title: Olfactory Aversive Conditioning during Sleep Reduces Cigarette-Smoking Behavior

abstract: Recent findings suggest that novel associations can be learned during sleep. However, whether associative learning during sleep can alter later waking behavior and whether such behavioral changes last for minutes, hours, or days remain unknown. We tested the hypothesis that olfactory aversive conditioning during sleep will alter cigarette-smoking behavior during ensuing wakefulness. A total of 66 human subjects wishing to quit smoking participated in the study (23 females; mean age, 28.7 ± 5.2 years). Subjects completed a daily smoking diary detailing the number of cigarettes smoked during 7 d before and following a 1 d or night protocol of conditioning between cigarette odor and profoundly unpleasant odors. We observed significant reductions in the number of cigarettes smoked following olfactory aversive conditioning during stage 2 and rapid eye movement (REM) sleep but not following aversive conditioning during wakefulness ( p p


Machine Reading


title: ON THE APPROPRIATENESS OF THEORY BORROWING IN IS: AN INTERDISCIPLINARY EVALUATION

abstract: This essay discusses whether “stealing” theories from other disciplines should always be avoided or whether there is any appropriate way of using theories developed in other disciplines in an information systems (IS) context. We take a look at the benefits and problems that have arisen in the IS field as a result of theory borrowing and suggest that – when done appropriately – such borrowing may well benefit the field. In the process, we also draw on the marketing and psychology literatures to evaluate the adequacy of theory borrowing in general and derive specific recommendations about what appropriate borrowing could look like. By synthesizing concepts from these literatures, we derive a comprehensive set of recommendations that may improve the process of theory borrowing in the field; thereby moving the discipline forward.


title: Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGX

abstract: Side-channel risks of Intel SGX have recently attracted great attention. Under the spotlight is the newly discovered page-fault attack, in which an OS-level adversary induces page faults to observe the page-level access patterns of a protected process running in an SGX enclave. With almost all proposed defense focusing on this attack, little is known about whether such efforts indeed raise the bar for the adversary, whether a simple variation of the attack renders all protection ineffective, not to mention an in-depth understanding of other attack surfaces in the SGX system. In the paper, we report the first step toward systematic analyses of side-channel threats that SGX faces, focusing on the risks associated with its memory management. Our research identifies 8 potential attack vectors, ranging from TLB to DRAM modules. More importantly, we highlight the common misunderstandings about SGX memory side channels, demonstrating that high frequent AEXs can be avoided when recovering EdDSA secret key through a new page channel and fine-grained monitoring of enclave programs (at the level of 64B) can be done through combining both cache and cross-enclave DRAM channels. Our findings reveal the gap between the ongoing security research on SGX and its side-channel weaknesses, redefine the side-channel threat model for secure enclaves, and can provoke a discussion on when to use such a system and how to use it securely.


title: Position Estimation of the PMSM High Dynamic Drive at Low Speed Range

abstract: Permanent Magnet Synchronous Motors (PMSM) are widely used in industrial drives due to their high power density, high torque-toinertia ratio, small torque ripple and precise control at low speed range, possibility to torque control at zero speed, high efficiency and small size. To exploit presented advantages, a vector control should be used. Vector control allows the drive a good dynamic, effective performance especially during transients and prevents overload of the motor by controlling the torque. However, a motor shaft position sensor is required to enable the effective vector control of a PMSM. Such sensors increase the overall cost of the drive and decrease its reliability. That sensor occupies usually the end of the shaft, so elimination of the need for its installation allows new applications of such drive. This scientific problem remains an open question for and is the subject for extensive research in many scientific centers. Several approaches to this problem are reported in the literature, which are based on state observers [1,2], various versions of the Kalman filters [3,4], sliding-mode observers [5,6] or methods of applying motor saliency [7,8,9]. State observers and Kalman filters based on a motor model require complex computational operations to obtain proper accuracy, which always causes problems in real time operation. Several approaches apply motor magnetic saliency and detect the rotor position by measuring phase inductances. These methods provide a real solution at small speeds and during standstill operation, but requirements with respect to hardware and software are high. The additional scientific problem is to obtain high dynamic sensorless drive [10,11]. Assumption of the limitation the applications up to speed control (without position control) gives possibility to achieve well performance of high dynamic sensorless drive for low speed operating range.


title: Overlooked aspects of COTS-based development

abstract: Although developing with commercial-off-the-shelf components is gaining more attention from both research and industrial communities, most literature on the topic doesn't clearly identify context variables such as the type of products, projects, and systems. In particular, the literature often lacks a definition of "COTS product," or, when a definition is present, it invariably disagrees with other studies. A shared definition would improve discourse and enable researchers to meta-analyze published empirical data. After a speculative effort to define and classify COTS products, we decided to obtain this much-needed understanding from the bottom up. We asked people involved in industrial projects to name the key features in COTS-based development and to tell us what they think constitutes a COTS product. We present the interview results in the form of six theses, which contradict widely accepted (or simply undisputed) ideas. We also present a definition of "COTS product" that captures the key features.


title: News Comments: Exploring, Modeling, and Online Prediction

abstract: Online news agents provide commenting facilities for their readers to express their opinions or sentiments with regards to news stories. The number of user supplied comments on a news article may be indicative of its importance, interestingness, or impact. We explore the news comments space, and compare the log-normal and the negative binomial distributions for modeling comments from various news agents. These estimated models can be used to normalize raw comment counts and enable comparison across different news sites. We also examine the feasibility of online prediction of the number of comments, based on the volume observed shortly after publication. We report on solid performance for predicting news comment volume in the long run, after short observation. This prediction can be useful for identifying news stories with the potential to “take off,” and can be used to support front page optimization for news sites.


title: A Real-Time Pulse Peak Detection Algorithm for the Photoplethysmogram

abstract: In this paper, we propose a simple and low complexity pulse peak detection algorithm using cascaded recursive digital filters and a slope sum function (SSF) with an adaptive thresholding scheme. The algorithm first eliminates noises in the photoplethysmogram (PPG) using the cascaded lowpass and highpass digital filters. The filters have been designed with 3-dB cutoff frequencies of 11 Hz and 0.5 Hz, respectively. The filtered PPG signal is then transformed by the SSF. The SSF simplifies detecting the pulse peaks by enhancing the upslope of the PPG signal and suppressing the remainder. A threshold for identifying SSF peaks is updated using the median filter with an order of 5. This update makes the threshold adaptive to variations of SSF heights. The detected SSF peaks localize ranges for pulse peak detection. Finally, the pulse peak is identified by picking the local maxima within the range from an onset index of the SSF signal to the following zero index. In order to cope with over-detected and missed information, the proposed algorithm employs knowledge-based rules as post- processing. The algorithm is tested on a database where PPG waveforms are collected from 127 subjects. The results are promising, suggesting that the method provides simpler but accurate pulse peak detection in real applications. 


Steganography in MPEG Video Files using MACROBLOCKS


title: Improving The Completeness Of Product Carbon Footprints Using A Global Link Input-Output Model: The Case Of Japan

abstract: This paper is concerned with the main activities of Japan's 'Disclosure of CO2 emissions' programme, aimed at illustrating the CO2 emissions associated with consumer products as a 'carbon footprint' (CF). Although the current, provisional guidelines for calculating product carbon footprints specify that only the bottom-up approach is to be used for this purpose, this paper presents useful applications of input-output analysis that can improve the reliability of the method considerably, by taking full advantage of the strengths of input-output analysis. To this end, we first estimated the global carbon footprint (GCF) of food and consumables in Japan, using a global link input-output (GLIO) model comprising 804 economic sectors in Japan and 230 foreign countries and regions. By visualizing the GCF on a world map, the global distribution (including Middle East and African countries) of the induced CO2 emissions of each of the Japanese sectors were identified. To investigate the scope for reducing the data collection burden for CF practitioners, GCFs were compared with CFs obtained using a single-region input-output model. This showed that there are certain commodity groups with a CF equating to 70% to over 90% of the corresponding GCF, even if the imported goods used for producing a Japanese domestic product are considered environmentally equivalent to their domestically produced counterparts. Furthermore, it was identified which data should preferably be collected by the bottom-up approach to secure CO2 emissions coverage greater than a certain predefined level and keep data and labour costs at a minimum.


title: Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach

abstract: The Iterated Prisoner's Dilemma has guided research on social dilemmas for decades. However, it distinguishes between only two atomic actions: cooperate and defect. In real-world prisoner's dilemmas, these choices are temporally extended and different strategies may correspond to sequences of actions, reflecting grades of cooperation. We introduce a Sequential Prisoner's Dilemma (SPD) game to better capture the aforementioned characteristics. In this work, we propose a deep multiagent reinforcement learning approach that investigates the evolution of mutual cooperation in SPD games. Our approach consists of two phases. The first phase is offline: it synthesizes policies with different cooperation degrees and then trains a cooperation degree detection network. The second phase is online: an agent adaptively selects its policy based on the detected degree of opponent cooperation. The effectiveness of our approach is demonstrated in two representative SPD 2D games: the Apple-Pear game and the Fruit Gathering game. Experimental results show that our strategy can avoid being exploited by exploitative opponents and achieve cooperation with cooperative opponents.


MoocViz : A Large Scale , Open Access , Collaborative , Data Analytics Platform for MOOCs


title: Using neural network ensembles for bankruptcy prediction and credit scoring

abstract: Bankruptcy prediction and credit scoring have long been regarded as critical topics and have been studied extensively in the accounting and finance literature. Artificial intelligence and machine learning techniques have been used to solve these financial decision-making problems. The multilayer perceptron (MLP) network trained by the back-propagation learning algorithm is the mostly used technique for financial decision-making problems. In addition, it is usually superior to other traditional statistical models. Recent studies suggest combining multiple classifiers (or classifier ensembles) should be better than single classifiers. However, the performance of multiple classifiers in bankruptcy prediction and credit scoring is not fully understood. In this paper, we investigate the performance of a single classifier as the baseline classifier to compare with multiple classifiers and diversified multiple classifiers by using neural networks based on three datasets. By comparing with the single classifier as the benchmark in terms of average prediction accuracy, the multiple classifiers only perform better in one of the three datasets. The diversified multiple classifiers trained by not only different classifier parameters but also different sets of training data perform worse in all datasets. However, for the Type I and Type II errors, there is no exact winner. We suggest that it is better to consider these three classifier architectures to make the optimal financial decision.


Queue based Job Scheduling algorithm for Cloud computing


title: High quality depth map estimation of object surface from light-field images

abstract: Light-field imaging provides a novel solution to the passive 3D imaging technology. However the dense multi-view sub-aperture images decoded from the light-field raw image have extremely narrow baselines, which lead to inconsistent matching with terrible blurriness and ambiguities. This paper presents an accurate depth estimation algorithm for object surface using a lenslet light-field camera. The input data for depth estimation can be both light-field videos and images under indoor and outdoor environment. To tackle the continuously changing outdoor illumination and take full advantage of rays, rendering enhancement is performed through denoising and local vignetting correction for obtaining high-fidelity 4D light fields. The novel sub-aperture image pair selection and stereo matching algorithm are proposed for disparity computation. Then we apply the disparity refinement for recovering high quality surface details and handling disparity discontinuities. Finally both commercial and self-developed light-field cameras are used to capture real-world scenes with various lighting conditions and poses. The accuracy and robustness of the proposed algorithm are evaluated both on synthetic light-field datasets and real-world scenes by comparing with state-of-the-art algorithms. The experimental results show that high quality depth maps are recovered with smooth surfaces and accurate geometry structures.


title: A 0.18-$ \mu{\hbox {m}}$ Monolithic Li-Ion Battery Charger for Wireless Devices Based on Partial Current Sensing and Adaptive Reference Voltage

abstract: An Li-ion battery charger based on a charge-control buck regulator operating at 2.2 MHz is implemented in 180 nm CMOS technology. The novelty of the proposed charge-control converter consists of regulating the average output current by only sensing a portion of the inductor current and using an adaptive reference voltage. By adopting this approach, the charger average output current is set to a constant value of 900 mA regardless of the battery voltage variation. In constant-voltage (CV) mode, a feedback loop is established in addition to the preexisting current control loop, preserving the smoothness of the output voltage at the transition from constant-current (CC) to CV mode. A small-signal model has been developed to analyze the system stability and subharmonic oscillations at low current levels. Transistor-level simulations of the proposed switching charger are presented. The output voltage ranges from 2.1 to 4.2 V, and the power efficiency at 900 mA has been measured to be 86% for an input voltage of 10 V. The accuracy of the output current using the proposed sensing technique is 9.4% at 10 V.


title: Ultra-wideband low noise amplifier with shunt resistive feedback in 0.18µm CMOS process

abstract: A CMOS low noise amplifier (LNA) for ultra-wideband (UWB) systems is presented. The proposed LNA achieve wide operating bandwidth for 3–10.6 GHz by using resistive shunt feedback topology. Two stage amplifiers and an inter stage circuit are designed to achieve wider gain bandwidth. The shunt resistive feedback are employed in input and output stage to provide wideband input matching with low noise figure (NF). This work is designed and fabricated in TSMC 0.18µm CMOS process. The proposed UWB LNA achieves a measured flat gain 15 dB and has a noise figure of 4 dB over the entire band while consuming 21.5 mW of power. The measured third order intercept point IIP3 is 2.5 dBm.


title: Implementation of Kalman Filter with Python Language

abstract: In this paper, we investigate the implementation of a Python code for a Kalman Filter using the Numpy package. A Kalman Filtering is carried out in two steps: Prediction and Update. Each step is investigated and coded as a function with matrix input and output. These different functions are explained and an example of a Kalman Filter application for the localization of mobile in wireless networks is given.


title: Decision Support Systems in Fisheries and Aquaculture: A systematic review

abstract: Decision support systems help decision makers make better decisions in the face of complex decision problems (e.g. investment or policy decisions). Fisheries and Aquaculture is a domain where decision makers face such decisions since they involve factors from many different scientific fields. No systematic overview of literature describing decision support systems and their application in fisheries and aquaculture has been conducted. This paper summarizes scientific literature that describes decision support systems applied to the domain of Fisheries and Aquaculture. We use an established systematic mapping survey method to conduct our literature mapping. Our research questions are: What decision support systems for fisheries and aquaculture exists? What are the most investigated fishery and aquaculture decision support systems topics and how have these changed over time? Do any current DSS for fisheries provide real- time analytics? Do DSSes in Fisheries and Aquaculture build their models using machine learning done on captured and grounded data? The paper then detail how we employ the systematic mapping method in answering these questions. This results in 27 papers being identified as relevant and gives an exposition on the primary methods concluded in the study for designing a decision support system. We provide an analysis of the research done in the studies collected. We discovered that most literature does not consider multiple aspects for multiple stakeholders in their work. In addition we observed that little or no work has been done with real-time analysis in these decision support systems.


title: Optimal rotor shape with third harmonic for maximizing torque and minimizing torque ripple in IPM motors

abstract: This paper presents optimal rotor shape design to increase the torque capability in IPM machines by introducing optimal 3rd harmonic to the inverse cosine shaped rotor. The impact of the optimal 3rd harmonic to the rotor shape on the electromagnetic performance is investigated by finite element method. It is demonstrated that under the same amount of permanent magnets as the inverse cosine shaped IPM machine, the average torque for the machine of inverse cosine with optimal 3rd harmonic shaped rotor can be improved by >5% while the torque ripple can be maintained almost the same. In addition, the inductance saliency ratio is also improved, which increases the average torque further. The impact of 3rd harmonic on stator and rotor iron and rotor eddy current magnet losses is also compared with the machine without 3rd harmonic in the rotor shape, which shows that the iron loss increases slightly. All results have been verified with finite element method.


title: A Collaborative DDoS Defence Framework Using Network Function Virtualization

abstract: High-profile and often destructive distributed denial of service (DDoS) attacks continue to be one of the top security concerns as the DDoS attacks volumes are increasing constantly. Among them, the SYN Flood attack is the most common type. Conventional DDoS defense solutions may not be preferable, since they demand highly capable hardware resources, which induce high cost and long deployment cycle. The emerging of network function virtualization (NFV) technology introduces new opportunities to decrease the amount of proprietary hardware that is needed to launch and operate network services. In this paper, we propose a DDoS defense mechanism named CoFence, which facilitates a “domain-helps-domain” collaboration network among NFV-based domain networks. CoFence allows domain networks to help each other in handling large volume of DDoS attacks through resource sharing. Specifically, we design a dynamic resource allocation mechanism for domains so that the resource allocation is fair, efficient, and incentive-compatible. The resource sharing mechanism is modeled as a multi-leader-follower Stackelberg game. In this game, all domains have a degree of control to maximize their own utility. The resource supplier domains determine the amount of resource to each requesting peer based on optimizing a reciprocal-based utility function. On the other hand, the resource requesting domains decide the level of demand to send to the resource supplier domains in order to reach sufficient support. Our simulation results demonstrate that the designed resource allocation game is effective, incentive compatible, fair, and reciprocal under its Nash equilibrium.


title: Reinforcement learning in robotics: A survey

abstract: Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.


title: Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition

abstract: As facial appearance is subject to significant intra-class variations caused by the aging process over time, age-invariant face recognition (AIFR) remains a major challenge in face recognition community. To reduce the intra-class discrepancy caused by the aging, in this paper we propose a novel approach (namely, Orthogonal Embedding CNNs, or OE-CNNs) to learn the age-invariant deep face features. Specifically, we decompose deep face features into two orthogonal components to represent age-related and identity-related features. As a result, identity-related features that are robust to aging are then used for AIFR. Besides, for complementing the existing cross-age datasets and advancing the research in this field, we construct a brand-new large-scale Cross-Age Face dataset (CAF). Extensive experiments conducted on the three public domain face aging datasets (MORPH Album 2, CACD-VS and FG-NET) have shown the effectiveness of the proposed approach and the value of the constructed CAF dataset on AIFR. Benchmarking our algorithm on one of the most popular general face recognition (GFR) dataset LFW additionally demonstrates the comparable generalization performance on GFR.


title: Universal Factor Graphs for Every NP-Hard Boolean CSP

abstract: An instance of a Boolean constraint satisfaction problem can be divided into two parts. One part, that we refer to as the factor graph of the instance, specifies for each clause the set of variables that are associated with the clause. The other part, specifies for each of the given clauses what is the constraint that is evaluated on the respective variables. Depending on the allowed choices of constraints, it is known that Boolean constraint satisfaction problems fall into one of two classes, being either NP-hard or in P. ::: ::: This paper shows that every NP-hard Boolean constraint satisfaction problem (except for an easy to characterize set of natural exceptions) has a universal factor graph. That is, for every NP-hard Boolean constraint satisfaction problem, there is a family of at most one factor graph of each size, such that the problem, restricted to instances that have a factor graph from this family, cannot be solved in polynomial time unless NP is contained in P/poly. Moreover, we extend this classification to one that establishes hardness of approximation.


title: A Statistical Analysis of Cryptocurrencies

abstract: We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.


Moving Robot and Image Processing based Analysis for Tomato Growth Measurement


Towards User Personality Profiling from Multiple Social Networks.


title: Assessment of Comprehensive Analysis Calculation of Airloads on Helicopter Rotors

abstract: Blade section normal force and pitching moment were investigated for six rotors operating at transition and high speeds: H-34 in flight and wind tunnel, SA 330 (research Puma), SA 349/2, UH-60A full-scale, and BO-105 model (Higher-Harmonic Acoustics Rotor Test I). The measured data from flight and wind-tunnel tests were compared with calculations obtained using the comprehensive analysis CAMRAD II. The calculations were made using two free-wake models: rolled up and multiple trailer with consolidation models. At transition speed, there is fair to good agreement for the blade section normal force between the test data and analysis for the H-34, research Puma, and SA 349/2 with the rolled-up wake. The calculated airloads differ significantly from the measurements for the UH-60A and BO-105. Better correlation is obtained for the UH-60A and BO-105 by using the multiple trailer with consolidation wake model. In the high-speed condition, the analysis shows generally good agreement with the research Puma flight data in both magnitude and phase. However, poor agreement is obtained for the other rotors examined. The analysis shows that the aerodynamic tip design (chord length and quarter-chord location) of the research Puma has an important influence on the phase correlation.


title: Learning Query and Document Relevance from a Web-scale Click Graph

abstract: Click-through logs over query-document pairs provide rich and valuable information for multiple tasks in information retrieval. This paper proposes a vector propagation algorithm on the click graph to learn vector representations for both queries and documents in the same semantic space. The proposed approach incorporates both click and content information, and the produced vector representations can directly improve ranking performance for queries and documents that have been observed in the click log. For new queries and documents that are not in the click log, we propose a two-step framework to generate the vector representation, which significantly improves the coverage of our vectors while maintaining the high quality. Experiments on Web-scale search logs from a major commercial search engine demonstrate the effectiveness and scalability of the proposed method. Evaluation results show that NDCG scores are significantly improved against multiple baselines by using the proposed method both as a ranking model and as a feature in a learning-to-rank framework.


title: A Scalable Hybrid Classifier for Music Genre Classification using Machine Learning Concepts and Spark

abstract: Music genre classification has been a challenging task in the field of Music Information Retrieval (MIR). Using a machine to automate this classification process is a more complex task. Audio feature extraction is the first important step of MIR. Essentia, a tool is used for the audio feature extraction. In this paper, a comparative study of the standard machine learning classifiers including K-Nearest Neighbor Classifier, Support Vector Machine, Naive Bayes Classifier, Neural Network, Quadratic Discriminant Analysis and Fuzzy Classifiers is undertaken in context of music genre classification. In this paper, a two phase hybrid classifier is proposed to overcome the problem of blurry classification of Pop, Rock and Electronic genres. It is observed that these genres are not classified accurately by above mentioned classifiers. The behavior of the proposed classifier on the standard music genre dataset GTZAN Genre Collection (1000 songs) and Free Music Archive (FMA) Dataset (approx. 20K songs) is investigated. The overall result of proposed approach on both the datasets is approximately 90% and 70% respectively.


title: An Overview of Smart Contract and Use Cases in Blockchain Technology

abstract: In the last decade blockchain technology become mainstream research topic because of its decentralized, peer to peer transaction, distributed consensus, and anonymity properties. The blockchain technology overshadows regulatory problem and technical challenges. A smart contract is a set of programs which are self-verifying, self-executing and tamper resistant. Smart contract with the integration of blockchain technology capable of doing a task in real time with low cost and provide a greater degree of security. This paper firstly, explains the various components and working principle of smart contract. Secondly, identify and analyse the various use cases of smart contract along with the advantage of using smart contract in blockchain application. Lastly, the paper concludes with challenges lie in implementing smart contract the future real-life scenario.


“Mega Champs” Field Trip: A Lesson in Character Development


title: Compact Mixed-Mode Filter Based on ${\rm{TE}}_{\mathrm {101}}$ Cavity Mode and ${\rm{TE}}_{\mathrm {01\delta }}$ Dielectric Mode

abstract: A new class of compact in-line filters with pseudoelliptic responses is presented in this paper. The proposed filters employ a new type of mixed-mode resonator. Such a resonator consists of a cavity loaded with a suspended high permittivity dielectric puck, so that both cavity TE101 mode and dielectric TE $_{\mathrm {01\delta }}$ mode are exploited within the same volume. This structure realizes the transverse doublet topology and it is therefore capable of generating a transmission zero (TZ) that can be either located above or below the passband. Multiple mixed-mode resonators can be used as basic building blocks to obtain higher order filters by cascading them through nonresonating nodes. These filters are capable of implementing TZs that are very close to the passband edges, thus realizing an extreme close-in rejection. As a result of the dielectric loading, the proposed solution leads to a very compact structure with improved temperature stability. To validate the proposed class of filters, a second-order filter with 2.0% fractional bandwidth (FBW) and a fourth-order filter with 2.5% FBW have been designed and manufactured.


title: Linking pharmacy and laboratory data to assess the appropriateness of care in patients with diabetes.

abstract: OBJECTIVE ::: To use pharmacy and laboratory data to assess diabetes care within a medical group and between medical groups and to determine dispensing patterns and the extent to which providers change therapy based on HbA1c results. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: Participating groups submitted 1 year of data for continuously enrolled patients. Required data included date of birth, all diabetes-specific prescriptions (oral hypoglycemic agents and insulin), date of prescription, National Drug Code, all HbA1c values, lower and upper normal limits, and date of testing. ::: ::: ::: RESULTS ::: Few changes in therapy were noted despite the large percentages of patients with suboptimal control. Nearly 90% of the patients treated with medications received a monotherapy regimen involving one of three therapeutic agents: sulfonylureas, metformin, or insulin. More than three-fourths of the patients remained on the same therapy during the observation period despite the fact that 27% of these patients had HbA1c values > or = 8%. Nearly one-fifth (18%) of patients had an HbA1c level of > or = 8% and no further testing for at least 90 days after the "actionable" HbA1C result was obtained. Furthermore, 54% of patients with actionable HbA1c results did not have a change in therapy initiated after the result was available. ::: ::: ::: CONCLUSIONS ::: The American Diabetes Association recommendations to act on HbA1c values > or = 8% and to follow up regularly on patients found to be in suboptimal control do not appear to be applied in a consistent manner based on the pharmacy and laboratory data analyzed in this sample.


title: Prevalence of Circadian Rhythm Sleep-Wake Disorders and Associated Factors in Euthymic Patients with Bipolar Disorder

abstract: Recent studies have suggested that there are certain pathophysiological relationships between bipolar disorder (BD) and circadian rhythm dysfunction. However, apparently no studies have clarified the prevalence of circadian rhythm sleep-wake disorders (CRSWD) in patients with BD. This study was set out to investigate the prevalence of CRSWD and associated factors in patients with BD. One hundred four euthymic BD outpatients participated in this study. The subjects were asked to answer questionnaires including demographic variables, clinical course of BD, and family history of psychiatric disorders and suicide. Severity of BD was assessed by the Montgomery-Asberg Depression Rating Scale and Young Mania Rating Scale. CRSWD was diagnosed by clinical interview, together with sleep logs, according to the International Classification of Sleep Disorders, third edition (ICSD-3). Thirty-five subjects (32.4%) met the criteria for CRSWD. The age at the time of investigation and that at the onset of BD were both lower in the CRSWD group than in the non-CRSWD group. The rates of family history of psychiatric disorders and suicide in the CRSWD group were higher than those in the non-CRSWD group. Multiple logistic regression analysis revealed that the presence of CRSWD was significantly associated with younger onset age of BD and family history of suicide. The prevalence of CRSWD could be quite high in BD patients. Younger onset age of BD and family history of suicide were associated with presence of CRSWD in BD patients.


title: Compact UWB Bandpass Filter With Improved Upper-Stopband Performance

abstract: In this letter, a novel ultra-wideband (UWB) bandpass filter with compact size and improved upper-stopband performance has been studied and implemented using multiple-mode resonator (MMR). The MMR is formed by attaching three pairs of circular impedance-stepped stubs in shunt to a high impedance microstrip line. By simply adjusting the radius of the circles of the stubs, the resonant modes of the MMR can be roughly allocated within the 3.1-10.6 GHz UWB band while suppressing the spurious harmonics in the upper-stopband. In order to enhance the coupling degree, two interdigital coupled-lines are used in the input and output sides. Thus, a predicted UWB passband is realized. Meanwhile, the insertion loss is higher than 30.0 dB in the upper-stopband from 12.1 to 27.8 GHz. Finally, the filter is successfully designed and fabricated. The EM-simulated and the measured results are presented in this work where excellent agreement between them is obtained.


title: Spined hybrid soft robot for high speed, high force, tunable bistability, and wide-range stiffness modulation

abstract: The ease deformation of soft-bodied robots enables a variety of intriguing locomotion modes, yet their speed is typically slow owing to intrinsic soft materials limitation. Bioinspired by active actuation of spine deformation during high-speed galloping of cheetahs, the fastest animal on land, we propose spined hybrid soft bending actuators (SBAs) that enable high-speed locomotion and high force in soft robots. They are composed of rigid links as skeletal spines, SBAs as skeletal muscles, and an amplifier to enhance force output and velocity, as well as enable high precision, wide-range stiffness, and tunable snap-through instabilities. We demonstrated their integrations for a few high-performance soft machines, including high-speed yet energy-efficient crawlers with locomotion speed of 2.49 BL/s, fast-moving underwater swimmers (0.78 BL/s), soft grippers with over x1000 stiffness modulation (maximum load capacity is 11.4 kg), bistable soft oscillators, and ultra-high-strength adhesive device that has ~700 N pull-off force.


title: Methods for Evaluating Gameplay Experience in a Serious Gaming Context

abstract: Gameplay experience (GX) is created during the process of player-game interaction, where this interaction has the goal to provide a motivating, fun experience for the player. Since GX is an important factor for the success of failure of a game, a formal classification of how to design for and evaluate GX is necessary. Using appropriate mechanisms for evaluation and measurement of GX allows the validation of good gameplay experiences. This paper presents an approach to formalize such evaluative methods and a roadmap for applying these mechanisms in the context of serious games. We first discuss related work of user experience (UX) and player experience models, based on which we propose a three-layer framework of GX. For each layer, a number of measurement methodologies are listed and our focus is put on physiological and technical metrics for game evaluation. Finally, we point out the potential use of this framework within the field of game-based learning and serious gaming for sports and health. GAMEPLAY, AFFECT, PSYCHOPHYSIOLOGY, USER EXPERIENCE (UX), SERIOUS GAMING


Strengths, Weaknesses, and Combinations of Model-based and Model-free Reinforcement Learning


title: Volcanic ash observation in active volcano areas using teleoperated mobile robots - Introduction to our robotic-volcano-observation project and field experiments

abstract: Observation of an active volcano is very important to determine a strategy for estimating its eruptive activity and providing residents with an evacuation warning. However, it is too dangerous for humans to install cameras during eruptive activity to determine the status of a volcano. Furthermore, permanently installed cameras might be damaged by eruptions, and craters can emerge in unanticipated positions. To handle this situation, we proposed robotic observations in a volcanic area after an eruption using a multi-rotor UAV (unmanned aerial vehicle) and a small ground robot. Field experiments are effective at promoting this type of research and development. Therefore, we performed several field experiments at Mt. Asama. In this paper, we introduce our robotic observation project, and report on the field experiments conducted with teleoperated mobile robots in October 2012 at Mt. Asama.


title: Efficient Zero-Knowledge Contingent Payments in Cryptocurrencies Without Scripts

abstract: One of the most promising innovations offered by the cryptographic currencies (like Bitcoin) are the so-called smart contracts, which can be viewed as financial agreements between mutually distrusting participants. Their execution is enforced by the mechanics of the currency, and typically has monetary consequences for the parties. The rules of these contracts are written in the form of so-called “scripts”, which are pieces of code in some “scripting language”. Although smart contracts are believed to have a huge potential, for the moment they are not widely used in practice. In particular, most of Bitcoin miners allow only to post standard transactions (i.e.: those without the non-trivial scripts) on the blockchain. As a result, it is currently very hard to create non-trivial smart contracts in Bitcoin.


title: Feature-selection-based dynamic transfer ensemble model for customer churn prediction

abstract: Customer churn prediction is one of the key steps to maximize the value of customers for an enterprise. It is difficult to get satisfactory prediction effect by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study proposes a feature-selection-based dynamic transfer ensemble (FSDTE) model that aims to introduce transfer learning theory for utilizing the customer data in both the target and related source domains. The model mainly conducts a two-layer feature selection. In the first layer, an initial feature subset is selected by GMDH-type neural network only in the target domain. In the second layer, several appropriate patterns from the source domain to target training set are selected, and some features with higher mutual information between them and the class variable are combined with the initial subset to construct a new feature subset. The selection in the second layer is repeated several times to generate a series of new feature subsets, and then, we train a base classifier in each one. Finally, a best base classifier is selected dynamically for each test pattern. The experimental results in two customer churn prediction datasets show that FSDTE can achieve better performance compared with the traditional churn prediction strategies, as well as three existing transfer learning strategies.


title: Parallel programming framework for large batch transaction processing on scale-out systems

abstract: A scale-out system is a cluster of commodity machines, and offers a good platform to support steadily increasing workloads that process growing data sets. Sharding [4] is a method of partitioning data and processing a computation on a scale-out system. In a database system, a large table can be partitioned into small tables so each node can process its part of the computation. The sharding approach in a large batch transaction processing, which is important in financial area, presents two hard problems to programmers. Programmers have to write complex code (1) to transfer the input data so as to align the computations with the data partitions, and (2) to manage the distributed transactions. This paper presents a new parallel programming framework that makes parallel transactional programming easier by specifying transaction scopes and partitioners to simplify the code. Transaction scopes include series of subtransactions, each of which performs local operations. The system manages the distributed transactions automatically. A partitioner represents how the computation should be decomposed and aligned with the data partitions to avoid remote database accesses. Between paired of subtransactions, the system handles the data shuffling across the network. We implemented our parallel programming framework as a new Java class library. We hide all of the complex details of data transfer and distributed transaction management in the library. Our programming framework can eliminate almost 66% of the lines of code compared to a current programming approach without programming framework support. We also confirmed good scalability, with a scaling factor of 20.6 on 24 nodes using our modified batch program for the TPC-C benchmark.


title: Automation Architecture for Single Operator- Multiple UAV Command and Control

abstract: Abstract : In light of the Office of the Secretary Defense's Roadmap for unmanned aircraft systems (UAS), there is a critical need for research examining human interaction with heterogeneous unmanned vehicles. The OSD Roadmap clearly delineates the need to investigate the "appropriate conditions and requirements under which a single pilot would be allowed to control multiple airborne UA (unmanned aircraft) simultaneously." Toward this end, in this paper, we provide a meta-analysis of research studies across unmanned aerial and ground vehicle domains that investigated single operator control of multiple vehicles. As a result, a hierarchical control model for single operator control of multiple unmanned vehicles (UV) is proposed that demonstrates those requirements that will need to be met for operator cognitive support of multiple UV control, with an emphasis on the introduction of higher levels of autonomy. The challenge in achieving effective management of multiple UV systems in the future is not only to determine whether automation can be used to improve human and system performance, but how and to what degree across hierarchical control loops, as well as determining the types of decision support that will be needed by operators given the high-workload environment. We address when and how increasing levels of automation should be incorporated in multiple UV systems and discuss the impact on not only human performance, but more importantly, on system performance.


Opinion Consciousness cannot be separated from


title: Remote monitoring system design for photovoltaic panels

abstract: The design and implementation studies of a remote monitoring system are presented in this study. The proposed design is improved in terms of smart metering infrastructure based on current, voltage, and power measurements. The communication among the photovoltaic (PV) panels and host PC is performed by designed ZigBee transceiver system. Sensing circuits are very important devices to measure for both current and voltage magnitudes of electrical systems. The measured data are processed by the microcontroller unit (MCU) and are modulated after signal processing operations for wireless transmission. Although the transceiver is configured to execute bi-directional communication, unidirectional data flow is performed where the smart metering system transmits data to monitoring substructure. The monitoring software is also implemented in the context of the presented study. The Visual Studio.Net software development kit is used to design and to code the software. The performed experimental studies showed that the proposed real time monitoring and smart metering system could be exploited to monitor current, voltage and power magnitudes of the PV panels in an efficient way.


title: Effect of textile materials on wearable antenna performance: a case study of GPS antennas

abstract: Recently, textile antennas have been presented in international conferences. These studies have demonstrated that fabrics are suitable for microstrip antenna substrates. In this paper, our study focuses on the characterization of different synthetic fabrics suitable for circular polarized micro-strip antennas. Special attention is paid to GPS antennas. Five different synthetic textile materials are chosen for the comparison. The word textile is typically used for antenna class and fabric to describe a particular material. However, in many cases these words are interchangeable.


title: A printed array antenna for multi-layer PCB design

abstract: This paper presents a design of 1×4 printed array antenna excited by proximity coupling. The gain of patch antenna built on thin substrate is usually inferior to the one with thicker substrate of the same material. The goal of this work is to design an antenna with realized gain less responsive to substrate thickness. Patch antenna fed by two-layer proximity coupled feeding line is introduced to alleviate gain degradation caused by thin substrate. Proposed antenna is compared with conventional patch antenna in simulation. 1×4 array antenna is fabricated on multi-layer PCB to validate our design. Array antenna fabricated on FR-4 substrate exhibits good radiation characteristics and a flat gain response from 7.7 to 8.3 GHz is measured. Measured radiation patterns of array antenna are similar to simulations.


title: Understanding User Attention and Engagement in Online News Reading

abstract: Prior work on user engagement with online media identified web page dwell time as a key metric reflecting level of user engagement with online news articles. While on average, dwell time gives a reasonable estimate of user experience with a news article, it is not able to capture important aspects of user interaction with the page, such as how much time a user spends reading the article vs. viewing the comment posted by other users, or the actual proportion of article read by the user. In this paper, we propose a set of user engagement classes along with new user engagement metrics that, unlike dwell time, more accurately reflect user experience with the content. Our user engagement classes provide clear and interpretable taxonomy of user engagement with online news, and are defined based on amount of time user spends on the page, proportion of the article user actually reads and the amount of interaction users performs with the comments. Moreover, we demonstrate that our metrics are relatively easier to predict from the news article content, compared to the dwell time, making optimization of user engagement more attainable goal.


title: AP17-OLR challenge: Data, plan, and baseline

abstract: We present the data profile and the evaluation plan of the second oriental language recognition (OLR) challenge AP17-OLR. Compared to the event last year (AP16-OLR), the new challenge involves more languages and focuses more on short utterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two types of baselines are constructed to assist the participants, one is based on the i-vector model and the other is based on various neural networks. We report the baseline results evaluated with various metrics defined by the AP17-OLR evaluation plan and demonstrate that the combined database is a reasonable data resource for multilingual research. All the data is free for participants, and the Kaldi recipes for the baselines have been published online.


title: Fast webpage classification using URL features

abstract: We demonstrate the usefulness of the uniform resource locator (URL) alone in performing web page classification. This approach is faster than typical web page classification, as the pages do not have to be fetched and analyzed. Our approach segments the URL into meaningful chunks and adds component, sequential and orthographic features to model salient patterns. The resulting features are used in supervised maximum entropy modeling. We analyze our approach's effectiveness on two standardized domains. Our results show that in certain scenarios, URL-based methods approach the performance of current state-of-the-art full-text and link-based methods.


title: Automated Analysis and Verification of TLS 1.3: 0-RTT, Resumption and Delayed Authentication

abstract: After a development process of many months, the TLS 1.3 specification is nearly complete. To prevent past mistakes, this crucial security protocol must be thoroughly scrutinised prior to deployment. In this work we model and analyse revision 10 of the TLS 1.3 specification using the Tamarin prover, a tool for the automated analysis of security protocols. We specify and analyse the interaction of various handshake modes for an unbounded number of concurrent TLS sessions. We show that revision 10 meets the goals of authenticated key exchange in both the unilateral and mutual authentication cases. We extend our model to incorporate the desired delayed client authentication mechanism, a feature that is likely to be included in the next revision of the specification, and uncover a potential attack in which an adversary is able to successfully impersonate a client during a PSK-resumption handshake. This observation was reported to, and confirmed by, the IETF TLS Working Group. Our work not only provides the first supporting evidence for the security of several complex protocol mode interactions in TLS 1.3, but also shows the strict necessity of recent suggestions to include more information in the protocol's signature contents.


A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology


title: Recommender Systems

abstract: The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.


title: High voltage dual active bridge with series connected high voltage silicon carbide (SiC) devices

abstract: Using 6.5 kV Silicon (Si) IGBTs, high voltage high power DC to DC converters are realized either by multi-level converters or series connected devices based two level converters or modular multi-level converter using series connected devices two level converter as building blocks. The introduction of high voltage SiC devices (10 kV to 20 kV) reduces the component count significantly while improving the efficiency and power density of the converter. To explore further, this paper investigates the state of the art high voltage (>10 kV) SiC devices for high voltage dual active bridge (DAB) with series connected devices for high power applications. Experimental results for static and dynamic voltage balancing of 15 kV, 20 A SiC IGBT devices are given to validate the feasibility of series connection and also the experimental characterization of 10kV SiC MOSFET and 15 kV SiC IGBT with RC snubber are reported. By using energy loss data from the experimental characterization, HV side switching loss of a 1 MVA 16 kV/2 kV DAB topology has been evaluated for two independent cases with 10 kV, 10 A SiC MOSFET and 15 kV, 20 A SiC IGBT on the HV side of the converter, while using 1.7 kV Si IGBTs on LV side of the converter.


title: Probabilistic Boolean decision trees and the complexity of evaluating game trees

abstract: The Boolean Decision tree model is perhaps the simplest model that computes Boolean functions; it charges only for reading an input variable. We study the power of randomness (vs. both determinism and non-determinism) in this model, and prove separation results between the three complexity measures. These results are obtained via general and efficient methods for computing upper and lower bounds on the probabilistic complexity of evaluating Boolean formulae in which every variable appears exactly once (AND/OR tree with distinct leaves). These bounds are shown to be exactly tight for interesting families of such tree functions. We then apply our results to the complexity of evaluating game trees, which is a central problem in AI. These trees are similar to Boolean tree functions, except that input variables (leaves) may take values from a large set (of valuations to game positions) and the AND/OR nodes are replaced by MIN/MAX nodes. Here the cost is the number of positions (leaves) probed by the algorithm. The best known algorithm for this problem is the alpha-beta pruning method. As a deterministic algorithm, it will in the worst case have to examine all positions. Many papers studied the expected behavior of alpha-beta pruning (on uniform trees) under the unreasonable assumption that position values are drawn independently from some distribution. We analyze a randomized variant of alphabeta pruning, show that it is considerably faster than the deterministic one in worst case, and prove it optimal for uniform trees.


title: Coordinating investment, production and subcontracting

abstract: We value the option of subcontracting to improve financial performance and system coordination by analyzing a competitive stochastic investment game with recourse. The manufacturer and subcontractor decide separately on their capacity investment levels. Then demand uncertainty is resolved and both parties have the option to subcontract when deciding on their production and sales. We analyze and present outsourcing conditions for three contract types: (1) price-only contracts where an ex-ante transfer price is set for each unit supplied by the subcontractor; (2) incomplete contracts, where both parties negotiate over the subcontracting transfer; and (3) state-dependent price-only and incomplete contracts for which we show an equivalence result. While subcontracting with these three contract types can coordinate production decisions in the supply system, only state-dependent contracts can eliminate all decentralization costs and coordinate capacity investment decisions. The minimally sufficient price-only contract that coordinates our supply chain specifies transfer prices for a small number (6 in our model) of contingent scenarios. Our game-theoretic model allows the analysis of the role of transfer prices and of the bargaining power of buyer and supplier. We find that sometimes firms may be better off leaving some contract parameters unspecified ex-ante and agreeing to negotiate ex-post. Also, a price-focused strategy for managing subcontractors can backfire because a lower transfer price may decrease the manufacturer's profit. Finally, as with financial options, the option value of subcontracting increases as markets are more volatile or more negatively correlated.


GPCAD: a tool for CMOS op-amp synthesis


title: Training Invariant Support Vector Machines

abstract: Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide experimental results, and discuss their respective merits. One of the significant new results reported in this work is our recent achievement of the lowest reported test error on the well-known MNIST digit recognition benchmark task, with SVM training times that are also significantly faster than previous SVM methods.


title: Phoolproof Phishing Prevention

abstract: Phishing, or web spoofing, is a growing problem: the Anti-Phishing Working Group (APWG) received almost 14,000 unique phishing reports in August 2005, a 56% jump over the number of reports in December 2004 [3]. For financial institutions, phishing is a particularly insidious problem, since trust forms the foundation for customer relationships, and phishing attacks undermine confidence in an institution. Phishing attacks succeed by exploiting a user's inability to distinguish legitimate sites from spoofed sites. Most prior research focuses on assisting the user in making this distinction; however, users must make the right security decision every time. Unfortunately, humans are ill-suited for performing the security checks necessary for secure site identification, and a single mistake may result in a total compromise of the user's online account. Fundamentally, users should be authenticated using information that they cannot readily reveal to malicious parties. Placing less reliance on the user during the authentication process will enhance security and eliminate many forms of fraud. We propose using a trusted device to perform mutual authentication that eliminates reliance on perfect user behavior, thwarts Man-in-the-Middle attacks after setup, and protects a user's account even in the presence of keyloggers and most forms of spyware. We demonstrate the practicality of our system with a prototype implementation.


title: Potential Markers of Endometriosis: Latest Update

abstract: Endometriosis is a complicated chronic disease that is characterized by the presence of ectopic lesions commonly located in the ovaries, peritoneum, and rectovaginal septum. It can cause irregular bleeding, pain, and infertility in the millions of affected women globally. While it is associated with abnormal mechanisms of retrograde menstruation, inflammation, and oxidative stress, but its etio-pathogenesis is not clearly characterized. Previous and recent literature supports the presence of altered levels of immunological signaling molecules growth factors, oxidants, and antioxidants in patients with endometriosis. This article reviews such literature regarding these biomarkers in four biological windows: serum, peritoneal fluid, follicular fluid, and relevant endometrial tissue samples. The investigation of changes in the proteome expression and oxidative stress markers in endometriosis has been shown to reflect the presence and severity of the condition, suggesting these molecules may possess the relevance and capability to function as early diagnostic markers. Current standard of laparoscopic diagnosis can delay detection by approximately nine years; therefore, establishing a non-invasive, inexpensive diagnostic methodology could simplify detection, reduce the use of invasive procedures, improve management, and address worldwide public health concerns. Characterization of multiple highly specific and sensitive biomarkers is necessary to create a reliable testing panel for early detection of endometriosis. The focus of the review is to summarize the relevant biomarkers as potential diagnostic tools for endometriosis and understand the roles of immunological and chemical modulators in this pathology. Our review also suggests that future research direction regarding diagnostic potential of biomarkers should further showcase reproducible results to establish the credibility of associated molecules.


title: Subgroup identification based on differential effect search--a recursive partitioning method for establishing response to treatment in patient subpopulations.

abstract: We propose a novel recursive partitioning method for identifying subgroups of subjects with enhanced treatment effects based on a differential effect search algorithm. The idea is to build a collection of subgroups by recursively partitioning a database into two subgroups at each parent group, such that the treatment effect within one of the two subgroups is maximized compared with the other subgroup. The process of data splitting continues until a predefined stopping condition has been satisfied. The method is similar to 'interaction tree' approaches that allow incorporation of a treatment-by-split interaction in the splitting criterion. However, unlike other tree-based methods, this method searches only within specific regions of the covariate space and generates multiple subgroups of potential interest. We develop this method and provide guidance on key topics of interest that include generating multiple promising subgroups using different splitting criteria, choosing optimal values of complexity parameters via cross-validation, and addressing Type I error rate inflation inherent in data mining applications using a resampling-based method. We evaluate the operating characteristics of the procedure using a simulation study and illustrate the method with a clinical trial example.


title: How neuroscience contributes to neuromarketing

abstract: The purpose of this study is to show how and where neuroscience contributes to neuromarketing. In order to show the contribution to neuromarketing, a literature research on neuroscience, consumer neuroscience and neuromarketing has been done. Main findings of this paper suggest that the combination of neuroscientific tools and non-neuroimaging tools add validity to research findings and it will establish brain behavior relationships in order to understand underlying consumer preferences and choices. Thus far, the proposed framework for assessing the contribution of neuroscience to consumer research cannot be used for neuromarketing due to the lack of general converging evidence. However, some neuromarketing findings do support some of it, though it is not sufficient enough to contribute to neuromarketing yet. The implication for neuroscience, and thus for neuromarketing, is the presence of reverse inference. If the activation of a specific area of the brain is relatively selectively, only then it is possible to infer with sufficient confidence that the particular mental process is engaged when there is activation in that area of the brain.


title: 'Free lunch' enhancement for collaborative filtering with factorization machines

abstract: The advantage of Factorization Machines over other factorization models is their ability to easily integrate and efficiently exploit auxiliary information to improve Collaborative Filtering. Until now, this auxiliary information has been drawn from external knowledge sources beyond the user-item matrix. In this paper, we demonstrate that Factorization Machines can exploit additional representations of information inherent in the user-item matrix to improve recommendation performance. We refer to our approach as 'Free Lunch' enhancement since it leverages clusters that are based on information that is present in the user-item matrix, but not otherwise directly exploited during matrix factorization. Borrowing clustering concepts from codebook sharing, our approach can also make use of 'Free Lunch' information inherent in a user-item matrix from a auxiliary domain that is different from the target domain of the recommender. Our approach improves performance both in the joint case, in which the auxiliary and target domains share users, and in the disjoint case, in which they do not. Although 'Free Lunch' enhancement does not apply equally well to any given domain or domain combination, our overall conclusion is that Factorization Machines present an opportunity to exploit information that is ubiquitously present, but commonly under-appreciated by Collaborative Filtering algorithms.


title: End-fire Quasi-Yagi antennas with pattern diversity on LTCC technology for 5G mobile communications

abstract: We have designed two end-fire antennas on LTCC with horizontal and vertical polarizations respectively. The antennas operate at 38GHz, a potential frequency for 5G applications. The horizontally-polarized antenna provide a broadband performance about 27% and 6dB end-fire gain and the vertically-polarized one provide a 12.5% bandwidth and 5dB gain. Both antenna are integrated under a compact substrate. Excellent isolation is achieved between the nearby elements making these antennas suitable for corner elements in 5G mobile system.


title: Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation

abstract: Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems. SGD estimates the gradient by uniform sampling with sample size one. There have been several other works that suggest faster epoch-wise convergence by using weighted non-uniform sampling for better gradient estimates. Unfortunately, the per-iteration cost of maintaining this adaptive distribution for gradient estimation is more than calculating the full gradient itself, which we call the chicken-and-the-egg loop. As a result, the false impression of faster convergence in iterations, in reality, leads to slower convergence in time. In this paper, we break this barrier by providing the first demonstration of a scheme, Locality sensitive hashing (LSH) sampled Stochastic Gradient Descent (LGD), which leads to superior gradient estimation while keeping the sampling cost per iteration similar to that of the uniform sampling. Such an algorithm is possible due to the sampling view of LSH, which came to light recently. As a consequence of superior and fast estimation, we reduce the running time of all existing gradient descent algorithms, that relies on gradient estimates including Adam, Ada-grad, etc. We demonstrate the effectiveness of our proposal with experiments on linear models as well as the non-linear BERT, which is a recent popular deep learning based language representation model.


title: Rapid and accurate 3D selection by progressive refinement

abstract: Issues such as hand and tracker jitter negatively affect user performance with the ray-casting selection technique in 3D environments. This makes it difficult for users to perform tasks that require them to select objects that have a small visible area, since small targets require high levels of precision. We introduce an approach to address this issue that uses progressive refinement of the set of selectable objects to reduce the required precision of the task. We present a design space of progressive refinement techniques and an exemplar technique called Sphere-casting refined by QUAD-menu (SQUAD). We explore the tradeoffs between progressive refinement and immediate selection techniques in an evaluation comparing SQUAD to ray-casting. Both an analytical evaluation based on a distal pointing model and an empirical evaluation demonstrate that progressive refinement selection can be better than immediate selection. SQUAD was much more accurate than ray-casting, and SQUAD was faster than ray-casting with small targets and less cluttered environments.


title: Circuits for bistable spike-timing-dependent plasticity neuromorphic VLSI synapses

abstract: We present analog neuromorphic circuits for implementing bistable synapses with spike-timing-dependent plasticity (STDP) properties. In these types of synapses, the short-term dynamics of the synaptic efficacies are governed by the relative timing of the preand post-synaptic spikes, while on long time scales the efficacies tend asymptotically to either a potentiated state or to a depressed one. Test results from a prototype VLSI chip, containing a learning synapse connected to a preand post-synaptic integrate and fire neuron, demonstrate the synapse’s STDP learning properties, and its long-term bistable characteristics.


title: Lung segmentation in chest radiographs using distance regularized level set and deep-structured learning and inference

abstract: Computer-aided diagnosis of digital chest X-ray (CXR) images critically depends on the automated segmentation of the lungs, which is a challenging problem due to the presence of strong edges at the rib cage and clavicle, the lack of a consistent lung shape among different individuals, and the appearance of the lung apex. From recently published results in this area, hybrid methodologies based on a combination of different techniques (e.g., pixel classification and deformable models) are producing the most accurate lung segmentation results. In this paper, we propose a new methodology for lung segmentation in CXR using a hybrid method based on a combination of distance regularized level set and deep structured inference. This combination brings together the advantages of deep learning methods (robust training with few annotated samples and top-down segmentation with structured inference and learning) and level set methods (use of shape and appearance priors and efficient optimization techniques). Using the publicly available Japanese Society of Radiological Technology (JSRT) dataset, we show that our approach produces the most accurate lung segmentation results in the field. In particular, depending on the initialization used, our methodology produces an average accuracy on JSTR that varies from 94.8% to 98.5%.


title: From machine learning to machine reasoning

abstract: A plausible definition of "reasoning" could be "algebraically manipulating previously acquired knowledge in order to answer a new question". This definition covers first-order logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by first training a character segmenter, an isolated character recognizer, and a language model, using appropriate labelled training sets. Adequately concatenating these modules and fine tuning the resulting system can be viewed as an algebraic operation in a space of models. The resulting model answers a new question, that is, converting the image of a text page into a computer readable text. ::: ::: This observation suggests a conceptual continuity between algebraically rich inference systems, such as logical or probabilistic inference, and simple manipulations, such as the mere concatenation of trainable learning systems. Therefore, instead of trying to bridge the gap between machine learning systems and sophisticated "all-purpose" inference mechanisms, we can instead algebraically enrich the set of manipulations applicable to training systems, and build reasoning capabilities from the ground up.


Prediction of Driving Behavior Through Probabilistic Inference


title: Semantic Arabic Information Retrieval Framework

abstract: The continuous increasing in the amount of the published and stored information requires a special Information Retrieval (IR) frameworks to search and get information accurately and speedily. Currently, keywords-based techniques are commonly used in information retrieval. However, a major drawback of the keywords approach is its inability of handling the polysemy and synonymy phenomenon of the natural language. For instance, the meanings of words and understanding of concepts differ in different communities. Same word use for different concepts (polysemy) or use different words for the same concept (synonymy). Most of information retrieval frameworks have a weakness to deal with the semantics of the words in term of (indexing, Boolean model, Latent Semantic Analysis (LSA) , Latent semantic Index (LSI) and semantic ranking, etc.). Traditional Arabic Information Retrieval (AIR) models performance insufficient with semantic queries, which deal with not only the keywords but also with the context of these keywords. Therefore, there is a need for a semantic information retrieval model with a semantic index structure and ranking algorithm based on semantic index.


title: {\Omega}-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks.

abstract: Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present ${\Omega}$-Net (Omega-Net): a novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image, second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation, and third, a final segmentation is performed on the transformed image. In this work, ${\Omega}$-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA, four-chamber, 4C, two-chamber, 2C), without prior knowledge of the view being segmented. The architecture was trained on a cohort of patients with hypertrophic cardiomyopathy and healthy control subjects. Network performance as measured by weighted foreground intersection-over-union (IoU) was substantially improved in the best-performing ${\Omega}$- Net compared with U-Net segmentation without localization or orientation. In addition, {\Omega}-Net was retrained from scratch on the 2017 MICCAI ACDC dataset, and achieves state-of-the-art results on the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally.


title: Genome analysis of E. coli isolated from Crohn’s disease patients

abstract: BACKGROUND ::: Escherichia coli (E. coli) has been increasingly implicated in the pathogenesis of Crohn's disease (CD). The phylogeny of E. coli isolated from Crohn's disease patients (CDEC) was controversial, and while genotyping results suggested heterogeneity, the sequenced strains of E. coli from CD patients were closely related. ::: ::: ::: RESULTS ::: We performed the shotgun genome sequencing of 28 E. coli isolates from ten CD patients and compared genomes from these isolates with already published genomes of CD strains and other pathogenic and non-pathogenic strains. CDEC was shown to belong to A, B1, B2 and D phylogenetic groups. The plasmid and several operons from the reference CD-associated E. coli strain LF82 were demonstrated to be more often present in CDEC genomes belonging to different phylogenetic groups than in genomes of commensal strains. The operons include carbon-source induced invasion GimA island, prophage I, iron uptake operons I and II, capsular assembly pathogenetic island IV and propanediol and galactitol utilization operons. ::: ::: ::: CONCLUSIONS ::: Our findings suggest that CDEC are phylogenetically diverse. However, some strains isolated from independent sources possess highly similar chromosome or plasmids. Though no CD-specific genes or functional domains were present in all CD-associated strains, some genes and operons are more often found in the genomes of CDEC than in commensal E. coli. They are principally linked to gut colonization and utilization of propanediol and other sugar alcohols.


title: The ADT evaluation method based on MCMC

abstract: This paper proposes an accelerated degradation testing (ADT) evaluation method based on Markov Chain Monte Carlo (MCMC) method. Firstly the degradation model, reliability model and accelerated model of ADT are introduced; secondly, with the information above, the ADT evaluation method based on MCMC is proposed; Thirdly, the evaluation results of this method would be studied and compared with the evaluation results of the maximum likelihood estimation method by two simulation examples. Finally, the method proposed is taken to evaluate the lifetime and reliability of super luminescent diode (SLD) as an engineering application.


title: Additive Pattern Database Heuristics

abstract: We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases (Culberson & Schaeffer, 1998), which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint sub-problems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously (Korf & Felner, 2002) we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective. while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem.


title: 3D unsteady numerical analysis of conjugate heat transport and turbulent/laminar flows in LEC growth of GaAs crystals

abstract: Abstract We present the conjugated 3D unsteady numerical analysis of industrial-scale LEC GaAs crystal growth, including the calculation of heat transfer in the crystal and crucibles, melt convection, and the encapsulant flow. The analysis of unsteady turbulent melt convection is performed in terms of the large eddy simulation approach. A special procedure was introduced into the calculations to predict the geometry of the crystallization front. The results of the 3D unsteady calculations are compared to the results obtained in terms of the conventional steady-state Reynolds averaged approach with respect to the calculation of the geometry of the crystallization front. The effect of convective heat transfer in the encapsulant is specially studied using the 3D unsteady analysis. To investigate details of dynamic interaction between two immiscible liquids having a plane interface, preliminary computational tests were performed in a model setup.


title: R-TTWD: Robust Device-Free Through-The-Wall Detection of Moving Human With WiFi

abstract: Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human–computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit–receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.


title: An image encryption algorithm with a chaotic dynamical system based Sudoku Grid

abstract: Nowadays, increasingly more, it seems that using the most popular games' sets of rules, particularly on new images' encryption algorithms designing branch, leads to crystallization of a new paradigm in the field of cryptography. Motivated by this, present paper aims to assess performances of a digital images' encryption scheme that uses chaos-based Sudoku Grids.


title: A simple and efficient oscillator based read-out scheme for LVDT

abstract: A novel read-out circuitry for Linear Variable Differential Transformers (LVDT) is presented in this paper. The proposed method avoids the use of low distortion sine wave oscillators for primary excitation and the associated phase compensation circuitry. The read-out circuit uses the secondary coil of an LVDT as part of a Colpitts oscillator along with a frequency counter to derive the position information. The proposed method also utilizes ratiometric computation for better linearity. Results of the measurements carried out for the proposed oscillator based read-out circuit are comparable to those obtained from commercially available LVDT signal conditioners. The measurements provide an accuracy of 12 bits over a 26 mm range with an update rate in excess of 4 kS/s, and a linearity error of +/− 0.17% of the Full Scale (FS).


title: Five-Level Diode-Clamped Inverter With Three-Level Boost Converter

abstract: This paper proposes an active front-end solution to balance the dc-link capacitor voltage of the five-level diode-clamped inverter. Capacitor voltage balancing is performed by a three-level boost converter connected to the two inner capacitors of a five-level diode-clamped inverter and additional balancing circuits at the other two outer capacitors. The proposed configuration is tested through simulation and experiment for various load power factor conditions at a high modulation index. The result demonstrates the reliability of the proposed configuration to balance the dc-link capacitor voltage at the desired level.


title: SINR and Throughput of Dense Cellular Networks With Stretched Exponential Path Loss

abstract: Distance-based attenuation is a critical aspect of wireless communications. As opposed to the ubiquitous power-law path loss model, this paper proposes a stretched exponential path loss model that is suitable for short-range communication. In this model, the signal power attenuates over a distance $r$ as $e^{-\alpha r^{\beta}}$, where $\alpha,\beta$ are tunable parameters. Using experimental propagation measurements, we show that the proposed model is accurate for short to moderate distances in the range $r \in (5,300)$ meters and so is a suitable model for dense and ultradense networks. We integrate this path loss model into a downlink cellular network with base stations modeled by a Poisson point process, and derive expressions for the coverage probability, potential throughput, and area spectral efficiency. Although the most general result for coverage probability has a double integral, several special cases are given where the coverage probability has a compact or even closed form. We then show that the potential throughput is maximized for a particular BS density and then collapses to zero for high densities, assuming a fixed SINR threshold. We next prove that the area spectral efficiency, which assumes an adaptive SINR threshold, is non-decreasing with the BS density and converges to a constant for high densities.


title: Graph similarity algorithm evaluation

abstract: This paper considers the problem of Graph Similarity. We propose a benchmark framework which can be used to study and evaluate graph matching algorithms. Each benchmark in the framework generates similarity scores for a pair of graphs, according to how similar or dissimilar they are. Many algorithms exist, but each implementation uses a different emphasis on graph properties emphasised in the similarity scores. Some place more emphasis on number of nodes, while others place more emphasis on number of edges, etc. Our proposed benchmark framework is a set of graph pairs; each pair contains graphs which are similar/dissimilar according to certain properties, as identified by experts. Graph matching algorithms being evaluated take this benchmark as input and, according to the similarity scores generated for each pair in the benchmark, it can be determined what a particular algorithm focuses on and is biased towards. Thus a classification system for graph similarity algorithms is produced.


title: Dynamic Prefetching of Data Tiles for Interactive Visualization

abstract: In this paper, we present ForeCache, a general-purpose tool for exploratory browsing of large datasets. ForeCache utilizes a client-server architecture, where the user interacts with a lightweight client-side interface to browse datasets, and the data to be browsed is retrieved from a DBMS running on a back-end server. We assume a detail-on-demand browsing paradigm, and optimize the back-end support for this paradigm by inserting a separate middleware layer in front of the DBMS. To improve response times, the middleware layer fetches data ahead of the user as she explores a dataset. We consider two different mechanisms for prefetching: (a) learning what to fetch from the user's recent movements, and (b) using data characteristics (e.g., histograms) to find data similar to what the user has viewed in the past. We incorporate these mechanisms into a single prediction engine that adjusts its prediction strategies over time, based on changes in the user's behavior. We evaluated our prediction engine with a user study, and found that our dynamic prefetching strategy provides: (1) significant improvements in overall latency when compared with non-prefetching systems (430% improvement); and (2) substantial improvements in both prediction accuracy (25% improvement) and latency (88% improvement) relative to existing prefetching techniques.


title: A Software-Managed Approach to Die-Stacked DRAM

abstract: Advances in die-stacking (3D) technology have enabled the tight integration of significant quantities of DRAM with high-performance computation logic. How to integrate this technology into the overall architecture of a computing system is an open question. While much recent effort has focused on hardware-based techniques for using die-stacked memory (e.g., caching), in this paper we explore what it takes for a software-driven approach to be effective. First we consider exposing die-stacked DRAM directly to applications, relying on the static partitioning of allocations between fast on-chip and slow off-chip DRAM. We see only marginal benefits from this approach (9% speedup). Next, we explore OS-based page caches that dynamically partition application memory, but we find such approaches to be worse than not having stacked DRAM at all! We analyze the performance bottlenecks in OS page caches, and propose two simple techniques that make the OS approach viable. The first is a hardware-assisted TLB shoot-down, which is a more general mechanism that is valuable beyond stacked DRAM, and enables OS-managed page caches to achieve a 27% speedup, the second is a software-implemented prefetcher that extends classic hardware prefetching algorithms to the page level, leading to 39% speedup. With these simple and lightweight components, the OS page cache can provide 70% of the performance benefit that would be achievable with an ideal and unrealistic system where all of main memory is die-stacked. However, we also found that applications with poor locality (e.g., graph analyses) are not amenable to any page-caching schemes -- whether hardware or software -- and therefore we recommend that the system still provides APIs to the application layers to explicitly control die-stacked DRAM allocations.


title: Large scale predictive process mining and analytics of university degree course data

abstract: For students, in particular freshmen, the degree pathway from semester to semester is not that transparent, although students have a reasonable idea what courses are expected to be taken each semester. An often-pondered question by students is: "what can I expect in the next semester?" More precisely, given the commitment and engagement I presented in this particular course and the respective performance I achieved, can I expect a similar outcome in the next semester in the particular course I selected? Are the demands and expectations in this course much higher so that I need to adjust my commitment and engagement and overall workload if I expect a similar outcome? Is it better to drop a course to manage expectations rather than to (predictably) fail, and perhaps have to leave the degree altogether? Degree and course advisors and student support units find it challenging to provide evidence based advise to students. This paper presents research into educational process mining and student data analytics in a whole university scale approach with the aim of providing insight into the degree pathway questions raised above. The beta-version of our course level degree pathway tool has been used to shed light for university staff and students alike into our university's 1,300 degrees and associated 6 million course enrolments over the past 20 years.


title: A BEME systematic review of the effects of interprofessional education: BEME Guide No. 39.

abstract: BACKGROUND ::: Interprofessional education (IPE) aims to bring together different professionals to learn with, from, and about one another in order to collaborate more effectively in the delivery of safe, high-quality care for patients/clients. Given its potential for improving collaboration and care delivery, there have been repeated calls for the wider-scale implementation of IPE across education and clinical settings. Increasingly, a range of IPE initiatives are being implemented and evaluated which are adding to the growth of evidence for this form of education. ::: ::: ::: AIM ::: The overall aim of this review is to update a previous BEME review published in 2007. In doing so, this update sought to synthesize the evolving nature of the IPE evidence. ::: ::: ::: METHODS ::: Medline, CINAHL, BEI, and ASSIA were searched from May 2005 to June 2014. Also, journal hand searches were undertaken. All potential abstracts and papers were screened by pairs of reviewers to determine inclusion. All included papers were assessed for methodological quality and those deemed as "high quality" were included. The presage-process-product (3P) model and a modified Kirkpatrick model were employed to analyze and synthesize the included studies. ::: ::: ::: RESULTS ::: Twenty-five new IPE studies were included in this update. These studies were added to the 21 studies from the previous review to form a complete data set of 46 high-quality IPE studies. In relation to the 3P model, overall the updated review found that most of the presage and process factors identified from the previous review were further supported in the newer studies. In regard to the products (outcomes) reported, the results from this review continue to show far more positive than neutral or mixed outcomes reported in the included studies. Based on the modified Kirkpatrick model, the included studies suggest that learners respond well to IPE, their attitudes and perceptions of one another improve, and they report increases in collaborative knowledge and skills. There is more limited, but growing, evidence related to changes in behavior, organizational practice, and benefits to patients/clients. ::: ::: ::: CONCLUSIONS ::: This updated review found that key context (presage) and process factors reported in the previous review continue to have resonance on the delivery of IPE. In addition, the newer studies have provided further evidence for the effects on IPE related to a number of different outcomes. Based on these conclusions, a series of key implications for the development of IPE are offered.


title: Attention-based neural network for short-text question answering

abstract: Question answering (QA) has been a popular topic in information retrieval tasks. Several studies rely on classifiers with a large number of handcrafted syntactic and semantic features and various external resources, such as WordNet, which is an English dictionary based on cognitive linguistics. Deep learning approaches have recently gained advanced performance in QA. However, these approaches have to be combined with additional features, such as word overlap. In this work, the factoid query answer retrieval task is introduced; moreover, the effective solving of this task under a deep learning framework is investigated. An attention-based convolutional neural network model is proposed to obtain word- and phrase-level interactive information and generate correct probability to re-rank candidate answers. The performance of the proposed model is compared with other models using the popular benchmark text retrieval conference QA data. Results show that the proposed model can obtain a significant performance improvement.