title: Reduced prefrontal and increased subcortical brain functioning assessed using positron emission tomography in predatory and affective murderers

abstract: There appear to be no brain imaging studies investigating which brain mechanisms subserve affective, impulsive violence versus planned, predatory violence. It was hypothesized that affectively violent offenders would have lower prefrontal activity, higher subcortical activity, and reduced prefrontal/subcortical ratios relative to controls, while predatory violent offenders would show relatively normal brain functioning. Glucose metabolism was assessed using positron emission tomography in 41 comparisons, 15 predatory murderers, and nine affective murderers in left and right hemisphere prefrontal (medial and lateral) and subcortical (amygdala, midbrain, hippocampus, and thalamus) regions. Affective murderers relative to comparisons had lower left and right prefrontal functioning, higher right hemisphere subcortical functioning, and lower right hemisphere prefrontal/subcortical ratios. In contrast, predatory murderers had prefrontal functioning that was more equivalent to comparisons, while also having excessively high right subcortical activity. Results support the hypothesis that emotional, unplanned impulsive murderers are less able to regulate and control aggressive impulses generated from subcortical structures due to deficient prefrontal regulation. It is hypothesized that excessive subcortical activity predisposes to aggressive behaviour, but that while predatory murderers have sufficiently good prefrontal functioning to regulate these aggressive impulses, the affective murderers lack such prefrontal control over emotion regulation. © 1998 John Wiley & Sons, Ltd.


title: Sequence-specific bias correction for RNA-seq data using recurrent neural networks

abstract: BACKGROUND ::: The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. ::: ::: ::: RESULT ::: We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. ::: ::: ::: CONCLUSTIONS ::: RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.


title: A Reliable Effective Terascale Linear Learning System

abstract: We present a system and a set of techniques for learning linear predictors with convex losses on terascale data sets, with trillions of features, billions of training examples and millions of parameters in an hour using a cluster of 1000 machines. Individually none of the component techniques are new, but the careful synthesis required to obtain an efficient implementation is. The result is, up to our knowledge, the most scalable and efficient linear learning system reported in the literature. We describe and thoroughly evaluate the components of the system, showing the importance of the various design choices.


title: 2015 Equilibrium Committee Amendment to the 1995 AAO-HNS Guidelines for the Definition of Ménière’s Disease

abstract: Ménière's disease is a disorder of the inner ear that causes attacks of vertigo and hearing loss, tinnitus, aural fullness in the involved ear. Over the past 4 decades, the Equilibrium Committee of the AAO-HNS has issued guidelines for diagnostic criteria, with the latest version being published in 1995. These criteria were reviewed in 2015 by the Equilibrium Committee, and revisions were approved at the recent meeting of the committee at the 2015 AAO-HNSF Annual Meeting. The following commentary outlines the amended and approved criteria.


title: Learning to play Tic-tac-toe

abstract: This paper reports our experiment on applying Q Learning algorithm for learning to play Tic-tac-toe. The original algorithm is modified by updating the Q value only when the game terminates, propagating the update process from the final move backward to the first move, and incorporating a new update rule. We evaluate the agent performance using full-board and partial-board representations. In this evaluation, the agent plays the tic-tac-toe game against human players. The evaluation results show that the performance of modified Q Learning algorithm with partial-board representation is comparable to that of human players.


title: Scalable Gaussian Process Classification via Expectation Propagation

abstract: Variational methods have been recently considered for scaling the training process of Gaussian process classifiers to large datasets. As an alternative, we describe here how to train these classifiers efficiently using expectation propagation. The proposed method allows for handling datasets with millions of data instances. More precisely, it can be used for (i) training in a distributed fashion where the data instances are sent to different nodes in which the required computations are carried out, and for (ii) maximizing an estimate of the marginal likelihood using a stochastic approximation of the gradient. Several experiments indicate that the method described is competitive with the variational approach.


title: Uncertainty and Political Perceptions

abstract: While the world of politics is uncertain, previous work, both theoretical and empirical, has largely failed to incorporate this uncertainty into the analysis of public opinion and electoral behavior. In this article we discuss measures designed to elicit the uncertainty survey respondents feel about their political perceptions. These measures exhibit response patterns which are interpretable, substantively interesting, and consistent with a model relating uncertainty to citizen information costs. We also find that variation in respondent uncertainty leads to different models of perception of political figures and speaks to models of the survey response. As a practical matter, our measures can easily be incorporated into existing surveys with no disruption of continuity.


title: Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition

abstract: Body joints, directly obtained from a pose estimation model, have proven effective for action recognition. Existing works focus on analyzing the dynamics of human joints. However, except joints, humans also explore motions of limbs for understanding actions. Given this observation, we investigate the dynamics of human limbs for skeleton-based action recognition. Specifically, we represent an edge in a graph of a human skeleton by integrating its spatial neighboring edges (for encoding the cooperation between different limbs) and its temporal neighboring edges (for achieving the consistency of movements in an action). Based on this new edge representation, we devise a graph edge convolutional neural network (CNN). Considering the complementarity between graph node convolution and edge convolution, we further construct two hybrid networks by introducing different shared intermediate layers to integrate graph node and edge CNNs. Our contributions are twofold, graph edge convolution and hybrid networks for integrating the proposed edge convolution and the conventional node convolution. Experimental results on the Kinetics and NTU-RGB+D data sets demonstrate that our graph edge convolution is effective at capturing the characteristics of actions and that our graph edge CNN significantly outperforms the existing state-of-the-art skeleton-based action recognition methods.


title: Simulation design of compact stepped-frequency continuous-wave through-wall radar

abstract: In this paper, an approach of designing a compact stepped-frequency continuous-wave (SFCW) through-wall radar is presented using simulation software. The radar which is designed to operate at carrier frequency of 2GHz and working bandwidth of 1.5GHz works by transmitting the signal and then processing the reflected signal from the object behind the wall to be analyzed for obtaining the object information. Here, the use of simulation software aims to do modeling in investigating the information contained in reflected signal. From the result, it shows that the SFCW method is suitable for designing a compact through-wall radar which can range an object at the distance of 2 meters form the radar.


title: Vehicle Number Plate Recognition System: A Literature Review and Implementation using Template Matching

abstract: growing affluence of urban India has made the ownership of vehicles a necessity. This has resulted in an unexpected civic problem - that of traffic control and vehicle identification. Parking areas have become overstressed due to the growing numbers of vehicles on the roads today. The Automatic Number Plate Recognition System (ANPR) plays an important role in addressing these issues as its application ranges from parking admission to monitoring urban traffic and to tracking automobile thefts. There are numerous ANPR systems available today which are based on different methodologies. In this paper, we attempt to review the various techniques and their usage. The ANPR system has been implemented using template Matching and its accuracy was found to be 80.8% for Indian number plates.


title: Ripple: communicating through physical vibration

abstract: This paper investigates the possibility of communicating through vibrations. By modulating the vibration motors available in all mobile phones, and decoding them through accelerometers, we aim to communicate small packets of information. Of course, this will not match the bit rates available through RF modalities, such as NFC or Bluetooth, which utilize a much larger bandwidth. However, where security is vital, vibratory communication may offer advantages. We develop Ripple, a system that achieves up to 200 bits/s of secure transmission using off-the-shelf vibration motor chips, and 80 bits/s on Android smartphones. This is an outcome of designing and integrating a range of techniques, including multicarrier modulation, orthogonal vibration division, vibration braking, side-channel jamming, etc. Not all these techniques are novel; some are borrowed and suitably modified for our purposes, while others are unique to this relatively new platform of vibratory communication.


title: CMOS transceiver with baud rate clock recovery for optical interconnects

abstract: An efficient baud rate clock and data recovery architecture is applied to a double sampling/integrating front-end receiver for optical interconnects. Receiver performance is analyzed and projected for future technologies. This front-end allows use of a 1:5 demux architecture to achieve 5Gb/s in a 0.25 /spl mu/m CMOS process. A 5:1 multiplexing transmitter is used to drive VCSELs for optical transmission. The transceiver chip consumes 145mW per link at 5Gb/s with a 2.5V supply.


title: Bayesian model selection for group studies

abstract: Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM). However, so far, combining BMS results from several subjects has relied on simple (fixed effects) metrics, e.g. the group Bayes factor (GBF), that do not account for group heterogeneity or outliers. In this paper, we compare the GBF with two random effects methods for BMS at the between-subject or group level. These methods provide inference on model-space using a classical and Bayesian perspective respectively. First, a classical (frequentist) approach uses the log model evidence as a subject-specific summary statistic. This enables one to use analysis of variance to test for differences in log-evidences over models, relative to inter-subject differences. We then consider the same problem in Bayesian terms and describe a novel hierarchical model, which is optimised to furnish a probability density on the models themselves. This new variational Bayes method rests on treating the model as a random variable and estimating the parameters of a Dirichlet distribution which describes the probabilities for all models considered. These probabilities then define a multinomial distribution over model space, allowing one to compute how likely it is that a specific model generated the data of a randomly chosen subject as well as the exceedance probability of one model being more likely than any other model. Using empirical and synthetic data, we show that optimising a conditional density of the model probabilities, given the log-evidences for each model over subjects, is more informative and appropriate than both the GBF and frequentist tests of the log-evidences. In particular, we found that the hierarchical Bayesian approach is considerably more robust than either of the other approaches in the presence of outliers. We expect that this new random effects method will prove useful for a wide range of group studies, not only in the context of DCM, but also for other modelling endeavours, e.g. comparing different source reconstruction methods for EEG/MEG or selecting among competing computational models of learning and decision-making.


title: Is Bitcoin a Decentralized Currency?

abstract: Bitcoin has achieved popularity by promising users a fully decentralized, low-cost virtual currency system. However, a limited set of entities controls Bitcoin's services, decision-making, mining, and incident resolution processes. These entities can decide Bitcoin's fate, bypassing the will of the multitude of users that populate the network.


title: Secure Data Storage and Searching for Industrial IoT by Integrating Fog Computing and Cloud Computing

abstract: With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices’ energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.


title: EthoHand: A dexterous robotic hand with ball-joint thumb enables complex in-hand object manipulation

abstract: Our dexterous hand is a fundmanetal human feature that distinguishes us from other animals by enabling us to go beyond grasping to support sophisticated in-hand object manipulation. Our aim was the design of a dexterous anthropomorphic robotic hand that matches the human hand's 24 degrees of freedom, under-actuated by seven motors. With the ability to replicate human hand movements in a naturalistic manner including in-hand object manipulation. Therefore, we focused on the development of a novel thumb and palm articulation that would facilitate in-hand object manipulation while avoiding mechanical design complexity. Our key innovation is the use of a tendon-driven ball joint as a basis for an articulated thumb. The design innovation enables our under-actuated hand to perform complex in-hand object manipulation such as passing a ball between the fingers or even writing text messages on a smartphone with the thumb's end-point while holding the phone in the palm of the same hand. We then proceed to compare the dexterity of our novel robotic hand design to other designs in prosthetics, robotics and humans using simulated and physical kinematic data to demonstrate the enhanced dexterity of our novel articulation exceeding previous designs by a factor of two. Our innovative approach achieves naturalistic movement of the human hand, without requiring translation in the hand joints, and enables teleoperation of complex tasks, such as single (robot) handed messaging on a smartphone without the need for haptic feedback. Our simple, under-actuated design outperforms current state-of-the-art prostheses or robotic and prosthetic hands regarding abilities that encompass from grasps to activities of daily living which involve complex in-hand object manipulation.


title: 3D Printed Dielectric Reflectarrays: Low-Cost High-Gain Antennas at Sub-Millimeter Waves

abstract: Dielectric reflectarray antennas are proposed as a promising low-loss and low-cost solution for high gain terahertz (THz) antennas. Variable height dielectric elements are used in the reflectarray designs, which allow for the use of low dielectric-constant materials. Polymer-jetting 3-D printing technology is utilized to fabricate the antenna, which makes it possible to achieve rapid prototyping at a low-cost. Numerical and experimental results are presented for 3 different prototypes operating at 100 GHz, which show a good performance. Moreover the methodology proposed here is readily scalable, and with the current material and fabrication technology, designs up to 1.0 THz can be realized. This study reveals that the proposed design approach is well suited for low-cost high-gain THz antennas.


title: Wavelet Analysis of Experimental Blade Vibrations During Interaction With an Abradable Coating

abstract: Minimizing the clearance between turbofan blades and the surrounding casing is a key factor to achieving compressor efficiency. The deposition of an abradable coating on casings is one of the technologies used to reduce this blade-casing clearance and ensure blade integrity in the event of blade-casing contact. Aircraft in-service condi-tions may lead to interactions between the blade tip and the coated casing, during which wear of the abradable coating, blade dynamics and interacting force are critical yet little-understood issues. In order to study blade/abradable-coating interactions of a few tens of milliseconds, experiments were conducted on a dedicated test rig. The experi-mental data were analyzed with the aim of determining the friction-induced vibrational modes of the blade. This involved a time-frequency analysis of the experimental blade strain using the Continuous Wavelet Transform, combined with a modal analysis of the blade. The latter was carried out with two kinds of kinematic boundary conditions at the blade tip: free and modified, by imposing contact with the abradable coating. The interaction data show that the blade vibration modes identified during interactions cor-respond to the free boundary condition, due to the transitional nature of the phenomena and the very short duration of contacts. The properties of the Continous Wavelet Trans-form were then used to identify the occurence of blade-coating contact. Two kinds of blade/abradable-coating interactions were identified: bouncing of the blade over short time periods associated with loss of abradable material, and isolated contacts capable of amplifying the blade vibrations without causing significant wear of the abradable coat-ing. The results obtained were corroborated by high-speed imaging of the interactions.


title: EVALUATION OF THE MICROHARDNESS OF ROOT CANAL DENTIN AFTER DIFFERENT IRRIGATION PROTOCOLS (IN VITRO STUDY)

abstract: INTRODUCTION: Different irrigations solutions may cause alteration in the physicochemical properties of dentin structure thereby affectingthe microhardness of root canal dentin.OBJECTIVES: to evaluate the effect of different irrigation protocols on microhardness of human root canal dentin.MATERIALS AND METHODS: Forty extracted single rooted lower premolars were used. All teeth were instrumented using manualstainless steel files and irrigated by 2ml distilled water between each file, then were sectioned by longitudinal splitting of each tooth. The roothalves were randomly assigned into 4 parallel groups (n=10) and immersed for 5 minutes with one of the following irrigants: Group I: 10 mlof 2.5% Sodium Hypochlorite (NaOCL), Group II: 10 ml of 17% ethylene diamine tetra-acetic acid (EDTA) followed by 10 ml of 2.5%NaOCL, Group III: 10 ml of 2.5% NaOCL followed by 10 ml of 2% chlorhexidine digluconate (CHX), Group IV: 10 ml of 2.5% NaOCLfollowed by 10 ml distilled water then were followed by 10ml of 2% CHX. Ten root halves from each group were prepared to measure dentinmicrohardness at baseline measurement and after treatment to determine the change in microhardness, using Vickers tester.RESULTS: Data were analysed using t-test, ANOVA test and Post Hoc test.Group II showed the highest percentage decrease in microhardnessvalues, followed by group III, then group IV and the lowest was group I. All groups showed a significant difference between each other (P <0.05), except group III and IV. The coronal third showed the highest percentage decrease with significant difference between apical and middlethirds (P < 0.05), in which there was no significant difference between them.CONCLUSIONS: CHX is the best final irrigant if there is excellent intermediate flush for prevention of its precipitation with NaOCL.Thecoronal third needs conservative approach as it is the most affected third.


title: Intrusion Detection in the RPL-connected 6LoWPAN Networks

abstract: The interconnectivity of 6LoWPAN networks with the Internet raises serious security concerns, as constrained 6LoWPAN devices are accessible anywhere from the untrusted global Internet. Also, 6LoWPAN devices are mostly deployed in unattended environments, hence easy to capture and clone. Despite that state of the art crypto solutions provide information security, IPv6 enabled smart objects are vulnerable to attacks from outside and inside 6LoWPAN networks that are aimed to disrupt networks. This paper attempts to identify intrusions aimed to disrupt the Routing Protocol for Low-Power and Lossy Networks (RPL).In order to improve the security within 6LoWPAN networks, we extend SVELTE, an intrusion detection system for the Internet of Things, with an intrusion detection module that uses the ETX (Expected Transmissions) metric. In RPL, ETX is a link reliability metric and monitoring the ETX value can prevent an intruder from actively engaging 6LoWPAN nodes in malicious activities. We also propose geographic hints to identify malicious nodes that conduct attacks against ETX-based networks. We implement these extensions in the Contiki OS and evaluate them using the Cooja simulator.


title: A Smartphone-Based Multi-Functional Hearing Assistive System to Facilitate Speech Recognition in the Classroom

abstract: In this paper, we propose a smartphone-based hearing assistive system (termed SmartHear) to facilitate speech recognition for various target users, who could benefit from enhanced listening clarity in the classroom. The SmartHear system consists of transmitter and receiver devices (e.g., smartphone and Bluetooth headset) for voice transmission, and an Android mobile application that controls and connects the different devices via Bluetooth or WiFi technology. The wireless transmission of voice signals between devices overcomes the reverberation and ambient noise effects in the classroom. The main functionalities of SmartHear include: 1) configurable transmitter/receiver assignment, to allow flexible designation of transmitter/receiver roles; 2) advanced noise-reduction techniques; 3) audio recording; and 4) voice-to-text conversion, to give students visual text aid. All the functions are implemented as a mobile application with an easy-to-navigate user interface. Experiments show the effectiveness of the noise-reduction schemes at low signal-to-noise ratios in terms of standard speech perception and quality indices, and show the effectiveness of SmartHear in maintaining voice-to-text conversion accuracy regardless of the distance between the speaker and listener. Future applications of SmartHear are also discussed.


title: Paternal postpartum depression: how can nurses begin to help?

abstract: Men's emotional health can be overlooked during their partner's pregnancy and throughout the first postpartum year. Postpartum depression, once expected only in new mothers, is now estimated to occur in 4-25% of new fathers as well. The incidence of paternal postpartum depression is greater in couples where maternal postpartum depression is also present. Paternal postpartum depression can be difficult to assess. New fathers may seem more angry and anxious than sad. And yet, depression is present. When left untreated, paternal postpartum depression limits men's capacity to provide emotional support to their partners and children. This article reviews the incidence and prevalence of paternal postpartum depression, comments on tools to measure the disorder, identifies paternal behaviors that may indicate depression, examines the effects of parental depression on families and discusses what nurses can do to begin to help.


title: The Continuator: Musical Interaction with Style

abstract: We propose a system, the Continuator, that bridges the gap between two classes of traditionally incompatible musical systems: (1) interactive musical systems, limited in their ability to generate stylistically consistent material, and (2) music imitation systems, which are fundamentally not interactive. Our purpose is to allow musicians to extend their technical ability with stylistically consistent, automatically learnt material. This goal requires the ability for the system to build operational representations of musical styles in a real time context. Our approach is based on a Markov model of musical styles augmented to account for musical issues such as management of rhythm, beat, harmony, and imprecision. The resulting system is able to learn and generate music in any style, either in standalone mode, as continuations of musician’s input, or as interactive improvisation back up. Lastly, the very design of the system makes possible new modes of musical collaborative playing. We describe the architectu...


title: Exploring the Realization of Irony in Twitter Data

abstract: Handling figurative language like irony is currently a challenging task in natural language processing. Since irony is commonly used in user-generated content, its presence can significantly undermine accurate analysis of opinions and sentiment in such texts. Understanding irony is therefore important if we want to push the state-of-the-art in tasks such as sentiment analysis. In this research, we present the construction of a Twitter dataset for two languages, being English and Dutch, and the development of new guidelines for the annotation of verbal irony in social media texts. Furthermore, we present some statistics on the annotated corpora, from which we can conclude that the detection of contrasting evaluations might be a good indicator for recognizing irony.


title: Multiscale Dictionary Learning via Cross-Scale Cooperative Learning and Atom Clustering for Visual Signal Processing

abstract: For sparse signal representation, the sparsity across the scales is a promising yet underinvestigated direction. In this paper, we aim to design a multiscale sparse representation scheme to explore such potential. A multiscale dictionary (MD) structure is designed. A cross-scale matching pursuit algorithm is proposed for multiscale sparse coding. Two dictionary learning methods, cross-scale cooperative learning and cross-scale atom clustering, are proposed each focusing on one of the two important attributes of an efficient MD: the similarity and uniqueness of corresponding atoms in different scales. We analyze and compare their different advantages in the application of image denoising under different noise levels, where both methods produce state-of-the-art denoising results.


title: Least squares quantization in pcm

abstract: It has long been realized that in pulse-code modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as the number of quanta becomes infinite, the asymptotic fractional density of quanta per unit voltage should vary as the one-third power of the probability density per unit voltage of signal amplitudes. In this paper the corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy. The optimization criterion used is that the average quantization noise power be a minimum. It is shown that the result obtained here goes over into the Panter and Dite result as the number of quanta become large. The optimum quautization schemes for 2^{b} quanta, b=1,2, \cdots, 7 , are given numerically for Gaussian and for Laplacian distribution of signal amplitudes.


title: Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR

abstract: Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning intervention, and for tracking tumor changes over time. However, this task is complicated by the lack of prior knowledge concerning tumor location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and tumor segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package—a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal Brain Tumor Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade glioma tumor four-modality MRI from five different institutions. Average Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for “complete”, “core”, and “enhanced” tumor components, respectively.


title: Design and Implementation of a Low Cost VLF Metal Detector with Metal-Type Discrimination Capabilities

abstract: Land mine detectors, security metal doors that we all go through in buildings and airports, as well as metal mining detectors are just examples of metal detector use in our daily lives. In this paper we present a cost efficient, low complexity very low frequency (VLF) handheld reconfigurable metal detector (MD) design and implementation that has the capability of discriminating between various metal types. A concentric coil consisting of an outer transmitter coil, an inner receiver coil and a feedback coil. The coils operate in the 5.5 KHz frequency range. An amplitude and phase detection circuits were designed to detect and distinguish between metal types. The detection algorithm was implemented using a PIC microcontroller. The results from the actual implemented metal detector are shown and discussed for 3 types of metals: Iron, Copper, and Aluminum at various distances. The MD can detect metals about 16 cm below its coils.


title: Recommendation Systems in Requirements Discovery

abstract: Recommendation systems offer the opportunity for supporting and enhancing a wide variety of activities in requirements engineering. We discuss several potential uses. In particular we highlight the role of recommendation systems in online forums that are used for capturing and discussing feature requests. The recommendation system is used to mitigate problems introduced when face-to-face communication is replaced with potentially high-volume online discussions. In this context, recommendation systems can be used to suggest relevant topics to stakeholders and conversely to recommend expert stakeholders for each discussion topic. We also explore the use of recommendation systems in the domain analysis process, where they can be used to recommend sets of features to include in new products.


title: A direct three-phase to single-phase AC/AC converter for contactless electric vehicle charger

abstract: This paper proposes an attractive direct line-frequency three-phase to a higher frequency single-phase AC/AC converter for a contactless electric vehicle charger. It removes a DC-link capacitor of a conventional AC/AC converter but has a smaller number of bi-directional switches than a matrix converter. It operates using injection and free-oscillation modes based on a zero-current switching mechanism. Circuit descriptions including a topology, commutations and a modulation strategy are discussed using some illustrations and diagrams. An on-off control is used to simplify control of resonant current. Simulation results to study some effects due to different coupling factor and load are then presented to show converter's performance and capabilities in contactless power transfer.


title: In vivo evaluation of bacterial cellulose/acrylic acid wound dressing hydrogel containing keratinocytes and fibroblasts for burn wounds

abstract: The healing of wounds, including those from burns, currently exerts a burden on healthcare systems worldwide. Hydrogels are widely used as wound dressings and in the field of tissue engineering. The popularity of bacterial cellulose-based hydrogels has increased owing to their biocompatibility. Previous study demonstrated that bacterial cellulose/acrylic acid (BC/AA) hydrogel increased the healing rate of burn wound. This in vivo study using athymic mice has extended the use of BC/AA hydrogel by the addition of human epidermal keratinocytes and human dermal fibroblasts. The results showed that hydrogel loaded with cells produces the greatest acceleration on burn wound healing, followed by treatment with hydrogel alone, compared with the untreated group. The percentage wound reduction on day 13 in the mice treated with hydrogel loaded with cells (77.34 ± 6.21%) was significantly higher than that in the control-treated mice (64.79 ± 6.84%). Histological analysis, the expression of collagen type I via immunohistochemistry, and transmission electron microscopy indicated a greater deposition of collagen in the mice treated with hydrogel loaded with cells than in the mice administered other treatments. Therefore, the BC/AA hydrogel has promising application as a wound dressing and a cell carrier.


title: Performance evaluation of OFDMA random access in IEEE802.11ax

abstract: Random access Orthogonal Frequency Division Multiple Access (OFDMA), a method for uplink multi-user transmission (UL-MU) for STAs with unknown transmit buffer has been adopted in the upcoming IEEE802.11ax next generation wireless LAN standard. In this paper, we propose a performance analysis model which use bidimensional Markov chain model of OFDMA random access. Via computer simulation, we show the usefulness of the proposed method even in the case of dense environment. In addition, we present a method in calculating the optimal number of resource units, which in 802.11ax refer to a subset of subcarriers to assign the user for OFDMA random access from numerical results.


title: Experiments with Infinite-Horizon, Policy-Gradient Estimation

abstract: In this paper, we present algorithms that perform gradient ascent of the average reward in a partially observable Markov decision process (POMDP). These algorithms are based on GPOMDP, an algorithm introduced in a companion paper (Baxter & Bartlett, 2001), which computes biased estimates of the performance gradient in POMDPs. The algorithm's chief advantages are that it uses only one free parameter β ∈ [0, 1], which has a natural interpretation in terms of bias-variance trade-off, it requires no knowledge of the underlying state, and it can be applied to infinite state, control and observation spaces. We show how the gradient estimates produced by GPOMDP can be used to perform gradient ascent, both with a traditional stochastic-gradient algorithm, and with an algorithm based on conjugate-gradients that utilizes gradient information to bracket maxima in line searches. Experimental results are presented illustrating both the theoretical results of Baxter and Bartlett (2001) on a toy problem, and practical aspects of the algorithms on a number of more realistic problems.


title: Next Generation Intrusion Detection: Autonomous Reinforcement Learning of Network Attacks

abstract: The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. Existing intrusion detection approaches require either manual coding of new attacks in expert systems or the complete retraining of a neural network to improve analysis or learn new attacks. This paper presents a new approach to applying adaptive neural networks to intrusion detection that is capable of autonomously learning new attacks rapidly through the use of a modified reinforcement learning method that uses feedback from the protected system. The approach has been demonstrated to be extremely effective in learning new attacks, detecting previously learned attacks in a network data stream, and in autonomously improving its analysis over time using feedback from the protected system.


title: Back to the future: how human induced pluripotent stem cells will transform regenerative medicine

abstract: Based on cloning studies in mammals, all adult human cells theoretically contain DNA that is capable of creating a whole new person. Cells are maintained in their differentiated state by selectively activating some genes and silencing. The dogma until recently was that cell differentiation was largely fixed unless exposed to the environment of an activated oocyte. However, it is now possible to activate primitive pluripotent genes within adult human cells that take them back in time to a pluripotent state (termed induced pluripotent stem cells). This technology has grown at an exponential rate over the past few years, culminating in the Nobel Prize in medicine. Discussed here are recent developments in the field as they relate to regenerative medicine, with an emphasis on creating functional cells, editing their genome, autologous transplantation and how this ground-breaking field may eventually impact human aging.


Exploring the Influence of Online Consumers ’ Perception on Purchase Intention as Exemplified with an Online Bookstore


title: It’s a Contradiction – no, it’s not: A Case Study using Functional Relations

abstract: Contradiction Detection (CD) in text is a difficult NLP task. We investigate CD over functions (e.g., BornIn(Person)=Place), and present a domain-independent algorithm that automatically discovers phrases denoting functions with high precision. Previous work on CD has investigated hand-chosen sentence pairs. In contrast, we automatically harvested from the Web pairs of sentences that appear contradictory, but were surprised to find that most pairs are in fact consistent. For example, "Mozart was born in Salzburg" does not contradict "Mozart was born in Austria" despite the functional nature of the phrase "was born in". We show that background knowledge about meronyms (e.g., Salzburg is in Austria), synonyms, functions, and more is essential for success in the CD task.


title: An efficient filtering based approach improving LSB image steganography using status bit along with AES cryptography

abstract: In Steganography, the total message will be invisible into a cover media such as text, audio, video, and image in which attackers don't have any idea about the original message that the media contain and which algorithm use to embed or extract it. In this paper, the proposed technique has focused on Bitmap image as it is uncompressed and convenient than any other image format to implement LSB Steganography method. For better security AES cryptography technique has also been used in the proposed method. Before applying the Steganography technique, AES cryptography will change the secret message into cipher text to ensure two layer security of the message. In the proposed technique, a new Steganography technique is being developed to hide large data in Bitmap image using filtering based algorithm, which uses MSB bits for filtering purpose. This method uses the concept of status checking for insertion and retrieval of message. This method is an improvement of Least Significant Bit (LSB) method for hiding information in images. It is being predicted that the proposed method will able to hide large data in a single image retaining the advantages and discarding the disadvantages of the traditional LSB method. Various sizes of data are stored inside the images and the PSNR are also calculated for each of the images tested. Based on the PSNR value, the Stego image has higher PSNR value as compared to other method. Hence the proposed Steganography technique is very efficient to hide the secret information inside an image.


title: Hospice nurses’ emotional challenges in their encounters with the dying

abstract: The purpose of this study was to explore nurses’ emotional challenges when caring for the dying in hospices. The study has a qualitative design, and knowledge was developed through a dialectical exchange between theory and data. Ten individual in-depth interviews were conducted with nurses recruited from two hospices in Denmark. Although all of the nurses said that they experienced emotional challenges or felt emotionally touched during their work, the study found a variety of opinions related to the extent to which their emotional reactions should be revealed in their role as a hospice professional. The participants described their emotional challenges as being simultaneously draining and enriching experiences leading to personal and professional growth and development. The study may contribute to increased awareness of emotional challenges for hospice nurses, which involve continuous reflection and balancing between meeting the dying as a human being and meeting the dying as a hospice professional. Key words: Nurses, emotional dimension, palliative care, professional identity, qualitative interviews (Published: 1 June 2016) Citation: Int J Qualitative Stud Health Well-being 2016, 11: 31170 - http://dx.doi.org/10.3402/qhw.v11.31170


title: The Research on Key Generation in RSA Public-Key Cryptosystem

abstract: In order to reduce the time and space complexity, this paper presents a study on key generation of RSA public-key cryptosystem. An improved prescreening algorithm is introduced to make a screen for the large random number which is generated by the generator. After that, Miller-Rabin algorithm is used to make a final prime test. At last, Stain algorithm is introduced to generate the public and private keys. The results show that the research effectively improved the efficiency of RSA key generation.


title: Enhanced Modelling and Performance in Braided Pneumatic Muscle Actuators

abstract: Pneumatic technology has been successfully applied for over two millennia. Even today, pneumatic cylinder based technology forms the keystone of many manufacturing processes where there is a need for simple, high-speed, low-cost, reliable motion. But when the system requires accurate control of position, velocity or acceleration profiles, these actuators form a far from satisfactory solution. Braided pneumatic muscle actuators (pMAs) form an interesting development of the pneumatic principle offering even higher power/weight performance, operation in a wide range of environments and accurate control of position, motion and force. This technology provides an interesting and potentially very successful alternative actuation source for robots as well as other applications. However, there are difficulties with this approach due to the following.(i)Modeling errors. Models of the force response are still nonoptimal and for good results these models are highly complex, which makes accurate design difficult.(ii)L...


title: CodeMend: Assisting Interactive Programming with Bimodal Embedding

abstract: Software APIs often contain too many methods and parameters for developers to memorize or navigate effectively. Instead, developers resort to finding answers through online search engines and systems such as Stack Overflow. However, the process of finding and integrating a working solution is often very time-consuming. Though code search engines have increased in quality, there remain significant language- and workflow-gaps in meeting end-user needs. Novice and intermediate programmers often lack the language to query, and the expertise in transferring found code to their task. To address this problem, we present CodeMend, a system to support finding and integration of code. CodeMend leverages a neural embedding model to jointly model natural language and code as mined from large Web and code datasets. We also demonstrate a novel, mixed-initiative, interface to support query and integration steps. Through CodeMend, end-users describe their goal in natural language. The system makes salient the relevant API functions, the lines in the end-user's program that should be changed, as well as proposing the actual change. We demonstrate the utility and accuracy of CodeMend through lab and simulation studies.


title: Nearest Neighbor Algorithms Using xBR-Trees

abstract: One of the common queries in spatial databases is the (K) Nearest Neighbor Query that discovers the (K) closest objects to a query object. Processing of spatial queries, in most cases, is accomplished by indexing spatial data by an access method. In this paper, we present algorithms for Nearest Neighbor Queries using a disk based structure that belongs to the Quad tree family, the xBR-tree, that can be used for indexing large point datasets. We demonstrate performance results (I/O efficiency and execution time) of alternative Nearest Neighbor algorithms, using real datasets.


title: Empirical Study of Face Authentication Systems Under OSNFD Attacks

abstract: Face authentication has been widely available on smartphones, tablets, and laptops. As numerous personal images are published in online social networks (OSNs), OSN-based facial disclosure (OSNFD) creates significant threat against face authentication. We make the first attempt to quantitatively measure OSNFD threat to real-world face authentication systems on smartphones, tablets, and laptops. Our results show that the percentage of vulnerable users that are subject to spoofing attacks is high, which is about 64 percent for laptop users, and 93 percent smartphone/tablet users. We investigate liveness detection methods in the real-world face authentication systems against OSNFD threat. We discover that under protection of liveness detection, the percentage of vulnerable images is 18.8 percent, but the percentage of vulnerable users is as high as 73.3 percent. This evidence suggests that the current face authentication systems are not strong enough under OSNFD attacks. Finally, we develop a risk estimation tool based on logistic regression, and analyze the impacts of key attributes of facial images on the OSNFD risk. Our statistical analysis reveals that the most influential attributes of facial images are image resolution, facial makeup, occluded eyes, and illumination. This tool can be used to evaluate OSNFD risk for OSN images to increase users’ awareness of OSNFD.


title: CRITICAL SUCCESS FACTORS FOR CUSTOMER RELATIONSHIP MANAGEMENT IMPLEMENTATIONS

abstract: The growing forces of increasing global competition, continuing customer demands, and the significant revolution in Commercial Off The Shelf (COTS) solutions, especially Customer Relationship Management (CRM) applications, have together put pressure upon many organisations to implement CRM solutions and to switch their organisational processes from being product-centric to being customer-centric. A CRM initiative is not only technology; it is a business strategy supported by technology which automates and enhances the processes associated with managing customer relationships. By the end of 2010, it is predicted that companies will be spending almost $11 billion yearly on CRM solutions. However, studies have found that 70% of CRM projects have failed. Understanding the factors that enable success of CRM is vital. There is very few existing specific research into Critical Success Factors (CSFs) of CRM implementations, and there is no comprehensive view that captures all the aspects for successful CRM implementation and their inter-relationships. Therefore, the aim of this paper is to explore the current literature base of CSFs for CRM implementations and proposes a taxonomy for them. Future research work will continue to investigate in depth these factors by exploring the complex system links between CSFs using systems thinking techniques such as causal maps to investigate the complex, systemic networks of CSFs in organisations which result in emergent effects which themselves influence the failure or success of a CRM.


title: On the determinant of multiplication map of a monomial complete intersection ring

abstract: In this article, we consider the monomial complete intersection algebra $\mathbb{K}[x,y]/\langle x^d,y^q\rangle$ in two variables. For elements $l_1,\ldots,l_{d+q-2k}$ of degree $1$, we give a formula of the deteminant of linear map from the homogeneous component of degree $k$ to the homogenous component of degree $d+q-k$ defined by the multiplication of $l_1 \cdots l_{d+q-2k}$.


title: Combined coverage path planning for autonomous cleaning robots in unstructured environments

abstract: Complete coverage path planning is a key problem for autonomous cleaning robots, which concerns not only the cleaning efficiency but also the adaptability to unstructured environments. But the diversity of environments and limited perception ability of the robot make the problem still unsolved. In this paper, a novel strategy of combined coverage path planning is proposed, which combines the random path planning and local complete coverage path planning. The random planning lets the robot run straight until an obstacle is encountered. After turning a random angle, the robot continues the straight run. This mode is easy to implement and can provide the robot with the flexibility to environments. And local complete coverage path planning works out a comb-like path depending on dead reckoning. The comb-like path can cover every part in a relative small area. All these functions are just based on general hardware: ultrasonic sensors, infrared sensors, incremental encoders, DC motors, vacuum, etc. Finally the experiment shows that this strategy can work efficiently and robustly in common family environments.


title: Simulation of smart functionalities of photovoltaic inverters by interfacing OpenDSS and Matlab

abstract: With the increase in demand for clean and green power, several energy utilities are increasing renewable energy in their generation portfolio. The high penetration of PV can create problems of voltage fluctuation and frequency variation. The two major smart functionalities that address high PV penetration include dynamic Volt/VAR control for voltage fluctuations and active power control and curtailment to achieve frequency regulation. Quasi-static time series simulation of the power grid that includes the two smart functionalities of the Photovoltaic (PV) generator is needed. In this paper, the modeling and analysis of the power grid with PV generators is presented using the open source simulation tool OpenDSS. Specifically, the COM interface capability of OpenDSS is used to interface with Matlab to simulate the advanced functionalities of Volt/VAR and active power control and at the same time observe the effect of these controls in the power grid. The IEEE 13 bus feeder is used to demonstrate the usefulness of the proposed simulation methodology interfacing both Matlab and OpenDSS.


title: Metallic Glass Hemispherical Shell Resonators

abstract: By utilizing bulk metallic glasses' (BMGs) unique combination of amorphous structure, material properties, and fabrication opportunities, ultrasmooth and symmetric 3-D metallic glass resonators that are complimentary metal oxide semiconductor (CMOS) post-processing compatible are fabricated. Surface roughness to size ratio fabrication precision in the order of 100 parts per billion is demonstrated with a 3-mm diameter Pt ::: 57.5 ::: Cu ::: 14.7 ::: Ni ::: 5.3 ::: P ::: 22.5 ::: BMG hemispherical shell with a thickness variation <;100 nm and a surface roughness of <;1 nm Ra. The resonator exhibits a resonant frequency of 13.9440 kHz ± 0.1 Hz with 0.035% frequency mismatch between degenerate N = 2 wineglass modes with a quality factor of 6200. This performance was obtained in the asmolded state without any device tuning or trimming. Another resonator with N = 2 resonant modes at 9.393 and 9.401 kHz, and quality factors of 7800 and 6500 was mounted into an integrated electrode system. Electrical readout by capacitive sensing in both time and frequency domains showed a resonance shift to 9.461 and 9.483 kHz, respectively. The quality factor was reduced to 5400 and 5300, respectively. This investigation demonstrates that BMG resonators may serve as a basis for robust microelectromechanical systems resonator devices with increased performance and low-cost fabrication techniques that exploits the atomic structure, unique softening behavior, strength, formability, and toughness of metallic glasses.


title: Evaluating face trustworthiness: a model based approach

abstract: Judgments of trustworthiness from faces determine basic approach/avoidance responses and approximate the valence evaluation of faces that runs across multiple person judgments. Here, based on trustworthiness judgments and using a computer model for face representation, we built a model for representing face trustworthiness (study 1). Using this model, we generated novel faces with an increased range of trustworthiness and used these faces as stimuli in a functional Magnetic Resonance Imaging study (study 2). Although participants did not engage in explicit evaluation of the faces, the amygdala response changed


title: Collaborative Visual SLAM Using Compressed Feature Exchange

abstract: In the field of robotics, collaborative simultaneous localization and mapping (SLAM) is still a challenging problem. The exploration of unknown large-scale environments benefits from sharing the work among multiple agents possibly equipped with different abilities, such as aerial or ground-based vehicles. In this letter, we specifically address data-efficiency for the exchange of visual information in a collaborative visual SLAM setup. For efficient data exchange, we extend a compression scheme for local binary features by two additional modes providing support for local features with additional depth information and an inter-view coding mode exploiting the spatial relations between views of a stereo camera system. To demonstrate the coding framework, we use a centralized system architecture based on ORB-SLAM2, where energy-constrained agents extract local binary features and send a compressed version over a network to a more powerful agent, which is capable of running several visual SLAM instances in parallel. We exploit the information from other agents by detecting the overlap between already mapped areas and subsequent merging of the maps. Henceforth, the participants contribute to a joint representation and benefit from shared map information. We show a reduction in terms of data-rate by 70.8% using the feature compression and a reduction in absolute trajectory error by 53.7% using the collaborative mapping strategy with three agents on the well-known KITTI dataset. For the benefit of the community, we provide a public version of the source code.


Incorporating Semantic and Geometric Priors in Deep Pose Regression


title: A Ka-Band CMOS Wilkinson Power Divider Using Synthetic Quasi-TEM Transmission Lines

abstract: This work presents a Ka-band two-way 3 dB Wilkinson power divider using synthetic quasi-transverse electromagnetic (TEM) transmission lines (TLs). The synthetic quasi-TEM TL, also called complementary-conducting-strip TL (CCS TL), is theoretically analyzed. The equivalent TL model, whose production is based on the extracted results, is applied to the power divider design. The prototype is fabricated by the standard 0.18 mum 1P6M CMOS technology, showing the circuit size of 210.0 mumtimes390.0 mum without contact pads. The measurement results, which match the 50 Omega system, reveal perfect agreements with those of the simulations. The comparison reveals the following characteristics. The divider exhibits an equal power-split with the insertion losses (S21 and S31) of 3.65 dB. The return losses (S11, S22 and S33) of the prototype are higher than 10.0 dB from 30.0 to 40.0 GHz.


title: New Horizons in MR Technology: RF Coil Designs and Trends

abstract: Parallel imaging techniques have developed very rapidly, and realization of their full potential has required the design of magnetic resonance (MR) scanners with ever-increasing numbers of receiver channels (32 to 128). In particular, 1.5- and 3-Tesla fast MR imaging applications are now used in everyday clinical practice. Both strengths require maximum achievable signal-to-noise ratio (SNR) and multi-detector array coil optimization within the framework of the parallel imaging scheme for more advanced and faster clinical MR scanning. Preamplifiers are key components in the detector array coils and serve many functions beyond mere signal amplification. One critical function is to aid in the decoupling of individual coils, which is essential for optimal SNR and the performance of parallel imaging. To support a large number of detector array coils for parallel imaging, preamplifiers must be physically very small so that they may be tightly packed together to form an optimized detector array. The author herein reviews the state-of-the-art work reported by those skilled in the art to consider the rationale for determining how many channels are enough and how fast we can go. The paper explores the important and fundamental principles of RF array coils for MR imaging and reviews cutting-edge array coils, including those for transmit-SENSE or parallel transmission applications. The future of radiofrequency (RF) coil technology is also considered.


title: PDFX: fully-automated PDF-to-XML conversion of scientific literature

abstract: PDFX is a rule-based system designed to reconstruct the logical structure of scholarly articles in PDF form, regardless of their formatting style. The system's output is an XML document that describes the input article's logical structure in terms of title, sections, tables, references, etc. and also links it to geometrical typesetting markers in the original PDF, such as paragraph and column breaks. The key aspect of the presented approach is that the rule set used relies on relative parameters derived from font and layout specifics of each article, rather than on a template-matching paradigm. The system thus obviates the need for domain- or layout-specific tuning or prior training, exploiting only typographical conventions inherent in scientific literature. Evaluated against a significantly varied corpus of articles from nearly 2000 different journals, PDFX gives a 77.45 F1 measure for top-level heading identification and 74.03 for extracting individual bibliographic items. The service is freely available for use at http://pdfx.cs.man.ac.uk/.


title: Improved simultaneous localization and mapping (SLAM) algorithms for aerial vehicle under dynamic sea surface environment

abstract: The aim of this work is to design a SLAM algorithm for localization and mapping of aerial platform for ocean observation. The aim is to determine the direction of travel, given that the aerial platform flies over the water surface and in an environment with few static features and dynamic background. This approach is inspired by the bird techniques which use landmarks as navigation direction. In this case, the blimp is chosen as the platform, therefore the payload is the most important concern in the design so that the desired lift can be achieved. The results show the improved SLAM is were able to achieve the desired waypoint.


title: A digital signature scheme secure against adaptive chosen-message attacks

abstract: We present a digital signature scheme based on the computational difficulty of integer factorization.The scheme possesses the novel property of being robust against an adaptive chosen-message attack: an adversary who receives signatures for messages of his choice (where each message may be chosen in a way that depends on the signatures of previously chosen messages) cannot later forge the signature of even a single additional message. This may be somewhat surprising, since in the folklore the properties of having forgery being equivalent to factoring and being invulnerable to an adaptive chosen-message attack were considered to be contradictory.More generally, we show how to construct a signature scheme with such properties based on the existence of a “claw-free” pair of permutations—a potentially weaker assumption than the intractibility of integer factorization.The new scheme is potentially practical: signing and verifying signatures are reasonably fast, and signatures are compact.


title: Wavelength selection for low-saturation pulse oximetry

abstract: Conventional pulse oximeters are accurate at high oxygen saturation under a variety of physiological conditions but show worsening accuracy at lower saturation (below 70%). Numerical modeling suggests that sensors fabricated with 735 and 890 nm emitters should read more accurately at low saturation under a variety of conditions than sensors made with conventionally used 660 and 900 nm band emitters. Recent animal testing confirms this expectation. It is postulated that the most repeatable and stable accuracy of the pulse oximeter occurs when the fractional change in photon path lengths due to perturbations in the tissue (relative to the conditions present during system calibration) is equivalent at the two wavelengths. Additionally, the penetration depth (and/or breadth) of the probing light needs to be well matched at the two wavelengths in order to minimize the effects of tissue heterogeneity. At high saturation these conditions are optimally met with 660 and 900 nm band emitters, while at low saturation 735 and 890 nm provide better performance.


title: Bullying, Cyberbullying, and Suicide

abstract: Empirical studies and some high-profile anecdotal cases have demonstrated a link between suicidal ideation and experiences with bullying victimization or offending. The current study examines the extent to which a nontraditional form of peer aggression—cyberbullying—is also related to suicidal ideation among adolescents. In 2007, a random sample of 1,963 middle-schoolers from one of the largest school districts in the United States completed a survey of Internet use and experiences. Youth who experienced traditional bullying or cyberbullying, as either an offender or a victim, had more suicidal thoughts and were more likely to attempt suicide than those who had not experienced such forms of peer aggression. Also, victimization was more strongly related to suicidal thoughts and behaviors than offending. The findings provide further evidence that adolescent peer aggression must be taken seriously both at school and at home, and suggest that a suicide prevention and intervention component is essential within...


title: The pleasure of food: underlying brain mechanisms of eating and other pleasures

abstract: As all chefs know, great food can have a transformational impact. A great deal of recent research has gone into using the new techniques from molecular gastronomy and gastrophysics to create innovative meals with delicious original textures and flavours. These novel creations have elicited much excitement from food critiques and diners alike. Much stands to be gained if these developments were to be matched by a better understanding of how the pleasure of food comes about in the brain. This review summarises the current state-of-the-art of the science of pleasure and specifically the brain’s fundamental computational principles for eating and the pleasures evoked. It is shown how the study of food has advanced our understanding of the unitary pleasure system that is used for all pleasures. As such, these novel insights may come to serve as a guide for chefs of how to combine science and art in order to maximise pleasure—and perhaps even increase happiness.


title: A helpfulness modeling framework for electronic word-of-mouth on consumer opinion platforms

abstract: Electronic Word-of-Mouth (eWOM) is growing exponentially with the rapid development of electronic commerce. As a result, consumers are increasingly crowded by a huge amount of eWOM contents and therefore there is a need to automatically recommend eWOM contents that are helpful to them. Existing helpfulness assessment approaches that deterministically estimate the helpfulness of eWOM contents lack a generative formulation and are limited to the training set that has been voted by many readers. This article presents a rigorous probabilistic framework for inferring the “helpfulness” of eWOM contents which can build a “helpfulness” model from a low number of votes on eWOM contents. Furthermore, we introduce a measurement, “helpfulness” bias, as the benchmark for the “helpfulness” of eWOM documents. We also propose a model that exploits the graphical model and expectation maximization algorithm, under this probabilistic framework, to demonstrate the versatility of our framework. Our algorithm is compared experimentally to other existing helpfulness discovering algorithms and the experimental results show that our framework can effectively model the helpfulness of eWOM contents better than other approaches, and therefore indicate the capability of our framework to recommend helpful eWOMs to potential consumers.


title: Using the 'Arts' to Teach Indigenous Australian Studies in Higher Education

abstract: This paper will discuss some personal philosophies and rationales in approaching and delivering Indigenous Australian studies - approaches that have been influenced and informed by experienced and knowledgeable educators both Indigenous and non- Indigenous. It doesn't aim or pretend to be unique or innovative in its arguments or approach; instead, it is a reflection on the pathway taken by one Indigenous educator and the thinking behind this - at least at this point in time. As such, it adopts a philosophy that 'it's important to know where we've come from in order to make sense of where we are and where we might be heading'. It is hoped that through this reflection and by offering an example of how art (as performance/ role-play) has been used in one of my teaching sessions that I might make more sense of my personal practice and contribute in some small way to the important and ongoing debates surrounding teaching Indigenous Australian studies.


title: Algebraic and Slide Attacks on KeeLoq

abstract: The DES key schedule is linear and yet defeats related-key cryptanalysis and other attacks that exploit weaknesses in key schedules, for example the slide attacks. In this paper we present new interesting key-pairs of the DES that can be used in related-key attacks to produce sliding properties of the full-round DES key schedule. This is a sort of key schedule weakness from a slide attack point of view. Our results demonstrate the first known sliding property of the original DES key schedule for its full 16 rounds. Finally, we consider potential applications of these findings in extending the slide attacks.


title: Recruitment of circulating allergen-specific T lymphocytes to the lung on allergen challenge in asthma.

abstract: Abstract Background: In allergic subjects with asthma, the migration of CD4 T cells to the lungs in the hours after allergen exposure may contribute to allergic inflammation in the target organ. Objective: We studied allergen-specific T cells from the peripheral blood and lungs of allergic subjects with asthma at baseline and after allergen challenge. Methods: In each patient, blood samples were taken 10 minutes before and 24 hours after the inhalation of a major sensitizing allergen. In vitro proliferation of peripheral blood CD4 T cells specific for the same allergen used in the in vivo challenge was assessed. In one patient two Dermatophagoides pteronyssinus –specific T-cell clones (TCCs) were derived from peripheral blood, and their T-cell receptors were sequenced to determine their clonotypic determinants on the β chains. The T-cell receptor determinants of the allergen-specific TCCs were sought in blood and bronchoalveolar lavage samples taken from this patient. Results: We found that allergen inhalation is followed by a decrement in the specific proliferation of peripheral CD4+ T cells to the same allergen used for bronchial provocation. In one patient the clonotypic determinants of two allergen-specific TCCs diminished in the peripheral blood, whereas they were simultaneously expanded in the lower respiratory tract. Conclusion: Our data suggest that allergen-specific T cells are recruited from the peripheral blood to the bronchial lumen after allergen challenge. (J Allergy Clin Immunol 1997;100:669-78


title: Reflections on Culturally Responsive Teaching: Embedding Theory into Practices of Instructional and Behavioral Support

abstract: This paper offers reflections on the embedding of culturally responsive teaching practices into Response-to-Intervention (RtI) and School-wide Positive Behavior Support (SW-PBS) models. These types of systemic change models are increasingly being adopted by schools to address academic achievement and to foster a positive school climate. Moreover, they hold promise for addressing the disproportionality that many students who are culturally and linguistically diverse students experience in our schools. Following a review of the existing literature on systemic change, three guiding principles are offered that demonstrate how culturally responsive teaching can be embedded into models of instructional and behavioral support. Thousands of schools across the United States have adopted Response to Intervention (RtI) and School-wide Positive Behavior Support (SW-PBS) programs over the past decade. These models hold promise in helping educators implement research-based instructional and behavioral support strategies that are intended to meet the academic and social/behavioral needs of all learners. While more similar than different, each model has the potential for creating systemic change in schools that can result in improved academic and social outcomes. Proponents believe that this is especially true for students from culturally and linguistically diverse backgrounds who have experienced marginalization, including disproportionately higher rates of academic failure, placement in special education, suspensions, expulsions, and school dropout (Skiba et al., 2008). Scholars have cautioned that "if we do not engage in dialogue about how culture mediates learning, RTI models will simply be like old wine in a new bottle, in other words, another deficit-based approach to sorting children, particularly children from marginalized communities" (NCCRESt, 2005, p. 1). The purpose of this paper is share reflections on the need for embedding culturally responsive teaching (CRT) into RtI and SW-PBS implementation efforts. The first section of this paper will provide a brief overview of RtI and SW-PBS highlighting similarities and providing examples from work being done in Colorado. Terms such as "Instructional Support" and "Behavioral Support" will be introduced and a new definition of Culturally Responsive Teaching that is more closely aligned to systemic change models is offered. The second part of the paper will highlight three guiding principles, gleaned from the extant literature, that can assist classroom teachers,


title: An information theoretic approach to improve semantic similarity assessments across multiple ontologies

abstract: Semantic similarity has become, in recent years, the backbone of numerous knowledge-based applications dealing with textual data. From the different methods and paradigms proposed to assess semantic similarity, ontology-based measures and, more specifically, those based on quantifying the Information Content (IC) of concepts are the most widespread solutions due to their high accuracy. However, these measures were designed to exploit a single ontology. They thus cannot be leveraged in many contexts in which multiple knowledge bases are considered. In this paper, we propose a new approach to achieve accurate IC-based similarity assessments for concept pairs spread throughout several ontologies. Based on Information Theory, our method defines a strategy to accurately measure the degree of commonality between concepts belonging to different ontologies--this is the cornerstone for estimating their semantic similarity. Our approach therefore enables classic IC-based measures to be directly applied in a multiple ontology setting. An empirical evaluation, based on well-established benchmarks and ontologies related to the biomedical domain, illustrates the accuracy of our approach, and demonstrates that similarity estimations provided by our approach are significantly more correlated with human ratings of similarity than those obtained via related works.


NEW FAST AND ACCURATE JACOBI SVD ALGORITHM: II.


title: Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality

abstract: Matching for causal inference is a well-studied problem, but standard methods fail when the units to match are text documents: the high-dimensional and rich nature of the data renders exact matching infeasible, causes propensity scores to produce incomparable matches, and makes assessing match quality difficult. In this paper, we characterize a framework for matching text documents that decomposes existing methods into: (1) the choice of text representation, and (2) the choice of distance metric. We investigate how different choices within this framework affect both the quantity and quality of matches identified through a systematic multifactor evaluation experiment using human subjects. Altogether we evaluate over 100 unique text matching methods along with 5 comparison methods taken from the literature. Our experimental results identify methods that generate matches with higher subjective match quality than current state-of-the-art techniques. We enhance the precision of these results by developing a predictive model to estimate the match quality of pairs of text documents as a function of our various distance scores. This model, which we find successfully mimics human judgment, also allows for approximate and unsupervised evaluation of new procedures. We then employ the identified best method to illustrate the utility of text matching in two applications. First, we engage with a substantive debate in the study of media bias by using text matching to control for topic selection when comparing news articles from thirteen news sources. We then show how conditioning on text data leads to more precise causal inferences in an observational study examining the effects of a medical intervention.


EFFECTS OF TREATMENT METHODS ON TOTAL POLYPHENOL CONTENT AND ANTIOXIDANT ACTIVITY OF POLYGONUM MULTIFLORUM THUNB ROOT EXTRACT


The neoepitope landscape in pediatric cancers


On-Device Neural Language Model Based Word Prediction


Correcting Chinese Word Usage Errors for Learning Chinese as a Second Language


title: A multi-arm hand-held robotic system for transurethral laser Prostate surgery

abstract: Benign prostatic hyperplasia is the most common symptomatic disease in men. A new transurethral surgical intervention is available that has been shown to reduce bleeding, catheterization time, and hospitalization time in comparison to traditional Transurethral Resection of the Prostate (TURP). However, this new procedure, Holmium Laser Enucleation of the Prostate (HoLEP), is so challenging to accomplish that only a small number of expert surgeons are able to offer it. Toward facilitating broader use of HoLEP, we propose a new hand-held robotic system for the purpose of making the surgery easier to perform. In current HoLEP, the only way to aim the laser and/or manipulate tissue is to move the entire endoscope, stretching a large quantity of tissue. In contrast, our new robotic approach provides the surgeon with two concentric tube manipulators that can aim the laser and manipulate tissue simultaneously. The manipulators are deployed through a 5 mm working channel in a 26 French (8.66 mm) endoscope clinically used for transurethral procedures. This paper describes the design of the robot and experiments illustrating its ability to perform the motions expected to be useful in HoLEP.


title: Cross-Point Resistive RAM Based on Field-Assisted Superlinear Threshold Selector

abstract: We report a 3-D-stackable 1S1R passive cross-point resistive random access memory (RRAM). The sneak (leakage) current challenge in the cross-point RRAM integration has been overcome utilizing a field-assisted superlinear threshold selector. The selector offers high selectivity of $>10^{7}$ , sharp switching slope of $ mV/decade, ability to tune the threshold voltage, stable operation at 125 °C, and endurance of $>10^{11}$ . Furthermore, we demonstrate 1S1R integration in which the selector-subthreshold current is $ pA while offering $>10^{2}$ memory ON/OFF ratio and $>10^{6}$ selectivity during cycling. Combined with self-current-controlled RRAM, the 1S1R enables high-density and high-performance memory applications.


title: Radio Map Recovery and Noise Reduction Method for Green WiFi Indoor Positioning System Based on Inexact Augmented Lagrange Multiplier Algorithm

abstract: Currently, WiFi indoor positioning system based on IEEE 802.11 is widely attractive for its free infrastructure and high localization performance. However, due to working on-demand strategy in green WiFi scenario, the access points are not always available for mobile when radio map is built in the offline phase. Radio map with unknown received signal strength is not valid for positioning and usually be replaced by the minimum value, which leads to poor positioning performance. In This paper we propose a radio map recovery method based on inexact augmented Lagrange multiplier (IALM) algorithm, which achieves to precisely recover the missing received signal strength in the radio map for those access points unavailable in the offline. By solving the nuclear norm minimization, the IALM algorithm could not only recover the missing received signal strength, but also reduce the noise effectively. We have implemented the proposed method in our lab and evaluated its performances. The experiment results indicate the proposed method could precisely recover the radio map and achieve good positioning performance.


KNOWLEDGE ACQUISITION AND CYBER SICKNESS: A COMPARISON OF VR DEVICES IN VIRTUAL TOURS


Clinical perspectives on the utility of aromatase inhibitors for the adjuvant treatment of breast cancer


title: Review and Classification of Multichannel MAC Protocols for Low-Power and Lossy Networks

abstract: The so-called Internet of Things (IoT) aims at connecting every single object to the Internet with the purpose to automate every aspect of daily life. The IoT relies heavily on wireless low-power and lossy networks (LLNs) that collect information from the physical world and send the measurements to data aggregation and processing nodes. Most LLNs operate in the non-licensed industrial, scientific, and medical radio band, which is shared by a considerable number of systems. Coexisting wireless systems cause interference to each other, limiting their achievable performance. Multichannel communications enable frequency diversity, which in turn provides robustness against interference as well as increased network capacity. There is a considerable interest in multichannel medium access control (MAC) protocols for LLNs, including an evolving standard for the MAC layer of LLNs. In this paper, we review the latest advances in the topic and introduce a new classification framework for multichannel MAC protocols for LLNs. While our framework builds on previous review and classification studies, it adds aspects of a MAC protocol that reflect its interactions with the surrounding network stack. Seeing the resource constraints of the LLN devices, the study of such interactions—which is missing in prior classification efforts—can be the key for improving future designs. Relevant protocols published since 2006 are discussed and classified using the presented framework, including the recent multichannel MAC protocols for LLNs, such as the latest version of the IEEE 802.15.4 standard for time slotted channel hopping.


title: A Review of Key Issues That Concern the Feasibility of Mobile Cloud Computing

abstract: Recently, the arising of cloud computing (CC) concept and explosive growth of mobile applications induce a novel technology, i.e., mobile cloud computing (MCC). By integrating CC into mobile environment, MCC alleviates limitations of mobile devices and proliferates a variety of new fascinating mobile services. The key to enable MCC is to equip mobile users with the data processing and storage abilities in the clouds via wireless networks. In this paper, aiming at facilitating the research of MCC and inducing more research topics regarding MCC, we offer a review of the key issues that concern the feasibility of MCC and identify the corresponding novel techniques to mitigate these key issues. Moreover, we propose a potential framework to enhance the robustness of MCC incorporating these novel techniques.


title: 3DHoPD: A Fast Low-Dimensional 3-D Descriptor

abstract: Three-dimensional feature descriptors are heavily employed in various 3-D perception applications to find keypoint correspondences between two point clouds. The availability of mobile devices equipped with depth sensors compels the developed applications to be both memory and computationally efficient. Toward this, in this letter, we present 3DHoPD, a new low-dimensional 3-D feature descriptor that is extremely fast to compute. The novelty lies in compactly encoding the “3-D” keypoint position by transforming it to a new 3-D space, where the keypoints arising from similar 3-D surface patches lie close to each other. Then, we propose histograms of point distributions (HoPD) to capture the neighborhood structure, thus forming 3DHoPD (3D+HoPD). We propose a tailored feature descriptor matching technique, wherein the “3-D” keypoint position in the new 3-D space is used to remove false positive matches, effectively reducing the search space by 90%, and then, the exact match is found using the “HoPD” descriptor. Experimental evaluation on multiple publicly available datasets shows that 3DHoPD is robust to noise and offers stable and competitive keypoint matching performance to the existing state-of-the-art 3-D descriptors with similar dimensionality across datasets, while requiring dramatically low-computational time (10 $\times$ faster). The source code and additional experimental results are available at https://sites.google.com/site/3dhopd/


title: A millimeter-wave connected antenna array for 5G applications

abstract: In this work, a beam switched antenna system based on a planar connected antenna array (CAA) is proposed at 28 GHz for 5G applications. The antenna system consists of a 4 × 4 connected slot antenna elements. It is covering frequency band from 27.4 GHz to 28.23 GHz with at least −10dB bandwidth of 830 MHz. It is modeled on a commercially available RO3003 substrate with ∊ r equal to 3.3. The dimensions of the board are equal to 61×54×0.13 mm3. The proposed design is compact and low profile. A Butler matrix based feed network is used to steer the beam at different locations.


Matrix Factorization Techniques for Recommender Systems


title: Enhancing Sensor Pattern Noise via Filtering Distortion Removal

abstract: In this work, we propose a method to obtain higher quality sensor pattern noise (SPN) for identifying source cameras. We believe that some components of SPN have been severely contaminated by the errors introduced by denoising filters and the quality of SPN can be improved by abandoning those components. In our proposed method, some coefficients with higher denoising errors are abandoned in the wavelet representation of SPN and the remaining wavelet coefficients are further enhanced to suppress the scene details in the SPN. These two steps aim to provide better SPN with higher signalto-noise ::: ratio (SNR) and therefore improve the identification performance. The experimental results on 2,000 images captured by 10 cameras (each responsible for 200 images), show that our method achieves better receiver operating characteristic (ROC) performance when compared with some state-of-the-art methods.


title: Knowledge-based Query Expansion in Real-Time Microblog Search

abstract: Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques usually suffer severely from the vocabulary mismatch problem such that they cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we propose a new language modeling approach for microblog retrieval by inferring various types of context information. In particular, we expand the query using knowledge terms derived from Freebase so that the expanded one can better reflect the information need. Besides, in order to further answer users’ real-time information need, we incorporate temporal evidences into the expansion methods so that the proposed approach can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on two official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.


title: Backstepping Based Control of PWM DC-DC Boost Power Converters

abstract: This paper focuses on the problem of controlling DC-to-DC switched power converter of Boost type. The system nonlinear feature is coped with by resorting to the backstepping control approach. Both adaptive and nonadaptive versions are designed and shown to yield quite interesting tracking and robustness performances. A comparison study shows that backstepping nonlinear controllers perform as well as passivity- based controllers. For both the choice of design parameters proves to be crucial to ensure robustness with respect to load resistance variations. From this viewpoint, adaptive backstepping controllers are more interesting as they prove to be less sensitive to design parameters.


title: Micro-blog topic detection method based on BTM topic model and K-means clustering algorithm

abstract: The development of micro-blog, generating large-scale short texts, provides people with convenient communication. In the meantime, discovering topics from short texts genuinely becomes an intractable problem. It was hard for traditional topic model-to-model short texts, such as probabilistic latent semantic analysis (PLSA) and Latent Dirichlet Allocation (LDA). They suffered from the severe data sparsity when disposed short texts. Moreover, K-means clustering algorithm can make topics discriminative when datasets is intensive and the difference among topic documents is distinct. In this paper, BTM topic model is employed to process short texts–micro-blog data for alleviating the problem of sparsity. At the same time, we integrating K-means clustering algorithm into BTM (Biterm Topic Model) for topics discovery further. The results of experiments on Sina micro-blog short text collections demonstrate that our method can discover topics effectively.


title: Supervised Hashing with Deep Neural Networks

abstract: In this paper, we propose training very deep neural networks (DNNs) for supervised learning of hash codes. Existing methods in this context train relatively "shallow" networks limited by the issues arising in back propagation (\eg vanishing gradients) as well as computational efficiency. We propose a novel and efficient training algorithm inspired by alternating direction method of multipliers (ADMM) that overcomes some of these limitations. Our method decomposes the training process into independent layer-wise local updates through auxiliary variables. Empirically we observe that our training algorithm always converges and its computational complexity is linearly proportional to the number of edges in the networks. Empirically we manage to train DNNs with 64 hidden layers and 1024 nodes per layer for supervised hashing in about 3 hours using a single GPU. Our proposed very deep supervised hashing (VDSH) method significantly outperforms the state-of-the-art on several benchmark datasets.


title: Improved ACLR by Cancellation Carrier Insertion in GFDM Based Cognitive Radios

abstract: Generalized Frequency Division Multiplexing (GFDM) is a recent multicarrier modulation technique with low out-of-band radiation that makes it an attractive choice for the PHY layer of cognitive radio. In orthogonal frequency division multiplexing (OFDM) the out of band leakage is around −13 dB; with raised cosine or root raised cosine pulses, the out of band leakage of a GFDM system is −35 dB. To improve the adjacent channel leakage ratio (ACLR) even further, a technique of inserting cancellation carriers is implemented. Cancellation carriers are inserted at the vicinity of interference avoidance notch and are designed such that these cancellation carriers mitigate the interference from other subcarriers to the adjacent band. With this technique the out of band leakage is lowered to around −65 dB. This stunning improvement in the GFDM adjacent channel leakage ratio satisfies stringent FCC requirements for cognitive radio transmissions in TV white space scenarios.


title: Deep hashing learning networks

abstract: Hashing-based methods seek compact and efficient binary codes that preserve the similarity between data. For most existing hashing methods, an input (e.g. image) is first encoded as a vector of hand-crafted visual feature, followed by a hash projection and quantization step to obtain the compact binary vector. Most of hand-crafted features only encode low-level information of the input, the feature may not preserve semantic similarities of pairwise inputs. Meanwhile, the hash function learning process is independent with the feature representation, so that the feature may not be optimal for the hash projection. In this paper, we propose a supervised hashing learning method based on a well designed deep convolutional neural network, which tries to learn hashing code and compact representations of data simultaneously. Particularly, the proposed model learns binary codes by adding a compact sigmoid layer before the classifier layer. Experiments on several image data sets show that the proposed model outperforms other state-of-the-art hashing learning approaches.


Estimating Machine Translation Post-Editing Effort with HTER Lucia Specia


title: A New Current Phasor-Controlled ZVS Twin Half-Bridge High-Frequency Resonant Inverter for Induction Heating

abstract: A novel soft-switching high-frequency (HF) resonant (HF-R) inverter for induction heating (IH) applications is presented in this paper. By adopting the current phasor control of changing a phase shift (PS) angle between two half-bridge inverter units, the IH load resonant current can be regulated continuously under the condition of wide-range soft-switching operations. In addition to this, the dual-mode power regulation scheme-based PS angle control and asymmetrical pulsewidth modulation in one inverter unit is proposed for improving the efficiency in low output power settings. The essential performances on the output power regulation and soft-switching operations are demonstrated in an experiment using its 1-kW 60-kHz HF-R inverter prototype, and then, the topological validity is evaluated from a practical point of view.


title: Finite Element Analysis of the Lateral Crushing Behavior of Segmented Composite Tubes

abstract: This paper aims to develop a finite element model for a lateral crushing behavior of segmented composite tubes. In the first step, the finite element modeling is established. Then, the model is modified according to the experimental results. Once the model was proven do yield the correct results, dynamic crushing was simulated. We varied the tube's geometry and the composite's material properties to make conclusions about the optimum composite tube configuration.


title: A High-Power DC-DC Converter Topology for Battery Charging Applications

abstract: A DC-DC converter that can be applied for battery chargers with the power-capacity of over 7-kW for electric vehicles (EVs) is presented in this paper. Due to a new architecture, the proposed converter achieves a reduction of conduction losses at the primary side by as much as 50% and has many benefits such as much smaller circulating current, less duty-cycle loss, and lower secondary-voltage stress. In addition, its power handing capacity can be upsized easily with the use of two full-bridge inverters and two transformers. Besides, all the switches in the converter achieve zero-voltage switching (ZVS) during whole battery charging process, and the size of output filter can be significantly reduced. The circuit configuration, operation, and relevant analysis are presented, followed by the experiment on a prototype realized with a 7-kW charger. The experimental results validate the theoretical analysis and show the effectiveness of the proposed converter as battery charger.


title: Improving pairwise learning for item recommendation from implicit feedback

abstract: Pairwise algorithms are popular for learning recommender systems from implicit feedback. For each user, or more generally context, they try to discriminate between a small set of selected items and the large set of remaining (irrelevant) items. Learning is typically based on stochastic gradient descent (SGD) with uniformly drawn pairs. In this work, we show that convergence of such SGD learning algorithms slows down considerably if the item popularity has a tailed distribution. We propose a non-uniform item sampler to overcome this problem. The proposed sampler is context-dependent and oversamples informative pairs to speed up convergence. An efficient implementation with constant amortized runtime costs is developed. Furthermore, it is shown how the proposed learning algorithm can be applied to a large class of recommender models. The properties of the new learning algorithm are studied empirically on two real-world recommender system problems. The experiments indicate that the proposed adaptive sampler improves the state-of-the art learning algorithm largely in convergence without negative effects on prediction quality or iteration runtime.


title: One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities

abstract: The softmax representation of probabilities for categorical variables plays a prominent role in modern machine learning with numerous applications in areas such as large scale classification, neural language modeling and recommendation systems. However, softmax estimation is very expensive for large scale inference because of the high cost associated with computing the normalizing constant. Here, we introduce an efficient approximation to softmax probabilities which takes the form of a rigorous lower bound on the exact probability. This bound is expressed as a product over pairwise probabilities and it leads to scalable estimation based on stochastic optimization. It allows us to perform doubly stochastic estimation by subsampling both training instances and class labels. We show that the new bound has interesting theoretical properties and we demonstrate its use in classification problems.


title: Human-Computer Interaction : Overview on State of the Art

abstract: The intention of this paper is to provide an overview on the subject of Human-Computer Interaction. The overview includes the basic definitions and terminology, a survey of existing technologies and recent advances in the field, common architectures used in the design of HCI systems which includes unimodal and multimodal configurations, and finally the applications of HCI. This paper also offers a comprehensive number of references for each concept, method, and application in the HCI.