “pedagogical value” of the document to the learner.While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of “pedagogical roles” of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles.


Bilevel Model-Based Discriminative Dictionary Learning for Recognition

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the  $\ell _{0}$  or  $\ell _{1}$  norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn–Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.


Understanding Replication in Databases and Distributed Systems

Replication is an area of interest to both distributed systems and databases. The solutions developed from these two perspectives are conceptually similar but differ in many aspects: model, assumptions, mechanisms, guarantees provided, and implementation. In this paper, we provide an abstract and “neutral” framework to compare replication techniques from both communities in spite of the many subtle differences. The framework has been designed to emphasize the role played by different mechanisms and to facilitate comparisons. With this, it is possible to get a functional comparison of many ideas that is valuable for both didactic and practical purposes. The paper describes the replication techniques used in both communities, compares them, and points out ways in which they can be integrated to arrive to better, more robust replication protocols.


The Ontogenesis of Social Dominance : A Strategy-Based Evolutionary Perspective

Social dominance results when members of a social group vary in their ability to acquire resources in the presence of others (i.e., compete). Traditional approaches to social dominance often emphasize coercive behavior, but nonetheless suggest that dominant individuals are socially central (e.g., watched, attractive social partners). These patterns, however, apply to humans only up to a certain age. This apparent discontinuity may give the false impression that social dominance is less relevant to human social organization than it is to animal social organization. This paper reintroduces the ethological concept of social dominance, but reinterprets it from a strategy-based perspective. That is, if social dominance is defined as differential ability to control resources—without reference to how this is done—then children evidently employ different strategies to compete with peers (e.g., coercive and prosocial). Furthermore, the type of strategy children employ and peers’ responses to it depend largely on the ages of the children. By adopting a strategy-based approach to social dominance and explicitly incorporating developmental processes and uniquely human capacities, human social dominance patterns appear to be more similar to primate patterns than commonly believed. Implications for social competence, peer relationships, and the development of the self are discussed.  1999


PaDNet: Pan-Density Crowd Counting

Crowd counting in varying density scenes is a challenging problem in artificial intelligence (AI) and pattern recognition. Recently, deep convolutional neural networks (CNNs) are used to tackle this problem. However, the single-column CNN cannot achieve high accuracy and robustness in diverse density scenes. Meanwhile, multi-column CNNs lack effective way to accurately learn the features of different scales for estimating crowd density. To address these issues, we propose a novel pan-density level deep learning model, named as Pan-Density Network (PaDNet). Specifically, the PaDNet learns multi-scale features by three steps. First, several sub-networks are pre-trained on crowd images with different density-levels. Then, a Scale Reinforcement Net (SRN) is utilized to reinforce the scale features. Finally, a Fusion Net fuses all of the scale features to generate the final density map. Experiments on four crowd counting benchmark datasets, the ShanghaiTech, the UCF CC 50, the UCSD, and the UCF-QRNF, indicate that the PaDNet achieves the best performance and has high robustness in pan-density crowd counting compared with other state-of-the-art algorithms.


Offline handwritten signature verification — Literature review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.


Improving MQTT by Inclusion of Usage Control

Due to the increasing pervasiveness of Internet of Things (IoT) and Internet of Everything (IoE) devices, securing both their communications and operations has become of capital importance. Among the several existing IoT protocols, Message Queue Telemetry Transport (MQTT) is a widely-used general purpose one, usable in both constrained and powerful devices, which coordinates data exchanges through a publish/subscribe approach. In this paper, we propose a methodology to increase the security of the MQTT protocol, by including Usage Control in its operative workflow. The inclusion of usage control enables a fine-grained dynamic control of the rights of subscribers to access data and data-streams over time, by monitoring mutable attributes related to the subscriber, the environment or data itself. We will present the architecture and workflow of MQTT enhanced through Usage Control, also presenting a real implementation on Raspberry Pi 3 for performance


A three-phase ZVS PWM DC/DC converter with asymmetrical duty cycle associated with a three-phase version of the hybridge rectifier

This paper proposes the use of a three-phase version of the hybridge rectifier in the three-phase zero-voltage switch (ZVS) DC/DC converter with asymmetrical duty cycle. The use of this new rectifier improves the efficiency of the converter because only three diodes are responsible for the conduction losses in the secondary side. The current in the secondary side of the transformer is half the output current. In addition to this, all the advantages of the three-phase DC/DC converter, i.e., the increased frequency of the output and input currents, the improved distribution of the losses, as well as the soft commutation for a wide load range, are preserved. Therefore, the resulting topology is capable of achieving high efficiency and high power density at high power levels. The theoretical analysis, simulation, and experimental results obtained from a 6-kW prototype, and also a comparison of the efficiency of this converter with the full-bridge rectifier are presented.


Towards a Deeper Understanding of Adversarial Losses

Recent work has proposed various adversarial losses for training generative adversarial networks. Yet, it remains unclear what certain types of functions are valid adversarial loss functions, and how these loss functions perform against one another. In this paper, we aim to gain a deeper understanding of adversarial losses by decoupling the effects of their component functions and regularization terms. We first derive some necessary and sufficient conditions of the component functions such that the adversarial loss is a divergence-like measure between the data and the model distributions. In order to systematically compare different adversarial losses, we then propose DANTest—a new, simple framework based on discriminative adversarial networks. With this framework, we evaluate an extensive set of adversarial losses by combining different component functions and regularization approaches. This study leads to some new insights into the adversarial losses. For reproducibility, all source code is available at https: //github.com/salu133445/dan.


Grammatical error correction in non-native English

Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Previous research has mainly focussed on individual error types and current commercial proofreading tools only target limited error types. As sentences produced by learners may contain multiple errors of different types, a practical error correction system should be able to detect and correct all errors. In this thesis, we investigate GEC for learners of English as a Second Language (ESL). Specifically, we treat GEC as a translation task from incorrect into correct English, explore new models for developing end-to-end GEC systems for all error types, study system performance for each error type, and examine model generali-sation to different corpora. First, we apply Statistical Machine Translation (SMT) to GEC and prove that it can form the basis of a competitive all-errors GEC system. We implement an SMT-based GEC system which contributes to our winning system submitted to a shared task in 2014. Next, we propose a ranking model to re-rank correction candidates generated by an SMT-based GEC system. This model introduces new linguistic information and we show that it improves correction quality. Finally, we present the first study using Neural Machine Translation (NMT) for GEC. We demonstrate that NMT can be successfully applied to GEC and help capture new errors missed by an SMT-based GEC system. While we focus on GEC for English, our methods presented in this thesis can be easily applied to any language. Acknowledgements First and foremost, I owe a huge debt of gratitude to my supervisor, Ted Briscoe, who has patiently guided me through my PhD and always been very helpful, understanding and supportive. I cannot thank him enough for providing me with opportunities that helped me grow as a researcher and a critical thinker. I am immensely grateful to my examiners, Paula Buttery and Stephen Pulman, for their thorough reading of my thesis, their valuable comments and an enjoyable viva. My appreciation extends to my fellow members of the Natural Language and Information Processing research group, with whom I have always enjoyed discussing our work and other random things. My gratitude goes to Stephen Clark and Ann Copestake for giving me early feedback on my work as well as Christopher Bryant for generously reading my thesis draft. I would especially like to thank Mariano Felice for being not just a great colleague but also a dear friend. A special mention has …


D ecisions by senior management lay the groundwork for lower corporate levels to develop policies and procedures for various corporate activities. However, the potential business contribution of these activities depends on the quality of the decisions and in turn on the quality of the data used to make them. Some inputs are judgmental, others are from transactional systems, and still others are from external sources, but all must have a level of quality appropriate for the decisions they will be part of. Although concern about the quality of one’s data is not new, what is fairly recent is using the same data for multiple purposes, which can be quite different from their original purposes. Users working with a particular data set come to know and internalize its deficiencies and idiosyncrasies. This knowledge is lost when data is made available to other parties, like when data needed for decision making is collected in repositories called data warehouses. Here, we offer a conceptual framework for enhancing data quality in data warehouse environments. We explore the factors that should be considered, such as the current level of data quality, the levels of quality needed by the relevant decision processes, and the potential benefits of projects designed to enhance data quality. Those responsible for data quality have to understand the importance of such factors, as well as the interaction among them. This understanding is mandatory in data warehousing environments characterized by multiple users with differing needs for data quality. For warehouses supporting a limited number of decision processes, awareness of these issues coupled with good judgment should suffice. For more complex situations, however, the number and diversity of trade-offs make reliance on judgment alone problematic. For such situations, we offer a methodology that Nothing is more likely to undermine the performance and business value of a data warehouse than inappropriate, misunderstood, or ignored data quality. Enhancing DataQuality in DataWarehouse Environments


Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming.

Normative data stratified by three levels of age (16-59, 60-79, and 80-95 years) and three levels of education (0-8, 9-12, and 13-21 years) are presented for phonemic verbal fluency (FAS) and categorical verbal fluency (Animal Naming). The normative sample, aged 16 to 95 years, consisted of 1,300 cognitively intact individuals who resided in the community. Years of education ranged from 0 to 21. The total number of words in 1 minute for each of the letters F, A, and S was correlated r =.52 with the number of animal names generated in 1 minute. Regression analyses showed that FAS was more sensitive to the effects of education (18.6% of the variance) than age (11.0% of the variance). The opposite relationship occurred for Animal Naming, where age accounted for 23.4% of the variance and education accounted only for 13.6%. Gender accounted for less than 1% of variance for FAS and Animal Naming. The clinical utility of these norms is discussed.


Impact of carboplatin plus paclitaxel combined with endostar against A375 melanoma cells: An in vitro and in vivo analysis.

PURPOSE
The aim of the present study was to investigate the efficacy of carboplatin plus paclitaxel (CP) combined with endostar against A375 melanoma cells in vitro and in vivo.

METHODS
Effects on the cell viability and apoptosis induction were estimated with the Cell counting Kit-8 assay and Annexin V-FITC/Propidium Iodide staining. Fifty female BALB/c-nude mice with subcutaneous injection of A375 cells were randomized to be treated with normal saline, dacarbazine alone, dacarbazine plus endostar, carboplatin plus paclitaxel, and CP plus endostar. Tumor volume of mice was monitored after injection and survival time was adopted for survival analysis.

RESULTS
CP plus endostar significantly decreased the cell survival rate compared with CP (P<0.01). Combination of CP and endostar showed higher cytotoxicity to A375 cells in vitro than endostar plus dacarbazine (P<0.01). The percentage of apoptotic cells in A375 cells treated with CP plus endostar was appreciably higher when compared to CP group (P<0.05). The mean relative tumor size in CP group was definitely larger (p<0.05) than CP plus endostar group. In addition, the mean survival time in CP plus endostar group was notably elevated compared with the CP group (P<0.05).

CONCLUSIONS
Our data indicated that treatment with CP plus endostar significantly reduced cell growth and induced a high rate of apoptotic cells in the A375 melanoma cell line. CP and endostar exhibited synergistic anti-tumor activities in A375 melanoma cells in vitro. CP plus endostar suppressed the growth of xenograft tumors and prolonged the survival time of mice with xenograft tumors. Combination of CP and endostar may be a promising treatment for melanoma.


Heritability for Alzheimer's disease: the study of dementia in Swedish twins.

BACKGROUND
Alzheimer's disease has been thought to have familial and sporadic forms, and several genetic defects have been identified that chiefly explain early-onset familial cases. In this study, our purpose was to detect all cases of dementia in an established twin registry and to estimate total extent of genetic contribution to liability to Alzheimer's disease.

METHODS
At the first stage, members of the registry were screened for dementia, using in-person or telephone mental status testing. At the second stage, those who screened positively and their partners were referred for clinical work-ups, including neuropsychological assessment, physician examination, laboratory tests, and neuroimaging. Clinical diagnoses were assigned at a multidisciplinary consensus conference. Probandwise concordance rates were examined by zygosity, and structural modeling was applied to the data to estimate genetic and environmental influences, using both single- and multiple-threshold models.

RESULTS
Sixty-five pairs were identified in which one or both was demented. The probandwise concordance rate for Alzheimer's disease among monozygotic pairs was 67%; the corresponding figure for dizygotic pairs was 22%. Heritability of liability to Alzheimer's disease was estimated to be .74; to any dementia, .43. The other variance is attributable to environmental influences.

CONCLUSIONS
Findings indicate a substantial genetic effect for these predominantly late-onset Alzheimer's disease cases. At the same time, structural modeling results and large intra-pair differences in age of onset suggest that environmental factors are also important in determining whether and when an individual may develop dementia.


SANE: System for Fine Grained Named Entity Typing on Textual Data

Assignment of fine-grained types to named entities is gaining popularity as one of the major Information Extraction tasks due to its applications in several areas of Natural Language Processing. Existing systems use huge knowledge bases to improve the accuracy of the fine-grained types. We designed and developed SANE, a system that uses Wikipedia categories to fine grain the type of the named entities recognized in the textual data. The main contribution of this work is building a named entity typing system without the use of knowledge bases. Through our experiments, 1) we establish the usefulness of Wikipedia categories to Named Entity Typing and 2) we show that SANE’s performance is on par with the state-ofthe-art.


Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation

In its basic form, the reverse mode of computational differentiation yields the gradient of a scalar-valued function at a cost that is a small multiple of the computational work needed to evaluate the function itself. However, the corresponding memory requirement is proportional to the run-time of the evaluation program. Therefore, the practical applicability of the reverse mode in its original formulation is limited despite the availability of ever larger memory systems. This observation leads to the development of checkpointing schedules to reduce the storage requirements. This article presents the function revolve, which generates checkpointing schedules that are provably optimal with regard to a primary and a secondary criterion. This routine is intended to be used as an explicit “controller” for running a time-dependent applications program.


Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks

Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices. This imposes an upper-bound on the reduction of complexity of multiply-accumulate units. We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training. Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation. We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet. In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline. We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds. Overall this analysis enables precise tailoring of computation hardware to the application, yielding areaand power-optimal systems.


Sorry, I only speak natural language: a pattern-based, data-driven and guided approach to mapping natural language queries to SPARQL

We present a new interface based on natural language to support users in specifying their queries with respect to RDF datasets. The approach relies on a number of predefined patterns that uniquely determine a type of SPARQL query. The approach is incremental and assisted in that it guides a user step by step in specifying a query by incrementally parsing the input and providing suggestions for completion at every stage. The methodology to specify the patterns is informed by empirical distributions of SPARQL query types as found in standard query logs. So far, we have implemented the approach using two patterns only as proof-of-concept. The coverage of the pattern library will be extended in the future. We will also provide an evaluation of the approach on the well-known QALD dataset.


Recent and Emerging Topics in Wireless Industrial Communications: A Selection

In this paper we discuss a selection of promising and interesting research areas in the design of protocols and systems for wireless industrial communications. We have selected topics that have either emerged as hot topics in the industrial communications community in the last few years (like wireless sensor networks), or which could be worthwhile research topics in the next few years (for example cooperative diversity techniques for error control, cognitive radio/opportunistic spectrum access for mitigation of external interferences).


Nano-Power Wireless Wake-Up Receiver With Serial Peripheral Interface

We designed, implemented, tested and measured an ultra low power Wake Up Receiver (WUR), intended for use in Wireless Body Area Networks (WBAN). Gaussian On-Off Keying (GOOK) and Pulse Width Modulation (PWM) are used to modulate and encode, respectively, the preamble signal. The receiver incorporates a decoder to enable Serial Peripheral Interface (SPI). WUR was also comprehensively tested for power consumption and robustness to RF interference from wireless devices commonly found in the vicinity of persons utilising WBAN technology. Our results and comparative evaluation demonstrate that the achieved listening power of 270nW for the Wake Up Receiver is significantly lower power consumption than for the other state-of-the-art. The proposed preamble detection scheme can significantly reduce false wake ups due to other wireless devices in a WBAN. Additionally, SPI significantly reduces the overall power consumption for packet reception and decoding.


Performance Evaluation and Comparative Analysis of SubCarrier Modulation Wake-up Radio Systems for Energy-Efficient Wireless Sensor Networks

Energy-efficient communication is one of the main concerns of wireless sensor networks nowadays. A commonly employed approach for achieving energy efficiency has been the use of duty-cycled operation of the radio, where the node's transceiver is turned off and on regularly, listening to the radio channel for possible incoming communication during its on-state. Nonetheless, such a paradigm performs poorly for scenarios of low or bursty traffic because of unnecessary activations of the radio transceiver. As an alternative technology, Wake-up Radio (WuR) systems present a promising energy-efficient network operation, where target devices are only activated in an on-demand fashion by means of a special radio signal and a WuR receiver. In this paper, we analyze a novel wake-up radio approach that integrates both data communication and wake-up functionalities into one platform, providing a reconfigurable radio operation. Through physical experiments, we characterize the delay, current consumption and overall operational range performance of this approach under different transmit power levels. We also present an actual single-hop WuR application scenario, as well as demonstrate the first true multi-hop capabilities of a WuR platform and simulate its performance in a multi-hop scenario. Finally, by thorough qualitative comparisons to the most relevant WuR proposals in the literature, we state that the proposed WuR system stands out as a strong candidate for any application requiring energy-efficient wireless sensor node communications.


The many faces of publish/subscribe

Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.


A 37.5 /spl mu/W Body Channel Communication Wake-Up Receiver With Injection-Locking Ring Oscillator for Wireless Body Area Network

An ultra-low power wake-up receiver for body channel communication (BCC) is implemented in 130 nm CMOS process. The proposed wake-up receiver uses the injection-locking ring oscillator (ILRO) to replace the RF amplifier with low power consumption. Through the ILRO, the frequency modulated input signal is amplified to the full swing rectangular signal directly demodulated by the following low power PLL-based FSK demodulator. In addition, the energy-efficient BCC link mitigates the sensitivity and selectivity requirements for the receiver, which significantly reduces the power consumption. Furthermore, the auto frequency calibrator (AFC) is adopted to compensate the free running frequency of the ring oscillator which is caused by temperature variation and leakage current. The AFC reuses the PLL-based demodulator to periodically set the free running frequency to the desired frequency without any area overhead. As a result, the proposed wake-up receiver achieves a sensitivity of -62.7 dBm at a data rate of 200 kbps while consuming only 37.5 μW from the 0.7 V supply.


2D-to-3D image conversion by learning depth from examples

Among 2D-to-3D image conversion methods, those involving human operators have been most successful but also time-consuming and costly. Automatic methods, that typically make use of a deterministic 3D scene model, have not yet achieved the same level of quality as they often rely on assumptions that are easily violated in practice. In this paper, we adopt the radically different approach of “learning” the 3D scene structure. We develop a simplified and computationally-efficient version of our recent 2D-to-3D image conversion algorithm. Given a repository of 3D images, either as stereopairs or image+depth pairs, we find k pairs whose photometric content most closely matches that of a 2D query to be converted. Then, we fuse the k corresponding depth fields and align the fused depth with the 2D query. Unlike in our original work, we validate the simplified algorithm quantitatively on a Kinect-captured image+depth dataset against the Make3D algorithm. While far from perfect, the presented results demonstrate that online repositories of 3D content can be used for effective 2D-to-3D image conversion.


User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop

Collaborative Filtering(CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.


Corn Disease Identification from Leaf Images Using Convolutional Neural Networks

Plant diseases are one of the major factors that can negatively impact crop yields. Therefore, we want to create an automated system that can identify plant diseases as easily as possible. Our research goal is to help crop growers who might not be experts in plant pathology be able to identify diseases as soon as they occur in order to minimize the spread of disease and the damage to the crops. We propose a very light-weight convolutional neural network architecture that is trained to classify images of corn leaves into one of the four categories: common rust disease, northern leaf blight disease, gray leaf spot disease, or healthy. The dataset used is the PlantVillage dataset taken from plantvillage.org. The dataset consists of 8506 images of corn leaves from 4 categories. We evaluated our model using 5-fold cross-validation, and we achieves an accuracy of 97.09 % on average. The result of our model is slightly worse than the performance of other well-known CNNs architectures, but the complexity of our architecture is much lower, making it more suitable for real-time inferences on resource-constrained devices such as smartphones.


Argument discovery via crowdsourcing

The amount of controversial issues being discussed on the Web has been growing dramatically. In articles, blogs, and wikis, people express their points of view in the form of arguments, i.e., claims that are supported by evidence. Discovery of arguments has a large potential for informing decision-making. However, argument discovery is hindered by the sheer amount of available Web data and its unstructured, free-text representation. The former calls for automatic text-mining approaches, whereas the latter implies a need for manual processing to extract the structure of arguments. In this paper, we propose a crowdsourcing-based approach to build a corpus of arguments, an argumentation base, thereby mediating the trade-off of automatic text-mining and manual processing in argument discovery. We develop an end-to-end process that minimizes the crowd cost while maximizing the quality of crowd answers by: (1) ranking argumentative texts, (2) pro-actively eliciting user input to extract arguments from these texts, and (3) aggregating heterogeneous crowd answers. Our experiments with real-world datasets highlight that our method discovers virtually all arguments in documents when processing only 25% of the text with more than 80% precision, using only 50% of the budget consumed by a baseline algorithm.


Comparative study of automatic speech recognition techniques

Over the past decades, extensive research has been carried out on various possible implementations of automatic speech recognition (ASR) systems. The most renowned algorithms in the field of ASR are the mel-frequency cepstral coefficients and the hidden Markov models. However, there are also other methods, such as wavelet-based transforms, artificial neural networks and support vector machines, which are becoming more popular. This review article presents a comparative study on different approaches that were proposed for the task of ASR, and which are widely used nowadays.


Computational approaches to motor control

This review will focus on four areas of motor control which have recently been enriched both by neural network and control system models: motor planning, motor prediction, state estimation and motor learning. We will review the computational foundations of each of these concepts and present specific models which have been tested by psychophysical experiments. We will cover the topics of optimal control for motor planning, forward models for motor prediction, observer models of state estimation arid modular decomposition in motor learning. The aim of this review is to demonstrate how computational approaches, as well as proposing specific models, provide a theoretical framework to formalize the issues in motor control.


Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning

We introduce a framework for learning regrasping behaviors based on tactile data. First, we present a grasp stability predictor that uses spatio-temporal tactile features collected from the early-object-lifting phase to predict the grasp outcome with a high accuracy. Next, the trained predictor is used to supervise and provide feedback to a reinforcement learning algorithm that learns the required grasp adjustments based on tactile feedback. Our results gathered over more than 50 hours of real robot experiments indicate that the robot is able to predict the grasp outcome with 93% accuracy. In addition, the robot is able to improve the grasp success rate from 42% when randomly grasping an object to up to 97% when allowed to regrasp the object in case of a predicted failure.


Cross-Modal Multistep Fusion Network With Co-Attention for Visual Question Answering

Visual question answering (VQA) is receiving increasing attention from researchers in both the computer vision and natural language processing fields. There are two key components in the VQA task: feature extraction and multi-modal fusion. For feature extraction, we introduce a novel co-attention scheme by combining Sentence-guide Word Attention (SWA) and Question-guide Image Attention in a unified framework. To be specific, the textual attention SWA relies on the semantics of the whole question sentence to calculate contributions of different question words for text representation. For the multi-modal fusion, we propose a “Cross-modal Multistep Fusion (CMF)” network to generate multistep features and achieve multiple interactions for two modalities, rather than focusing on modeling complex interactions between two modals like most current feature fusion methods. To avoid the linear increase of the computational cost, we share the parameters for each step in the CMF. Extensive experiments demonstrate that the proposed method can achieve competitive or better performance than the state of the art.


Frontal cortex function as derived from hierarchical predictive coding

The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications for understanding the frontal cortex across multiple levels of description, from the level of single neurons to behavior.


Comparison of Collision Avoidance Systems and Applicability to Rail Transport

The paper presents an overview of the state of the art in collision avoidance related with transportation systems like the automatic identification system (AIS) for maritime transportation, traffic alert and collision avoidance system/automatic dependent surveillance-broadcast (TCAS/ADS-B) for aircraft, and the car-2-car communication system (C2C) for road transportation. The examined systems rely on position detection and direct communication among vehicles. Alike a collision avoidance system for railway transportation "RCAS" is introduced. Focussing on the communication aspects, possible applicability of the examined state of the art systems to RCAS is studied. The analysis are performed at different communication system layers, namely application (APP) layer, media access control (MAC) layer and physical layer (PHY), which are the most relevant for a single hop network broadcast system as favorized in RCAS. Since multihop and addressed communication are not foreseen in a first RCAS approach, the network layer is not taken into account.


MICROCONTROLLER BASED POWER INVERTER

1 CHAPTER ONE: BACKGROUND STUDY 2 1.


KYPO Cyber Range: Design and Use Cases

The physical and cyber worlds are increasingly intertwined and exposed to cyber attacks. The KYPO cyber range provides complex cyber systems and networks in a virtualized, fully controlled and monitored environment. Time-efficient and cost-effective deployment is feasible using cloud resources instead of a dedicated hardware infrastructure. This paper describes the design decisions made during it’s development. We prepared a set of use cases to evaluate the proposed design decisions and to demonstrate the key features of the KYPO cyber range. It was especially cyber training sessions and exercises with hundreds of participants which provided invaluable feedback for KYPO platform development.


Information systems interoperability: What lies beneath?

Interoperability is the most critical issue facing businesses that need to access information from multiple information systems. Our objective in this research is to develop a comprehensive framework and methodology to facilitate semantic interoperability among distributed and heterogeneous information systems. A comprehensive framework for managing various semantic conflicts is proposed. Our proposed framework provides a unified view of the underlying representational and reasoning formalism for the semantic mediation process. This framework is then used as a basis for automating the detection and resolution of semantic conflicts among heterogeneous information sources. We define several types of semantic mediators to achieve semantic interoperability. A domain-independent ontology is used to capture various semantic conflicts. A mediation-based query processing technique is developed to provide uniform and integrated access to the multiple heterogeneous databases. A usable prototype is implemented as a proof-of-concept for this work. Finally, the usefulness of our approach is evaluated using three cases in different application domains. Various heterogeneous datasets are used during the evaluation phase. The results of the evaluation suggest that correct identification and construction of both schema and ontology-schema mapping knowledge play very important roles in achieving interoperability at both the data and schema levels.


Feature Selection for SVMs

We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA micro array data.


Toward Automatic Program Synthesis

An elementary outline of the theorem-proving approach to automatic program synthesis is given, without dwelling on technical details. The method is illustrated by the automatic construction of both recursive and iterative programs operating on natural numbers, lists, and trees.
In order to construct a program satisfying certain specifications, a theorem induced by those specifications is proved, and the desired program is extracted from the proof. The same technique is applied to transform recursively defined functions into iterative programs, frequently with a major gain in efficiency.
It is emphasized that in order to construct a program with loops or with recursion, the principle of mathematical induction must be applied. The relation between the version of the induction rule used and the form of the program constructed is explored in some detail.


Developing a Feasible and Maintainable Ontology for Automatic Landscape Design

In general, landscape architecture includes analysis, planning, design, administration and management of natural and artificial. An important aspect is the formation of socalled sustainable landscapes that allow maximum use of the environment, natural resources and promote sustainable restoration of ecosystems. For such purposes, a designer needs a complete database with existing and suitable plants, but no designing tool has one. Therefore it is presented the structure and the development of on ontology suitable for storing and managing all information and knowledge about plants. The advantage is that the format of the ontology allows the storage of any plant species (e.g. live or fossil) and automated reasoning. Ontology is a formal conceptualization of a particular knowledge about the world, through the explicit representation of basic concepts, relations, and inference rules about themselves. Therefore the ontology may be used by a design tool for helping the designer and choosing the best options for a sustainable landscape. Keywords—environment; landscapes; ontology; ontology-based simulation; sustainable landscapes