title: REIFICATION: A MARXIST PERSPECTIVE

abstract: The concept of reification is used by Marx to describe a form of social consciousness in which human relations come to be identified with the physical properties of things, thereby acquiring an appearance of naturalness and inevitability. This essay presents a systematic reconstruction of Marx's theory of reification, with an emphasis on the social-structural dimensions of the concept. This reconstruction differs both from the conceptions of reification that are found in non-Marxist sociology and from the interpretations of some of Marx's followers. Marx's concept of reification is then taken as the model for a more general theory of ideology. In the final section of the essay, I show how this theory can be used to analyze the emergence of new forms of reification in capitalist society, including those that are based on the growth of technology, the spread of bureaucracy, and the rationalization of occupational selection. Sociology has been aptly described as a graveyard of critical concepts. Nowhere is this more true than in its appropriation and "operationalization" of concepts deriving from the Marxist theoretical tradition. Several critics have noted, for example, the emasculation which the concept of alienation has suffered at the hands of empirically oriented sociologists (Horton, 1964; Blackburn, 1969). In this instance, it is precisely the most crucial and distinguishing features of Marx's theory that are lost in the translation to mainstream sociology: the multi-dimensionality of the concept of alienation, the insight it provides into the inner structure of capitalist work relations, and the unity it forges between empirical analysis and radical critique. For Marx, the concept of alienation describes the situation of the individual worker, but also implies a theoretical conception of capitalist society as a whole. It offers an analysis of objective social relations which is also a critique of the false or "inverted" nature of those relations (Meszaros, 1970; Ollman, 1971; Colletti, 1972; Geras, 1973). By contrast, as appropriated by mainstream sociology, the descriptive elements of the concept of alienation have been separated from Marx's critique of capitalism, and the focus has been narrowed from an analysis of capitalist production relations to the study of individual attitudes (Seeman, 1957; Dean, 1961). The result is a dehistoricized and psychologized concept which, in becoming grist for interview questionnaires and statistical correlations, has been stripped of its capacity to illuminate the hidden structure of capitalist society and


deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets


title: Adding Semantic Role Annotation to a Corpus of Written Dutch

abstract: We present an approach to automatic semantic role labeling (SRL) carried out in the context of the Dutch Language Corpus Initiative (D-Coi) project. Adapting earlier research which has mainly focused on English to the Dutch situation poses an interesting challenge especially because there is no semantically annotated Dutch corpus available that can be used as training data. Our automatic SRL approach consists of three steps: bootstrapping from a syntactically annotated corpus by means of a rule-based tagger developed for this purpose, manual correction on the basis of the Prop-Bank guidelines which have been adapted to Dutch and training a machine learning system on the manually corrected data.


title: A Real-Time Coded OFDM Acoustic Modem in Very Shallow Underwater Communications

abstract: In this paper, we describe the design and implementation of a high-rate modem for use in very-shallow water communications. The modem employs an Orthogonal-Frequency-Division-Multiplexing (OFDM) scheme with a powerful forward error-correcting code (FEC) - turbo block code. Bit error rate (BER) measurements obtained from coastal sea trials showed that this modem is capable of delivering good error rate performance for information rates of up to nearly 10 kbits/s at transmission distances below 1700m.


title: Discontinuities in Continuous Scatter Plots

abstract: The concept of continuous scatterplot (CSP) is a modern visualization technique. The idea is to define a scalar density value based on the map between an n-dimensional spatial domain and an m-dimensional data domain, which describe the CSP space. Usually the data domain is two-dimensional to visually convey the underlying, density coded, data. In this paper we investigate kinds of map-based discontinuities, especially for the practical cases n = m = 2 and n = 3 | m = 2, and we depict relations between them and attributes of the resulting CSP itself. Additionally, we show that discontinuities build critical line structures, and we introduce algorithms to detect them. Further, we introduce a discontinuity-based visualization approach - called contribution map (CM) -which establishes a relationship between the CSP's data domain and the number of connected components in the spatial domain. We show that CMs enhance the CSP-based linking & brushing interaction. Finally, we apply our approaches to a number of synthetic as well as real data sets.


title: A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity

abstract: We present a mixed-mode analog/digital VLSI device comprising an array of leaky integrate-and-fire (I&F) neurons, adaptive synapses with spike-timing dependent plasticity, and an asynchronous event based communication infrastructure that allows the user to (re)configure networks of spiking neurons with arbitrary topologies. The asynchronous communication protocol used by the silicon neurons to transmit spikes (events) off-chip and the silicon synapses to receive spikes from the outside is based on the "address-event representation" (AER). We describe the analog circuits designed to implement the silicon neurons and synapses and present experimental data showing the neuron's response properties and the synapses characteristics, in response to AER input spike trains. Our results indicate that these circuits can be used in massively parallel VLSI networks of I&F neurons to simulate real-time complex spike-based learning algorithms.


title: Detection of QRS Complexes Based on Biorthogonal Spline Wavelet

abstract: Biorthogonal spline wavelet is used to detect the QRS complex of ECG signal. Mallat algorithm is applied in the decomposition of ECG signal by using the equivalent filter of a biorthogonal spline wavelet. Lipschitz exponent is introduced to investigate the relationship between the signal singularity (R Peak) and the zero-crossing point of the modulus maximum pair of the signal's wavelet transform. Adaptive threshold, refractory period and expiating are applied to improve the anti-interference performance. Experimental results demonstrated that this method is robust against time varying characteristics of QRS complex and noise. A correct detection rate of 99.905% has been achieved when the MIT-BIH arrhythmia database is used to test the proposed QRS complex detection algorithm.


title: Modeling of SiC MOSFET with temperature dependent parameters and its applications

abstract: A model of Silicon carbide (SiC) MOSFET based on PSpice is proposed in this paper, which is suitable for a wide temperature range especially at low temperature. The static characteristics of SiC MOSFET are described by introducing temperature dependent voltage source and current source. The effect of negative turn-off gate drive voltage is also taken into account in the modeling. In order to reflect the low temperature characteristics of SiC MOSFET accurately, low temperature (-25°C) measurements are carried out, which provide the modeling basis. The determinations of key parameters in the model are analyzed in detail, including on-state resistor, internal gate resistor, temperature dependent sources, and some capacitors. The proposed model is verified by the experimental tests on a Buck converter prototype at different input voltages, input currents and temperatures. Simulation results on the proposed model coincide well with the experimental results, in terms of switching waveforms and power losses even at low temperature. The comparison between SiC MOSFET and Si MOSFET on switching characteristics and the efficiency comparison of Buck converter using SiC devices and Si devices are presented, which demonstrate the attractive advantage of SiC devices in power loss reduction.


Women's Preferences for Penis Size: A New Research Method Using Selection among 3D Models


title: Statistical Modeling of 4D Respiratory Lung Motion Using Diffeomorphic Image Registration

abstract: Modeling of respiratory motion has become increasingly important in various applications of medical imaging (e.g., radiation therapy of lung cancer). Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient's anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D computed tomography (CT) data of different patients to extend the motion modeling capabilities. Our modeling process consists of three steps: an intra-subject registration to generate subject-specific motion models, the generation of an average shape and intensity atlas of the lung as anatomical reference frame, and the registration of the subject-specific motion models to the atlas in order to build a statistical 4D mean motion model (4D-MMM). Furthermore, we present methods to adapt the 4D mean motion model to a patient-specific lung geometry. In all steps, a symmetric diffeomorphic nonlinear intensity-based registration method was employed. The Log-Euclidean framework was used to compute statistics on the diffeomorphic transformations. The presented methods are then used to build a mean motion model of respiratory lung motion using thoracic 4D CT data sets of 17 patients. We evaluate the model by applying it for estimating respiratory motion of ten lung cancer patients. The prediction is evaluated with respect to landmark and tumor motion, and the quantitative analysis results in a mean target registration error (TRE) of 3.3 ±1.6 mm if lung dynamics are not impaired by large lung tumors or other lung disorders (e.g., emphysema). With regard to lung tumor motion, we show that prediction accuracy is independent of tumor size and tumor motion amplitude in the considered data set. However, tumors adhering to non-lung structures degrade local lung dynamics significantly and the model-based prediction accuracy is lower in these cases. The statistical respiratory motion model is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in radiation therapy and image guided diagnosis.


title: Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation

abstract: Data augmentation is a key element in training high-dimensional models. In this approach, one synthesizes new observations by applying pre-specified transformations to the original training data; e.g.~new images are formed by rotating old ones. Current augmentation schemes, however, rely on manual specification of the applied transformations, making data augmentation an implicit form of feature engineering. With an eye towards true end-to-end learning, we suggest learning the applied transformations on a per-class basis. Particularly, we align image pairs within each class under the assumption that the spatial transformation between images belongs to a large class of diffeomorphisms. We then learn a class-specific probabilistic generative models of the transformations in a Riemannian submanifold of the Lie group of diffeomorphisms. We demonstrate significant performance improvements in training deep neural nets over manually-specified augmentation schemes. Our code and augmented datasets are available online.


title: Applying electric field sensing to human-computer interfaces

abstract: A non-contact sensor based on the interaction of a person with electric fields for human-computer interface is investigated. Two sensing modes are explored: an external electric field shunted to ground through a human body, and an external electric field transmitted through a human body to stationary receivers. The sensors are low power (milliwatts), high resolution (millimeter) low cost (a few dollars per channel), have low latency (millisecond), high update rate (1 kHz), high immunity to noise (>72 dB), are not affected by clothing, surface texture or reflectivity, and can operate on length scales from microns to meters. Systems incorporating the sensors include a finger mouse, a room that knows the location of its occupant, and people-sensing furniture. Haptic feedback using passive materials is described. Also discussed are empirical and analytical approaches to transform sensor measurements into position information.


title: DREAM: Distributed RDF Engine with Adaptive Query Planner and Minimal Communication

abstract: The Resource Description Framework (RDF) and SPARQL query language are gaining wide popularity and acceptance. In this paper, we present DREAM, a distributed and adaptive RDF system. As opposed to existing RDF systems, DREAM avoids partitioning RDF datasets and partitions only SPARQL queries. By not partitioning datasets, DREAM offers a general paradigm for different types of pattern matching queries, and entirely averts intermediate data shuffling (only auxiliary data are shuffled). Besides, by partitioning queries, DREAM presents an adaptive scheme, which automatically runs queries on various numbers of machines depending on their complexities. Hence, in essence DREAM combines the advantages of the state-of-the-art centralized and distributed RDF systems, whereby data communication is avoided and cluster resources are aggregated. Likewise, it precludes their disadvantages, wherein system resources are limited and communication overhead is typically hindering. DREAM achieves all its goals via employing a novel graph-based, rule-oriented query planner and a new cost model. We implemented DREAM and conducted comprehensive experiments on a private cluster and on the Amazon EC2 platform. Results show that DREAM can significantly outperform three related popular RDF systems.


title: CANcentrate: an active star topology for CAN networks

abstract: Distributed embedded systems that require real-time performance need a network capable of deterministic access delay. CAN is one such network that became widespread in recent years due to its electrical robustness, low price, and priority-based access control. However, its use in safety-critical applications has been controversial due to dependability limitations that arise from its bus topology and non-guaranteed atomic broadcast. In this paper, we propose an active star topology that allows solving many of the limitations related to the first aspect by means of strong error confinement. Nodes are interconnected through an active hub that is fully compatible with existing CAN controllers. The paper compares bus and star topologies, analyzes related work and discusses the hub implementation and dependability properties.


title: IR-UWB Radar Demonstrator for Ultra-Fine Movement Detection and Vital-Sign Monitoring

abstract: In this paper an impulse-radio ultra-wideband (IR-UWB) hardware demonstrator is presented, which can be used as a radar sensor for highly precise object tracking and breath-rate sensing. The hardware consists of an impulse generator integrated circuit (IC) in the transmitter and a correlator IC with an integrating baseband circuit as correlation receiver. The radiated impulse is close to a fifth Gaussian derivative impulse with $\sigma=51\ {\hbox {ps}}$ , efficiently using the Federal Communications Commission indoor mask. A detailed evaluation of the hardware is given. For the tracking, an impulse train is radiated by the transmitter, and the reflections of objects in front of the sensor are collected by the receiver. With the reflected signals, a continuous hardware correlation is computed by a sweeping impulse correlation. The correlation is applied to avoid sampling of the RF impulse with picosecond precision. To localize objects precisely in front of the sensor, three impulse tracking methods are compared: Tracking of the maximum impulse peak, tracking of the impulse slope, and a slope-to-slope tracking of the object's reflection and the signal of the static direct coupling between transmit and receive antenna; the slope-to-slope tracking showing the best performance. The precision of the sensor is shown by a measurement with a metal plate of 1-mm sinusoidal deviation, which is clearly resolved. Further measurements verify the use of the demonstrated principle as a breathing sensor. The breathing signals of male humans and a seven-week-old infant are presented, qualifying the IR-UWB radar principle as a useful tool for breath-rate determination.


title: Algorithm Selection and Model Adaptation for ESL Correction Tasks

abstract: We consider the problem of correcting errors made by English as a Second Language (ESL) writers and address two issues that are essential to making progress in ESL error correction - algorithm selection and model adaptation to the first language of the ESL learner. ::: ::: A variety of learning algorithms have been applied to correct ESL mistakes, but often comparisons were made between incomparable data sets. We conduct an extensive, fair comparison of four popular learning methods for the task, reversing conclusions from earlier evaluations. Our results hold for different training sets, genres, and feature sets. ::: ::: A second key issue in ESL error correction is the adaptation of a model to the first language of the writer. Errors made by non-native speakers exhibit certain regularities and, as we show, models perform much better when they use knowledge about error patterns of the non-native writers. We propose a novel way to adapt a learned algorithm to the first language of the writer that is both cheaper to implement and performs better than other adaptation methods.


title: Conceptual framework for high-end graphical password

abstract: User authentication depends largely on the concept of passwords. However, users find it difficult to remember alphanumerical passwords over time. When user is required to choose a secure password, they tend to choose an easy, short and insecure password. Graphical password method is proposed as an alternative solution to text-based alphanumerical passwords. The reason of such proposal is that human brain is better in recognizing and memorizing pictures compared to traditional alphanumerical string. Therefore, in this paper, we propose a conceptual framework to better understand the user performance for new high-end graphical password method. Our proposed framework is based on hybrid approach combining different features into one. The user performance experimental analysis pointed out the effectiveness of the proposed framework.


Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications


title: Fast and Accurate Texture Recognition with Multilayer Convolution and Multifractal Analysis

abstract: A fast and accurate texture recognition system is presented. The new approach consists in extracting locally and globally invariant representations. The locally invariant representation is built on a multi-resolution convolutional network with a local pooling operator to improve robustness to local orientation and scale changes. This representation is mapped into a globally invariant descriptor using multifractal analysis. We propose a new multifractal descriptor that captures rich texture information and is mathematically invariant to various complex transformations. In addition, two more techniques are presented to further improve the robustness of our system. The first technique consists in combining the generative PCA classifier with multiclass SVMs. The second technique consists of two simple strategies to boost classification results by synthetically augmenting the training set. Experiments show that the proposed solution outperforms existing methods on three challenging public benchmark datasets, while being computationally efficient.


title: Infants understand deceptive intentions to implant false beliefs about identity: New evidence for early mentalistic reasoning

abstract: © 2015 Elsevier Inc. Are infants capable of representing false beliefs, as the mentalistic account of early psychological reasoning suggests, or are they incapable of doing so, as the minimalist account suggests? The present research sought to shed light on this debate by testing the minimalist claim that a signature limit of early psychological reasoning is a specific inability to understand false beliefs about identity: because of their limited representational capabilities, infants should be unable to make sense of situations where an agent mistakes one object for another, visually identical object. To evaluate this claim, three experiments examined whether 17-month-olds could reason about the actions of a deceptive agent who sought to implant in another agent a false belief about the identity of an object. In each experiment, a thief attempted to secretly steal a desirable rattling toy during its owner's absence by substituting a less desirable silent toy. Infants realized that this substitution could be effective only if the silent toy was visually identical to the rattling toy (Experiment 1) and the owner did not routinely shake her toy when she returned (Experiment 2). When these conditions were met, infants expected the owner to be deceived and to mistake the silent toy for the rattling toy she had left behind (Experiment 3). Together, these results cast doubt on the minimalist claim that infants cannot represent false beliefs about identity. More generally, these results indicate that infants in the 2nd year of life can reason not only about the actions of agents who hold false beliefs, but also about the actions of agents who seek to implant false beliefs, thus providing new support for the mentalistic claim that an abstract capacity to reason about false beliefs emerges early in human development.


title: Battery Management System (BMS) simulation environment for electric vehicles

abstract: The wide diffusion of Full and Hybrid Electric Vehicles is stimulating research on electric energy storage systems and Battery Management Systems (BMS). The Battery management system must ensure many complex features such as charge control, battery-capacity monitoring, remaining run-time information and charge-cycle counting. An optimization of the BMS can allow an improvement on security of the vehicle, performance of the engine, energy optimization and extension of the life of the battery. The main objective of this work is to develop a simulation environment based on SystemC to design and optimize the Battery Management System including a lithium-ion battery model and CAN communication interface. The BMS has been validated using real-world scenarios and data.


title: Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics

abstract: Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent's behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the solution of the inverse problem is significantly influenced by both. Current IRL approaches assume that if the transition model is unknown, additional samples from the system's dynamics are accessible, or the observed behavior provides enough samples of the system's dynamics to solve the inverse problem accurately. These assumptions are often not satisfied. To overcome this, we present a gradient-based IRL approach that simultaneously estimates the system's dynamics. By solving the combined optimization problem, our approach takes into account the bias of the demonstrations, which stems from the generating policy. The evaluation on a synthetic MDP and a transfer learning task shows improvements regarding the sample efficiency as well as the accuracy of the estimated reward functions and transition models.


title: Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification

abstract: We provide a simple but novel supervised weighting scheme for adjusting term frequency in tf-idf for sentiment analysis and text classification. We compare our method to baseline weighting schemes and find that it outperforms them on multiple benchmarks. The method is robust and works well on both snippets and longer documents.


title: Many Facets of Reasoning Under Uncertainty, Inconsistency, Vagueness, and Preferences: A Brief Survey

abstract: In this paper, we give an introduction to reasoning under uncertainty, inconsistency, vagueness, and preferences in artificial intelligence (AI), including some historic notes and a brief survey to previous approaches.


title: Role of primary stability for successful osseointegration of dental implants: Factors of influence and evaluation

abstract: Abstract A secure implant primary (mechanical) stability is positively associated with a successful implant integration and long-term successful clinical outcome. Therefore, it is essential to assess the initial stability at different time-points to ensure a successful osseointegration. The present study critically reviews the factors that may play a role in achieving a successful initial stability in dental implants. Databases were searched from 1983 up to and including October 2013 using different combinations of various keywords. Bone quality and quantity, implant geometry, and surgical technique adopted may significantly influence primary stability and overall success rate of dental implants.


title: Stress control on polymeric outdoor insulators using Zinc oxide microvaristor composites

abstract: An approach to high field control, particularly in the areas near the high voltage (HV) and ground terminals of an outdoor insulator, is proposed using a nonlinear grading material; Zinc Oxide (ZnO) microvaristors compounded with other polymeric materials to obtain the required properties and allow easy application. The electrical properties of the microvaristor compounds are characterised by a nonlinear field-dependent conductivity. This paper describes the principles of the proposed field-control solution and demonstrates the effectiveness of the proposed approach in controlling the electric field along insulator profiles. A case study is carried out for a typical 11 kV polymeric insulator design to highlight the merits of the grading approach. Analysis of electric potential and field distributions on the insulator surface is described under dry clean and uniformly contaminated surface conditions for both standard and microvaristor-graded insulators. The grading and optimisation principles to allow better performance are investigated to improve the performance of the insulator both under steady state operation and under surge conditions. Furthermore, the dissipated power and associated heat are derived to examine surface heating and losses in the grading regions and for the complete insulator. Preliminary tests on inhouse prototype insulators have confirmed better flashover performance of the proposed graded insulator with a 21 % increase in flashover voltage.


title: A behavioral study of emotions in south indian classical music andits implications in music recommendation systems

abstract: In order to model a culture-specific content-based music recommendatio/n system, a total of 750 subjective emotional responses to tunes composed in popular raagas of South Indian classical (Carnatic) music are empirically investigated to find out the long speculated relation between raagas (indian music scales) and rasas (emotion clusters). We discuss the results from analysis of this survey, which show that raagas are quite useful as a first step in a different direction towards content-based music recommendation. Along the way, we discriminate Carnatic and North-Indian classical (Hindustani) music traditions. We also convey the definition of rasa, which is different from being a single emotional state. We use a classification based on a novel approach in conceptualization of emotions based on navarasa, which is a emotion classification given by Bharata, that suits behavioral studies with Indian arts. Pitch-class profiles which were previously shown to give high accuracies in Hindustani raaga recognition are tested in a preliminary experiment to automatically recognize Carnatic raagas. The results are discussed and additional challenges in dealing with melodies in Carnatic tradition are highlighted.


title: Can neural machine translation do simultaneous translation?

abstract: We investigate the potential of attention-based neural machine translation in simultaneous translation. We introduce a novel decoding algorithm, called simultaneous greedy decoding, that allows an existing neural machine translation model to begin translating before a full source sentence is received. This approach is unique from previous works on simultaneous translation in that segmentation and translation are done jointly to maximize the translation quality and that translating each segment is strongly conditioned on all the previous segments. This paper presents a first step toward building a full simultaneous translation system based on neural machine translation.


title: Conceptual Model of Website Design Elements that Influences Credibility and Trustworthiness

abstract: In today’s competitive environment, it is important for web designers to understand what design elements are essential for attracting visitors, making the visitor trust the website, and perform intended actions like placing an order and revisiting the site. Trustworthiness of a website heavily relies on a good first impression and visitor’s visual perception of the user interface. Therefore, the focus of this research is to investigate the effects of visual design elements on trustworthiness of a site. In this research-in-progress paper, we develop a conceptual model to depict the relationships among website design elements and trustworthiness. This conceptual model aids website designers understand what elements are appealing to the visual senses, and conjures credibility and trust.


title: Sex, trauma, stress hormones and depression

abstract: Although few studies dispute that there are gender differences in depression, the etiology is still unknown. In this review, we cover a number of proposed factors and the evidences for and against these factors that may account for gender differences in depression. These include the possible role of estrogens at puberty, differences in exposure to childhood trauma, differences in stress perception between men and women and the biological differences in stress response. None of these factors seem to explain gender differences in depression. Finally, we do know that when depressed, women show greater hypothalamic–pituitary–adrenal (HPA) axis activation than men and that menopause with loss of estrogens show the greatest HPA axis dysregulation. It may be the constantly changing steroid milieu that contributes to these phenomena and vulnerability to depression.


title: Automatic Curriculum Graph Generation for Reinforcement Learning Agents

abstract: In recent years, research has shown that transfer learning methods can be leveraged to construct curricula that sequence a series of simpler tasks such that performance on a final target task is improved. A major limitation of existing approaches is that such curricula are handcrafted by humans that are typically domain experts. To address this limitation, we introduce a method to generate a curriculum based on task descriptors and a novel metric of transfer potential. Our method automatically generates a curriculum as a directed acyclic graph (as opposed to a linear sequence as done in existing work). Experiments in both discrete and continuous domains show that our method produces curricula that improve the agent’s learning performance when compared to the baseline condition of learning on the target task from scratch.


title: Nonparametric General Reinforcement Learning

abstract: Reinforcement learning (RL) problems are often phrased in terms of Markov decision processes (MDPs). In this thesis we go beyond MDPs and consider RL in environments that are non-Markovian, non-ergodic and only partially observable. Our focus is not on practical algorithms, but rather on the fundamental underlying problems: How do we balance exploration and exploitation? How do we explore optimally? When is an agent optimal? We follow the nonparametric realizable paradigm. ::: We establish negative results on Bayesian RL agents, in particular AIXI. We show that unlucky or adversarial choices of the prior cause the agent to misbehave drastically. Therefore Legg-Hutter intelligence and balanced Pareto optimality, which depend crucially on the choice of the prior, are entirely subjective. Moreover, in the class of all computable environments every policy is Pareto optimal. This undermines all existing optimality properties for AIXI. However, there are Bayesian approaches to general RL that satisfy objective optimality guarantees: We prove that Thompson sampling is asymptotically optimal in stochastic environments in the sense that its value converges to the value of the optimal policy. We connect asymptotic optimality to regret given a recoverability assumption on the environment that allows the agent to recover from mistakes. Hence Thompson sampling achieves sublinear regret in these environments. ::: Our results culminate in a formal solution to the grain of truth problem: A Bayesian agent acting in a multi-agent environment learns to predict the other agents' policies if its prior assigns positive probability to them (the prior contains a grain of truth). We construct a large but limit computable class containing a grain of truth and show that agents based on Thompson sampling over this class converge to play Nash equilibria in arbitrary unknown computable multi-agent environments.


title: Design of WLAN/LTE/UWB Antenna with Improved Pattern Uniformity Using Ground-Cooperative Radiating Structure

abstract: The radiating effect of metal ground is rarely considered in most antenna designs. In this communication, we show that a properly designed metal ground can serve as a cooperative lower frequency radiator for reducing the antenna dimensions, broadening impedance bandwidth, and achieving almost uniform omnidirectional patterns over a wide frequency band. By introducing a ground-cooperative radiating structure (GCRS) into the metal ground of a prototype defected hexagonal monopole antenna (HMA), a more compact and broadband planar antenna of almost uniform omnidirectional patterns and stable linear polarizations is developed. The new antenna exhibits excellent omnidirectional radiation uniformity over the frequency range of $2.38 \leq f\;{\text{(GHz)}} \leq 11.80$ , together with group delay of ${\tau _g} \leq $ 0.8 ns and a compact size of $27 \times 31\;{\text{mm}}^2$ , which makes it very promising for applications in wireless local area network (WLAN), long-term evolution (LTE), and ultrawideband (UWB) systems as a compact multifunctional antenna to reduce the number of antennas installed in wireless devices for accessing multiple wireless networks.


title: Learning spatial preconditions of manipulation skills using random forests

abstract: Robots working in everyday and unstructured environments will need to perform manipulation skills using different sets of objects. To determine if a manipulation skill can be executed in a given situation, a robot will need to learn the preconditions of the skill. The robot will need to check both that the required objects are present in the scene and that they are arranged in a suitable manner for the skill to be executed. We propose a random forest approach to learn the set of spatial configurations of objects that fulfill a skill's preconditions. We also explore how parts of objects and interactions between parts can be incorporated into the scene models to improve the generalization performance. The proposed approach was evaluated on the preconditions of six manipulation skills. The experiments show that using the ensemble approach, and including the parts and interactions, results in an increase in accuracy of 16.4%.


title: Fast generation of realistic virtual humans

abstract: In this paper we present a complete pipeline to create ready-to-animate virtual humans by fitting a template character to a point set obtained by scanning a real person using multi-view stereo reconstruction. Our virtual humans are built upon a holistic character model and feature a detailed skeleton, fingers, eyes, teeth, and a rich set of facial blendshapes. Furthermore, due to the careful selection of techniques and technology, our reconstructed humans are quite realistic in terms of both geometry and texture. Since we represent our models as single-layer triangle meshes and animate them through standard skeleton-based skinning and facial blendshapes, our characters can be used in standard VR engines out of the box. By optimizing for computation time and minimizing manual intervention, our reconstruction pipeline is capable of processing whole characters in less than ten minutes.


title: A Three-Dimensional Model of Women’s Empowerment: Implications in the Field of Microfinance and Future Directions

abstract: Women's empowerment is an important goal in achieving sustainable development worldwide. Offering access to microfinance services to women is one way to increase women's empowerment. However, empirical evidence provides mixed results with respect to its effectiveness. We reviewed previous research on the impact of microfinance services on different aspects of women's empowerment. We propose a Three-Dimensional Model of Women's Empowerment to integrate previous findings and to gain a deeper understanding of women's empowerment in the field of microfinance services. This model proposes that women's empowerment can take place on three distinct dimensions: (1) the micro-level, referring to an individuals' personal beliefs as well as actions, where personal empowerment can be observed (2) the meso-level, referring to beliefs as well as actions in relation to relevant others, where relational empowerment can be observed and (3) the macro-level, referring to outcomes in the broader, societal context where societal empowerment can be observed. Importantly, we propose that time and culture are important factors that influence women's empowerment. We suggest that the time lag between an intervention and its evaluation may influence when empowerment effects on the different dimensions occur and that the type of intervention influences the sequence in which the three dimensions can be observed. We suggest that cultures may differ with respect to which components of empowerment are considered indicators of empowerment and how women's position in society may influence the development of women's empowerment. We propose that a Three-Dimensional Model of Women's Empowerment should guide future programs in designing, implementing, and evaluating their interventions. As such our analysis offers two main practical implications. First, based on the model we suggest that future research should differentiate between the three dimensions of women's empowerment to increase our understanding of women's empowerment and to facilitate comparisons of results across studies and cultures. Second, we suggest that program designers should specify how an intervention should stimulate which dimension(s) of women's empowerment. We hope that this model inspires longitudinal and cross-cultural research to examine the development of women's empowerment on the personal, relational, and societal dimension.


title: Urban road localization by using multiple layer map matching and line segment matching

abstract: In recent years, automated vehicle researches move on to the next stage, that is, auto-driving experiments on public roads. This study focuses on how to realize accurate localization based on the use of Lidar data and precise map. On different roads such as urban roads and expressways, the observed information of surrounding is significantly different. For example, on the urban roads, many buildings can be observed around the upper part of the vehicle. Such observation realizes accurate map matching. On the other hand, the upper part has no specific observation on the expressway. Therefore, it is necessary to observe the lower part for the map matching. To adapt the situation changes, we propose a localization method based on self-adaptive multi-layered scan matching and road line segment matching. The main idea is to effectively match the features observed from different heights and to improve the results by applying the line segment matching in certain scenes. Localization experiments show the ability to estimate accurate vehicle pose in urban driving.


title: Graded Causation and Defaults

abstract: Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality. A number of philosophers and computer scientists have also suggested that an appeal to such factors can help deal with problems facing existing accounts of actual causation. This paper develops a flexible formal framework for incorporating defaults, typicality, and normality into an account of actual causation. The resulting account takes actual causation to be both graded and comparative. We then show how our account would handle a number of standard cases.


title: Iris localization based on integro-differential operator for unconstrained infrared iris images

abstract: Iris localization is an important step for high accuracy iris recognition systems and it becomes difficult for iris images captured in unconstrained environments. The proposed method localizes irises in unconstrained infrared iris images having non-ideal issues such as severe reflections, eyeglasses, low contrast, low illumination and occlusions by eyebrow hair, eyelids and eyelashes. In the proposed method, the iris image is first preprocessed using morphological operation to remove reflections and make it suitable for subsequent steps. The proposed method detects pupil using Daugman's integro-differential operator (IDO) and iris's outer boundary is detected using proposed modified Daugman's IDO. The proposed method proposes a technique based on thresholding and morphological operation to reduce the number of pixels on which the IDO is applied for detecting pupil which improves the time performance and accuracy as well. The method was tested with CASIA-Iris-Thousand, version 4.0 (CITHV4) iris database which contains challenging images having non-ideal issues as described before. The average accuracy of the proposed method is 99.3% and average time cost per image is 1.86 seconds for CITHV4. The proposed method shows improvement in both accuracy and time when compared with some published state-of-the-art iris localization methods in the literature.


title: Face recognition techniques, their advantages, disadvantages and performance evaluation

abstract: A human brain can store and remember thousands of faces in a person's life time, however it is very difficult for an automated system to reproduce the same results. Faces are complex and multidimensional which makes extraction of facial features to be very challenging, yet it is imperative for our face recognition systems to be better than our brain's capabilities. The face like many physiological biometrics that include fingerprint, hand geometry, retina, iris and ear uniquely identifies each individual. In this paper we focus mainly on the face recognition techniques. This review looks at three types of recognition approaches namely holistic, feature based (geometric) and the hybrid approach. We also look at the challenges that are face by the approaches.


title: High frequency link DC/AC converter with PWM cycloconverter

abstract: A high-frequency link DC/AC converter developed for flexible, compact, and high-efficiency uninterruptible power supply (UPS) systems is discussed. The DC/AC converter consists of a 50% duty ratio rectangular voltage output inverter, a high-frequency transformer, a pulse-width modulation (PWM) cycloconverter, and an LC filter. For this converter, a three-phase output DC/AC converter can be easily realized with only one inverter and one three-phase cycloconverter. Conversion efficiency is inherently high because the inverter can utilize zero-current switching to minimize the switching loss. Output waveform control is improved because the dead time in the cycloconverter PWM can be eliminated. The main circuit configuration, the PWM method of the cycloconverter used to obtain a sinusoidal output voltage and the switching method of the inverter are described. The experimental results of a 1 kVA DC/AC converter using a high-frequency link of 20 kHz in both single-phase and three-phase output are discussed. >


title: Block-based adaptive ROI for remote photoplethysmography

abstract: Remote photoplethysmography (rPPG) can achieve contactless human vital signs monitoring, but its signal quality is limited by the remote operation nature. In practical applications, improving the rPPG signal quality becomes an essential task. As a remote imaging technique, rPPG utilizes a camera to capture a video of a skin area, especially the facial area, then focuses on a particular sub-area as the region of interest (ROI). In this paper, we investigated a novel adaptive ROI (AROI) approach for improving the rPPG signal quality. In this approach, block-based spatial-temporal division is performed on a captured face video. Based on these segmented video pipelines, the spatial-temporal quality distribution of the rPPG signals is estimated using a signal-to-noise ratio (SNR) feature. Afterwards, AROIs are calculated through mean-shift clustering and adaptive thresholding in SNR maps. As the AROI can be dynamically adjusted according to the spatial-temporal quality distribution of rPPG signals on the face, the quality of the final recovered rPPG signal is improved. The performance of the proposed AROI approach was evaluated with both still and moving subjects. Compared to conventional ROI methods for rPPG, the proposed AROI obtained a higher accuracy in heart rate measurement. And the state-of-the-art motion-resistant rPPG techniques can be effectively enhanced through being integrated with the AROI.


title: On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction

abstract: Several contributions have been made so far to develop optimal multichannel linear filtering approaches and show their ability to reduce the acoustic noise. However, there has not been a clear unifying theoretical analysis of their performance in terms of both noise reduction and speech distortion. To fill this gap, we analyze the frequency-domain (non-causal) multichannel linear filtering for noise reduction in this paper. For completeness, we consider the noise reduction constrained optimization problem that leads to the parameterized multichannel non-causal Wiener filter (PMWF). Our contribution is fivefold. First, we formally show that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction. Second, we propose new simplified expressions for the PMWF, the MVDR, and the generalized sidelobe canceller (GSC) that depend on the signals' statistics only. In contrast to earlier works, these expressions are explicitly independent of the channel transfer function ratios. Third, we quantify the theoretical gains and losses in terms of speech distortion and noise reduction when using the PWMF by establishing new simplified closed-form expressions for three performance measures, namely, the signal distortion index, the noise reduction factor (originally proposed in the paper titled ldquoNew insights into the noise reduction Wiener filter,rdquo by J. Chen (IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, no. 4, pp. 1218-1234, Jul. 2006) to analyze the single channel time-domain Wiener filter), and the output signal-to-noise ratio (SNR). Fourth, we analyze the effects of coherent and incoherent noise in addition to the benefits of utilizing multiple microphones. Fifth, we propose a new proof for the a posteriori SNR improvement achieved by the PMWF. Finally, we provide some simulations results to corroborate the findings of this work.


title: Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning

abstract: Functional magnetic resonance imaging and a meta-analysis of prior neuroimaging studies were used to characterize cortical changes resulting from extensive practice and to evaluate a dual-processing account of the neural mechanisms underlying human learning. Three core predictions of the dual processing theory are evaluated: 1) that practice elicits generalized reductions in regional activity by reducing the load on the cognitive control mechanisms that scaffold early learning; 2) that these control mechanisms are domain-general; and 3) that no separate processing pathway emerges as skill develops. To evaluate these predictions, a meta-analysis of prior neuroimaging studies and a within-subjects fMRI experiment contrasting unpracticed to practiced performance in a paired-associate task were conducted. The principal effect of practice was found to be a reduction in the extent and magnitude of activity in a cortical network spanning bilateral dorsal prefrontal, left ventral prefrontal, medial frontal (anterior cingulate), left insular, bilateral parietal, and occipito-temporal (fusiform) areas. These activity reductions are shown to occur in common regions across prior neuroimaging studies and for both verbal and nonverbal paired-associate learning in the present fMRI experiment. The implicated network of brain regions is interpreted as a domain-general system engaged specifically to support novice, but not practiced, performance.


title: A Hybrid Architecture for Multi-Party Conversational Systems

abstract: Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems. From the moment that an utterance is sent to a group, to the moment that it is replied in the group by a member, several activities must be done by the system: utterance understanding, information search, reasoning, among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed hybrid architecture using both rules and machine learning and some insights after implementing and evaluating one on the finance domain.


title: Joint Prediction of Topics in a URL Hierarchy

abstract: We study the problem of jointly predicting topics for all web pages within URL hierarchies. We employ a graphical model in which latent variables represent the predominant topic within a subtree of the URL hierarchy. The model is built around a generative process that infers how web site administrators hierarchically structure web site according to topic, and how web page content is generated depending on the page topic. The resulting predictive model is linear in a joint feature map of content, topic labels, and the latent variables. Inference reduces to message passing in a tree-structured graph; parameter estimation is carried out using concave-convex optimization. We present a case study on web page classification for a targeted advertising application.


title: Consistency Tradeoffs in Modern Distributed Database System Design: CAP is Only Part of the Story

abstract: The CAP theorem's impact on modern distributed database system design is more limited than is often perceived. Another tradeoff-between consistency and latency -has had a more direct influence on several well-known DDBSs. A proposed new formulation, PACELC, unifies this tradeoff with CAP.


title: Seeking a Foundation for Context-Aware Computing

abstract: Context-aware computing is generally associated with elements of the ubiquitous computing program, and the opportunity to distribute computation and interaction through the environment rather than concentrating it at the desktop computer. However, issues of context have also been important in other areas of human-computer interaction research. I argue that the scope of context-based computing should be extended to include not only ubiquitous computing, but also recent trends in tangible interfaces as well as work on sociological investigations of the organization of interactive behavior. By taking a view of context-aware computing that integrates these different perspectives, we can begin to understand the foundational relations that tie them all together, and that provide a framework for understanding the basic principles behind these various forms of embodied interaction. In particular, I point to phenomenology as a basis for the development of a new framework for design and evaluation of context-aware technologies.


title: EAGr: Supporting Continuous Ego-centric Aggregate Queries over Large Dynamic Graphs

abstract: In this paper, we present EAGr, a system for supporting large numbers of continuous neighborhood-based ("ego-centric") aggregate queries over large, highly dynamic, rapidly evolving graphs. Examples of such queries include computation of personalized, tailored trends in social networks, anomaly or event detection in communication or financial transaction networks, local search and alerts in spatio-temporal networks, to name a few. Key challenges in supporting such continuous queries include very high update rates typically seen in these situations, large numbers of queries that need to be executed simultaneously, stringent low latency requirements. In this paper, we propose a flexible, general, extensible in-memory framework for executing different types of ego-centric aggregate queries over large dynamic graphs with low latencies. Our framework is built around the notion of an aggregation overlay graph, a pre-compiled data structure that encodes the computations to be performed when an update or a query is received. The overlay graph enables sharing of partial aggregates across different ego centric queries (corresponding to different nodes in the graph), also allows partial pre-computation of the aggregates to minimize the query latencies. We present several highly scalable techniques for constructing an overlay graph given an aggregation function, also design incremental algorithms for handling changes to the structure of the underlying graph itself, that may result in significant changes to the neighborhoods on which queries are posed. We also present an optimal, polynomial-time algorithm for making the pre-computation decisions given an overlay graph. Although our approach is naturally parallelizable, we focus on a single-machine deployment in this paper and show that our techniques can easily handle graphs of size up to 320 million nodes and edges, achieve update and query throughputs of over 500,000/s using a single, powerful machine.


title: Düppel: retrofitting commodity operating systems to mitigate cache side channels in the cloud

abstract: This paper presents the design, implementation and evaluation of a system called Duppel that enables a tenant virtual machine to defend itself from cache-based side-channel attacks in public clouds. Duppel includes defenses for time-shared caches such as per-core L1 and L2 caches. Experiments in the lab and on public clouds show that Duppel effectively obfuscates timing signals available to an attacker VM via these caches and incurs modest performance overheads (at most 7% and usually much less) in the common case of no side-channel attacks. Moreover, Duppel requires no changes to hypervisors or support from cloud operators.


title: Bayes Optimality in Linear Discriminant Analysis

abstract: We present an algorithm which provides the one-dimensional subspace where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex Bayes error function g(v). This allows for the minimization of the error function using standard convex optimization algorithms. Our algorithm is then extended to the minimization of the Bayes error in the more general case of heteroscedastic distributions. This is done by means of an appropriate kernel mapping function. This result is further extended to obtain the d-dimensional solution for any given d, by iteratively applying our algorithm to the null space of the (d - 1)-dimensional solution. We also show how this result can be used to improve up on the outcomes provided by existing algorithms, and derive a low-computational cost, linear approximation. Extensive experimental validations are provided to demonstrate the use of these algorithms in classification, data analysis and visualization.


title: The Saccharomyces cerevisiae DNA repair gene RAD2 is regulated in meiosis but not during the mitotic cell cycle.

abstract: The expression of the RAD2 gene of Saccharomyces cerevisiae is elevated upon DNA damage. Here, we show that RAD2 transcript levels also rise approximately eightfold during meiosis but remain constant during the mitotic cell cycle. The period of maximal RAD2 mRNA accumulation during meiosis is consistent with a possible role of RAD2 in a late stage of recombination, in mismatch repair of heteroduplexes, or both.


title: Conditional Density Estimation via Least-Squares Density Ratio Estimation

abstract: Estimating the conditional mean of an inputoutput relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation. Our basic idea is to express the conditional density in terms of the ratio of unconditional densities, and the ratio is directly estimated without going through density estimation. Experiments using benchmark and robot transition datasets illustrate the usefulness of the proposed approach.


title: Men’s Experiences of the Criminal Justice System Following Female Perpetrated Intimate Partner Violence

abstract: The current study aimed to explore men’s experience of the UK Criminal Justice System (CJS) following female-perpetrated intimate partner violence (IPV). Unstructured face-to-face and Skype interviews were conducted with six men aged between 40–65 years. Interviews were transcribed and analysed using interpretative phenomenological analysis (IPA). Due to the method of analysis and the sensitive nature of the research, the researcher engaged in a process of reflexivity. Four main themes were identified, including ‘Guilty until Proven Innocent: Victim Cast as Perpetrator;’ ‘Masculine Identity;’ ‘Psychological Impact’ and ‘Light at the End of the Tunnel.’ Themes were discussed and illustrated with direct quotes drawn from the transcripts. Directions for future research, criminal justice interventions, and therapeutic interventions were discussed.


title: Graphic literacies for a digital age : the survival of layout

abstract: Page layout is dominant in many genres of physical documents, but it is frequently overlooked in academic analyses of texts and in digitized versions. Its presence is largely determined by available technologies and skills: If no provision is made for creating, preserving, or describing layout, then it tends not to be created, preserved or described. However, I argue, the significance and utility of layout for readers is such that it will survive or reemerge. I review how layout has been treated in the literature of graphic design and linguistics, and consider its role as a memory tool. I distinguish between fixed, flowed, fluid, and fugitive layouts, determined not only by authorial intent, but by technical constraints. Finally, I describe graphic literacy as a component of functional literacy and suggest that corresponding graphic literacies are needed not only by readers, but by creators of documents and by the information management technologies that produce, deliver, and store them.


title: Imposing Hard Constraints on Deep Networks: Promises and Limitations

abstract: Imposing constraints on the output of a Deep Neural Net is one way to improve the quality of its predictions while loosening the requirements for labeled training data. Such constraints are usually imposed as soft constraints by adding new terms to the loss function that is minimized during training. An alternative is to impose them as hard constraints, which has a number of theoretical benefits but has not been explored so far due to the perceived intractability of the problem. ::: In this paper, we show that imposing hard constraints can in fact be done in a computationally feasible way and delivers reasonable results. However, the theoretical benefits do not materialize and the resulting technique is no better than existing ones relying on soft constraints. We analyze the reasons for this and hope to spur other researchers into proposing better solutions.


title: Quality Physical Education: A Commentary on Effective Physical Education Teaching

abstract: In my commentary in response to the 3 articles (McKenzie & Lounsbery, 2013; Rink, 2013; Ward, 2013), I focus on 3 areas: (a) content knowledge, (b) a holistic approach to physical education, and (c) policy impact. I use the term quality teaching rather than “teacher effectiveness.” Quality teaching is a term with the potential to move our attention beyond a focus merely on issues of effectiveness relating to the achievement of prespecified objectives. I agree with Ward that teacher content knowledge is limited in physical education, and I argue that if the student does not have a connection to or relationship with the content, this will diminish their learning gains. I also argue for a more holistic approach to physical education coming from a broader conception. Physical educators who teach the whole child advocate for a plethora of physical activity, skills, knowledge, and positive attitudes that foster healthy and active playful lifestyles. Play is a valuable educational experience. I also endorse view...


title: Situated analytics: Demonstrating immersive analytical tools with augmented reality

abstract: Abstract This paper introduces the use of Augmented Reality as an immersive analytical tool in the physical world. We present Situated Analytics, a novel combination of real-time interaction and visualization techniques that allows exploration and analysis of information about objects in the user's physical environment. Situated Analytics presents both situated and abstract summary and contextual information to a user. We conducted a user study to evaluate its use in three shopping analytics tasks, comparing the use of a Situated Analytics prototype with manual analysis. The results showed that users preferred the Situated Analytics prototype over the manual method, and that tasks were performed more quickly and accurately using the prototype.


title: Actual causation and the art of modeling

abstract: [Introduction] In The Graduate, Benjamin Braddock (Dustin Hoffman) is told that the future can ::: be summed up in one word: “Plastics”. One of us (Halpern) recalls that in roughly ::: 1990, Judea Pearl told him that the future was in causality. Pearl’s own research ::: was largely focused on causality in the years after that; his seminal contributions ::: are widely known. We were among the many influenced by his work. We discuss one ::: aspect of it, actual causation, in this article, although a number of our comments ::: apply to causal modeling more generally.


title: Social media analytics and internet of things: survey

abstract: Due to the emergence of social media, there is a paradigm shift in the area of information production, processing and consumption. Hence, investigation in the utilization of open social media data is a relevant research topic. The openness of data, social media data, enables innovation and societal value creation. Social media analytics is an evolving research domain with interdisciplinary methods that are common in data mining such as text mining, social network analysis, trend analysis, and sentiment analysis. Also, social media analytics deals with development and evaluation of frameworks and informatics tools to process noisy and unstructured social media data. On the other hand, Internet of Things (IoT) enables the utilization of digital artifacts with well-established solutions and allows things to be connected regardless of location and time. However, a literature review about social media analytics and IoT integration is missing. In this paper, we conducted a systematic literature review of social media analytics and IoT integration. The literature review indicates that there are fewer research works done in the area of social media analytics and IoT compared to Data Mining and IoT. This paper facilitates discussion and elicits research potentials in social media analytics and IoT integration.


title: Lateral Control of an Autonomous Vehicle

abstract: The asymptotic stabilization problem for a class of nonlinear under-actuated systems is studied and solved. Its solution, together with the back-stepping and the forwarding control design methods, is exploited in the control of the nonlinear lateral dynamics of a vehicle. Even though the theoretical studies of the lateral control of autonomous vehicles are traditionally applied to lane keeping cases, the results can be applied to broader range of areas, such as lane changing cases. The comparison between the performances of the closed-loop systems with the given controller and a typical human driver is given and demonstrates the speediness and the effectiveness of the feedback controller.


title: Advanced organic light-emitting devices for enhancing display performances

abstract: Various advanced organic light-emitting devices (OLEDs) for enhancing performances of OLED displays, particularly active-matrix OLED displays, are reviewed in this paper. These include top-emitting OLEDs, inverted OLEDs, high-contrast OLEDs, microcavity OLEDs, and their combinations. How these device structures enhance display performances, such as color saturation, brightness or emission efficiency, contrast, and aperture ratio of pixels, etc., are discussed. The critical technical issues and the status of development associated with each device technology are reviewed.


title: Minimal MapReduce algorithms

abstract: MapReduce has become a dominant parallel computing paradigm for big data, i.e., colossal datasets at the scale of tera-bytes or higher. Ideally, a MapReduce system should achieve a high degree of load balancing among the participating machines, and minimize the space usage, CPU and I/O time, and network transfer at each machine. Although these principles have guided the development of MapReduce algorithms, limited emphasis has been placed on enforcing serious constraints on the aforementioned metrics simultaneously. This paper presents the notion of minimal algorithm, that is, an algorithm that guarantees the best parallelization in multiple aspects at the same time, up to a small constant factor. We show the existence of elegant minimal algorithms for a set of fundamental database problems, and demonstrate their excellent performance with extensive experiments.


title: Coverage-based Greybox Fuzzing as Markov Chain

abstract: Coverage-based Greybox Fuzzing (CGF) is a random testing approach that requires no program analysis. A new test is generated by slightly mutating a seed input. If the test exercises a new and interesting path, it is added to the set of seeds; otherwise, it is discarded. We observe that most tests exercise the same few "high-frequency" paths and develop strategies to explore significantly more paths with the same number of tests by gravitating towards low-frequency paths. We explain the challenges and opportunities of CGF using a Markov chain model which specifies the probability that fuzzing the seed that exercises path i generates an input that exercises path j. Each state (i.e., seed) has an energy that specifies the number of inputs to be generated from that seed. We show that CGF is considerably more efficient if energy is inversely proportional to the density of the stationary distribution and increases monotonically every time that seed is chosen. Energy is controlled with a power schedule. We implemented the exponential schedule by extending AFL. In 24 hours, AFLFAST exposes 3 previously unreported CVEs that are not exposed by AFL and exposes 6 previously unreported CVEs 7x faster than AFL. AFLFAST produces at least an order of magnitude more unique crashes than AFL.


title: HyperLogLog in practice: algorithmic engineering of a state of the art cardinality estimation algorithm

abstract: Cardinality estimation has a wide range of applications and is of particular importance in database systems. Various algorithms have been proposed in the past, and the HyperLogLog algorithm is one of them. In this paper, we present a series of improvements to this algorithm that reduce its memory requirements and significantly increase its accuracy for an important range of cardinalities. We have implemented our proposed algorithm for a system at Google and evaluated it empirically, comparing it to the original HyperLogLog algorithm. Like HyperLogLog, our improved algorithm parallelizes perfectly and computes the cardinality estimate in a single pass.


title: Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet

abstract: Major winning Convolutional Neural Networks (CNNs), such as VGGNet, ResNet, DenseNet, \etc, include tens to hundreds of millions of parameters, which impose considerable computation and memory overheads. This limits their practical usage in training and optimizing for real-world applications. On the contrary, light-weight architectures, such as SqueezeNet, are being proposed to address this issue. However, they mainly suffer from low accuracy, as they have compromised between the processing power and efficiency. These inefficiencies mostly stem from following an ad-hoc designing procedure. In this work, we discuss and propose several crucial design principles for an efficient architecture design and elaborate intuitions concerning different aspects of the design procedure. Furthermore, we introduce a new layer called {\it SAF-pooling} to improve the generalization power of the network while keeping it simple by choosing best features. Based on such principles, we propose a simple architecture called {\it SimpNet}. We empirically show that SimpNet provides a good trade-off between the computation/memory efficiency and the accuracy solely based on these primitive but crucial principles. SimpNet outperforms the deeper and more complex architectures such as VGGNet, ResNet, WideResidualNet \etc, on several well-known benchmarks, while having 2 to 25 times fewer number of parameters and operations. We obtain state-of-the-art results (in terms of a balance between the accuracy and the number of involved parameters) on standard datasets, such as CIFAR10, CIFAR100, MNIST and SVHN. The implementations are available at \href{url}{this https URL}.


title: Online multiple kernel regression

abstract: Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this paper presents a novel scheme of Online Multiple Kernel Regression (OMKR), which sequentially learns the kernel-based regressor in an online and scalable fashion, and dynamically explore a pool of multiple diverse kernels to avoid suffering from a single fixed poor kernel so as to remedy the drawback of manual/heuristic kernel selection. The OMKR problem is more challenging than regular kernel-based regression tasks since we have to on-the-fly determine both the optimal kernel-based regressor for each individual kernel and the best combination of the multiple kernel regressors. In this paper, we propose a family of OMKR algorithms for regression and discuss their application to time series prediction tasks. We also analyze the theoretical bounds of the proposed OMKR method and conduct extensive experiments to evaluate its empirical performance on both real-world regression and times series prediction tasks.


title: Argument Mining: Extracting Arguments from Online Dialogue

abstract: Online forums are now one of the primary venues for public dialogue on current social and political issues. The related corpora are often huge, covering any topic imaginable. Our aim is to use these dialogue corpora to automatically discover the semantic aspects of arguments that conversants are making across multiple dialogues on a topic. We frame this goal as consisting of two tasks: argument extraction and argument facet similarity. We focus here on the argument extraction task, and show that we can train regressors to predict the quality of extracted arguments with RRSE values as low as .73 for some topics. A secondary goal is to develop regressors that are topic independent: we report results of cross-domain training and domain-adaptation with RRSE values for several topics as low as .72, when trained on topic independent features.


title: Interpreting tree ensembles with inTrees

abstract: Tree ensembles such as random forests and boosted trees are accurate but difficult to understand. In this work, we provide the interpretable trees (inTrees) framework that extracts, measures, prunes, selects, and summarizes rules from a tree ensemble, and calculates frequent variable interactions. The inTrees framework can be applied to multiple types of tree ensembles, e.g., random forests, regularized random forests, and boosted trees. We implemented the inTrees algorithms in the “inTrees” R package.


title: Semi-supervised forecasting of fraudulent financial statements

abstract: Prediction of potential fraudulent activities may prevent both the stakeholders and the appropriate regulatory authorities of national or international level from being deceived. The objective difficulties on collecting adequate data that are obsessed by completeness affects the reliability of the most supervised Machine Learning methods. This work examines the effectiveness of forecasting fraudulent financial statements using semi-supervised classification techniques (SSC) that require just a few labeled examples for achieving robust learning behaviors mining useful data patterns from a larger pool of unlabeled examples. Based on data extracted from Greek firms, a number of comparisons between supervised and semi-supervised algorithms has been conducted. According to the produced results, the later algorithms are favored being examined over several scenarios of different Labeled Ratio (R) values.


title: Temporal frequency of subthreshold oscillations scales with entorhinal grid cell field spacing.

abstract: Grid cells in layer II of rat entorhinal cortex fire to spatial locations in a repeating hexagonal grid, with smaller spacing between grid fields for neurons in more dorsal anatomical locations. Data from in vitro whole-cell patch recordings showed differences in frequency of subthreshold membrane potential oscillations in entorhinal neurons that correspond to different positions along the dorsal-to-ventral axis, supporting a model of physiological mechanisms for grid cell responses.


title: AUTOMATED IRRIGATION SYSTEM USING THERMOELECTRIC GENERATOR AS SOIL MOISTURE DETECTOR

abstract: This paper shows the design and fabrication of a Thermo-electric generator (TEG) and the implementation of an automated irrigation system using this TEG as a soil moisture detector. The TEG inserted in two heat exchangers is capable of finding the thermal difference between the air and the soil that establishes a relationship with the soil's moisture condition. Being able to obtain the soil moisture level from the TEG's output, a microcontroller is used to automate the irrigation system. The irrigation system adapts to the soil area's condition it irrigates based from the moisture it detects via the TEG. The water consumption of the soil is controlled by the automated irrigation system based on the soil's condition and therefore, promotes water conservation compared to that of the water consumption of manual irrigation system. It also optimizes plant growth in that it waters it to the correct moisture level at the right time.


title: A new hardware architecture for genomic and proteomic sequence alignment

abstract: We describe a novel hardware architecture for genomic and proteomic sequence alignment which achieves a speed-up of two to three orders of magnitude over Smith-Waterman dynamic programming (DP) in hardware. In our previous papers, we introduce several features of our search algorithm, DASH, which outperforms NCII-Blast (BLAST) by an order of magnitude in software, and has better sensitivity. Indeed, DASH has been shown to have excellent sensitivity compared to Smith-Waterman. It is designed around the principle of considering genomic and proteomic sequence alignments to typically consist of regions of high homology (the diagonals) interspersed with regions of low homology. In DASH, the optimal solution consists of such diagonals joined by regions of exact DP. This is affordable due to the small area of these interconnecting regions. Accordingly, we have designed a chip which finds the diagonals and performs the inter-region DP directly in hardware. On a Xilinx Vitex II, XC2V6000, FPGA, it performs over 10/sup 12/ base comparisons/second.


title: When Rule Engine Meets Big Data: Design and Implementation of a Distributed Rule Engine Using Spark

abstract: Rule Engines have been widely used both in industry and academia since they can separate rule knowledge from implementation logic conveniently and flexibly. However, traditional rule engine systems can not deal with big data, because of the limitations of memory and computing capacity of one single computer. Consequently, some researchers have proposed distributed rule engine to meet this challenge. But these solutions still do not work well when enormous amounts of facts are involved, for the reasons such as high cost of moving data, data imbalance and so on. We present the SparkRE system, a distributed rule engine based on Spark, to support big data reasoning. In particular, SparkRE uses DataFrame representing distributed working memory which holds enormous amounts of facts, and implements the rule condition-testing mechanism by the query execution engine of Spark SQL. In addition, we design a rule language tailored for SparkRE and an efficient inference engine. An experimental evaluation shows SparkRE's capability of matching 26 million facts, and reveals its scalability and performance.


title: 3D Game Engine Design: A Practical Approach to Real-Time Computer Graphics

abstract: *Preface *1 Introduction *2 The Graphics System *2.1 The Foundation *2.1.1 Coordinate Systems *2.1.2 Handedness and Cross Products *2.1.3 Points and Vectors *2.2 Transformations *2.2.1 Linear Transformations *2.2.2 Affine Transformations *2.2.3 Projective Transformations *2.2.4 Properties of Perspective Projection *2.2.5 Homogeneous Points and Matrices *2.3 Cameras *2.3.1 The Perspective Camera Model *2.3.2 Model or Object Space *2.3.3 World Space *2.3.4 View, Camera, or Eye Space *2.3.5 Clip, Projection, or Homogeneous Space *2.3.6 Window Space *2.3.7 Putting Them All Together *2.4 Culling and Clipping *2.4.1 Object Culling *2.4.2 Back Face Culling *2.4.3 Clipping to the View Frustum *2.5 Rasterizing *2.5.1 Line Segments *2.5.2 Circles *2.5.3 Ellipses *2.5.4 Triangles *2.6 Vertex Attributes *2.6.1 Colors *2.6.2 Lighting and Materials *2.6.3 Textures *2.6.4 Transparency and Opacity *2.6.5 Fog *2.6.6 And Many More *2.6.7 Rasterizing Attributes *2.7 Issues of Software, Hardware, and APIs *2.7.1 A General Discussion *2.7.2 Portability versus Performance *2.8 API Conventions *2.8.1 Matrix Representation and Storage *2.8.2 Matrix Composition *2.8.3 View Matrices *2.8.4 Projection Matrices *2.8.5 Window Handedness *2.8.6 Rotations *2.8.7 Fast Computations using the Graphics API *3 Renderers *3.1 Software Rendering *3.2 Hardware Rendering *3.3 The Fixed-Function Pipeline *3.4 Vertex and Pixel Shaders *3.5 An Abstract Rendering API *4 Special Effects Using Shaders *4.1 Vertex Colors *4.2 Lighting and Materials *4.3 Textures *4.4 Multitextures *4.5 Bump Maps *4.6 Gloss Maps *4.7 Sphere Maps *4.8 Cube Maps *4.9 Refraction *4.10 Planar Reflection *4.11 Planar Shadows *4.12 Projected Textures *4.13 Shadow Maps *4.14 Volumetric Fog *4.15 Skinning *4.16 Miscellaneous *4.16.1 Iridescence *4.16.2 Water Effects *4.16.3 Volumetric Textures *5 Scene Graphs *5.1 The Need for High-Level Data Management *5.2 The Need for Low-Level Data Structures *5.3 Geometric State *5.3.1 Vertices and Vertex Attributes *5.3.2 Transformations *5.3.3 Bounding Volumes *5.4 Render State *5.4.1 Global State *5.4.2 Lights *5.4.3 Effects *5.5 The Update Pass *5.5.1 Geometric State Updates *5.5.2 Render State Updates *5.6 The Culling Pass *5.6.1 Hierarchical Culling *5.6.2 Sorted Culling *5.7 The Drawing Pass *5.7.1 Single-Pass Drawing *5.7.2 Single Effect, Multipass Drawing *5.7.3 Multiple Effect, Multipass Drawing *5.7.4 Caching Data on the Graphics Hardware *5.7.5 Sorting to Reduce State Changes *5.8 Scene Graph Design Issues *5.8.1 Organization Based on Geometric State *5.8.2 Organization Based on Render State *5.8.3 Scene Graph Operations and Threading *5.8.4 The Producer-Consumer Model *6 Scene Graph Compilers *6.1 The Need for Platform-Specific Optimization *6.2 The Need for Reducing Memory Fragmentation *6.3 A Scene Graph as a Dynamic Expression *6.4 Compilation from High-Level to Low-Level Data *6.5 Control of Compilation via Node Tags *7 Memory Management *7.1 Memory Budgets for Game Consoles *7.2 General Concepts for Memory Management *7.2.1 Allocation, Deallocation, and Fragmentation *7.2.2 Sequential-Fit Methods *7.2.3 Buddy-System Methods *7.2.4 Segregated-Storage Methods *7.3 Design Choices *7.3.1 Memory Utilization *7.3.2 Fast Allocation and Deallocation *8 Controller-Based Animation *8.1 Vertex Morphing *8.2 Keyframe Animation *8.3 Inverse Kinematics *8.4 Skin and Bones *8.5 Particle Systems *9 Spatial Sorting *9.1 Spatial Partitioning *9.1.1 Quadtrees and Octrees *9.1.2 BSP Trees *9.1.3 User-Defined Maps *9.2 Node-Based Sorting *9.3 Portals *9.4 Occlusion Culling *10 Level of Detail *10.1 Discrete Level of Detail *10.1.1 Sprites and Billboards *10.1.2 Model Switching *10.2 Continuous Level of Detail *10.2.1 General Concepts *10.2.2 Application to Regular Meshes *10.2.3 Application to General Meshes *10.3 Infinite Level of Detail *10.3.1 General Concepts *10.3.2 Application to Parametric Curves *10.3.3 Application to Parametric Surfaces *11 Terrain *11.1 Data Representations *11.2 Level of Detail for Height Fields *11.3 Terrain Pages and Memory Management *12 Collision Detection *12.1 Static Line-Object Intersections *12.2 Static Object-Object Intersections *12.3 Dynamic Line-Object Intersections *12.3.1 Distance-Based Approach *12.3.2 Intersection-Based Approach *12.4 Dynamic Object-Object Intersections *12.4.1 Distance-Based Approach *12.4.2 Intersection-Based Approach *12.5 Path Finding to Avoid Collisions *13 Physics *13.1 Basic Concepts *13.2 Particle Systems *13.3 Mass-Spring Systems *13.4 Deformable Bodies *13.5 Rigid Bodies *14 Object-Oriented Infrastructure *14.1 Object-Oriented Software Construction *14.2 Style, Naming Conventions, and Namespaces *14.3 Run-Time Type Information *14.4 Templates *14.5 Shared Objects and Reference Counting *14.6 Streaming *14.7 Startup and Shutdown *14.8 An Application Layer *15 Mathematical Topics *15.1 Standard Objects *15.2 Curves *15.3 Surfaces *15.4 Distance Algorithms *15.5 Intersection Algorithms *15.6 Numerical Algorithms *15.7 All About Rotations *15.7.1 Rotation Matrices *15.7.2 Quaternions *15.7.3 Euler Angles *15.7.4 Performance Issues *15.8 The Curse of Nonuniform Scaling * Bibliography * Index


title: Be Appropriate and Funny: Automatic Entity Morph Encoding

abstract: Internet users are keen on creating different kinds of morphs to avoid censorship, express strong sentiment or humor. For example, in Chinese social media, users often use the entity morph “¹ ? b (Instant Noodles)” to refer to “h 8 · (Zhou Yongkang)” because it shares one character “· (Kang)” with the well-known brand of instant noodles “·� (Master Kang)”. We developed a wide variety of novel approaches to automatically encode proper and interesting morphs, which can effectively pass decoding tests 1 .


title: Unsupervised neuron selection for mitigating catastrophic forgetting in neural networks

abstract: Catastrophic forgetting is a well studied problem in artificial neural networks in which past representations are rapidly lost as new representations are constructed. We hypothesize that such forgetting occurs due to overlap in the hidden layers, as well as the global nature in which neurons encode information. We introduce a novel technique to mitigate forgetting which effectively minimizes activation overlapping by using online clustering to effectively select neurons in the feedforward and back-propagation phases. We demonstrate the memory retention properties of the proposed scheme using the MNIST digit recognition data set.


title: Local Fisher Discriminant Analysis for Pedestrian Re-identification

abstract: Metric learning methods, for person re-identification, estimate a scaling for distances in a vector space that is optimized for picking out observations of the same individual. This paper presents a novel approach to the pedestrian re-identification problem that uses metric learning to improve the state-of-the-art performance on standard public datasets. Very high dimensional features are extracted from the source color image. A first processing stage performs unsupervised PCA dimensionality reduction, constrained to maintain the redundancy in color-space representation. A second stage further reduces the dimensionality, using a Local Fisher Discriminant Analysis defined by a training set. A regularization step is introduced to avoid singular matrices during this stage. The experiments conducted on three publicly available datasets confirm that the proposed method outperforms the state-of-the-art performance, including all other known metric learning methods. Further-more, the method is an effective way to process observations comprising multiple shots, and is non-iterative: the computation times are relatively modest. Finally, a novel statistic is derived to characterize the Match Characteristic: the normalized entropy reduction can be used to define the 'Proportion of Uncertainty Removed' (PUR). This measure is invariant to test set size and provides an intuitive indication of performance.


title: Comparing images using joint histograms

abstract: Color histograms are widely used for content-based image retrieval due to their efficiency and robustness. However, a color histogram only records an image's overall color composition, so images with very different appearances can have similar color histograms. This problem is especially critical in large image databases, where many images have similar color histograms. In this paper, we propose an alternative to color histograms called a joint histogram, which incorporates additional information without sacrificing the robustness of color histograms. We create a joint histogram by selecting a set of local pixel features and constructing a multidimensional histogram. Each entry in a joint histogram contains the number of pixels in the image that are described by a particular combination of feature values. We describe a number of different joint histograms, and evaluate their performance for image retrieval on a database with over 210,000 images. On our benchmarks, joint histograms outperform color histograms by an order of magnitude.


title: Text classification with kernels on the multinomial manifold

abstract: Support Vector Machines (SVMs) have been very successful in text classification. However, the intrinsic geometric structure of text data has been ignored by standard kernels commonly used in SVMs. It is natural to assume that the documents are on the multinomial manifold, which is the simplex of multinomial models furnished with the Riemannian structure induced by the Fisher information metric. We prove that the Negative Geodesic Distance (NGD) on the multinomial manifold is conditionally positive definite (cpd), thus can be used as a kernel in SVMs. Experiments show the NGD kernel on the multinomial manifold to be effective for text classification, significantly outperforming standard kernels on the ambient Euclidean space.


title: Suicide pact by drowning with bound wrists: A case of medico-legal importance

abstract: Suicide pacts are uncommon and mainly committed by male–female pairs in a consortial relationship. The victims frequently choose methods such as hanging, poisoning, using a firearm, etc; however, a case of a suicide pact by drowning is rare in forensic literature. We report a case where a male and a female, both young adults, in a relationship of adopted “brother of convenience” were found drowned in a river. The victims were bound together at their wrists which helped with our conclusion this was a suicide pact. The medico-legal importance of wrist binding in drowning cases is also discussed in this article.


title: Investigating Players' Engagement, Immersion, and Experiences in Playing Pokémon Go

abstract: In recent years, Augmented Reality (AR) based mobile games have become popular among players. The Pokemon Go is one of the well-known examples. Although Pokemon Go game has become a global phenomenon, there is a limited study about players' experiences in the gameplay. Especially, little is known about players' engagement and immersion in playing AR-based mobile games in which physical movements in the real world are largely required to play the game. In this study, we conducted a pilot study with eight participants to investigate players' engagement, immersion, and experiences in the Pokemon Go gameplay. We report and discuss the preliminary findings from the study, which can help game designers understand players' experiences in the gameplay so that they can design more creative AR games in the future. Furthermore, the findings are also useful for future studies with larger sample size.


title: Active strokes: coherent line stylization for animated 3D models

abstract: This paper presents a method for creating coherently animated line drawings that include strong abstraction and stylization effects. These effects are achieved with active strokes: 2D contours that approximate and track the lines of an animated 3D scene. Active strokes perform two functions: they connect and smooth unorganized line samples, and they carry coherent parameterization to support stylized rendering. Line samples are approximated and tracked using active contours ("snakes") that automatically update their arrangment and topology to match the animation. Parameterization is maintained by brush paths that follow the snakes but are independent, permitting substantial shape abstraction without compromising fidelity in tracking. This approach renders complex models in a wide range of styles at interactive rates, making it suitable for applications like games and interactive illustrations.


title: Springer-Verlag, 2008. Twisted Edwards Curves Revisited

abstract: This paper introduces fast algorithms for performing group operations on twisted Edwards curves, pushing the recent speed limits of Elliptic Curve Cryptography (ECC) forward in a wide range of applications. Notably, the new addition algorithm uses for suitably selected curve constants. In comparison, the fastest point addition algorithms for (twisted) Edwards curves stated in the literature use . It is also shown that the new addition algorithm can be implemented with four processors dropping the effective cost to . This implies an effective speed increase by the full factor of 4 over the sequential case. Our results allow faster implementation of elliptic curve scalar multiplication. In addition, the new point addition algorithm can be used to provide a natural protection from side channel attacks based on simple power analysis (SPA).


title: A Binocular, Foveated Active Vision System

abstract: This report documents the design and implementation of a binocular, foveated active vision system as part of the Cog project at the MIT Artificial Intelligence Laboratory. The active vision system features a three degree of freedom mechanical platform that supports four color cameras, a motion control system, and a parallel network of digital signal processors for image processing. To demonstrate the capabilities of the system, we present results from four sample visual-motor tasks.


title: Text normalization in social media: progress, problems and applications for a pre-processing system of casual English

abstract: Abstract The rapid expansion in user-generated content on the Web of the 2000s, characterized by social media, has led to Web content featuring somewhat less standardized language than the Web of the 1990s. User creativity and individuality of language creates problems on two levels. The first is that social media text is often unsuitable as data for Natural Language Processing tasks such as Machine Translation, Information Retrieval and Opinion Mining, due to the irregularity of the language featured. The second is that non-native speakers of English, older Internet users and non-members of the “in-group” often find such texts difficult to understand. This paper discusses problems involved in automatically normalizing social media English, various applications for its use, and our progress thus far in a rule-based approach to the issue. Particularly, we evaluate the performance of two leading open source spell checkers on data taken from the microblogging service Twitter, and measure the extent to which their accuracy is improved by pre-processing with our system. We also present our database rules and classification system, results of evaluation experiments, and plans for expansion of the project.


title: A New Nine-Phase Permanent Magnet Synchronous Motor With Consequent Pole Rotor for High-Power Traction Applications

abstract: Although three-phase permanent magnet (PM) motors are quite common in industry, multi-phase PM motors are used in special applications where high power and redundancy are required. Multi-phase PM motors offer higher torque/power density than conventional three-phase PM motors. In this paper, a novel multi-phase consequent pole PM (CPPM) synchronous motor is proposed. The constant power–speed range of the proposed motor is quite wide as opposed to conventional PM motors. The design and the detailed finite-element analysis of the proposed nine-phase CPPM motor and performance comparison with a nine-phase surface mounted PM motor are completed to illustrate the benefits of the proposed motor.


title: Recognizing lower face action units for facial expression analysis

abstract: Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions (e.g., happiness and anger). Such prototypic expressions, however, occur infrequently. Human emotions and intentions are communicated more often by changes in one or two discrete facial features. We develop an automatic system to analyze subtle changes in facial expressions based on both permanent (e.g., mouth, eye, and brow) and transient (e.g., furrows and wrinkles) facial features in a nearly frontal image sequence. Multi-state facial component models are proposed for tracking and modeling different facial features. Based on these multi-state models, and without artificial enhancement, we detect and track the facial features, including mouth, eyes, brow, cheeks, and their related wrinkles and facial furrows. Moreover we recover detailed parametric descriptions of the facial features. With these features as the inputs, 11 individual action units or action unit combinations are recognized by a neural network algorithm. A recognition rate of 96.7% is obtained. The recognition results indicate that our system can identify action units regardless of whether they occur singly or in combinations.