title: Spare register aware prefetching for graph algorithms on GPUs

abstract: More and more graph algorithms are being GPU enabled. Graph algorithm implementations on GPUs have irregular control flow and are memory-intensive with many irregular/data-dependent memory accesses. Due to these factors graph algorithms on GPUs have low execution efficiency. In this work we propose a mechanism to improve the execution efficiency of graph algorithms by improving their memory access latency tolerance. We propose a mechanism for prefetching data for load pairs that have one load dependent on the other - such pairs are common in graph algorithms. Our mechanism detects the target loads in hardware and injects instructions into the pipeline to prefetch data into spare registers that are not being used by any active threads. By prefetching data into registers, early eviction of prefetched data can be eliminated. We also propose a mechanism that uses the compiler to identify the target loads. Our mechanism improves performance over no prefetching by 10% on average and upto 51% for nine memory intensive graph algorithm kernels.


title: MOOC performance prediction via clickstream data and social learning networks

abstract: We study student performance prediction in Massive Open Online Courses (MOOCs), where the objective is to predict whether a user will be Correct on First Attempt (CFA) in answering a question. In doing so, we develop novel techniques that leverage behavioral data collected by MOOC platforms. Using video-watching clickstream data from one of our MOOCs, we first extract summary quantities (e.g., fraction played, number of pauses) for each user-video pair, and show how certain intervals/sets of values for these behaviors quantify that a pair is more likely to be CFA or not for the corresponding question. Motivated by these findings, our methods are designed to determine suitable intervals from training data and to use the corresponding success estimates as learning features in prediction algorithms. Tested against a large set of empirical data, we find that our schemes outperform standard algorithms (i.e., without behavioral data) for all datasets and metrics tested. Moreover, the improvement is particularly pronounced when considering the first few course weeks, demonstrating the “early detection” capability of such clickstream data. We also discuss how CFA prediction can be used to depict graphs of the Social Learning Network (SLN) of students, which can help instructors manage courses more effectively.


title: Side-Information based Exponential Discriminant Analysis for face verification in the wild

abstract: Recently, there is an extensive research efforts devoted to the challenging problem of face verification in unconstrained settings and weakly labeled data, where the task is to determine whether pairs of images are from the same person or not. In this paper, we propose a novel discriminative dimensionality reduction technique called Side-Information Exponential Discriminant Analysis (SIEDA) which inherits the advantages of both Side-Information Linear Discriminant (SILD) and Exponential Discriminant Analysis (EDA). SIEDA transforms the problem of face verification under weakly labeled data into a generalized eigenvalue problem while alleviating the preprocessing step of PCA dimensionality reduction. To further boost the performance, the multi-scale variant of the binarized statistical image features histograms are adopted for efficient and rich facial texture representation. Extensive experimental evaluation on the challenging Labeled Faces in the Wild LFW benchmark database demonstrates the superiority of SIEDA over SILD. Moreover, the obtained verification accuracy is impressive and compares favorably against the state-of-the-art.


title: Document-level Entity Linking: CMCRC at TAC 2010

abstract: This paper describes the CMCRC systems entered in the TAC 2010 entity linking challenge. The best performing system we describe implements the document-level entity linking system from Cucerzan (2007), with several additions that exploit global information. Our implementation of Cucerzan’s method achieved a score of 74.9% in development experiments. Additional global information improves performance to 78.4%. On the TAC 2010 test data, our best system achieves a score of 84.4%, which is second in the overall rankings of submitted systems.


title: Formal verification and simulation for platform screen doors and collision avoidance in subway control systems

abstract: For hybrid systems, hybrid automata-based tools are capable of verification, while Matlab Simulink/Stateflow is proficient in simulation. We propose a co-verification procedure, in which the verification tool SpaceEx/PHAVer and simulation tool Matlab are integrated to analyze and verify hybrid systems. For the application of this procedure, a platform screen door system (PSDS, a subsystem of the subway control system), is modeled with hybrid automata and Simulink/Stateflow charts, respectively. The models of PSDS are simulated by Matlab and verified by SpaceEx/PHAVer. The simulation and verification results indicate that the sandwiched situation can be avoided under time interval conditions. We improve the model with four trains and four stations on a subway line and analyze the urgent control scenario for the safety distance requirement. In this paper, the Simulink/Stateflow model is a refinement of the SpaceEx/PHAVer model, which is closer to a final implementation. Moreover, the two models are complementary for some features (e.g.,visualization of simulation, correctness proving by verification), stressing different aspects of the overall system and permitting complementary analysis techniques, i.e., verification versus simulation. We conclude that this integration procedure is competent in verifying subway control systems.


title: Underactuated modular finger with pull-in mechanism for a robotic gripper

abstract: This paper presents an underactuated modular finger with a pull-in mechanism for a robotic gripper. Most existing robotic grippers do not have an infinite rotating mechanism. However, this movement has the potential for industrial applications such as pulling in and pushing out an object from the plane. Increasing functionality tends to lead to more operational difficulties. Therefore, we have developed a three-fingered underactuated gripper that not only grasps but also pulls in the object. We have described the design of a modular underactuated finger and a differential mechanism for the same. Lastly, to test the grasping and underactuated object-pulling-in capabilities of the gripper, we conducted experiments with objects of various shapes.


title: Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning

abstract: High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy.


title: The Inexorable Rise of Gender and the Decline of Sex: Social Change in Academic Titles, 1945–2001

abstract: More than 30 million titles of “academic” articles, from the years 1945–2001, were surveyed for occurrences of the wordssex andgender. At the beginning of this period, uses ofgender were much rarer than uses ofsex, and often used in the sense of a grammatical category. By the end of this period, uses ofgender outnumbered uses ofsex in the social sciences, arts, and humanities. Within the natural sciences, there was now more than 1 use ofgender for every 2 uses ofsex. The beginnings of this change in usage can be traced to Money's introduction of the concept of “gender role” in 1955 (J. Money, 1955). However, the major expansion in the use of gender followed its adoption by feminists to distinguish the social and cultural aspects of differences between men and women (gender) from biological differences (sex). Since then, the use of gender has tended to expand to encompass the biological, and a sex/gender distinction is now only fitfully observed.


title: A low-noise differential front-end and its controller for capacitive touch screen panels

abstract: A low-noise front-end and its controller are proposed for capacitive touch screen panels. The proposed front-end circuit based on a ΔΣ ADC uses differential sensing and integration scheme to maximize the input dynamic range. In addition, supply and internal reference voltage noise are effectively removed in the sensed touch signal. Furthermore, the demodulation process in front of the ΔΣ ADC provides the maximized oversampling ratio (OSR) so that the scan rate can be increased at the targeted resolution. The proposed IC is implemented in a mixed-mode 0.18-μm CMOS process. The measurement is performed on a bar-patterned 4.3-inch touch screen panel with 12 driving lines and 8 sensing channels. The report rate is 100 Hz, and SNR and spatial jitter are 54 dB and 0.11 mm, respectively. The chip area is 3 × 3 mm2 and total power consumption is 2.9 mW with 1.8-V and 3.3-V supply.


title: Fruit and vegetables classification system using image saliency and convolutional neural network

abstract: Fruit and vegetables classification and recognition are still challenging in daily production and life. In this paper, we propose an efficient fruit and vegetables classification system using image saliency to draw the object regions and convolutional neural network (CNN) model to extract image features and implement classification. Image saliency is utilized to select main saliency regions according to saliency map. A VGG model is chosen to train for fruit and vegetables classification. Another contribution in this paper is that we establish a fruit and vegetables images database spanning 26 categories, which covers the major types in real life. Experiments are conducted on our own database, and the results show that our classification system achieves an excellent accuracy rate of 95.6%.


title: HDD: hierarchical delta debugging

abstract: Inputs causing a program to fail are usually large and often contain information irrelevant to the failure. It thus helps debugging to simplify program inputs. The Delta Debugging algorithm is a general technique applicable to minimizing all failure-inducing inputs for more effective debugging. In this paper, we present HDD, a simple but effective algorithm that significantly speeds up Delta Debugging and increases its output quality on tree structured inputs such as XML. Instead of treating the inputs as one flat atomic list, we apply Delta Debugging to the very structure of the data. In particular, we apply the original Delta Debugging algorithm to each level of a program's input, working from the coarsest to the finest levels. We are thus able to prune the large irrelevant portions of the input early. All the generated input configurations are syntactically valid, reducing the number of inconclusive configurations that need to be tested and accordingly the amount of time spent simplifying. We have implemented HDD and evaluated it on a number of real failure-inducing inputs from the GCC and Mozilla bugzilla databases. Our Hierarchical Delta Debugging algorithm produces simpler outputs and takes orders of magnitude fewer test cases than the original Delta Debugging algorithm. It is able to scale to inputs of considerable size that the original Delta Debugging algorithm cannot process in practice. We argue that HDD is an effective tool for automatic debugging of programs expecting structured inputs.


title: The Next Era: Deep Learning in Pharmaceutical Research

abstract: Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.


title: Threats to Security in DSRC/WAVE

abstract: Dedicated Short Range Communications (DSRC) enabled road vehicles are on the brink of actualizing an important application of mobile ad hoc networks. It is crucial that the messages exchanged between the vehicles and between the vehicles and specialized infrastructure be reliable, accurate and confidential. To this end, we propose to identify the security threats inherent in the emerging DSRC Wireless Access in Vehicular Environments (WAVE) architecture. We rank the identified threats according to the European Telecommunications Standards Institute's (ETSI) threat analysis methodology. We also discuss possible countermeasures to the most critical threats.


title: Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood

abstract: We directly sequenced cell-free DNA with high-throughput shotgun sequencing technology from plasma of pregnant women, obtaining, on average, 5 million sequence tags per patient sample. This enabled us to measure the over- and underrepresentation of chromosomes from an aneuploid fetus. The sequencing approach is polymorphism-independent and therefore universally applicable for the noninvasive detection of fetal aneuploidy. Using this method, we successfully identified all nine cases of trisomy 21 (Down syndrome), two cases of trisomy 18 (Edward syndrome), and one case of trisomy 13 (Patau syndrome) in a cohort of 18 normal and aneuploid pregnancies; trisomy was detected at gestational ages as early as the 14th week. Direct sequencing also allowed us to study the characteristics of cell-free plasma DNA, and we found evidence that this DNA is enriched for sequences from nucleosomes.


title: Medical Students' Experience of and Reaction to Stress: The Role of Depression and Anxiety

abstract: Background. Medical school is recognized as a stressful environment that often has a negative effect on students’ academic performance, physical health, and psychosocial well-being. Previous studies have not identified differences between depressed and nondepressed and anxious and nonanxious medical students’ experiences of stress or their reactions to stressors. The present study aimed to identify the prevalence of depression and anxiety among a sample of 358 medical students attending a private university in Malaysia and to examine differences according to participants’ gender, year of study, and stage of training (preclinical and clinical). Additionally, this study examined the extent to which stress predicts depression and anxiety, differences between depressed and nondepressed medical students’ experiences of and reactions to stressors, and differences between anxious and nonanxious medical students’ experiences of and reactions to stressors. Methods. The Student Life Stress Inventory was used to measure stress and reaction to stressors and the Depression, Anxiety, and Stress Scale was used to measure depression and anxiety. Results. The results showed that 44% () of the students were anxious and 34.9% () were depressed. More female students exhibited anxiety compared to male students. Stress is a predictor for depression and anxiety. A significant difference was found between depressed and nondepressed and anxious and nonanxious students’ experience of stressors due to frustration, change, and their emotional reaction to stressors. Conclusion. Overall, depressed and anxious students were found to experience more stress and react differently to stressors compared to nondepressed and nonanxious students.


title: Mode-seeking by Medoidshifts

abstract: We present a nonparametric mode-seeking algorithm, called medoidshift, based on approximating the local gradient using a weighted estimate of medoids. Like meanshift, medoidshift clustering automatically computes the number of clusters and the data does not have to be linearly separable. Unlike meanshift, the proposed algorithm does not require the definition of a mean. This property allows medoidshift to find modes even when only a distance measure between samples is defined. In this sense, the relationship between the medoidshift algorithm and the meanshift algorithm is similar to the relationship between the k-medoids and the k-means algorithms. We show that medoidshifts can also be used for incremental clustering of growing datasets by recycling previous computations. We present experimental results using medoidshift for image segmentation, incremental clustering for shot segmentation and clustering on nonlinearly separable data.


title: Past, Present, and Future of Industrial Agent Applications

abstract: Industrial agents technology leverages the benefits of multiagent systems, distributed computing, artificial intelligence techniques and semantics in the field of production, services and infrastructure sectors, providing a new way to design and engineer control solutions based on the decentralization of control over distributed structures. The key drivers for this application are the benefits of agent-based industrial systems, namely in terms of robustness, scalability, reconfigurability and productivity, all of which translate to a greater competitive advantage. This manuscript monitors the chronology of research and development of the industrial applications of multiagent and holonic systems. It provides the comprehensive overview of methodologies, architectures and applications of agents in industrial domain from early nineties up to present. It also gives an outlook of the current trends as well as challenges and possible future application domains of industrial agents.


title: Compact wideband metasurface-based circularly polarized antenna for Ka-band phased array

abstract: A metasurface-based compact wideband circularly polarized (CP) antenna is proposed. The antenna is composed of dual-layer overlapping metasurface coupled by cross-slot through single series stripline feed. A 64-element metasurface-based CP phased array with a scan angle up to 30 degree off the boresight direction shows the gain higher than 22 dBic, active reflection coefficient less than −9 dB, axial ratio less than 3 dB, and side-lobe levels less than −10 dB from 28 GHz to 31 GHz.


title: Feature Selection for Time Series Forecasting: A Case Study

abstract: The integration of feature selection techniques within the modeling process of a time series forecaster can improve dealing with some usual important problems in this type of tasks, such as noise reduction, the curse of dimensionality and reducing the complexity of both the problem and the solution. In this paper we show how a convenient combination of feature selection procedures with soft computing techniques can be used to solve satisfactorily a real world problem. The problem is a rather hard one and consists of forecasting the amount of incoming calls for an emergency call center, so that the center managers can make a better resource planning.


title: CloudTracker: Using Execution Provenance to Optimize the Cost of Cloud Use

abstract: In this work, we investigate tools that enable dollar cost optimization of scientific simulations using commercial clouds. We present a framework, called CloudTracker, that transparently records information from a simulation that is executed in a commercial cloud so that it may be “replayed” exactly to reproduce its results. Using the automated CloudTracker provenance and replay facilities, scientists can choose either to store the results of a simulation or to reproduce it on-demand – whichever is more cost efficient in terms of the dollar cost charged for storage and computing by the commercial cloud provider. We present a prototype implementation of CloudTracker for the Amazon AWS commercial cloud and the StochSS stochastic simulation system. Using this prototype, we analyze the storage-versus-compute cost tradeoffs for different classes of StochSS simulations when deployed and executed in AWS.


title: Counterparty Risk and Funding: The Four Wings of the TVA

abstract: The credit crisis and the ongoing European sovereign debt crisis have highlighted the native form of credit risk, namely the counterparty risk. The related Credit Valuation Adjustment, (CVA), Debt Valuation Adjustment (DVA), Liquidity Valuation Adjustment (LVA) and Replacement Cost (RC) issues, jointly referred to in this paper as Total Valuation Adjustment (TVA), have been thoroughly investigated in the theoretical papers [Cr12a] and [Cr12b]. The present work provides an executive summary and numerical companion to these papers, through which the TVA pricing problem can be reduced to Markovian pre-default TVA BSDEs. The first step consists in the counterparty clean valuation of a portfolio of contracts, which is the valuation in a hypothetical situation where the two parties would be risk-free and funded at a risk-free rate. In the second step, the TVA is obtained as the value of an option on the counterparty clean value process called Contingent Credit Default Swap (CCDS). Numerical results are presented for interest rate swaps in the Vasicek, as well as in the inverse Gaussian Hull-White short rate model, also allowing one to assess the related model risk issue.


title: On the Behavior of Convolutional Nets for Feature Extraction

abstract: Deep neural networks are representation learning techniques. During training, a deep net is capable of generating a descriptive language of unprecedented size and detail in machine learning. Extracting the descriptive language coded within a trained CNN model (in the case of image data), and reusing it for other purposes is a field of interest, as it provides access to the visual descriptors previously learnt by the CNN after processing millions of images, without requiring an expensive training phase. Contributions to this field (commonly known as feature representation transfer or transfer learning) have been purely empirical so far, extracting all CNN features from a single layer close to the output and testing their performance by feeding them to a classifier. This approach has provided consistent results, although its relevance is limited to classification tasks. In a completely different approach, in this paper we statistically measure the discriminative power of every single feature found within a deep CNN, when used for characterizing every class of 11 datasets. We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning. Our results confirm that low and middle level features may behave differently to high level features, but only under certain conditions. We find that all CNN features can be used for knowledge representation purposes both by their presence or by their absence, doubling the information a single CNN feature may provide. We also study how much noise these features may include, and propose a thresholding approach to discard most of it. All these insights have a direct application to the generation of CNN embedding spaces.


title: Towards Empathetic Human-Robot Interactions

abstract: Since the late 1990s when speech companies began providing their customer-service software in the market, people have gotten used to speaking to machines. As people interact more often with voice and gesture controlled machines, they expect the machines to recognize different emotions, and understand other high level communication features such as humor, sarcasm and intention. In order to make such communication possible, the machines need an empathy module in them which can extract emotions from human speech and behavior and can decide the correct response of the robot. Although research on empathetic robots is still in the early stage, we described our approach using signal processing techniques, sentiment analysis and machine learning algorithms to make robots that can "understand" human emotion. We propose Zara the Supergirl as a prototype system of empathetic robots. It is a software based virtual android, with an animated cartoon character to present itself on the screen. She will get "smarter" and more empathetic through its deep learning algorithms, and by gathering more data and learning from it. In this paper, we present our work so far in the areas of deep learning of emotion and sentiment recognition, as well as humor recognition. We hope to explore the future direction of android development and how it can help improve people's lives.


title: Binary Code Ranking with Weighted Hamming Distance

abstract: Binary hashing has been widely used for efficient similarity search due to its query and storage efficiency. In most existing binary hashing methods, the high-dimensional data are embedded into Hamming space and the distance or similarity of two points are approximated by the Hamming distance between their binary codes. The Hamming distance calculation is efficient, however, in practice, there are often lots of results sharing the same Hamming distance to a query, which makes this distance measure ambiguous and poses a critical issue for similarity search where ranking is important. In this paper, we propose a weighted Hamming distance ranking algorithm (WhRank) to rank the binary codes of hashing methods. By assigning different bit-level weights to different hash bits, the returned binary codes are ranked at a finer-grained binary code level. We give an algorithm to learn the data-adaptive and query-sensitive weight for each hash bit. Evaluations on two large-scale image data sets demonstrate the efficacy of our weighted Hamming distance for binary code ranking.


title: A practical VEP-based brain-computer interface

abstract: This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to >90% of people with a high ITR in living environments.


title: A DCVSL Delay Cell for Fast Low Power Frequency Synthesis Applications

abstract: In this paper, a low-cost, power efficient and fast Differential Cascode Voltage-Switch-Logic (DCVSL) based delay cell (named DCVSL-R) is proposed. We use the DCVSL-R cell to implement high frequency and power-critical delay cells and flip-flops of ring oscillators and frequency dividers. When compared to TSPC, DCVSL circuits offer small input and clock capacitance and a symmetric differential loading for previous RF stages. When compared to CML, they offer low transistor count, no headroom limitation, rail-to-rail swing and no static current consumption. However, DCVSL circuits suffer from a large low-to-high propagation delay, which limits their speed and results in asymmetrical output waveforms. The proposed DCVSL-R circuit embodies the benefits of DCVSL while reducing the total propagation delay, achieving faster operation. DCVSL-R also generates symmetrical output waveforms which are critical for differential circuits. Another contribution of this work is a closed-form delay model that predicts the speed of DCVSL circuits with 8% worst case accuracy. We implement two ring-oscillator-based VCOs in 0.13 μm technology with DCVSL and DCVSL-R delay cells. Measurements show that the proposed DCVSL-R based VCO consumes 30% less power than the DCVSL VCO for the same oscillation frequency (2.4 GHz) and same phase noise (-113 dBc/Hz at 10 MHz). DCVSL-R circuits are also used to implement the high frequency dual modulus prescaler (DMP) of a 2.4 GHz frequency synthesizer in 0.18 μm technology. The DMP consumes only 0.8 mW at 2.48 GHz, a 40% reduction in power when compared to other reported DMPs with similar division ratios and operating frequencies. The RF buffer that drives the DMP consumes only 0.27 mW, demonstrating the lowest combined DMP and buffer power consumption among similar synthesizers in literature.


title: Adding Interpretable Attention to Neural Translation Models Improves Word Alignment

abstract: Multi-layer models with multiple attention heads per layer provide superior translation quality compared to simpler and shallower models, but determining what source context is most relevant to each target word is more challenging as a result. Therefore, deriving high-accuracy word alignments from the activations of a state-of-the-art neural machine translation model is an open challenge. We propose a simple model extension to the Transformer architecture that makes use of its hidden representations and is restricted to attend solely on encoder information to predict the next word. It can be trained on bilingual data without word-alignment information. We further introduce a novel alignment inference procedure which applies stochastic gradient descent to directly optimize the attention activations towards a given target word. The resulting alignments dramatically outperform the naive approach to interpreting Transformer attention activations, and are comparable to Giza++ on two publicly available data sets.


title: Implementation of an Induction Motor Mathematical Model for Stator Winding Fault Analysis

abstract: Induction motors are versatile, affordable and highly efficient machines, which makes them the principal source of electromechanical power in industrial application. However, induction motors eventually undergo failures, mostly due to insulation between turns or coils of stator windings, which are the structure with the highest incidence of electrical faults. In this work, it is presented an asymmetric mathematical model of induction motor that characterizes the electrical behavior of machine under stator winding fault. The model, derived from the symmetric one, was implemented on MATLAB/Simulink® virtual environment and the simulation results are compared to experimental tests, which were carried out on a motor previously modified for non-destructive trials. Additionally, the absolute and RMS values of stator currents from simulation and from experimental tests was analyzed, and results show the proposed model validation.


title: Exploiting emoticons in polarity classification of text

abstract: With people increasingly using emoticons in written text on the Web in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and we subsequently propose and evaluate a novel method for exploiting this with a manually created emoticon sentiment lexicon in a lexicon-based polarity classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons. We validate our findings on 10,069 English reviews of apps, some of which contain emoticons. We find that accounting for the sentiment conveyed by emoticons on a paragraph level - and, to a lesser extent, on a sentence level - significantly improves polarity classification performance. Whenever emoticons are used, their associated sentiment tends to dominate the sentiment conveyed by textual cues and forms a good proxy for the polarity of text.


title: Behavioral/Systems/Cognitive Two Retinotopic Visual Areas in Human Lateral Occipital Cortex

abstract: We describe two visual field maps, lateral occipital areas 1 (LO1) and 2 (LO2), in the human lateral occipital cortex between the dorsal part of visual area V3 and visual area V5/MT+. Each map contained a topographic representation of the contralateral visual hemifield. The eccentricity representations were shared with V1/V2/V3. The polar angle representation in LO1 extended from the lower vertical meridian (at the boundary with dorsal V3) through the horizontal to the upper vertical meridian (at the boundary with LO2). The polar angle representation in LO2 was the mirror-reversal of that in LO1. LO1 and LO2 overlapped with the posterior part of the object-selective lateral occipital complex and the kinetic occipital region (KO). The retinotopy and functional properties of LO1 and LO2 suggest that they correspond to two new human visual areas, which lack exact homologues in macaque visual cortex. The topography, stimulus selectivity, and anatomical location of LO1 and LO2 indicate that they integrate shape information from multiple visual submodalities in retinotopic coordinates.


title: Gait recognition using wifi signals

abstract: In this paper, we propose WifiU, which uses commercial WiFi devices to capture fine-grained gait patterns to recognize humans. The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking human generates unique variations in the Channel State Information (CSI) on the WiFi receiver. To profile human movement using CSI, we use signal processing techniques to generate spectrograms from CSI measurements so that the resulting spectrograms are similar to those generated by specifically designed Doppler radars. To extract features from spectrograms that best characterize the walking pattern, we perform autocorrelation on the torso reflection to remove imperfection in spectrograms. We evaluated WifiU on a dataset with 2,800 gait instances collected from 50 human subjects walking in a room with an area of 50 square meters. Experimental results show that WifiU achieves top-1, top-2, and top-3 recognition accuracies of 79.28%, 89.52%, and 93.05%, respectively.


title: There’s more to auditory cortex than meets the ear

abstract: The auditory cortex is typically defined as that portion of the cortex containing neurons that respond to sound. This definition is adequate in a narrow context, but does not take into account sufficiently the subtleties associated with more complex behaviors and cognitive processes. Thus, it is easy to demonstrate that cortical regions essentially unrelated to sound processing may nevertheless be activated by an auditory stimulus; conversely, it is possible to demonstrate responses within classical auditory cortical regions in the complete absence of sound. We give several examples that indicate that responses in auditory cortex cannot be predicted based solely on knowledge of stimulus features. Rather, factors such as memory, attention, and mental imagery can be shown to play a major role in modulating or producing neural responses within auditory cortex. We argue that the interactions between classically defined auditory regions and other sensory, motor, and cognitive systems underlie many behaviors of interest; and that a more complete understanding of these processes will emerge from a consideration of the distributed nature of auditory cortical function.


title: N-Acylation of ethanolamine phospholipids by acyl transfer does not involve hydrolysis.

abstract: N-Acylethanolamine phospholipids occur in infarcted but not in normal canine myocardium. Their synthesis is catalyzed by a membrane-bound, Ca2+-requiring N-acyltransferase (transacylase) which transfers acyl groups from the sn-1 position of various phospholipids including phosphatidylethanolamine to the amino group of ethanolamine phospholipids. When dog heart mitochondria are incubated in media containing Ca2+ and H2(18)O, the resulting N-acylethanolamine phospholipids do not accumulate 18O in either the amide or 1-O-acyl groups. The results indicate that acyl transfer occurs without hydrolysis, most likely through an acyl-enzyme complex which may be covalently linked.


title: A 14 nm SoC platform technology featuring 2nd generation Tri-Gate transistors, 70 nm gate pitch, 52 nm metal pitch, and 0.0499 um2 SRAM cells, optimized for low power, high performance and high density SoC products

abstract: A leading edge 14 nm SoC platform technology based upon the 2nd generation Tri-Gate transistor technology [5] has been optimized for density, low power and wide dynamic range. 70 nm gate pitch, 52 nm metal pitch and 0.0499 um2 HDC SRAM cells are the most aggressive design rules reported for 14/16 nm node SoC process to achieve Moore's Law 2x density scaling over 22 nm node. High performance NMOS/PMOS drive currents of 1.3/1.2 mA/um, respectively, have been achieved at 0.7 V and 100 nA/um off-state leakage, 37%/50% improvement over 22 nm node. Ultra-low power NMOS/PMOS drives are 0.50/0.32 mA/um at 0.7 V and 15pA/um Ioff. This technology also deploys high voltage I/O transistors to support up to 3.3 V I/O. A full suite of analog, mixed-signal and RF features are also supported.


title: Eye Fixations Indicate Men’s Preference for Female Breasts or Buttocks

abstract: Evolutionary psychologists have been interested in male preferences for particular female traits that are thought to signal health and reproductive potential. While the majority of studies have focused on what makes specific body traits attractive—such as the waist-to-hip ratio, the body mass index, and breasts shape and size—there is little empirical research that has examined individual differences in male preferences for specific traits (e.g., favoring breasts over buttocks). The current study begins to fill this empirical gap. In the first experiment (Study 1), 184 male participants were asked to report their preference between breasts and buttocks on a continuous scale. We found that (1) the distribution of preference was bimodal, indicating that Argentinean males tended to define themselves as favoring breasts or buttocks but rarely thinking that these traits contributed equally to their choice and (2) the distribution was biased towards buttocks. In a second experiment (Study 2), 19 male participants were asked to rate pictures of female breasts and buttocks. This study was necessary to generate three categories of pictures with statistically different ratings (high, medium, and low). In a third experiment (Study 3), we recorded eye-movements of 25 male participants while they chose the more attractive between two women, only seeing their breasts and buttock. We found that the first and last fixations were systematically directed towards the self-reported preferred trait.


title: Automatic Facial Expression Recognition Using Features of Salient Facial Patches

abstract: Extraction of discriminative features from salient facial patches plays a vital role in effective facial expression recognition. The accurate detection of facial landmarks improves the localization of the salient patches on face images. This paper proposes a novel framework for expression recognition by using appearance features of selected facial patches. A few prominent facial patches, depending on the position of facial landmarks, are extracted which are active during emotion elicitation. These active patches are further processed to obtain the salient patches which contain discriminative features for classification of each pair of expressions, thereby selecting different facial patches as salient for different pair of expression classes. One-against-one classification method is adopted using these features. In addition, an automated learning-free facial landmark detection technique has been proposed, which achieves similar performances as that of other state-of-art landmark detection methods, yet requires significantly less execution time. The proposed method is found to perform well consistently in different resolutions, hence, providing a solution for expression recognition in low resolution images. Experiments on CK+ and JAFFE facial expression databases show the effectiveness of the proposed system.


title: Design of hybrid resistive-capacitive DAC for SAR A/D converters

abstract: While hybrid capacitive-resistive D/A Converter (DAC) has been known for many years, its potential for energy-efficient operation is sometimes overlooked. This paper investigates the utilization of hybrid DACs in successive-approximation register A/D converters. To improve energy efficiency of SAR ADCs, a new hybrid DAC is introduced. In an exemplar 10-bit 100-MS/s ADC, simulation results show that the energy efficiency and chip area (of passive devices) can be improved by more than an order of magnitude.


title: Flicker-Free Electrolytic Capacitor-Less Universal Input Offline LED Driver With PFC

abstract: Recent developments in improving lighting efficiency and cost reduction of LEDs have made them suitable alternatives to the current lighting systems. In this paper, a novel offline structure is proposed to drive LEDs. The proposed circuit has a high-input power factor, high efficiency, a long lifetime, and it produces no flicker. To increase the lifetime of the converter, the proposed circuit does not include any electrolytic capacitors in the power stage. The proposed circuit consists of a transition mode flyback converter in order to improve power factor. Additionally, a buck converter is added to the third winding of the flyback transformer in order to create two parallel paths for the electrical power to feed the output load. DC power reaches the load through one stage (flyback) and ac power reaches the load through two stages of conversion (flyback + buck). Therefore, in the proposed one-and-a-half stage circuit, the efficiency is improved compared to a regular two-stage circuit. Although the proposed structure has some output current ripple, it is low enough (less than 8%) that the structure can be rendered flicker free, as shall be discussed. Principles of operation and design equations are presented as well as experimental results for a 700 mA/20 W universal input prototype.


title: Discovery and characterization of chromatin states for systematic annotation of the human genome

abstract: A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation and the onset of disease. Although individual modifications have been linked to the activity levels of various genetic functional elements, their combinatorial patterns are still unresolved and their potential for systematic de novo genome annotation remains untapped. Here, we use a multivariate Hidden Markov Model to reveal 'chromatin states' in human T cells, based on recurrent and spatially coherent combinations of chromatin marks. We define 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, large-scale repressed and repeat-associated states. Each chromatin state shows specific enrichments in functional annotations, sequence motifs and specific experimentally observed characteristics, suggesting distinct biological roles. This approach provides a complementary functional annotation of the human genome that reveals the genome-wide locations of diverse classes of epigenetic function.


title: 30+ years of research and practice of outsourcing – Exploring the past and anticipating the future

abstract: Outsourcing is a phenomenon that as a practice originated in the 1950s, but it was not until the 1980s when the strategy became widely adopted in organizations. Since then, the strategy has evolved from a strictly cost focused approach towards more cooperative nature, in which cost is only one, often secondary, decision-making criterion. In the development of the strategy, three broad and somewhat overlapping, yet distinct phases can be identified: the era of the Big Bang, the era of the Bandwagon, and the era of Barrierless Organizations. This paper illustrates that the evolution of the practice has caused several contradictions among researchers, as well as led to the situation where the theoretical background of the phenomenon has recently become much richer. Through examining existing research, this paper intends to identify the development of outsourcing strategy from a practical as well as a theoretical perspective from its birth up to today. In addition, through providing insights from managers in the information technology industry, this paper aims at providing a glimpse from the future - that is - what may be the future directions and research issues in this complex phenomenon?


title: COFI RANK Maximum Margin Matrix Factorization for Collaborative Ranking

abstract: In this paper, we consider collaborative filtering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes ranking instead of rating. We employ structured output prediction to optimize directly for ranking scores. Experimental results show that our method gives very good ranking scores and scales well on collaborative filtering tasks.


Uncompressed Capture and Mosaicking with a High-Bandwidth PC Camera Array


title: Volley: Automated Data Placement for Geo-Distributed Cloud Services

abstract: As cloud services grow to span more and more globally distributed datacenters, there is an increasingly urgent need for automated mechanisms to place application data across these datacenters. This placement must deal with business constraints such as WAN bandwidth costs and datacenter capacity limits, while also minimizing user-perceived latency. The task of placement is further complicated by the issues of shared data, data inter-dependencies, application changes and user mobility. We document these challenges by analyzing month-long traces from Microsoft's Live Messenger and Live Mesh, two large-scale commercial cloud services. ::: ::: We present Volley, a system that addresses these challenges. Cloud services make use of Volley by submitting logs of datacenter requests. Volley analyzes the logs using an iterative optimization algorithm based on data access patterns and client locations, and outputs migration recommendations back to the cloud service. ::: ::: To scale to the data volumes of cloud service logs, Volley is designed to work in SCOPE [5], a scalable MapReduce-style platform; this allows Volley to perform over 400 machine-hours worth of computation in less than a day. We evaluate Volley on the month-long Live Mesh trace, and we find that, compared to a state-of-the-art heuristic that places data closest to the primary IP address that accesses it, Volley simultaneously reduces datacenter capacity skew by over 2×, reduces inter-datacenter traffic by over 1.8× and reduces 75th percentile user-latency by over 30%.


title: Consumers’ Preferred Criteria for Hotel Online Booking

abstract: Online bookings of hotels have increased drastically in recent years. Studies in tourism and hospitality have investigated the relevance of hotel attributes influencing choice but not yet explore them in an online booking setting. This paper presents findings about consumers’ stated preferences for decision criteria from an adaptive conjoint study among 346 respondents. The results show that recommendations of friends and online reviews are the most important factors that influence online hotel booking. Partitioning the importance values of the decision criteria reveals group-specific differences indicating the presence of market segments.


title: A measurement study regarding quality of service and its impact on multiplayer online games

abstract: In this paper we study the impact of network parameters including delay, delay jitter, and packet loss on the outcome of multiplayer online games. We use a measurement methodology that allows for automating game experiments using an artificial intelligence that runs on the game clients and eliminates the need for human interaction. Thus, we were able to perform thousands of experiments with different network configurations to investigate their influence on the game outcome systematically. Complementary to previous studies, we provide a solid data basis for statistical analysis. We derive probabilistic models that allow for calculating score and win probabilities and verify their accuracy using our measurements. As a result we are able to predict the outcome statistics of an ideal game with respect to network conditions.


title: A Qualitative Study on Causes and Effects of Employee Turnover in the Private Sector in Malaysia

abstract: The term "employee turnover" is an important metric that is often central to organizations' workforce planning and strategy. The reasons why employees leave their current positions; not just the fact that they leave have crucial implications for future retention rates among current staff, job satisfaction and employee engagement and an organization's ability to attract talented people for job vacancies. The impact of turnover has received considerable attention by senior management, human resources professionals and industrial psychologists. It has proven to be one of the most costly and seemingly intractable human resource challenges confronting several organizations globally. The purpose of this research is therefore, to find out the actual reasons behind turnover and its damaging effects on the productivity of manufacturing industries in Malaysia. To explore turnover in greater detail, this article will examine the main sources of employee turnover, its effects and recommend some strategies on how an organization can retain employees and reduce employee turnover.


title: Political Campaigns and Big Data

abstract: Modern campaigns develop databases of detailed information about citizens to inform electoral strategy and to guide tactical efforts. Despite sensational reports about the value of individual consumer data, the most valuable information campaigns acquire comes from the behaviors and direct responses provided by citizens themselves. Campaign data analysts develop models using this information to produce individual-level predictions about citizens' likelihoods of performing certain political behaviors, of supporting candidates and issues, and of changing their support conditional on being targeted with specific campaign interventions. The use of these predictive scores has increased dramatically since 2004, and their use could yield sizable gains to campaigns that harness them. At the same time, their widespread use effectively creates a coordination game with incomplete information between allied organizations. As such, organizations would benefit from partitioning the electorate to not duplicate efforts, but legal and political constraints preclude that possibility.


title: Social Networks, Personalized Advertising, and Privacy Controls

abstract: This article investigates how Internet users' perceptions of control over their personal information affect how likely they are to click on online advertising on a social networking website. The analysis uses data from a randomized field experiment that examined the effectiveness of personalizing ad text with user-posted personal information relative to generic text. The website gave users more control over their personally identifiable information in the middle of the field test. However, the website did not change how advertisers used data to target and personalize ads. Before the policy change, personalized ads did not perform particularly well. However, after this enhancement of perceived control over privacy, users were nearly twice as likely to click on personalized ads. Ads that targeted but did not use personalized text remained unchanged in effectiveness. The increase in effectiveness was larger for ads that used more unique private information to personalize their message and for target groups t...


title: Road Detection by One-Class Color Classification: Dataset and Experiments

abstract: Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. A common approach to road detection consists of exploiting color features to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. Furthermore, the lack of labeled datasets has motivated the development of algorithms performing on single images based on the assumption that the bottom part of the image belongs to the road surface. ::: In this paper, we first introduce a dataset of road images taken at different times and in different scenarios using an onboard camera. Then, we devise a simple online algorithm and conduct an exhaustive evaluation of different classifiers and the effect of using different color representation to characterize pixels.


title: The infrastructure problem in HCI

abstract: HCI endeavors to create human-centered computer systems, but underlying technological infrastructures often stymie these efforts. We outline three specific classes of user experience difficulties caused by underlying technical infrastructures, which we term constrained possibilities, unmediated interaction, and interjected abstractions. We explore how prior approaches in HCI have addressed these issues, and discuss new approaches that will be required for future progress. We argue that the HCI community must become more deeply involved with the creation of technical infrastructures. Doing so, however, requires a substantial expansion to the methodological toolbox of HCI.


title: Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation

abstract: Most of the Neural Machine Translation (NMT) models are based on the sequence-to-sequence (Seq2Seq) model with an encoder-decoder framework equipped with the attention mechanism. However, the conventional attention mechanism treats the decoding at each time step equally with the same matrix, which is problematic since the softness of the attention for different types of words (e.g. content words and function words) should differ. Therefore, we propose a new model with a mechanism called Self-Adaptive Control of Temperature (SACT) to control the softness of attention by means of an attention temperature. Experimental results on the Chinese-English translation and English-Vietnamese translation demonstrate that our model outperforms the baseline models, and the analysis and the case study show that our model can attend to the most relevant elements in the source-side contexts and generate the translation of high quality.


title: An Overview of Datatype Quantization Techniques for Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) are becoming increasingly popular due to their superior performance in the domain of computer vision, in applications such as objection detection and recognition. However, they demand complex, power-consuming hardware which makes them unsuitable for implementation on low-power mobile and embedded devices. In this paper, a description and comparison of various techniques is presented which aim to mitigate this problem. This is primarily achieved by quantizing the floating-point weights and activations to reduce the hardware requirements, and adapting the training and inference algorithms to maintain the network's performance.


title: Easy High-Dimensional Likelihood-Free Inference

abstract: We introduce a framework using Generative Adversarial Networks (GANs) for likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC) where we replace the black-box simulator model with an approximator network and generate a rich set of summary features in a data driven fashion. On benchmark data sets, our approach improves on others with respect to scalability, ability to handle high dimensional data and complex probability distributions.


title: Semantically guided location recognition for outdoors scenes

abstract: The problem of image based localization has a long history both in robotics and computer vision and shares many similarities with image based retrieval problem. Existing techniques use either local features or (semi)-global image signatures in the context of topological mapping or loop closure detection. Difficulties of the location recognition problem are often affected by large appearance and viewpoint variation between the query view and reference dataset and presence of non-discriminative features due to vegetation, sky and road. In this work we show that semantic segmentation labeling of man-made structures can inform the traditional bag-of-visual words models to obtain proper feature weighting and improve the overall location recognition accuracy. We also demonstrate additional capability of identifying individual buildings and estimating their extent in images, providing the essential building block for semantic localization. Towards this end we introduce a new challenging outdoors urban dataset exhibiting large variations in appearance and viewpoint.


title: A hybrid elephant herding optimization and support vector machines for human behavior identification

abstract: Human behavior identification has a great importance in daily life. Security, gaming, and medical diagnosis are vital applications in that field. This paper introduces a hybrid classification approach for human behavior identification employing support vector machines (SVMs) classifier hybrid with Elephant Herding Optimization algorithm (EHO). The Elephant Herding Optimization algorithm used to fine-tune SVM parameters and to select most discriminant features. Validation of the proposed approach will be accomplished using a computer vision-based data set named Vicon. It was acquired from multiple human action detection experiments. Results show superiority for the proposed approach over other techniques on the same data set regarding classification accuracy. EHO-SVM hybrid algorithm reaches 91.21 % and 90.62 % accuracies for two test cases with different action class selections.


title: Strategic assets and organizational rent

abstract: We build on an emerging strategy literature that views the firm as a bundle of resources and capabilities, and examine conditions that contribute to the realization of sustainable economic rents. Because of (1) resource-market imperfections and (2) discretionary managerial decisions about resource development and deployment, we expect firms to differ (in and out of equilibrium) in the resources and capabilities they control. This asymmetry in turn can be a source of sustainable economic rent. The paper focuses on the linkages between the industry analysis framework, the resource-based view of the firm, behavioral decision biases and organizational implementation issues. It connects the concept of Strategic Industry Factors at the market level with the notion of Strategic Assets at the firm level. Organizational rent is shown to stem from imperfect and discretionary decisions to develop and deploy selected resources and capabilities, made by boundedly rational managers facing high uncertainty, complexity, and intrafirm conflict.


title: Self-Biased Y-Junction Circulators Using Lanthanum- and Cobalt-Substituted Strontium Hexaferrites

abstract: In this paper, we propose the application of polycrystalline lanthanum- and cobalt-substituted strontium hexaferrites in the realization of self-biased circulators. These materials present a high anisotropy field, dependent on the substitution rate, which makes it possible to reach operating frequencies in the millimeter-wave range. A first demonstrator was successfully designed and realized using a 20% rate of substitution $(\hbox {Sr}_{0.8}\hbox {La}_{0.2}\hbox {Fe}_{11.8}\hbox {Co}_{0.2}\hbox {O}_{19})$ . This circulator showed insertion losses of 1.79 dB and an isolation level of 28.1 dB at 41.4 GHz without magnets. Performances can be significantly improved by applying a low magnetic field $(\hbox {H}_{\rm app}=2100\ \hbox {Oe})$ . According to the literature, increasing the substitution rate makes it possible to increase the anisotropy field, and thus, the internal field. Consequently, a 30% substituted strontium hexaferrite was tested. It appears that the anisotropy field was not higher in this case. However, magnetic losses are much lower and enabled us to halve insertion losses of the self-biased circulator (0.87 dB at 41 GHz).


title: Hand Gesture Recognition with Leap Motion

abstract: The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with Leap Motion Controller. A series of features are extracted from Leap Motion tracking data, we feed these features along with HOG feature extracted from sensor images into a multi-class SVM classifier to recognize performed gesture, dimension reduction and feature weighted fusion are also discussed. Our results show that our model is much more accurate than previous work.


title: Autonomous pipeline inspection and maintenance robot with inch worm mobile mechanism

abstract: A new type of mobile robots with the inch worm mechanism is presented in this paper for inspecting pipelines from the outside of pipe surfaces under hostile environments. This robot, Mark III, is made after the successful investigation of the prototypes, Mark I and II, which can pass over obstacles on pipelines, such as flanges and T-joints and others. Newly developed robot, Mark III, can move vertically along the pipeline and move to the adjacent pipeline for the inspection. The sensors, infra ray proximity sensor and ultra sonic sensors and others, are installed to detect these obstacles and can move autonomously controlled by the microprocessor. The control method of this robot can be carried out by the dual control mode proposed in this paper.


title: Learning from class-imbalanced data

abstract: 527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.


title: Determinants of mobile value-added service continuance: The mediating role of service experience

abstract: In previous research, MVAS subscribers have been regarded as technology users. By adopting the service-dominant logic perspective from the service management discipline, this study proposes and empirically tests a model integrating both technology user and service consumer perspectives, emphasizing the role of service experience in MVAS continuance. The findings show that both technology excellence and service experience are principal determinants of MVAS continuance intention. Moreover, this study proposes the second-order factor structure of service experience and demonstrates the mediating role of service experience on the relationship between technology excellence and MVAS subscribers' continuance intentions. The theoretical and practical implications are discussed.


title: A full frequency range average model for Vienna-type rectifiers

abstract: This paper presents a new mathematical model for three-level non-regenerative Vienna-type rectifiers exploiting the equivalence of this topology with three-level neutral-point-clamped (NPC) converters. Specifically, it models the rectifier operation using a positive- and one negative-rail switching function-the standard approach for NPC converters. This equivalent modeling renders the state space model of Vienna-type rectifiers structurally time invariant, enabling its conversion into the synchronous d-q frame, and its averaging over a switching cycle basis. The resultant model is consequently valid up to half of the switching frequency. An in-depth small-signal analysis is then presented showing that only the d-d channel and dc port of the rectifier present truly time-invariant dynamics, given the intrinsically pulsating power transfer between its ac and dc terminals; quasi-stationary small-signal analysis is thus mandatory for this type of topology. Simulation and experimental results with a 20 kW motor drive and 2 kW experimental prototype are used for validation purposes.


title: A result on the weight distributions of binary quadratic residue codes

abstract: Truong et al. [7]proved that the weight distribution of a binary quadratic residue code C with length congruent to ?1 modulo 8 can be determined by the weight distribution of a certain subcode of C containing only one-eighth of the codewords of C. In this paper, we prove that the same conclusion holds for any binary quadratic residue codes.


title: Investigation of Photon-Generated Leakage Current for High-Performance Active Matrix Micro-LED Displays

abstract: In this paper, we investigated leakage current generated by light emission of micro-light emitting diode (LED) pixels in active matrix (AM) micro-LED displays. Custom-designed structures of metal cover with different coverage lengths were designed and fabricated to completely eliminate the photon-generated leakage current, thus enhancing the performance of AM driving circuits. Working principle of two transistors and one capacitor AM pixel and requirement of its driving transistors were also systematically discussed for liquid crystal displays, organic LED displays, and LED microdisplays. Transistors with the optimized metal-cover design have an ON/OFF-current ratio of 108, which is two orders of magnitude higher than those without metal cover. This concept was successfully implemented in a high-resolution and fine-pitch AM micro-LED display with a resolution of $400\times 240$ and a pixel pitch of $30~\mu \text{m}$ . Vivid images and movies were presented to show the great potential for wearable applications.


title: Is there a relationship between tracking ability, joint position sense, and functional level in patellofemoral pain syndrome?

abstract: Purpose ::: This prospective cohort study investigated proprioception and motor control changes in patients with patellofemoral pain syndrome (PFPS), and how these changes related to knee function, pain, muscle strength and muscle endurance.


title: A Single-Participants Investigation of the Effects of Various Biofeedback-Assisted Breathing Patterns on Heart Rate Variability: A Practitioner's Approach

abstract: Abstract The objective of this study was to define specific breathing patterns and examine the effects these patterns have on indicators of heart rate variability. Perceived accounts of ease and comfort in relation to the patterned breathing applications also were assessed. A breathing pattern is the within-cycle respiratory timing or the ratio between the inspiration and expiration of breath, including the pause time at the top of the inspiration and at the bottom of the expiration. The within-participants analysis (N = 14), including the subjective components, revealed significant differences and illuminated the strength of the effects the varied breathing patterns had on each participant.


title: Current challenges and visions in music recommender systems research

abstract: Music recommender systems (MRSs) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user’s fingertip. While today’s MRSs considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user–item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art toward solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field.


title: A survey and classification of principles for domain-specific ontology design patterns development

abstract: Dynamic, networked service-oriented systems, like those found in manufacturing, logistics or transportation, require efficient communication of capabilities of their services to enable on-the-fly integrations as a result of changing requirements. Previously, in a case of a manufacturing services network, we have shown the manufacturing service capability (MSC) in- formation communication can be enhanced by introducing a reference MSC ontology - a formal, OWL DL domain-specific ontology. However, consistent and quality development of reference ontology for a large and evolving domain such as manu- facturing is a challenge. Therefore, we propose to utilize the notion of OWL ontology design patterns (ODPs) to develop such reference ontology. However, despite the existence of rich design patterns for information modeling in general, there has been no documentation detailing the principles for development of domain-specific ODPs for domain-specific semantic models. This survey paper fills this void by providing a survey and systematic synthesis of applicable principles for domain-specific ODP development in an investigation of the prior works in data modeling, object-oriented software analysis and ontology modeling design patterns. The paper discusses applicability of the revealed principles in regards to requirements of MSC domain-specific ODPs. Although the paper is concerned with the MSC domain, the findings apply to any domain-specific ODP development. Further research is identified to operationalize principles towards domain-specific ODP development.


title: Self-Activated Transparent All-Graphene Gas Sensor with Endurance to Humidity and Mechanical Bending

abstract: Graphene is considered as one of leading candidates for gas sensor applications in the Internet of Things owing to its unique properties such as high sensitivity to gas adsorption, transparency, and flexibility. We present self-activated operation of all graphene gas sensors with high transparency and flexibility. The all-graphene gas sensors which consist of graphene for both sensor electrodes and active sensing area exhibit highly sensitive, selective, and reversible responses to NO2 without external heating. The sensors show reliable operation under high humidity conditions and bending strain. In addition to these remarkable device performances, the significantly facile fabrication process enlarges the potential of the all-graphene gas sensors for use in the Internet of Things and wearable electronics.


title: A novel Si-Tunnel FET based SRAM design for ultra low-power 0.3V VDD applications

abstract: Steep sub-threshold transistors are promising candidates to replace the traditional MOSFETs for sub-threshold leakage reduction. In this paper, we explore the use of Inter-Band Tunnel Field Effect Transistors (TFETs) in SRAMs at ultra low supply voltages. The uni-directional current conducting TFETs limit the viability of 6T SRAM cells. To overcome this limitation, 7T SRAM designs were proposed earlier at the cost of extra silicon area. In this paper, we propose a novel 6T SRAM design using Si-TFETs for reliable operation with low leakage at ultra low voltages. We also demonstrate that a functional 6T TFET SRAM design with comparable stability margins and faster performances at low voltages can be realized using proposed design when compared with the 7T TFET SRAM cell. We achieve a leakage reduction improvement of 700X and 1600X over traditional CMOS SRAM designs at V DD of 0.3V and 0.5V respectively which makes it suitable for use at ultra-low power applications.


title: Analogy Just Looks Like High Level Perception: Why a Domain-General Approach to Analogical Mapping is Right

abstract: Hofstadter and his colleagues have criticized current accounts of analogy, claiming that such accounts do not accurately capture interactions between processes of representation construction and processes of mapping. They suggest instead that analogy should be viewed as a form of high level perception that encompasses both representation building and mapping as indivisible operations within a single model. They argue speci® cally against SME, our model of analogical matching, on the grounds that it is modular, and oŒer instead programs such as Mitchell and Hofstadter's Copycat as examples of the high level perception approach. In this paper we argue against this position on two grounds. First, we demonstrate that most of their speci® c arguments involving SME and Copycat are incorrect. Second, we argue that the claim that analogy is high-level perception, while in some ways an attractive metaphor, is too vague to be useful as a technical proposal. We focus on ® ve issues: (1) how perception relates to analogy,(2) howexibilityarises in analogical processing, (3) whether analogy is a domain-general process, (4) how micro-worlds should be used in the study of analogy, and (5) how best to assess the psychological plausibility of a model of analogy. We illustrate our discussion with examples taken from computer models embodying both views.


title: Data Integration for the Relational Web

abstract: The Web contains a vast amount of structured information such as HTML tables, HTML lists and deep-web databases; there is enormous potential in combining and re-purposing this data in creative ways. However, integrating data from this relational web raises several challenges that are not addressed by current data integration systems or mash-up tools. First, the structured data is usually not published cleanly and must be extracted (say, from an HTML list) before it can be used. Second, due to the vastness of the corpus, a user can never know all of the potentially-relevant databases ahead of time (much less write a wrapper or mapping for each one); the source databases must be discovered during the integration process. Third, some of the important information regarding the data is only present in its enclosing web page and needs to be extracted appropriately. ::: ::: This paper describes Octopus, a system that combines search, extraction, data cleaning and integration, and enables users to create new data sets from those found on the Web. The key idea underlying Octopus is to offer the user a set of best-effort operators that automate the most labor-intensive tasks. For example, the Search operator takes a search-style keyword query and returns a set of relevance-ranked and similarity-clustered structured data sources on the Web; the Context operator helps the user specify the semantics of the sources by inferring attribute values that may not appear in the source itself, and the Extend operator helps the user find related sources that can be joined to add new attributes to a table. Octopus executes some of these operators automatically, but always allows the user to provide feedback and correct errors. We describe the algorithms underlying each of these operators and experiments that demonstrate their efficacy.


title: Multiple extended object tracking using Gaussian processes

abstract: The goal of multi-object tracking is to estimate the number of objects and their states recursively over time. In the presence of extended objects, i.e. objects with an extent that is not negligible in comparison to sensor resolution and which give rise to multiple measurements per time step, the tracking task is even more challenging: To fully utilize all available information and to achieve accurate estimation results, measurement models that are able to represent the object shape as well as algorithms that tackle the aggravated track-to-measurements association problem are necessary. In this work, the Gaussian process measurement model is integrated in the recently developed labeled multi-Bernoulli filter for extended objects. Thus, the ability of Gaussian processes to estimate and represent a wide range of free-from shapes is combined with a principled approach to the multiple extended objects tracking problem. The filter performance is demonstrated for simulated and experimental vehicle tracking using a laser scanner. For this particular application, a new, approximately axis-symmetric covariance function is additionally introduced.


A direct lda algorithm for high-dimensional data with application to face recognition


title: Head Finalization: A Simple Reordering Rule for SOV Languages

abstract: English is a typical SVO (Subject-Verb-Object) language, while Japanese is a typical SOV language. Conventional Statistical Machine Translation (SMT) systems work well within each of these language families. However, SMT-based translation from an SVO language to an SOV language does not work well because their word orders are completely different. Recently, a few groups have proposed rule-based preprocessing methods to mitigate this problem (Xu et al., 2009; Hong et al., 2009). These methods rewrite SVO sentences to derive more SOV-like sentences by using a set of handcrafted rules. In this paper, we propose an alternative single reordering rule: Head Finalization. This is a syntax-based preprocessing approach that offers the advantage of simplicity. We do not have to be concerned about part-of-speech tags or rule weights because the powerful Enju parser allows us to implement the rule at a general level. Our experiments show that its result, Head Final English (HFE), follows almost the same order as Japanese. We also show that this rule improves automatic evaluation scores.


title: The Internet of Things

abstract: A ski binding comprises a base and a jaw pivotally mounted on the base for engaging a ski boot. A releasable locking mechanism locks the jaw from pivoting to secure the ski boot to a ski and releases the jaw to pivot to release the ski boot if a lateral force having a magnitude greater than a selected predetermined magnitude of a break away force is applied thereto. The releasable locking mechanism is adjustable by a user of the ski binding to set the break away force and the ski binding is provided with an indicator mechanism to indicate the break away force for which the binding is set. The indicator mechanism includes a marker moveable relative to indicia indicating the break away force and the marker and indicia are disposed for viewing thereof by the user of the binding at a surface of the jaw or the base opposite the ski.


title: Structural matching by discrete relaxation

abstract: This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we focus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations of the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter.


title: A computer vision approach for weeds identification through Support Vector Machines

abstract: This paper outlines an automatic computer vision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the Support Vector Machines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the Support Vector Machines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.


title: Introducing the Geneva emotion recognition test: an example of Rasch-based test development.

abstract: Existing tests to measure the ability to recognize other people’s emotional expressions (emotion recognition ability [ERA]) mostly focus on a single modality (usually the face) and include only a small number of emotions, restricting their ecological validity. Further, their reliability is often unsatisfactory. The goal of the present study was to develop a new ERA test (Geneva Emotion Recognition Test [GERT]) that (a) features dynamic and multimodal actor portrayals (short videos with sound), (b) contains a large number of emotions, and (c) is based on modern psychometric principles (item response theory). We asked 295 participants to watch 108 actor portrayals and to choose, for each portrayal, which of 14 emotions had been expressed by the actor. We then applied the Rasch model independently to each of the 14 emotion portrayal subsets to select 83 final items for the GERT. Results showed that the model fits the emotion subtests and the overall GERT and that measurement precision is satisfactory. Consistent with previous findings, we found a decline in ERA with increasing age and an ERA advantage for women. To conclude, the GERT is a promising instrument to measure ERA in a more ecologically valid and comprehensive fashion than previous tests.


title: Anomaly Detection with Robust Deep Autoencoders

abstract: Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, $X = L_{D} + S$, where $L_{D}$ can be effectively reconstructed by a deep autoencoder and $S$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems.


title: Superhuman AI for heads-up no-limit poker: Libratus beats top professionals

abstract: No-limit Texas hold’em is the most popular form of poker. Despite artificial intelligence (AI) successes in perfect-information games, the private information and massive game tree have made no-limit poker difficult to tackle. We present Libratus, an AI that, in a 120,000-hand competition, defeated four top human specialist professionals in heads-up no-limit Texas hold’em, the leading benchmark and long-standing challenge problem in imperfect-information game solving. Our game-theoretic approach features application-independent techniques: an algorithm for computing a blueprint for the overall strategy, an algorithm that fleshes out the details of the strategy for subgames that are reached during play, and a self-improver algorithm that fixes potential weaknesses that opponents have identified in the blueprint strategy.


title: Knowledge representation and Question Answering

abstract: Publisher Summary A search engine or a typical information retrieval (IR) system, such as Google, does not go far enough as it takes keywords and only gives a ranked list of documents that may contain those keywords. Often this list is very long, and an analyst might have to read the documents in the list. Other reasons behind the unsuitability of an IR system (for an analyst) are that the nuances of a question in a natural language cannot be adequately expressed through keywords, most IR systems ignore synonyms, and most IR systems cannot reason. There should be a system that can take the documents and the analyst's question as input, access the data in fact books, and do commonsense reasoning based on them to provide answers to questions. Such a system is referred to as a “question answering system” or a “QA system.” A precursor to question answering is database querying where one queries a database using a database query language. Question answering takes this to a whole new dimension where the system has a huge body of documents (in natural languages, possibly including multimedia objects, situated in the Web and described in a Web language), and it is asked a query in a natural language. It is expected to give an answer to the question not only using the documents but also using appropriate commonsense knowledge. The system needs to be able to accommodate new additions to the body of documents.


title: Efficient iris segmentation method in unconstrained environments

abstract: Recently, iris recognition systems have gained increased attention especially in non-cooperative environments. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction and iris matching steps. Traditional iris segmentation methods provide excellent results when iris images are captured using near infrared cameras under ideal imaging conditions, but the accuracy of these algorithms significantly decreases when the iris images are taken in visible wavelength under non-ideal imaging conditions. In this paper, a new algorithm is proposed to segments iris images captured in visible wavelength under unconstrained environments. The proposed algorithm reduces the error percentage even in the presence of types of noise include iris obstructions and specular reflection. The proposed algorithm starts with determining the expected region of the iris using the K-means clustering algorithm. The Circular Hough Transform (CHT) is then employed in order to estimate the iris radius and center. A new efficient algorithm is developed to detect and isolate the upper eyelids. Finally, the non-iris regions are removed. Results of applying the proposed algorithm on UBIRIS iris image databases demonstrate that it improves the segmentation accuracy and time.


title: RefocusGAN: Scene Refocusing Using a Single Image

abstract: Post-capture control of the focus position of an image is a useful photographic tool. Changing the focus of a single image involves the complex task of simultaneously estimating the radiance and the defocus radius of all scene points. We introduce RefocusGAN, a deblur-then-reblur approach to single image refocusing. We train conditional adversarial networks for deblurring and refocusing using wide-aperture images created from light-fields. By appropriately conditioning our networks with a focus measure, an in-focus image and a refocus control parameter \(\delta \), we are able to achieve generic free-form refocusing over a single image.


title: Unsupervised feature selection on networks: a generative view

abstract: In the past decade, social and information networks have become prevalent, and research on the network data has attracted much attention. Besides the link structure, network data are often equipped with the content information (i.e, node attributes) that is usually noisy and characterized by high dimensionality. As the curse of dimensionality could hamper the performance of many machine learning tasks on networks (e.g., community detection and link prediction), feature selection can be a useful technique for alleviating such issue. In this paper, we investigate the problem of unsupervised feature selection on networks. Most existing feature selection methods fail to incorporate the linkage information, and the state-of-the-art approaches usually rely on pseudo labels generated from clustering. Such cluster labels may be far from accurate and can mislead the feature selection process. To address these issues, we propose a generative point of view for unsupervised features selection on networks that can seamlessly exploit the linkage and content information in a more effective manner. We assume that the link structures and node content are generated from a succinct set of high-quality features, and we find these features through maximizing the likelihood of the generation process. Experimental results on three real-world datasets show that our approach can select more discriminative features than state-of-the-art methods.


title: Ontogeny of thyroid tissue and tissue thyroid hormone clearance in rainbow trout embryos reared at two temperatures

abstract: The development of the thyroid tissue and clearance of thyroid hormones was studied in rainbow trout embryos reared at 8.5 and 5.5 °C, in order to determine if these events are affected by temperature in the same manner as growth rate. Although the change in growth rate was found to be predictable using the degree-day model, the pattern of whole body thyroid hormone clearance was not similarly related to rearing temperature. Further, although the thyroid follicles appeared at different times in embryos reared at the two temperatures, the differences could not be predicted using the degree-day model. These results suggest that the development of the thyroid tissue and the clearance of yolk hormones are not directly related to rearing temperature. Immunohistochemical staining of the thyroid tissue indicated two phases of thyroid activity, one associated with hormonogenesis, and the other a later phase of hormone release. However, there was no increase in 'whole body' thyroid hormone level associated with the time of appearance of colloid in the follicles. Further, evidence was presented to suggest that the extraction method used did not remove thyroid hormone associated with the colloid in the follicles. There was also evidence of sporadic increases in 'whole body' thyroid hormone content, after the development of phase 2 thyroid follicles which might indicate pulsatile release of T4 and T3.


title: A scientometric review of emerging trends and new developments in recommendation systems

abstract: Recommendation systems have drawn an increasingly broad range of interest since early 1990s. Recently, a search with the query of "recommendation systems" on Google Scholar found over 32,000 documents. As the volume of the literature grows rapidly, thus, a systematic review of the diverse research field and its current challenges becomes essential. This study surveys the literature of recommendation systems between 1992 and 2014. The overall structure of its intellectual landscape is illustrated in terms of thematic concentrations of co-cited references and emerging trends of bursting keywords and citations to references. Our review is based on two sets of bibliographic records retrieved from the Web of Science. The core dataset, obtained through a topic search, contains 2573 original research and review articles. The expanded dataset, consisting of 12,916 articles and reviews, was collected by citation expansion. We identified intellectual landscapes, landmark articles and bursting keywords of the domain in core and broader perspectives. We found that a number of landmark studies in 1980s and 1990s and techniques such as LDA, pLSI, and matrix factorization have tremendously influenced the development of the recommendation systems research. Furthermore, our study reveals that the field of recommendation systems is still evolving and developing. Thematic trends in recommendation systems research reflect the development of a wide variety of information systems such as the World Wide Web and social media. Finally, collaborative filtering has been a dominant research concept of the field. Recent emerging topics focus on enhancing the effectiveness of recommendation systems by addressing diverse challenges.


title: Robust Visual Tracking Using Oblique Random Forests

abstract: Random forest has emerged as a powerful classification technique with promising results in various vision tasks including image classification, pose estimation and object detection. However, current techniques have shown little improvements in visual tracking as they mostly rely on piece wise orthogonal hyperplanes to create decision nodes and lack a robust incremental learning mechanism that is much needed for online tracking. In this paper, we propose a discriminative tracker based on a novel incremental oblique random forest. Unlike conventional orthogonal decision trees that use a single feature and heuristic measures to obtain a split at each node, we propose to use a more powerful proximal SVM to obtain oblique hyperplanes to capture the geometric structure of the data better. The resulting decision surface is not restricted to be axis aligned and hence has the ability to represent and classify the input data better. Furthermore, in order to generalize to online tracking scenarios, we derive incremental update steps that enable the hyperplanes in each node to be updated recursively, efficiently and in a closed-form fashion. We demonstrate the effectiveness of our method using two large scale benchmark datasets (OTB-51 and OTB-100) and show that our method gives competitive results on several challenging cases by relying on simple HOG features as well as in combination with more sophisticated deep neural network based models. The implementations of the proposed random forest are available at https://github.com/ZhangLeUestc/ Incremental-Oblique-Random-Forest.