Design and implementation of a single-phase buck-boost inverter

In order to simplify the configuration of photovoltaic (PV) grid-connection system, this paper proposes to adopt a buck-boost dc-dc converter, and then develops a single-phase inverter via the connection with an H-bridge unfolding circuit with line-commutated. Depending on the conditions of dc input-voltage and ac output-voltage, the proposed circuit can work functionally as either a step-down or step-up inverter. It is suitable for the applications with wide voltage-variation range. Since only one switch is operated with high-frequency, the switching loss can be significantly reduced to improve the efficiency. Finally, a laboratory prototype with 110 Vrms / 60 Hz output voltage is then simulated and implemented accordingly to verify the feasibility of the proposed inverter.


Stream-processing workloads and modern shared cluster environments exhibit high variability and unpredictability. Combined with the large parameter space and the diverse set of user SLOs, this makes modern streaming systems very challenging to statically configure and tune. To address these issues, in this paper we investigate a novel control-plane design, Chi, which supports continuous monitoring and feedback, and enables dynamic re-configuration. Chi leverages the key insight of embedding control-plane messages in the data-plane channels to achieve a low-latency and flexible control plane for stream-processing systems. Chi introduces a new reactive programming model and design mechanisms to asynchronously execute control policies, thus avoiding global synchronization. We show how this allows us to easily implement a wide spectrum of control policies targeting different use cases observed in production. Large-scale experiments using production workloads from a popular cloud provider demonstrate the flexibility and efficiency of our approach. PVLDB Reference Format: Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanan Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao. Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems. PVLDB, 11 (10): xxxx-yyyy, 2018. DOI: https://doi.org/10.14778/3231751.3231765


Using Dynamic Programming for Solving Variational Problems in Vision

Variational approaches have been proposed for solving many inverse problems in early vision, such as in the computation of optical flow, shape from shading, and energy-minimizing active contour models. In general however, variational approaches do not guarantee global optimality of the solution, require estimates of higher order derivatives of the discrete data, and do not allow direct and natural enforcement of constraints. In this paper we discuss dynamic programming as a novel approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, it is numerically stable, and it allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the efficacy of the proposed approach, application of dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a " time-delayed " discrete dynamic programming algorithm. A parallel procedure is discussed that can result in savings in computational costs.


VisTrails: visualization meets data management

Scientists are now faced with an incredible volume of data to analyze. To successfully analyze and validate various hypothesis, it is necessary to pose several queries, correlate disparate data, and create insightful visualizations of both the simulated processes and observed phenomena. Often, insight comes from comparing the results of multiple visualizations. Unfortunately, today this process is far from interactive and contains many error-prone and time-consuming tasks. As a result, the generation and maintenance of visualizations is a major bottleneck in the scientific process, hindering both the ability to mine scientific data and the actual use of the data. The VisTrails system represents our initial attempt to improve the scientific discovery process and reduce the time to insight. In VisTrails, we address the problem of visualization from a data management perspective: VisTrails manages the data and metadata of a visualization product. In this demonstration, we show the power and flexibility of our system by presenting actual scenarios in which scientific visualization is used and showing how our system improves usability, enables reproducibility, and greatly reduces the time required to create scientific visualizations.


The twisted string actuation system: Modeling and control

This paper describes a novel actuation system for very compact and light-weight robotic devices, like artificial hands. The actuation concept presented here allows the implementation of powerful tendon-based driving systems, using as actuators small-size DC motors characterized by high speed and low torque. After the presentation of the basic concept of this novel actuation system, the constitutive equations of the system are given, validated by means of laboratory tests. Moreover, the problem of tracking a desired actuation force profile is taken into account, considering as load a mass-spring-damper system. A control algorithm based on a second-order sliding manifold has been firstly evaluated by means of simulations, and then validated by experiments. This output-feedback controller has been chosen to guarantee a high level of robustness against disturbances, parameter variations and uncertainties while maintaining a low computational burden.


An Empirical Evaluation of Resources for the Identification of Diseases and Adverse Effects in Biomedical Literature

The mentions of human health perturbations such as the diseases and adverse effects denote a special entity class in the biomedical literature. They help in understanding the underlying risk factors and develop a preventive rationale. The recognition of these named entities in texts through dictionary-based approaches relies on the availability of appropriate terminological resources. Although few resources are publicly available, not all are suitable for the text mining needs. Therefore, this work provides an overview of the well known resources with respect to human diseases and adverse effects such as the MeSH, MedDRA, ICD-10, SNOMED CT, and UMLS. Individual dictionaries are generated from these resources and their performance in recognizing the named entities is evaluated over a manually annotated corpus. In addition, the steps for curating the dictionaries, rule-based acronym disambiguation and their impact on the dictionary performance is discussed. The results show that the MedDRA and UMLS achieve the best recall. Besides this, MedDRA provides an additional benefit of achieving a higher precision. The combination of search results of all the dictionaries achieve a considerably high recall. The corpus is available on http://www.scai.fraunhofer.de/disease-ae-corpus.html


The iSLIP scheduling algorithm for input-queued switches

h increasing number of high performance internetworking protocol routers, LAN and asynchronous transfer mode (ATM) switches use a switched backplane based on a crossbar switch. Most often, these systems use input queues to hold packets waiting to traverse the switching fabric. It is well known that if simple first in fh-st out (FIFO) input queues are used to hold packets then, even under benign conditions, head-of-line (HOL) blocking limits the achievable bandwidth to approximately 58.6% of the maximum. HOL blocking can be overcome by the use of virtual output queueing, which is described in this paper. A scheduling algorithm is used to configure the crossbar switch, deciding the order in which packets will be served. Recent results have shown that with a suitable scheduling algorithm, 100% throughput can he achieved. In this paper, we present a scheduling algorithm called iSLIP. An iterative, round-robin algorithm, iSLIP can achieve 100% throughput for uniform traffic, yet is simple to implement in hardware. Iterative and noniterative versions of the algorithms are presented, along with modified versions for prioritized traffic. Simulation results are presented to indicate the performance of iSLIP under benign and bursty traffic conditions. Prototype and commercial implementations of SLIP exist in systems with aggregate bandwidths ranging from 50 to SO0 Gb/s. When the traffic is nonuniform, SLIP quickly adapts to a fair scheduling policy that is guaranteed never to starve an input queue. Finally, we describe the implementation complexity of SLIP. Based on a two-dimensional (2-D) array of priority encoders, single-chip schedulers have been built supporting up to 32 ports, and making approximately 100 million scheduling decisions per second. I~l&x TermsATM switch, crossbar switch, input-queueing, IP router, scheduling.


Reliable detection and skew correction method of license plate for PTZ camera-based license plate recognition system

The skewness of license plates affects negatively all processing steps of license plate recognition including character segmentation and character classification. The skewness of the license plate in the captured image may appear severely under PTZ camera environments since license plate appears to have various postures in the captured image due to panning and tilting positions of PTZ cameras. Thus far, most of previous works on this subject have mainly resolved skewness problem due to rotation in plane, but not rotation in depth. In this paper, by utilizing planar projective transformation (planar homography), we propose a reliable skew correction method of license plates which handles also the skewness due to rotation in depth. For more reliable deskewing process, the license plate needs to be detected more reliably so that this paper also proposes a reliable license plate detection method. The effectiveness of the proposed skew correction method as well as the proposed license plate detection method is verified though experiments.


Rapid Graph Layout Using Space Filling Curves

Network data frequently arises in a wide variety of fields, and node-link diagrams are a very natural and intuitive representation of such data. In order for a node-link diagram to be effective, the nodes must be arranged well on the screen. While many graph layout algorithms exist for this purpose, they often have limitations such as high computational complexity or node colocation. This paper proposes a new approach to graph layout through the use of space filling curves which is very fast and guarantees that there will be no nodes that are colocated. The resulting layout is also aesthetic and satisfies several criteria for graph layout effectiveness.


Context-Free Transductions with Neural Stacks

This paper analyzes the behavior of stackaugmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, contextfree language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.


Radio resource allocation and power control scheme to mitigate interference in device-to-device communications underlaying LTE-A uplink cellular networks

The integration of Device-to-Device (D2D) communication into a cellular network improves system spectral efficiency, increases capacity, reduces power consumption and also reduces traffic to the evolved NodeB (eNB). However, D2D communication generates interference to the conventional cellular network which deteriorates the system performance. In this paper, we propose a radio resource allocation and power control scheme to mitigate the interference using cell sectorization scheme while reusing uplink cellular resources by the D2D pairs. By simulations, we show that our proposed scheme improves the overall system performance compared with the existing methods.


Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles

The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in this paper to give autonomous vehicles the ability to make decisions like a human. In our method, a convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method.


Robot Learning from Demonstration: A Task-level Planning Approach:

In this paper, we deal with the problem of task level learning and planning for robotic applications that involve object manipulation. In dynamic environments, preprogramming robots for execution of complex tasks such as for example setting a dinner table is of little use, since the same order of subtasks may not be conceivable due to the changed state of the world in the execution phase. In our approach, we aim to learn the goal of the task, and use a task planner to reach the goal given different initial states of the world. However, for some tasks, there are underlying constraints that must be fulfilled, and knowing just the final goal is not sufficient. We propose two techniques for constraint identification. In the first case, the teacher can directly instruct the system about the underlying constraints. In the second case, the constraints are identified by the robot itself through the merging of multiple observations. The constraints are then considered in the planning phase, allowing the task to be executed without violating any of them. We evaluate our work on a real robot performing pick-and-place tasks with several objects of various size.


Does Life Satisfaction influence the intention (We-Intention) to use Facebook?

Several studies have investigated a variety of factors affecting use of social networking sites (SNSs), but the investigation of these factors is still under development. In this study, we aim to contribute to the literature by extending and testing an existent conceptual model in a new context, including Life Satisfaction as an independent variable to explain the intention (We-Intention) to use Facebook. The previous models has Subjective Norm, Group Norms, Social Identity, Purposive Value, Self-Discovery, Maintaining Interpersonal Interconnectivity, Social Enhancement, Entertainment Value and Social Presence as the predictor variables. An online survey with Brazilians (n = 1111) was conducted. Our structural equation modeling reveals that Life Satisfaction influence on We-Intention is mediated by Subjective Norm, Group Norm, Social Identity, Entertainment Value, and Maintaining Interpersonal Interconnectivity (R-squared value is 0.36). Our findings, while consistent with existing literature in terms of theories, reveal different arrangements among factors influencing Brazilian consumers’ behavior. 2015 Elsevier Ltd. All rights reserved.


NailTactors: eyes-free spatial output using a nail-mounted tactor array

This paper investigates the feasibility of using a nail-mounted array of tactors, NailTactors, as an eyes-free output device. By rim-attached eccentric-rotating-mass (ERM) vibrators to artificial nails, miniature high-resolution tactile displays were realized as an eyes-free output device. To understand how to deliver rich signals to users for valid signal perception, three user studies were conducted. The results suggest that users can not only recognized absolute and relative directional cues, but also recognized numerical characters in EdgeWrite format with an overall 89% recognition rate. Experiments also identified the optimal placement of ERM actuators for maximizing information transfer.


Automatic medical image annotation in ImageCLEF 2007: Overview, results, and discussion

In this paper, the automatic medical annotation task of the 2007 CLEF cross-language image retrieval campaign (ImageCLEF) is described. The paper focusses on the images used, the task setup, and the results obtained in the evaluation campaign. Since 2005, the medical automatic image annotation task exists in ImageCLEF with increasing complexity to evaluate the performance of state-of-the-art methods for completely automatic annotation of medical images based on visual properties. The paper also describes the evolution of the task from its origin in 2005 to 2007. The 2007 task, comprising 11,000 fully annotated training images and 1,000 test images to be annotated, is a realistic task with a large number of possible classes at different levels of detail. Detailed analysis of the methods across participating groups is presented with respect to the (i) image representation, (ii) classification method, and (iii) use of the class hierarchy. The results show that methods which build on local image descriptors and discriminative models are able to provide good predictions of the image classes, mostly by using techniques that were originally developed in the machine learning and computer vision domain for object recognition in non-medical images.


Stochastic Backpropagation and Approximate Inference in Deep Generative Models

We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent an approximate posterior distribution and uses this for optimisation of a variational lower bound. We develop stochastic backpropagation – rules for gradient backpropagation through stochastic variables – and derive an algorithm that allows for joint optimisation of the parameters of both the generative and recognition models. We demonstrate on several real-world data sets that by using stochastic backpropagation and variational inference, we obtain models that are able to generate realistic samples of data, allow for accurate imputations of missing data, and provide a useful tool for high-dimensional data visualisation.


Generalized Denoising Auto-Encoders as Generative Models

Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued. This has led to various proposals for sampling from this implicitly learned density function, using Langevin and Metropolis-Hastings MCMC. However, it remained unclear how to connect the training procedure of regularized auto-encoders to the implicit estimation of the underlying datagenerating distribution when the data are discrete, or using other forms of corruption process and reconstruction errors. Another issue is the mathematical justification which is only valid in the limit of small corruption noise. We propose here a different attack on the problem, which deals with all these issues: arbitrary (but noisy enough) corruption, arbitrary reconstruction loss (seen as a log-likelihood), handling both discrete and continuous-valued variables, and removing the bias due to non-infinitesimal corruption noise (or non-infinitesimal contractive penalty).


Sampling Generative Networks: Notes on a Few Effective Techniques

We introduce several techniques for sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation prevents diverging from a model’s prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifolds created by analogies and nearest neighbors. We demonstrate two new techniques for deriving attribute vectors: bias-corrected vectors with data replication and synthetic vectors with data augmentation. Most techniques are intended to be independent of model type and examples are shown on both Variational Autoencoders and Generative Adversarial Networks. Keywords—Generative; VAE; GAN; Sampling; Manifold


Discriminative Regularization for Generative Models

We explore the question of whether the representations learned by classifiers can be used to enhance the quality of generative models. Our conjecture is that labels correspond to characteristics of natural data which are most salient to humans: identity in faces, objects in images, and utterances in speech. We propose to take advantage of this by using the representations from discriminative classifiers to augment the objective function corresponding to a generative model. In particular we enhance the objective function of the variational autoencoder, a popular generative model, with a discriminative regularization term. We show that enhancing the objective function in this way leads to samples that are clearer and have higher visual quality than the samples from the standard variational autoencoders.


Light emitting diodes reliability review

0026-2714/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.microrel.2011.07.063 ⇑ Corresponding author at: CALCE Center for Adva University of Maryland, College Park, MD 20742, Un 5323; fax: +1 301 314 9269. E-mail address: pecht@calce.umd.edu (M. Pecht). The increasing demand for light emitting diodes (LEDs) has been driven by a number of application categories, including display backlighting, communications, medical services, signage, and general illumination. The construction of LEDs is somewhat similar to microelectronics, but there are functional requirements, materials, and interfaces in LEDs that make their failure modes and mechanisms unique. This paper presents a comprehensive review for industry and academic research on LED failure mechanisms and reliability to help LED developers and end-product manufacturers focus resources in an effective manner. The focus is on the reliability of LEDs at the die and package levels. The reliability information provided by the LED manufacturers is not at a mature enough stage to be useful to most consumers and end-product manufacturers. This paper provides the groundwork for an understanding of the reliability issues of LEDs across the supply chain. We provide an introduction to LEDs and present the key industries that use LEDs and LED applications. The construction details and fabrication steps of LEDs as they relate to failure mechanisms and reliability are discussed next. We then categorize LED failures into thirteen different groups related to semiconductor, interconnect, and package reliability issues. We then identify the relationships between failure causes and their associated mechanisms, issues in thermal standardization, and critical areas of investigation and development in LED technology and reliability. 2011 Elsevier Ltd. All rights reserved.


Computability and λ-Definability

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Mathematical models of cancer metabolism.

Metabolism is essential for life, and its alteration is implicated in multiple human diseases. The transformation from a normal to a cancerous cell requires metabolic changes to fuel the high metabolic demands of cancer cells, including but not limited to cell proliferation and cell migration. In recent years, there have been a number of new discoveries connecting known aberrations in oncogenic and tumour suppressor pathways with metabolic alterations required to sustain cell proliferation and migration. However, an understanding of the selective advantage of these metabolic alterations is still lacking. Here, we review the literature on mathematical models of metabolism, with an emphasis on their contribution to the identification of the selective advantage of metabolic phenotypes that seem otherwise wasteful or accidental. We will show how the molecular hallmarks of cancer can be related to cell proliferation and tissue remodelling, the two major physiological requirements for the development of a multicellular structure. We will cover different areas such as genome-wide gene expression analysis, flux balance models, kinetic models, reaction diffusion models and models of the tumour microenvironment. We will also highlight current challenges and how their resolution will help to achieve a better understanding of cancer metabolism and the metabolic vulnerabilities of cancers.


A Direct AC–AC Converter for Inductive Power-Transfer Systems

This paper proposes a direct ac-ac converter to generate a high-frequency current for inductive power-transfer (IPT) systems. Unlike traditional dc-ac converters for IPT systems, the proposed converter can achieve a high-frequency current generation directly from an ac power source without a dc link. The power converter can maintain high-frequency resonance by natural circuit oscillation and discrete energy injection control. A variable-frequency control strategy is developed to vary the switching frequency to keep the high-frequency current operating with zero-current switching. Thus, the switching stress, power losses, and electromagnetic interference of the converter are reduced. Theoretical analysis, simulation, and experimental results show that the output track current is fully controllable with good waveforms for contactless power-transfer applications.


MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.


Investigation of a new motor assessment scale for stroke patients.

The purpose of this paper is to present and describe a motor assessment scale (MAS) for stroke patients and to report on the investigation of two aspects of its reliability. The MAS is a brief and easily administered assessment of eight areas of motor function and one item related to muscle tone. Each item is scored on a scale from 0 to 6. To check interrater reliability, we videotaped five stroke patients while they were being assessed with the MAS. These scores were used as the criterion ratings. Twenty raters then assessed these patients, and their results were correlated with the criterion ratings. We determined test-retest reliability by assessing on two occasions, separated by a four-week interval, 14 stroke patients whose recovery was considered to be stable and by correlating these scores. The MAS was found to be highly reliable with an average interrater correlation of .95 and an average test-retest correlation of .98.


Time-Domain Finite-Difference and Finite-Element Methods for Maxwell Equations in Complex Media

Extensions of finite-difference time-domain (FDTD) and finite-element time-domain (FETD) algorithms are reviewed for solving transient Maxwell equations in complex media. Also provided are a few representative examples to illustrate the modeling capabilities of FDTD and FETD for complex media. The term complex media refers here to media with dispersive, (bi)anisotropic, inhomogeneous, and/or nonlinear properties present in the constitutive tensors.


Creating SERS hot spots on MoS(2) nanosheets with in situ grown gold nanoparticles.

Herein, a reliable surface-enhanced Raman scattering (SERS)-active substrate has been prepared by synthesizing gold nanoparticles (AuNPs)-decorated MoS2 nanocomposite. The AuNPs grew in situ on the surface of MoS2 nanosheet to form efficient SERS hot spots by a spontaneous redox reaction with tetrachloroauric acid (HAuCl4) without any reducing agent. The morphologies of MoS2 and AuNPs-decorated MoS2 nanosheet were characterized by TEM, HRTEM, and AFM. The formation of hot spots greatly depended on the ratio of MoS2 and HAuCl4. When the concentration of HAuCl4 was 2.4 mM, the as-prepared AuNPs@MoS2-3 nanocomposite exhibited a high-quality SERS activity toward probe molecule due to the generated hot spots. The spot-to-spot SERS signals showed that the relative standard deviation (RSD) in the intensity of the main Raman vibration modes (1362, 1511, and 1652 cm(-1)) of Rhodamine 6G were about 20%, which displayed good uniformity and reproducibility. The AuNPs@MoS2-based substrate was reliable, sensitive, and reproducible, which showed great potential to be an excellent SERS substrate for biological and chemical detection.


SIFT Features Tracking for Video Stabilization

This paper presents a video stabilization algorithm based on the extraction and tracking of scale invariant feature transform features through video frames. Implementation of SIFT operator is analyzed and adapted to be used in a feature-based motion estimation algorithm. SIFT features are extracted from video frames and then their trajectory is evaluated to estimate interframe motion. A modified version of iterative least squares method is adopted to avoid estimation errors and features are tracked as they appear in nearby frames to improve video stability. Intentional camera motion is eventually filtered with adaptive motion vector integration. Results confirm the effectiveness of the method.


Compact Single- and Dual-Band Filtering Patch Antenna Arrays Using Novel Feeding Scheme

This paper presents two compact patch antenna arrays using a novel feeding scheme to integrate bandpass responses. The two arrays have no filtering circuits but reveal high filtering performance. For each array, only a microstrip feed line is coupled to radiating patches and specific coupling regions are employed to obtain the desired current distribution on the radiating patches. In the operating band, the induced currents on the patches are in phase, resulting in high boresight gain. At the out-of-band frequencies, the patches are not excited or the induced currents on them are 180° out of phase, resulting in radiation suppression. Using this method, out-of-band radiation can be suppressed without degrading the in-band performance. For demonstration, a four-element planar patch array is first implemented. Single-band bandpass response is observed in the experiment. Measured in-band gains are 12.6 dBi, whereas out-of-band suppression is more than 20 dB. Then the feeding scheme is employed to design two three-element subarrays to form a planar dual-band patch array. Also, dual-band bandpass responses are obtained with high frequency selectivity.


A broadband substrate integrated waveguide (SIW) filter

An eleven-stage broadband SIW filter has been designed, fabricated and measured. The measured results show that the SIW filter can be operated from 9.25 GHz to 11.25 GHz with good selectivity performance. The filter has a small size, low weight, and can be integrated with other SIW components without any transitions. It's suitable for the design of microwave and millimeter wave integrated circuits


Efficient Pedestrian Detection in Urban Traffic Scenes

Pedestrians are important participants in urban traffic environments, and thus act as an interesting category of objects for autonomous cars. Automatic pedestrian detection is an essential task for protecting pedestrians from collision. In this thesis, we investigate and develop novel approaches by interpreting spatial and temporal characteristics of pedestrians, in three different aspects: shape, cognition and motion. The special up-right human body shape, especially the geometry of the head and shoulder area, is the most discriminative characteristic for pedestrians from other object categories. Inspired by the success of Haar-like features for detecting human faces, which also exhibit a uniform shape structure, we propose to design particular Haar-like features for pedestrians. Tailored to a pre-defined statistical pedestrian shape model, Haar-like templates with multiple modalities are designed to describe local difference of the shape structure. Cognition theories aim to explain how human visual systems process input visual signals in an accurate and fast way. By emulating the center-surround mechanism in human visual systems, we design multi-channel, multi-direction and multi-scale contrast features, and boost them to respond to the appearance of pedestrians. In this way, our detector is considered as a top-down saliency system. In the last part of this thesis, we exploit the temporal characteristics for moving pedestrians and then employ motion information for feature design, as well as for regions of interest (ROIs) selection. Motion segmentation on optical flow fields enables us to select those blobs most probably containing moving pedestrians; a combination of Histogram of Oriented Gradients (HOG) and motion self difference features further enables robust detection. We test our three approaches on image and video data captured in urban traffic scenes, which are rather challenging due to dynamic and complex backgrounds. The achieved results demonstrate that our approaches reach and surpass state-of-the-art performance, and can also be employed for other applications, such as indoor robotics or public surveillance.


Exploiting Tri-Relationship for Fake News Detection

Social media for news consumption is becoming popular nowadays. The low cost, easy access and rapid information dissemination of social media bring benefits for people to seek out news timely. However, it also causes the widespread of fake news, i.e., low-quality news pieces that are intentionally fabricated. The fake news brings about several negative effects on individual consumers, news ecosystem, and even society trust. Previous fake news detection methods mainly focus on news contents for deception classification or claim fact-checking. Recent Social and Psychology studies show potential importance to utilize social media data: 1) Confirmation bias effect reveals that consumers prefer to believe information that confirms their existing stances; 2) Echo chamber effect suggests that people tend to follow likeminded users and form segregated communities on social media. Even though users social engagements towards news on social media provide abundant auxiliary information for better detecting fake news, but existing work exploiting social engagements is rather limited. In this paper, we explore the correlations of publisher bias, news stance, and relevant user engagements simultaneously, and propose a Tri-Relationship Fake News detection framework (TriFN). We also provide two comprehensive real-world fake news datasets to facilitate fake news research. Experiments on these datasets demonstrate the effectiveness of the proposed approach. Introduction People nowadays tend to seek out and consume news from social media rather than traditional news organizations. For example, 62% of U.S. adults get news on social media in 2016, while in 2012, only 49 percent reported seeing news on social media. However, social media for news consumption is a double-edged sword. The quality of news on social media is much lower than traditional news organizations. Large volumes of “fake news”, i.e., those news articles with intentionally false information, are produced online for a variety of purposes, such as financial and political gain (Klein and Wueller 2017; Allcott and Gentzkow 2017). Fake news can have detrimental effects on individuals and the society. First, people may be misled by fake Copyright c © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. http://www.journalism.org/2016/05/26/news-use-acrosssocial-media-platforms-2016/ news and accept false beliefs (Nyhan and Reifler 2010; Paul and Matthews 2016). Second, fake news could change the way people respond to true news. Third, the widespread of fake news could break the trustworthiness of entire news ecosystem. Thus, it is important to detect fake news on social media. Fake news is intentionally written to mislead consumers, which makes it nontrivial to detect simply based on news content. Thus, it is necessary to explore auxiliary information to improve detection. For example, several style-based approaches try to capture the deceptive manipulators originated from the particular writing style of fake news (Rubin and Lukoianova 2015; Potthast et al. 2017). In addition, previous approaches try to aggregate users’ responses from relevant social engagements to infer the veracity of original news (Castillo, Mendoza, and Poblete 2011; Gupta, Zhao, and Han 2012). The news ecosystem on social media involves three basic entities, i.e., news publisher, news and social media users. Figure 1 gives an illustration of such ecosystem. In Figure 1, p1, p2 and p3 are news publishers who publish news a1, . . . , a4 and u1, . . . , u6 are users who have engaged in posting these news. In addition, users with similar interests can also form social links. The tri-relationship among publisher, news, and social engagements contains additional information to help detect fake news. First, sociallogical studies on journalism have theorized the correlation between the partisan bias of publisher and news contents veracity (Gentzkow, Shapiro, and Stone 2014; Entman 2007), where partisan means the perceived bias of the publisher in the selection of how news is reported and covered. For example, in Figure 1, for p1 with extreme left partisan bias and p2 with extreme right partisan bias, to support their own partisan, they have high degree to report fake news, such as a1 and a3; while for a mainstream publisher p3 that has least partisan bias, she has lower degree to manipulate original news events, and is more likely to write true news a4. Thus, exploiting publisher partisan information can bring additional benefits to predict fake news. Second, mining user engagements on social media towards the news also help fake news detection. Different users have different credibility levels on social media, and https://www.nytimes.com/2016/11/28/opinion/fake-newsand-the-internet-shell-game.html? Figure 1: Tri-relationship among publishers, news pieces, and social media users for news dissemination ecosystem. the credibility score which means “the quality of being trustworthy” (Abbasi and Liu 2013) has a strong indication of whether the user is more likely to engage fake news or not. Those less credible users, such as malicious accounts or normal users who are vulnerable to fake news, are more likely to spread fake news. For example, u2 and u4 are users with low credibility scores, and they tend to spread fake news more than other highly credible users. In addition, users tend to form relationships with like-minded people. For example, user u5 and u6 are friends on social media, so they tend to engage those news that confirm their own views, such as a4. Publisher partisan information can bridge the publishernews relationship, while social engagements can capture the news-user relationship. In other words, they provide complementary information that has potential to improve fake news prediction. Thus, it’s important to integrate these two components and model the tri-relationship simultaneously. In this paper, we study the novel problem of exploiting trirelationship for fake news detection. In essence, we need to address the following challenges (1) how to mathematically model the tri-relationship to extract news feature representations; and (2) how to take the advantage of tri-relationship learning for fake news detection. In an attempt to address these challenges, we propose a novel framework TriFN that captures the Tri-relationship for Fake News detection. The main contributions are: • We provide a principled way to model tri-relationship among publisher, news, and relevant user engagements simultaneously; • We propose a novel framework TriFN that exploits trirelationship for fake news prediction; and • We evaluate the effectiveness of the proposed framework for fake news detection through extensive experiments on newly collected real-world datasets. Problem Statement Even though fake news has been existed for long time, there is no agreed definition. In this paper, we follow the definition of fake news that is widely used in recent research (Shu et al. 2017; Zubiaga et al. 2017; Allcott and Gentzkow 2017), which has been shown to be able to 1) provide theoretical and practical values for fake news topic; and 2) eliminate the ambiguities between fake news and related concepts. DEFINITION 1 (FAKE NEWS) Fake news is a news article that is intentionally and verifiably false. Let A = {a1, a2, ..., an} be the set of n news articles and U = {u1, u2, ..., um} be the set of m users on social media engaging the news spreading process. We denote X ∈ R as the news feature matrix. Users can become friends with other users and we use A ∈ {0, 1} to denote the user-user adjacency matrix. On social media sites, users can easily share, comment and discuss about the news pieces. This kind of social engagements provide auxiliary information for fake news detection. We denote the social news engagement matrix as W ∈ {0, 1}, where Wij = 1 indicate that user ui has engaged in the spreading process of the news piece aj ; otherwise Wij = 0. It’s worth mentioning that we focus on those engagements that show that users agree with the news. For example, For example, we only utilize those users that directly post the news, or repost the news without adding comments. More details will introduced in Section . We also denote P = {p1, p2, ..., pl} as the set of l news publishers. In addition, we denote B ∈ R as the publisher-news relation matrix, and Bkj = 1 means news publisher pk publishes the news article aj ; otherwise Bkj = 0. We assume that the partisan labels of some publishers are given and available. We define o ∈ {−1, 0, 1} as the partisan label vectors, where -1, 0, 1 represents left-, neutral-, and right-partisan bias. Similar to previous research (Shu et al. 2017), we treat fake news detection problem as a binary classification problem. In other words, each news piece can be true or fake, and we use y = {y1;y2; ...;yn} ∈ R n×1 to represent the labels, and yj = 1 means news piece aj is fake news; yj = −1 means true news. With the notations given above, the problem is formally defined as, Given news article feature matrix X, user adjacency matrix A, user social engagement matrix W, publishernews publishing matrix B, publisher partisan label vector o, and partial labeled news vector yL, we aim to predict remaining unlabeled news label vector yU . A Tri-Relationship Embedding Framework In this section, we propose a semi-supervised detection framework by exploiting tri-relationship. The idea of modeling tri-relationship is demonstrated in Figure 2. Specifically, we first introduce the news latent feature embedding from news content, and then show how to model user social engagements and publisher partisan separately; At last, we integrate the components to model tri-relationship and provide a semi-supervised detection framework. A Basic Model for News Content Embedding The inherent manipulators of fake news can be reflected in the news content. Thus, it’s important to extract


Which User Interactions Predict Levels of Expertise in Work-Integrated Learning?

Predicting knowledge levels from user’s implicit interactions with an adaptive system is a difficult task, particularly in learning systems that are used in the context of daily work tasks. We have collected interactions of six persons working with the adaptive work-integrated learning system APOSDLE over a period of two months to find out whether naturally occurring interactions with the system can be used to predict their level of expertise. One set of interactions is based on the tasks they performed, the other on a number of additional Knowledge Indicating Events (KIE). We find that the addition of KIE significantly improves the prediction as compared to using tasks only. Both approaches are superior to a model that uses only the frequencies of events.


Application of a LabVIEW for Real-Time Control of Ball and Beam System


Data-Confined HTML5 Applications

Rich client-side applications written in HTML5 proliferate diverse platforms such as mobile devices, commodity PCs, and the web platform. These client-side HTML5 applications are increasingly accessing sensitive data, including users’ personal and social data, sensor data, and capability-bearing tokens. To fulfill their security and privacy guarantees, these applications need to maintain certain data-confinement invariants. These invariants are not explicitly stated in today’s HTML5 applications and are enforced using ad-hoc mechanisms. The complexity of web applications, coupled with hard-to-analyze client-side languages, leads to low-assurance data-confinement mechanisms in which the whole application needs to be in the TCB to ensure data-confinement in-


Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia

Convolutions have long been regarded as fundamental to applied mathematics, physics and engineering. Their mathematical elegance allows for common tasks such as numerical differentiation to be computed efficiently on large data sets. Efficient computation of convolutions is critical to artificial intelligence in real-time applications, like machine vision, where convolutions must be continuously and efficiently computed on tens to hundreds of kilobytes per second. In this paper, we explore how convolutions are used in fundamental machine vision applications. We present an accelerated n-dimensional convolution package in the high performance computing language, Julia, and demonstrate its efficacy in solving the time to contact problem for machine vision. Results are measured against synthetically generated videos and quantitatively assessed according to their mean squared error from the ground truth. We achieve over an order of magnitude decrease in compute time and allocated memory for comparable machine vision applications. All code is packaged and integrated into the official Julia Package Manager to be used in various other scenarios.


Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement

Community question answering services (CQAS) (e.g., Yahoo! Answers) provides a platform where people post questions and answer questions posed by others. Previous works analyzed the answer quality (AQ) based on answer-related features, but neglect the question-related features on AQ. Previous work analyzed how asker- and question-related features affect the question quality (QQ) regarding the amount of attention from users, the number of answers and the question solving latency, but neglect the correlation between QQ and AQ (measured by the rating of the best answer), which is critical to quality of service (QoS). We handle this problem from two aspects. First, we additionally use QQ in measuring AQ, and analyze the correlation between a comprehensive list of features (including answer-related features) and QQ. Second, we propose the first method that estimates the probability for a given question to obtain high AQ. Our analysis on the Yahoo! Answers trace confirmed that the list of our identified features exert influence on AQ, which determines QQ. For the correlation analysis, the previous classification algorithms cannot consider the mutual interactions between multiple ($>$ 2) classes of features. We then propose a novel Coupled Semi-Supervised Mutual Reinforcement-based Label Propagation (CSMRLP) algorithm for this purpose. Our extensive experiments show that CSMRLP outperforms the Mutual Reinforcement-based Label Propagation (MRLP) and five other traditional classification algorithms in the accuracy of AQ classification, and the effectiveness of our proposed method in AQ prediction. Finally, we provide suggestions on how to create a question that will receive high AQ, which can be exploited to improve the QoS of CQAS.


The longitudinal relationship of personality traits and disorders.

Personality disorders are presumed to be stable because of underlying stable and maladaptive personality traits, but while previous research has demonstrated a link between personality traits and personality disorders cross-sectionally, personality disorders and personality traits have not been linked longitudinally. This study explores the extent to which relevant personality traits are stable in individuals diagnosed with 4 personality disorders (schizotypal, borderline, avoidant, and obsessive-compulsive personality disorders) and examines the assumption that these personality disorders are stable by virtue of stable personality traits. This assumption was tested via the estimation of a series of latent longitudinal models that evaluated whether changes in relevant personality traits lead to subsequent changes in personality disorders. In addition to offering large consistency estimates for personality traits and personality disorders, the results demonstrate significant cross-lagged relationships between trait change and later disorder change for 3 of the 4 personality disorders studied.


Human-robot physical interaction and collaboration using an industrial robot with a closed control architecture

In physical Human-Robot Interaction, the basic problem of fast detection and safe robot reaction to unexpected collisions has been addressed successfully on advanced research robots that are torque controlled, possibly equipped with joint torque sensors, and for which an accurate dynamic model is available. In this paper, an end-user approach to collision detection and reaction is presented for an industrial manipulator having a closed control architecture and no additional sensors. The proposed detection and reaction schemes have minimal requirements: only the outer joint velocity reference to the robot manufacturer's controller is used, together with the available measurements of motor currents and joint positions. No a priori information on the robot dynamic model and existing low-level joint controllers is strictly needed. A suitable on-line processing of the motor currents allows to distinguish between accidental collisions and intended human-robot contacts, so as to switch the robot to a collaboration mode when needed. Two examples of reaction schemes for collaboration are presented, with the user pushing/pulling the robot at any point of its structure (e.g., for manual guidance) or with a compliant-like robot behavior in response to forces applied by the human. The actual performance of the methods is illustrated through experiments on a KUKA KR5 manipulator.


TR 03-002 Item-Based Top-N Recommendation Algorithms


ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews

Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semisupervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.


Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them

Research on recommender systems is a challenging task, as is building and operating such systems. Major challenges include non-reproducible research results, dealing with noisy data, and answering many questions such as how many recommendations to display, how often, and, of course, how to generate recommendations most effectively. In the past six years, we built three research-article recommender systems for digital libraries and reference managers, and conducted research on these systems. In this paper, we share some experiences we made during that time. Among others, we discuss the required skills to build recommender systems, and why the literature provides little help in identifying promising recommendation approaches. We explain the challenge in creating a randomization engine to run A/B tests, and how low data quality impacts the calculation of bibliometrics. We further discuss why several of our experiments delivered disappointing results, and provide statistics on how many researchers showed interest in our recommendation dataset.


Object Segmentation by Long Term Analysis of Point Trajectories

Unsupervised learning requires a grouping step that defines which data belong together. A natural way of grouping in images is the segmentation of objects or parts of objects. While pure bottom-up segmentation from static cues is well known to be ambiguous at the object level, the story changes as soon as objects move. In this paper, we present a method that uses long term point trajectories based on dense optical flow. Defining pair-wise distances between these trajectories allows to cluster them, which results in temporally consistent segmentations of moving objects in a video shot. In contrast to multi-body factorization, points and even whole objects may appear or disappear during the shot. We provide a benchmark dataset and an evaluation method for this so far uncovered setting.


"GrabCut": interactive foreground extraction using iterated graph cuts


LabelMe video: Building a video database with human annotations

Currently, video analysis algorithms suffer from lack of information regarding the objects present, their interactions, as well as from missing comprehensive annotated video databases for benchmarking. We designed an online and openly accessible video annotation system that allows anyone with a browser and internet access to efficiently annotate object category, shape, motion, and activity information in real-world videos. The annotations are also complemented with knowledge from static image databases to infer occlusion and depth information. Using this system, we have built a scalable video database composed of diverse video samples and paired with human-guided annotations. We complement this paper demonstrating potential uses of this database by studying motion statistics as well as cause-effect motion relationships between objects.


The structure of images

In practice the relevant details of images exist only over a restricted range of scale. Hence it is important to study the dependence of image structure on the level of resolution. It seems clear enough that visual perception treats images on several levels of resolution simultaneously and that this fact must be important for the study of perception. However, no applicable mathematically formulated theory to deal with such problems appers to exist. In this paper it is shown that any image can be embedded in a one-parameter family of derived images (with resolution as the parameter) in essentially only one unique way if the constraint that no spurious detail should be generated when the resolution is diminished, is applied. The structure of this family is governed by the well known diffusion equation (a parabolic, linear, partial differential equation of the second order). As such the structure fits into existing theories that treat the front end of the visual system as a continuous tack of homogeneous layer, characterized by iterated local processing schemes. When resolution is decreased the images becomes less articulated because the extrem (“light and dark blobs”) disappear one after the other. This erosion of structure is a simple process that is similar in every case. As a result any image can be described as a juxtaposed and nested set of light and dark blobs, wherein each blod has a limited range of resolution in which it manifests itself. The structure of the family of derived images permits a derivation of the sampling density required to sample the image at multiple scales of resolution. The natural scale along the resolution axis (leading to an informationally uniform sampling density) is logarithmic, thus the structure is apt for the description of size invariances.


Gaussian processes for multi-sensor environmental monitoring

Efficiently monitoring environmental conditions across large indoor spaces (such as warehouses, factories or data centers) is an important problem with many applications. Deployment of a sensor network across the space can provide very precise readings at discrete locations. However, construction of a continuous model from this discrete sensor data is a challenge. The challenge is made harder by economic and logistical constraints that may limit the number of sensor motes in the network. The required model, therefore, must be able to interpolate sparse data and give accurate predictions at unsensed locations, as well as provide some notion of the uncertainty on those predictions. We propose a Gaussian process based model to answer both of these issues. We use Gaussian processes to model temperature and humidity distributions across an indoor space as functions of a 3-dimensional point. We study the model selection process and show that good results can be obtained, even with sparse sensor data. Deployment of a sensor network across an indoor lab provides real-world data that we use to construct an environmental model of the lab space. We seek to refine the model obtained from the initial deployment by using the uncertainty estimates provided by the Gaussian process methodology to modify sensor distribution such that each sensor is most advantageously placed. We explore multiple sensor placement techniques and experimentally validate a near-optimal criterion.


A Parking Occupancy Detection Algorithm Based on AMR Sensor

Recently, with the explosive increase of automobiles in cities, parking problems are serious and even worsen in many cities. This paper proposes a new algorithm for parking occupancy detection based on the use of anisotropic magnetoresistive sensors. Parking occupancy detection is abstracted as binary pattern recognition problem. According to the status of the parking space, the recognition result contains two categories: vacant and occupied. The feature extraction method of the parking magnetic signal is proposed. In addition, the classification criteria are derived based on the distance discriminate analysis method. Eighty-two sensor nodes are deployed on the roadside parking spaces. By running the system for six months, we observed that the accuracy rate of the proposed parking occupancy detection algorithm is better than 98%.


Evidence-based practice: functional rhinoplasty.

The cause of nasal obstruction can often be attributed to pathologic conditions of the nasal valve. The key physical examination finding in nasal valve compromise is inspiratory collapse of the nasal sidewall. Validated subjective and objective measures evaluating nasal obstruction exist, although with weak correlation. Functional rhinoplasty encompasses the surgical techniques used to address obstruction occurring in this area. These techniques aim to increase the size of the nasal valve opening and/or strengthen the lateral nasal wall and nasal ala, preventing dynamic collapse. Much of the supporting evidence for functional rhinoplasty consists of observational studies that are universally favorable.


Kalermo, Jonna Maria Rissanen, Jenni Karoliina Agile software development in theory and practice / Jonna Kalermo and Jenni Rissanen Jyväskylä, University of Jyväskylä, 2002. 188 pp. Master's thesis In turn of the millennium, new software development ideas were presented in the form of Agile Manifesto as a counteraction to the traditional, rigorous development methods and process models. Agile Manifesto consists of four values and twelve principles of which the authors of this thesis formed a conceptual framework. It assists in analysing different aspects of agile software development one by one but also as a whole. Agile software development aims at fast, light and efficient development that supports customer's business without being chaotic or following any rigorous method. The aim of this study was to analyse Agile Manifesto and its applicability. This was done by conducting a literature review and an empirical case study. Agile Manifesto gives an ideological background for agile methods, such as Extreme Programming (XP). Such methods are most feasible for small organisations developing innovative software products. We analysed Agile Manifesto and found out that literature supported the ideas of it, although no revolutionary ideas were discovered. We used an empirical case study as an example of an agile in-house software development method in corporate venturing context. The case study supported principles and values of Agile Manifesto and confirmed the assumption that agility is heavily based on tacit knowledge, skilled and motivated employees and frequent communication. This sets several requirements for management and employees working in an agile mode: they have to be communicative, social, responsible and skilled.


Randomized, Placebo-Controlled Trial of Methyl B12 for Children with Autism.

OBJECTIVE
Children with autism spectrum disorder (ASD) have been reported to have reduced ability to methylate DNA and elevated markers of oxidative stress. We sought to determine if methyl B12, a key metabolic cofactor for cellular methylation reactions and antioxidant defense, could improve symptoms of ASD.

METHODS
A total of 57 children with ASD were randomly assigned to 8 weeks of treatment with methyl B12 (75 μg/kg) or saline placebo every 3 days in a subcutaneous injection. The primary outcome measure was overall improvement in symptoms of ASD as measured by the Clinical Global Impressions-Improvement (CGI-I) score. Secondary outcome measures included changes in the Aberrant Behavior Checklist (ABC) and the Social Responsiveness Scale (SRS). Laboratory measures of methionine methylation and antioxidant glutathione metabolism were assessed at baseline and 8 weeks.

RESULTS
A total of 50 children (mean age 5.3 years, 79% male) completed the study. The primary outcome measure - the clinician rated CGI-I score - was statistically significantly better (lower) in the methyl B12 group (2.4) than in the placebo group (3.1) (0.7 greater improvement in the methyl B12 group, 95% CI 1.2-0.2, p = 0.005). Clinical improvement among children treated with methyl B12 was positively correlated with increases in plasma methionine (p = 0.05), decreases in S-adenosyl-l-homocysteine (SAH) (p = 0.007) and improvements in the ratio of S-adenosylmethionine (SAM) to SAH (p = 0.007), indicating an improvement in cellular methylation capacity. No improvements were observed in the parent-rated ABC or SRS.

CONCLUSIONS
Methyl B12 treatment improved clinician-rated symptoms of ASD that were correlated with improvements in measures of methionine metabolism and cellular methylation capacity. Clinical Trial Registry: Efficacy Study of Subcutaneous Methyl B12 in Children with Autism: NCT01039792 ( clinicaltrials.gov1 ).


The Insider Threat Security Reference Architecture, ITSRA, provides an enterprise wide solution to insider threat. The architecture consists of four security layers - Business, Information, Data, and Application. Organizations should deploy and enforce controls at each layer in order to address insider attacks. Each layer does not function in isolation or independently of other layers. Rather, the correlation of indicators and application of controls across all four layers forms the crux of this approach. Based on empirical data consisting of over 600 cases of insider crimes, insider attacks proved successful in inflicting damage when an organization failed to implement adequate controls in any of three security principles - authorized access, acceptable use, and continuous monitoring. The ITSRA draws from existing best practices and standards as well as from analysis of these cases to provide actionable guidance for organizations to improve their posture against the insider threat.


Exploring multicriteria decision strategies in GIS with linguistic quantifiers: A case study of residential quality evaluation

Commonly used GIS combination operators such as Boolean conjunction/disjunction and weighted linear combination can be generalized to the ordered weighted averaging (OWA) family of operators. This multicriteria evaluation method allows decision-makers to define a decision strategy on a continuum between pessimistic and optimistic strategies. Recently, OWA has been introduced to GIS-based decision support systems. We propose to extend a previous implementation of OWA with linguistic quantifiers to simplify the definition of decision strategies and to facilitate an exploratory analysis of multiple criteria. The linguistic quantifier-guided OWA procedure is illustrated using a dataset for evaluating residential quality of neighborhoods in London, Ontario.


The efficacy and safety of postoperative adjuvant transarterial embolization and radiotherapy in hepatocellular carcinoma patients with portal vein tumor thrombus

OBJECTIVE
This study aims to find out the safety and efficiency of postoperative adjuvant transarterial chemoembolization (TACE) and radiotherapy (RT) in hepatocellular carcinoma (HCC) patients with portal vein tumor thrombus (PVTT).

METHODS
From 2009 to 2010, a total of 92 HCC patients with PVTT were enrolled in this retrospective study. Patients were divided into three groups according to their adjuvant therapies (conservative group, n=51; TACE group, n=31; RT group, n=10).

RESULTS
In our analysis, median survival in patients with postoperative adjuvant TACE (21.91±3.60 months) or RT (14.53±1.61 months) was significantly longer than patients with hepatectomy alone (8.99±1.03 months). But the difference between adjuvant TACE and RT was of no significance (P=0.716). Also a similar result could be observed in median disease-free survival: conservative group (6.51±1.44 months), TACE group (13.98±3.38 months), and RT group (14.03±2.40 months). Treatment strategies (hazard ratio [HR] =0.411, P<0.001) and PVTT type (HR =4.636, P<0.001) were the independent prognostic factors for overall survival. Similarly, the risk factors were the same when multivariate analysis was conducted in disease-free survival (treatment strategies, HR =0.423, P<0.001; PVTT type, HR =4.351, P<0.001) and recurrence (treatment strategies, HR =0.459, P=0.030; PVTT type, HR =2.908, P=0.047). Patients with PVTT type I had longer overall survival than patients with PVTT type II (median survival: 18.43±2.88 months vs 11.59±1.45 months, P=0.035).

CONCLUSION
Postoperative adjuvant TACE and RT may be a choice for HCC patients with PVTT.


Minimum Inhibitory Concentration ( MIC ) Of various synthetic and natural antimicrobial agents using E coli screened from VIT sewage treatment plant *

The Minimum Inhibitory Concentration (MIC) Test is an important evaluating tool in the field of microbiology. It gives us an idea of the microbial activity assessment in any source sample containing microbes. Validating the effectiveness of disinfection and decontamination is a vital and often challenging task. In clinical and laboratory environments, presence of sterile conditions is a compulsion and various disinfectants are used to sterilize the surroundings from bacteria, fungi and other microorganisms. The broth dilution and agar diffusion methods of MIC Test tell us about the efficiency of the disinfectant under use. By subjecting a given disinfectant sample to the MIC Test, we can find out its efficiency in lysing the microorganisms present in the inoculums. Thus two or more disinfectants can be compared also at the same time to test their lysing capacity under normal conditions. The MIC Test also helps to choose the more economic disinfectant from a given batch of disinfectants. The results obtained from the MIC Test can also be used to prepare a new anti microbial agent which has greater efficiency in lysing the microorganisms and is economic financially. Minimum Inhibitory Concentration (MIC) of various synthetic like chloramphenical, chloroxylenol and 10% hydrochloric acid with butyl oleylamine in aqueous solution), and natural antimicrobial agents like Sample C (Grass extract) and Sample D (Grass+Neem extract) using E coli screened from VIT sewage treatment plant will be discussed.


Evaluation of the Wireless M-Bus standard for future smart water grids

The most recent Wireless Sensor Networks technologies can provide viable solutions to perform automatic monitoring of the water grid, and smart metering of water consumptions. However, sensor nodes located along water pipes cannot access power grid facilities, to get the necessary energy imposed by their working conditions. In this sense, it is of basic importance to design the network architecture in such a way as to require the minimum possible power. This paper investigates the suitability of the Wireless Metering Bus protocol for possible adoption in future smart water grids, by evaluating its transmission performance, through simulations and experimental tests executed by means of prototype sensor nodes.


Evaluation of a dynamic role-playing platform for simulations based on Octalysis gamification framework

The use of serious games (SG) in education and their pedagogical benefit is being widely recognized. However, effective integration of SGs in education depends on addressing two big challenges: successful incorporation of motivation and engagement that can lead to learning; and high cost and specialised skills associated with customised development to achieve the required pedagogical results. This paper presents a SG platform that offers tools to educators to dynamically create three dimensional (3D) scenes and verbal and non-verbal interaction with fully embodied conversational agents (ECA) that can be used to simulate numerous educational scenarios. We then evaluate the effectiveness of the platform in terms of supporting the creation of motivating and engaging educational simulations based on the Octalysis gamification framework. The latter includes a number of game design elements that can be used as appropriate in the optimization of the experience in each phase of the player’s journey. We conclude with directions of the further extension to our platform to address the aforementioned gamification framework, as well as directions for further development of the Octalysis framework.


Ad Exchanges: Research Issues

An emerging way to sell and buy display ads on the Internet is via ad exchanges. RightMedia [1], AdECN [2] and DoubleClick Ad Exchange [3] are examples of such real-time two-sided markets. We describe an abstraction of ad exchanges. Based on that abstraction, we present several research directions and discuss some insights.


Joint Entity and Event Coreference Resolution across Documents

We introduce a novel coreference resolution system that models entities and events jointly. Our iterative method cautiously constructs clusters of entity and event mentions using linear regression to model cluster merge operations. As clusters are built, information flows between entity and event clusters through features that model semantic role dependencies. Our system handles nominal and verbal events as well as entities, and our joint formulation allows information from event coreference to help entity coreference, and vice versa. In a cross-document domain with comparable documents, joint coreference resolution performs significantly better (over 3 CoNLL F1 points) than two strong baselines that resolve entities and events separately.


Understanding Difficulties and Resulting Confusion in Learning: An Integrative Review

Difficulties are often an unavoidable but important part of the learning process. This seems particularly so for complex conceptual learning. Challenges in the learning process are however, particularly difficult to detect and respond to in educational environments where growing class sizes and the increased use of digital technologies mean that teachers are unable to provide nuanced and personalized feedback and support to help students overcome their difficulties. Individual differences, the specifics of the learning activity, and the difficulty of giving individual feedback in large classes and digital environments all add to the challenge of responding to student difficulties and confusion. In this integrative review, we aim to explore difficulties and resulting emotional responses in learning. We will review the primary principles of cognitive disequilibrium and contrast these principles with work on desirable difficulties, productive failure, impasse driven learning, and pure discovery-based learning. We conclude with a theoretical model of the zones of optimal and sub-optimal confusion as a way of conceptualizing the parameters of productive and non-productive difficulties experienced by students while they learn.


Hand Gesture Recognition for Sign Language Recognition: A Review

For deaf and dumb people, Sign language is the only way of communication. With the help of sign language, these physical impaired people express their emotions and thoughts to other person. Since for the common person, it is difficult to understand these language therefore often these physically challenged has to keep the translator along with them to communicate with the world. Hence sign language recognition has become empirical task. Since sign language consist of various movement and gesture of hand therefore the accuracy of sign language depends on the accurate recognition of hand gesture. This paper present various method of hand gesture and sign language recognition proposed in the past by various researchers. Keywords—SL, DCT, Self organizing Map, Camshift


Using Ontology and Data Provenance to Improve Software Processes

Provenance refers to the origin of a particular object. In computational terms, provenance is a historical record of the derivation of data that can help to understand the current record. In this context, this work presents a proposal for software processes improvement using a provenance data model and an ontology. This improvement can be obtained by process data execution analysis with an approach called PROV-Process, which uses a layer for storing process provenance and an ontology based on PROV-O.


A Bayesian Classification Model for Fraud Detection over ATM Platforms Milgo , Carolyne

The banking system relies greatly on the use of Automated Teller Machines, debit and credit cards as a vital element of its payment processing systems. The efficient functioning of payment processing systems allows transactions to be completed safely and on time thereby contributing to operational performance. Customer accounts are equally exposed to risk with respect to fraud. The major security risk identified is identity and access management of customer funds. This research sought to use classification techniques to implement a novel fraud detection solution to establish legitimacy of customer transactions. It also sought to identify the main security issues encountered with use of ATM cards and establish internal control mechanisms which can be implemented to deter possibility of card fraud. The data was obtained from a local bank and was consequently preprocessed and fed into WEKA which was used to develop the training model which was to be used for classification of incoming transactions.


Should Investors Avoid All Actively Managed Mutual Funds ? A Study in Bayesian Performance Evaluation

This paper analyzes mutual-fund performance from an investor’s perspective. We study the portfolio-choice problem for a mean-variance investor choosing among a risk-free asset, index funds, and actively managed mutual funds. To solve this problem, we employ a Bayesian method of performance evaluation; a key innovation in our approach is the development of a f lexible set of prior beliefs about managerial skill. We then apply our methodology to a sample of 1,437 mutual funds. We find that some extremely skeptical prior beliefs nevertheless lead to economically significant allocations to active managers. ACTIVELY MANAGED EQUITY MUTUAL FUNDS have trillions of dollars in assets, collect tens of billions in management fees, and are the subject of enormous attention from investors, the press, and researchers. For years, many experts have been saying that investors would be better off in low-cost passively managed index funds. Notwithstanding the recent growth in index funds, active managers still control the vast majority of mutual-fund assets. Are any of these active managers worth their added expenses? Should investors avoid all actively managed mutual funds? Since Jensen ~1968!, most studies have found that the universe of mutual funds does not outperform its benchmarks after expenses.1 This evidence indicates that the average active mutual fund should be avoided. On the other hand, recent studies have found that future abnormal returns ~“alphas”! can be forecast using past returns or alphas,2 past fund * Baks and Metrick are from the Department of Finance, The Wharton School, University of Pennsylvania. Wachter is from the Department of Finance, The Stern School, New York University. We thank Nick Barberis, Gary Chamberlain, Ken French, Will Goetzmann, Karsten Hansen, Chris Jones, Tom Knox, Tony Lancaster, L̆ubos̆ Pástor, André Perold, Steve Ross, Andrei Shleifer, Rob Stambaugh, René Stulz, Sheridan Titman, an anonymous referee, and seminar participants at Columbia, Wharton, the NBER, the 1999 NBER Summer Institute, and the 2000 AFA meetings for helpful comments. Wachter thanks Lehman Brothers for financial support. 1 Recently, Carhart ~1995!, Malkiel ~1995!, and Daniel et al. ~1997! all find small or zero average abnormal returns by using modern performance-evaluation methods on samples that are relatively free of survivorship bias. 2 Carlson ~1970!, Lehman and Modest ~1987!, Grinblatt and Titman ~1988, 1992!, Hendricks, Patel, and Zechhauser ~1993!, Goetzmann and Ibbotson ~1994!, Brown and Goetzmann ~1995!, Elton, Gruber, and Blake ~1996!, and Carhart ~1997!. THE JOURNAL OF FINANCE • VOL. LVI, NO. 1 • FEBRUARY 2001


The world of emotions is not two-dimensional.

For more than half a century, emotion researchers have attempted to establish the dimensional space that most economically accounts for similarities and differences in emotional experience. Today, many researchers focus exclusively on two-dimensional models involving valence and arousal. Adopting a theoretically based approach, we show for three languages that four dimensions are needed to satisfactorily represent similarities and differences in the meaning of emotion words. In order of importance, these dimensions are evaluation-pleasantness, potency-control, activation-arousal, and unpredictability. They were identified on the basis of the applicability of 144 features representing the six components of emotions: (a) appraisals of events, (b) psychophysiological changes, (c) motor expressions, (d) action tendencies, (e) subjective experiences, and (f) emotion regulation.


On the Origin of CRISPR-Cas Technology: From Prokaryotes to Mammals.

Clustered regularly-interspaced short palindromic repeat (CRISPR) sequences cooperate with CRISPR-associated (Cas) proteins to form the basis of CRISPR-Cas adaptive immune systems in prokaryotes. For more than 20 years, these systems were of interest only to specialists, mainly molecular microbiologists, who tried to understand the properties of this unique defense mechanism. In 2012, the potential of CRISPR-Cas systems was uncovered and these were presented as genome-editing tools with an outstanding capacity to trigger targeted genetic modifications that can be applied to virtually any organism. Shortly thereafter, in early 2013, these tools were shown to efficiently drive specific modification of mammalian genomes. This review attempts to summarize, in a comprehensive manner, the key events and milestones that brought CRISPR-Cas technology from prokaryotes to mammals.


Deep Analysis of Binary Code to Recover Program Structure

Title of dissertation: DEEP ANALYSIS OF BINARY CODE TO RECOVER PROGRAM STRUCTURE Khaled ElWazeer, Doctor of Philosophy, 2014 Dissertation directed by: Professor Rajeev Barua Department of Electrical and Computer Engineering Reverse engineering binary executable code is gaining more i nterest in the research community. Agencies as diverse as anti-virus companies, se curity consultants, code forensics consultants, law-enforcement agencies and nati onal security agencies routinely try to understand binary code. Engineers also often need to d ebug, optimize or instrument binary code during the software development process. In this dissertation, we present novel techniques to extend he capabilities of existing binary analysis and rewriting tools to be more scalab le, handling a larger set of stripped binaries with better and more understandable outp uts as well as ensuring correct recovered intermediate representation (IR) from binar ies such that any modified or rewritten binaries compiled from this representation work correctly. In the first part of the dissertation, we present techniques t o recover accurate function boundaries from stripped executables. Our techniques as opposed to current techniques ensure complete live executable code coverage, high quality recovered code, and functional behavior for most application binaries. We use s tatic and dynamic based techniques to remove as much spurious code as possible in a safe ma nner that does not hurt code coverage or IR correctness. Next, we present static tec hniques to recover correct prototypes for the recovered functions. The recovered prot otypes include the complete set of all arguments and returns. Our techniques ensure corr ct behavior of rewritten binaries for both internal and external functions. Finally, we present scalable and precise techniques to reco ver l cal variables for every function obtained as well as global and heap variables . Different techniques are represented for floating point stack allocated variables an d memory allocated variables. Data type recovery techniques are presented to declare mean ingful data types for the detected variables. Our data type recovery techniques can rec over integer, pointer, structural and recursive data types. We discuss the correctness of the r ecovered representation. The evaluation of all the methods proposed is conducted on Se condWrite, a binary rewriting framework developed by our research group. An imp ortant metric in the evaluation is to be able to recompile the IR with the recovered info rmation and run it producing the same answer that is produced when running the original ex ecutable. Another metric is the analysis time. Some other metrics are proposed to meas ure the quality of the IR with respect to the IR with source code information availabl e. DEEP ANALYSIS OF BINARY CODE TO RECOVER PROGRAM STRUCTURE


Ultrasonography for clinical decision-making and intervention in airway management: from the mouth to the lungs and pleurae

OBJECTIVES
To create a state-of-the-art overview of the new and expanding role of ultrasonography in clinical decision-making, intervention and management of the upper and lower airways, that is clinically relevant, up-to-date and practically useful for clinicians.

METHODS
This is a narrative review combined with a structured Medline literature search.

RESULTS
Ultrasonography can be utilised to predict airway difficulty during induction of anaesthesia, evaluate if the stomach is empty or possesses gastric content that poses an aspiration risk, localise the essential cricothyroid membrane prior to difficult airway management, perform nerve blocks for awake intubation, confirm tracheal or oesophageal intubation and facilitate localisation of tracheal rings for tracheostomy. Ultrasonography is an excellent diagnostic tool in intraoperative and emergency diagnosis of pneumothorax. It also enables diagnosis and treatment of interstitial syndrome, lung consolidation, atelectasis, pleural effusion and differentiates causes of acute breathlessness during pregnancy. Patient safety can be enhanced by performing procedures under ultrasound guidance, e.g. thoracocentesis, vascular line access and help guide timing of removal of chest tubes by quantification of residual pneumothorax size.

CONCLUSIONS
Ultrasonography used in conjunction with hands-on management of the upper and lower airways has multiple advantages. There is a rapidly growing body of evidence showing its benefits.

TEACHING POINTS
• Ultrasonography is becoming essential in management of the upper and lower airways. • The tracheal structures can be identified by ultrasonography, even when unidentifiable by palpation. • Ultrasonography is the primary diagnostic approach in suspicion of intraoperative pneumothorax. • Point-of-care ultrasonography of the airways has a steep learning curve. • Lung ultrasonography allows treatment of interstitial syndrome, consolidation, atelectasis and effusion.


Challenges of design and practical application of LTCC chip antennas

In this paper key challenges related to design, manufacturing and practical application of small LTCC antennas are described and discussed. A summary of the state of the art in LTCC antenna technology is presented and then the focus is put on limitations that have to be taken into account in the course of antenna design and manufacturing. Finally, some aspects related to practical application of LTCC antennas are discussed.


The radiation properties of electrically small folded spherical helix antennas

The radiation properties of several electrically small, folded spherical helix antennas are presented. The primary variables considered in the design of these antennas are the number of helical turns and the number of helical arms. The principle design objectives are to achieve self resonance, a low quality factor (Q), and a practical radiation resistance for small values of ka. Designs are presented for ka less than 0.5, where the antennas are self resonant, exhibiting an efficiency in excess of 95%, a Q within 1.5 times the fundamental limit, and a radiation resistance near 50 /spl Omega/. The relationship between the number of helical turns, the number of helical arms, and achieving self resonance at low frequencies is discussed.


Small Spherical Antennas Using Arrays of Electromagnetically Coupled Planar Elements

This letter presents the design, fabrication, and experimental characterization of small spherical antennas fabricated using arrays of non-interconnected planar conductor elements. The antennas are based upon spherical resonator structures with radiation Q-factors approaching $1.5\times$ the fundamental lower limit. The antennas are formed by coupling these resonators to an impedance-matched coplanar strip transmission line. Direct electrical connection between the feed and the antenna are made only to conductor elements coplanar with the transmission line, simplifying the fabrication process. The incident energy excites a collective resonant mode of the entire sphere (an electric dipole resonance), thereby inducing currents in each of the rings of the structure. The presence of the conductor elements outside of the feed plane is critical towards achieving the excellent bandwidth behavior observed here. The fabricated antennas have a normalized size $ka=0.54$ (where $k$ is the wavenumber and $a$ is the radius of the sphere) and exhibit high radiative efficiencies ($>$ 90%) and bandwidth performance near the fundamental limit for their size.


The Spherical Coil as an Inductor, Shield, or Antenna

The spherical coil is an idealized form of inductor having, on a spherical surface, a single-layer winding of constant axial pitch. Its magnetic field inside is uniform and outside is that of a small coil or magnetic dipole. Its properties exemplify exactly some of the rules that are approximately applicable to practical inductors. Simple formulas are given for self-inductance, mutual inductance, coupling coefficient, effect of iron core, and radiation power factor in free space or sea water. This coil is the basis for evaluating the shielding effect of a closed conductive (nonmagnetic) metal shell. A special winding is described which enables simple and exact computation of self-resonance (the length of wire being just 1/2 wavelength in some cases).


Substrate-Integrated Millimeter-Wave and Terahertz Antenna Technology

Significant advances in the development of millimeter-wave and terahertz (30-10 000 GHz) technologies have been made to cope with the increasing interest in this still not fully explored electromagnetic spectrum. The nature of electromagnetic waves over this frequency range is well suited for the development of high-resolution imaging applications, molecular-sensitive spectroscopic devices, and ultrabroadband wireless communications. In this paper, millimeter-wave and terahertz antenna technologies are overviewed including the conventional and nonconventional planar/nonplanar antenna structures based on different platforms. As a promising technological platform, substrate-integrated circuits (SICs) attract more and more attention. Various substrate-integrated waveguide (SIW) schemes and other synthesized guide techniques have been widely employed in the design of antennas and arrays. Different types of substrate-integrated antennas and beamforming networks are discussed with respect to theoretical and experimental results in connection with electrical and mechanical performances.


An overlap invariant entropy measure of 3D medical image alignment

This paper is concerned with the development of entropy-based registration criteria for automated 3D multi-modality medical image alignment. In this application where misalignment can be large with respect to the imaged field of view, invariance to overlap statistics is an important consideration. Current entropy measures are reviewed and a normalised measure is proposed which is simply the ratio of the sum of the marginal entropies and the joint entropy. The effect of changing overlap on current entropy measures and this normalised measure are compared using a simple image model and experiments on clinical image data. Results indicate that the normalised entropy measure provides significantly improved behaviour over a range of imaged fields of view. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.


Node Representation Learning for Multiple Networks: The Case of Graph Alignment

Recent advances in representation learning produce node embeddings that may be used successfully in many downstream tasks (e.g., link prediction), but do not generally extend beyond a single network. Motivated by the prevalence of multi-network problems, such as graph alignment, similarity, and transfer learning, we introduce an elegant and principled node embedding formulation, Cross-network Matrix Factorization or xNetMF, to effectively and efficiently learn embeddings from similarities based on the nodes’ structural and attribute identities (if available), which are comparable across networks. Leveraging the Nyström method for low-rank approximation, we achieve significant reduction in space and time compared to the naïve approach of directly factorizing the node similarity matrix, with xNetMF running up to 30× faster than existing representation learning methods. We study its effectiveness on the task of network alignment, and introduce REGAL (REpresentationbased Graph ALignment), the first method to use node representation learning for this task. Extensive experiments on a variety of challenging real-world networks with and without attributes show that REGAL outperforms existing network alignment methods by 20 to 30% in accuracy on average, uses attributes up to 40× faster, and scales to networks with millions of nodes each.


Canine computer interaction: towards designing a touchscreen interface for working dogs

Touchscreens can provide a way for service dogs to relay emergency information about their handlers from a home or office environment. In this paper, we build on work exploring the ability of canines to interact with touchscreen interfaces. We observe new requirements for training and explain best practices found in training techniques. Learning from previous work, we also begin to test new dog interaction techniques such as lift-off selection and sliding gestural motions. Our goal is to understand the affordances needed to make touchscreen interfaces usable for canines and help the future design of touchscreen interfaces for assistance dogs in the home.


Exploring large scale data for multimedia QA: an initial study

With the explosive growth of multimedia contents on the internet, multimedia search has become more and more important. However, users are often bewildered by the vast quantity of information content returned by the search engines. In this scenario, Multimedia Question-Answering (MMQA) emerges as a way to return precise answers by leveraging advanced media content and linguistic analysis as well as domain knowledge. This paper performs an initial study on exploring large scale data for MMQA. First, we construct a web video dataset and discuss its query strategy, statistics, feature description and groundtruth. We then conduct experiments based on the dataset to answer definition event questions using three schemes. We finally conclude the study with discussion for future work.


Industrial Robotics


An experimental study of big spatial data systems

With the rise of location-aware IoT devices, there is an increased desire to process and manage the stationary and moving trajectory data generated by these real-time sensors. There has been a corresponding evolution of distributed database and compute technology to handle the increasing data load. Here we describe challenges in managing this kind of data and survey the technologies that address those challenges. We evaluate two distributed database technologies against a simulated high volume sensor data source and discuss the performance of predominant moving trajectory access patterns.


Time-Frequency Distributions Based on Compact Support Kernels: Properties and Performance Evaluation

This paper presents two new time-frequency distributions (TFDs) based on kernels with compact support (KCS) namely the separable (CB) (SCB) and the polynomial CB (PCB) TFDs. The implementation of this family of TFDs follows the method developed for the Cheriet-Belouchrani (CB) TFD. The mathematical properties of these three TFDs are analyzed and their performance is compared to the best classical quadratic TFDs using several tests on multicomponent signals with linear and nonlinear frequency modulation (FM) components including the noise effects. Instead of relying solely on visual inspection of the time-frequency domain plots, comparisons include the time slices' plots and the evaluation of the Boashash-Sucic's normalized instantaneous resolution performance measure that permits to provide the optimized TFD using a specific methodology. In all presented examples, the KCS-TFDs show a significant interference rejection, with the component energy concentration around their respective instantaneous frequency laws yielding high resolution measure values.