title: Clustering with feature selection using alternating minimization, Application to computational biology

abstract: This paper deals with unsupervised clustering with feature selection. The problem is to estimate both labels and a sparse projection matrix of weights. To address this combinatorial non-convex problem maintaining a strict control on the sparsity of the matrix of weights, we propose an alternating minimization of the Frobenius norm criterion. We provide a new efficient algorithm named K-sparse which alternates k-means with projection-gradient minimization. The projection-gradient step is a method of splitting type, with exact projection on the $\ell^1$ ball to promote sparsity. The convergence of the gradient-projection step is addressed, and a preliminary analysis of the alternating minimization is made. The Frobenius norm criterion converges as the number of iterates in Algorithm K-sparse goes to infinity. Experiments on Single Cell RNA sequencing datasets show that our method significantly improves the results of PCA k-means, spectral clustering, SIMLR, and Sparcl methods, and achieves a relevant selection of genes. The complexity of K-sparse is linear in the number of samples (cells), so that the method scales up to large datasets.


title: Assisting Versus Repelling Force-Feedback for Learning of a Line Following Task in a Wheelchair

abstract: Previous work has shown that training with the "assist-as-needed" method using a force-feedback joystick can improve the driving performance of children and adults. This paper is the first study to evaluate training with a repelling force versus an assisting force for learning of a line following task in a wheelchair through a force-feedback joystick. We designed a robotic training wheelchair, that can accurately localize itself in the training environment, and implemented assisting and repelling force fields on the force-feedback joystick. The training protocol included three groups. The control (CT) group received no force feedback. The assisting force (AF) group was trained using the "assist-as-needed" paradigm. The repelling force (RF) group was trained with the repelling force field. We observed that both the AF and RF groups improved their driving skills. The error reductions of both groups were not statistically different under the current setting. We believe that this pilot study could provide a promising foundation regarding the effects of a robotic wheelchair training algorithm on motor learning.


title: Quality by Design Based Optimization of a High Performance Liquid Chromatography Method for Assay Determination of Low Concentration Preservatives in Complex Nasal Formulations

abstract: The effects of seven different chromatographic parameters and five sample preparation parameters in a high performance liquid chromatography (HPLC) method for assay determination of benzalkonium chloride (BKC) in a nasal formulation were evaluated using two fractional factorial experimental designs. The design space of the analytical method was modeled using Umetrics Modde software and the optimal method conditions were predicted. The optimum HPLC chromatographic conditions were obtained using a Luna CN column (150 x 4.6 mm, 3 µm). The results show that mobile phase pH, amount of acetonitrile in the mobile phase and column temperature are the most important factors in obtaining good separation of BKC homologs from an interfering peak. In the sample preparation step, the use of an aqueous solution for dissolving the samples was the most important factor since it eliminated the interfering effect of the active compound. The optimal method was validated for linearity, accuracy and precision. The use of experimental designs enables obtaining the maximum amount of information with the least possible number of experiments. Such designs are an economical manner in evaluating a variety of different factors and their interactions.


title: Automatic Construction of a Context-Aware Sentiment Lexicon: An Optimization Approach

abstract: The explosion of Web opinion data has made essential the need for automatic tools to analyze and understand people's sentiments toward different topics. In most sentiment analysis applications, the sentiment lexicon plays a central role. However, it is well known that there is no universally optimal sentiment lexicon since the polarity of words is sensitive to the topic domain. Even worse, in the same domain the same word may indicate different polarities with respect to different aspects. For example, in a laptop review, "large" is negative for the battery aspect while being positive for the screen aspect. In this paper, we focus on the problem of learning a sentiment lexicon that is not only domain specific but also dependent on the aspect in context given an unlabeled opinionated text collection. We propose a novel optimization framework that provides a unified and principled way to combine different sources of information for learning such a context-dependent sentiment lexicon. Experiments on two data sets (hotel reviews and customer feedback surveys on printers) show that our approach can not only identify new sentiment words specific to the given domain but also determine the different polarities of a word depending on the aspect in context. In further quantitative evaluation, our method is proved to be effective in constructing a high quality lexicon by comparing with a human annotated gold standard. In addition, using the learned context-dependent sentiment lexicon improved the accuracy in an aspect-level sentiment classification task.


title: An 800-MHz–6-GHz Software-Defined Wireless Receiver in 90-nm CMOS

abstract: A software-defined radio receiver is designed from a low-power ADC perspective, exploiting programmability of windowed integration sampler and clock-programmable discrete-time analog filters. To cover the major frequency bands in use today, a wideband RF front-end, including the low-noise amplifier (LNA) and a wide tuning-range synthesizer, spanning over 800 MHz to 6 GHz is designed. The wideband LNA provides 18-20 dB of maximum gain and 3-3.5 dB of noise figure over 800 MHz to 6 GHz. A low 1/f noise and high-linearity mixer is designed which utilizes the passive mixer core properties and provides around +70 dBm IIP2 over the bandwidth of operation. The entire receiver circuits are implemented in 90-nm CMOS technology. Programmability of the receiver is tested for GSM and 802.11g standards


title: Comparison of linear precoding schemes for downlink massive MIMO

abstract: We consider the downlink of a time-division duplexing (TDD) multicell multiuser MIMO system where the base stations (BSs) are equipped with a very large number of antennas. Assuming channel estimation through uplink pilots, arbitrary antenna correlation and user distributions, we derive approximations of achievable rates with linear precoding techniques, namely eigenbeamforming (BF) and regularized zero-forcing (RZF). The approximations are tight in the large system limit with an infinitely large number of antennas and user terminals (UTs), but match our simulations for realistic system dimensions. We further show that a simple RZF precoding scheme can achieve the same performance as BF with one order of magnitude fewer antennas in both uncorrelated and correlated fading channels.


title: Reasoning with conditionals: A test of formal

abstract: The four dominant theories of reasoning from conditionals are translated into formal models: The theory of mental models (Johnson-Laird, P. N., & Byrne, R. M. J. (2002). Conditionals: a theory of meaning, pragmatics, and inference. Psychological Review, 109, 646–678), the suppositional theory (Evans, J. S. B. T., & Over, D. E. (2004). If. Oxford: Oxford University Press), a dual-process variant of the model theory (Verschueren, N., Schaeken, W., & d’Ydewalle, G. (2005). A dual-process specification of causal conditional reasoning. Thinking & Reasoning, 11, 278–293), and the probabilistic theory (Oaksford, M., Chater, N., & Larkin, J. (2000). Probabilities and polarity biases in conditional inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 883–899). The first three theories are formalized as multinomial models. The models are applied to the frequencies of patterns of acceptance or rejection across the four basic inferences modus ponens, acceptance of the consequent, denial of the antecedent, and modus tollens. Model fits are assessed for two large data sets, one representing reasoning with abstract, basic conditionals, the other reflecting reasoning with pseudo-realistic causal and non-causal conditionals. The best account of the data was provided by a modified version of the mental-model theory, augmented by directionality, and by the dual-process model.


title: Molecular modeling studies of some substituted chalcone derivatives as cysteine protease inhibitors

abstract: The quantitative structure–activity relationship (QSAR) studies were performed on a series of 42 chalcone derivatives to find out the structural requirements of their antimalarial activities. The multiple linear regression (MLR) and partial least square (PLS) regression methods coupled with various feature selection methods, viz., stepwise (SW), genetic algorithm (GA) and simulated annealing (SA) were applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The statistically significant 2D-QSAR model having r 2 = 0.8892 and q 2 = 0.7508 with pred_r 2 = 0.7403 was developed by SW-MLR and best Group-based QSAR (GQSAR) model having r 2 = 0.7884 and q 2 = 0.7038 with pred_r 2 = 0.7339 was developed by SW-PLS. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbour method, showing good correlative and predictive capabilities in terms of q 2 = 0.6818 and pred_r 2 = 0.7708. A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (Gln36, Cys39, Lys37, Asp35, Trp206) of falcipain-2 enzyme (PDB ID: 3BPF). Both QSAR and docking studies of such derivatives provide guidance for further lead optimization and designing of more potent antimalarial agents.


title: Improving System Efficiency for the More Electric Aircraft: A Look at dc\/dc Converters for the Avionic Onboard dc Microgrid

abstract: The More Electric Aircraft (MEA) initiative aims to increase the penetration of electrical systems in aircraft to decrease weight and further develop overall efficiency and reliability [1]. Newer aircraft, such as the Boeing 787 and the Airbus A380, have more electrical power installed compared with older models, and this trend is not expected to change any time soon. Although most aircraft feature some amount of electrical power, this is often limited to the electronics (i.e., flight or entertainment systems) or auxiliary systems (i.e., lighting or anti-icing). The core systems of the actuation still rely on hydraulic power.


title: EvalUMAP: Towards Comparative Evaluation in User Modeling, Adaptation and Personalization

abstract: There is currently no established or standardized means for the comparative evaluation of algorithms and systems developed by researchers in the User Modeling, Adaptation and Personalization (UMAP) space. The design and establishment of such methodologies has proven to be extremely difficult, but would be highly rewarding, as demonstrated by initiatives such as CLEF, TREC and NTCIR in the Information Retrieval domain. Privacy concerns, the challenges of working with interactive scenarios, and individual differences in behaviour between users must all be addressed in order to facilitate repeatable and comparable evaluation, and to advance research in this domain. In this paper we present EvalUMAP, a new concerted drive towards the establishment of shared challenges for comparative evaluation within the UMAP community.


title: Applying image analysis to assess food aesthetics and uniqueness

abstract: This paper describes our latest work on assessing the aesthetics of food dishes by analyzing its visual appearance. Specifically, our framework builds upon work in the areas of color science, psychology and statistics to enable users to computationally assess whether the image of a dish is visually appealing. Moreover, we further assess the uniqueness of a dish by comparing its color composition against a dish image repository. The assessment engine has been recently integrated into a Google Glass Application which brings a new dimension of wearable computing to this work. Our preliminary experiments with the assessment system have shown encouraging results, and the initial deployment of the Google Glass Application has raised quite some interests.


title: Protection Method for Data Communication between ADS-B Sensor and Next-Generation Air Traffic Control Systems

abstract: Communications, Navigation, Surveillance/Air Traffic Management (CNS/ATM) systems utilize digital technologies, satellite systems, and various levels of automation to facilitate seamless global air traffic management. Automatic Dependent Surveillance-Broadcast (ADS-B), the core component of CNS/ATM, broadcasts important monitoring information, such as the location, altitude, and direction of aircraft, to the ground. However, ADS-B data are transmitted in an unencrypted (or unprotected) communication channel between ADS-B sensors and Air Traffic Control (ATC). Consequently, these data are vulnerable to security threats, such as spoofing, eavesdropping, and data modification. In this paper, we propose a method that protects the ADS-B data transmitted between ADS-B sensors and ATC using Simple Public Key Infrastructure (SPKI) certificates and symmetric cryptography. The SPKI certificates are used to grant transmission authorization to the ADS-B sensors, while symmetric cryptography is used to encrypt/decrypt the ADS-B data transmitted between the ADS-B sensors and ATC. The proposed security framework comprises an ADS-B sensor authentication module, an encrypted data processing module, and an ADS-B sensor information management module. We believe that application of the proposed security framework to CNS/ATM will enable it to effectively obviate security threats, such as ground station flood denial, ground station target ghost injection, and ADS-B data modification.


title: Reliability Modeling and Analysis of Service-Oriented Architectures

abstract: Service selection and composition are central activities in service-oriented computing, and the prediction of the QoS attributes of a Service-Oriented Architecture (SOAs) plays a key role to appropriately drive these activities. Software composition driven by QoS criteria (e.g., optimization of performance, maximization of reliability) has been mostly studied in the Component-Based Software Engineering domain, whereas methodological approaches are not well established in the service-oriented area. Indeed, prediction methodologies for service-oriented systems should be supported by automated and efficient tools to remain compliant with the requirement that most of the activities connected with service discovery and composition must be performed automatically. Moreover, the adopted implementation should respect the autonomy and independence of each provider of the services we want to include in our analysis. In this chapter we focus on the modeling and analysis of the reliability attribute in Service-Oriented Architectures, with particular emphasis on two aspects of this problem: (i) the mathematical foundations of reliability modeling of a Service-Oriented Architecture as a function of the reliability characteristics of its basic elements and (ii) the automatization of service composition driven by reliability criteria.


title: Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability

abstract: Anomaly detection (AD) task corresponds to identifying the true anomalies from a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies, which are then analyzed by a human to discover the true anomalies. However, this process can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. ::: In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered. We make four main contributions towards this goal. First, we present an important insight that explains the practical successes of AD ensembles and how ensembles are naturally suited for active learning. Second, we present several algorithms for active learning with tree-based AD ensembles. These algorithms help us to improve the diversity of discovered anomalies, generate rule sets for improved interpretability of anomalous instances, and adapt to streaming data settings in a principled manner. Third, we present a novel algorithm called GLocalized Anomaly Detection (GLAD) for active learning with generic AD ensembles. GLAD allows end-users to retain the use of simple and understandable global anomaly detectors by automatically learning their local relevance to specific data instances using label feedback. Fourth, we present extensive experiments to evaluate our insights and algorithms. Our results show that in addition to discovering significantly more anomalies than state-of-the-art unsupervised baselines, our active learning algorithms under the streaming-data setup are competitive with the batch setup.


title: A Cubic Unsharp Masking Technique for Contrast Enhancement

abstract: Abstract The cubic unsharp masking method is introduced in this paper. It is demonstrated, through both a statistical study and some computer simulations, that the proposed method has a much reduced noise sensitivity with respect to the linear unsharp masking technique and it permits to obtain perceptually pleasant results. The proposed operator also compares favourably with other algorithms which recently have been studied to improve the behaviour of the unsharp masking approach.


title: Enterprise architecture principles: literature review and research directions

abstract: This paper presents a literature review on enterprise architecture principles. The results show that there are various gaps in the research literature: No accepted definition of enterprise architecture principles has emerged yet. A detailed conceptual framework is still lacking. Business principles, IT principles and enterprise architecture principles are often mixed up. Research into generic design principles is still in its infancy. Our review illustrates the necessity to conduct more research on enterprise architecture principles. We describe conceptual foundations and provide guidance for further research in this field.


Low Cost Eye Tracking: The Current Panorama


title: Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard

abstract: This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full-scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboard’s patterns. Powell’s method is introduced for resolving the discontinuity problem in optimization. The corners of the fitted model are considered as the 3D corners of the chessboard. Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem. The corresponding 3D-2D points are used to calculate the absolute pose of the two sensors with Unified Perspective-n-Point (UPnP). Further, the calculated parameters are regarded as initial values and are refined using the Levenberg-Marquardt method. The performance of the proposed corner detection method from the 3D point cloud is evaluated using simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR and a Ladybug3 camera under the proposed re-projection error metric, qualitatively and quantitatively demonstrate the accuracy and stability of the final extrinsic calibration parameters.


title: Preparation of highly selective, sensitive and stable hydrogen sensors based on Pd-doped tungsten trioxide

abstract: Abstract In this paper, highly selective, sensitive and stable hydrogen sensors based on Pd-doped tungsten trioxide were successfully prepared by promoting way. Two different morphologies of WO 3 were obtained by chemical route synthesis (WO 3 nano-particles and WO3 micro-lamellae), their morphologies were observed by SEM and TEM microscopy and their crystalline structures were determined by X-ray diffraction (XRD). To prepare Pd-doped WO3 films, palladium chloride salt/WO 3 was highly dispersed in organic solution using sonication, the resultant Pd–WO 3 dispersion was drop coated on the alumina substrate fitted with gold electrodes and annealed at 500 ∘ C. The gas sensing properties of Pd-doped WO 3 films to reducing gases (NH 3 , CO, H 2 ) were studied at different operating temperatures. The sensors have shown high sensitivity and selectivity to hydrogen at working temperature of 200 ∘ C. The sensors with low Pd loading (0.4 wt%) has shown the best stability and reproducibility. In these last conditions, the sensors have shown no obvious response to ammonia (≤1 ppm) and insignificant response to carbon monoxide (≤200 ppm).


title: Cortical High-Density Counterstream Architectures

abstract: Small-world networks provide an appealing description of cortical architecture owing to their capacity for integration and segregation combined with an economy of connectivity. Previous reports of low-density interareal graphs and apparent small-world properties are challenged by data that reveal high-density cortical graphs in which economy of connections is achieved by weight heterogeneity and distance-weight correlations. These properties define a model that predicts many binary and weighted features of the cortical network including a core-periphery, a typical feature of self-organizing information processing systems. Feedback and feedforward pathways between areas exhibit a dual counterstream organization, and their integration into local circuits constrains cortical computation. Here, we propose a bow-tie representation of interareal architecture derived from the hierarchical laminar weights of pathways between the high-efficiency dense core and periphery.


title: Competition between local and electronic markets: How the benefit of buying online depends on where you live

abstract: Our paper shows that the parameters in existing theoretical models of channel substitution such as offline transportation cost, online disutility cost, and the prices of online and offline retailers interact to determine consumer choice of channels. In this way, our results provide empirical support for many such models. In particular, we empirically examine the trade-off between the benefits of buying online and the benefits of buying in a local retail store. How does a consumer's physical location shape the relative benefits of buying from the online world? We explore this problem using data from Amazon.com on the top-selling books for 1,497 unique locations in the United States for 10 months ending in January 2006. We show that when a store opens locally, people substitute away from online purchasing, even controlling for product-specific preferences by location. These estimates are economically large, suggesting that the disutility costs of purchasing online are substantial and that offline transportation costs matter. We also show that offline entry decreases consumers' sensitivity to online price discounts. However, we find no consistent evidence that the breadth of the product line at a local retail store affects purchases.


title: The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections

abstract: Internet search rankings have a significant impact on consumer choices, mainly because users trust and choose higher-ranked results more than lower-ranked results. Given the apparent power of search rankings, we asked whether they could be manipulated to alter the preferences of undecided voters in democratic elections. Here we report the results of five relevant double-blind, randomized controlled experiments, using a total of 4,556 undecided voters representing diverse demographic characteristics of the voting populations of the United States and India. The fifth experiment is especially notable in that it was conducted with eligible voters throughout India in the midst of India’s 2014 Lok Sabha elections just before the final votes were cast. The results of these experiments demonstrate that (i) biased search rankings can shift the voting preferences of undecided voters by 20% or more, (ii) the shift can be much higher in some demographic groups, and (iii) search ranking bias can be masked so that people show no awareness of the manipulation. We call this type of influence, which might be applicable to a variety of attitudes and beliefs, the search engine manipulation effect. Given that many elections are won by small margins, our results suggest that a search engine company has the power to influence the results of a substantial number of elections with impunity. The impact of such manipulations would be especially large in countries dominated by a single search engine company.


title: Classification Of Spatio-Temporal Patterns With Applications To Recognition Of Sonar Sequences

abstract: Many tasks performed by humans and animals involve decision-making and behavioral responses to spatiotemporally patterned stimuli. Thus the recognition and processing of time-varying signals is fundamental to a wide range of cognitive processes. Classification of such signals is also basic to many engineering applications such as speech recognition, seismic event detection, sonar classification and real-time control (Lippmann, 1989; Maren, 1990).


title: Entity Linking to One Thousand Knowledge Bases

abstract: We address the task of entity linking to multiple knowledge bases (KB). In particular, we investigate the use of over one thousand domain-specific KBs derived from Wikia.com collections in conjunction with the Wikipedia collection as a background-knowledge repository. Our system employs a two-step approach: for each document, a supervised model with a large set of features detects whether there exists a Wikia collection whose domain matches the document; when such a collection is available, the system extracts and resolves the entity mentions in the document to the KB obtained by merging the Wikipedia KB and the KB corresponding to the matched Wikia collection. Otherwise, the system employs only the background KB for analysis, in a standard entity-detection-and-linking framework. On a Web news articles dataset, our system achieves 90% precision in detecting domain-accurate Wikia collections while providing also high linking accuracy (93%) to the KB of the matched Wikia collection.


title: Project correlation in portfolio theory

abstract: Abstract The use of portfolio theory allows the consideration of correlation between projects and may rationalize the selection of the projects and capital budgeting. Positive coefficient of correlation increases the risk for a portfolio, while negative coefficient decreases the risk. On the other hand, the expected return of a portfolio may increase or decrease due to project correlation. The correlation between projects is due to several casual factors which are of different importance and contribution to project correlation. The importance and the level of contribution of each factor can be estimated based on experience and judgement. Experience and judgement may easily be expressed in semantic measures rather than mathematical terms. Classical portfolio theory fails to incorporate subjective information. The semantic measures can be translated into mathematical values using the fuzzy set theory. A method by which project correlation may be estimated based on experience and judgement is proposed. The m...


title: An automatic laser scanning system for accurate 3D reconstruction of indoor scenes

abstract: 3D reconstruction of indoor scenes is important for lots of applications like SLAM (Simultaneous Localization and Mapping), VR (virtual reality) and AR (augmented reality) etc. This paper presents a precise and low-cost 3D reconstruction system, which consists of a laser emitter, a monocular camera and a turntable. The system is firstly calibrated to determine the intrinsic and extrinsic parameters of the camera and laser plane. By rotating the scanning device, a serial of laser strip images can be obtained. With the proposed point cloud registration algorithm, multiple 3D profiles of the target scene can be computed precisely and aligned automatically. As a result, a complete 3D model of the indoor scene with color information and high accuracy can be obtained. In the experiments, some real scenes are reconstructed and evaluated from the aspects of both visual quality and measurement accuracy.


title: Examining Representational Similarity in ConvNets and the Primate Visual Cortex

abstract: We compare several ConvNets with different depth and regularization techniques with multi-unit macaque IT cortex recordings and assess the impact of the same on representational similarity with the primate visual cortex. We find that with increasing depth and validation performance, ConvNet features are closer to cortical IT representations.


title: Exploring complex networks

abstract: The study of networks pervades all of science, from neurobiology to statistical physics. The most basic issues are structural: how does one characterize the wiring diagram of a food web or the Internet or the metabolic network of the bacterium Escherichia coli? Are there any unifying principles underlying their topology? From the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems — be they neurons, power stations or lasers — will behave collectively, given their individual dynamics and coupling architecture. Researchers are only now beginning to unravel the structure and dynamics of complex networks.


title: Trichoscopy in alopecias: Diagnosis simplified

abstract: Trichoscopy is the term coined for dermoscopic imaging of the scalp and hair. This novel diagnostic technique, both simple and non-invasive, can be used as a handy bed side tool for diagnosing common hair and scalp disorders. Trichoscopic observations can be broadly grouped as hair signs, vascular patterns, pigment patterns and interfollicular patterns. In this article, we have briefly described the trichoscopic findings in the common categories of cicatricial and non-cicatricial alopecias such as androgenetic alopecia, alopecia areata, telogen effluvium, tinea capitis, trichotillomania, lichen planopilaris, discoid lupus erythematosus and hair shaft disorders. Besides diagnosing alopecia, it has the potential for obviating unnecessary biopsies and when a biopsy is still needed it is helpful in choosing an ideal biopsy site. Moreover, trichoscopy is a valuable tool for evaluating the treatment response photographically at each follow-up. The last statement here is deleted as asked.


title: Comprehensive approach for prediction and assessment of power system transient stability in real-time

abstract: Secure operation of power systems is receiving a renewed interest due to considerable structural changes and greater uncertainties. Thus, motivated by implementation of wide-area measurement systems in power systems worldwide, recent research work is being devoted to the development of enhanced tools for fast security assessment within reduced time frames. The work presented in this paper introduces an approach that exploits the post-disturbance data acquired via phasor measurement units (PMUs) by using a finite difference based method for the prediction of rotor angles, data-mining-based clustering for identification of critical machines, and single-machine-infinite bus (SMIB) transformation to evaluate the system transient stability in terms of unstable margin and time to instability. Numerical results obtained by applying the approach on the New England test system demonstrates the feasibility and effectiveness that could be achieved in predicting the critical machines and unstable conditions, which is also of great value for defining suitable emergency control actions.


title: A compact association of particle filtering and kernel based object tracking

abstract: Particle filtering (PF) and kernel based object tracking (KBOT) algorithms have shown their promises in a wide range of visual tracking contexts. This paper mainly addresses the association of PF and KBOT. Unlike other related association approaches which usually directly use KBOT to refine the position states of propagated particles for more accurate mode seeking, we elucidate the problem of what kind of particles is suitable for employing KBOT to refine their position states from a theoretical point of view. In accordance with the theoretical analysis, a two-stage solution is also proposed to resample propagated particles that are suitable for invoking KBOT from a computational perspective. The incremental Bhattacharyya dissimilarity (IBD) based stage is designed to consistently distinguish the particles located in the object region from the others placed in the background, while the matrix condition number based stage is formulated to further eliminate the particles positioned at the ill-posed conditions for running KBOT. Once the appropriate particles are obtained, constrained gradient based mean shift optimization enables us to efficiently refine the particles' position states. Besides, a state transition model embodying object-scale oriented information and prior motion cues is presented to adapt to fast movement scenarios. These ingredients lead to a new tracking algorithm. Experiments demonstrate that the proposed association approach is more robust to handle complex tracking conditions in comparison with related methods. Also, a limited number of particles are used in our association algorithm to maintain multiple hypotheses.


title: Features selection in video fall detection

abstract: Falls are a common problem for old people. They can result in dangerous consequences even death. Therefore, automatic tools for fall detection using camera vision can be very useful for helping the elderly. These methods are based on analyzing extracted features. Different features are used such as vertical and horizontal gradient, motion history of image, shape analysis and posture. In this paper, we try to do an investigation of many proposed methods for the fall detection and compare their performances.


title: 3D Solid Modeling and Reveal in CNC Simulation Based on the Improved Voxel Model

abstract: In the process of dealing with the cutting calculation of Multi-axis CNC Simulation, the traditional Voxel Model not only will cost large computation time when judging whether the cutting happens or not, but also the data points may occupy greater storage space. So it cannot satisfy the requirement of real-time emulation, In the construction method of Compressed Voxel Model, it can satisfy the need of Multi-axis CNC Simulation, and storage space is relatively small. Also the model reconstruction speed is faster, but the Boolean computation in the cutting judgment is very complex, so it affects the real-time of CNC Simulation indirectly. Aimed at the shortcomings of these methods, we propose an improved solid modeling technique based on the Voxel model, which can meet the demand of real-time in cutting computation and Graphic display speed.


title: The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective

abstract: Recently, deep learning, an emerging machine learning technique, is garnering a lot of research attention in several computer science areas. However, to the best of our knowledge, its application to improve heterogeneous network traffic control (which is an important and challenging area by its own merit) has yet to appear because of the difficult challenge in characterizing the appropriate input and output patterns for a deep learning system to correctly reflect the highly dynamic nature of large-scale heterogeneous networks. In this vein, in this article, we propose appropriate input and output characterizations of heterogeneous network traffic and propose a supervised deep neural network system. We describe how our proposed system works and how it differs from traditional neural networks. Also, preliminary results are reported that demonstrate the encouraging performance of our proposed deep learning system compared to a benchmark routing strategy (Open Shortest Path First (OSPF)) in terms of significantly better signaling overhead, throughput, and delay.


title: Biochemistry of Statins.

abstract: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. Elevated blood lipids may be a major risk factor for CVD. Due to consistent and robust association of higher low-density lipoprotein (LDL)-cholesterol levels with CVD across experimental and epidemiologic studies, therapeutic strategies to decrease risk have focused on LDL-cholesterol reduction as the primary goal. Current medication options for lipid-lowering therapy include statins, bile acid sequestrants, a cholesterol-absorption inhibitor, fibrates, nicotinic acid, and omega-3 fatty acids, which all have various mechanisms of action and pharmacokinetic properties. The most widely prescribed lipid-lowering agents are the HMG-CoA reductase inhibitors, or statins. Since their introduction in the 1980s, statins have emerged as the one of the best-selling medication classes to date, with numerous trials demonstrating powerful efficacy in preventing cardiovascular outcomes (Kapur and Musunuru, 2008 [1]). The statins are commonly used in the treatment of hypercholesterolemia and mixed hyperlipidemia. This chapter focuses on the biochemistry of statins including their structures, pharmacokinetics, and mechanism of actions as well as the potential adverse reactions linked to their clinical uses.


title: An orbiting sample capture and orientation system architecture for potential mars sample return

abstract: An orbiting sample capture and orientation system architecture for a Rendezvous and Orbiting Sample Capture System (ROCS) concept was developed to enable spacecraft-based, on-orbit capture, orientation, and transfer of a Mars sample container into a containment vessel as part of a potential Mars Sample Return (MSR) campaign. An analysis of the system functions, requirements, interactions, and constraints was performed. A trade study was carried out on relevant technologies, and a set of evaluation criteria was developed to help determine the most feasible concepts for implementation. The MArs CApture and ReOrientation for the potential NExt Mars Orbiter (MACARONE) concept is proposed as a promising system architecture for the ROCS Capture and Orient Module (COM). The concept uses a sliding trap door for Orbiting Sample (OS) capture, a Motorized Cups Mechanism for OS orientation, and a 2 DOF Turret Arm with a paddle for transferring the OS into a containment vessel. This approach facilitates modularity, development flexibility, testability in a 1G environment, analyzability without the need to simulate or test for 0G contact dynamics, ability to encapsulate potential dust surrounding the OS, and ability to be ejected to reduce the probability of Earth exposure to unsterilized Mars material.


title: Solid Waste Collection Routes Optimization via GIS Techniques in Ipoh City, Malaysia

abstract: Abstract Waste collection becomes more complex in developing countries in terms oflogistic, fuel and labor cost and air pollutantsemission. In this study, solid waste collection routes optimizationusing Geographical Information System (GIS-ArcView) was investigated. Five routes were selected in different area of Ipoh city for pilot study and the present routes were optimized to reduce the length of the routes and consequently the time taken to complete the collection. Results indicate up to 22% length minimization in the routes. The collection duration was also reduced from 6934s to 4602 s.


title: Power Density Limits and Design Trends of High-Speed Permanent Magnet Synchronous Machines

abstract: Electrical machines will always be a subcomponent in a larger system and should therefore be designed in conjunction with other major parts. Only a limited set of parameters, such as weight, size, or speed, is often relevant at a system level, but accurately determining those parameters requires performing a time-consuming detailed design of the electrical machine. This conflicts with a top down system design approach, where initially many different system topologies need to be considered. Hence, there is a need for a consistent and detailed description of the dependency of the interface parameters on machine properties. This paper proposes a method to obtain detailed design trends of electrical machines with a very high level of detail, based on large-scale application of multiobjective optimization using finite element models. The method is demonstrated by quantifying the power density of surface-mounted permanent magnet (SPM) machines versus rotor surface speed, power level, and cooling approach. It is found that power density strongly depends on cooling approach, followed by rotor surface speed, but barely on power level. Ultimately, this paper can be used both as an example when performing a similar design study and as a reference on detailed high-level and low-level machine design trends.


title: On schema matching with opaque column names and data values

abstract: Most previous solutions to the schema matching problem rely in some fashion upon identifying "similar" column names in the schemas to be matched, or by recognizing common domains in the data stored in the schemas. While each of these approaches is valuable in many cases, they are not infallible, and there exist instances of the schema matching problem for which they do not even apply. Such problem instances typically arise when the column names in the schemas and the data in the columns are "opaque" or very difficult to interpret. In this paper we propose a two-step technique that works even in the presence of opaque column names and data values. In the first step, we measure the pair-wise attribute correlations in the tables to be matched and construct a dependency graph using mutual information as a measure of the dependency between attributes. In the second stage, we find matching node pairs in the dependency graphs by running a graph matching algorithm. We validate our approach with an experimental study, the results of which suggest that such an approach can be a useful addition to a set of (semi) automatic schema matching techniques.


title: Reduced-Reference Image Quality Assessment in Free-Energy Principle and Sparse Representation

abstract: The free-energy principle in recent studies of brain theory and neuroscience models the perception and understanding of the outside scene as an active inference process, in which the brain tries to account for the visual scene with an internal generative model. Specifically, with the internal generative model, the brain yields corresponding predictions for its encountered visual scenes. Then, the discrepancy between the visual input and its brain prediction should be closely related to the quality of perceptions. On the other hand, sparse representation has been evidenced to resemble the strategy of the primary visual cortex in the brain for representing natural images. With the strong neurobiological support for sparse representation, in this paper, we approximate the internal generative model with sparse representation and propose an image quality metric accordingly, which is named FSI (free-energy principle and sparse representation-based index for image quality assessment). In FSI, the reference and distorted images are, respectively, predicted by the sparse representation at first. Then, the difference between the entropies of the prediction discrepancies is defined to measure the image quality. Experimental results on four large-scale image databases confirm the effectiveness of the FSI and its superiority over representative image quality assessment methods. The FSI belongs to reduced-reference methods, and it only needs a single number from the reference image for quality estimation.


title: A framework of energy efficient mobile sensing for automatic user state recognition

abstract: Urban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, continuously capturing this contextual information on mobile devices consumes huge amount of energy. In this paper, we present a novel design framework for an Energy Efficient Mobile Sensing System (EEMSS). EEMSS uses hierarchical sensor management strategy to recognize user states as well as to detect state transitions. By powering only a minimum set of sensors and using appropriate sensor duty cycles EEMSS significantly improves device battery life. We present the design, implementation, and evaluation of EEMSS that automatically recognizes a set of users' daily activities in real time using sensors on an off-the-shelf high-end smart phone. Evaluation of EEMSS with 10 users over one week shows that our approach increases the device battery life by more than 75% while maintaining both high accuracy and low latency in identifying transitions between end-user activities.


Modeling soil: realtime dynamic models for soil slippage and manipulation


title: An Efficient Computational Architecture for a Collision Early-Warning System for Vehicles, Pedestrians, and Bicyclists

abstract: We describe a computational architecture of a collision early-warning system for vehicles and other principals. Early-warnings allow drivers to make good judgments and to avoid emergency stopping or dangerous maneuvering. With many principals (vehicles, pedestrians, bicyclists, etc.) coexisting in a dense intersection, it is difficult to predict, even a few seconds in advance, since there are many possible scenarios. It is a major challenge to manage computational resources and human attention resources so that only the more plausible collisions are tracked, and of those, only the most critical collisions prompt warnings to drivers. In this paper, we propose a two-stage collision risk assessment process, including the following: 1) a preliminary assessment via simple efficient geometric computations, which thoroughly considers surrounding principals and identifies likely potential accidents, and 2) a specialized assessment that computes more accurate collision probabilities via sophisticated statistical inference. The whole process delivers an expected utility assessment to available user interfaces (UIs), allowing the UIs to make discriminating choices of when to warn drivers or other principals.


title: FATODE: A Library for Forward, Adjoint, and Tangent Linear Integration of ODEs

abstract: Fatode is a Fortran library for the integration of ordinary differential equations with direct and adjoint sensitivity analysis capabilities. The paper describes the capabilities, implementation, code organization, and usage of this package. Fatode implements four families of methods: explicit Runge--Kutta for nonstiff problems, fully implicit Runge--Kutta, singly diagonally implicit Runge--Kutta, and Rosenbrock for stiff problems. Each family contains several methods with different orders of accuracy; users can add new methods by simply providing their coefficients. For each family the forward, adjoint, and tangent linear models are implemented. General purpose solvers for dense and sparse linear algebra are used; users can easily incorporate problem-tailored linear algebra routines. The performance of the package is demonstrated on several test problems. To the best of our knowledge Fatode is the first publicly available general purpose package that offers forward and adjoint sensitivity analysis capabi...


title: REGAL: Representation Learning-based Graph Alignment

abstract: Problems involving multiple networks are prevalent in many scientific and other domains. In particular, network alignment, or the task of identifying corresponding nodes in different networks, has applications across the social and natural sciences. Motivated by recent advancements in node representation learning for single-graph tasks, we propose REGAL (REpresentation learning-based Graph ALignment), a framework that leverages the power of automatically-learned node representations to match nodes across different graphs. Within REGAL we devise xNetMF, an elegant and principled node embedding formulation that uniquely generalizes to multi-network problems. Our results demonstrate the utility and promise of unsupervised representation learning-based network alignment in terms of both speed and accuracy. REGAL runs up to 30x faster in the representation learning stage than comparable methods, outperforms existing network alignment methods by 20 to 30% accuracy on average, and scales to networks with millions of nodes each.


Correction to "Electromagnetic energy propagation in power electronic converters: toward future electromagnetic integration"


title: Cyber-physical System Risk Assessment

abstract: Cyber Physical System (CPS) is a combination of physical systems with cyber systems, where there is a tight coupling between the two systems. It is widely used in critical national infrastructure, such as electric power, petroleum and chemical industries. Once an attack against the CPS obtains success, the consequence will be unimaginable. A well-designed risk assessment of CPS will provide an overall view of CPS security status and support efficient allocations of safeguard resources. Though there is much relationship between CPS and IT system, they are still different in various aspects, especially the requirement for real-time. Therefore, traditional risk assessment method for IT system can't be directly applied in CPS. New ideas on CPS risk assessment are in urgent need and one idea about this is addressed in this paper. Firstly, it presents a depict description of a three-level CPS architecture and makes an analysis on the corresponding security features in each level. Secondly, it sums up traditional risk assessment methods analyzes the differences between cyber physical system security and traditional IT system security. Finally, the authors blaze a trail under the new perspective of CPS after breaking the restriction of traditional risk assessment methods and propose a risk assessment idea for CPS.


title: A Convex Framework for Fair Regression

abstract: We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the rang from notions of group fairness to strong individual fairness. By varying the weight on the fairness regularizer, we can compute the efficient frontier of the accuracy-fairness trade-off on any given dataset, and we measure the severity of this trade-off via a numerical quantity we call the Price of Fairness (PoF). The centerpiece of our results is an extensive comparative study of the PoF across six different datasets in which fairness is a primary consideration.


title: On the Instability of Bitcoin Without the Block Reward

abstract: Bitcoin provides two incentives for miners: block rewards and transaction fees. The former accounts for the vast majority of miner revenues at the beginning of the system, but it is expected to transition to the latter as the block rewards dwindle. There has been an implicit belief that whether miners are paid by block rewards or transaction fees does not affect the security of the block chain. We show that this is not the case. Our key insight is that with only transaction fees, the variance of the block reward is very high due to the exponentially distributed block arrival time, and it becomes attractive to fork a "wealthy" block to "steal" the rewards therein. We show that this results in an equilibrium with undesirable properties for Bitcoin's security and performance, and even non-equilibria in some circumstances. We also revisit selfish mining and show that it can be made profitable for a miner with an arbitrarily low hash power share, and who is arbitrarily poorly connected within the network. Our results are derived from theoretical analysis and confirmed by a new Bitcoin mining simulator that may be of independent interest. We discuss the troubling implications of our results for Bitcoin's future security and draw lessons for the design of new cryptocurrencies.


title: Syntactic Representation Learning for Open Information Extraction on Web

abstract: This paper proposes a representation learning based method to discover new relations between entities from web, which is more general than existing Open Information Extraction(OIE) methods. Given dependency sequences on the expandPath as input, a convolutional neural network(CNN) is adopted to learn the representation layer features of the syntactic dependency patterns which indicate the relations. Experimental results show that compared with the state-of-art OIE methods, the proposed method obviously improves recall without much expense of precision.


title: From the ventral to the dorsal striatum: Devolving views of their roles in drug addiction

abstract: Abstract We revisit our hypothesis that drug addiction can be viewed as the endpoint of a series of transitions from initial voluntarily drug use to habitual, and ultimately compulsive drug use. We especially focus on the transitions in striatal control over drug seeking behaviour that underlie these transitions since functional heterogeneity of the striatum was a key area of Ann Kelley's research interests and one in which she made enormous contributions. We also discuss the hypothesis in light of recent data that the emergence of a compulsive drug seeking habit both reflects a shift to dorsal striatal control over behaviour and impaired prefontal cortical inhibitory control mechanisms. We further discuss aspects of the vulnerability to compulsive drug use and in particular the impact of impulsivity. In writing this review we acknowledge the untimely death of an outstanding scientist and a dear personal friend.


Automatic Marker Field Calibration


title: Neuro-Symbolic Program Synthesis

abstract: Recent years have seen the proposal of a number of neural architectures for the problem of Program Induction. Given a set of input-output examples, these architectures are able to learn mappings that generalize to new test inputs. While achieving impressive results, these approaches have a number of important limitations: (a) they are computationally expensive and hard to train, (b) a model has to be trained for each task (program) separately, and (c) it is hard to interpret or verify the correctness of the learnt mapping (as it is defined by a neural network). In this paper, we propose a novel technique, Neuro-Symbolic Program Synthesis, to overcome the above-mentioned problems. Once trained, our approach can automatically construct computer programs in a domain-specific language that are consistent with a set of input-output examples provided at test time. Our method is based on two novel neural modules. The first module, called the cross correlation I/O network, given a set of input-output examples, produces a continuous representation of the set of I/O examples. The second module, the Recursive-Reverse-Recursive Neural Network (R3NN), given the continuous representation of the examples, synthesizes a program by incrementally expanding partial programs. We demonstrate the effectiveness of our approach by applying it to the rich and complex domain of regular expression based string transformations. Experiments show that the R3NN model is not only able to construct programs from new input-output examples, but it is also able to construct new programs for tasks that it had never observed before during training.


title: A Smooth Path Tracking Algorithm for Wheeled Mobile Robots with Dynamic Constraints

abstract: In order to avoid wheel slippage or mechanical damage during the mobile robot navigation, it is necessary tosmoothly change driving velocity or direction of the mobile robot. This means that dynamic constraints of the mobile robotshould be considered in the design of path tracking algorithm. In the study, a path tracking problem is formulated asfollowing a virtual target vehicle which is assumed to move exactly along the path with specified velocity. The drivingvelocity control law is designed basing on bang-bang control considering the acceleration bounds of driving wheels. Thesteering control law is designed by combining the bang-bang control with an intermediate path called the landing curve whichguides the robot to smoothly land on the virtual target's tangential line. The curvature and convergence analyses providesufficient stability conditions for the proposed path tracking controller. A series of path tracking simulations and experimentsconducted for a two-wheel driven mobile robot show the validity of the proposed algorithm.


title: Smartphone addiction among university students in the light of some variables

abstract: We explored the frequency and indices of smartphone addiction in a group of King Saud University students and investigated whether there were differences in smartphone addiction based on gender, social status, educational level, monthly income and hours of daily use. We developed a questionnaire probing smartphone addiction consisting of five dimensions: 1) overuse of smartphone, 2) the psychological-social dimension, 3) the health dimension, 4) preoccupation with smartphones, and 5) the technological dimension. After being validated, the questionnaire was administered to 416 students, both male and female, at King Saud University. Results revealed that addiction percentage among participants was 48%. The order of smartphone addiction indices were as follows: overuse of smartphone, the technological dimension, the psychological-social dimension, preoccupation with smartphones, and the health dimension. Significant gender differences were found in the degree of addiction on the whole questionnaire and all of its dimensions with the exception of the technological dimension in favor of males. Significant differences by social status were found in favor of the unmarried. Bachelor degree students were found to have the highest degree of addiction. Significant differences by hours of daily use were also detected in favor of participants using the smartphone for more than 4źh a day. As to the monthly income dimension, significant differences were found on the health dimension in favor of participants with lower monthly income. The addiction percentage among participants was 48%.There are Significant gender and social status differences were found in the degree of addiction.There are significant differences due to monthly income were found in the degree of addiction.Bachelor degree students were found to have the highest degree of addiction.There are significant differences due to hours of daily use were found in degree of addiction.


title: An Improved Control Strategy for Grid-Connected Voltage Source Inverters With an LCL Filter

abstract: This paper proposes a novel control strategy for grid-connected voltage source inverters (VSI) with an LCL-filter. The strategy "split" the capacitor of LCL-filter into two parts, and the current flowing between these two parts is measured and used as the feedback of a current regulator to stabilize and improve the system performances. By this way, the inverter control system is simplified from third-order to first-order, and the close loop control system can easily be optimized for minimum steady-state error and current harmonic distortion. The characteristics of the inverter system with the proposed controller are investigated and compared with the traditional strategy. Simulations results are provided, and the new current control strategy has been experimentally tested on a 5kW fuel cell inverter.


title: A clique-based method for mining fuzzy graph patterns in anti-money laundering systems

abstract: In this paper, we present a clique-based method for mining fuzzy graph patterns of money laundering and financing terrorism. The method will contribute to a new generation of intelligent anti-money laundering systems that incorporate comprehensive information from various information sources as well as from human subject matter experts. A fuzzy degree of confidence can therefore be associated to each relation between any two actors in a graph of transactions. We reduce the problem of fuzzy subgraph isomorphism to the problem of finding a fuzzy set of maximal cliques in a fuzzy extension of a mathematical construct that is similar to Vizing's modular product of graphs.


title: Latent-Data Privacy Preserving With Customized Data Utility for Social Network Data

abstract: Social network data can help with obtaining valuable insight into social behaviors and revealing the underlying benefits. New big data technologies are emerging to make it easier to discover meaningful social information from market analysis to counterterrorism. Unfortunately, both diverse social datasets and big data technologies raise stringent privacy concerns. Adversaries can launch inference attacks to predict sensitive latent information, which is unwilling to be published by social users. Therefore, there is a tradeoff between data benefits and privacy concerns. In this paper, we investigate how to optimize the tradeoff between latent-data privacy and customized data utility. We propose a data sanitization strategy that does not greatly reduce the benefits brought by social network data, while sensitive latent information can still be protected. Even considering powerful adversaries with optimal inference attacks, the proposed data sanitization strategy can still preserve both data benefits and social structure, while guaranteeing optimal latent-data privacy. To the best of our knowledge, this is the first work that preserves both data benefits and social structure simultaneously and combats against powerful adversaries.


title: Distributed dynamic programming

abstract: We consider distributed algorithms for solving dynamic programming problems whereby several processors participate simultaneously in the computation while maintaining coordination by information exchange via communication links. A model of asynchronous distributed computation is developed which requires very weak assumptions on the ordering of computations, the timing of information exchange, the amount of local information needed at each computation node, and the initial conditions for the algorithm. The class of problems considered is very broad and includes shortest path problems, and finite and infinite horizon stochastic optimal control problems. When specialized to a shortest path problem the algorithm reduces to the algorithm originally implemented for routing of messages in the ARPANET.


title: Design and energetic evaluation of a prosthetic knee joint actuator with a lockable parallel spring.

abstract: There are disadvantages to existing damping knee prostheses which cause an asymmetric gait and higher metabolic cost during level walking compared to non-amputees. Most existing active knee prostheses which could benefit the amputees use a significant amount of energy and require a considerable motor. In this work, a novel semi-active actuator with a lockable parallel spring for a prosthetic knee joint has been developed and tested. This actuator is able to provide an approximation of the behavior of a healthy knee during most of the gait cycle of level walking. This actuator is expanded with a series-elastic actuator to mimic the full gait cycle and enable its use in other functional tasks like stair climbing and sit-to-stance. The proposed novel actuator reduces the energy consumption for the same trajectory with respect to a compliant or directly-driven prosthetic active knee joint and improves the approximation of healthy knee behavior during level walking compared to passive or variable damping knee prostheses.


title: InterpNET: Neural Introspection for Interpretable Deep Learning

abstract: Humans are able to explain their reasoning. On the contrary, deep neural networks are not. This paper attempts to bridge this gap by introducing a new way to design interpretable neural networks for classification, inspired by physiological evidence of the human visual system's inner-workings. This paper proposes a neural network design paradigm, termed InterpNET, which can be combined with any existing classification architecture to generate natural language explanations of the classifications. The success of the module relies on the assumption that the network's computation and reasoning is represented in its internal layer activations. While in principle InterpNET could be applied to any existing classification architecture, it is evaluated via an image classification and explanation task. Experiments on a CUB bird classification and explanation dataset show qualitatively and quantitatively that the model is able to generate high-quality explanations. While the current state-of-the-art METEOR score on this dataset is 29.2, InterpNET achieves a much higher METEOR score of 37.9.


title: Simulated Annealing Algorithm for Graph Coloring

abstract: The goal of this Random Walks project is to code and experiment the Markov Chain Monte Carlo (MCMC) method for the problem of graph coloring. In this report, we present the plots of cost function \(\mathbf{H}\) by varying the parameters like \(\mathbf{q}\) (Number of colors that can be used in coloring) and \(\mathbf{c}\) (Average node degree). The results are obtained by using simulated annealing scheme, where the temperature (inverse of \(\mathbf{\beta}\)) parameter in the MCMC is lowered progressively.


title: Thermal performance analysis of a novel linear cavity receiver for parabolic trough solar collectors

abstract: Abstract The trough solar thermal power generation system is one of the most mature solar thermal power generation systems. A novel major arc-shaped linear cavity receiver with a lunate channel based on the black cavity effect principle for parabolic trough solar collectors is proposed in this work. The effects of the inclination angle, collecting temperature, surface emissivity and aperture width on the heat loss are thoroughly analyzed with a two-dimensional numerical model coupling natural convection with surface radiation. In addition, the thermal performance of the proposed linear cavity receiver is compared with that of the Solel’s UVAC series evacuated collector tube. The results show that: (1) The natural convection heat loss is significantly affected by the inclination angle, while the radiation heat loss is mainly affected by the surface emissivity and the collecting temperature. (2) The aperture width of the receiver has a great impact on the thermal performance. The larger the aperture width is, the greater the heat losses. However, the aperture width can also affect the optical performance and the manufacturing cost. The reasonable aperture width for this kind of linear cavity receiver is about 50–70 mm with consideration of all these factors. (3) The proposed linear cavity receiver demonstrates comparative or even better thermal performance as traditional evacuated collector tubes, especially in high temperature range. In general, the proposed linear cavity receiver has the comparative shape and size as the traditional evacuated collector tubes. More importantly, it has the advantages of raising the collecting temperature and reducing the production and maintenance costs. Therefore, it can be used to replace the evacuated collector tube which has poor performance due to long-term operation.


title: Immunogenicity of protein therapeutics.

abstract: Protein therapeutics, such as monoclonal antibodies, enzymes and toxins, hold significant promise for improving human health. However, repeated administration of protein therapeutics, whether natural or recombinant, often leads to the induction of undesirable anti-drug antibodies (ADAs), which interfere with or neutralize the effect of the drug. Although an immune response to foreign proteins can be expected and is well understood, the basis for the development of responses to therapeutic autologous proteins is the subject of some debate. Inflammatory components of the drug delivery vehicle, T cell responses, T and B cell epitopes in the protein drug, and the associated B cell response are all targets for interventions aimed at reducing ADA responses. Here, we review some theories put forward to explain the immunogenicity of therapeutic proteins and describe some emerging protein-engineering approaches that might prevent the development of anti-drug antibodies.


End-to-end Relation Extraction using Neural Networks and Markov Logic Networks


title: The rationale behind complete mesocolic excision (CME) and a central vascular ligation for colon cancer in open and laparoscopic surgery

abstract: Background ::: It has been evident for a while that the result after resection for colon cancer may not have been optimal. Several years ago, this was showed by some leading surgeons in the USA but a concept of improving results was not consistently pursued. Later, surgeons in Europe and Japan have increasingly adopted the more radical principle of complete mesocolic excision (CME) as the optimal approach for colon cancer. The concept of CME is a similar philosophy to that of total mesorectal excision for rectal cancer and precise terminology and optimal surgery are key factors.


title: A Roadmap towards Machine Intelligence

abstract: The development of intelligent machines is one of the biggest unsolved challenges in computer science. In this paper, we propose some fundamental properties these machines should have, focusing in particular on communication and learning. We discuss a simple environment that could be used to incrementally teach a machine the basics of natural-language-based communication, as a prerequisite to more complex interaction with human users. We also present some conjectures on the sort of algorithms the machine should support in order to profitably learn from the environment.


title: Towards general motion-based face recognition

abstract: Motion-based face recognition is a young research topic, inspired mainly by psychological studies on motion-based perception of human faces. Unlike its close relative, appearance-based face recognition, motion-based face recognition extracts personal characteristics from facial motion (e.g. smile) and uses the information to recognize human identity. However, existing studies in this field are limited to fixed motion, that is - a subject must perform a specific type of facial motion in order to be correctly recognized. In this paper, we try to overcome this limitation by investigating the patterns of local skin deformation exhibited in facial motion. We are pushing the state-of-the-art towards general motion-based face recognition. Our approach is able to extract identity evidence from various types of facial motion, as long as those facial motions are at least, in some part of the face, locally similar to the facial motions used in training. We call our approach Local Deformation Profile (or LDP). This approach is tested through several experiments conducted over a video database of facial expression. The experiment results demonstrate the potential of LDP to be used for biometrics. We also evaluate LDP under extremely heavy face makeup, showing its usefulness to recognize faces even in disguise.


title: Polyester (Parietex) mesh for total extraperitoneal laparoscopic inguinal hernia repair

abstract: Polypropylene mesh is the most commonly used mesh for open and laparoscopic hernia repair in the United States. A variety of newly developed polyester mesh products have recently become available. This is the first U.S. multiinstitutional study evaluating the initial experience of polyester mesh use for total extraperitoneal (TEP) laparoscopic inguinal hernia repair. Between January 2000 and June 2001, 337 patients underwent 495 TEP laparoscopic inguinal hernia repairs using polyester mesh. There were 309 men and 28 women in the study, whose average age was 45 years (range, 17-80 years). The average operative time for all cases was 54.3 min (range, 18-157 min). There were no conversions to open repair and no mortality. Complications included 12 seromas/hematomas (six aspirated), chronic pain in three patients, urinary retention in two patients, and one incidence each of the following: epididimitis, prostatitis, hydrocele, and port-site cellulitis. Additionally, one patient had carbon dioxide (CO2) in the Foley bag at the end of the surgery, but a normal cystogram showed no identified bladder injury. There has been one recurrence (0.2%), occurring 4 months after surgery, which was repaired using a transabdominal laparoscopic approach. The mean follow-up period was 11 months (range, 2-22 months). There have been no documented infections of the mesh, and no mesh has been removed. This study documents a favorable initial experience with polyester mesh for TEP laparoscopic inguinal hernia repair. There were no complications related to the mesh. There may be technical and long-term advantages with the use of polyester mesh for laparoscopic inguinal hernia repair. Longer follow-up evaluation and additional studies are warranted to evaluate these potential advantages.


title: Illumination invariant face recognition based on neural network ensemble

abstract: An illumination invariant face recognition method based on neural network ensemble architecture is proposed. Given a face image with an arbitrary illumination direction, it can complete recognition in a uniform way with high performance without knowing or estimating the illumination direction. Experimental result shows that the recognition ratio of the ensemble architecture is higher than the conventional approach that uses a single neural network to recognize faces of a specific illumination direction.


title: Analytical Solution of the Magnetic Field in Permanent-Magnet Motors Taking Into Account Slotting Effect: No-Load Vector Potential and Flux Density Calculation

abstract: We present a general computation taking into account the slotting effect on the no-load magnetic field distribution in multipole surface mounted permanent-magnet motors (SMPMM). A new analytical solution based on 2-D analysis in polar coordinates is developed. It includes both radial and parallel magnetization and the main originality is to consider the stator slotting. The saturation effect of the magnetic circuit is neglected. The 2-D analytical calculation of the no-load magnetic field with the slotting effect has been validated by a corresponding finite-element analysis (FEA). The analytical results are in very good agreement with those obtained by the FEA, considering both amplitude and waveform.


title: A Novel Image Classification Method with CNN-XGBoost Model

abstract: Image classification problem is one of most important research directions in image processing and has become the focus of research in many years due to its diversity and complexity of image information. In view of the existing image classification models’ failure to fully utilize the information of images, this paper proposes a novel image classification method of combining the Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost), which are two outstanding classifiers. The presented CNN-XGBoost model provides more precise output by integrating CNN as a trainable feature extractor to automatically obtain features from input and XGBoost as a recognizer in the top level of the network to produce results. Experiments are implemented on the well-known MNIST and CIFAR-10 databases. The results prove that the new method performs better compared with other methods on the same databases, which verify the effectiveness of the proposed method in image classification problem.


title: Probiotics in the dental practice: a review.

abstract: During the last decade an increased interest in alternative, preventive, and therapeutic strategies in dentistry has arisen. Probiotics are living microorganisms which, if administered in sufficient amounts, provide a health benefit to the host. Their precise mechanisms of action have not been identified, but they are able to interfere with the imbalance occurring in biofilm-associated infections. In other fields of medicine, mainly in gastroenterology, their usefulness is already proven. Concerning oral threats, probiotic bacteria may reduce the numbers of pathogens associated with dental caries (mutans streptococci). Clinically, results are encouraging, but further research is needed to demonstrate apparent effects of certain probiotic strains on oral health as well as their desired concentration and vehicle. The use of probiotics in prevention and treatment of caries, periodontal diseases, halitosis, and other oral diseases needs to be further investigated.


title: Distributed QR Decomposition Framework for Training Support Vector Machines

abstract: Support Vector Machines (SVM) belong to a class of supervised machine learning algorithms with applications inclassification and regression analysis. SVM training is modeled as a convex optimization problem that is computationally tedious and has large memory requirements. Specifically, it is a quadratic programming problem which scales rapidly with the training set size rather than the dimensionality of the feature space. In this work, we first present a novel QR decomposition framework (QRSVM) to efficiently model and solve a large scale SVM problem by capitalizing on low-rank representations of the full kernel matrix rather than solving the problem as a sequence of smaller sub-problems. The low-rank structure of the kernel matrix is leveraged to transform the dense matrix into one with a sparse and separable structure. The modified SVM problem requires significantly lesser memory and computation. Our approach scales linearly with the training set size which makes it applicable to large datasets. This motivates towards our another contribution; exploring a distributed QRSVM framework to solve large-scale SVM classification problems in parallel across a cluster of computing nodes. We also derive an optimal step size for fast convergence of the dual ascent method which is used to solve the quadratic programming problem.


title: The Atari Grand Challenge Dataset

abstract: Recent progress in Reinforcement Learning (RL), fueled by its combination, with Deep Learning has enabled impressive results in learning to interact with complex virtual environments, yet real-world applications of RL are still scarce. A key limitation is data efficiency, with current state-of-the-art approaches requiring millions of training samples. A promising way to tackle this problem is to augment RL with learning from human demonstrations. However, human demonstration data is not yet readily available. This hinders progress in this direction. The present work addresses this problem as follows. We (i) collect and describe a large dataset of human Atari 2600 replays -- the largest and most diverse such data set publicly released to date, (ii) illustrate an example use of this dataset by analyzing the relation between demonstration quality and imitation learning performance, and (iii) outline possible research directions that are opened up by our work.


title: Effects of the rotor design parameters on the torque production of a PMaSynRM for washing machine applications

abstract: This paper presents the effects and importance of the rotor design parameters on the performance of a permanent magnet assisted synchronous reluctance motor (PMaSynRM). At the beginning of the design, number of stator slots and rotor flux barriers are considered. Rotor design parameters such as different insulation ratios and magnet positions are investigated after choosing the number of stator slots and rotor flux barriers combination. The rotor design process is iterated with different d−q axis insulation ratios on rotor to determine the best value for maximum average torque and minimum torque ripple. Permanent magnets are placed in different combinations inside the rotor barriers and results are compared in terms of average torque, torque ripple and efficiency as well. The chosen best model is prototyped and tested. The comparison between the experimental and the simulation results are also presented.


Event Embeddings for Semantic Script Modeling


title: Do English and Mandarin speakers think about time differently?

abstract: Time is a fundamental domain of experience. In this paper we ask whether aspects of language and culture affect how people think about this domain. Specifically, we consider whether English and Mandarin speakers think about time differently. We review all of the available evidence both for and against this hypothesis, and report new data that further support and refine it. The results demonstrate that English and Mandarin speakers do think about time differently. As predicted by patterns in language, Mandarin speakers are more likely than English speakers to think about time vertically (with earlier time-points above and later time-points below).


title: RotationNet: Learning Object Classification Using Unsupervised Viewpoint Estimation.

abstract: The recent popularization of depth sensors and the availability of large-scale 3D model databases such as ShapeNet have drawn increased attention to 3D object recognition. Despite the convenience of using 3D models captured offline, we are allowed to observe only a single view of an object at once, with the exception of the use of special environments such as multi-camera studios. This impedes the recognition of diverse objects in a real environment. If a mechanical system (or a robot) has access to multi-view models of objects and is able to estimate the viewpoint of a currently observed object, it can rotate the object to a better view for classification. In this paper, we propose a novel method to learn a deep convolutional neural network that both classifies an object and estimates the rotation path to its best view under the predicted object category. We conduct experiments on a 3D model database as well as a real image dataset to demonstrate that our system can achieve an effective strategy of object rotation for category classification.


title: An artificial immune system architecture for computer security applications

abstract: With increased global interconnectivity and reliance on e-commerce, network services and Internet communication, computer security has become a necessity. Organizations must protect their systems from intrusion and computer virus attacks. Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities. Current anti-virus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral strains and intrusion patterns. To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture. A prototype interactive system is designed, implemented in Java and tested. The results validate the use of a distributed-agent biological system approach toward the computer security problems of virus elimination and ID.


title: A recurrent neural network for solving a class of generalized convex optimization problems.

abstract: Abstract In this paper, we propose a penalty-based recurrent neural network for solving a class of constrained optimization problems with generalized convex objective functions. The model has a simple structure described by using a differential inclusion. It is also applicable for any nonsmooth optimization problem with affine equality and convex inequality constraints, provided that the objective function is regular and pseudoconvex on feasible region of the problem. It is proven herein that the state vector of the proposed neural network globally converges to and stays thereafter in the feasible region in finite time, and converges to the optimal solution set of the problem.


title: Intelligent approaches for prognosticating post-operative life expectancy in the lung cancer patients

abstract: The aim of this research is to evaluate the performance of two feature selection methods on seven different machine learning methods applied over thoracic surgery data. Feature selection is a crucial pre-processing step in determining factors responsible for post-operative life expectancy in the patients suffering with lung cancer. Postoperative life expectancy complications are the most common fatality following major types of thoracic surgery. In particular, we want to examine the underlying health factors of patients that could potentially be a powerful predictor for deaths which are surgically related. Seven machine learning methods namely Naive Bayes, Linear SVM, MLP, RBF Network, SMO, KNN and CART are employed for analyzing the performance of feature selection methods. Maximum accuracy of 85.11% was obtained with correlation-based feature selection in comparison with consistency-based feature selection which was 84.89 %.


title: Comparative Study of Big Data Computing and Storage Tools: A Review

abstract: As a result of tremendous rise in internet usage like social media and forums, mail systems, scholarly and research articles, daily online transactions from multiple sources like health care systems, meteorological and environmental organizations etc., the data collected has shoot up exponentially. This vast collection of data, called Big Data, has caused the traditional tools incompetent for managing it from either of storage, computing or analytical perspective. There is an immense need of architectures, platforms, tools, techniques and algorithms to handle Big Data. The available technologies deal with two broad aspects related to Big Data that are Big Data Storage Management and Big Data Computing, focused to overcome various challenges such as scalability, faster processing speed, multiple format data processing, availability, faster response time and analytics etc. This paper reviews recent trends of storage and computing tools with their relative capabilities, limitations and environment they are suitable to work with.


Anorectal Malformations (Part 1)


Towards Deep Style Transfer: A Content-Aware Perspective


title: Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation

abstract: This paper provides a holistic study of how stock prices vary in their response to financial disclosures across different topics. Thereby, we specifically shed light into the extensive amount of filings for which no a priori categorization of their content exists. For this purpose, we utilize an approach from data mining - namely, latent Dirichlet allocation - as a means of topic modeling. This technique facilitates our task of automatically categorizing, ex ante, the content of more than 70,000 regulatory 8-K filings from U.S. companies. We then evaluate the subsequent stock market reaction. Our empirical evidence suggests a considerable discrepancy among various types of news stories in terms of their relevance and impact on financial markets. For instance, we find a statistically significant abnormal return in response to earnings results and credit rating, but also for disclosures regarding business strategy, the health sector, as well as mergers and acquisitions. Our results yield findings that benefit managers, investors and policy-makers by indicating how regulatory filings should be structured and the topics most likely to precede changes in stock valuations.


title: Quality assessment of TanDEM-X Raw DEMs oriented to a fusion with CartoSAT-1 DEMs

abstract: This paper addresses a quality assessment of TanDEM-X standard Raw DEMs, with a resolution of 12 meters, for three different terrain configurations: urban areas, moderate topography and complex topography. The analysis is performed in the geospatial and in the spectral domain. Beside TanDEM-X, the same analysis is also carried out for CartoSAT-1 and LiDAR DEMs. The latter one is used as a reference. Nevertheless, the focus is centered on TanDEM-X, whose geometric limitations and their impacts on the DEM are analyzed here in detail. How the DEM appears in layover, shadow and phase unwrapping error areas is one of the objectives of the paper. The chosen test site is around Terrassa/Barcelona (Spain), offering all kinds of terrain variations. The final scope of the analysis is to learn about the potentials and the limitations of the two systems, radar (TanDEM-X) and optical (CartoSAT-1), in a way to optimally fuse them and to create an enhanced DEM. A simple fusion processing chain, based on a weighted average depending on the quality of the DEMs adapted to the local geometry, is tested. First results show that in urban and complex terrain areas the improvements are limited, mainly due to the previously analyzed geometrical issues, whereas in moderate terrain areas the enhancement is significant, with a drop in the RMSE of about 25% for TanDEM-X and 30% for CartoSAT-1.


title: Improving Document Ranking with Dual Word Embeddings

abstract: This paper investigates the popular neural word embedding method Word2vec as a source of evidence in document ranking. In contrast to NLP applications of word2vec, which tend to use only the input embeddings, we retain both the input and the output embeddings, allowing us to calculate a different word similarity that may be more suitable for document ranking. We map the query words into the input space and the document words into the output space, and compute a relevance score by aggregating the cosine similarities across all the query-document word pairs. We postulate that the proposed Dual Embedding Space Model (DESM) provides evidence that a document is about a query term, in addition to and complementing the traditional term frequency based approach.


title: Multifactor Adaptation for Mandarin Broadcast News and Conversation Speech Recognition

abstract: Abstract Weexploretheintegration of multiplefactorssuch asgenre andspeaker gender for acoustic model adaptation tasks to improveMandarin ASR system performance on broadcast news andbroadcast conversation audio. We investigate the use of mul-tifactor clustering of acoustic model training data and the ap-plication of MPE-MAP and fMPE-MAP acoustic model adap-tations. We found that by effectively combining these adapta-tion approaches, we achieve 6% relative reduction in recogni-tion error rate compared to a Mandarin recognition system thatdoes not use genre-speciﬁc acoustic models, and 5% relativeimprovement if the genre-adaptive system is combined withan-other, genre-independent state-of-the-art system. Index Terms : large vocabulary automatic speech recognition,broadcast news, broadcast conversation, genre classiﬁcation,MAP adaptation, MPE-MAP, fMPE-MAP 1. Introduction Building a large vocabulary automatic speech recognition(ASR) system for a new domain is a difﬁcult task. One majorreasonisthatcollectionofthetrainingdataforacousticandlan-guagemodeldevelopment isexpensive andverylaborintensive.Acoustic model (AM) and language model (LM) adaptation at-tempttoadjustparametersofawell-trainedexistingmodelfromone domain to a new domain, by exploring statistics obtainedfrom a small in-domain seed corpus. There has been muchwork on AM adaptation approaches for improving ASR perfor-mance. In this paper, we investigate AM adaptation for a Man-darin large vocabulary ASR system for transcribing broadcastnews (BN) and broadcast conversation (BC) audio under theDefense Advanced ResearchProjects Agency (DARPA)GlobalAutonomous Language Exploitation (GALE) program.


title: On the value of temporal information in information retrieval

abstract: Time is an important dimension of any information space and can be very useful in information retrieval. Current information retrieval systems and applications do not take advantage of all the time information available in the content of documents to provide better search results and user experience. In this paper we show some of the areas that can benefit from exploiting such temporal information.


title: A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy

abstract: Objective: To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia. Methods: Pre-treatment EEG data are collected in 23 + 14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results. First, a feature selection scheme is employed to select a reduced subset of features extracted from the subjects’ EEG that is most statistically relevant to our treatment-response prediction. These features are then entered into a classifier, which is realized in the form of a kernel partial least squares regression method that performs response prediction. Various scales, including the positive and negative syndrome scale (PANSS) are used as treatment-response indicators. Results: We determined that a set of discriminating EEG features do exist. A low-dimensional representation of the feature space showed significant clustering into clozapine responder and non-responder groups. The minimum level of performance of the proposed prediction methodology, tested over a range of conditions using the leave-one-out cross-validation method using the original 23 subjects, with further testing in an independent sample of 14 subjects, was 85%. Conclusions: These findings indicate that analysis of pre-treatment EEG data can predict the clinical response to clozapine in treatment resistant schizophrenia. Significance: If replicated in a larger population, this novel approach to EEG analysis may assist the clinician in determining treatment-efficacy.


A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science


title: Maximizing speedup through self-tuning of processor allocation

abstract: Addresses the problem of maximizing application speedup through run-time self-selection of an appropriate number of processors on which to run. Automatic run-time selection of processor allocations is important because many parallel applications exhibit peak speedups at allocations that are data- or time-dependent. We propose the use of a run-time system that: (a) dynamically measures job efficiencies at different allocations, (b) uses these measurements to calculate speedups, and (c) automatically adjusts a job's processor allocation to maximize its speedup. Using a set of 10 applications that includes both hand-coded parallel programs and compiler-parallelized sequential programs, we show that our run-time system can reliably determine dynamic allocations that match the best possible static allocation, and that it has the potential to find dynamic allocations that outperform any static allocation.


title: Strengths and limitations of high-resolution electromagnetic field measurements for side-channel analysis

abstract: The electromagnetic field as a side-channel of cryptographic devices has been linked to several advantages in past contributions. We provide a comprehensive study using high-resolution horizontal and vertical magnetic field probes at close distance to an integrated circuit die. We configured an FPGA device with two uncorrelated digital structures showing similar leakage behavior as symmetric cryptography implementations. We found that measurements from the frontside of the die using a horizontal probe lead to the highest signal-to-noise ratios. Further, high sampling rates are required and no trace compression should be applied. Contrary to previous contributions, we successfully demonstrate that the leakage of design parts is locally restricted and matches their placement. This proves the feasibility of localized side-channel analysis after a profiling phase, however, also means that other locations will lead to inferior results, which is an important limitation. Our analysis confirmed an advantage of measuring localized electromagnetic fields instead of current consumption due to the fact that less parasitic capacitances are involved.


Product , Process , and Service Product , Process , and Service : A New Industry Lifecycle Model


title: Game engine learning from video

abstract: Intelligent agents need to be able to make predictions about their environment. In this work we present a novel approach to learn a forward simulation model via simple search over pixel input. We make use of a video game, Super Mario Bros., as an initial test of our approach as it represents a physics system that is significantly less complex than reality. We demonstrate the significant improvement of our approach in predicting future states compared with a baseline CNN and apply the learned model to train a game playing agent. Thus we evaluate the algorithm in terms of the accuracy and value of its output model.