title: Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images

abstract: An abdominal ultrasound examination, which is the most common ultrasound examination, requires substantial manual efforts to acquire standard abdominal organ views, annotate the views in texts, and record clinically relevant organ measurements. Hence, automatic view classification and landmark detection of the organs can be instrumental to streamline the examination workflow. However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e.g., low contrast and large variations, but also the heterogeneity across tasks, i.e., one classification task for all views, and then one landmark detection task for each relevant view. While convolutional neural networks (CNN) have demonstrated more promising outcomes on ultrasound image analytics than traditional machine learning approaches, it becomes impractical to deploy multiple networks (one for each task) due to the limited computational and memory resources on most existing ultrasound scanners. To overcome such limits, we propose a multi-task learning framework to handle all the tasks by a single network. This network is integrated to perform view classification and landmark detection simultaneously; it is also equipped with global convolutional kernels, coordinate constraints, and a conditional adversarial module to leverage the performances. In an experimental study based on 187,219 ultrasound images, with the proposed simplified approach we achieve (1) view classification accuracy better than the agreement between two clinical experts and (2) landmark-based measurement errors on par with inter-user variability. The multi-task approach also benefits from sharing the feature extraction during the training process across all tasks and, as a result, outperforms the approaches that address each task individually.


title: An Active Matrix Micro-Pixelated LED Display Driver for High Luminance Uniformity Using Resistance Mismatch Compensation Method

abstract: This brief proposes an active matrix micro-pixelated light-emitting diode (AMLED) display driver to achieve a high luminance uniformity. The proposed AMLED display driver enhances the forward current uniformity of micro-LEDs by compensating for resistance mismatch between the micro-pixel circuits. The proposed AMLED display driver ICs were fabricated using a standard 0.18- $\mu \text{m}$ CMOS process with 6-V high voltage devices. They were assembled with a micro-LED array having a resolution of $16 \times 16 \times \text{RGB}$ and measured to verify the luminance uniformity of the AMLED display. The measured average deviation of the micro-LED forward current over all 768 pixels was 0.91% with an average current of 12.03 $\mu \text{A}$ , indicating that the proposed display driver achieved highly uniform luminance. Therefore, the proposed AMLED display driver is suitable for high-end display applications, offering high image quality.


title: Respiratory metabolism of L-929 cells at different water contents and volumes

abstract: Oxygen consumption was measured in mouse L-929 cells whose volumes and water contents were reduced by adding sorbitol to the medium. The volume of water lost due to a given sorbitol supplement exceeded the loss in apparent cell volume. An explanation is given for this discrepancy. The rate of oxygen uptake in the absence of exogenous respiratory substrate was essentially the same in cells whose total volume was reduced by 45%, amounting to a loss of about 70% of the total cell water, compared to controls at ‘physiological’ volume and water content. Cells under these same conditions responded to added substrates (pyruvate, glucose, and glutamine) and inhibitors (iodoacetate and 2-deoxyglucose) in nearly the same way as control cells. These observations are in accord with and add to previous work showing that very large fluctuations in cell volume and water content have only modest effects on the rates and directions of a variety of metabolic processes. The results are interpreted in terms of current views on the composition and organization of the aqueous compartments of eucaryotic cells.


title: Design Evaluation Method for Miniature Machine Tools Utilizing Form-Shaping Theory

abstract: A microfactory is a system that can perform manufacturing processes within a very limited space such as a desktop. However, design optimization of miniature machine tools in microfactories have not been studied enough. Since the miniature machine tool designs are not supported by existing design experience as normal machine tools are, design guidelines for miniature machine tool are strongly demanded. And a design tool to analyze machine performance without prototyping will be also necessary because the miniature machines have wider design choices than normal machine tools have, based on its small size and less constraints. This paper focuses on a robust design tool combining form-shaping theory with the Taguchi method, to roughly estimate performance of miniature machine tools at its conceptual design stages. The effort not only identifies critical design parameters that have significant influence on the machining tolerance, but also determines which structure has the best theoretical performance. The paper tells that the proposing design evaluation method can help machine tool designers in determining the optimum structure of a miniature machine tool. The study also realizes two designs of miniature mills to measure positioning errors. The measurement ensures the design evaluation method can predict the machine performance well enough for usage in conceptual design stages. The paper concludes that the design evaluation method is applicable to a systematic miniaturization of a machine tool.Copyright © 2003 by ASME


title: Low-power energy harvester for wiegand transducers

abstract: This paper discusses the use of Wiegand magnetic sensors as energy harvesters for powering low-power electronic equipments. A Wiegand device typically releases a ~10 μs voltage pulse several volts wide when subject to an external, time-varying magnetic field. Due to the sharpness of the magnetic transition, pulse generation occurs regardless of how slow the magnetic field variation is, an attractive feature which enables its use in energy harvesting scenarios even when low-frequency sources are considered. The paper first identifies the theoretical conditions for maximum energy extraction. An efficient energy harvesting circuit is then proposed which interfaces the Wiegand source to a fixed DC voltage provided, for instance, by a rechargeable Lithium-Ion battery. Simulations and experimental results are provided supporting the developed theoretical framework and the effectiveness of the proposed implementation.


title: Hypersensitivity pneumonitis--pathology and pathogenesis.

abstract: Early reports of hypersensitivity pneumonitis postulated that the disease was infectious or resulted from the toxic properties of the inhaled organic dusts. The finding of precipitating antibodies to moldy hay in farmers afflicted with farmer's lung suggested a role for antibody in pathogenesis, and a type III (antigen-antibody complex-mediated or Arthus) hypersensitivity reaction based on the classification of allergic reactions by Gell and Coombs was postulated. Subsequent studies have indicated the importance of cell-mediated (delayed) hypersensitivity (type IV). It must be recognized that hypersensitivity mechanisms are quite complicated and that the classification of Gell and Coombs is an oversimplification; interreacting humoral and cellular responses are typical of most hypersensitivity reactions of whatever classic type as originally defined. The prime importance of T-cell- and macrophage-mediated inflammation in HP, however, is indicated by histopathology, animal models, and in vitro correlates in humans. Major difficulties in defining completely the exact effector mechanisms involved in the pathogenesis of HP include the absence of a reliable in vitro correlate of antigen-specific effector T cells (the so called TDH cell) and the overwhelming versatility of the macrophage. There is no direct evidence to support contributions by precipitins, complement, or genetic host factors in the pathogenesis of hypersensitivity pneumonitis, nor are there studies as yet of cellular cytotoxicity contributions. Cellular and antibody interactions may lead to immunosuppressive processes modulating inflammatory responses and preventing disease despite immunogenesis. Animal models are helpful in dissecting mechanisms and defining effector functions. The eventual goal in studies of pathogenesis is to provide better tools for definitive diagnosis and methods of disease prevention, modulation, and cure.


title: Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks

abstract: Shot boundary detection (SBD) is an important pre-processing step for video manipulation. Here, each segment of frames is classified as either sharp, gradual or no transition. Current SBD techniques analyze hand-crafted features and attempt to optimize both detection accuracy and processing speed. However, the heavy computations of optical flow prevents this. To achieve this aim, we present an SBD technique based on spatio-temporal Convolutional Neural Networks (CNN). Since current datasets are not large enough to train an accurate SBD CNN, we present a new dataset containing more than 3.5 million frames of sharp and gradual transitions. The transitions are generated synthetically using image compositing models. Our dataset contain additional 70,000 frames of important hard-negative no transitions. We perform the largest evaluation to date for one SBD algorithm, on real and synthetic data, containing more than 4.85 million frames. In comparison to the state of the art, we outperform dissolve gradual detection, generate competitive performance for sharp detections and produce significant improvement in wipes. In addition, we are up to 11 times faster than the state of the art.


title: A Sample of Monte Carlo Methods in Robotics and Vision

abstract: Presented at the ISM International Symposium on the Science of Modeling - The 30th Anniversary of the Information Criterion (AIC), 14-17 December 2003, Pacifico Yokohama, Japan.


title: Auto-Calibration Around-View Monitoring System

abstract: This work is to establish an auto-calibration around-view monitoring system to avoid the inconsistent performance from deviations of camera installation. First, a vehicle is parked at a specific area which has a special pattern on the ground. Second, the captured images from four embedded cameras are compared with the reference pattern images to obtain the deviations in angle, X-axis and Y-axis. Finally, these deviations are used to compensate the coefficients of transformation matrices associated with view-point conversion and image stitching. Particularly, the adaptive edge recovery scheme is developed to help on precise alignments. The experimental results show that the proposed system implemented at the TI DaVinci DSP platform can accomplish auto-calibration of a vehicle within 60 seconds and have deviation rates smaller than 38% at stitched regions and 25% at the other regions. Additionally, ±2° deviation errors at angles, X-axis and Y-axis are allowed. Particularly, the proposed system can generate precise 2-meter around-view pictures at a vehicle speed of 30km/h. Therefore, the auto-calibration system proposed herein can be widely applied to vehicle manufacture, verification and repair.


title: School Psychologists' Diagnostic Decision-Making Processes: Objective-Subjective Discrepancies.

abstract: Abstract A body of literature suggests that decision makers often lack insight into the cues or variables they use to reach conclusions. We studied this hypothesis by comparing school psychologists’ subjective versus objective cue use when analyzing three clinical cases. Working from a set of assessment data, the psychologists selected 1 of 16 categories of information at a time, based on preference. After each selection, the psychologists formulated a preliminary diagnosis, rated their confidence in that diagnosis, and rated the importance of the information in that category for the diagnosis. They continued this process until they felt they had enough information to make a final diagnosis. For all three cases, results indicated that there was little correspondence between the psychologists’ subjective impressions about the importance of information and objective measures of the impact that information had on diagnostic judgments.


title: Sensing and Characterization of the Wrist Using Dart Thrower’s Movement

abstract: The dart-throwing movement is engaged to capture wrist movements involved in numerous daily activities. This paper focuses on robust implementation in a wearable platform-based inertial sensors enhance opportunities for exploring day-to-day changes of wrist joint when performing dart-throwing motion. The kinematic characterization of the underlying movement is described in terms of time-series quaternions measured from gyroscope data. The distance metrics between these representations are used to compare quantitatively wrist kinematic performance against clinical observations. A clinical trial was conducted engaging five normal subjects and 10 patients undergoing a series of post-surgical rehabilitation programs. The approach classifies effectively the patients from normal subjects and alleviates the need for range of motion measurements of the wrist joint implying the quaternion trajectory associated with classical dynamic time warping as a useful kinematic description for dart thrower’s movement in assessing and characterizing the wrist performance. Clustering and classification results confirm that this proposed method is well-correlated with clinical assessments based on high positive correlation coefficients. The primary objective of this paper is to enhance the uptake and promote the uses of wearable sensors in longer term monitoring scenarios particularly relevant to non-clinical environments.


title: Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG

abstract: How humans solve the cocktail party problem remains unknown. However, progress has been made recently thanks to the realization that cortical activity tracks the amplitude envelope of speech. This has led to the development of regression methods for studying the neurophysiology of continuous speech. One such method, known as stimulus-reconstruction, has been successfully utilized with cortical surface recordings and magnetoencephalography (MEG). However, the former is invasive and gives a relatively restricted view of processing along the auditory hierarchy, whereas the latter is expensive and rare. Thus it would be extremely useful for research in many populations if stimulus-reconstruction was effective using electroencephalography (EEG), a widely available and inexpensive technology. Here we show that single-trial (≈60 s) unaveraged EEG data can be decoded to determine attentional selection in a naturalistic multispeaker environment. Furthermore, we show a significant correlation between our EEG-based measure of attention and performance on a high-level attention task. In addition, by attempting to decode attention at individual latencies, we identify neural processing at ∼200 ms as being critical for solving the cocktail party problem. These findings open up new avenues for studying the ongoing dynamics of cognition using EEG and for developing effective and natural brain– computer interfaces.


title: Prior Knowledge based mutation prioritization towards causal variant finding in rare disease

abstract: How do we determine the mutational effects in exome sequencing data with little or no statistical evidence? Can protein structural information fill in the gap of not having enough statistical evidence? In this work, we answer the two questions with the goal towards determining pathogenic effects of rare variants in rare disease. We take the approach of determining the importance of point mutation loci focusing on protein structure features. The proposed structure-based features contain information about geometric, physicochemical, and functional information of mutation loci and those of structural neighbors of the loci. The performance of the structure-based features trained on 80\% of HumDiv and tested on 20\% of HumDiv and on ClinVar datasets showed high levels of discernibility in the mutation's pathogenic or benign effects: F score of 0.71 and 0.68 respectively using multi-layer perceptron. Combining structure- and sequence-based feature further improve the accuracy: F score of 0.86 (HumDiv) and 0.75 (ClinVar). Also, careful examination of the rare variants in rare diseases cases showed that structure-based features are important in discerning importance of variant loci.


title: From real-time attention assessment to “with-me-ness” in human-robot interaction

abstract: Measuring "how much the human is in the interaction" -- the level of engagement -- is instrumental in building effective interactive robots. Engagement, however, is a complex, multi-faceted cognitive mechanism that is only indirectly observable. This article formalizes "with-me-ness" as one of such indirect measures. With-me-ness, a concept borrowed from the field of Computer-Supported Collaborative Learning, measures in a well-defined way to what extent the human is "with" the robot over the course of an interactive task. As such, it is a meaningful precursor of engagement. We expose in this paper the full methodology, from real-time estimation of the human's focus of attention (relying on a novel, open-source, vision-based head pose estimator), to on-line computation of with-me-ness. We report as well on the experimental validation of this approach, using a naturalistic setup involving children during a complex robot-teaching task.


title: An empirical study of multifactorial PSO and multifactorial DE

abstract: Recently, the notion of Multifactorial Optimization (MFO) has emerged as a promising approach for evolutionary multi-tasking by automatically exploiting the latent synergies between optimization problems, simply through solving them together in an unified representation space [1]. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge between them. In [1], the efficacy of MFO has been studied by a specific mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. Here we further explore the generality of MFO when diverse population based search mechanisms are employed. In particular, in this paper, we present the first attempt to conduct MFO with the popular particle swarm optimization and differential evolution search. Two specific multi-tasking paradigms, namely multifactorial particle swarm optimization (MFPSO) and multifactorial differential evolution (MFDE) are proposed. To evaluate the performance of MFPSO and MFDE, comprehensive empirical studies on 9 single objective MFO benchmark problems are provided.


title: Highly stereoselective synthesis of the indolo[2,3-a]quinolizine ring system and application to indole natural product synthesis

abstract: We report a novel, facile and highly stereoselective approach to the indolo[2,3-a]quinolizine ring system from a readily available, non-racemic chiral template. We demonstrate the potential for application of this methodology to natural product synthesis through conversion of the template to a simple indole alkaloid with high enantiomeric purity.


title: Online Learning for Offloading and Autoscaling in Renewable-Powered Mobile Edge Computing

abstract: Mobile edge computing (a.k.a. fog computing) has recently emerged to enable \emph{in-situ} processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in renewable-powered mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning.


THE RELATIONSHIP BETWEEN TEACHER IMMEDIACY BEHAVIORS AND LEARNERS ’ PERCEPTIONS OF SOCIAL PRESENCE AND SATISFACTION IN OPEN AND DISTANCE EDUCATION : THE CASE OF ANADOLU UNIVERSITY OPEN EDUCATION


title: Inhibitory effects of sweet cherry anthocyanins on the obesity development in C57BL/6 mice

abstract: In the present study, purified sweet cherry anthocyanins (CACN) were evaluated to determine their inhibitory effects on adipocyte differentiation of 3T3-L1 cells and their anti-obesity properties in male C57BL/6 mice fed with high-fat diet (HFD). CACN prevented HFD-induced obesity in C57BL/6 mice. In vivo experiment revealed that 40 and 200 mg/kg of CACN in food reduced the body weight by 5.2% and 11.2%, respectively. CACN supplementation could also reduce the size of adipocytes, leptin secretion, serum glucose, triglyceride, total cholesterol, LDL-cholesterol and liver triglycerides. Furthermore, CACN could effectively reduce the expression levels of IL-6 and TNFα genes, markedly increase the SOD and GPx activity. Our results indicated that CACN slowed down the development of HFD-induced obesity in male C57BL/6 mice.


title: Online and offline character recognition: A survey

abstract: Handwritten character recognition has been one of the most fascinating research among the various researches in field of image processing. In Handwritten character recognition method the input is scanned from images, documents and real time devices like tablets, tabloids, digitizers etc. which are then interpreted into digital text. There are basically two approaches — Online Handwritten recognition which takes the input at run time and Offline Handwritten Recognition which works on scanned images. In this paper we have discussed the architecture, the steps involved, and the various proposed methodologies of offline and online character recognition along with their comparison and few applications.


title: Using Design Thinking to Differentiate Useful From Misleading Evidence in Observational Research.

abstract: Few issues can be more important to physicians or patients than that treatment decisions are based on reliable information about benefits and harms. While randomized clinical trials (RCTs) are generally regarded as the most valid source of evidence about benefits and some harms, concerns about their generalizability, costs, and heterogeneity of treatment effects have led to the search for other sources of information to augment or possibly replace trials. This is embodied in the recently passed 21st Century Cures Act, which mandates that the US Food and Drug Administration develop rules for the use of “real world evidence” in drug approval, defined as “data…derived from sources other than randomized clinical trials.”1 A second push toward the use of nontrial evidence is based on the perception that the torrent of electronic health-related data—medical record, genomic, and lifestyle (ie, “Big Data”)—can be transformed into reliable evidence with the use of powerful modern analytic tools


title: JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling

abstract: JAGS is a program for Bayesian Graphical modelling which aims for compatibility with Classic BUGS. The program could eventually be developed as an R package. This article explains the motivations for this program, briefly describes the architecture and then discusses some ideas for a vectorized form of the BUGS language.


The effects of a high protein diet on indices of health and body composition – a crossover trial in resistance-trained men


title: Towards a Cortical Prosthesis: Implementing A Spike-Based HMAX Model of Visual Object Recognition in Silico

abstract: Object recognition and categorization are computationally difficult tasks that are performed effortlessly by humans. Attempts have been made to emulate the computations in different parts of the primate cortex to gain a better understanding of the cortex and to design brain–machine interfaces that speak the same language as the brain. The HMAX model proposed by Riesenhuber and Poggio and extended by Serre attempts to truly model the visual cortex. In this paper, we provide a spike-based implementation of the HMAX model, demonstrating its ability to perform biologically-plausible MAX computations as well as classify basic shapes. The spike-based model consists of 2514 neurons and 17 $\thinspace$ 305 synapses (S1 Layer: 576 neurons and 7488 synapses, C1 Layer: 720 neurons and 2880 synapses, S2 Layer: 576 neurons and 1152 synapses, C2 Layer: 640 neurons and 5760 synapses, and Classifier: 2 neurons and 25 synapses). Without the limits of the retina model, it will take the system 2 min to recognize rectangles and triangles in 24 $\,\times\,$ 24 pixel images. This can be reduced to 4.8 s by rearranging the lookup table so that neurons which have similar responses to the same input(s) can be placed on the same row and affected in parallel.


title: The effects of different coverage of Nitrogen adsorbed on wurtzite ZnO(0001) surface

abstract: The adsorption of nitrogen on wurtzite ZnO (0001) surface were studied by First-principles. Different adsorption sites and the effect of different coverages have been considered. The clean ZnO (0001) surface and different coverages of Nitrogen adsorbed on wurtzite ZnO(0001) surface show n-type conduction. We found that the H3 adsorption site is energy-favored in each coverage considered, and that 0.25 and 0.5ML N adsorption leads to an indirect band gap semiconducting surface. Analyzing the electronic structures and the density of states of the adsorbed systems, we proposed that the strong hybridization between the Zn-3d and N-2p results in the consequent saturation of Zn dangling bonds and the formation of a polarized covalent Zn-N bonding. Our study may be able to provide a guidance for the growth of N-doped ZnO.


title: Natural Logic for Textual Inference

abstract: This paper presents the first use of a computational model of natural logic---a system of logical inference which operates over natural language---for textual inference. Most current approaches to the PASCAL RTE textual inference task achieve robustness by sacrificing semantic precision; while broadly effective, they are easily confounded by ubiquitous inferences involving monotonicity. At the other extreme, systems which rely on first-order logic and theorem proving are precise, but excessively brittle. This work aims at a middle way. Our system finds a low-cost edit sequence which transforms the premise into the hypothesis; learns to classify entailment relations across atomic edits; and composes atomic entailments into a top-level entailment judgment. We provide the first reported results for any system on the FraCaS test suite. We also evaluate on RTE3 data, and show that hybridizing an existing RTE system with our natural logic system yields significant performance gains.


title: Quality of life impairment in hidradenitis suppurativa: a study of 61 cases.

abstract: OBJECTIVE ::: We sought to determine quality of life impairment in hidradenitis suppurativa. ::: ::: ::: METHODS ::: Questionnaires were administered to 61 patients. ::: ::: ::: RESULTS ::: Quality of life impact in hidradenitis was much greater than that of several other dermatologic conditions. ::: ::: ::: LIMITATION ::: This hospital-based population may not be representative. ::: ::: ::: CONCLUSION ::: Hidradenitis is one of the most distressing conditions observed in dermatology.


title: Automatic test generation for mutation testing on database applications

abstract: To assure high quality of database applications, testing database applications remains the most popularly used approach. In testing database applications, tests consist of both program inputs and database states. Assessing the adequacy of tests allows targeted generation of new tests for improving their adequacy (e.g., fault-detection capabilities). Comparing to code coverage criteria, mutation testing has been a stronger criterion for assessing the adequacy of tests. Mutation testing would produce a set of mutants (each being the software under test systematically seeded with a small fault) and then measure how high percentage of these mutants are killed (i.e., detected) by the tests under assessment. However, existing test-generation approaches for database applications do not provide sufficient support for killing mutants in database applications (in either program code or its embedded or resulted SQL queries). To address such issues, in this paper, we propose an approach called MutaGen that conducts test generation for mutation testing on database applications. In our approach, we first apply an existing approach that correlates various constraints within a database application through constructing synthesized database interactions and transforming the constraints from SQL queries into normal program code. Based on the transformed code, we generate program-code mutants and SQL-query mutants, and then derive and incorporate query-mutant-killing constraints into the transformed code. Then, we generate tests to satisfy query-mutant-killing constraints. Evaluation results show that MutaGen can effectively kill mutants in database applications, and MutaGen outperforms existing test-generation approaches for database applications in terms of strong mutant killing.


title: Neural CRF Parsing

abstract: This paper describes a parsing model that combines the exact dynamic programming of CRF parsing with the rich nonlinear featurization of neural net approaches. Our model is structurally a CRF that factors over anchored rule productions, but instead of linear potential functions based on sparse features, we use nonlinear potentials computed via a feedforward neural network. Because potentials are still local to anchored rules, structured inference (CKY) is unchanged from the sparse case. Computing gradients during learning involves backpropagating an error signal formed from standard CRF sufficient statistics (expected rule counts). Using only dense features, our neural CRF already exceeds a strong baseline CRF model (Hall et al., 2014). In combination with sparse features, our system achieves 91.1 F1 on section 23 of the Penn Treebank, and more generally outperforms the best prior single parser results on a range of languages.


title: A High-Efficiency Dimmable LED Driver for Low-Power Lighting Applications

abstract: This paper presents a dimmable light-emitting diode (LED) driver with adaptive feedback control for low-power lighting applications. An improved pulsewidth modulation dimming technique is studied for regulating the LED current and brightness. Under universal input voltage operation, high efficiency and high power factor can be achieved by a coupled inductor single-ended primary inductance converter power factor correction (PFC) converter with a simple commercial transition-mode PFC controller. The operation principles and design considerations of the studied LED driver are analyzed and discussed. A laboratory prototype is also designed and tested to verify the feasibility.


title: Capacitor voltage balancing in a 5-L full-bridge flying capacitor inverter

abstract: An active control method to balance the flying capacitor's voltage is presented for a single-phase full-bridge flying capacitor inverter. Based on the equations of flying capacitors, an analytical method that represents the natural capacitor's voltage balancing is introduced. In this technique, the duty cycle of the switches is modified to control the voltage of the capacitors using a proportional integrative controller. This method considers the fact that current direction influences the capacitor's voltage balance. The controller generates small differences between the duty cycles of the switches, which change the charge process of the capacitors. Simulation and experimental implementation results show that the control is efficient, maintaining the capacitor's voltage regulated.


title: Feeding behavior and digestive physiology in sea cucumber Apostichopus japonicus

abstract: The feeding behavior and digestive physiology of the sea cucumber, Apostichopus japonicus are not well understood. A better understanding may provide useful information for the development of the aquaculture of this species. In this article the tentacle locomotion, feeding rhythms, ingestion rate (IR), feces production rate (FPR) and digestive enzyme activities were studied in three size groups (small, medium and large) of sea cucumber under a 12h light/12h dark cycle. Frame-by-frame video analysis revealed that all size groups had similar feeding strategies using a grasping motion to pick up sediment particles. The tentacle insertion rates of the large size group were significantly faster than those of the small and medium-sized groups (P<0.05). Feeding activities investigated by charge coupled device cameras with infrared systems indicated that all size groups of sea cucumber were nocturnal and their feeding peaks occurred at 02:00-04:00. The medium and large-sized groups also had a second feeding peak during the day. Both IR and FPR in all groups were significantly higher at night than those during the daytime (P<0.05). Additionally, the peak activities of digestive enzymes were 2-4h earlier than the peak of feeding. Taken together, these results demonstrated that the light/dark cycle was a powerful environment factor that influenced biological rhythms of A. japonicus, which had the ability to optimize the digestive processes for a forthcoming ingestion.


title: Towards handling architecture design, variability and evolution with model transformations

abstract: Software systems have to face evolving requirements from information system stakeholders, infrastructure modifications, and evolving rationales about the implementation. This increases the rate of migration and redeployment of systems. Recent approaches intend to abstract architectural element specifications from the implementing technology and manage software design through model transformations. Based on an Architecture Description Language integrating infrastructure modelling facilities and a requirement modelling language, the present work manages architecturally significant requirements and infrastructure evolutions by model transformations. Our approach offers support for evolution and variability management tasks as it makes explicit the rationales concerning requirements, infrastructure and implementation alternatives that guide both the software architecture and the infrastructure definition.


title: Intel RealSense Stereoscopic Depth Cameras

abstract: We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging systems. We discuss these systems' mode-of-operation, functional behavior and include models of their expected performance, shortcomings, and limitations. We provide information about the systems' optical characteristics, their correlation algorithms, and how these properties can affect different applications, including 3D reconstruction and gesture recognition. Our discussion covers the Intel RealSense R200 and the Intel RealSense D400 (formally RS400).


title: Modified ATO algorithm based high accuracy RDC using pulse excitation

abstract: Most important requirement in the modern control industry applications is the measurement of motor rotor shaft angle. Resolvers are absolute angular position measurement transducers, but it requires some signal processing techniques to improve the tracking accuracy of the angle. This paper discusses the mathematical design analysis of Resolver to Digital Converter (RDC) using modified Angle Tracking Observer (ATO) algorithm with pulse excitation. The proposed software based RDC system is implemented in MATLAB® SIMULINK® and the angle tracking performance is verified through various ranges of speed. The results show that the designed RDC system provides better tracking performance with negligible error and it reduces the hardware cost, weight and size.


title: A survey on IoT based road traffic surveillance and accident detection system (A smart way to handle traffic and concerned problems)

abstract: Road Traffic is one of the most vital problem in our hastily developing world. This paper presents of study of different aspects and issues related to the problem. This paper emphasizes on using prominent technology — Internet of Things (IoT) for developing smart system to monitor various parameters related to road traffic and using it for effective solution. The survey of the existing systems and concerned techniques related to the problem area are discussed. Different issues like vehicle detection, occlusion detection, lane projectile detection, accident detection and related methods to solve these issues are explored. We propose our “IoT based traffic monitoring and accident detection system” consisting of Raspberry pi, and pi camera as hardware which will use live video as input and process it for gathering information about the live traffic. The system generates information regarding the traffic such as number of vehicles, emergency accident situations, and improper projectile of vehicle. The generated information can be used to handle and divert the live traffic as needed to avoid the problems related to road traffic.


title: Is Post-Traumatic Stress Disorder Associated with Premature Senescence? A Review of the Literature

abstract: Objective: Post-traumatic stress disorder (PTSD) has major public health significance. Evidence that PTSD may be associated with premature senescence (early or accelerated aging) would have major implications for quality of life and healthcare policy. We conducted a comprehensive review of published empirical studies relevant to early aging in PTSD. Method: Our search included the PubMed, PsycINFO, and PILOTS databases for empirical reports published since the year 2000 relevant to early senescence and PTSD, including: 1) biomarkers of senescence (leukocyte telomere length [LTL] and pro-inflammatory markers), 2) prevalence of senescence-associated medical conditions, and 3) mortality rates. Results: All six studies examining LTL indicated reduced LTL in PTSD (pooled Cohen's d = 0.76). We also found consistent evidence of increased pro-inflammatory markers in PTSD (mean Cohen's ds), including C-reactive protein = 0.18, Interleukin-1 beta = 0.44, Interleukin-6 = 0.78, and tumor necrosis factor alpha = 0.81. The majority of reviewed studies also indicated increased medical comorbidity among several targeted conditions known to be associated with normal aging, including cardiovascular disease, type 2 diabetes mellitus, gastrointestinal ulcer disease, and dementia. We also found seven of 10 studies indicated PTSD to be associated with earlier mortality (average hazard ratio: 1.29). Conclusion: In short, evidence from multiple lines of investigation suggests that PTSD may be associated with a phenotype of accelerated senescence. Further research is critical to understand the nature of this association. There may be a need to re-conceptualize PTSD beyond the boundaries of mental illness, and instead as a full systemic disorder.


title: Joint auto-encoders: a flexible multi-task learning framework

abstract: The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest. Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a way that commonalities and differences between the tasks are learned in a data-driven fashion. We develop a framework for learning multiple tasks simultaneously, based on sharing features that are common to all tasks, achieved through the use of a modular deep feedforward neural network consisting of shared branches, dealing with the common features of all tasks, and private branches, learning the specific unique aspects of each task. Once an appropriate weight sharing architecture has been established, learning takes place through standard algorithms for feedforward networks, e.g., stochastic gradient descent and its variations. The method deals with domain adaptation and multi-task learning in a unified fashion, and can easily deal with data arising from different types of sources. Numerical experiments demonstrate the effectiveness of learning in domain adaptation and transfer learning setups, and provide evidence for the flexible and task-oriented representations arising in the network.


title: A Unifying Framework to Enhance the Practice Environment

abstract: The application of theory to practice is multifaceted. It requires a nursing theory that is compatible with an institution’s values and mission and that is easily understood and simple enough to guide practice. Comfort Theory was chosen because of its universality. The authors describe how Kolcaba’s Comfort Theory was used by a not-forprofit New England hospital to provide a coherent and consistent pattern for enhancing care and promoting professional practice, as well as to serve as a unifying framework for applying for Magnet Recognition Status.


title: Rejection Sensitivity and the Rejection-Hostility Link in Romantic Relationships

abstract: Rejection sensitivity is the disposition to anxiously expect, readily perceive, and intensely react to rejection. In response to perceived social exclusion, highly rejection sensitive people react with increased hostile feelings toward others and are more likely to show reactive aggression than less rejection sensitive people in the same situation. This paper summarizes work on rejection sensitivity that has provided evidence for the link between anxious expectations of rejection and hostility after rejection. We review evidence that rejection sensitivity functions as a defensive motivational system. Thus, we link rejection sensitivity to attentional and perceptual processes that underlie the processing of social information. A range of experimental and diary studies shows that perceiving rejection triggers hostility and aggressive behavior in rejection sensitive people. We review studies that show that this hostility and reactive aggression can perpetuate a vicious cycle by eliciting rejection from those who rejection sensitive people value most. Finally, we summarize recent work suggesting that this cycle can be interrupted with generalized self-regulatory skills and the experience of positive, supportive relationships.


title: © 2006 Science Publications Empirical Study on Applications of Data Mining Techniques in Healthcare

abstract: The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, decision tree and Artificial Neural Network to massive volume of healthcare data. In particular we consider a case study using classification techniques on a medical data set of diabetic patients.


title: A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds

abstract: Due to the complexity and volume, outsourcing ciphertexts to a cloud is deemed to be one of the most effective approaches for big data storage and access. Nevertheless, verifying the access legitimacy of a user and securely updating a ciphertext in the cloud based on a new access policy designated by the data owner are two critical challenges to make cloud-based big data storage practical and effective. Traditional approaches either completely ignore the issue of access policy update or delegate the update to a third party authority; but in practice, access policy update is important for enhancing security and dealing with the dynamism caused by user join and leave activities. In this paper, we propose a secure and verifiable access control scheme based on the NTRU cryptosystem for big data storage in clouds. We first propose a new NTRU decryption algorithm to overcome the decryption failures of the original NTRU, and then detail our scheme and analyze its correctness, security strengths, and computational efficiency. Our scheme allows the cloud server to efficiently update the ciphertext when a new access policy is specified by the data owner, who is also able to validate the update to counter against cheating behaviors of the cloud. It also enables (i) the data owner and eligible users to effectively verify the legitimacy of a user for accessing the data, and (ii) a user to validate the information provided by other users for correct plaintext recovery. Rigorous analysis indicates that our scheme can prevent eligible users from cheating and resist various attacks such as the collusion attack.


title: Annapurna: Building a Real-World Smartwatch-Based Automated Food Journal

abstract: We describe the design and implementation of a smartwatch-based, completely unobtrusive, food journaling system, where the smartwatch helps to intelligently capture useful images of food that an individual consumes throughout the day. The overall system, called Annapurna, is based on three key components: (a) a smartwatch-based gesture recognizer to identify eating gestures, (b) a smartwatch-based image capturer that obtains a small set of relevant and useful images with a low energy overhead, and (c) a server-based image filtering engine that removes irrelevant uploaded images, and then catalogs them through a portal. Our primary challenge is to make the system robust to the huge diversity in natural eating habits and food choices. We show how we address this by an appropriate coupling between a smartwatch's camera sensor and inertial sensor-based tracking of eating gestures, thereby helping to capture multiple likely-to-be-useful images with low energy overhead. Through a series of real-world, in-the-wild studies, we demonstrate the end-to-end working of Annapurna, which captures useful images in over 95% of all natural eating episodes.


title: Big Data Analysis-Based Security Situational Awareness for Smart Grid

abstract: Advanced communications and data processing technologies bring great benefits to the smart grid. However, cyber-security threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness.


title: Antitumor activity of HPA3P through RIPK3-dependent regulated necrotic cell death in colon cancer

abstract: The antimicrobial peptide HPA3 shows anticancer activity in gastric cancer and leukaemia. However, how HPA3 exerts its anticancer activity, as well as whether it also exhibits activity in other cancers, remains unknown. Therefore, the aim of this study was to evaluate the anticancer activity of HPA3 and its analogues in colon cancer and to elucidate the mechanisms responsible for this activity. HPA3P decreased cell viability, whereas HPA3 and HPA3P2 did not decrease cell viability in colon cancer cells compared with control cells. This reduction in cell viability occurred through necrosis, a conclusion supported by our observation of the release of cellular contents, our intracellular PI staining results, and our observation of the release of HMGB1. Moreover, RIPK3 inhibition blocks the reduction of cell viability by HPA3P. Consistent with this finding, we found that knocking down RIPK3 and MLKL, key necroptosis proteins, attenuates the reductions in cell viability induced by HPA3P. Furthermore, HPA3P can improve the anticancer activity of chemotherapeutic agents and exhibits anticancer activity in other cancer cells. These results suggest that HPA3P may have potential as an anticancer agent in the treatment of colon cancer.


title: Constructing Category-Specific Models for Monocular Object-SLAM

abstract: We present a new paradigm for real-time object-oriented SLAM with a monocular camera. Contrary to previous approaches, that rely on object-level models, we construct category-level models from CAD collections which are now widely available. To alleviate the need for huge amounts of labeled data, we develop a rendering pipeline that enables synthesis of large datasets from a limited amount of manually labeled data. Using data thus synthesized, we learn category-level models for object deformations in 3D, as well as discriminative object features in 2D. These category models are instance-independent and aid in the design of object landmark observations that can be incorporated into a generic monocular SLAM framework. Where typical object-SLAM approaches usually solve only for object and camera poses, we also estimate object shape on-the-fly, allowing for a wide range of objects from the category to be present in the scene. Moreover, since our 2D object features are learned discriminatively, the proposed object-SLAM system succeeds in several scenarios where sparse feature-based monocular SLAM fails due to insufficient features or parallax. Also, the proposed category-models help in object instance retrieval, useful for Augmented Reality (AR) applications. We evaluate the proposed framework on multiple challenging real-world scenes and show --- to the best of our knowledge --- first results of an instance-independent monocular object-SLAM system and the benefits it enjoys over feature-based SLAM methods.


title: Comparison of PWM AC chopper topologies

abstract: In this paper, a commonly reported single-phase four-switch AC chopper topology is compared with a six-switch topology in terms of the total harmonic distortion (THD) of the input current, power factor (PF), commutation behavior, robustness and efficiency. Working principles of both topologies are presented along with simulation results. Based on a comparison of the findings, the four-switch topology is found to be better choice for the given application. Thus, the implementation of a four-switch topology is carried out at a power level of 10 kW. Experimental evaluation is carried out in order to verify the suitability of the circuit for the intended application. The experimental setup validates the findings of the simulations. Thus, the proposed approach is a practical way to improve input current THD and PF compared to thyristor choppers.


title: An Edge Driven Wavelet Frame Model for Image Restoration

abstract: Abstract Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images. In this paper, we introduce a new edge driven wavelet frame model for image restoration by approximating images as piecewise smooth functions. With an implicit representation of image singularities sets, the proposed model inflicts different strength of regularization on smooth and singular image regions and edges. The proposed edge driven model is robust to both image approximation and singularity estimation. The implicit formulation also enables an asymptotic analysis of the proposed models and a rigorous connection between the discrete model and a general continuous variational model. Finally, numerical results on image inpainting and deblurring show that the proposed model is compared favorably against several popular image restoration models.


title: Space-Time Wireless Channels

abstract: A practical, "first-principles" approach to space-time wireless channel design. A practical approach to space-time wireless channel design Integrates essential principles from communications, electromagnetics, and random process theory Includes detailed coverage of diversity, multipath applications, and antenna array design Contains extensive examples, illustrations, and problem setsNext-generation broadband radio systems must deliver unprecedented performance and higher data rates, while coping with increased spectral congestion. To achieve these goals, engineers need an in-depth understanding of radio channels that fade in time, frequency, and space. In Space-Time Wireless Channels, leading researcher Gregory D. Durgin presents a pragmatic, first-principles approach that integrates crucial concepts and techniques from communications, electromagnetics, and random process theory.Durgin focuses on comprehension and practicality, offering extensive examples, illustrations, and problem sets, while avoiding gratuitious mathematics and moving most derivations to end-of-chapter appendices. Coverage includes: Fundamentals of space, time, and frequency transmission and random process theory Electromagnetic description of space-time channels and the physics of small-scale fading First- and second-order statistics of fading channels Angle spectrum concepts and applications, including vector/scalar space and multipath shape factors Antenna diversity, temporal diversity, and bit error rates Multipath channels: separation, signaling, block coding, and antenna array design Appendices list special functions, Fourier transform examples, and random process theory concepts, as well as all relevant mathematical symbols, conventions, and acronyms.


title: MIZAN: A Large Persian-English Parallel Corpus

abstract: One of the most major and essential tasks in natural language processing is machine translation that is now highly dependent upon multilingual parallel corpora. Through this paper, we introduce the biggest Persian-English parallel corpus with more than one million sentence pairs collected from masterpieces of literature. We also present acquisition process and statistics of the corpus, and experiment a base-line statistical machine translation system using the corpus.


Ontology Mapping: The State of the Art


title: Distributed Adaptive Model Rules for mining big data streams

abstract: Decision rules are among the most expressive data mining models. We propose the first distributed streaming algorithm to learn decision rules for regression tasks. The algorithm is available in samoa (Scalable Advanced Massive Online Analysis), an open-source platform for mining big data streams. It uses a hybrid of vertical and horizontal parallelism to distribute Adaptive Model Rules (AMRules) on a cluster. The decision rules built by AMRules are comprehensible models, where the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of the attributes. Our evaluation shows that this implementation is scalable in relation to CPU and memory consumption. On a small commodity Samza cluster of 9 nodes, it can handle a rate of more than 30000 instances per second, and achieve a speedup of up to 4.7x over the sequential version.


title: Can social media help us reason about mental health

abstract: Millions of people each year suffer from depression, which makes mental illness one of the most serious and widespread health challenges in our society today. There is therefore a need for effective policies, interventions, and prevention strategies that enable early detection and diagnosis of mental health concerns in populations. This talk reports some findings on the potential of leveraging social media postings as a new type of lens in understanding mental illness in individuals and populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements of behavior over time while radically expanding population sample sizes. The talk highlights how this research direction may be useful in developing tools for identifying the onset of depressive disorders, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from mental illness to be more proactive about their mental health.


title: An extensive empirical comparison of ensemble learning methods for binary classification

abstract: We present an extensive empirical comparison between nineteen prototypical supervised ensemble learning algorithms, including Boosting, Bagging, Random Forests, Rotation Forests, Arc-X4, Class-Switching and their variants, as well as more recent techniques like Random Patches. These algorithms were compared against each other in terms of threshold, ranking/ordering and probability metrics over nineteen UCI benchmark data sets with binary labels. We also examine the influence of two base learners, CART and Extremely Randomized Trees, on the bias---variance decomposition and the effect of calibrating the models via Isotonic Regression on each performance metric. The selected data sets were already used in various empirical studies and cover different application domains. The source code and the detailed results of our study are publicly available.


title: Identifying Hierarchical Structures in Sequences on GPU

abstract: Identifying hierarchical structures in sequences is an important problem with applications in lossless data-compression to program profiling. A popular algorithm for identifying hierarchical structures in sequences is the Sequitur algorithm developed by Nevill-Manning and Witten. Sequitur is not just a compression algorithm, it attempts to learn the hierarchical structure of the input sequence as a context-free grammar. However, Sequitur is difficult to parallelize. Inspired by Sequitur, we have developed a new GPU algorithm, that reveals the hierarchical structure in sequences and is also concurrency-friendly. Our algorithm, Pequitur, is built as a series of fast kernels (for intermittent synchronization), where each kernel attempts to minimize inter-thread communication and achieve a good load balance among the GPU threads. As opposed to Sequitur, Pequitur follows a greedy strategy to find good productions, that are productions formed by long and frequent substrings. We have implemented and evaluated our algorithm on the NVIDIA K20c card on random strings drawn from multiple distributions. On our benchmarks, Pequitur achieves an average speedup of more than 3X over an optimized Sequitur implementation with similar compression ratios.


title: Reliable Estimation of Dense Optical Flow Fields with Large Displacements

abstract: In this paper we show that a classic optical flow technique by Nagel and Enkelmann (1986, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, pp. 565–593) can be regarded as an early anisotropic diffusion method with a diffusion tensor. We introduce three improvements into the model formulation that (i) avoid inconsistencies caused by centering the brightness term and the smoothness term in different images, (ii) use a linear scale-space focusing strategy from coarse to fine scales for avoiding convergence to physically irrelevant local minima, and (iii) create an energy functional that is invariant under linear brightness changes. Applying a gradient descent method to the resulting energy functional leads to a system of diffusion–reaction equations. We prove that this system has a unique solution under realistic assumptions on the initial data, and we present an efficient linear implicit numerical scheme in detail. Our method creates flow fields with 100 % density over the entire image domain, it is robust under a large range of parameter variations, and it can recover displacement fields that are far beyond the typical one-pixel limits which are characteristic for many differential methods for determining optical flow. We show that it performs better than the optical flow methods with 100 % density that are evaluated by Barron et al. (1994, Int. J. Comput. Vision, Vol. 12, pp. 43–47). Our software is available from the Internet.


title: Self and social identity

abstract: ▪ Abstract In this chapter, we examine the self and identity by considering the different conditions under which these are affected by the groups to which people belong. From a social identity perspective we argue that group commitment, on the one hand, and features of the social context, on the other hand, are crucial determinants of central identity concerns. We develop a taxonomy of situations to reflect the different concerns and motives that come into play as a result of threats to personal and group identity and degree of commitment to the group. We specify for each cell in this taxonomy how these issues of self and social identity impinge upon a broad variety of responses at the perceptual, affective, and behavioral level.


title: Defensive Distillation is Not Robust to Adversarial Examples

abstract: We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.


The Word Game: The ontology of an undefinable object


title: Wireless Fetal Monitoring Device with Provisions for Multiple Births

abstract: Cardiotocography is used to monitor the effect of contractions on fetal heart rate (FHR) and is used in the final trimester of pregnancy to detect fetal distress. This simple and efficient (patent-pending) architecture for Cardiotocography employs the Doppler technique to measure Fetal Heart Rate (FHR) and is scalable to any number of FHR monitors. The Doppler circuitry is designed for low cost and low power consumption to facilitate portability and compatibility with wireless data transmission. Measured results indicate performance consistent with conventional bedside systems, i.e. 8 bits of resolution with update rates =2 samples per second.


title: A dense semantic mapping system based on CRF-RNN network

abstract: Geometric structure and appearance information of environments are main outputs of Visual Simultaneous Localization and Mapping (Visual SLAM) systems. They serve as the fundamental knowledge for robotic applications in unknown environments. Nowadays, more and more robotic applications require semantic information in visual maps to achieve better performance. However, most of the current Visual SLAM systems are not equipped with the semantic annotation capability. In order to address this problem, we develop a novel system to build 3-D Visual maps annotated with semantic information in this paper. We employ the CRF-RNN algorithm for semantic segmentation, and integrate the semantic algorithm with ORB-SLAM to achieve the semantic mapping. In order to get real-scale 3-D visual maps, we use the RGB-D data as the input of our system. We test our semantic mapping system with our self-generated RGB-D dataset. The experimental results demonstrate that our system is able to reliably annotate the semantic information in the resulting 3-D point-cloud maps.


Visual Analysis of Optical Coherence Tomography Data in Ophthalmology


title: Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection

abstract: Engaging in a debate with oneself or others to take decisions is an integral part of our day-today life. A debate on a topic (say, use of performance enhancing drugs) typically proceeds by one party making an assertion/claim (say, PEDs are bad for health) and then providing an evidence to support the claim (say, a 2006 study shows that PEDs have psychiatric side effects). In this work, we propose the task of automatically detecting such evidences from unstructured text that support a given claim. This task has many practical applications in decision support and persuasion enhancement in a wide range of domains. We first introduce an extensive benchmark data set tailored for this task, which allows training statistical models and assessing their performance. Then, we suggest a system architecture based on supervised learning to address the evidence detection task. Finally, promising experimental results are reported.


title: STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset

abstract: In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention. In this paper, we particularly consider generating Japanese captions for images. Since most available caption datasets have been constructed for English language, there are few datasets for Japanese. To tackle this problem, we construct a large-scale Japanese image caption dataset based on images from MS-COCO, which is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions for 164,062 images. In the experiment, we show that a neural network trained using STAIR Captions can generate more natural and better Japanese captions, compared to those generated using English-Japanese machine translation after generating English captions.


title: The efficacy of topical royal jelly on healing of diabetic foot ulcers: a double-blind placebo-controlled clinical trial

abstract: Foot ulcers are major sources of morbidity in individuals with diabetes mellitus. As royal jelly (RJ, a worker honey bee product) contains enzymatic, antibacterial and vasodilative properties, it can potentially help in healing of diabetic foot ulcers (DFUs). This study aimed to evaluate the efficacy of topical RJ on healing of DFUs. Diabetic patients with foot ulcers who were referred to us at Khorshid Hospital, Isfahan, Iran, were managed by offloading, infection control, vascular improvement and debridement (if required). Then, all ulcers were randomly selected to receive either 5% sterile topical RJ or placebo on their total surface area. Patients were followed for 3 months or until complete healing. Twenty-five patients (6 females and 19 males) and a total of 64 ulcers were included and randomly allocated to case or control group (32 per group). Four ulcers were excluded and 60 ulcers included in the final analysis. Healing parameters including depth, length and width reduction rate, duration of complete healing and incidence of complete healing did not show any significant difference (P = 0·69, 0·95, 0·7, 0·74 and 0·6, respectively) between groups. We did not observe any side effect of topical RJ application. This study could not confirm any significant superiority of 5% topical RJ over placebo for the treatment of DFUs.


title: Design of a Miniature Ku-Band monopulse waveguide slotted array antenna

abstract: Slotted waveguide array antennas find wide applications in many microwave and radar systems requiring narrow beam and low side lobe level. Light weight, geometric simplicity, high power, limited scan volume and reliability make waveguide slotted arrays very useful for airborne radar application. A Miniature Ku-Band monopulse slotted waveguide array antenna in quarter height waveguide configuration for on-board radar application is presented. Antenna consists of five layers including monopulse comparator. All the layers of the antenna have been designed and analyzed using CST Microwave Studio. Taylor's aperture distribution is selected to obtain narrow beam with low sidelobes. This paper describes design, fabrication technique and electrical performance study to produce desired radiation pattern. The gain of the sum pattern is better than 23 dB while the side lobe level is −21dB in H-plane and −14dB in E-plane. The crossover of difference channels in both planes are around 3.8dB and the null depth in H-plane is 36dB and in E-plane is 25dB.


cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information


title: Automated sizing of coarse-grained sediments: image processing procedures

abstract: This is the first in a pair of papers in which we present image-processing-based procedures for the measurement of fluvial gravels. The spatial and temporal resolution of surface grain-size characterization is constrained by the time-consuming and costly nature of traditional measurement techniques. Several groups have developed image-processing-based procedures, but none have demonstrated the transferability of these techniques between sites with different lithological, clast form and textural characteristics. Here we focus on image-processing procedures for identifying and measuring image objects (i.e. grains); the second paper examines the application of such procedures to the measurement of fluvially deposited gravels. Four image-segmentation procedures are developed, each having several internal parameters, giving a total of 416 permutations. These are executed on 39 images from three field sites at which the clasts have contrasting physical properties. The performance of each procedure is evaluated against a sample of manually digitized grains in the same images, by comparing three derived statistics. The results demonstrate that it is relatively straightforward to develop procedures that satisfactorily identify objects in any single image or a set of images with similar sedimentary characteristics. However, the optimal procedure is that which gives consistently good results across sites with dissimilar sedimentary characteristics. We show that neighborhood-based operations are the most powerful, and a morphological bottom-hat transform with a double threshold is optimal. It is demonstrated that its performance approaches that of the procedures giving the best results for individual sites. Overall, it out-performs previously published, or improvements to previously published, methods.


title: Defining structural similarity

abstract: There is general agreement that structural similarity — a match in relational structure — is crucial in analogical processing. However, theories differ in their definitions of structural similarity: in particular, in whether there must be conceptual similarity between the relations in the two domains or whether parallel graph structure is sufficient. In two studies, we demonstrate, first, that people draw analogical correspondences based on matches in conceptual relations, rather than on purely structural graph matches; and, second, that people draw analogical inferences between passages that have matching conceptual relations, but not between passages with purely structural graph matches.


title: Domain Adaptation for Named Entity Recognition in Online Media with Word Embeddings

abstract: Content on the Internet is heterogeneous and arises from various domains like News, Entertainment, Finance and Technology. Understanding such content requires identifying named entities (persons, places and organizations) as one of the key steps. Traditionally Named Entity Recognition (NER) systems have been built using available annotated datasets (like CoNLL, MUC) and demonstrate excellent performance. However, these models fail to generalize onto other domains like Sports and Finance where conventions and language use can differ significantly. Furthermore, several domains do not have large amounts of annotated labeled data for training robust Named Entity Recognition models. A key step towards this challenge is to adapt models learned on domains where large amounts of annotated training data are available to domains with scarce annotated data. ::: In this paper, we propose methods to effectively adapt models learned on one domain onto other domains using distributed word representations. First we analyze the linguistic variation present across domains to identify key linguistic insights that can boost performance across domains. We propose methods to capture domain specific semantics of word usage in addition to global semantics. We then demonstrate how to effectively use such domain specific knowledge to learn NER models that outperform previous baselines in the domain adaptation setting.


title: An improved categorization of classifier's sensitivity on sample selection bias

abstract: A recent paper categorizes classifier learning algorithms according to their sensitivity to a common type of sample selection bias where the chance of an example being selected into the training sample depends on its feature vector x but not (directly) on its class label y. A classifier learner is categorized as "local" if it is insensitive to this type of sample selection bias, otherwise, it is considered "global". In that paper, the true model is not clearly distinguished from the model that the algorithm outputs. In their discussion of Bayesian classifiers, logistic regression and hard-margin SVMs, the true model (or the model that generates the true class label for every example) is implicitly assumed to be contained in the model space of the learner, and the true class probabilities and model estimated class probabilities are assumed to asymptotically converge as the training data set size increases. However, in the discussion of naive Bayes, decision trees and soft-margin SVMs, the model space is assumed not to contain the true model, and these three algorithms are instead argued to be "global learners". We argue that most classifier learners may or may not be affected by sample selection bias; this depends on the dataset as well as the heuristics or inductive bias implied by the learning algorithm and their appropriateness to the particular dataset.


title: Pooling Objects for Recognizing Scenes without Examples

abstract: In this paper we aim to recognize scenes in images without using any scene images as training data. Different from attribute based approaches, we do not carefully select the training classes to match the unseen scene classes. Instead, we propose a pooling over ten thousand of off-the-shelf object classifiers. To steer the knowledge transfer between objects and scenes we learn a semantic embedding with the aid of a large social multimedia corpus. Our key contributions are: we are the first to investigate pooling over ten thousand object classifiers to recognize scenes without examples; we explore the ontological hierarchy of objects and analyze the influence of object classifiers from different hierarchy levels; we exploit object positions in scene images and we demonstrate new scene retrieval scenarios with complex queries. Finally, we outperform attribute representations on two challenging scene datasets, SUNAttributes and Places2.


title: Internalized Timing of Isochronous Sounds Is Represented in Neuromagnetic Beta Oscillations

abstract: Moving in synchrony with an auditory rhythm requires predictive action based on neurodynamic representation of temporal information. Although it is known that a regular auditory rhythm can facilitate rhythmic movement, the neural mechanisms underlying this phenomenon remain poorly understood. In this experiment using human magnetoencephalography, 12 young healthy adults listened passively to an isochronous auditory rhythm without producing rhythmic movement. We hypothesized that the dynamics of neuromagnetic beta-band oscillations (∼20 Hz)—which are known to reflect changes in an active status of sensorimotor functions—would show modulations in both power and phase-coherence related to the rate of the auditory rhythm across both auditory and motor systems. Despite the absence of an intention to move, modulation of beta amplitude as well as changes in cortico-cortical coherence followed the tempo of sound stimulation in auditory cortices and motor-related areas including the sensorimotor cortex, inferior-frontal gyrus, supplementary motor area, and the cerebellum. The time course of beta decrease after stimulus onset was consistent regardless of the rate or regularity of the stimulus, but the time course of the following beta rebound depended on the stimulus rate only in the regular stimulus conditions such that the beta amplitude reached its maximum just before the occurrence of the next sound. Our results suggest that the time course of beta modulation provides a mechanism for maintaining predictive timing, that beta oscillations reflect functional coordination between auditory and motor systems, and that coherence in beta oscillations dynamically configure the sensorimotor networks for auditory-motor coupling.


title: Model Uncertainty in Cross-Country Growth Regressions

abstract: We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is spread widely among many models, suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results support this claim. In contrast to Levine and Renelt (1992), our results broadly support the more 'optimistic' conclusion of Sala-i-Martin (1997b), namely that some variables are important regressors for explaining cross-country growth patterns. However, care should be taken in the methodology employed. The approach proposed here is firmly grounded in statistical theory and immediately leads to posterior and predictive inference. Copyright © 2001 John Wiley & Sons, Ltd.


THE CONTINGENT VALUE OF BRIDGING AND BONDING SOCIAL CAPITAL ON FIRM PERFORMANCE: A META-ANALYSIS


title: Robust object detection via soft cascade

abstract: We describe a method for training object detectors using a generalization of the cascade architecture, which results in a detection rate and speed comparable to that of the best published detectors while allowing for easier training and a detector with fewer features. In addition, the method allows for quickly calibrating the detector for a target detection rate, false positive rate or speed. One important advantage of our method is that it enables systematic exploration of the ROC surface, which characterizes the trade-off between accuracy and speed for a given classifier.


title: Feeding and nutrient excretion of the New Zealand freshwater mussel Echyridella menziesii (Hyriidae, Unionida): implications for nearshore nutrient budgets in lakes and reservoirs

abstract: Native freshwater mussels (Bivalvia: Unionoida) can represent a large portion of benthic biomass, but their functional role is still poorly understood. We sampled Echyridella menziesii (Hyriidae) in six lakes from two regions of the North Island of New Zealand to develop general allometric relationships for predicting filtration, excretion and biodeposition rates. Experimental containers were set up in the field (no-flow conditions) to measure filtration and biodeposition using natural phytoplankton communities, and excretion in filtered lake water. Filtration rates were 0.02–1.3 L mussel−1 h−1 and increased with increasing mussel size (R2 = 0.13, P = 0.023). Stable isotope analysis suggests that mussels also assimilate food from non-planktonic origins. Nitrogen excretion rates were 4–50 μg N mussel−1 h−1 and increased with mussel size (R2 = 0.70, P 0.3). In contrast, phosphorus excretion rates did not vary with mussel size, and were much lower in Rotorua than Waikato lakes (2 vs 5 μg P mussel−1 h−1). The reason for this regional difference is unclear, but suggests that mussels could contribute different N:P ratios to nearshore nutrient budgets in different types of lakes. Biodeposits represented 50–70% of the N, and 25–70% of the P recycled. Echyridella filters and excretes nutrients at rates similar to those measured in North American and European mussels (Unionidae, Margaritiferidae). Mussels could be important contributors of nutrients in areas where they are abundant, and their inclusion into nutrient budget models could improve the predictions of nearshore nutrient fluxes in lakes and reservoirs.


title: Antisaccade performance in patients with multiple sclerosis

abstract: Abstract Commonly used measures of disability in patients with Multiple sclerosis (MS) inadequately reflect disease severity and progression. Further, cognitive deficits experienced by up to 70% of patients, are poorly represented by these measures. Saccadic eye movements may provide a powerful tool for the analysis of cognitive changes in MS, providing a surrogate measure of performance that extends more conventional measures. The cognitive control of eye movements has not previously been investigated in patients with MS. We studied antisaccade (AS) performance in 25 patients with MS and compared the results with 25 age matched healthy controls, to evaluate the resolution of response conflict between volitional and automatic processes. Experimental measures were also correlated with a battery of neuropsychological tests evaluating attention, working memory and executive processes, the most commonly reported cognitive deficits in MS. Compared to controls, patients with MS generated significantly more prosaccade errors, and AS latencies were prolonged and more variable. Error rates correlated significantly with scores on the commonly used PASAT. MS patients also exhibited poor spatial accuracy, with mean absolute error significantly larger and more variable than control subjects. The sensitivity of this task in dissociating function in MS, as well as clear correlation with a key measure of cognition, suggests that eye movements, may provide a surrogate measure of cognitive function in MS, with the potential to sensitively assess disease severity and progression.


title: A new PWM dimmer using two active switches for AC LED lamp

abstract: This paper presents a new dimmer using only two active switches for AC LED lamp. The control method of the proposed dimmer is pulse width control (PWM) method. Compared with the conventional phase- controlled dimmer, the proposed PWM dimmer can produce sine wave and it does not cause harmonics problem. Furthermore, the proposed control method does not amplify the light flicker due to independence of the input voltage. Therefore, the proposed PWM dimmer can be used as the AC LED lamp's dimmer instead of the conventional phase-controlled dimmer. The experimental result shows that the proposed PWM dimmer has good performances.


title: Taxonomy Development and Its Impact on a Self-learning e-Recruitment System

abstract: In this work we present a novel approach for evaluating job applicants in online recruitment systems, using machine learning algorithms to solve the candidate ranking problem and performing semantic matching techniques. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants’ LinkedIn profile, and compares them semantically to the job’s prerequisites. It also infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters, thus it can be trusted for the automation of applicant ranking and personality mining.


title: Monitoring civil structures with a wireless sensor network

abstract: Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a building's structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures.


1 Causality and graphical models in time series analysis


title: Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains

abstract: Link prediction is a key technique in many applications such as recommender systems, where potential links between users and items need to be predicted. A challenge in link prediction is the data sparsity problem. In this paper, we address this problem by jointly considering multiple heterogeneous link prediction tasks such as predicting links between users and different types of items including books, movies and songs, which we refer to as the collective link prediction (CLP) problem. We propose a nonparametric Bayesian framework for solving the CLP problem, which allows knowledge to be adaptively transferred across heterogeneous tasks while taking into account the similarities between tasks. We learn the inter-task similarity automatically. We also introduce link functions for different tasks to correct their biases and skewness of distributions in their link data. We conduct experiments on several real world datasets and demonstrate significant improvements over several existing state-of-the-art methods.


Speaker Age Classification and Regression Using i-Vectors.


Hierarchical Cognitive Control and the Functional Organization of the Frontal Cortex


title: LDA-Based Unified Topic Modeling for Similar TV User Grouping and TV Program Recommendation

abstract: Social TV is a social media service via TV and social networks through which TV users exchange their experiences about TV programs that they are viewing. For social TV service, two technical aspects are envisioned: grouping of similar TV users to create social TV communities and recommending TV programs based on group and personal interests for personalizing TV. In this paper, we propose a unified topic model based on grouping of similar TV users and recommending TV programs as a social TV service. The proposed unified topic model employs two latent Dirichlet allocation (LDA) models. One is a topic model of TV users, and the other is a topic model of the description words for viewed TV programs. The two LDA models are then integrated via a topic proportion parameter for TV programs, which enforces the grouping of similar TV users and associated description words for watched TV programs at the same time in a unified topic modeling framework. The unified model identifies the semantic relation between TV user groups and TV program description word groups so that more meaningful TV program recommendations can be made. The unified topic model also overcomes an item ramp-up problem such that new TV programs can be reliably recommended to TV users. Furthermore, from the topic model of TV users, TV users with similar tastes can be grouped as topics, which can then be recommended as social TV communities. To verify our proposed method of unified topic-modeling-based TV user grouping and TV program recommendation for social TV services, in our experiments, we used real TV viewing history data and electronic program guide data from a seven-month period collected by a TV poll agency. The experimental results show that the proposed unified topic model yields an average 81.4% precision for 50 topics in TV program recommendation and its performance is an average of 6.5% higher than that of the topic model of TV users only. For TV user prediction with new TV programs, the average prediction precision was 79.6%. Also, we showed the superiority of our proposed model in terms of both topic modeling performance and recommendation performance compared to two related topic models such as polylingual topic model and bilingual topic model.


title: Carry speculative adder with variable latency for low power VLSI

abstract: Arithmetic logic units and digital signal processors widely uses adders. It is the most complicated arithmetic circuits in digital electronics. The existing adders suffer from critical path delay, area overhead and power consumption. Speculative adders are designed with variable latency that combines speculation technique along with correction methodology to attain high performance in terms of low area overhead over the existing adders. In speculative adders the sum and carry generation part is separated to reduce the area overhead. Carry Speculative Adder (CSPA) uses carry predictor circuit to reduce power consumption and to reduce the computational time and it uses error recognition and error correction circuit to find the fault occurred in the partial sum generator and to recover it to get accurate results. This speculative adder can reduce the delay upto 11.88 %.


title: Reproductive outcome following hysteroscopic adhesiolysis in patients with infertility due to Asherman’s syndrome

abstract: OBJECTIVES ::: To evaluate the outcome of hysteroscopic adhesiolysis in patients with infertility due to Asherman's syndrome. ::: ::: ::: MATERIALS AND METHODS ::: This was a retrospective clinical analysis of 89 patients who underwent hysteroscopic adhesiolysis by monopolar electrode knife. A second-look office hysteroscopy was performed in all cases after 2 months. On second-look hysteroscopy, 12 patients showed reformation of adhesions and needed a repeat procedure. ::: ::: ::: RESULTS ::: The mean age of the patient was 28.4 years. In the majority (64%) of patients, the possible cause of Asherman's syndrome was previous curettage on gravid uterus. The overall conception rate was 40.4% after hysteroscopic adhesiolysis. The mean conception time after surgery was 12.8 months. There was no conception in patients who needed repeat adhesiolysis. The conception rate was higher (58%) in mild Asherman's syndrome compared to 30% conception rate in moderate and 33.3% conception rate in severe cases. There was no significant association between conception rate and preoperative menstrual pattern. There was significant higher likelihood of conception rate (44.3%) in those who continued to have improved menstrual pattern compared to only 10% likelihood of conception in those who continued to have amenorrhea after adhesiolysis. The live birth rate was 86.1% and miscarriage rate was 11.1%. Cumulative pregnancy rate showed that 97.2% patients conceived within 24 months. There was increased incidence (43.8%) of cesarean section. Four (12.5%) patients had postpartum hemorrhage for adherent placenta. ::: ::: ::: CONCLUSION ::: Hysteroscopic adhesiolysis for Asherman's syndrome is a safe and effective method of choice for restoring menstrual function and fertility.


title: Adversarial Learning for Semi-Supervised Semantic Segmentation

abstract: We propose a method for semi-supervised semantic segmentation using the adversarial network. While most existing discriminators are trained to classify input images as real or fake on the image level, we design a discriminator in a fully convolutional manner to differentiate the predicted probability maps from the ground truth segmentation distribution with the consideration of the spatial resolution. We show that the proposed discriminator can be used to improve the performance on semantic segmentation by coupling the adversarial loss with the standard cross entropy loss on the segmentation network. In addition, the fully convolutional discriminator enables the semi-supervised learning through discovering the trustworthy regions in prediction results of unlabeled images, providing additional supervisory signals. In contrast to existing methods that utilize weakly-labeled images, our method leverages unlabeled images without any annotation to enhance the segmentation model. Experimental results on both the PASCAL VOC 2012 dataset and the Cityscapes dataset demonstrate the effectiveness of our algorithm.


title: Online multi-object tracking with convolutional neural networks

abstract: In this paper, we propose a novel online multi-object tracking (MOT) framework, which exploits features from multiple convolutional layers. In particular, we use the top layer to formulate a category-level classifier and use a lower layer to identify instances from one category under the intuition that lower layers contain much more details. To avoid the computational cost caused by online fine-tuning, we train our appearance model with an offline learning strategy using the historical appearance reserved for each object. We evaluate the proposed tracking framework on a popular MOT benchmark to demonstrate the effectiveness and the state-of-the-art performance of our tracker.


title: EmEx, a Tool for Automated Emotive Face Recognition Using Convolutional Neural Networks

abstract: The work described in this paper represents the study and the attempt to make a contribution to one of the most stimulating and promising sectors in the field of emotion recognition, which is health care management. Multidisciplinary studies in artificial intelligence, augmented reality and psychology stressed out the importance of emotions in communication and awareness. The intent is to recognize human emotions, processing images streamed in real-time from a mobile device. The adopted techniques involve the use of open source libraries of visual recognition and machine learning approaches based on convolutional neural networks (CNN).


The Holy Grail: Imagination and Belief


title: CRF-net: Single Image Radiometric Calibration using CNNs

abstract: In this paper we present CRF-net, a CNN-based solution for estimating the camera response function from a single photograph. We follow the recent trend of using synthetic training data, and generate a large set of training pairs based on a small set of radio-metrically linear images and the DoRF database of camera response functions. The resulting CRF-net estimates the parameters of the EMoR camera response model directly from a single photograph. Experimentally, we show that CRF-net is able to accurately recover the camera response function from a single photograph under a wide range of conditions.


title: Straight until proven gay: A systematic bias toward straight categorizations in sexual orientation judgments.

abstract: Perceivers achieve above chance accuracy judging others' sexual orientations, but they also exhibit a notable response bias by categorizing most targets as straight rather than gay. Although a straight categorization bias is evident in many published reports, it has never been the focus of systematic inquiry. The current studies therefore document this bias and test the mechanisms that produce it. Studies 1-3 revealed the straight categorization bias cannot be explained entirely by perceivers' attempts to match categorizations to the number of gay targets in a stimulus set. Although perceivers were somewhat sensitive to base rate information, their tendency to categorize targets as straight persisted when they believed each target had a 50% chance of being gay (Study 1), received explicit information about the base rate of gay targets in a stimulus set (Study 2), and encountered stimulus sets with varying base rates of gay targets (Study 3). The remaining studies tested an alternate mechanism for the bias based upon perceivers' use of gender heuristics when judging sexual orientation. Specifically, Study 4 revealed the range of gendered cues compelling gay judgments is smaller than the range of gendered cues compelling straight judgments despite participants' acknowledgment of equal base rates for gay and straight targets. Study 5 highlighted perceptual experience as a cause of this imbalance: Exposing perceivers to hyper-gendered faces (e.g., masculine men) expanded the range of gendered cues compelling gay categorizations. Study 6 linked this observation to our initial studies by demonstrating that visual exposure to hyper-gendered faces reduced the magnitude of the straight categorization bias. Collectively, these studies provide systematic evidence of a response bias in sexual orientation categorization and offer new insights into the mechanisms that produce it. (PsycINFO Database Record


title: Terahertz Communication for Vehicular Networks

abstract: With the never-ending increase in the number of mobile connected devices and the need for higher data rates anywhere, anytime, higher frequency bands are being considered for communications. As millimeter-wave technology moves from research to commercial deployments, and motivated by the still limited bandwidth, the terahertz (THz) band is envisioned as the next frontier for communications. When it comes to vehicular networks, communication at much higher frequencies and, consequently, with much higher data rates brings many exciting opportunities as well as challenges. In this paper, an overview of the opportunities and challenges in THz communications for vehicular networks is provided. In addition, the papers in this Special Section which provide first-time solutions to some of these challenges, are introduced.


title: A wearable wireless fall detection system with accelerators

abstract: Falls in elderly is a major health problem and a cost burden to social services. Thus automatic fall detectors are needed to support the independence and security of the elderly. The goal of this research is to develop a real-time portable wireless fall detection system, which is capable of automatically discriminating between falls and Activities of Daily Life (ADL). The fall detection system contains a portable fall-detection terminal and a monitoring centre, both of which communicate with ZigBee protocol. To extract the features of falls, falls data and ADL data obtained from young subjects are analyzed. Based on the characteristics of falls, an effective fall detection algorithm using tri-axis accelerometers is introduced, and the results show that falls can be distinguished from ADL with a sensitivity over 95% and a specificity of 100%, for a total set of 270 movements.