title: ConceptRDF: An RDF presentation of ConceptNet knowledge base

abstract: The exponential growth of information on the Web has prompted the introduction of new technologies such as Semantic Web and Common Sense knowledge bases. While there are different models available to present information, RDF, as the cornerstone of Semantic Web technologies, has a dominant place for formats suitable both for human interactions and machine understanding. In this paper we present ConceptRDF, a conversion of ConceptNet, as one of the largest common sense knowledge bases available for public use, to RDF/XML format, suitable for use in different fields of information retrieval.


title: Large-Scale Wi-Fi Hotspot Classification via Deep Learning

abstract: We describe the problem of classifying hundreds of millions of Wi-Fi hotspots using only connection and user count characteristics. We use a combination of deep learning and frequency analysis. Specifically, Convolution Neural Networks (CNN) capture the spatio-temporal relationship between adjacent connection/user counts across a 24 hour×7 day matrix, while FFT (Fast Fourier Transforms) extract user and connection frequencies. Our production system has been deployed to classify 239 million hotspots in 12 hours on a SPARK 2.0 cluster, achieving close to 80% F1-score for binary classification.


title: On the role of design in information visualization

abstract: Every information visualization developer is engaged in a complex process of design - balancing the three requirements of utility, soundness and attractiveness within given constraints. This paper investigates the oftoverlooked requirement of attractiveness (or 'aesthetics'] in visualization, and, in doing so, discusses a wider rote for the design process which emphasizes the benefits of disseminating design rationale. In presenting a model of three potential roles for design in information visualization, we suggest that the field would benefit from encompassing a broader scope that includes visualizations produced as part of commercial practice or artistic exploration. We conclude with a discussion on the practical consequences to the academic community of adopting our model - consequences that wiLL require a paradigm shift in the way we value, teach and conduct information visualization research.


title: A Scalable Parallel Deduplication Algorithm

abstract: The identification of replicas in a database is fundamental to improve the quality of the information. Deduplication is the task of identifying replicas in a database that refer to the same real world entity. This process is not always trivial, because data may be corrupted during their gathering, storing or even manipulation. Problems such as misspelled names, data truncation, data input in a wrong format, lack of conventions (like how to abbreviate a name), missing data or even fraud may lead to the insertion of replicas in a database. The deduplication process may be very hard, if not impossible, to be performed manually, since actual databases may have hundreds of millions of records. In this paper, we present our parallel deduplication algorithm, called FER- APARDA. By using probabilistic record linkage, we were able to successfully detect replicas in synthetic datasets with more than 1 million records in about 7 minutes using a 20- computer cluster, achieving an almost linear speedup. We believe that our results do not have similar in the literature when it comes to the size of the data set and the processing time.


title: Remote Heart Rate Measurement from RGB-NIR Video Based on Spatial and Spectral Face Patch Selection

abstract: In this paper, we propose a novel heart rate (HR) estimation method using simultaneously recorded RGB and near-infrared (NIR) face videos. The key idea of our method is to automatically select suitable face patches for HR estimation in both spatial and spectral domains. The spatial and spectral face patch selection enables us to robustly estimate HR under various situations, including scenes under which existing RGB camera-based methods fail to accurately estimate HR. For a challenging scene in low light and with light fluctuations, our method can successfully estimate HR for all 20 subjects $( \pm 3$ beats per minute), while the RGB camera-based methods succeed only for 25% of the subjects.


title: Methods for Recognition and Classification of Human Motion Patterns – A Prerequisite for Intelligent Devices Assisting in Sports Activities

abstract: Abstract Sensor and computing technologies provide people with information on their performance and load when doing sports. In order to automatically give advices on how to continue exercising and/or to adjust the sports equipment during the physical activity, intelligent devices are required. These devices rely on models for recognition and classification of patterns in the motion currently performed. Different methods and models, such as Neural Networks, Hidden Markov models or Support Vector Machines have proven to be applicable for this purpose. Pros and cons of the different approaches are discussed. Practical applications are presented and experiences reported.


title: Overview of deep learning

abstract: Deep learning is a new way of training multi-layer neural network.The optimization difficulty associated with the deep models can be alleviated,it has induced great attention of machine learning community.Firstly,this paper discussed the origin of deep learning,then analyzed virtue brought by deep learning.It introduced the main stream deep learning algorithm and their applications.Finally it concluded the problem remaining and development orientation.


title: Named Entity Recognition System for Urdu

abstract: Named Entity Recognition (NER) is a task which helps in finding out Persons name, Location names, Brand names, Abbreviations, Date, Time etc and classifies the m into predefined different categories. NER plays a major role in various Natural Language Processing (NLP) fields like Information Extraction, Machine Translations and Question Answering. This paper describes the problems of NER in the context of Urdu Language and provides relevant solutions. The system is developed to tag thirteen different Named Entities (NE), twelve NE proposed by IJCNLP-08 and Izaafats. We have used the Rule Based approach and developed the various rules to extract the Named Entities in the given Urdu text.


title: Coordination of Cooperative Autonomous Vehicles: Toward safer and more efficient road transportation

abstract: While intelligent transportation systems come in many shapes and sizes, arguably the most transformational realization will be the autonomous vehicle. As such vehicles become commercially available in the coming years, first on dedicated roads and under specific conditions, and later on all public roads at all times, a phase transition will occur. Once a sufficient number of autonomous vehicles is deployed, the opportunity for explicit coordination appears. This article treats this challenging network control problem, which lies at the intersection of control theory, signal processing, and wireless communication. We provide an overview of the state of the art, while at the same time highlighting key research directions for the coming decades.


Keystones and Dominators: Framing Operating and Technology Strategy in a Business Ecosystem


Understanding Mutual Information and its Use in InfoGAN


title: A Study on Threshold Selection for Multi-label

abstract: Multi-label classification is useful for text categorization, multimedia retrieval, and many other areas. A commonly used multi-label approach is the binary method, which constructs a decision function for each label. For some applications, adjusting thresholds in decision functions of the binary method significantly improves the performance, but few studies have been done on this subject. This article gives a detailed investigation on the selection of thresholds. Experiments on real-world data sets demonstrate the usefulness of some simple selection strategies.


title: Communication Quantization for Data-Parallel Training of Deep Neural Networks

abstract: We study data-parallel training of deep neural networks on high-performance computing infrastructure. The key problem with scaling data-parallel training is avoiding severe communication/computation imbalance. We explore quantizing gradient updates before communication to reduce bandwidth requirements and compare it against a baseline implementation that uses the MPI allreduce routine. We port two existing quantization approaches, one-bit and threshold, and develop our own adaptive quantization algorithm. The performance of these algorithms is evaluated and compared with MPI_Allreduce when training models for the MNIST dataset and on a synthetic benchmark. On an HPC system, MPI_Allreduce outperforms the existing quantization approaches. Our adaptive quantization is comparable or superior for large layers without sacrificing accuracy. It is 1.76 times faster than the next best approach for the largest layers in our benchmark and achieves near-linear speedup in data-parallel training.


title: A Scalable Architecture For High-Throughput Regular-Expression Pattern Matching

abstract: We present and evaluate an architecture for highthroughput pattern matching of regular expressions. Our approach matches multiple patterns concurrently, responds rapidly to changes in the pattern set, and is well suited for synthesis in an ASIC or FPGA. Our approach is based on a new and easily pipelined state-machine representation that uses encoding and compression techniques to improve density. We have written a compiler that translates a set of regular expressions and optimizes their deployment in the structures used by our architecture. We analyze our approach in terms of its throughput, density, and efficiency. We present experimental results from an implementation in a commodity FPGA, showing better throughput and density than the best known approaches.


title: TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition

abstract: Recent two-stream deep Convolutional Neural Networks (ConvNets) have made significant progress in recognizing human actions in videos. Despite their success, methods extending the basic two-stream ConvNet have not systematically explored possible network architectures to further exploit spatiotemporal dynamics within video sequences. Further, such networks often use different baseline two-stream networks. Therefore, the differences and the distinguishing factors between various methods using Recurrent Neural Networks (RNN) or convolutional networks on temporally-constructed feature vectors (Temporal-ConvNet) are unclear. In this work, we first demonstrate a strong baseline two-stream ConvNet using ResNet-101. We use this baseline to thoroughly examine the use of both RNNs and Temporal-ConvNets for extracting spatiotemporal information. Building upon our experimental results, we then propose and investigate two different networks to further integrate spatiotemporal information: 1) temporal segment RNN and 2) Inception-style Temporal-ConvNet. We demonstrate that using both RNNs (using LSTMs) and Temporal-ConvNets on spatiotemporal feature matrices are able to exploit spatiotemporal dynamics to improve the overall performance. However, each of these methods require proper care to achieve state-of-the-art performance; for example, LSTMs require pre-segmented data or else they cannot fully exploit temporal information. Our analysis identifies specific limitations for each method that could form the basis of future work. Our experimental results on UCF101 and HMDB51 datasets achieve state-of-the-art performances, 94.1% and 69.0%, respectively, without requiring extensive temporal augmentation.


title: Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem

abstract: In this paper we introduce Ant-Q, a family of algorithms which present many similarities with Q-learning (Watkins, 1989), and which we apply to the solution of symmetric and asymmetric instances of the traveling salesman problem (TSP). Ant-Q algorithms were inspired by work on the ant system (AS), a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in (Dorigo, 1992; Dorigo, Maniezzo and Colorni, 1996). We show that AS is a particular instance of the Ant-Q family, and that there are instances of this family which perform better than AS. We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP's are competitive with those obtained by other heuristic approaches based on neural networks or local search. Finally, we apply Ant-Q to some difficult asymmetric TSP's obtaining very good results: Ant-Q was able to find solutions of a quality which usually can be found only by very specialized algorithms.


title: Common criteria compliant software development (CC-CASD)

abstract: In order to gain their customers' trust, software vendors can certify their products according to security standards, e.g., the Common Criteria (ISO 15408). However, a Common Criteria certification requires a comprehensible documentation of the software product. The creation of this documentation results in high costs in terms of time and money. We propose a software development process that supports the creation of the required documentation for a Common Criteria certification. Hence, we do not need to create the documentation after the software is built. Furthermore, we propose to use an enhanced version of the requirements-driven software engineering process called ADIT to discover possible problems with the establishment of Common Criteria documents. We aim to detect these issues before the certification process. Thus, we avoid expensive delays of the certification effort. ADIT provides a seamless development approach that allows consistency checks between different kinds of UML models. ADIT also supports traceability from security requirements to design documents. We illustrate our approach with the development of a smart metering gateway system.


title: Questions, options, and criteria: elements of design space analysis

abstract: A parachute deployment system and a connector for use in this system. The deployment system utilizes a pyrotechnic device to launch a weighted projectile which is connected to the ripcord and pilot chute of a parachute pack. The connector is to be interposed between the projectile and the ripcord and comprises a mounting plate and a support releasably retained thereon. The support carries a movable member which, when pressure is exerted on a part thereof, releases the support from the mounting plate and completes a connection between the projectile and the ripcord and pilot chute.


title: A Low-Power 26-GHz Transformer-Based Regulated Cascode SiGe BiCMOS Transimpedance Amplifier

abstract: Low-power high-speed optical receivers are required to meet the explosive growth in data communication systems. This paper presents a 26 GHz transimpedance amplifier (TIA) that employs a transformer-based regulated cascode (RGC) input stage which provides passive negative-feedback gain that enhances the effective transconductance of the TIA's input common-base transistor; reducing the input resistance and isolating the parasitic photodiode capacitance. This allows for considerable bandwidth extension without significant noise degradation or power consumption. Further bandwidth extension is achieved through series inductive peaking to isolate the photodetector capacitance from the TIA input. The optimum choice of series inductive peaking value and key transformer parameters for bandwidth extension and jitter minimization is analyzed. Fabricated in a 0.25-µm SiGe BiCMOS technology and tested with an on-chip 150 fF capacitor to emulate a photodiode, the TIA achieves a 53 dBΩ single-ended transimpedance gain with a 26 GHz bandwidth and 21.3 pA/ $\sqrt{\rm Hz}$ average input-referred noise current spectral density. Total chip power including output buffering is 28.2 mW from a 2.5 V supply, with the core TIA consuming 8.2 mW, and the chip area including pads is 960 µm x 780 µm.


title: Online Adaptation for Joint Scene and Object Classification

abstract: Recent efforts in computer vision consider joint scene and object classification by exploiting mutual relationships (often termed as context) between them to achieve higher accuracy. On the other hand, there is also a lot of interest in online adaptation of recognition models as new data becomes available. In this paper, we address the problem of how models for joint scene and object classification can be learned online. A major motivation for this approach is to exploit the hierarchical relationships between scenes and objects, represented as a graphical model, in an active learning framework. To select the samples on the graph, which need to be labeled by a human, we use an information theoretic approach that reduces the joint entropy of scene and object variables. This leads to a significant reduction in the amount of manual labeling effort for similar or better performance when compared with a model trained with the full dataset. This is demonstrated through rigorous experimentation on three datasets.


Critical Success Factors Analysis on Effective Information Security Management : A Literature Review Completed Research Paper


title: 55-GHz bandwidth short-cavity distributed reflector laser and its application to 112-Gb/s PAM-4

abstract: A short-cavity distributed reflector (DR) laser achieved a 3-dB BW of 55 GHz. The DR laser was used to demonstrate single-channel 112-Gb/s PAM-4 transmission without pre-emphasis.


A post GWAS association study of SNPs associated with cleft lip with or without cleft palate in submucous cleft palate


title: Parallel Cat Swarm Optimization

abstract: We investigate a parallel structure of cat swarm optimization (CSO) in this paper, and we call it parallel cat swarm optimization (PCSO). In the experiments, we compare particle swarm optimization (PSO) with CSO and PCSO. The experimental results indicate that both CSO and PCSO perform well. Moreover, PCSO is an effective scheme to improve the convergent speed of cat swarm optimization in case the population size is small and the whole iteration is less.


Deep LSTM-Based Goal Recognition Models for Open-World Digital Games.


title: Hybrid video object tracking in H.265/HEVC video streams

abstract: In this paper we propose a hybrid tracking method which detects moving objects in videos compressed according to H.265/HEVC standard. Our framework largely depends on motion vectors (MV) and block types obtained by partially decoding the video bit stream and occasionally uses pixel domain information to distinguish between two objects. The compressed domain method is based on a Markov Random Field (MRF) model that captures spatial and temporal coherence of the moving object and is updated on a frame-to-frame basis. The hybrid nature of our approach stems from the usage of a pixel domain method that extracts the color information from the fully-decoded I frames and is updated only after completion of each Group-of-Pictures (GOP). We test the tracking accuracy of our method using standard video sequences and show that our hybrid framework provides better tracking accuracy than a state-of-the-art MRF model.


title: Partially Isolated Single-Magnetic Multiport Converter Based on Integration of Series-Resonant Converter and Bidirectional PWM Converter

abstract: Recent power systems, such as renewable energy systems consisting of solar panels and rechargeable batteries, contain multiple power sources and need multiple converters in proportion to the number of power sources, resulting in increased system complexity and cost. This paper proposes a multiport converter (MPC) integrating a bidirectional PWM converter and series-resonant converter. Not only is the switch count halved with the proposed MPC, but also magnetic components can be integrated, reducing the circuit complexity and volume. A 150-W prototype of the proposed MPC was built for the experimental verification. The measured voltage conversion characteristics and transient responses demonstrated that the proposed MPC could control load and battery voltages independently by pulse width modulation and pulse frequency modulation controls, respectively.


title: The relationship between ethical leadership and core job characteristics

abstract: Summary In the current study, we draw on the original job characteristics model (JCM) and on an elaborated model of work design to examine relationships between ethical leadership, task significance, job autonomy, effort, and job performance. We suggest that leaders with strong ethical commitments who regularly demonstrate ethically normative behavior can have an impacton the JCM elementsof tasksignificance and autonomy, thereby affecting anemployee’s motivation (willingness to exert effort), which in turn will be evidenced by indications of enhanced task performance and organizational citizenship behavior. We conducted a field study by surveying pairs of co-workers in a diverse set of organizations. Results provide support for a fully mediated model whereby task significance and effort fully mediate relationships between ethical leadership and subordinates’ job performance. Implications for future research on job design are discussed. Copyright # 2010 John Wiley & Sons, Ltd. At the heart of the job characteristics model (JCM, Hackman & Oldham, 1976) is the premise that leaders canshape intrinsic motivation bytheway inwhich theystructuretheobjectivecharacteristics of the work itself. 1 The theoretical model of job design was rooted in an examination of the ‘‘basic conditions that promote high performance motivation and satisfaction at work,’’ in order ‘‘to determine how those conditions can be created’’ (Hackman & Oldham, 1976; p. 71). We argue that the conceptual and empirical developments of the JCM to date can be expanded to encompass a broader role for leaders. In particular, we suggest that leaders with strong ethical commitments who regularly demonstrate ethically normative behavior can have an impact on the JCM elements of task significance and autonomy, thereby affecting an employee’s motivation (willingness to exert effort), which in turn will be evidenced by indications of enhanced task performance and organizational citizenship behaviors.


title: Frequency-Modulation Control of a DC/DC Current-Source Parallel-Resonant Converter

abstract: This paper proposes a frequency-modulation control scheme for a dc/dc current-source parallel-resonant converter with two possible configurations. The basic configuration comprises an external voltage loop, an internal current loop, and a frequency modulator: the voltage loop is responsible for regulating the output voltage, the current loop makes the system controllable and limits the input current, and the modulator provides robustness against variations in resonant component values. The enhanced configuration introduces the output inductor current as a feed-forward term and clearly improves the transient response to fast load changes. The theoretical design of these control schemes is performed systematically by first deriving their small-signal models and second using Bode diagram analysis. The actual performance of the proposed control schemes is experimentally validated by testing on a laboratory prototype.


title: Event Detection using Twitter: A Spatio-Temporal Approach

abstract: Background ::: Every day, around 400 million tweets are sent worldwide, which has become a rich source for detecting, monitoring and analysing news stories and special (disaster) events. Existing research within this field follows key words attributed to an event, monitoring temporal changes in word usage. However, this method requires prior knowledge of the event in order to know which words to follow, and does not guarantee that the words chosen will be the most appropriate to monitor. ::: ::: ::: Methods ::: This paper suggests an alternative methodology for event detection using space-time scan statistics (STSS). This technique looks for clusters within the dataset across both space and time, regardless of tweet content. It is expected that clusters of tweets will emerge during spatio-temporally relevant events, as people will tweet more than expected in order to describe the event and spread information. The special event used as a case study is the 2013 London helicopter crash. ::: ::: ::: Results and Conclusion ::: A spatio-temporally significant cluster is found relating to the London helicopter crash. Although the cluster only remains significant for a relatively short time, it is rich in information, such as important key words and photographs. The method also detects other special events such as football matches, as well as train and flight delays from Twitter data. These findings demonstrate that STSS is an effective approach to analysing Twitter data for event detection.


title: Catadioptric sensors that approximate wide-angle perspective projections

abstract: We present two families of reflective surfaces that are capable of providing a wide field of view, and yet still approximate a perspective projection to a high degree. These surfaces are derived by considering a plane perpendicular to the axis of a surface of revolution and finding the equations governing the distortion of the image of the plane in this surface. We then view this relation as a differential equation and prescribe the distortion term to be linear. By choosing appropriate initial conditions for the differential equation and solving it numerically, we derive the surface shape and obtain a precise estimate as to what degree the resulting sensor can approximate a perspective projection. Thus these surfaces act as computational sensors, allowing for a wide-angle perspective view of a scene without processing the image in software. The applications of such a sensor should be numerous, including surveillance, robotics and tradition photography.


title: Web-image driven best views of 3D shapes

abstract: The rapid advance of the Internet provides available huge database of web images. In this paper, we introduce a novel approach for automatically computing the best views of 3D shapes based on their web images. Best view selection is generally an intuitive task of getting the most information of a 3D shape. The novelty of our approach is to directly explore human perception on observing 3D shapes from the relevant web images. Those images are captured from biased views of different people, thus sufficiently reflecting view choice when observing the 3D shapes. By collecting web images possibly captured from the similar views, the best view is selected as the one possessing the most web images. We experiment our method with the shapes in Princeton Shape Benchmark (PSB), as well make comparisons with traditional geometric descriptor based approaches. The results demonstrate that our method is not only robust but also able to produce more canonical views in accordance with human perception.


title: A Low Power 8-bit Magnitude Comparator with Small Transistor Count using Hybrid PTL/CMOS Logic

abstract: Magnitude comparison is one of the basic functions used for sorting in microprocessor, digital signal processing, so a high performance, effective magnitude comparator is required. The main objective of this paper is to provide new low power, area solution for Very Large Scale Integration (VLSI) designers. At circuit level, Hybrid PTL/CMOS Logic style gives best results over CMOS only and PTL only. A fine cost-performance ratio comparator design based on modified 1‟s complement principle and conditional sum adder scheme using Hybrid PTL/CMOS logic style has been proposed in this paper and the proposed design has small power dissipation and less area over various supply voltages. Simulations based on BSIM 3V3 90nm CMOS technology. It shows an 8-b comparator of the proposed architecture only needs 154 transistors.


title: Platelet function analysis.

abstract: Since the last guidelines for BCSH platelet function testing were written in the late 1980s, many new tests have become available. Previously most platelet function tests were traditionally utilized to aid in the diagnosis and management of patients with platelet and haemostatic disorders. Most traditional tests were also largely restricted to the specialized laboratory or centre. However, nowadays there is also much renewed interest in monitoring the efficacy of anti-platelet therapy and measuring platelet hyper-function. A number of dedicated platelet function instruments have now become available that are much simpler to use and are beginning to be utilized as point of care instruments. These can now provide measurement of platelet function within whole blood without the requirement of sample processing. Some are also beginning to be incorporated into routine clinical use and can be utilized as not only as general screening tests of platelet function but to monitor anti-platelet therapy and to potentially assess both risk of bleeding and/or thrombosis. Modern flow cytometric-based platelet function analysis now also provides a wide variety of specific tests that can assess different aspects of platelet biology that are useful for diagnostic purposes. This review will highlight some of these of new tests/instruments and discuss their potential utility both within the haemostasis laboratory but also as potential point of care instruments.


title: Effect of adjunctive L-methylfolate 15 mg among inadequate responders to SSRIs in depressed patients who were stratified by biomarker levels and genotype: results from a randomized clinical trial.

abstract: Objective: Specific genetic or biological markers may predict inadequate response to therapy for major depressive disorder (MDD). The objective of the current post hoc analysis was to evaluate the effect of specific biological and genetic markers on the antidepressant efficacy of adjunctive l-methylfolate 15 mg versus placebo from a trial of inadequate responders to selective serotonin reuptake inhibitors (SSRIs). Method: The double-blind, randomized, placebo-controlled trial used the sequential parallel comparison design. Outpatients with SSRI-resistant MDD (DSM-IV criteria) received l-methylfolate 15 mg/d for 60 days, placebo for 30 days followed by l-methylfolate 15 mg/d for 30 days, or placebo for 60 days. The effects of baseline levels of select biological and genetic markers individually and combined on treatment response to l-methylfolate versus placebo were evaluated; the primary response measure was the 28-Item Hamilton Depression Rating Scale (HDRS-28). The first patient was enrolled July 14, 2009, and the last patient completed April 28, 2011. Results: Seventy-five patients were enrolled. Patients with specific biological (body mass index ≥ 30 kg/m2, elevated plasma levels of high-sensitivity C-reactive protein or 4-hydroxy-2nonenal, low S-adenosylmethionine/S-adenosylhomocysteine ratio) and genetic markers at baseline had significantly (P ≤ .05) greater pooled mean change from baseline on the HDRS-28 with l-methylfolate versus placebo. Pooled mean change from baseline on the Clinical Global Impressions-Severity of Illness scale was significantly (P < .05) greater with l-methylfolate versus placebo for most genetic markers. Most combinations of baseline biological and genetic markers predicted significantly (P ≤ .05) greater reductions in pooled mean change from baseline in HDRS-28 scores with l-methylfolate versus placebo. Conclusions: Biomarkers associated with inflammation or metabolism and genomic markers associated with l-methylfolate synthesis and metabolism may identify patients with SSRIresistant depression who are responsive to adjunctive therapy with l-methylfolate 15 mg. Confirmatory studies are needed. Trial Registration: ClinicalTrials.gov identifier: NCT00955955 J Clin Psychiatry 2014;75(00):000–000 © Copyright 2014 Physicians Postgraduate Press, Inc. Submitted: December 17, 2013; accepted March 6, 2014. Online ahead of print: April 15, 2014 (doi:10.4088/JCP.13m08947). Corresponding author: George I. Papakostas, MD, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, One Bowdoin Sq, 6th Fl, Boston, MA 02114 (gpapakostas@partners.org). D the availability of numerous antidepressant drugs, over 60% of patients with major depressive disorder (MDD) fail to experience a complete remission of symptoms following their first antidepressant treatment, and the majority of those who do remit experience relapse or recurrence.1 An opportunity exists to develop more efficacious treatment strategies with improved success rates for specific patients identified with the use of clinical or biological markers.2 Disturbances in metabolic systems have been implicated in the pathophysiology and course of MDD.3–5 For instance, a prospective cohort study found that participants with depression and comorbid metabolic syndrome had a higher risk of developing chronic, recurrent depression.4 In parallel, in recent years, an association has also been recognized between MDD and altered cellular immunity and inflammation, characterized by elevated interleukin-6, tumor necrosis factor α, and high-sensitivity C-reactive protein (hsCRP) levels.5–8 Furthermore, activation of inflammatory pathways within the brain also may contribute to oxidative stress leading to the neuropathologic characteristics of MDD.7,9,10 In a prospective study of patients hospitalized for cardiac intervention, use of statins, which have antiinflammatory and antioxidative properties, was associated with a significant reduction in the risk of MDD at 9 months.9,10 An association has been observed between folate deficiency, metabolic dysregulation, and inflammation.4,6–8 The benefits of folic acid and its biologically active form, l-methylfolate, for treating MDD have been recognized; also recently recognized are links between folate deficiency and an increased risk for MDD, reduced antidepressant effectiveness, and a more chronic course of illness.4,11–14 More recently, our group published the results from a randomized, placebo-controlled trial in MDD patients not achieving an adequate response to selective serotonin reuptake inhibitors (SSRIs), which demonstrated greater efficacy for adjunctive treatment with 15 mg daily of l-methylfolate versus placebo using the sequential parallel comparison design (SPCD).15 In light of these findings, we surmised that it would be interesting to examine the treatment effect of 15 mg of l-methylfolate versus placebo as a function of baseline biomarker levels or genotype, focusing on markers of metabolic or inflammatory status. Specifically, in light of


title: Picture changes during blinks: Looking without seeing and seeing without looking

abstract: Observers inspected normal, high quality colour displays of everyday visual scenes while their eye movements were recorded. A large display change occurred each time an eye blink occurred. Display changes could either involve “Central Interest” or “Marginal Interest” locations, as determined from descriptions obtained from independent judges in a prior pilot experiment. Visual salience, as determined by luminance, colour, and position of the Central and Marginal Interest changes were equalized. The results obtained were very similar to those obtained in prior experiments showing failure to detect changes occurring simultaneously with saccades, flicker, or “mudsplashes” in the visual scene: Many changes were very hard to detect, and Marginal Interest changes were harder to detect than Central Interest changes. Analysis of eye movements showed, as expected, that the probability of detecting a change depended on the eye's distance from the change location. However a surprising finding was that both for Centr...


Game-based programming towards developing algorithmic thinking skills in primary education


title: High Power Terahertz and Millimeter-Wave Oscillator Design: A Systematic Approach

abstract: A systematic approach to designing high frequency and high power oscillators using activity condition is introduced. This method finds the best topology to achieve frequencies close to the fmax of the transistors. It also determines the maximum frequency of oscillation for a fixed circuit topology, considering the quality factor of the passive components. Using this technique, in a 0.13 μm CMOS process, we design and implement 121 GHz and 104 GHz fundamental oscillators with the output power of -3.5 dBm and -2.7 dBm, respectively. Next, we introduce a novel triple-push structure to realize 256 GHz and 482 GHz oscillators. The 256 GHz oscillator was implemented in a 0.13 μm CMOS process and the output power of -17 dBm was measured. The 482 GHz oscillator generates -7.9 dBm (0.16 mW) in a 65 nm CMOS process.


title: Explanatory dialogues with argumentative faculties over inconsistent knowledge bases

abstract: We introduce a new formal model of explanatory dialogues.We extend the model by argumentative capacities.We provide an implementation of the model.We show its usefulness through a case study on knowledge acquisition. We introduce a formal model of explanatory dialogue called EDS. We extend this model by including argumentation capacities to facilitate knowledge acquisition in inconsistent knowledge bases. To prove the relevance of such model we provide the dalek (DiALectical Explanation in Knowledge-bases) framework that implements this model. We show the usefulness of the framework on a real-world application in the domain of Durum Wheat sustainability improvement within the ANR (French National Agency) funded Dur-Dur project. The preliminary pilot evaluation of the framework with agronomy experts gives a promising indication on the impact of explanation dialogues on the improvement of the knowledges content.


title: Is interleukin-1β a culprit in macrophage-adipocyte crosstalk in obesity?

abstract: Adipose tissue remodeling occurs in obesity, characterized by adipocyte hypertrophy and increased infiltration of macrophages which also shift to a proinflammatory phenotype. Factors derived from these macrophages significantly alter adipocyte function, such as repressing adipogenesis, inducing inflammatory response and desensitizing insulin action. As macrophages produce a cocktail of inflammatory signals, identifying the key factors that mediate the detrimental effects may offer effective therapeutic targets. IL-1β, a major cytokine produced largely by macrophages, is implicated in the development of obesity-associated insulin resistance. In this article, we discuss recent advances in our understanding of the role of IL-1β in macrophage-adipocyte crosstalk in obesity. IL-1β impairs insulin sensitivity in adipose tissue by inhibition of insulin signal transduction. Blocking the activity of IL-1β, its receptor binding or production improves insulin signaling and action in human adipocytes. This is in parallel with a reduction in macrophage-stimulated proinflammatory profile and lipolysis. Targeting IL-1β may be beneficial for protecting against obesity-related insulin resistance at the tissue and systemic levels.


title: Smart bag tracking and alert system using RFID

abstract: The problem with deployed bag tracking system lies in the fact that the procedure is too much human dependent. This leads to a high error rates, thus resulting in misplaced bags and misrouted deliveries. A trivial solution to this would be automating the process that goes into transporting a bag from point A to point B cost-effectively and accurately while giving the users a real-time feedback and location update. Our solution exploits the Radio Frequency Identification (RFID) technology which uses electromagnetic fields to automatically identify and track tags attached to objects thus reducing human labour as well as error rates. RFID is a low power consuming technology. We have made use of reusable passive RFID tags for the postal service. Every postal node (post offices), is equipped with RFID reader which is coupled to a micro-controller which syncs its data to our backend system. The backend processes the data and displays the same to the respective persons as well as to the respective authority. The system also determines the most cost optimized path — calculated before the parcel is dispatched and updates itself according to the accident points. Our solution makes use of the RFID solution to track the booked parcel bags. As a baggage is sealed, it is allotted with a RFID tag and tracking ID generated using RFID tag, Date and time stamp. There is a feedback mechanism to check if the parcel has reached the desired destination. This technology tracks out the accident points and hence necessary actions can be taken at those points. This makes the whole system automated and reduces error to a great extent.


Balanced Neighborhoods for Multi-sided Fairness in Recommendation


title: Polyclonal antibody specific to human tumor metastasis-associated mag-2 gene: preparation and identification

abstract: Objective:To prepare and identify a polyclonal antibody specific to human tumor metastasis-associated mag-2.Methods:Based on the data of antigen anticipating of mag-2 produced by a biosoftware,a mag-2-specific peptide of 17 amino acids was synthesized and conjugated with key hole limpet hemocyanin(KLH) to immunize Japanese rabbit subcutaneously and intradermally on its back.The rabbit serum was collected.Its titer was determined by indirect enzyme linked immunosorbent assay(ELISA).Western blotting assay was performed to test the serum′s specificity and the expression of mag-2 in several tumor ceml lines.Results: The Japanese rabbit was successfully immunized.The ELISA revealed that the serum obtained could well combine with the immunogen peptide and that the polyclonal antibody′s titer was high.A band was detected by the polyclonal antibody in Western blotting assay,and its molecular mass was 55×103,which was the very molecular mass of mag-2 gene's theoretically coded protein.The mag-2 protein was expressed in several tumor cell lines.Conclusion:A polyclonal antibody specific to human tumor metastasis-associated mag-2 was successfully prepared,which would provide a useful tool for the further study of the functions of mag2 in tumor metastasis process.


title: Mechanisms of synesthesia: cognitive

abstract: Abstract Synesthesia is a conscious experience of systematically induced sensory attributes that are not experienced by most people under comparable conditions. Recent findings from cognitive psychology, functional brain imaging and electrophysiology have shed considerable light on the nature of synesthesia and its neurocognitive underpinnings. These cognitive and physiological findings are discussed with respect to a neuroanatomical framework comprising hierarchically organized cortical sensory pathways. We advance a neurobiological theory of synesthesia that fits within this neuroanatomical framework.


title: A double-differential-input/differential-output fully complementary and self-biased asynchronous CMOS comparator

abstract: A novel fully complementary and fully differential asynchronous CMOS comparator architecture, that consists of a two-stage preamplifier cas- caded with a latch, achieves a sub-100 ps propagation delay for a 50mVpp and higher input signal amplitudes under 1.1V supply and 2.1mWpower consumption. The proposed voltage comparator topology features two differential pairs of inputs (four in total) thus increasing signal-to-noise ratio (SNR) and noise immunity through rejection of the coupled noise components, reduced even-order harmonic distortion, and doubled output voltage swing. In addition to that, the comparator is truly self-biased via negative feedback loop thereby eliminating the need for a voltage reference and suppressing the influence of process, supply voltage and ambient temperature variations. The described analog comparator prototype occupies 0.001mm2 in a purely digital 40 nm LP (low-power) CMOS process technology. All the above mentioned merits make it highly attractive for use as a building block in implementation of the leading-edge system-on-chip (SoC) data transceivers and data converters.


Food waste within global food systems-September 2013


title: Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations

abstract: Multi-label learning has attracted significant interests in computer vision recently, finding applications in many vision tasks such as multiple object recognition and automatic image annotation. Associating multiple labels to a complex image is very difficult, not only due to the intricacy of describing the image, but also because of the incompleteness nature of the observed labels. Existing works on the problem either ignore the label-label and instance-instance correlations or just assume these correlations are linear and unstructured. Considering that semantic correlations between images are actually structured, in this paper we propose to incorporate structured semantic correlations to solve the missing label problem of multi-label learning. Specifically, we project images to the semantic space with an effective semantic descriptor. A semantic graph is then constructed on these images to capture the structured correlations between them. We utilize the semantic graph Laplacian as a smooth term in the multi-label learning formulation to incorporate the structured semantic correlations. Experimental results demonstrate the effectiveness of the proposed semantic descriptor and the usefulness of incorporating the structured semantic correlations. We achieve better results than state-of-the-art multi-label learning methods on four benchmark datasets.


title: Affinity aggregation for spectral clustering

abstract: Spectral clustering makes use of spectral-graph structure of an affinity matrix to partition data into disjoint meaningful groups. Because of its elegance, efficiency and good performance, spectral clustering has become one of the most popular clustering methods. Traditional spectral clustering assumes a single affinity matrix. However, in many applications, there could be multiple potentially useful features and thereby multiple affinity matrices. To apply spectral clustering for these cases, a possible way is to aggregate the affinity matrices into a single one. Unfortunately, affinity measures constructed from different features could have different characteristics. Careless aggregation might make even worse clustering performance. This paper proposes an affinity aggregation spectral clustering (AASC) algorithm which extends spectral clustering to a setting with multiple affinities available. AASC seeks for an optimal combination of affinity matrices so that it is more immune to ineffective affinities and irrelevant features. This enables the construction of similarity or distance-metric measures for clustering less crucial. Experiments show that AASC is effective in simultaneous clustering and feature fusion, thus enhancing the performance of spectral clustering by employing multiple affinities.


title: TD Models: Modeling the World at a Mixture of Time Scales

abstract: ARRAY(0x83fc18c) hierarchical or multi-level planning and reinforcement learning. In this paper we treat only the prediction problem--that of learning a model and value function for the case of fixed agent behavior. Within this context, we establish the theoretical foundations of multi-scale models and derive TD algorithms for learning them. Two small computational experiments are presented to test and illustrate the theory. This work is an extension and generalization of the work of Singh (1992), Dayan (1993), and Sutton and Pinette (1985).


title: Automatic scoring of online discussion posts

abstract: Online discussions forums, known as forums for short, are conversational social cyberspaces constituting rich repositories of content and an important source of collaborative knowledge. However, most of this knowledge is buried inside the forum infrastructure and its extraction is both complex and difficult. The ability to automatically rate postings in online discussion forums, based on the value of their contribution, enhances the ability of users to find knowledge within this content. Several key online discussion forums have utilized collaborative intelligence to rate the value of postings made by users. However, a large percentage of posts go unattended and hence lack appropriate rating. In this paper, we focus on automatic rating of postings in online discussion forums. A set of features derived from the posting content and the threaded discussion structure are generated for each posting. These features are grouped into five categories, namely (i) relevance, (ii) originality, (iii) forum-specific features, (iv) surface features, and (v) posting-component features. Using a non-linear SVM classifier, the value of each posting is categorized into one of three levels High, Medium, or Low. This rating represents a seed value for each posting that is leveraged in filtering forum content. Experimental results have shown promising performance on forum data.


title: A method for extracting the fundamental frequency positive-sequence voltage vector based on simple mathematical transformations

abstract: In this paper, a novel scheme for obtaining the fundamental-frequency positive-sequence grid voltage is proposed. The method is based on four simple mathematical transformations; two of them are in the stationary reference frame, which are able to eliminate odd harmonics from the original signals. The other two transformations are implemented in a synchronously rotating reference frame in order to eliminate even harmonics. The output of the last transformation block is the input to a synchronous reference-frame phase-locked loop for detecting the frequency and position of the positive-sequence voltage vector. The proposed algorithm was verified through simulations and experiments by applying distorted and unbalanced signals, containing positive and negative-sequence components. The results are in agreement with those theoretically predicted and indicate that the proposed scheme has a great potential for use in grid-connected converter synchronization algorithms.


title: Caterpillar locomotion: A new model for softbodied climbing and burrowing robots

abstract: .Abstract – Caterpillars are some of the most successful scansorial and burrowing animals and yet they lack a hard skeleton. Their hydrostatic body and prolegs provide astonishing fault-tolerant manoeuvrability and powerful, stable, passive attachment. This paper describes some of the biomechanics of caterpillar locomotion and gripping. It then describes our recent work to build a multifunctional robotic climbing machine based on the biomechanics and neural control system (neuromechanics) of the caterpillar, Manduca sexta. The new robot (“Softbot”) is continuously deformable and capable of collapsing and crumpling into a small volume. Eventually it will be able to climb textured surfaces and irregular objects, crawl along ropes and wires, or burrow into winding, confined spaces. These robots will be simple, cheap (disposable), and scaleable. They will have numerous applications including search and rescue in emergency situations and mine reconnaissance in complex environments such as rubble-fields.


Implicit Learning and Consciousness: A Graded, . . .


title: A Novel OFDM Chirp Waveform Scheme for Use of Multiple Transmitters in SAR

abstract: In this letter, we present a new waveform technique for the use of multiple transmitters in synthetic aperture radar (SAR) data acquisition. This approach is based on the principle of the orthogonal-frequency-division-multiplexing technique. Unlike multiple subband approaches, the proposed scheme allows the generation of multiple orthogonal waveforms on common spectral support and thereby enables to exploit the full bandwidth for each waveform. This letter introduces the modulation and the demodulation processing in regard to typical spaceborne SAR receive signals. The proposed processing techniques are verified by a simulation for the case of pointlike targets.


It takes two to tango: NAD+ and sirtuins in aging/longevity control


Microbial Phosphorus Solubilization and Its Potential for Use in Sustainable Agriculture


title: Pedestrian Detection in Low-Resolution Imagery by Learning Multi-scale Intrinsic Motion Structures (MIMS)

abstract: Detecting pedestrians at a distance from large-format wide-area imagery is a challenging problem because of low ground sampling distance (GSD) and low frame rate of the imagery. In such a scenario, the approaches based on appearance cues alone mostly fail because pedestrians are only a few pixels in size. Frame-differencing and optical flow based approaches also give poor detection results due to noise, camera jitter and parallax in aerial videos. To overcome these challenges, we propose a novel approach to extract Multi-scale Intrinsic Motion Structure features from pedestrian's motion patterns for pedestrian detection. The MIMS feature encodes the intrinsic motion properties of an object, which are location, velocity and trajectory-shape invariant. The extracted MIMS representation is robust to noisy flow estimates. In this paper, we give a comparative evaluation of the proposed method and demonstrate that MIMS outperforms the state of the art approaches in identifying pedestrians from low resolution airborne videos.


Time-shift Control of LLC Resonant Converters


title: Effect of Pet Insects on the Psychological Health of Community-Dwelling Elderly People: A Single-Blinded, Randomized, Controlled Trial

abstract: Background: There is evidence that animal-assisted therapy has positive effects on mental health, especially in elderly people. Caring for insects is easy, relatively inexpensive, and does not require much space. Objective: The aim of this 8-week randomized, controlled, single-blinded study was to investigate the effect of pet insects on the psychological health of community-dwelling elderly people. Methods: Elderly subjects (≥65 years old) attending a community center in Daegu, Korea, were enrolled in the study between April and May 2014 and randomized at a 1:1 ratio to receive insect therapy and health advice or only health advice. The insect group received 5 crickets in a cage with sufficient fodder and a detailed instruction manual. At baseline and at 8 weeks, all subjects underwent psychometric tests via a direct interview [Beck Anxiety Inventory, Geriatric Depression Scale (GDS-15), Mini-Mental State Examination (MMSE), 36-Item Short Form Health Survey, Insomnia Severity Index, Fatigue Severity Scale, and Brief Encounter Psychosocial Instrument] and laboratory analyses of inflammatory and oxidative stress markers (erythrocyte sedimentation rate, high-sensitivity C-reactive protein, biological antioxidant potential, and derivatives of reactive oxygen metabolites). Results: The insect-caring (n = 46) and control (n = 48) groups did not differ in baseline characteristics. The insect-caring group had significantly lower GDS-15 scores at week 8 (3.20 vs. 4.90, p = 0.004) and, after adjustment for baseline values, a significantly greater change in GDS-15 scores relative to baseline (-1.12 vs. 0.20, p = 0.011). They also had a significantly greater change in MMSE scores relative to baseline (1.13 vs. 0.31, p = 0.045). The two groups did not differ in terms of other psychometric and laboratory tests. No serious risks or adverse events were reported. Conclusion: Caring for insects, which is cost-effective and safe, was associated with a small to medium positive effect on depression and cognitive function in community-dwelling elderly people.


title: Identifying Topical Twitter Communities via User List Aggregation

abstract: A particular challenge in the area of social media analysis is how to find communities within a larger network of social interactions. Here a community may be a group of microblogging users who post content on a coherent topic, or who are associated with a specific event or news story. Twitter provides the ability to curate users into lists, corresponding to meaningful topics or themes. Here we describe an approach for crowdsourcing the list building efforts of many different Twitter users, in order to identify topical communities. This approach involves the use of ensemble community finding to produce stable groupings of user lists, and by extension, individual Twitter users. We examine this approach in the context of a case study surrounding the detection of communities on Twitter relating to the London 2012 Olympics.


title: Software-Defined and Virtualized Future Mobile and Wireless Networks: A Survey

abstract: With the proliferation of mobile demands and increasingly multifarious services and applications, mobile Internet has been an irreversible trend. Unfortunately, the current mobile and wireless network (MWN) faces a series of pressing challenges caused by the inherent design. In this paper, we extend two latest and promising innovations of Internet, software-defined networking and network virtualization, to mobile and wireless scenarios. We first describe the challenges and expectations of MWN, and analyze the opportunities provided by the software-defined wireless network (SDWN) and wireless network virtualization (WNV). Then, this paper focuses on SDWN and WNV by presenting the main ideas, advantages, ongoing researches and key technologies, and open issues respectively. Moreover, we interpret that these two technologies highly complement each other, and further investigate efficient joint design between them. This paper confirms that SDWN and WNV may efficiently address the crucial challenges of MWN and significantly benefit the future mobile and wireless network.


title: Assessing Hypersensitive Narcissism: A Reexamination of Murray's Narcism Scale

abstract: Abstract A new measure of hypersensitive narcissism was derived by correlating the items of H. A. Murray's (1938) Narcism Scale with an MMPI-based composite measure of covert narcissism. In three samples of college students (total N = 403), 10 items formed a reliable measure: the Hypersensitive Narcissism Scale (HSNS). The new HSNS and the MMPI-based composite showed similar patterns of correlations with the Big Five Inventory, and both measures correlated near zero with the Narcissistic Personality Inventory, which assesses overt narcissism. Results support P. Wink's (1991) distinction between covert and overt narcissistic tendencies in the normal range of individual differences and suggest that it would be beneficial for personality researchers to measure both types of narcissism in future studies.


Teaching ethics makes a difference


Network Optimization for High Performance Cloud Computing


Abstract A K-means-like Algorithm for K-medoids Clustering and Its Performance


title: Towards Text-based Emotion Detection A Survey and Possible Improvements

abstract: This paper presents an overview of the emerging field of emotion detection from text and describes the current generation of detection methods that are usually divided into the following three main categories: keyword-based, learning-based, and hybrid recommendation approaches. Limitations of current detection methods are examined, and possible solutions are suggested to improve emotion detection capabilities in practical systems, which emphasize on human-computer interactions. These solutions include extracting keywords with semantic analysis, and ontology design with emotion theory of appraisal. Furthermore, a case-based reasoning architecture is proposed to combine these solutions.


title: A Hierarchical Genetic Algorithm for System Identification and Curve Fitting with a Supercomputer Implementation

abstract: This paper describes a hierarchical genetic algorithm (GA) framework for identifying closed form functions for multi-variate data sets. The hierarchy begins with an upper GA that searches for appropriate functional forms given a user defined set of primitives and the candidate independent variables. Each functional form is encoded as a tree structure, where variables, coefficients and functional primitives are linked. The functional forms are sent to the second part of the hierarchy, the lower GA, that optimizes the coefficients of the function according to the data set and the chosen error metric. To avoid undue complication of the functional form identified by the upper GA, a penalty function is used in the calculation of fitness. Because of the computational effort required for this sequential optimization of each candidate function, the system has been implemented on a Cray supercomputer. The GA code was vectorized for parallel processing of 128 array elements, which greatly speeded the calculation of fitness. The system is demonstrated on five data sets from the literature. It is shown that this hierarchical GA framework identifies functions which fit the data extremely well, are reasonable in functional form, and interpolate and extrapolate well.


title: A meta-analysis of the relationship between organizational citizenship behavior and counterproductive work behavior.

abstract: Job performance is increasingly being seen to encompass constructs such as organizational citizenship behavior (OCB) and counterproductive work behavior (CWB). To clarify the OCB-CWB relationship, a meta-analysis was conducted. Results indicate a modest negative relationship (p = -0.32). The relationship strength did not increase appreciably when the target of the behavior (the organization vs. other employees) was the same. Moreover, OCB and CWB exhibited somewhat distinct patterns of relationships with antecedents. The OCB-CWB relationship was moderated by the source of the ratings, the presence of antithetical items, and the type of response options. An employee-centric perspective is proposed whereby both OCB and CWB are perceived as adaptive behavior. Implications for organizations are discussed.


title: Unexpected Effects of Activator Molecules' Polarity on the Electroreological Activity of Titanium Dioxide Nanopowders.

abstract: Titanium dioxide nanoparticles, obtained using the sol-gel method and modified with organic solvents, such as acetone, acetonitrile, benzene, diethyl ether, dimethyl sulfoxide, toluene, and chloroform, were used as the filler of polydimethylsiloxane-based electrorheological fluids. The effect of electric field strength on the shear stress and yield stress of electrorheological fluids was investigated, as well as the spectra of their dielectric relaxation in the frequency range from 25 to 106 Hz. Modification of titanium dioxide by polar molecules was found to enhance the electrorheological effect, as compared with unmodified TiO2, in accordance with the widely accepted concept of polar molecule dominated electrorheological effect (PM-ER). The most unexpected result of this study was an increase in the electrorheological effect during the application of nonpolar solvents with zero or near-zero dipole moments as the modifiers. It is suggested that nonpolar solvents, besides providing additional polarization effects at the filler particles interface, alter the internal pressure in the gaps between the particles. As a result, the filler particles are attracted to one another, leading to an increase in their aggregation and the formation of a network of bonds between the particles through liquid bridge contacts. Such changes in the electrorheological fluid structure result in a significant increase in the mechanical strength of the structures that arise when an electric field is applied, and an increase in the observed electrorheological effect in comparison with the unmodified titanium dioxide.


The Kolmogorov sampler


title: The Pathobiology of Vascular Dementia

abstract: Vascular cognitive impairment defines alterations in cognition, ranging from subtle deficits to full-blown dementia, attributable to cerebrovascular causes. Often coexisting with Alzheimer's disease, mixed vascular and neurodegenerative dementia has emerged as the leading cause of age-related cognitive impairment. Central to the disease mechanism is the crucial role that cerebral blood vessels play in brain health, not only for the delivery of oxygen and nutrients, but also for the trophic signaling that inextricably links the well-being of neurons and glia to that of cerebrovascular cells. This review will examine how vascular damage disrupts these vital homeostatic interactions, focusing on the hemispheric white matter, a region at heightened risk for vascular damage, and on the interplay between vascular factors and Alzheimer's disease. Finally, preventative and therapeutic prospects will be examined, highlighting the importance of midlife vascular risk factor control in the prevention of late-life dementia.


title: Granular SVM with Repetitive Undersampling for Highly Imbalanced Protein Homology Prediction

abstract: Highly imbalanced classification is important and increasingly common with emergence of new machine learning application domains including biomedical informatics. In order to solve this challenging class imbalance problem, a novel Granular Support Vector Machines - Repetitive Undersampling algorithm (GSVM-RU) is designed in this work. GSVM-RU creatively utilizes Support Vector Machines (SVM) themselves for undersampling to minimize the negative effect of information loss while maximizing the positive effect of data cleaning in the undersampling process. Consequently, an accurate and fast classifier can be modeled. GSVM-RU ranks as one of the best solutions in ACM KDDCUP 2004 competition for the extremely imbalanced protein homology prediction.


title: An unusual case of asymptomatic spontaneous umbilical endometriosis treated with skin-sparing excision

abstract: Spontaneous umbilical endometriosis is a rare extrapelvic manifestation of endometriosis. Very few such cases have been previously reported, almost always associated with a variety of symptoms, usually during menstruation. We present a case of asymptomatic umbilical endometriosis treated with skin-sparing excision. Differential diagnoses relevant to the clinician, as well as treatment options, are also presented. Surgeons should always consider umbilical endometriosis in their diagnostic approach when confronted with atypical umbilical nodules, regardless of whether they are symptomatic or not.


title: Sequence to sequence learning for unconstrained scene text recognition

abstract: In this work we present a state-of-the-art approach for unconstrained natural scene text recognition. We propose a cascade approach that incorporates a convolutional neural network (CNN) architecture followed by a long short term memory model (LSTM). The CNN learns visual features for the characters and uses them with a softmax layer to detect sequence of characters. While the CNN gives very good recognition results, it does not model relation between characters, hence gives rise to false positive and false negative cases (confusing characters due to visual similarities like "g" and "9", or confusing background patches with characters; either removing existing characters or adding non-existing ones) To alleviate these problems we leverage recent developments in LSTM architectures to encode contextual information. We show that the LSTM can dramatically reduce such errors and achieve state-of-the-art accuracy in the task of unconstrained natural scene text recognition. Moreover we manually remove all occurrences of the words that exist in the test set from our training set to test whether our approach will generalize to unseen data. We use the ICDAR 13 test set for evaluation and compare the results with the state of the art approaches [11, 18]. We finally present an application of the work in the domain of for traffic monitoring.


title: A Spectral Algorithm for Latent Tree Graphical Models

abstract: Latent variable models are powerful tools for probabilistic modeling, and have been successfully applied to various domains, such as speech analysis and bioinformatics. However, parameter learning algorithms for latent variable models have predominantly relied on local search heuristics such as expectation maximization (EM). We propose a fast, local-minimum-free spectral algorithm for learning latent variable models with arbitrary tree topologies, and show that the joint distribution of the observed variables can be reconstructed from the marginals of triples of observed variables irrespective of the maximum degree of the tree. We demonstrate the performance of our spectral algorithm on synthetic and real datasets; for large training sizes, our algorithm performs comparable to or better than EM while being orders of magnitude faster.


title: Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise

abstract: Biometric fingerprint scanners are positioned to provide improved security in a great span of applications from government to private. However, one highly publicized vulnerability is that it is possible to spoof a variety of fingerprint scanners using artificial fingers made from Play-Doh, gelatin and silicone molds. Therefore, it is necessary to offer protection for fingerprint systems against these threats. In this paper, an anti-spoofing detection method is proposed which is based on ridge signal and valley noise analysis, to quantify perspiration patterns along ridges in live subjects and noise patterns along valleys in spoofs. The signals representing gray level patterns along ridges and valleys are explored in spatial, frequency and wavelet domains. Based on these features, separation (live/spoof) is performed using standard pattern classification tools including classification trees and neural networks. We test this method on a larger dataset than previously considered which contains 644 live fingerprints (81 subjects with 2 fingers for an average of 4 sessions) and 570 spoof fingerprints (made from Play-Doh, gelatin and silicone molds in multiple sessions) collected from the Identix fingerprint scanner. Results show that the performance can reach 99.1% correct classification overall. The proposed anti-spoofing method is purely software based and integration of this method can provide protection for fingerprint scanners against gelatin, Play-Doh and silicone spoof fingers.


title: Hand Movements and Gestures Characterization Using Quaternion Dynamic Time Warping Technique

abstract: Trends show that wearable devices with high-range sensors offer new business opportunities in enriching user experience in swing-based outdoor games, such as tennis, golf, and/or indoor gaming applications. Such applications providing insights into a player's abilities to play these games require methods that efficiently distinguish and capture intricacies of hand movements and/or gestures. In this paper, we show that quaternions-based dynamic time warping (QDTW) technique provides an efficient means for characterizing different arm/hand movements and gestures. A complete methodology and results for the pursued case study of outdoor tennis game are provided in this paper. We propose a new and unique approach for training data for various tennis shots and then using DTW and QDTW at the two levels of a hierarchical classifier for classification of an incoming tennis shot. The achieved accuracy for tennis shots detection is more than 99% and that for classification is 90%. Furthermore, the concept of consistency in a player's shots and how a played shot differs from a professional's similar shot are considered to suggest recommendations for improvement to the player.


title: Implementation of real time moving object detection and tracking on FPGA for video surveillance applications

abstract: Computer vision has played a key role in developing object detection and tracking techniques for Surveillance system. Most of the implementations currently employed are based on Serial execution on General Purpose Processors. But the high cost and complexity of such implementations doesn't make it a viable option for real time surveillance system. The system proposed here is implemented on Field Programmable Gate Arrays (FPGA) Zynq XC7Z020 board using Modified Background Subtraction algorithm for real-time Object Detection and Tracking. The presence of numerous configurable logic blocks, distributed memory and hard Digital Signal Processing (DSP) modules offers a great flexibility in achieving Temporal and Spatial parallelism. This paper uses Xilinx ISE software for implementation which is programmed in VHDL. OV7670 camera used in the paper has a resolution of 0.3 Megapixel and it captures the video at a speed of 30fps. The reference frame and the subsequent incoming frames are stored in different memory modules before the Modified Background Subtraction algorithm is applied on these frames to obtain the difference image. After comparing it with the threshold, the resultant image is displayed and its addresses are stored in order to track it. The system works in real time with minimum time lag between the capture and display. Moreover the entire system is optimized in terms of speed, memory requirements as well as the number of logic elements used which makes it suitable for application in real-time surveillance system.


title: Place presence, social presence, co-presence, and satisfaction in virtual worlds

abstract: This study investigated the relationship among three types of presences, including place presence, social presence, and co-presence in virtual worlds and their relationship with satisfaction and immersive tendencies of students. Students' scores on a subjective questionnaire were analyzed. The results indicated that there was a significant relationship among the place presence, social presence, and co-presence. While social presence seemed to affect the satisfaction most, place and co-presence also affected students' satisfaction in the virtual world. Moreover, immersive tendencies of the students were related to their place and co-presence but not to their social presence. Findings highlighted the important issues for the design of virtual world environments to increase presence and satisfaction of students.


title: Joint bilateral upsampling

abstract: Image analysis and enhancement tasks such as tone mapping, colorization, stereo depth, and photomontage, often require computing a solution (e.g., for exposure, chromaticity, disparity, labels) over the pixel grid. Computational and memory costs often require that a smaller solution be run over a downsampled image. Although general purpose upsampling methods can be used to interpolate the low resolution solution to the full resolution, these methods generally assume a smoothness prior for the interpolation. ::: We demonstrate that in cases, such as those above, the available high resolution input image may be leveraged as a prior in the context of a joint bilateral upsampling procedure to produce a better high resolution solution. We show results for each of the applications above and compare them to traditional upsampling methods.


title: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification

abstract: Zero-shot Learners are models capable of predicting unseen classes. In this work, we propose a Zero-shot Learning approach for text categorization. Our method involves training model on a large corpus of sentences to learn the relationship between a sentence and embedding of sentence's tags. Learning such relationship makes the model generalize to unseen sentences, tags, and even new datasets provided they can be put into same embedding space. The model learns to predict whether a given sentence is related to a tag or not; unlike other classifiers that learn to classify the sentence as one of the possible classes. We propose three different neural networks for the task and report their accuracy on the test set of the dataset used for training them as well as two other standard datasets for which no retraining was done. We show that our models generalize well across new unseen classes in both cases. Although the models do not achieve the accuracy level of the state of the art supervised models, yet it evidently is a step forward towards general intelligence in natural language processing.


title: A new structure of universal motor using soft magnetic composites

abstract: The universal or AC commutator motor, widely used in hand tools or domestic appliances, generally uses a two-pole stator with a concentrated winding and an armature with interlocked coils elements. The copper volume and the axial length of the end windings of such conventional structures are then usually very important. In this paper, the authors present a new universal motor structure based on an efficient use of the isotropic magnetic properties of the soft magnetic composites (SMC) and on the concentrated winding technique. The stator core presents a claw pole structure and the armature has a concentrated winding with several coils wound around the same tooth. With this new AC commutator motor structure, a reduction of the total volume by a ratio equal to 200% is obtained when compared to classical universal motor structure with nearly identical performance.


title: Secure multi-party computation in large networks

abstract: We describe scalable protocols for solving the secure multi-party computation (MPC) problem among a significant number of parties. We consider both the synchronous and the asynchronous communication models. In the synchronous setting, our protocol is secure against a static malicious adversary corrupting less than a 1/3 fraction of the parties. In the asynchronous environment, we allow the adversary to corrupt less than a 1/8 fraction of parties. For any deterministic function that can be computed by an arithmetic circuit with m gates, both of our protocols require each party to send a number of messages and perform an amount of computation that is $$\tilde{O}(m/n + \sqrt{n})$$O~(m/n+n). We also show that our protocols provide statistical and universally-composable security. To achieve our asynchronous MPC result, we define the threshold counting problem and present a distributed protocol to solve it in the asynchronous setting. This protocol is load balanced, with computation, communication and latency complexity of $$O(\log {n})$$O(logn), and can also be used for designing other load-balanced applications in the asynchronous communication model.


Toxicities of the anti-PD-1 and anti-PD-L1 immune checkpoint antibodies


title: A comparative study of game mechanics and control laws for an adaptive physiological game

abstract: We present an adaptive biofeedback game that aims to maintain the player’s arousal by modifying game difficulty in response to the player’s physiological state, as measured with wearable sensors. Our approach models the interaction between human physiology and game difficulty during gameplay as a control problem, where game difficulty is the system input and player arousal its output. We validate the approach on a car-racing game with real-time adaptive game mechanics. Specifically, we use (1) car speed, road visibility, and steering jitter as three mechanisms to manipulate game difficulty, (2) electrodermal activity as physiological correlate of arousal, and (3) two types of control law: proportional (P) control, and proportional-integral-derivative (PID) control. We also propose quantitative measures to characterize the effectiveness of these game adaptations and controllers in manipulating the player’s arousal. Experimental trials with 25 subjects in both open-loop (no feedback) and closed-loop (negative feedback) conditions show statistically significant differences in effectiveness among the three game mechanics and also between the two control laws. Specifically, manipulating car speed provides higher control of arousal levels than changing road visibility or vehicle steering. Our results also confirm that PID control leads to lower error and reduced oscillations in the closed-loop response compared to proportional-only control. Finally, we discuss the theoretical and practical implications of our approach.


title: Satisfaction, trust and online purchase intention: A study of consumer perceptions

abstract: This paper tries to do the research of analyzing the relation of the customer trust, the customer satisfaction and purchase intention under the electronic commerce shopping environment. Drawing on the theory of customer trust, customer satisfaction and purchase intention, based on a survey of 102 respondents from the University in China, the United Kingdom, and the United States, the results showed that there is significant correlation between satisfaction and trust, satisfaction and purchase intention, trust and purchase intention, and more than 50% of respondents chose Taobao and Tmall, and the second is Amazon when they shopping onling. It could be expected that this kind of study is able to provide suggestion and guide for internet merchant.


title: Special feature: Computer hacking and cyber terrorism: the real threats in the new millennium?

abstract: As the new millennium approaches, we are living in a society that is increasingly dependent upon information technology. However, whilst technology can deliver a number of benefits, it also introduces new vulnerabilities that can be exploited by persons with the necessary technical skills. Hackers represent a well-known threat in this respect and are responsible for a significant degree of disruption and damage to information systems. However, they are not the only criminal element that has to be taken into consideration. Evidence suggests that technology is increasingly seen as potential tool for terrorist organizations. This is leading to the emergence of a new threat in the form of 'cyber terrorists', who attack technological infrastructures such as the Internet in order to help further their cause. The paper discusses the problems posed by these groups and considers the nature of the responses necessary to preserve the future security of our society.


Refining Duplicate Detection for Improved Data Quality


title: Decentralized Computation Offloading Game For Mobile Cloud Computing

abstract: Mobile cloud computing is envisioned as a promising approach to augment computation capabilities of mobile devices for emerging resource-hungry mobile applications. In this paper, we propose a game theoretic approach for achieving efficient computation offloading for mobile cloud computing. We formulate the decentralized computation offloading decision making problem among mobile device users as a decentralized computation offloading game. We analyze the structural property of the game and show that the game always admits a Nash equilibrium. We then design a decentralized computation offloading mechanism that can achieve a Nash equilibrium of the game and quantify its efficiency ratio over the centralized optimal solution. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.


title: Active Learning Based Rule Extraction for Regression

abstract: Advances in data mining have led to algorithms that produce accurate regression models for large and difficult to approximate data. Many of these use non-linear models to handle complex data-relationships in the input data. Their lack of transparency, however, is problematic since comprehensibility is a key requirement in many potential application domains. Rule-extraction algorithms have been proposed to solve this problem for classification by extracting comprehensible rule sets from the often better performing, complex models. We present a new pedagogical rule extraction algorithm for regression, based on active learning, which can be combined with any existing rule induction technique. Empirical results show that the proposed ALPA-R rule extraction method improves on classical rule induction techniques, both in accuracy and fidelity.


title: Efficient Architecture Search by Network Transformation

abstract: Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e.g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23\% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.


title: Cyber-Security-Aware Network Design of Industrial Control Systems

abstract: The pervasive adoption of traditional information and communication technologies hardware and software in industrial control systems (ICS) has given birth to a unique technological ecosystem encapsulating a variety of objects ranging from sensors and actuators to video surveillance cameras and generic PCs. Despite their invaluable advantages, these advanced ICS create new design challenges, which expose them to significant cyber threats. To address these challenges, an innovative ICS network design technique is proposed in this paper to harmonize the traditional ICS design requirements pertaining to strong architectural determinism and real-time data transfer with security recommendations outlined in the ISA-62443.03.02 standard. The proposed technique accommodates security requirements by partitioning the network into security zones and by provisioning critical communication channels, known as security conduits, between two or more security zones. The ICS network design is formulated as an integer linear programming (ILP) problem that minimizes the cost of the installation. Real-time data transfer limitations and security requirements are included as constraints imposing the selection of specific traffic paths, the selection of routing nodes, and the provisioning of security zones and conduits. The security requirements of cyber assets denoted by traffic and communication endpoints are determined by a cyber attack impact assessment technique proposed in this paper. The sensitivity of the proposed techniques to different parameters is evaluated in a first scenario involving the IEEE 14-bus model and in a second scenario involving a large network topology based on generated data. Experimental results demonstrate the efficiency and scalability of the ILP model.


title: Photo Layout with a Fast Evaluation Method and Genetic Algorithm

abstract: Photo collages are an effective form of visual communication and sharing. This paper is devoted to a particular style of photo layout, in which photos are tightly packed into a rectangular canvas while the aspect ratio and orientation of photos are preserved. In addition, users may specify a desired size for each photo within the canvas. BRIC (block recursive image composition) is a method that has been proposed for this problem [1]. In this paper, we present a better method based on the framework of genetic algorithm. Our method builds on three key components. First, we developed a fast method for computing the photo dimensions for a given layout tree. This makes it feasible to evaluate a large number of layout trees within a time limit that is acceptable for interactive multimedia applications. Second, we adapted a fitness measure that takes into account both the photo coverage of the canvas area and the size distribution of photos. Finally, we constructed appropriate genetic operators and incorporated the aforementioned evaluation method and fitness measure. Our experimental results showed that the proposed method is consistently better than BRIC.


title: Dynamical Approximation by Hierarchical Tucker and Tensor-Train Tensors

abstract: We extend results on the dynamical low-rank approximation for the treatment of time-dependent matrices and tensors (Koch and Lubich; see [SIAM J. Matrix Anal. Appl., 29 (2007), pp. 434--454], [SIAM J. Matrix Anal. Appl., 31 (2010), pp. 2360--2375]) to the recently proposed hierarchical Tucker (HT) tensor format (Hackbusch and Kuhn; see [J. Fourier Anal. Appl., 15 (2009), pp. 706--722]) and the tensor train (TT) format (Oseledets; see [SIAM J. Sci. Comput., 33 (2011), pp. 2295--2317]), which are closely related to tensor decomposition methods used in quantum physics and chemistry. In this dynamical approximation approach, the time derivative of the tensor to be approximated is projected onto the time-dependent tangent space of the approximation manifold along the solution trajectory. This approach can be used to approximate the solutions to tensor differential equations in the HT or TT format and to compute updates in optimization algorithms within these reduced tensor formats. By deriving and analyzing th...


title: Reinforcement Learning Design-Based Adaptive Tracking Control With Less Learning Parameters for Nonlinear Discrete-Time MIMO Systems

abstract: Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.


From the Industrial District to the District Group . An Insight into the Evolution of Local Capitalism in Italy *


title: Imputation of Missing Data in Industrial Databases

abstract: A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records. But every cell in a database is not an independent datum. Statistical relationships will constrain and, often determine, missing values. Data imputation, the filling in of missing values for partially missing data, can thus be an invaluable first step in many IDA projects. New imputation methods that can handle the large-scale problems and large-scale sparsity of industrial databases are needed. To illustrate the incomplete database problem, we analyze one database with instrumentation maintenance and test records for an industrial process. Despite regulatory requirements for process data collection, this database is less than 50% complete. Next, we discuss possible solutions to the missing data problem. Several approaches to imputation are noted and classified into two categories: data-driven and model-based. We then describe two machine-learning-based approaches that we have worked with. These build upon well-known algorithms: AutoClass and C4.5. Several experiments are designed, all using the maintenance database as a common test-bed but with various data splits and algorithmic variations. Results are generally positive with up to 80% accuracies of imputation. We conclude the paper by outlining some considerations in selecting imputation methods, and by discussing applications of data imputation for intelligent data analysis.


title: Musicmapper: interactive 2d representations of music samples for in-browser remixing and exploration

abstract: Much of the challenge and appeal in remixing music comes from manipulating samples. Typically, identifying distinct samples of a song requires expertise in music production software. Additionally, it is difficult to visualize similarities and differences between all samples of a song simultaneously and use this to select samples. MusicMapper is a web application that allows nonexpert users to find and visualize distinctive samples from a song without any manual intervention, and enables remixing by having users play back clusterings of such samples. This is accomplished by splitting audio from the Soundcloud API into appropriately-sized spectrograms, and applying the t-SNE algorithm to visualize these spectrograms in two dimensions. Next, we apply k-means to guide the user's eye toward related clusters and set k=26 to enable playback of the clusters by pressing keys on an ordinary keyboard. We present the source code (https://github.com/fatsmcgee/MusicMappr) and a demo video (http://youtu.be/mvD6e1uiO8k) of the MusicMapper web application that can be run in most modern browsers.


title: Image Matching Algorithm based on Feature-Point and DAISY Descriptor

abstract: Image matching technology is the research foundation of many computer vision problems, and the matching algorithm based on partial features of images is a research focus in this field. In order to overcome the unstable performance of classic SURF algorithm on rotation invariance, an image matching algorithm combined with SURF feature-point and DAISY descriptor is proposed. Based on the feature point detection of SURF algorithm, a principal direction distribution method for DAISY descriptor is put forward, and a novel DAISY descriptor is obtained according to the rotation of the principal direction. In this paper, our proposed algorithm, on the basis of slight increase in running time, improves the image matching capability of the classic SURF algorithm on image rotation. The experimental results show that our proposed algorithm has stronger robustness in a variety of complex cases, such as image blurring, illumination variation, JPEG compression ratio variation, field of view variation, etc. Our proposed algorithm can not only keep the merits of the original SURF algorithm on computation speed, but also improve the matching accuracy on rotation invariance.


title: Analyzing Ethereum's Contract Topology

abstract: Ethereum is the second most valuable cryptocurrency today, with a current market cap of over $68B. What sets Ethereum apart from other cryptocurrencies is that it uses the blockchain to not only store a record of transactions, but also smart contracts and a history of calls made to those contracts. Thus, Ethereum represents a new form of distributed system: one where users can implement contracts that can provide functionality such as voting protocols, crowdfunding projects, betting agreements, and many more. However, despite the massive investment, little is known about how contracts in Ethereum are actually created and used. In this paper, we examine how contracts in Ethereum are created, and how users and contracts interact with one another. We modify the geth client to log all such interactions, and find that contracts today are three times more likely to be created by other contracts than they are by users, and that over 60% of contracts have never been interacted with. Additionally, we obtain the bytecode of all contracts and look for similarity; we find that less than 10% of user-created contracts are unique, and less than 1% of contract-created contracts are so. Clustering the contracts based on code similarity reveals even further similarity. These results indicate that there is substantial code re-use in Ethereum, suggesting that bugs in such contracts could have wide-spread impact on the Ethereum user population.


title: Performance measurement in blind audio source separation

abstract: In this paper, we discuss the evaluation of blind audio source separation (BASS) algorithms. Depending on the exact application, different distortions can be allowed between an estimated source and the wanted true source. We consider four different sets of such allowed distortions, from time-invariant gains to time-varying filters. In each case, we decompose the estimated source into a true source part plus error terms corresponding to interferences, additive noise, and algorithmic artifacts. Then, we derive a global performance measure using an energy ratio, plus a separate performance measure for each error term. These measures are computed and discussed on the results of several BASS problems with various difficulty levels