title: A compact manufacturable 76-77-GHz radar module for commercial ACC applications

abstract: The design and measured results of a single-substrate transceiver module suitable for 76-77-GHz pulsed-Doppler radar applications are presented. Emphasis on ease of manufacture and cost reduction of commercial millimeter-wave systems is employed throughout as a design parameter. The importance of using predictive modeling techniques in understanding the robustness of the circuit design is stressed. Manufacturing techniques that conform to standard high-volume assembly constraints have been used. The packaged transceiver module, including three waveguide ports and intermediate-frequency output, measures 20 mm/spl times/22 mm/spl times/8 mm. The circuit is implemented using discrete GaAs/AlGaAs pseudomorphic high electron mobility transistors (pHEMTs), GaAs Schottky diodes, and varactor diodes, as well as GaAs p-i-n and pHEMT monolithic microwave integrated circuits mounted on a low-cost 127-/spl mu/m-thick glass substrate. A novel microstrip-to-waveguide transition is described to transform the planar microstrip signal into the waveguide launch. The module is integrated with a quasi-optical antenna. The measured performance of both the component parts and the complete radar transceiver module is described.


title: Non-Orthogonal Multiple Access (NOMA) for Downlink Multiuser MIMO Systems: User Clustering, Beamforming, and Power Allocation

abstract: We investigate the application of non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) in downlink multiuser multiple-input multiple-output (MIMO) cellular systems, where the total number of receive antennas at user equipment (UE) ends in a cell is more than the number of transmit antennas at the base station (BS). We first dynamically group the UE receive antennas into a number of clusters equal to or more than the number of BS transmit antennas. A single beamforming vector is then shared by all the receive antennas in a cluster. We propose a linear beamforming technique in which all the receive antennas can significantly cancel the inter-cluster interference. On the other hand, the receive antennas in each cluster are scheduled on the power domain NOMA basis with SIC at the receiver ends. For inter-cluster and intra-cluster power allocation, we provide dynamic power allocation solutions with an objective to maximizing the overall cell capacity. An extensive performance evaluation is carried out for the proposed MIMO-NOMA system and the results are compared with those for conventional orthogonal multiple access (OMA)-based MIMO systems and other existing MIMO-NOMA solutions. The numerical results quantify the capacity gain of the proposed MIMO-NOMA model over MIMO-OMA and other existing MIMO-NOMA solutions.


title: Location-based and preference-aware recommendation using sparse geo-social networking data

abstract: The popularity of location-based social networks provide us with a new platform to understand users' preferences based on their location histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are automatically learned from her location history and 2) Social opinions, which are mined from the location histories of the local experts. This recommender system can facilitate people's travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the user-locations matrix is very sparse, leading to a big challenge to traditional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to a new city. To this end, we propose a novel location recommender system, which consists of two main parts: offline modeling and online recommendation. The offline modeling part models each individual's personal preferences with a weighted category hierarchy (WCH) and infers the expertise of each user in a city with respect to different category of locations according to their location histories using an iterative learning model. The online recommendation part selects candidate local experts in a geospatial range that matches the user's preferences using a preference-aware candidate selection algorithm and then infers a score of the candidate locations based on the opinions of the selected local experts. Finally, the top-k ranked locations are returned as the recommendations for the user. We evaluated our system with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than baselines, while having a good efficiency of providing location recommendations.


title: Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model

abstract: We present a new morphological analysis model that considers semantic plausibility of word sequences by using a recurrent neural network language model (RNNLM). In unsegmented languages, since language models are learned from automatically segmented texts and inevitably contain errors, it is not apparent that conventional language models contribute to morphological analysis. To solve this problem, we do not use language models based on raw word sequences but use a semantically generalized language model, RNNLM, in morphological analysis. In our experiments on two Japanese corpora, our proposed model significantly outperformed baseline models. This result indicates the effectiveness of RNNLM in morphological analysis.


title: Implementation of Running Average Background Subtraction Algorithm in FPGA for Image Processing Applications

abstract: In this paper a new background subtraction algorithm was developed to detect moving objects from a stable system in which visual surveillance plays a major role. Initially it was implemented in MATLAB. Among all existing algorithms running average algorithm was choosen because of low computational complexity which is the major parameter of time in VLSI. The concept of the background subtraction is to subtract the current image with respect to the reference image and compare it with to the certain threshold values. We propose a new real time background subtraction algorithm which was implemented with verilog hdl in order to detect moving objects accurately. Our method involves three important modules background modelling; adaptive threshold estimation and finally fore ground extraction. Compared to all existing algorithms our method having low power consumption and low resource utilization. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. Area and the speed of the algorithm are also evaluated.


Review on Parallel and Distributed Computing


title: Rough set methods in feature selection and recognition

abstract: We present applications of rough set methods for feature selection in pattern recognition. We emphasize the role of the basic constructs of rough set approach in feature selection, namely reducts and their approximations, including dynamic reducts. In the overview of methods for feature selection we discuss feature selection criteria, including the rough set based methods. Our algorithm for feature selection is based on an application of a rough set method to the result of principal components analysis (PCA) used for feature projection and reduction. Finally, the paper presents numerical results of face and mammogram recognition experiments using neural network, with feature selection based on proposed PCA and rough set methods.


title: STDP-based Unsupervised Feature Learning using Convolution-over-time in Spiking Neural Networks for Energy-Efficient Neuromorphic Computing

abstract: Brain-inspired learning models attempt to mimic the computations performed in the neurons and synapses constituting the human brain to achieve its efficiency in cognitive tasks. In this work, we propose Spike Timing Dependent Plasticity-based unsupervised feature learning using convolution-over-time in Spiking Neural Network (SNN). We use shared weight kernels that are convolved with the input patterns over time to encode representative input features, thereby improving the sparsity as well as the robustness of the learning model. We show that the Convolutional SNN self-learns several visual categories for object recognition with limited number of training patterns while yielding comparable classification accuracy relative to the fully connected SNN. Further, we quantify the energy benefits of the Convolutional SNN over fully connected SNN on neuromorphic hardware implementation.


title: On Multilingual Training of Neural Dependency Parsers

abstract: We show that a recently proposed neural dependency parser can be improved by joint training on multiple languages from the same family. The parser is implemented as a deep neural network whose only input is orthographic representations of words. In order to successfully parse, the network has to discover how linguistically relevant concepts can be inferred from word spellings. We analyze the representations of characters and words that are learned by the network to establish which properties of languages were accounted for. In particular we show that the parser has approximately learned to associate Latin characters with their Cyrillic counterparts and that it can group Polish and Russian words that have a similar grammatical function. Finally, we evaluate the parser on selected languages from the Universal Dependencies dataset and show that it is competitive with other recently proposed state-of-the art methods, while having a simple structure.


title: Work Groups, Structural Diversity, and Knowledge Sharing in a Global Organization

abstract: Effective work groups engage in external knowledge sharing-the exchange of information, know-how, and feedback with customers, organizational experts, and others outside of the group. This paper argues that the value of external knowledge sharing increases when work groups are more structurally diverse. A structurally diverse work group is one in which the members, by virtue of their different organizational affiliations, roles, or positions, can expose the group to unique sources of knowledge. It is hypothesized that if members of structurally diverse work groups engage in external knowledge sharing, their performance will improve because of this active exchange of knowledge through unique external sources. A field study of 182 work groups in a Fortune 500 telecommunications firm operationalizes structural diversity as member differences in geographic locations, functional assignments, reporting managers, and business units, as indicated by corporate database records. External knowledge sharing was measured with group member surveys and performance was assessed using senior executive ratings. Ordered logit analyses showed that external knowledge sharing was more strongly associated with performance when work groups were more structurally diverse. Implications for theory and practice around the integration of work groups and social networks are addressed.


title: Accurate Modeling and Robust Hovering Control for a Quad–rotor VTOL Aircraft

abstract: Quad-robot type (QRT) unmanned aerial vehicles (UAVs) have been developed for quick detection and observation of the circumstances under calamity environment such as indoor fire spots. The UAV is equipped with four propellers driven by each electric motor, an embedded controller, an Inertial Navigation System (INS) using three rate gyros and accelerometers, a CCD (Charge Coupled Device) camera with wireless communication transmitter for observation, and an ultrasonic range sensor for height control. Accurate modeling and robust flight control of QRT UAVs are mainly discussed in this work. Rigorous dynamic model of a QRT UAV is obtained both in the reference and body frame coordinate systems. A disturbance observer (DOB) based controller using the derived dynamic models is also proposed for robust hovering control. The control input induced by DOB is helpful to use simple equations of motion satisfying accurately derived dynamics. The developed hovering robot shows stable flying performances under the adoption of DOB and the vision based localization method. Although a model is incorrect, DOB method can design a controller by regarding the inaccurate part of the model and sensor noises as disturbances. The UAV can also avoid obstacles using eight IR (Infrared) and four ultrasonic range sensors. This kind of micro UAV can be widely used in various calamity observation fields without danger of human beings under harmful environment. The experimental results show the performance of the proposed control algorithm.


title: Interpretable domain adaptation via optimization over the Stiefel manifold

abstract: In domain adaptation, the goal is to find common ground between two, potentially differently distributed, data sets. By finding common concepts present in two sets of words pertaining to different domains, one could leverage the performance of a classifier for one domain for use on the other domain. We propose a solution to the domain adaptation task, by efficiently solving an optimization problem through Stochastic Gradient Descent. We provide update rules that allow us to run Stochastic Gradient Descent directly on a matrix manifold: the steps compel the solution to stay on the Stiefel manifold. This manifold encompasses projection matrices of word vectors onto low-dimensional latent feature representations, which allows us to interpret the results: the rotation magnitude of the word vector projection for a given word corresponds to the importance of that word towards making the adaptation. Beyond this interpretability benefit, experiments show that the Stiefel manifold method performs better than state-of-the-art methods.


title: A digitally controlled oscillator system for SAW-less transmitters in cellular handsets

abstract: A complete digitally controlled oscillator (DCO) system for mobile phones is presented with a comprehensive study. The DCO is part of a single-chip fully compliant quad-band GSM transceiver realized in a 90-nm digital CMOS process. By operating the DCO at a 4 /spl times/ GSM low-band frequency followed by frequency dividers, the requirement of on-chip inductor Q and the amount of gate oxide stress are relaxed. It was found that a dynamic divider is needed for stringent TX output phase noise while a source-coupled-logic divider can be used for RX to save power. Both dividers are capable of producing a tight relation between four quadrature output phases at low voltage and low power. Frequency tuning is achieved through digital control of the varactors which serve as an RF DAC. Combining a MIM capacitor array and two nMOS transistor arrays of the varactors for the RF DAC, a highly linear oscillator gain which is also insensitive to process shift is achieved. The finest varactor step size is 12 kHz at the 1.6-2.0 GHz output. With a sigma-delta dithering, high frequency resolution is obtained while having negligible phase noise degradation. The measured phase noise of -167 dBc/Hz at 20 MHz offset from 915 MHz carrier and frequency tuning range of 24.5% proves that this DCO system can be used in SAW-less quad-band transmitters for mobile phones.


title: Deep Face Recognition with Center Invariant Loss

abstract: Convolutional Neural Networks (CNNs) have been widely used for face recognition and got extraordinary performance with large number of available face images of different people. However, it is hard to get uniform distributed data for all people. In most face datasets, a large proportion of people have few face images. Only a small number of people appear frequently with more face images. These people with more face images have higher impact on the feature learning than others. The imbalanced distribution leads to the difficulty to train a CNN model for feature representation that is general for each person, instead of mainly for the people with large number of face images. To address this challenge, we proposed a center invariant loss which aligns the center of each person to enforce the learned features to have a general representation for all people. The center invariant loss penalizes the difference between each center of classes. With center invariant loss, we can train a robust CNN that treats each class equally regardless the number of class samples. Extensive experiments demonstrate the effectiveness of the proposed approach. We achieve state-of-the-art results on LFW and YTF datasets.


title: EXCOL: An EXtract-and-COmplete Layering Approach to Cartoon Animation Reusing

abstract: We introduce the EXtract-and-COmplete Layering method (EXCOL)-a novel cartoon animation processing technique to convert a traditional animated cartoon video into multiple semantically meaningful layers. Our technique is inspired by vision-based layering techniques but focuses on shape cues in both the extraction and completion steps to reflect the unique characteristics of cartoon animation. For layer extraction, we define a novel similarity measure incorporating both shape and color of automatically segmented regions within individual frames and propagate a small set of user-specified layer labels among similar regions across frames. By clustering regions with the same labels, each frame is appropriately partitioned into different layers, with each layer containing semantically meaningful content. Then, a warping-based approach is used to fill missing parts caused by occlusion within the extracted layers to achieve a complete representation. EXCOL provides a flexible way to effectively reuse traditional cartoon animations with only a small amount of user interaction. It is demonstrated that our EXCOL method is effective and robust, and the layered representation benefits a variety of applications in cartoon animation processing.


title: New pharmacokinetic/pharmacodynamic studies of systemically administered colistin againstPseudomonas aeruginosaandAcinetobacter baumanniiin mouse thigh and lung infection models: smaller response in lung infection

abstract: OBJECTIVES ::: This study investigated the exposure-response relationships between unbound colistin in plasma and antibacterial activity in mouse thigh and lung infections. ::: ::: ::: METHODS ::: Dose fractionation studies (subcutaneous colistin sulphate at 1.25-160 mg/kg/day) were conducted in neutropenic mice in which infection (three strains of Pseudomonas aeruginosa and three strains of Acinetobacter baumannii) had been produced by intramuscular thigh injection or aerosol lung delivery. Bacterial burden was measured at 24 h after initiation of colistin treatment. Plasma protein binding was measured by rapid equilibrium dialysis and ultracentrifugation. The inhibitory sigmoid dose-effect model and non-linear least squares regression were employed to determine the relationship between exposure to unbound colistin and efficacy. ::: ::: ::: RESULTS ::: Plasma binding of colistin was constant over the concentration range ∼2-50 mg/L. The average ± SD percentage bound for all concentrations was 92.9 ± 3.3% by ultracentrifugation and 90.4 ± 1.1% by equilibrium dialysis. In the thigh model, across all six strains the antibacterial effect of colistin was well correlated with fAUC/MIC (R(2) = 0.82-0.94 for P. aeruginosa and R(2) = 0.84-0.95 for A. baumannii). Target values of fAUC/MIC for 2 log10 kill were 7.4-13.7 for P. aeruginosa and 7.4-17.6 for A. baumannii. In the lung model, for only two strains of P. aeruginosa and one strain of A. baumannii was it possible to achieve 2 log10 kill (fAUC/MIC target values 36.8-105), even at the highest colistin dose tolerated by mice. This dose was not able to achieve bacteriostasis for the other two strains of A. baumannii. ::: ::: ::: CONCLUSIONS ::: Colistin was substantially less effective in lung infection. The pharmacokinetic/pharmacodynamic target values will assist in the design of optimized dosage regimens.


title: Gossip in evolutionary perspective.

abstract: Conversation is a uniquely human phenomenon. Analyses of freely forming conversations indicate that approximately two thirds of conversation time is devoted to social topics, most of which can be given the generic label gossip. This article first explores the origins of gossip as a mechanism for bonding social groups, tracing these origins back to social grooming among primates. It then asks why social gossip in this sense should form so important a component of human interaction and presents evidence to suggest that, aside from servicing social networks, a key function may be related explicitly to controlling free riders. Finally, the author reviews briefly the role of social cognition in facilitating conversations of this kind.


title: PIECE: a database for plant gene structure comparison and evolution

abstract: Gene families often show degrees of differences in terms of exon–intron structures depending on their distinct evolutionary histories. Comparative analysis of gene structures is important for understanding their evolutionary and functional relationships within plant species. Here, we present a comparative genomics database named PIECE (http://wheat.pw.usda.gov/piece) for Plant Intron and Exon Comparison and Evolution studies. The database contains all the annotated genes extracted from 25 sequenced plant genomes. These genes were classified based on Pfam motifs. Phylogenetic trees were pre-constructed for each gene category. PIECE provides a user-friendly interface for different types of searches and a graphical viewer for displaying a gene structure pattern diagram linked to the resulting bootstrapped dendrogram for each gene family. The gene structure evolution of orthologous gene groups was determined using the GLOOME, Exalign and GECA software programs that can be accessed within the database. PIECE also provides a web server version of the software, GSDraw, for drawing schematic diagrams of gene structures. PIECE is a powerful tool for comparing gene sequences and provides valuable insights into the evolution of gene structure in plant genomes.


title: TakeLab: Systems for Measuring Semantic Text Similarity

abstract: This paper describes the two systems for determining the semantic similarity of short texts submitted to the SemEval 2012 Task 6. Most of the research on semantic similarity of textual content focuses on large documents. However, a fair amount of information is condensed into short text snippets such as social media posts, image captions, and scientific abstracts. We predict the human ratings of sentence similarity using a support vector regression model with multiple features measuring word-overlap similarity and syntax similarity. Out of 89 systems submitted, our two systems ranked in the top 5, for the three overall evaluation metrics used (overall Pearson -- 2nd and 3rd, normalized Pearson -- 1st and 3rd, weighted mean -- 2nd and 5th).


title: LHlf: lock-free linear hashing (poster paper)

abstract: LHlf is a new hash table designed to allow very high levels of concurrency. The table is lock free and grows and shrinks auto-matically according to the number of items in the table. Insertions, lookups and deletions are never blocked. LHlf is based on linear hashing but adopts recursive split-ordering of the items within a bucket to be able to split and merge lists in a lock free manner. LHlf is as fast as the best previous lock-free design and in addition it offers stable performance, uses less space, and supports both expansions and contractions.


title: Efficiently solving dynamic Markov random fields using graph cuts

abstract: In this paper, we present a fast new fully dynamic algorithm for the st-mincut/max-flow problem. We show how this algorithm can be used to efficiently compute MAP estimates for dynamically changing MRF models of labeling problems in computer vision, such as image segmentation. Specifically, given the solution of the max-flow problem on a graph, we show how to efficiently compute the maximum flow in a modified version of the graph. Our experiments showed that the time taken by our algorithm is roughly proportional to the number of edges whose weights were different in the two graphs. We test the performance of our algorithm on one particular problem: the object-background segmentation problem for video and compare it with the best known st-mincut algorithm. The results show that the dynamic graph cut algorithm is much faster than its static counterpart and enables real time image segmentation. It should be noted that our method is generic and can be used to yield similar improvements in many other cases that involve dynamic change in the graph


title: Motivations for Play in Online Games

abstract: An empirical model of player motivations in online games provides the foundation to understand and assess how players differ from one another and how motivations of play relate to age, gender, usage patterns, and in-game behaviors. In the current study, a factor analytic approach was used to create an empirical model of player motivations. The analysis revealed 10 motivation subcomponents that grouped into three overarching components (achievement, social, and immersion). Relationships between motivations and demographic variables (age, gender, and usage patterns) are also presented.


title: An effective document image deblurring algorithm

abstract: Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Traditional deblurring algorithms have been proposed to work for natural-scene images. However the natural-scene images are not consistent with document images. In this paper, the distinct characteristics of document images are investigated. We propose a content-aware prior for document image deblurring. It is based on document image foreground segmentation. Besides, an upper-bound constraint combined with total variation based method is proposed to suppress the rings in the deblurred image. Comparing with the traditional general purpose deblurring methods, the proposed deblurring algorithm can produce more pleasing results on document images. Encouraging experimental results demonstrate the efficacy of the proposed method.


title: Adaptive foot design for small quadruped robots

abstract: Biologically inspired robots that are used for research of the animal and the technological realm become more and more refined. Control schemes for sensor-less and sensorized robots were developed, are able to handle torque control and sometimes even adapt to a changing task set. Further mechanics and electronics have evolved and take part in more reliable and robust bio-inspired robots. Robots reproduce animal structures or use bio-mechanical principles to excel in a specific task. Never the less, during this evolution of robots the feet were often oversimplified compared to their animal counterparts. Our current work centers around the foot as a bio-mechanically complex but extremely important end-effector.


title: Contrastive auto-encoder for phoneme recognition

abstract: Speech data typically contains task irrelevant information lying within features. Specifically, phonetic information, speaker characteristic information, emotional information and noise are always mixed together and tend to impair one another for certain task. We propose a new type of auto-encoder for feature learning called contrastive auto-encoder. Unlike other variants of auto-encoders, contrastive auto-encoder is able to leverage class labels in constructing its representation layer. We achieve this by modeling two autoencoders together and making their differences contribute to the total loss function. The transformation built with contrastive auto-encoder can be seen as a task-specific and invariant feature learner. Our experiments on TIMIT clearly show the superiority of the feature extracted from contrastive auto-encoder over original acoustic feature, feature extracted from deep auto-encoder, and feature extracted from a model that contrastive auto-encoder originates from.


title: A process- and temperature-tolerant power-on reset circuit with a flexible detection level higher than the bandgap voltage

abstract: A process- and temperature-tolerant power-on reset (POR) circuit is described. The operation principle of the POR circuit is based on a Kuijk cell bandgap reference with one resistor added to enable a designed output voltage higher than the Bandgap voltage of 1.25V. A test circuit with a detection level of 2.0V was fabricated and measured with a temperature variation of 19mV from 25degC to 90degC and a standard deviation of 25mV across one wafer. The circuit occupies 0.064mm2 and dissipates 1.2A at 90degC.


title: Detecting stress and depression in adults with aphasia through speech analysis

abstract: Aphasia is an acquired communication disorder often resulting from stroke that can impact quality of life and may lead to high levels of stress and depression. Depression diagnosis in this population is often completed through subjective caregiver questionnaires. Stress diagnostic tests have not been modified for language difficulties. This work proposes to use speech analysis as an objective measure of stress and depression in patients with aphasia. Preliminary analysis used linear support vector regression models to predict depression scores and stress scores for a total of 19 and 18 participants respectively. Teager Energy Operator-Amplitude Modulation features performed the best in predicting the Perceived Stress Scale score based on various measures. The complications of speech in people with aphasia are examined and indicate the need for future work on this understudied population.


title: Automated facial expression recognition based on FACS action units

abstract: Automated recognition of facial expression is an important addition to computer vision research because of its relevance to the study of psychological phenomena and the development of human-computer interaction (HCI). We developed a computer vision system that automatically recognizes individual action units or action unit combinations in the upper face using hidden Markov models (HMMs). Our approach to facial expression recognition is based an the Facial Action Coding System (FACS), which separates expressions into upper and lower face action. We use three approaches to extract facial expression information: (1) facial feature point tracking; (2) dense flow tracking with principal component analysis (PCA); and (3) high gradient component detection (i.e. furrow detection). The recognition results of the upper face expressions using feature point tracking, dense flow tracking, and high gradient component detection are 85%, 93% and 85%, respectively.


title: Graph-Based Deformable 3D Object Matching

abstract: We present a method for efficient detection of deformed 3D objects in 3D point clouds that can handle large amounts of clutter, noise, and occlusion. The method generalizes well to different object classes and does not require an explicit deformation model. Instead, deformations are learned based on a few registered deformed object instances. The approach builds upon graph matching to find correspondences between scene and model points. The robustness is increased through a parametrization where each graph vertex represents a full rigid transformation. We speed up the matching through greedy multi-step graph pruning and a constant-time feature matching. Quantitative and qualitative experiments demonstrate that our method is robust, efficient, able to detect rigid and non-rigid objects and exceeds state of the art.


Theory of Mind Development in Adolescence and Early Adulthood: The Growing Complexity of Recursive Thinking Ability


title: Optimal Long Code Test with One Free Bit

abstract: For arbitrarily small constants epsilon, delta ≫ 0$, we present a long code test with one free bit, completeness 1-epsilon and soundness delta. Using the test, we prove the following two inapproximability results:1. Assuming the Unique Games Conjecture of Khot, given an n-vertex graph that has two disjoint independent sets of size (1/2-epsilon)n each, it is NP-hard to find an independent set of size delta n.2. Assuming a (new) stronger version of the Unique Games Conjecture, the scheduling problem of minimizing weighted completion time with precedence constraints is inapproximable within factor 2-epsilon.


title: Minimum-Current-Stress Scheme of Dual Active Bridge DC–DC Converter With Unified Phase-Shift Control

abstract: To reduce current stress and improve efficiency of dual active bridge (DAB) dc–dc converters, various control schemes have been proposed in recent decades. Most control schemes for directly minimizing power losses from power loss modeling analysis and optimization aspect of the adopted converter are too difficult and complicated to implement in real-time digital microcontrollers. Thus, this paper focuses on a simple solution to reduce current stress and improve the efficiency of the adopted DAB converter. However, traditional current-stress-optimized (CSO) schemes have some drawbacks, such as inductance dependency and an additional load-current sensor. In this paper, a simple CSO scheme with a unified phase-shift (UPS) control, which can be equivalent to the existing conventional phase-shift controls, is proposed for DAB dc–dc converters to realize current stress optimization. The simple CSO scheme can overcome those drawbacks of traditional CSO schemes, gain the minimum current stress, and improve efficiency. Then, a comparison of single-phase-shift (SPS) control, simple CSO scheme with dual-phase-shift (CSO-DPS) control, simple CSO scheme with extended-phase-shift (CSO-EPS) control, and simple CSO scheme with UPS (CSO-UPS) control is analyzed in detail. Finally, experimental results verify the excellent performance of the proposed CSO-UPS control scheme and the correctness of theoretical analysis.


title: Deep Reinforcement Learning From Raw Pixels in Doom

abstract: Using current reinforcement learning methods, it has recently become possible to learn to play unknown 3D games from raw pixels. In this work, we study the challenges that arise in such complex environments, and summarize current methods to approach these. We choose a task within the Doom game, that has not been approached yet. The goal for the agent is to fight enemies in a 3D world consisting of five rooms. We train the DQN and LSTM-A3C algorithms on this task. Results show that both algorithms learn sensible policies, but fail to achieve high scores given the amount of training. We provide insights into the learned behavior, which can serve as a valuable starting point for further research in the Doom domain.


title: 20 years of four HCI conferences: A visual exploration

abstract: We present a visual exploration of the field of human–computer interaction (HCI) through the author and article metadata of four of its major conferences: the ACM conferences on Computer-Human Interaction (CHI), User Interface Software and Technology, and Advanced Visual Interfaces and the IEEE Symposium on Information Visualization. This article describes many global and local patterns we discovered in this data set, together with the exploration process that produced them. Some expected patterns emerged, such as that–like most social networks–coauthorship and citation networks exhibit a power-law degree distribution, with a few widely collaborating authors and highly cited articles. Also, the prestigious and long-established CHI conference has the highest impact (citations by the others). Unexpected insights included that the years when a given conference was most selective are not correlated with those that produced its most highly referenced articles and that influential authors have distinct patterns...


title: Model Transport: Towards Scalable Transfer Learning on Manifolds

abstract: We consider the intersection of two research fields: transfer learning and statistics on manifolds. In particu- lar, we consider, for manifold-valued data, transfer learn- ing of tangent-space models such as Gaussians distribu- tions, PCA, regression, or classifiers. Though one would hope to simply use ordinary Rn-transfer learning ideas, the manifold structure prevents it. We overcome this by basing our method on inner-product-preserving parallel transport, a well-known tool widely used in other problems of statis- tics on manifolds in computer vision. At first, this straight- forward idea seems to suffer from an obvious shortcom- ing: Transporting large datasets is prohibitively expensive, hindering scalability. Fortunately, with our approach, we never transport data. Rather, we show how the statistical models themselves can be transported, and prove that for the tangent-space models above, the transport "commutes" with learning. Consequently, our compact framework, ap- plicable to a large class of manifolds, is not restricted by the size of either the training or test sets. We demonstrate the approach by transferring PCA and logistic-regression models of real-world data involving 3D shapes and image descriptors.


Lead poisoning in Chinese children: risk factors and preventive measures


title: Adversarial Examples - A Complete Characterisation of the Phenomenon

abstract: We provide a complete characterisation of the phenomenon of adversarial examples - inputs intentionally crafted to fool machine learning models. We aim to cover all the important concerns in this field of study: (1) the conjectures on the existence of adversarial examples, (2) the security, safety and robustness implications, (3) the methods used to generate and (4) protect against adversarial examples and (5) the ability of adversarial examples to transfer between different machine learning models. We provide ample background information in an effort to make this document self-contained. Therefore, this document can be used as survey, tutorial or as a catalog of attacks and defences using adversarial examples.


English transported : essays on Australasian English


title: Prevalence and Correlates of Prediabetes and Diabetes Results-I: A Screening Plan in a Selected Military Community in Central Saudi Arabia

abstract: Background: Diabetes is a chronic disease of a genetic and environmental background. If it is not diagnosed and controlled early, it can have devastating complications. Aim: Measure and analyze the prevalence and risks of prediabetes and diabetes among recruits enlisted in the Wadi Al-Dawasir (WD) military zone, central Saudi Arabia. The influence of some demographic criteria, weight, and blood pressure upon blood glucose level would be evaluated. Methods: A cross sectional design was utilized, whereas predesigned questionnaire and clinical interview were used to screen the study population. Results: The participants’ median age was 33.7 (IQR 11.9), mean body mass index (BMI) was 27.6 ± 5.4 kg/m2. Out of 531 recruits screened, almost two-thirds were either overweight (34.7%) or obese (29.9%). The mean waist circumference (WC) was 94.7 ± 13.3 cm; and 27.9% had central obesity (WC ≥102 cm). Random plasma glucose (RPG) median accounted 106 mg/dl (IQR 21). The participants’ RPG levels significantly increased by age (U = 19697.0), by BMI [H(df 5) = 59.6], and by WC (U = 25,670.5), (p rho(df 503) = 0.241, p U 3515, p 25 to ≥40 kg/m2) more than normal subjects [46 (87%) vs. 5 (0.9%)], [χ2(df 5) = 18.8, p < 0.0001]. Conclusions: Prediabetes and uncontrolled diabetes are relatively prevalent among Saudi recruits’ community. An insufficient influence of obesity for developing diabetes symptoms in the study population may well be attributed to time factor separating the two variables. A preventive approach to revert the predisposition of prediabetes in WD recruits population is quite promising.


title: Using and validating the strategic alignment model

abstract: The literature suggests that firms cannot be competitive if their business and information technology strategies are not aligned. Yet achieving strategic alignment continues to be a major concern for business executives. A number of alignment models have been offered in the literature, primary among them the strategic alignment model (SAM). However, there is little published research that attempts to validate SAM or describe its use in practice. This paper reports on the use of SAM in a financial services firm. Data from completed projects are applied to the model to determine whether SAM is useful as a management tool to create, assess and sustain strategic alignment between information technology and the business. The paper demonstrates that SAM has conceptual and practical value. The paper also proposes a practical framework that allows management, particularly technology management, to determine current alignment levels and to monitor and change future alignment as required. Through the use of this framework, alignment is more likely to be achieved in practice.


DEGRADATION OF CONVENTIONAL AND OXODEGRADABLE HIGH DENSITY POLYETHYLENE IN TROPICAL AQUEOUS AND OUTDOOR ENVIRONMENTS


title: Advantages of integrated and sustainability based assessment for metabolism based strategic planning of urban water systems.

abstract: Despite providing water-related services as the primary purpose of urban water system (UWS), all relevant activities require capital investments and operational expenditures, consume resources (e.g. materials and chemicals), and may increase negative environmental impacts (e.g. contaminant discharge, emissions to water and air). Performance assessment of such a metabolic system may require developing a holistic approach which encompasses various system elements and criteria. This paper analyses the impact of integration of UWS components on the metabolism based performance assessment for future planning using a number of intervention strategies. It also explores the importance of sustainability based criteria in the assessment of long-term planning. Two assessment approaches analysed here are: (1) planning for only water supply system (WSS) as a part of the UWS and (2) planning for an integrated UWS including potable water, stormwater, wastewater and water recycling. WaterMet(2) model is used to simulate metabolic type processes in the UWS and calculate quantitative performance indicators. The analysis is demonstrated on the problem of strategic level planning of a real-world UWS to where optional intervention strategies are applied. The resulting performance is assessed using the multiple criteria of both conventional and sustainability type; and optional intervention strategies are then ranked using the Compromise Programming method. The results obtained show that the high ranked intervention strategies in the integrated UWS are those supporting both water supply and stormwater/wastewater subsystems (e.g. rainwater harvesting and greywater recycling schemes) whilst these strategies are ranked low in the WSS and those targeting improvement of water supply components only (e.g. rehabilitation of clean water pipes and addition of new water resources) are preferred instead. Results also demonstrate that both conventional and sustainability type performance indicators are necessary for strategic planning in the UWS.


title: Augmenting curved robot surfaces with soft tactile skin

abstract: We present a novel, soft, tactile skin composed of a fabric-based, stretchable sensor technology based on the piezoresistive effect. Softness is achieved by a combination of a soft silicone padding covered by a skin of more durable, tearproof silicone with an imprinted surface pattern mimicking human glabrous skin, found e.g. in fingertips. Its very thin layer structure (starting from 2.5 mm) facilitates integration on existing robot surfaces, particularly on small and highly curved links. For example, we augmented our Shadow Dexterous Hand with 12 palm sensors, and 2 resp. 3 sensors in the middle resp. proximal phalanges of each finger. To demonstrate the usefulness and efficiency of the proposed sensor skin, we performed a challenging classification task distinguishing squeezed objects based on their varying stiffness.


title: CC-Hunter: Uncovering Covert Timing Channels on Shared Processor Hardware

abstract: As we increasingly rely on computers to process and manage our personal data, safeguarding sensitive information from malicious hackers is a fast growing concern. Among many forms of information leakage, covert timing channels operate by establishing an illegitimate communication channel between two processes and through transmitting information via timing modulation, thereby violating the underlying system's security policy. Recent studies have shown the vulnerability of popular computing environments, such as cloud computing, to these covert timing channels. In this work, we propose a new micro architecture-level framework, CC-Hunter, that detects the possible presence of covert timing channels on shared hardware. Our experiments demonstrate that Chanter is able to successfully detect different types of covert timing channels at varying bandwidths and message patterns.


title: Monocular Depth Estimation: A Survey

abstract: Monocular depth estimation is often described as an ill-posed and inherently ambiguous problem. Estimating depth from 2D images is a crucial step in scene reconstruction, 3Dobject recognition, segmentation, and detection. The problem can be framed as: given a single RGB image as input, predict a dense depth map for each pixel. This problem is worsened by the fact that most scenes have large texture and structural variations, object occlusions, and rich geometric detailing. All these factors contribute to difficulty in accurate depth estimation. In this paper, we review five papers that attempt to solve the depth estimation problem with various techniques including supervised, weakly-supervised, and unsupervised learning techniques. We then compare these papers and understand the improvements made over one another. Finally, we explore potential improvements that can aid to better solve this problem.


title: Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization

abstract: We consider the problem of sparse coding, where each sample consists of a sparse linear combination of a set of dictionary atoms, and the task is to learn both the dictionary elements and the mixing coefficients. Alternating minimization is a popular heuristic for sparse coding, where the dictionary and the coefficients are estimated in alternate steps, keeping the other fixed. Typically, the coefficients are estimated via $\ell_1$ minimization, keeping the dictionary fixed, and the dictionary is estimated through least squares, keeping the coefficients fixed. In this paper, we establish local linear convergence for this variant of alternating minimization and establish that the basin of attraction for the global optimum (corresponding to the true dictionary and the coefficients) is $\mathcal{O}(1/s^{2})$, where $s$ is the sparsity level in each sample and the dictionary satisfies restricted isometry property. Combined with the recent results of approximate dictionary estimation, this yields provable guar...


title: In Defense of Sparse Tracking: Circulant Sparse Tracker

abstract: Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.


title: Seasonal to Inter-annual Climate Prediction Using Data Mining KNN Technique

abstract: The impact of seasonal to inter-annual climate prediction on society, business, agriculture and almost all aspects of human life, force the scientist to give proper attention to the matter. The last few years show tremendous achievements in this field. All systems and techniques developed so far, use the Sea Surface Temperature (SST) as the main factor, among other seasonal climatic attributes. Statistical and mathematical models are then used for further climate predictions. In this paper, we develop a system that uses the historical weather data of a region (rain, wind speed, dew point, temperature, etc.), and apply the data-mining algorithm “K-Nearest Neighbor (KNN)” for classification of these historical data into a specific time span. The k nearest time spans (k nearest neighbors) are then taken to predict the weather. Our experiments show that the system generates accurate results within reasonable time for months in advance.


title: SAFEDISPATCH: Securing C++ Virtual Calls from Memory Corruption Attacks

abstract: Several defenses have increased the cost of traditional, low-level attacks that corrupt control data, e.g. return addresses saved on the stack, to compromise program execution. In response, creative adversaries have begun circumventing these defenses by exploiting programming errors to manipulate pointers to virtual tables, or vtables, of C++ objects. These attacks can hijack program control flow whenever a virtual method of a corrupted object is called, potentially allowing the attacker to gain complete control of the underlying system. In this paper we present SAFEDISPATCH, a novel defense to prevent such vtable hijacking by statically analyzing C++ programs and inserting sufficient runtime checks to ensure that control flow at virtual method call sites cannot be arbitrarily influenced by an attacker. We implemented SAFEDISPATCH as a Clang++/LLVM extension, used our enhanced compiler to build a vtable-safe version of the Google Chromium browser, and measured the performance overhead of our approach on popular browser benchmark suites. By carefully crafting a handful of optimizations, we were able to reduce average runtime overhead to just 2.1%.


title: Design and Analysis of a 60-GHz CMOS Doppler Micro-Radar System-in-Package for Vital-Sign and Vibration Detection

abstract: This paper presents the first flip-chip-packaged and fully integrated Doppler micro-radar in 90-nm CMOS for noncontact vital-sign and vibration detection. The use of a smaller wavelength compared with previous works achieves the highly compact system for portable devices, and the radar design considerations at 60 GHz are discussed from both system and circuits points of view. The compact 60-GHz core (0.73 $\hbox{mm}^{2}$ ) provides a 36-dB peak down-conversion gain and transmits a radar signal around 0 dBm at 55 GHz. Quadrature generation at the intermediate frequency stage of the heterodyne receiver gives a power- and area-efficient solution to the null detection point issue, ensuring robust detection. By using single-patch antennas and without a high-power amplifier, the system demonstrates the first-pass success of human vital-sign detection at 0.3 m. The small mechanical vibration with a displacement of 0.2 mm can be detected up to 2 m away. At 60 GHz, target displacement comparable to wavelength results in strong nonlinear phase modulation and increases detection difficulties. A signal-recovery algorithm is proposed to improve the accuracy of vital-sign detection.


title: Age‐dependent decline in β‐cell function assessed by an oral glucose tolerance test‐based disposition index

abstract: We evaluated age-dependent changes in β-cell function as assessed with an oral glucose tolerance test (OGTT)-based analog of the disposition index (oral disposition index). A total of 110 Japanese normoglycemic subjects (aged 22–59 years) was divided into decadal age groups (20, 30, 40 and 50 s) and subjected to an OGTT. The oral disposition index was calculated as the product of the Matsuda index and the ratio of the area under the insulin curve to the area under the glucose curve for 0–120 min during the OGTT (AUCins/gluc120). Although indexes of insulin secretion, including AUCins/gluc120 and the insulinogenic index, did not differ among age groups, the oral disposition index differed significantly among decadal ages and declined with age. The oral disposition index is thus a sensitive measure of β-cell function, and a natural decline in such function likely begins in early adulthood and progresses with age. (J Diabetes Invest, doi: 10.1111/j.2040-1124.2010.00099.x, 2011)


title: Information extraction from Wikipedia: moving down the long tail

abstract: Not only is Wikipedia a comprehensive source of quality information, it has several kinds of internal structure (e.g., relational summaries known as infoboxes), which enable self-supervised information extraction. While previous efforts at extraction from Wikipedia achieve high precision and recall on well-populated classes of articles, they fail in a larger number of cases, largely because incomplete articles and infrequent use of infoboxes lead to insufficient training data. This paper presents three novel techniques for increasing recall from Wikipedia's long tail of sparse classes: (1) shrinkage over an automatically-learned subsumption taxonomy, (2) a retraining technique for improving the training data, and (3) supplementing results by extracting from the broader Web. Our experiments compare design variations and show that, used in concert, these techniques increase recall by a factor of 1.76 to 8.71 while maintaining or increasing precision.


title: Binhunt: Automatically finding semantic differences in binary programs

abstract: We introduce BinHunt, a novel technique for finding semantic differences in binary programs. Semantic differences between two binary files contrast with syntactic differences in that semantic differences correspond to changes in the program functionality. Semantic differences are difficult to find because of the noise from syntactic differences caused by, e.g., different register allocation and basic block re-ordering. BinHunt bases its analysis on the control flow of the programs using a new graph isomorphism technique, symbolic execution, and theorem proving. We implement a system based on BinHunt and demonstrate the application of the system with three case studies in which BinHunt manages to identify the semantic differences between an executable and its patched version, revealing the vulnerability that the patch eliminates.


title: iMAP-CampUS: Developing an Intelligent Mobile Augmented Reality Program on Campus as a Ubiquitous System

abstract: Augmented Reality (AR) offers a combination of physical and virtual objects, drawing on the strengths of each. Therefore, it is different from virtual reality since it permits users to sight the real world enhanced with virtual objects. Thus, AR technology holds potential to support users with valuable information about the surrounding area. In this paper, we demonstrate the development of an AR app that provides the students with rich information about the buildings nearby. The application, which we name "iMAP-CampUS", is designed to assist students at Macquarie University in locating places of interest close to them by moving the camera of the device in all probable directions to overlay information of places around them. The iMAP-CampUS has been developed for both iOS and Android platforms and run on smartphones and tablets with different screen sizes.


title: Why You Should Run TPC-DS: A Workload Analysis

abstract: The Transaction Processing Performance Council (TPC) is completing development of TPC-DS, a new generation industry standard decision support benchmark. The TPC-DS benchmark, first introduced in the "The Making of TPC-DS" [9] paper at the 32nd International Conference on Very Large Data Bases (VLDB), has now entered the TPC's "Formal Review" phase for new benchmarks; companies and researchers alike can now download the draft benchmark specification and tools for evaluation. The first paper [9] gave an overview of the TPC-DS data model, workload model, and execution rules. This paper details the characteristics of different phases of the workload, namely: database load, query workload and data maintenance; and also their impact to the benchmark's performance metric. As with prior TPC benchmarks, this workload will be widely used by vendors to demonstrate their capabilities to support complex decision support systems, by customers as a key factor in purchasing servers and software, and by the database community for research and development of optimization techniques.


title: Human Activity Clustering for Online Anomaly Detection

abstract: This paper aims to address the problem of profiling human activities captured in surveillance videos for the applications of online normal human activity recognition and anomaly detection. A novel framework is developed for automatic human activity modeling and online anomaly detection without any manual labeling of the training dataset. The framework consists of the following key components: 1) A compact and effective activity representation method is developed based on a stochastic sequence of spatiotemporal actions. 2) The natural grouping of activities is discovered through a novel clustering algorithm with unsupervised model selection. 3) A runtime accumulative anomaly measure is introduced to detect abnormal activities, whereas normal human activities are recognized when sufficient visual evidence has become available based on an online Likelihood Ratio Test (LRT) method. This ensures robust and reliable anomaly detection and normal activity recognition at the shortest possible time. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse datasets collected from a real surveillance scenario.


title: Distributional Semantics Resources for Biomedical Text Processing

abstract: The openly available biomedical literature contains over 5 billion words in publication abstracts and full texts. Recent advances in unsupervised language processing methods have made it possible to make use of such large unannotated corpora for building statistical language models and inducing high quality vector space representations, which are, in turn, of utility in many tasks such as text classification, named entity recognition and query expansion. In this study, we introduce the first set of such language resources created from analysis of the entire available biomedical literature, including a dataset of all 1to 5-grams and their probabilities in these texts and new models of word semantics. We discuss the opportunities created by these resources and demonstrate their application. All resources introduced in this study are available under open licenses at http://bio.nlplab.org.


title: Charisma: orchestrating migratable parallel objects

abstract: The parallel programming paradigm based on migratable objects, as embodied in Charm++, improves programmer productivity by automating resource management. The programmer decomposes an application into a large number of parallel objects, while an intelligent run-time system assigns those objects to processors. It migrates objects among processors to effect dynamic load balance and communication optimizations. In addition, having multiple sets of objects representing distinct computations leads to improved modularity and performance. However, for complex applications involving many sets of objects, Charm++'s programming model tends to obscure the global flow of control in a parallel program: One must look at the code of multiple objects to discern how the multiple sets of objects are orchestrated in a given application. In this paper, we present Charisma, an orchestration notation that allows expression of Charm++ functionality without fragmenting the expression of control flow. Charisma separates expression of parallelism, including control flow and macro data-flow, from sequential components of the program. The sequential components only consume and publish data. Charisma expression of multiple patterns of communication among message-driven objects. A compiler generates Charm++ communication and synchronization code via static dependence analysis. As Charisma out puts standard Charm++ code, the functionality and performance benefits of the adaptive run-time system, such as automatic load balancing, are retained. In the paper, we show that Charisma programs scale up to 1024 processors without introducing undue overhead.


title: Combining Mobile and Fog Computing: Using CoAP to Link Mobile Device Clouds with Fog Computing

abstract: This paper focuses on the use of CoAP as a means of connecting clouds of sensors & smart devices via mobile devices with users. We present an alternative to the hierarchical view on fog-computing by enabling device clouds to interact in a P2P fashion with smart device/sensor clouds. Using the IoT protocol CoAP we expose smart device/sensors and resources in the mobile cloud in a uniform manner as RESful services. The paper presents an evaluation of an Erlang CoAP server and an evaluations of using BLE 4.1 for submitting CoAP messages that span between 1 -- 7 BLE packets.


title: Gender and ethnic differences in survival in a cohort of HIV positive clients.

abstract: Abstract Objectives: The purpose of this study was to examine gender and ethnic differences in survival of persons receiving treatment for HIV infection to determine if differences existed, and if they did, to assess the possibility of explaining these differences by examining other factors, such as age, disease severity when beginning treatment, alcohol, illicit drugs, tobacco, educational level, living arrangements, antiretroviral treatment, PCP prophylaxis, sexually transmitted diseases, mode of transmission and opportunistic infections. Design: A retrospective cohort study of all clients receiving treatment at an HIV only clinic from its opening in early 1988 until the end of May 1993. Statistical methods used to examine the data included incidence density ratios, Kaplan‐Meier survival curves, Breslow (generalized Wilcoxon) tests of equality of survival curves and Cox proportional hazards models both with and without time dependent covariates. Results: In the cohort (37% African American, 7% Hispanic ...


title: Computational Light Routing: 3D Printed Optical Fibers for Sensing and Display

abstract: Despite recent interest in digital fabrication, there are still few algorithms that provide control over how light propagates inside a solid object. Existing methods either work only on the surface or restrict themselves to light diffusion in volumes. We use multi-material 3D printing to fabricate objects with embedded optical fibers, exploiting total internal reflection to guide light inside an object. We introduce automatic fiber design algorithms together with new manufacturing techniques to route light between two arbitrary surfaces. Our implicit algorithm optimizes light transmission by minimizing fiber curvature and maximizing fiber separation while respecting constraints such as fiber arrival angle. We also discuss the influence of different printable materials and fiber geometry on light propagation in the volume and the light angular distribution when exiting the fiber. Our methods enable new applications such as surface displays of arbitrary shape, touch-based painting of surfaces, and sensing a hemispherical light distribution in a single shot.


title: IMPROVED DROOP CONTROL FOR PARALLEL INVERTER SYSTEM WITH LOAD

abstract: DC-AC converters are electronic devices used to change DC direct current to ::: alternating current. Three-phase inverter is widely used in power electronics system ::: applications consequently, the DC-AC converters requires a controller with a high ::: degree. Therefore the structure of two parallel three phase inverter with load system ::: has presented. In order to achieve load sharing between parallel inverters, the linear ::: transformed droop control is applied. This control strategy combines frequency and ::: voltage droop method and inverter voltage regulation control scheme. In the external ::: power control structure, the references frequency and magnitude of inverter output ::: voltage are obtained according to the droop characteristics. The improvement of the ::: droop control is made to obtain a more stable voltage and better load sharing ::: between two parallel inverters. The performance of the control strategy is verified in ::: simulation using Matlab/Simulink.


title: Scrum Method Implementation in a Software Development Project Management

abstract: To maximize the performance, companies conduct a variety of ways to increase the business profit. The work management between one company and the other company is different, so the differences in the management may cause the software to have a different business process. Software development can be defined as creating a new software or fixing the existing one. Technology developments led to increasing demand for software, Industrial Technology (IT) Companies should be able to project well maintenance. The methodology in software development is used in accordance with the company's needs based on the SDLC (Software Development Life Cycle). Scrum method is a part of the Agile method that is expected to increase the speed and flexibility in software development project management.


title: Anger detection in call center dialogues

abstract: We present a method to classify fixed-duration windows of speech as expressing anger or not, which does not require speech recognition, utterance segmentation, or separating the utterances of different speakers and can, thus, be easily applied to real-world recordings. We also introduce the task of ranking a set of spoken dialogues by decreasing percentage of anger duration, as a step towards helping call center supervisors and analysts identify conversations requiring further action. Our work is among the very few attempts to detect emotions in spontaneous human-human dialogues recorded in call centers, as opposed to acted studio recordings or human-machine dialogues. We show that despite the non-perfect performance (approx. 70% accuracy) of the window-level classifier, its decisions help produce a ranking of entire conversations by decreasing percentage of anger duration that is clearly better than a random ranking, which represents the case where supervisors and analysts randomly select conversations to inspect.


title: Can the Blockchain Technology Solve Trust Issues in Industrial Networks?

abstract: Since its inception about a decade ago, practitioners and researchers alike have been drawn to the blockchain technology vibe. Advocates of blockchain argue that the technology is taking us to trul ...


title: Surveyor: a system for generating coherent survey articles for scientific topics

abstract: We investigate the task of generating coherent survey articles for scientific topics. We introduce an extractive summarization algorithm that combines a content model with a discourse model to generate coherent and readable summaries of scientific topics using text from scientific articles relevant to the topic. Human evaluation on 15 topics in computational linguistics shows that our system produces significantly more coherent summaries than previous systems. Specifically, our system improves the ratings for coherence by 36% in human evaluation compared to C-Lexrank, a state of the art system for scientific article summarization.


title: Segmented handwritten text recognition with recurrent neural network classifiers

abstract: Recognition of handwritten text is a useful technique that can be applied in different applications, such as signature recognition, bank check recognition, etc. However, the off-line handwritten text recognition in an unconstrained situation is still a very challenging task due to the high complexity of text strokes and image background. This paper presents a novel segmented handwritten text recognition technique that ensembles recurrent neural network (RNN) classifiers. Two RNN models are first trained that take advantage of the widely used geometrical feature and the Histogram of Oriented Gradient (HOG) feature, respectively. Given a handwritten word image, the optimal recognition result is then obtained by integrating the two trained RNN models together with a lexicon. Experiments on public datasets show the superior performance of our proposed technique.


title: Stakeholder identification in the requirements engineering process

abstract: Adequate, timely and effective consultation of relevant stakeholders is of paramount importance in the requirements engineering process. However, the thorny issue of making sure that all relevant stakeholders are consulted has received less attention than other areas which depend on it, such as scenario-based requirements, involving users in development, negotiating between different viewpoints and so on. The literature suggests examples of stakeholders, and categories of stakeholder, but does not provide help in identifying stakeholders for a specific system. In this paper, we discuss current work in stakeholder identification, propose an approach to identifying relevant stakeholders for a specific system, and propose future directions for the work.


title: Knowledge Base Semantic Integration Using Crowdsourcing

abstract: The semantic web has enabled the creation of a growing number of knowledge bases (KBs), which are designed independently using different techniques. Integration of KBs has attracted much attention as different KBs usually contain overlapping and complementary information. Automatic techniques for KB integration have been improved but far from perfect. Therefore, in this paper, we study the problem of knowledge base semantic integration using crowd intelligence. There are both classes and instances in a KB, in our work, we propose a novel hybrid framework for KB semantic integration considering the semantic heterogeneity of KB class structures. We first perform semantic integration of the class structures via crowdsourcing, then apply the blocking-based instance matching approach according to the integrated class structure. For class structure (taxonomy) semantic integration, the crowd is leveraged to help identifying the semantic relationships between classes to handle the semantic heterogeneity problem. Under the conditions of both large scale KBs and limited monetary budget for crowdsourcing, we formalize the class structure (taxonomy) semantic integration problem as a Local Tree Based Query Selection (LTQS) problem. We show that the LTQS problem is NP-hard and propose two greedy-based algorithms, i.e., static query selection and adaptive query selection. Furthermore, the KBs are usually of large scales and have millions of instances, direct pairwise-based instance matching is inefficient. Therefore, we adopt the blocking-based strategy for instance matching, taking advantage of the class structure (taxonomy) integration result. The experiments on real large scale KBs verify the effectiveness and efficiency of the proposed approaches.


title: A Brain-Computer Interface Controlled Auditory Event-Related Potential (P300) Spelling System for Locked-In Patients

abstract: Using brain–computer interfaces (BCI) humans can select letters or other targets on a computer screen without any muscular involvement. An intensively investigated kind of BCI is based on the recording of visual event-related brain potentials (ERP). However, some severely paralyzed patients who need a BCI for communication have impaired vision or lack control of gaze movement, thus making a BCI depending on visual input no longer feasible. In an effort to render the ERP–BCI usable for this group of patients, the ERP–BCI was adapted to auditory stimulation. Letters of the alphabet were assigned to cells in a 5 × 5 matrix. Rows of the matrix were coded with numbers 1 to 5, and columns with numbers 6 to 10, and the numbers were presented auditorily. To select a letter, users had to first select the row and then the column containing the desired letter. Four severely paralyzed patients in the end-stage of a neurodegenerative disease were examined. All patients performed above chance level. Spelling accuracy was significantly lower with the auditory system as compared with a similar visual system. Patients reported difficulties in concentrating on the task when presented with the auditory system. In future studies, the auditory ERP–BCI should be adjusted by taking into consideration specific features of severely paralyzed patients, such as reduced attention span. This adjustment in combination with more intensive training will show whether an auditory ERP–BCI can become an option for visually impaired patients.


title: Empirical findings on team size and productivity in software development

abstract: The size of software project teams has been considered to be a driver of project productivity. Although there is a large literature on this, new publicly available software repositories allow us to empirically perform further research. In this paper we analyse the relationships between productivity, team size and other project variables using the International Software Benchmarking Standards Group (ISBSG) repository. To do so, we apply statistical approaches to a preprocessed subset of the ISBSG repository to facilitate the study. The results show some expected correlations between productivity, effort and time as well as corroborating some other beliefs concerning team size and productivity. In addition, this study concludes that in order to apply statistical or data mining techniques to these type of repositories extensive preprocessing of the data needs to be performed due to ambiguities, wrongly recorded values, missing values, unbalanced datasets, etc. Such preprocessing is a difficult and error prone activity that would need further guidance and information that is not always provided in the repository.


title: Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning

abstract: The encoder-decoder models for unsupervised sentence representation learning tend to discard the decoder after being trained on a large unlabelled corpus, since only the encoder is needed to map the input sentence into a vector representation. However, parameters learnt in the decoder also contain useful information about language. In order to utilise the decoder after learning, we present two types of decoding functions whose inverse can be easily derived without expensive inverse calculation. Therefore, the inverse of the decoding function serves as another encoder that produces sentence representations. We show that, with careful design of the decoding functions, the model learns good sentence representations, and the ensemble of the representations produced from the encoder and the inverse of the decoder demonstrate even better generalisation ability and solid transferability.


title: Short-term meaning shift: an exploratory distributional analysis.

abstract: We investigate diachronic meaning shift that takes place in short periods of time (short-term meaning shift) and in an online community of speakers. We create a small dataset and use it to assess the performance of a standard model for meaning shift detection on short-term meaning shift, and find that this phenomenon poses specific difficulties for models based on the Distributional Hypothesis.


title: Mori–Zwanzig formalism as a practical computational tool

abstract: An operational procedure is presented to compute explicitly the different terms in the generalized Langevin equation (GLE) for a few relevant variables obtained within Mori–Zwanzig formalism. The procedure amounts to introducing an artificial controlled parameter which can be tuned in such a way that the so-called projected dynamics becomes explicit and the GLE reduces to a Markovian equation. The projected dynamics can be realised in practice by introducing constraints, and it is shown that the Green–Kubo formulae computed with these dynamics do not suffer from the plateau problem. The methodology is illustrated in the example of star polymer molecules in a melt using their center of mass as relevant variables. Through this example, we show that not only the effective potentials, but also the friction forces and the noise play a very important role in the dynamics.


title: Blinking-based live face detection using conditional random fields

abstract: This paper presents a blinking-based liveness detection method for human face using Conditional Random Fields (CRFs). Our method only needs a web camera for capturing video clips. Blinking clue is a passive action and does not need the user to to any hint, such as speaking, face moving. We model blinking activity by CRFs, which accommodates long-range contextual dependencies among the observation sequence. The experimental results demonstrate that the proposed method is promising, and outperforms the cascaded Adaboost method and HMM method.


title: A millimeter-wave wide-band transition from a differential microstrip to a rectangular waveguide for 60 GHz applications

abstract: In this paper an integrated wide-band transition from a differential micro-strip line to a rectangular WR-15 waveguide is presented. The transition makes use of a cavity that is entirely integrated into the multilayer printed circuit board (PCB), which offers three layers (RF signal layer, ground plane and DC signal layer) for signal routing. The transition including the 18 mm long micro-strip feed lines provides a bandwidth of 20 GHz from 50 GHz to 70 GHz and an insertion loss of less than 2.3 dB. This makes the transition perfectly suited for differential wide-band transceivers operating in the 60 GHz band.


title: Technical Comment on: Mohr et al. Compound measure of hand-foot-eye preference masked opposite turning behavior in healthy right-handers and non-right-handers

abstract: A previous article reported opposite turning behavior in right-handers and non-right-handers (C. Mohr. T. Landis. H. S. Bracha, & P. Brugger, 2003). This observation appears contradictory to the 1st study on long-term spontaneous turning behavior in healthy participants (H. S. Bracha. D. J. Seitz. J. Otcmaa, & S. D. Click, 1987). These latter authors found a complex interaction between hemispheric dominance. preferred turning side, and sex. C. Mohr et al. (2003) argued that the differentiation of the population in hemisphere-dominant groups by a compound measure of hand-foot-eye preference might have masked their recent finding. Thus, this commentary presents a reanalysis of the original data set (H. S. Bracha et al., 1987). Replicating recent observation, right-handers preferred left-sided turns: and non-right-handers, right-sided turns. This replication strengthens the proposition that handedness and turning behavior might depend on interhemispheric dopamine asymmetries.


title: Automated transformation of Business Rules into Business Processes : from SBVR to BPMN

abstract: this paper presents a novel approach for transforming Business Rules expressed with Semantic of Business Vocabulary and Rules (SBVR) into (BPMN) Business Process models. This transformation provides several benefits to Information System project stakeholders, such as: enhancing requirement validation and refinement, improving Business Processes documentation, and reducing their overall modeling effort. To bridge the gap between SBVR and BPMN, we propose novel transformation rules that cover structural and behavioral SBVR rules. We illustrate and discuss our transformations with the EU-Rent case study, provided in the OMG specification.


title: Design of a Universal Inductive Charger for Multiple Electric Vehicle Models

abstract: Inductive power transfer technology has become a popular solution for battery charging of electric vehicles (EVs). However, problems such as varied magnetic coupling caused by coil misalignment still limit its practical applications, by safety and stability issues. Meanwhile, the growing market of EVs asks for a universal charger for various models. This paper presents the design process of a universal inductive charger (UIC) for EVs. The proposed UIC is capable of adaptively providing a constant or controllable charging voltage to various EVs, with a wide range of varied magnetic coupling between the transmitting and receiving coils. With a series-connected LC circuit, zero-voltage switching of the primary dc–ac inverter is universally realized in every charging cycle. A simple yet effective control method based on the frequency variation is used to automatically select the optimal frequency in different coupling conditions and adjust the frequency during the charging process. The design of the charging interface is also optimized with higher efficiency and power-transfer capability. Simulations and prototypes validate that the proposed UIC is accurate, robust, and applicable.


title: Design of millimeter-wave transformer balun with isolation circuit in silicon based technology

abstract: In this paper a novel method to design a transformer based balun with isolation between output ports and matching characteristic at all ports is proposed. The main issue of transformer balun at millimeter-wave frequencies is that it has poor matching and isolation performance. In this work, an isolation circuit is introduced to place between output ports in the transformer balun design. As a validation, comparison has been made between the balun with and without isolation circuit, which has shown great performance improvement of proposed design. Moreover, design example is given for millimeter-wave on-chip implementation of this isolation circuit in standard silicon based technology with 6 metal layers.


title: Rethinking Speech Recognition on Mobile Devices

abstract: In this paper, we describe our experiences and thoughts on building speech applications on mobile devices for developing countries. We describe three models of use for automatic speech recognition (ASR) systems on mobile devices that are currently used – embedded speech recognition, speech recognition in the cloud, and distributed speech recognition; evaluate their advantages and disadvantages; and finally propose a fourth model of use that we call Shared Speech Recognition with User-Based Adaptation. This proposed model exploits the advantages in all the three current models, while mitigating the challenges that make any of the current models less feasible, such as unreliable cellular connections or low processing power on mobile devices, which are typical needs of speech application in developing regions. We also propose open questions for future research to further evaluate our proposed model of use. Finally, we demonstrate the performance of two mobile speech recognizers that are either used in a lab setting to compare the recognition accuracy against a desktop, or used in real-world speech applications for mobile devices in the developing world. Author Keywords


title: Interactive Task Learning for Simple Games

abstract: We present an agent, called RosieTAG, that is implemented in Soar and interacts with an external robotic environment. Rosie learns new games through interactive instruction with a human via restricted natural language. Instead of learning policy or strategy information, as is common in other game learners, Rosie learns multiple game formulations (the objects, players, and rules of a game) and then uses its own general strategies to solve them. We describe the structure and functionality of Rosie, and evaluate its competence, generality, communication efficiency, communication accessibility, and ability to continuously learn and accumulate new tasks and new task knowledge.


SMART: Secure and Minimal Architecture for (Establishing Dynamic) Root of Trust


title: Learning Mixtures of Gaussians in High Dimensions

abstract: Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory. Given samples coming from a random one out of k Gaussian distributions in Rn, the learning problem asks to estimate the means and the covariance matrices of these Gaussians. This learning problem arises in many areas ranging from the natural sciences to the social sciences, and has also found many machine learning applications. Unfortunately, learning mixture of Gaussians is an information theoretically hard problem: in order to learn the parameters up to a reasonable accuracy, the number of samples required is exponential in the number of Gaussian components in the worst case. In this work, we show that provided we are in high enough dimensions, the class of Gaussian mixtures is learnable in its most general form under a smoothed analysis framework, where the parameters are randomly perturbed from an adversarial starting point. In particular, given samples from a mixture of Gaussians with randomly perturbed parameters, when n>{\Omega}(k^2), we give an algorithm that learns the parameters with polynomial running time and using polynomial number of samples. The central algorithmic ideas consist of new ways to decompose the moment tensor of the Gaussian mixture by exploiting its structural properties. The symmetries of this tensor are derived from the combinatorial structure of higher order moments of Gaussian distributions (sometimes referred to as Isserlis' theorem or Wick's theorem). We also develop new tools for bounding smallest singular values of structured random matrices, which could be useful in other smoothed analysis settings.


title: Aspects of anthocyanin absorption, metabolism and pharmacokinetics in humans

abstract: Interest in the health-promoting properties of berry anthocyanins is intensifying; however, findings are primarily based on in vitro characteristics, leaving mechanisms associated with absorption, metabolism and pharmacokinetics largely unexplored. The present review integrates the available anthocyanin literature with that of similar flavonoids or polyphenols in order to form hypotheses regarding absorption, metabolism and clearance in humans. Of the limited available literature regarding the absorption and clearance kinetics of anthocyanins, maximum plasma concentrations are reported anywhere between 1·4 and 592 nmol/l and occur at 0·5–4 h post-consumption (doses; 68–1300 mg). Average urinary excretion is reported between 0·03 and 4 % of the ingested dose, having elimination half-lives of 1·5–3 h. In addition, much is unknown regarding the metabolism of anthocyanins. The most commonly cited conjugation reactions involved in the metabolism of other flavonoids include glucuronidation, methylation and sulfation. It is reasonable to suspect that anthocyanins are metabolised in much the same manner; however, until recently, there was little evidence to suggest that anthocyanins were metabolised to any significant extent. New evidence now suggests that anthocyanins are absorbed and transported in human serum and urine primarily as metabolites, with recent studies documenting as much as 68–80 % of anthocyanins as metabolised derivatives in human urine. Further research is required to resolve mechanisms associated with the absorption, metabolism and clearance of anthocyanins in order to establish their true biological activities and health effects. The presented evidence will hopefully focus future research, refining study design and propagating a more complete understanding of anthocyanins' biological significance in humans.


title: LEVERAGING TRUST AND PRIVACY CONCERNS IN ONLINE SOCIAL NETWORKS: AN EMPIRICAL STUDY

abstract: Unprecedented success of Online Social Networks, such as Facebook, has been recently overshadowed by the privacy risks they imply. Weary of privacy concerns and unable to construct their identity in the desired way, users may restrict or even terminate their platform activities. Even though this means a considerable business risk for these platforms, so far there have been no studies on how to enable social network providers to address these problems. This study fills this gap by adopting a fairness perspective to analyze related measures at the disposal of the provider. In a Structural Equation Model with 237 subjects we find that ensuring interactional and procedural justice are two important strategies to support user participation on the platform.


title: Retail Bank Services Strategy: A Model of Traditional, Electronic, and Mixed Distribution Choices

abstract: Design of a retail banking distribution strategy is an important issue in that industry. This paper shows the effect of new electronic distribution technologies such as PC banking on the choice of a bank's distribution strategy. We present a competitive model of distribution strategy choice, including heterogeneous consumers and banks, that allows a rich variety of customer preference and technology cost parameters. Sensitivity analysis shows how several parameters affect the competitive outcome. This analysis suggests that changing consumer behavior and attitudes, instead of banks' cost structure with new technologies significantly affects the bank's distribution strategy choice. If the segment of consumers that prefers PC banking remains small relative to the segment that prefers branches, then there will still be a market for specialized branch banks. Branch banking without PC banking services will be a viable strategy until the segment that prefers PC banking grows larger (amounting to about 40 percent of all transactions). Banks offering both branch and PC banking services can prevent successful and profitable entry by virtual banks (Internet banks offering only PC banking services) as long as the segment of customers that prefer PC banking remains relatively small (less than two-thirds of all transactions). Beyond this fraction, virtual banks will be profitable. This analysis suggests that it may be a long time (if ever) before virtual banks turn a profit.


title: Square Localization for Efficient and Accurate Object Detection

abstract: The key contribution of this paper is the compact square object localization, which relaxes the exhaustive sliding window from testing all windows of different combinations of aspect ratios. Square object localization is category scalable. By using a binary search strategy, the number of scales to test is further reduced empirically to only O(log(min{H, W})) rounds of sliding CNNs, where H and W are respectively the image height and width. In the training phase, square CNN models and object co-presence priors are learned. In the testing phase, sliding CNN models are applied which produces a set of response maps that can be effectively filtered by the learned co-presence prior to output the final bounding boxes for localizing an object. We performed extensive experimental evaluation on the VOC 2007 and 2012 datasets to demonstrate that while efficient, square localization can output precise bounding boxes to improve the final detection result.


title: Control Issues in Single-Stage Photovoltaic Systems: MPPT, Current and Voltage Control

abstract: Photovoltaic Systems (PVS) can be easily integrated in residential buildings hence they will be the main responsible of making low-voltage grid power flow bidirectional. Control issues on both the PV side and on the grid side have received much attention from manufacturers, competing for efficiency and low distortion and academia proposing new ideas soon become state-of-the-art. This paper aims at reviewing part of these topics (MPPT, current and voltage control) leaving to a future paper to complete the scenario. Implementation issues on Digital Signal Processor (DSP), the mandatory choice in this market segment, are discussed.


title: Witnessing spouse abuse and experiencing physical abuse: A “double whammy”?

abstract: Child witnesses to parental violence, both abused (N =40) and nonabused (N =44), who were temporarily residing in a battered women's shelter were compared with children from a similar economic background (N =66) on measures of anxiety, depression, and behavior problems. Mothers of the three groups of children (comparison, witness, abused/witness) across the age range from 4 to 12 years completed a behavior problem inventory; the youngsters responded to paper-and-pencil self-report measures. Results indicated that the abused/witness children were manifesting significantly more distress on the behavior problem measure than the comparison youngsters, with the witness children showing a moderate amount and the comparison children the least. However, these patterns were mediated by the age of the child. Implications of these differential findings are discussed.


title: Fourier fringe analysis: the two-dimensional phase unwrapping problem.

abstract: A new phase unwrapping algorithm is described that uses local phase information to mask out those parts of the field that cause inconsistencies in the unwrapping. Unlike earlier techniques, which produce only a consistent unwrapping of the phase in the presence of discontinuities, this technique can produce an approximately correct unwrapping. The technique is tolerant of discontinuities and noise in the phase and is fast, efficient, and simple to implement. In the absence of discontinuities an rms signal-to-noise ratio in the wrapped phase of <2:1 can be tolerated.


title: Structure of the dengue virus envelope protein after membrane fusion

abstract: Dengue virus enters a host cell when the viral envelope glycoprotein, E, binds to a receptor and responds by conformational rearrangement to the reduced pH of an endosome. The conformational change induces fusion of viral and host-cell membranes. A three-dimensional structure of the soluble E ectodomain (sE) in its trimeric, postfusion state reveals striking differences from the dimeric, prefusion form. The elongated trimer bears three 'fusion loops' at one end, to insert into the host-cell membrane. Their structure allows us to model directly how these fusion loops interact with a lipid bilayer. The protein folds back on itself, directing its carboxy terminus towards the fusion loops. We propose a fusion mechanism driven by essentially irreversible conformational changes in E and facilitated by fusion-loop insertion into the outer bilayer leaflet. Specific features of the folded-back structure suggest strategies for inhibiting flavivirus entry.


title: Evidence and solution of over-RESET problem for HfOX based resistive memory with sub-ns switching speed and high endurance

abstract: The memory performances of the HfO X based bipolar resistive memory, including switching speed and memory reliability, are greatly improved in this work. Record high switching speed down to 300 ps is achieved. The cycling test shed a clear light on the wearing behavior of resistance states, and the correlation between over-RESET phenomenon and the worn low resistance state in the devices is discussed. The modified bottom electrode is proposed for the memory device to maintain the memory window and to endure resistive switching up to 1010 cycles.


title: A Multi-focus Image Fusion Method Based on Laplacian Pyramid

abstract: This paper presented a simple and efficient algorithm for multi-focus image fusion, which used a multi-resolution signal decomposition scheme called Laplacian pyramid method. The principle of Laplacian pyramid transform is introduced, and based on it the fusion strategy is described in detail. The method mainly composed of three steps. Firstly, the Laplacian pyramids of each source image are deconstructed separately, and then each level of new Laplacian pyramid is fused by adopting different fusion rules. To the top level, it adopts the maximum region information rule; and to the rest levels, it adopts the maximum region energy rule. Finally, the fused image is obtained by inverse Laplacian pyramid transform. Two sets of images are applied to verify the fusion approach proposed and compared it with other fusion approaches. By analyzing the experimental results, it showed that this method has good performance, and the quality of the fused image is better than the results of other methods.


title: ChargeCache: Reducing DRAM latency by exploiting row access locality

abstract: DRAM latency continues to be a critical bottleneck for system performance. In this work, we develop a low-cost mechanism, called Charge Cache, that enables faster access to recently-accessed rows in DRAM, with no modifications to DRAM chips. Our mechanism is based on the key observation that a recently-accessed row has more charge and thus the following access to the same row can be performed faster. To exploit this observation, we propose to track the addresses of recently-accessed rows in a table in the memory controller. If a later DRAM request hits in that table, the memory controller uses lower timing parameters, leading to reduced DRAM latency. Row addresses are removed from the table after a specified duration to ensure rows that have leaked too much charge are not accessed with lower latency. We evaluate ChargeCache on a wide variety of workloads and show that it provides significant performance and energy benefits for both single-core and multi-core systems.