title: A Super-Resolution Convolutional-Neural-Network-Based Approach for Subpixel Mapping of Hyperspectral Images

abstract: A new subpixel mapping (SPM) method based on a super-resolution convolutional neural network (SRCNN) is proposed to generate subpixel land cover maps for hyperspectral images. The SRCNN is used to restore the image spatial resolution from a coarse input image, which is equivalent to interpolation. First, an efficient subpixel convolutional neural network, which is a state-of-the-art SRCNN, is utilized to calculate the subpixel soft class value via a transfer learning strategy. Then, a classifier is used to transform the subpixel soft class values to hard-classified land cover maps with the constraint of fraction images. Experiments on three different hyperspectral images demonstrate that the SPM accuracy of the proposed SRCNN-based method is significantly better than those of three traditional SPM methods. In addition, the SRCNN-based SPM method has a simplified calculation process, does not require training data, and is less time consuming. This article provides a new solution for SPM of hyperspectral images.


title: Isolated SEPIC converter fed trinary hybrid cascaded H-bridge multilevel inverter for solar PV system

abstract: Solar electrical energy generation and its conversion techniques gain more attention because Photo Voltaic (PV) power generation system is more attractive due to its advantages among all the RESs (Renewable Energy Sources). In this paper, Isolated SEPIC(Single-Ended Primary-Inductance Converter)converter fed grid connected Multilevel Inverter is proposed. SEPIC converter is a buck-boost derived topology gives regulated output voltage with varying solar energy by using single switch. The output of the SEPIC is regulated at a trinary ratio and the cascaded H-bridge multilevel inverter is used here for maximum levels in the output voltage with minimum number of power electronic switches. A low THD output voltage is created and fed to the grid. Proposed system is validated with MATLAB/Simulink model and the results are compared with the existing models.


title: A comparative study of urban traffic signal control with reinforcement learning and Adaptive Dynamic Programming

abstract: This paper proposes a new algorithm that employs Adaptive Dynamic Programming(ADP) to solve the distributed control problem of urban traffic with an infinite horizon. Urban traffic congestions lead to a lot of time consumption and exhaust emissions. So alleviating congested situation will have a good impact on both economy and environment. The signal control at urban intersections is an effective and most important way to reduce the traffic jams and collisions. A lot of control theories including traditional mathematical ways and modern artificial intelligent ways have been exploited. ADP is an effective and amiable intelligent control method. We proposed an algorithm to adjust the signal time plan at urban traffic intersections based on ADP theory. Simulations are taken under a microscopic traffic simulation software, TSIS(Traffic Software Integrated System). Several criteria named MOEs(Measures of Effectiveness) are collected to compare with the widely used pre-timed control, actuated control, also with a machine learning method Q-learning control. Results show that ADP control method have a better adaptability to the various traffic simulating real traffic flows.


title: Fast wavelet transforms and numerical algorithms I

abstract: A class of algorithms is introduced for the rapid numerical application of a class of linear operators to arbitrary vectors. Previously published schemes of this type utilize detailed analytical information about the operators being applied and are specific to extremely narrow classes of matrices. In contrast, the methods presented here are based on the recently developed theory of wavelets and are applicable to all Calderon-Zygmund and pseudo-differential operators. The algorithms of this paper require order O(N) or O(N log N) operations to apply an N × N matrix to a vector (depending on the particular operator and the version of the algorithm being used), and our numerical experiments indicate that many previously intractable problems become manageable with the techniques presented here.


title: Contracting, equal, and expanding learning schedules: The optimal distribution of learning sessions depends on retention interval

abstract: In laboratory and applied learning experiments, researchers have extensively investigated the optimal distribution of two learning sessions (i.e., initial learning and one relearning session) for the learning of verbatim materials. However, research has not yet provided a satisfying and conclusive answer to the optimal scheduling of three learning sessions (i.e., initial learning and two relearning sessions) across educationally relevant time intervals. Should the to-be-learned material be repeated at decreasing intervals (contracting schedule), constant intervals (equal schedule), or increasing intervals (expanding schedule) between learning sessions? Different theories and memory models (e.g., study-phase retrieval theory, contextual variability theory, ACT-R, and the Multiscale Context Model) make distinct predictions about the optimal learning schedule. We discuss the extant theories and derive clear predictions from each of them. To test these predictions empirically, we conducted an experiment in which participants studied and restudied paired associates with a contracting, equal, or expanding learning schedule. Memory performance was assessed immediately, 1 day, 7 days, or 35 days later with free- and cued-recall tests. Our results revealed that the optimal learning schedule is conditional on the length of the retention interval: A contracting learning schedule was beneficial for retention intervals up to 7 days, but both equal and expanding learning schedules were better for a long retention interval of 35 days. Our findings can be accommodated best by the contextual variability theory and indicate that revisions are needed to existing memory models. Our results are practically relevant, and their implications for real-world learning are discussed.


title: Android App Collusion Threat and Mitigation Techniques.

abstract: With the digital breakthrough, smart phones have become very essential component. Mobile devices are very attractive attack surface for cyber thieves as they hold personal details (accounts, locations, contacts, photos) and have potential capabilities for eavesdropping (with cameras/microphone, wireless connections). Android, being the most popular, is the target of malicious hackers who are trying to use Android app as a tool to break into and control device. Android malware authors use many anti-analysis techniques to hide from analysis tools. Academic researchers and commercial anti-malware companies are putting great effort to detect such malicious apps. They are making use of the combinations of static, dynamic and behavior based analysis techniques. Despite of all the security mechanisms provided by Android, apps can carry out malicious actions through collusion. In collusion malicious functionality is divided across multiple apps. Each participating app accomplish its part and communicate information to another app through Inter Component Communication (ICC). ICC do not require any special permissions. Also, there is no compulsion to inform user about the communication. Each participating app needs to request a minimal set of privileges, which may make it appear benign to current state-of-the-art techniques that analyze one app at a time. There are many surveys on app analysis techniques in Android; however they focus on single-app analysis. This survey augments this through focusing only on collusion among multiple-apps. In this paper, we present Android vulnerabilities that may be exploited for a possible collusion attack. We cover the existing threat analysis, scenarios, and a detailed comparison of tools for intra and inter-app analysis. To the best of our knowledge this is the first survey on app collusion and state-of-the-art detection tools in Android.


title: Cancer risk among workers of a secondary aluminium smelter.

abstract: BACKGROUND ::: Cancer risk in secondary aluminium production is not well described. Workers in this industry are exposed to potentially carcinogenic agents from secondary smelters that reprocess aluminium scrap. ::: ::: ::: AIMS ::: To evaluate cancer risk in workers in a secondary aluminium plant in Spain. ::: ::: ::: METHODS ::: Retrospective cohort study of male workers employed at an aluminium secondary smelter (1960-92). Exposure histories and vital status through 2011 were obtained through personal interviews and hospital records, respectively. Standardized mortality (SMRs) and incidence ratios (SIRs) were calculated. ::: ::: ::: RESULTS ::: The study group consisted of 98 workers. We found increased incidence and mortality from bladder cancer [SIR = 2.85, 95% confidence interval (CI) 1.23-5.62; SMR = 5.90, 95% CI 1.58-15.11]. Increased incidence was also observed for prostate cancer and all other cancers but neither were statistically significant. No increased risk was observed for lung cancer. ::: ::: ::: CONCLUSIONS ::: Results of this study suggest that work at secondary aluminium smelters is associated with bladder cancer risk. Identification of occupational carcinogens in this industry is needed.


title: Boresight gain enhancement of an UWB planar monopole antenna

abstract: Techniques for boresight gain enhancement of an UWB planar monopole antenna is presented. The squinting effect of the radiation pattern in the broadside direction at high frequencies is alleviated through the introduction of a gate-like structure in the ground plane. The boresight gain is further improved by adding a parasitic element above the main patch.


title: Manifestation of an Analytic Hierarchy Process (AHP) Model on Fire Potential Zonation Mapping in Kathmandu Metropolitan City, Nepal

abstract: Even though fewer people die as a result of fire than other natural disasters, such as earthquake, flood, landslide, etc., the average loss of property due to fire is high. Kathmandu Metropolitan City is becoming more vulnerable to fire due to haphazard urbanization and increase in population. To control problems due to fire, systematic studies are necessary, including fire potential mapping and risk assessment. This study applies an Analytic Hierarchy Process (AHP) method in Kathmandu Metropolitan City, Nepal for generation of fire potential zonation map. The fire potential zonation map is prepared on the basis of available data of land use, fuel stations, and population density. This map shows that 58.04% of the study area falls under low fire potential zone, 32.92% falls under moderate fire potential zone and 9.04% falls under high fire potential zone. The map is also validated through major past fire incidents. The results show that the predicted fire potential zones are found to be in good agreement with past fire incidents, and, hence, the map can be used for future land-use planning.


title: Ontology of core data mining entities

abstract: In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines the most essential data mining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend. OntoDM-core is available at http://www.ontodm.com .


title: A Novel Dry Active Electrode for EEG Recording

abstract: The design and testing of a "dry" active electrode for electroencephalographic recording is described. A comparative study between the EEG signals recorded in human volunteers simultaneously with the classical Ag-AgCl and "dry" active electrodes was carried out and the reported preliminary results are consistent with a better performance of these devices over the conventional Ag-AgCl electrodes


title: Knowledge Base Population using Semantic Label Propagation

abstract: Training relation extractors for the purpose of automated knowledge base population requires the availability of sufficient training data. The amount of manual labeling can be significantly reduced by applying distant supervision, which generates training data by aligning large text corpora with existing knowledge bases. This typically results in a highly noisy training set, where many training sentences do not express the intended relation. In this paper, we propose to combine distant supervision with minimal human supervision by annotating features (in particular shortest dependency paths) rather than complete relation instances. Such feature labeling eliminates noise from the initial training set, resulting in a significant increase of precision at the expense of recall. We further improve on this approach by introducing the Semantic Label Propagation (SLP) method, which uses the similarity between low-dimensional representations of candidate training instances to again extend the (filtered) training set in order to increase recall while maintaining high precision. Our strategy is evaluated on an established test collection designed for knowledge base population (KBP) from the TAC KBP English slot filling task. The experimental results show that SLP leads to substantial performance gains when compared to existing approaches while requiring an almost negligible human annotation effort.


Word Sense Filtering Improves Embedding-Based Lexical Substitution


title: Efficient Clustering of Short Messages into General Domains.

abstract: The ever increasing activity in social networks is mainly manifested by a growing stream of status updating or microblogging. The massive stream of updates emphasizes the need for accurate and efficient clustering of short messages on a large scale. Applying traditional clustering techniques is both inaccurate and inefficient due to sparseness. This paper presents an accurate and efficient algorithm for clustering Twitter tweets. We break the clustering task into two distinctive tasks/stages: (1) batch clustering of user annotated data, and (2) online clustering of a stream of tweets. In the first stage we rely on the habit of `tagging', common in social media streams (e.g. hashtags), thus the algorithm can bootstrap on the tags for clustering of a large pool of hashtagged tweets. The stable clusters achieved in the first stage lend themselves for online clustering of a stream of (mostly) tagless messages. We evaluate our results against gold-standard classification and validate the results by employing multiple clusteringevaluation measures (information theoretic, paired, F and greedy). We compare our algorithm to a number of otherclustering algorithms and various types of feature sets. Results show that the algorithm presented is both accurate andefficient and can be easily used for large scale clustering of sparse messages as the heavy lifting is achieved ona sublinear number of documents.


title: Rotman lens development history at Raytheon Electronic Warfare Systems 1967-1995

abstract: During the three decade period 1967-1995, Electronic Warfare Systems Division utilized, developed and advanced Rotman lens (Rotman, W. and Turner, R.F., 1962; 1963) technology to a significant degree and implemented it in many of their multibeam systems (Archer, D., 1975; 1984). Most Raytheon multibeam programs use Rotman lenses and Raytheon's multibeam related technology has accounted for several billion dollars of business to date. The frequency independent beam pointing angle characteristic of the Rotman lens due to its true time delay nature makes it ideal for many broad bandwidth electronic warfare (EW) systems, such as those developed at Raytheon. The twelve most significant related technology developments and the six most significant Rotman lens products from Raytheon are summarized.


FORGETTING BY MAXIMIZING TRANSFER AND MINIMIZING INTERFERENCE


title: An review of fully digital audio class D amplifiers topologies

abstract: Class D Amplifiers are widely used in portable systems such as mobile phones to achieve high efficiency. This paper presents topologies of full digital class D amplifiers in order to remove the analog DAC in the amplification path. This approach increases the playback time of embedded system. In first session, open-loop digital class D and digital modulation are discussed. The characterization of an open-loop class D prototype, using a CMOS 130 nm ASIC and a FPGA, confirms the advantage of such systems to reach a long battery life. Then, solutions with closed-loop topologies are proposed to increase linearity and power supply rejection of digital class D. Simulation results of closed-loop topologies, showing an increased in sound quality, are presented as well.


title: Characterization and detection of taxpayers with false invoices using data mining techniques

abstract: In this paper we give evidence that it is possible to characterize and detect those potential users of false invoices in a given year, depending on the information in their tax payment, their historical performance and characteristics, using different types of data mining techniques. First, clustering algorithms like SOM and neural gas are used to identify groups of similar behaviour in the universe of taxpayers. Then decision trees, neural networks and Bayesian networks are used to identify those variables that are related to conduct of fraud and/or no fraud, detect patterns of associated behaviour and establishing to what extent cases of fraud and/or no fraud can be detected with the available information. This will help identify patterns of fraud and generate knowledge that can be used in the audit work performed by the Tax Administration of Chile (in Spanish Servicio de Impuestos Internos (SII)) to detect this type of tax crime.


title: Laptops in the K-12 classrooms: Exploring factors impacting instructional use

abstract: The purpose of this study was to examine factors affecting teachers' integration of laptops into classroom instruction. A research-based path model was tested based on data gathered from 379 K-12 school teachers to examine direct and indirect contributions of relevant institutional factors (overall support for school technology, technical support, and professional development) and teacher level factors (teacher readiness and teacher beliefs). The major premise of this study was that the hypothesized path model was powerful enough to explain a substantial amount of variance in teacher readiness (43%), beliefs (51%), and laptop integration (55%). The results suggest that teacher level factors (teacher readiness and teacher beliefs) strongly predict laptop integration, and that overall support for school technology and professional development have strong effects on teacher beliefs and readiness, respectively. All school-level factors also had a significant indirect impact on laptop integration, which is mediated by teacher readiness and beliefs.


title: Expression invariant 3D face recognition with a Morphable Model

abstract: We describe an expression-invariant method for face recognition by fitting an identity/expression separated 3D Morphable Model to shape data. The expression model greatly improves recognition and retrieval rates in the uncooperative setting, while achieving recognition rates on par with the best recognition algorithms in the face recognition great vendor test. The fitting is performed with a robust nonrigid ICP algorithm. It is able to perform face recognition in a fully automated scenario and on noisy data. The system was evaluated on two datasets, one with a high noise level and strong expressions, and the standard UND range scan database, showing that while expression invariance increases recognition and retrieval performance for the expression dataset, it does not decrease performance on the neutral dataset. The high recognition rates are achieved even with a purely shape based method, without taking image data into account.


title: Active Portfolio-Management based on Error Correction Neural Networks

abstract: This paper deals with a neural network architecture which establishes a portfolio management system similar to the Black / Litterman approach. This allocation scheme distributes funds across various securities or financial markets while simultaneously complying with specific allocation constraints which meet the requirements of an investor. ::: ::: The portfolio optimization algorithm is modeled by a feedforward neural network. The underlying expected return forecasts are based on error correction neural networks (ECNN), which utilize the last model error as an auxiliary input to evaluate their own misspecification. ::: ::: The portfolio optimization is implemented such that (i.) the allocations comply with investor's constraints and that (ii.) the risk of the portfolio can be controlled. We demonstrate the profitability of our approach by constructing internationally diversified portfolios across 21 different financial markets of the G7 contries. It turns out, that our approach is superior to a preset benchmark portfolio.


title: System architecture of a modular direct-DC PV charging station for plug-in electric vehicles

abstract: Plug-in hybrid electric vehicles (PHEVs) are an emerging technology in the market and are helping to offset the negative effects of existing transportation methods that primarily rely on fossil fuel sources. As PHEVs are being introduced into the market, renewable energy sources such as solar power are taking a larger part in the energy sector. A need for high efficiency battery charging is required to decrease the amount of time it takes to charge these cars in order for them to become a viable means of transportation. A novel solar carport architecture is proposed that will provide a three port interface to PHEVs, solar panels and the utility grid to create a seamless power flow between the three ports. Current battery chargers rely heavily on AC/DC conversion from the grid to the car battery, however a direct DC/DC interface is made in this solar carport thus increasing the overall efficiency. This paper1 will prove this concept and show the improved performance over available battery charging schemes.


title: Fuzzy sets and fuzzy logic: theory and applications

abstract: Fuzzy Sets and Fuzzy Logic is a true magnum opus. An enlargement of Fuzzy Sets, Uncertainty, ::: and Information—an earlier work of Professor Klir and Tina Folger—Fuzzy Sets and Fuzzy Logic ::: addresses practically every significant topic in the broad expanse of the union of fuzzy set theory ::: and fuzzy logic. To me Fuzzy Sets and Fuzzy Logic is a remarkable achievement; it covers its vast ::: territory with impeccable authority, deep insight and a meticulous attention to detail. ::: To view Fuzzy Sets and Fuzzy Logic in a proper perspective, it is necessary to clarify a point ::: of semantics which relates to the meanings of fuzzy sets and fuzzy logic. ::: A frequent source of misunderstanding fias to do with the interpretation of fuzzy logic. The ::: problem is that the term fuzzy logic has two different meanings. More specifically, in a narrow ::: sense, fuzzy logic, FLn, is a logical system which may be viewed as an extension and generalization ::: of classical multivalued logics. But in a wider sense, fuzzy logic, FL^ is almost synonymous ::: with the theory of fuzzy sets. In this context, what is important to recognize is that: (a) FLW is much ::: broader than FLn and subsumes FLn as one of its branches; (b) the agenda of FLn is very different ::: from the agendas of classical multivalued logics; and (c) at this juncture, the term fuzzy logic is ::: usually used in its wide rather than narrow sense, effectively equating fuzzy logic with FLW ::: In Fuzzy Sets and Fuzzy Logic, fuzzy logic is interpreted in a sense that is close to FLW. However, ::: to avoid misunderstanding, the title refers to both fuzzy sets and fuzzy logic. ::: Underlying the organization of Fuzzy Sets and Fuzzy Logic is a fundamental fact, namely, ::: that any field X and any theory Y can be fuzzified by replacing the concept of a crisp set in X and Y ::: by that of a fuzzy set. In application to basic fields such as arithmetic, topology, graph theory, probability ::: theory and logic, fuzzification leads to fuzzy arithmetic, fuzzy topology, fuzzy graph theory, ::: fuzzy probability theory and FLn. Similarly, hi application to applied fields such as neural network ::: theory, stability theory, pattern recognition and mathematical programming, fuzzification leads to ::: fuzzy neural network theory, fuzzy stability theory, fuzzy pattern recognition and fuzzy mathematical ::: programming. What is gained through fuzzification is greater generality, higher expressive ::: power, an enhanced ability to model real-world problems and, most importantly, a methodology for ::: exploiting the tolerance for imprecision—a methodology which serves to achieve tractability,


title: Automated Tissue Image Analysis Using Pattern Recognition

abstract: Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and pros ...


title: Reach for A∗: Efficient point-to-point shortest path algorithms

abstract: We study the point-to-point shortest path problem in a setting where preprocessing is allowed. We improve the reach-based approach of Gutman [17] in several ways. In particular, we introduce a bidirectional version of the algorithm that uses implicit lower bounds and we add shortcut arcs to reduce vertex reaches. Our modifications greatly improve both preprocessing and query times. The resulting algorithm is as fast as the best previous method, due to Sanders and Schultes [28]. However, our algorithm is simpler and combines in a natural way with A* search, which yields significantly better query times.


title: FAQ-based Question Answering via Word Alignment

abstract: In this paper, we propose a novel word-alignment-based method to solve the FAQ-based question answering task. First, we employ a neural network model to calculate question similarity, where the word alignment between two questions is used for extracting features. Second, we design a bootstrap-based feature extraction method to extract a small set of effective lexical features. Third, we propose a learning-to-rank algorithm to train parameters more suitable for the ranking tasks. Experimental results, conducted on three languages (English, Spanish and Japanese), demonstrate that the question similarity model is more effective than baseline systems, the sparse features bring 5% improvements on top-1 accuracy, and the learning-to-rank algorithm works significantly better than the traditional method. We further evaluate our method on the answer sentence selection task. Our method outperforms all the previous systems on the standard TREC data set.


title: A fully automated object extraction system for the World Wide Web

abstract: This paper presents a fully automated object extraction system Omini. A distinct feature of Omini is the suite of algorithms and the automatically learned information extraction rules for discovering and extracting objects from dynamic Web pages or static Web pages that contain multiple object instances. We evaluated the system using more than 2,000 Web pages over 40 sites. It achieves 100% precision (returns only correct objects) and excellent recall (between 99% and 98%, with very few significant objects left out). The object boundary identification algorithms are fast, about 0.1 second per page with a simple optimization.


CLEF 2017 eRisk Overview: Early Risk Prediction on the Internet: Experimental Foundations


title: Multi-period corporate default prediction with . . .

abstract: We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of firm-specific and macroeconomic covariates. For U.S. Industrial firms, based on over 390,000 firm-months of data spanning 1979 to 2004, the level and shape of the estimated term structure of conditional future default probabilities depends on a firm's distance to default (a volatility-adjusted measure of leverage), on the firm's trailing stock return, on trailing S&P 500 returns, and on U.S. interest rates, among other covariates. Distance to default is the most influential covariate. Default intensities are estimated to be lower with higher short-term interest rates. The out-of-sample predictive performance of the model is an improvement over that of other available models.


title: Deep learning for event-driven stock prediction

abstract: We propose a deep learning method for event-driven stock market prediction. First, events are extracted from news text, and represented as dense vectors, trained using a novel neural tensor network. Second, a deep convolutional neural network is used to model both short-term and long-term influences of events on stock price movements. Experimental results show that our model can achieve nearly 6% improvements on S&P 500 index prediction and individual stock prediction, respectively, compared to state-of-the-art baseline methods. In addition, market simulation results show that our system is more capable of making profits than previously reported systems trained on S&P 500 stock historical data.


title: ‘Internet Addiction’: A Critical Review

abstract: It has been alleged by some academics that excessive Internet use can be pathological and addictive. This paper reviews what is known from the empirical literature on ‘Internet addiction’ and its derivatives (e.g., Internet Addiction Disorder, Pathological Internet Use, etc.) and assesses to what extent it exists. Empirical research into ‘Internet addiction’ can roughly be divided into five areas: (1) survey studies that compare excessive Internet users with non-excessive users, (2) survey studies that have examined vulnerable groups of excessive Internet use, most notably students, (3) studies that examine the psychometric properties of excessive Internet use, (4) case studies of excessive Internet users and treatment case studies, and (5) correlational studies examining the relationship of excessive Internet use with other behaviours (e.g., psychiatric problems, depression, self-esteem, etc.). Each of these areas is reviewed. It is concluded that if ‘Internet addiction’ does indeed exist, it affects a relatively small percentage of the online population. However, exactly what it is on the Internet that they are addicted to still remains unclear.


title: Computer Vision: A Modern Approach

abstract: From the Publisher: ::: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.


title: A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression

abstract: Face recognition is receiving a significant attention due to the need of facing important challenges when developing real applications under unconstrained environments. The three most important challenges are facial occlusion, the problem of dealing with a single sample per subject (SSPS) and facial expression. This paper describes and analyzes various strategies that have been developed recently for overcoming these three major challenges that seriously affect the performance of real face recognition systems. This survey is organized in three parts. In the first part, approaches to tackle the challenge of facial occlusion are classified, illustrated and compared. The second part briefly describes the SSPS problem and the associated solutions. In the third part, facial expression challenge is illustrated. In addition, pros and cons of each technique are stated. Finally, several improvements for future research are suggested, providing a useful perspective for addressing new research in face recognition.


title: Independence of Containing Patterns Property and Its Application in Tree Pattern Query Rewriting Using Views

abstract: We show that several classes of tree patterns observe the independence of containing patterns property, that is, if a pattern is contained in the union of several patterns, then it is contained in one of them. We apply this property to two related problems on tree pattern rewriting using views. First, given view V and query Q, is it possible for Q to have an equivalent rewriting using V which is the union of two or more tree patterns, but not an equivalent rewriting which is a single pattern? This problem is of both theoretical and practical importance because, if the answer is no, then, to find an equivalent rewriting of a tree pattern using a view, we should use more efficient methods, such as the polynomial time algorithm of Xu and Ozsoyoglu (2005), rather than try to find the union of all contained rewritings (which takes exponential time in the worst case) and test its equivalence to Q. Second, given a set S of views, we want to know under what conditions a subset S' of S is redundant in the sense that for any queryQ, the contained rewritings of Q using the views in S' are contained in those using the views in S--S'. Solving this problem can help us to, for example, choose the minimum number of views to be cached, or better design the virtual schema in a mediated data integration system, or avoid repeated calculation in query optimization. For the first problem, we identify several classes of tree patterns for which the equivalent rewriting can be expressed as a single tree pattern. For the second problem, we present necessary and sufficient conditions for S to be redundant with respect to some classes of tree patterns. For both problems we consider extension to cases where there are rewritings using the intersection of multiple views and/or where a schema graph is present.


title: Input evaluation of an eye-gaze-guided interface: kalman filter vs. velocity threshold eye movement identification

abstract: This paper evaluates the input performance capabilities of Velocity Threshold (I-VT) and Kalman Filter (I-KF) eye movement detection models when employed for eye-gaze-guided interface control. I-VT is a common eye movement identification model employed by the eye tracking community, but it is neither robust nor capable of handling high levels of noise present in the eye position data. Previous research implies that use of a Kalman filter reduces the noise in the eye movement signal and predicts the signal during brief eye movement failures, but the actual performance of I-KF was never evaluated. We evaluated the performance of I-VT and I-KF models using guidelines for ISO 9241 Part 9 standard, which is designed for evaluation of non keyboard/mouse input devices with emphasis on performance, comfort, and effort. Two applications were implemented for the experiment: 1) an accuracy test 2) a photo viewing application specifically designed for eye-gaze-guided control. Twenty-one subjects participated in the evaluation of both models completing a series of tasks. The results indicates that I-KF allowed participants to complete more tasks with shorter completion time while providing higher general comfort, accuracy and operation speeds with easier target selection than the I-VT model. We feel that these results are especially important to the engineers of new assistive technologies and interfaces that employ eye-tracking technology in their design.


title: Beacon vector routing: scalable point-to-point routing in wireless sensornets

abstract: We propose a practical and scalable technique for point-to-point routing in wireless sensornets. This method, called Beacon Vector Routing (BVR), assigns coordinates to nodes based on the vector of hop count distances to a small set of beacons, and then defines a distance metric on these coordinates. BVR routes packets greedily, forwarding to the next hop that is the closest (according to this beacon vector distance metric) to the destination. We evaluate this approach through a combination of high-level simulation to investigate scaling and design tradeoffs, and a prototype implementation over real testbeds as a necessary reality check.


title: Monte Carlo localization for mobile robots

abstract: To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face considerable hurdles. In particular the problems encountered are closely related to the type of representation used to represent probability densities over the robot's state space. Earlier work on Bayesian filtering with particle-based density representations opened up a new approach for mobile robot localization based on these principles. We introduce the Monte Carlo localization method, where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. By using a sampling-based representation we obtain a localization method that can represent arbitrary distributions. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. It is faster, more accurate and less memory-intensive than earlier grid-based methods,.


Lexicalisation patterns: semantic structure in lexical forms


title: War! What Is It Good For? �

abstract: Whatever gains may come from fighting wars, economic growth is not among them. We examine the long-run impact of interstate conflict on real GDP per capita for a cross section of countries between 1960 and 2000. We construct a fatality-weighted conflict variable that accounts for both the severity and endogeneity of individual confrontations. We include our conflict measure in a deep determinants income regression in which we control for trade, institutions and geography. We find that a 10 percent increase in fatality-weighted conflict over the period 1960 to 2000 results in an average decrease of 1.2 to 1.6 percent in 2000 real GDP per capita.


title: Machine Learning-Based Malware Detection for Android Applications: History Matters!

abstract: Machine Learning-based malware detection is a promising scalable method for identifying suspicious applications. In particular, in today’s mobile computing realm where thousands of applications are daily poured into markets, such a technique could be valuable to guarantee a strong filtering of malicious apps. The success of machine-learning approaches however is highly dependent on (1) the quality of the datasets that are used for training and of (2) the appropriateness of the tested datasets with regards to the built classifiers. Unfortunately, there is scarce mention of these aspects in the evaluation of existing state-of-the-art approaches in the literature. In this paper, we consider the relevance of history in the construction of datasets, to highlight its impact on the performance of the malware detection scheme. Typically, we show that simply picking a random set of known malware to train a malware detector, as it is done in most assessment scenarios from the literature, yields significantly biased results. In the process of assessing the extent of this impact through various experiments, we were also able to confirm a number of intuitive assumptions about Android malware. For instance, we discuss the existence of Android malware lineages and how they could impact the performance of malware detection in the wild.


title: Online Learning of a Memory for Learning Rates

abstract: The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks. Here, we introduce a computationally efficient online meta-learning algorithm that builds and optimizes a memory model of the optimal learning rate landscape from previously observed gradient behaviors. While performing task specific optimization, this memory of learning rates predicts how to scale currently observed gradients. After applying the gradient scaling our meta-learner updates its internal memory based on the observed effect its prediction had. Our meta-learner can be combined with any gradient-based optimizer, learns on the fly and can be transferred to new optimization tasks. In our evaluations we show that our meta-learning algorithm speeds up learning of MNIST classification and a variety of learning control tasks, either in batch or online learning settings.


title: Faster and smaller inverted indices with treaps

abstract: We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using less space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression we represent the treap topology using compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. Results show that our index uses about 20% less space, and performs queries up to three times faster, than state-of-the-art compact representations.


Reliability Synthesis for UAV Flight Control System


PHYSIOTHERAPY TREATMENT IN LOW BACK PAIN PATIENTS WITH SACROILIAC JOINT DYSFUNCTION – A COMPARATIVE STUDY BETWEEN JOINT MANIPULATION WITH EXERCISES AND CONVENTIONAL PHYSIOTHERAPY EXERCISES


title: An automatic controller for a greenhouse using a supervisory expert system

abstract: The commercial production of crops and cut-flowers is a very labour intensive industry. In order to meet the high standards, the plants need to experience the least possible stress, i.e. requiring the environmental conditions to be as close as possible to an ideal one. To create an ideal environment, the main environmental parameters that need to be controlled are temperature, humidity, light intensity and carbon dioxide concentration. Because of the dead times and extreme interdependence of these four parameters, the control problem can be classified as nonlinear and multivariable. Two control techniques, PID-control and fuzzy logic control, are investigated in order to establish the applicability of each technique. In order to fully automate the control process, a supervisory expert system is implemented in conjunction with the controller, to serve as a user friendly interface with the operator.


title: Chronic Kidney Disease Prediction on Imbalanced Data by Multilayer Perceptron: Chronic Kidney Disease Prediction

abstract: Imbalanced data is an important problem for medical data analysis. Medical datasets are often not balanced in their class labels. The traditional classifiers can be seriously affected by the imbalanced class distribution in the data. This is because they aim to optimize the overall accuracy without considering the relative distribution of each class. This study searches the effect of class imbalance in training data when developing neural network classifier for medical decision making on chronic kidney disease. Neural networks are widely used in a number of applications including data mining and decision systems. Back propagation networks are a popular type of neural networks that can be trained to recognize different patterns. The importance of these networks was considered and a comparative study of some sampling algorithms was performed based on multilayer perceptron with different learning rate values for the prediction of chronic kidney disease. This study reveals that sampling algorithms can improve the performance of classification algorithms and learning rate is a crucial parameter which can significantly effect on multilayer perceptron.


title: Community-Affiliation Graph Model for Overlapping Network Community Detection

abstract: One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Communities in networks often overlap as nodes can belong to multiple communities at once. Identifying such overlapping communities is crucial for the understanding the structure as well as the function of real-world networks. Even though community structure in networks has been widely studied in the past, practically all research makes an implicit assumption that overlaps between communities are less densely connected than the non-overlapping parts themselves. Here we validate this assumption on 6 large scale social, collaboration and information networks where nodes explicitly state their community memberships. By examining such ground-truth communities we find that the community overlaps are more densely connected than the non-overlapping parts, which is in sharp contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves. Practically all existing community detection methods fail to detect communities with dense overlaps. We propose Community-Affiliation Graph Model, a model-based community detection method that builds on bipartite node-community affiliation networks. Our method successfully captures overlapping, non-overlapping as well as hierarchically nested communities, and identifies relevant communities more accurately than the state-of-the-art methods in networks ranging from biological to social and information networks.


title: Information vs. knowledge: the role of intranets in knowledge management

abstract: Knowledge has widely been acknowledged as one of the most important factors for corporate competitiveness, and we have witnessed an explosion of IS/IT solutions claiming to provide support for knowledge management (KM). A relevant question to ask, though, is how systems and technology intended for information such as the intranet can be able to assist in the managing of knowledge. To understand this, we must examine the relationship between information and knowledge. Building on Polanyi's theories, the author argues that all knowledge is tacit, and what can be articulated and made tangible outside the human mind is merely information. However, information and knowledge affect one another. By adopting a multi perspective of the intranet where information, awareness, and communication are all considered, this interaction can best be supported and the intranet can become a useful and people-inclusive KM environment.


title: Application of support vector machines in !nancial time series forecasting

abstract: This paper deals with the application of a novel neural network technique, support vector machine (SVM), in !nancial time series forecasting. The objective of this paper is to examine the feasibility of SVM in !nancial time series forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. The experiment shows that SVM outperforms the BP neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE), directional symmetry (DS) and weighted directional symmetry (WDS). Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast !nancial time series. ? 2001 Elsevier Science Ltd. All rights reserved.


title: Use of fluorogenic Scorpions for fast and sensitive detection of plant viruses

abstract: Very recently, a novel nucleic acid detection technology based on a primer with a quenched fluorophore and a probe complementary to an internal portion of the amplicon has been developed. The primer probe, named ‘Scorpion’, fluoresces only upon hybridization with its target, i.e. if the specific extension product of the primer is synthesized during the reaction. We have used Scorpion primers in RT-PCR assays to detect Tomato spotted wilt tospovirus and Plum pox potyvirus in naturally infected plants. Amplified products of the expected size were detected either by real-time measurements or by visual estimation of fluorescence emission from tubes placed over an ultraviolet light source at the end of the Scorpion-RT-PCR. In serially diluted plant sap, the visual method allowed detection of 7.5 pg of infected plant tissue and proved at least as sensitive as visual estimation of ethidium bromide-stained amplicons separated by gel electrophoresis.


title: Estimating or Propagating Gradients Through Stochastic Neurons

abstract: Stochastic neurons can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such s tochastic neurons, i.e., can we “back-propagate” through these stochastic neurons? We examine this question, existing approaches, and present two novel families of solutions, applicable in different settings. In particular, it is demonstrate d that a simple biologically plausible formula gives rise to an an unbiased (but noisy) estimator of the gradient with respect to a binary stochastic neuron firing proba bility. Unlike other estimators which view the noise as a small perturbation in order to estimate gradients by finite differences, this estimator is unbiased even w ithout assuming that the stochastic perturbation is small. This estimator is also in teresting because it can be applied in very general settings which do not allow gradient back-propagation, including the estimation of the gradient with respect to futur e rewards, as required in reinforcement learning setups. We also propose an approach to approximating this unbiased but high-variance estimator by learning to predict it using a biased estimator. The second approach we propose assumes that an estimator of the gradient can be back-propagated and it provides an unbiased estimator of the gradient, but can only work with non-linearities unlike the hard threshold, but like the rectifier, that are not flat for all of their range. This is similar to trad itional sigmoidal units but has the advantage that for many inputs, a hard decision (e.g., a 0 output) can be produced, which would be convenient for conditional computation and achieving sparse representations and sparse gradients.


title: Development of a biped robot with torque controlled joints

abstract: This paper gives an overview of the development of a novel biped walking machine. The robot is designed as an experimental system for studying biped locomotion based on torque controlled joints. As an underlying drive technology, the torque controlled joint units of the DLR-KUKA-Lightweight-Robot are employed. The relevant design choices for using this technology in a biped robot with integrated joint torque sensors are highlighted and some first experimental results using a conventional ZMP based control scheme are discussed.


AN ENHANCEMENT ALGORITHM USING GABOR FILTER FOR FINGERPRINT RECOGNITION 1 EINAS


title: Love, hate, arousal and engagement: exploring audience responses to performing arts

abstract: Understanding audience responses to art and performance is a challenge. New sensors are promising for measurement of implicit and explicit audience engagement. However, the meaning of biometric data, and its relationship to engagement, is unclear. We conceptually explore the audience engagement domain to uncover opportunities and challenges in the assessment and interpretation of audience engagement data. We developed a display that linked performance videos with audience biometric data and presented it to 7 performing arts experts, to explore the measurement, interpretation and application of biometric data. Experts were intrigued by the response data and reflective in interpreting it. We deepened our inquiry with an empirical study with 49 participants who watched a video of a dance performance. We related temporal galvanic skin response (GSR) data to two self-report scales, which provided insights on interpreting this measure. Our findings, which include strong correlations, support the interpretation of GSR as a valid representation of audience engagement.


title: Automated classification and analysis of internet malware

abstract: Numerous attacks, such as worms, phishing, and botnets, threaten the availability of the Internet, the integrity of its hosts, and the privacy of its users. A core element of defense against these attacks is anti-virus (AV) software--a service that detects, removes, and characterizes these threats. The ability of these products to successfully characterize these threats has far-reaching effects--from facilitating sharing across organizations, to detecting the emergence of new threats, and assessing risk in quarantine and cleanup. In this paper, we examine the ability of existing host-based anti-virus products to provide semantically meaningful information about the malicious software and tools (or malware) used by attackers. Using a large, recent collection of malware that spans a variety of attack vectors (e.g., spyware, worms, spam), we show that different AV products characterize malware in ways that are inconsistent across AV products, incomplete across malware, and that fail to be concise in their semantics. To address these limitations, we propose a new classification technique that describes malware behavior in terms of system state changes (e.g., files written, processes created) rather than in sequences or patterns of system calls. To address the sheer volume of malware and diversity of its behavior, we provide a method for automatically categorizing these profiles of malware into groups that reflect similar classes of behaviors and demonstrate how behavior-based clustering provides a more direct and effective way of classifying and analyzing Internet malware.


title: Weighted tensor nuclear norm minimization for color image denoising

abstract: Although non-local image denoising has attracted much research effort due to its superior performance, little attention has focused on its color extension. Most existing non-local color image denoising methods process the color channels of an input image separately. However, in order to improve the performance of color image denoising, all color channels should be processed jointly for fully utilizing the interchannel dependency. This paper proposes a new non-local and inter-channel dependency aware prior, named weighted tensor nuclear norm (WTNN), and it is defined on a 3rd-order tensor from a patch cluster of an input image. We also present an effective algorithm for color image denoising using the WTNN. Experimental results clearly show that the proposed algorithm outperforms a state-of-the-art color image denoising method, known as CBM3D.


title: Alobar holoprosencephaly, proboscis and cyclopia in a chromosomally normal fetus: Prenatal diagnosis and fetal outcome.

abstract: Holoprosencephaly is a brain malformation that develops as a result of a defect in development of prosencephalon during early gestation. Holoprosencephaly can be diagnosed with prenatal ultrasonography and magnetic resonance imaging. We report herein a case with cyclopia and holoprosencephaly detected by prenatal ultrasonography.


title: Curve tracking control for autonomous vehicles with rigidly mounted range sensors

abstract: In this paper, we present feedback control laws for an autonomous vehicle with rigidly mounted range sensors to track a desired curve. In particular, we consider a vehicle that has a group of rays around two center rays that are perpendicular to the velocity of the vehicle. Under such a sensor configuration, singularities are bound to occur in the curve tracking feedback control law when tracking concave curves. To overcome this singularity, we derive a hybrid strategy of switching between control laws when the vehicle gets close to singularities. Rigorous proof and extensive simulation results verify the validity of the proposed feedback control law.


title: RT-qPCR with chimeric dU stem-loop primer is efficient for the detection of bacterial small RNAs

abstract: Small non-coding RNAs are considered be involved in the regulation of multiple cellular processes. Quantitative reverse transcription PCR (RT-qPCR) is widely used in the detection of eukaryotic microRNA, and the stem-loop primers can improve the specificity and efficiency of reverse transcription. However, the loop structure of primers probably influence the next quantitative amplification due to the base stacking and steric hindrance. Here, we designed a chimeric stem-loop primer with a deoxyuracil (dU) base located near the RNA matching part. After the reverse transcription, uracil-DNA glycosylase (UDG) treatment was used to remove the dU base and destroy the stem-loop structure of RT product. Enzymatic assay confirmed that the recombinant UDG could efficiently eliminate the dU base in the oligonucleotide. Transcriptions of two small RNAs (TFF and ryeA) in Escherichia coli were detected by RT-qPCR with different primers. Results showed that the use of the chimeric dU stem-loop primer and UDG treatment could enhance the detection specificity and sensitivity about 1.1- to 3.4-fold, compared to those with traditional stem-loop primer and linear primer. Total RNA of 1–10 pg was enough for efficient detection with the chimeric stem-loop primers. In a word, this strategy could promote the RT-qPCR detection efficiency on the transcription of bacterial small RNAs even in trace samples and can facilitate the detection of exiguous change in cellular metabolism.


title: Design of Circular/Dual-Frequency Linear Polarization Antennas Based on the Anisotropic Complementary Split Ring Resonator

abstract: A novel kind of circular polarization and dual-frequency linear polarization microstrip antennas is proposed. The designs are based on the anisotropic property of the complementary split ring resonator. When the complementary split ring resonator is etched on the patch of the probe-fed microstrip antenna, the gap orientation asymmetric or symmetric to the current propagating direction will render the antenna to radiate circular polarization waves or dual-frequency linear polarization waves. Details of the experimental results compared with the simulated results are presented and discussed.


title: Induction cooker design with quasi resonant topology using jitter drive method

abstract: The principle of induction cooker is based on heating the metallic pan with circulating eddy currents induced by high frequency AC magnetic field. The coil within the induction cooker and the load (pan / pot) acts as a transformer if the load has an electrically conducting base. If the conducting base is ferromagnetic, then the power is transferred with high efficiency. In this document, the quasi resonant topology for induction heating is analyzed. Moreover, pan detection and the method to determine the amount of power delivered to the pan is covered. Jitter method and the three basic benefits of using jitter method during switching action is pointed out. An experimental prototype with a power rating of 2300W is constructed to test the validity of the proposed methods and control algorithms. It was observed that the quasi resonant topology is advantageous over half bridge topology when jitter method is used to drive the semiconductor switches. The test results of the prototype reveal that the current harmonics drawn from the utility is within the limits of the standard EN 61000-3-2.


title: Retrieval based interactive cartoon synthesis via unsupervised bi-distance metric learning

abstract: Cartoons play important roles in many areas, but it requires a lot of labor to produce new cartoon clips. In this paper, we propose a gesture recognition method for cartoon character images with two applications, namely content-based cartoon image retrieval and cartoon clip synthesis. We first define Edge Features (EF) and Motion Direction Features (MDF) for cartoon character images. The features are classified into two different groups, namely intra-features and inter-features. An Unsupervised Bi-Distance Metric Learning (UBDML) algorithm is proposed to recognize the gestures of cartoon character images. Different from the previous research efforts on distance metric learning, UBDML learns the optimal distance metric from the heterogeneous distance metrics derived from intra-features and inter-features. Content-based cartoon character image retrieval and cartoon clip synthesis can be carried out based on the distance metric learned by UBDML. Experiments show that the cartoon character image retrieval has a high precision and that the cartoon clip synthesis can be carried out efficiently.


title: Development and analysis of a folded shorted-patch antenna with reduced size

abstract: The length of a wall-shorted rectangular patch antenna can be reduced from /spl sim//spl lambda//sub 0//4 to /spl sim//spl lambda//sub 0//8 by a simple folding operation, which results in a stacked shorted-patch (S-P) structure with a resonant frequency that can be controlled by modifying the distance between the stacked (lower and upper) shorted-patches. A theoretical analysis based on a simple transmission-line model is presented and compared with numerical simulations, showing good agreement if the height of the folded patch is much smaller than the patch length. The physical insight of the variation of the resonant frequency for this reduced-size antenna can be understood by considering the antenna as a shorted patch loaded with a capacitor. An experimental verification is carried out for a 15 mm/spl times/15 mm/spl times/6 mm folded S-P antenna prototype designed for the 2.4 GHz ISM band that can achieve a 10-dB return loss bandwidth of 4% and results in a nearly omni-directional radiation pattern.


title: Design and characterization of temperature-robust piezoresistive micro-pressure sensor with double-Wheatstone-bridge structure

abstract: This study presents a silicon-based micro-pressure sensor in which the piezoresistive patches on the polysilicon membrane are configured in the form of two concentric Wheatstone bridge circuits. The sensors in the central circuit measure the membrane deflection caused by the combined effects of the pressure and the ambient temperature, respectively, while those in the outer circuit measure the deflection induced by the ambient temperature alone. Thus, by comparing the output signals of the two circuits, a reliable indication of the sensed pressure is obtained. ANSYS simulations are performed to establish the optimal positions on the membrane surface of the piezoresistors within the two Wheatstone bridge circuits. The sensor is then fabricated using micro-electro-mechanical systems (MEMS) techniques, and its sensing performance is compared with that of a micro-pressure sensor with a single-Wheatstone bridge configuration at pressures ranging from 0∼150 psi and temperatures in the range −10°C to 75°C. Moreover, the results confirm that the double-Wheatstone bridge sensor is significantly more robust to variations in the external temperature than the single-Wheatstone bridge sensor at all values of the considered pressure range.


title: Images of desire: food-craving activation during fMRI

abstract: Abstract Food craving (defined as an intense desire to eat a specific food) is of interest because it is extremely common and because it influences obesity or nutritional status. It has also been suggested that food craving may be the evolutionary source for cravings of all kinds including cravings for drugs and alcohol. Yet, little is known about the functional neuroanatomy of food craving. We report here the first functional magnetic resonance imaging (fMRI) study to explicitly examine food craving. A two-part technique was used to produce the food cravings. Threshold was reduced through a diet manipulation (monotonous diet) and cravings were triggered during blood oxygenation level-dependent (BOLD) fMRI sessions by having subjects imagine the sensory properties of favorite foods (a cue-induction technique). Subjects were also asked to imagine the monotonous diet (which they did not crave). Diet condition had an activating effect on both behavioral (reports of craving) and fMRI measures. Craving-related changes in fMRI signal were identified in the hippocampus, insula, and caudate, three areas reported to be involved in drug craving. Thus, this work supports the common substrate hypothesis for food and drug cravings.


title: A novel kill-chain framework for remote security log analysis with SIEM software

abstract: Network security investigations pose many challenges to security analysts attempting to identify the root cause of security alarms or incidents. Analysts are often presented with cases where either incomplete information is present, or an overwhelming amount of information is presented in a disorganized manner. Either scenario greatly impacts the ability for incident responders to properly identify and react to security incidents when they occur. The framework presented in this paper draws upon previous research pertaining to cyber threat modeling with kill-chains, as well as the practical application of threat modeling to forensic. Modifications were made to conventional kill-chain models to facilitate logical data aggregation within a relational database collecting data across disparate remote sensors resulting in more detailed alarms to security analysts. The framework developed in this paper proved effective in identifying the relationship of security alarms along a continuum of expected behaviors conducive to executing security investigations in a methodical manner. This framework effectively addressed incomplete or inadequate alarm information through aggregation, and provided a methodology for organizing related data and conducting standard investigations. Both improvements proved instrumental in the effective identification of security threats in a more expeditious manner.


title: Seeing Beyond Occlusions ( and other marvels of a finite lens aperture )

abstract: We present a novel algorithm to reconstruct the geometry and photometry of a scene with occlusions from a collection of defocused images. The presence of a finite lens aperture allows us to recover portions of the scene that would be occluded in a pin-hole projection, thus "uncovering" the occlusion. We estimate the shape of each object (a surface, including the occluding boundaries), and its radiance (a positive function defined on the surface, including portions that are occluded by other objects).


title: ECF: An MPTCP Path Scheduler to Manage Heterogeneous Paths

abstract: Multi-Path TCP (MPTCP) is a new standardized transport protocol that enables devices to utilize multiple network interfaces. The default MPTCP path scheduler prioritizes paths with the smallest round trip time (RTT). In this work, we examine whether the default MPTCP path scheduler can provide applications the ideal aggregate bandwidth, i.e., the sum of available bandwidths of all paths. Our experimental results show that heterogeneous paths cause under-utilization of the fast path, resulting in undesirable application behaviors such as lower video streaming quality than can be obtained using the available aggregate bandwidth. To solve this problem, we propose and implement a new MPTCP path scheduler, ECF (Earliest Completion First), that utilizes all relevant information about a path, not just RTT. Our results show that ECF consistently utilizes all available paths more efficiently than other approaches under path heterogeneity, particularly for streaming video.


POLYPHONIC PITCH DETECTION WITH CONVOLUTIONAL RECURRENT NEURAL NETWORKS


title: Structured Bayesian Pruning via Log-Normal Multiplicative Noise

abstract: Dropout-based regularization methods can be regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training. It was recently shown that Bayesian dropout procedure not only improves generalization but also leads to extremely sparse neural architectures by automatically setting the individual noise magnitude per weight. However, this sparsity can hardly be used for acceleration since it is unstructured. In the paper, we propose a new Bayesian model that takes into account the computational structure of neural networks and provides structured sparsity, e.g. removes neurons and/or convolutional channels in CNNs. To do this we inject noise to the neurons outputs while keeping the weights unregularized. We establish the probabilistic model with a proper truncated log-uniform prior over the noise and truncated log-normal variational approximation that ensures that the KL-term in the evidence lower bound is computed in closed-form. The model leads to structured sparsity by removing elements with a low SNR from the computation graph and provides significant acceleration on a number of deep neural architectures. The model is easy to implement as it can be formulated as a separate dropout-like layer.


title: RIPK1 Blocks Early Postnatal Lethality Mediated by Caspase-8 and RIPK3

abstract: Summary Receptor-interacting protein kinase (RIPK)-1 is involved in RIPK3-dependent and -independent signaling pathways leading to cell death and/or inflammation. Genetic ablation of ripk1 causes postnatal lethality, which was not prevented by deletion of ripk3 , caspase-8 , or fadd . However, animals that lack RIPK1, RIPK3, and either caspase-8 or FADD survived weaning and matured normally. RIPK1 functions in vitro to limit caspase-8-dependent, TNFR-induced apoptosis, and animals lacking RIPK1, RIPK3, and TNFR1 survive to adulthood. The role of RIPK3 in promoting lethality in ripk1 −/− mice suggests that RIPK3 activation is inhibited by RIPK1 postbirth. Whereas TNFR-induced RIPK3-dependent necroptosis requires RIPK1, cells lacking RIPK1 were sensitized to necroptosis triggered by poly I:C or interferons. Disruption of TLR (TRIF) or type I interferon (IFNAR) signaling delayed lethality in ripk1 −/− tnfr1 −/− mice. These results clarify the complex roles for RIPK1 in postnatal life and provide insights into the regulation of FADD-caspase-8 and RIPK3-MLKL signaling by RIPK1.


title: Collision Warning Timing, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator

abstract: Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly--and thereby avoid more collisions--than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0% and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input. Keywords: Driver distraction;


title: Virtualized and flexible ECC for main memory

abstract: We present a general scheme for virtualizing main memory error-correction mechanisms, which map redundant information needed to correct errors into the memory namespace itself. We rely on this basic idea, which increases flexibility to increase error protection capabilities, improve power efficiency, and reduce system cost; with only small performance overheads. We augment the virtual memory system architecture to detach the physical mapping of data from the physical mapping of its associated ECC information. We then use this mechanism to develop two-tiered error protection techniques that separate the process of detecting errors from the rare need to also correct errors, and thus save energy. We describe how to provide strong chipkill and double-chip kill protection using existing DRAM and packaging technology. We show how to maintain access granularity and redundancy overheads, even when using ×8 DRAM chips. We also evaluate error correction for systems that do not use ECC DIMMs. Overall, analysis of demanding SPEC CPU 2006 and PARSEC benchmarks indicates that performance overhead is only 1% with ECC DIMMs and less than 10% using standard Non-ECC DIMM configurations, that DRAM power savings can be as high as 27%, and that the system energy-delay product is improved by 12% on average.


title: Controlling Formations of Multiple Mobile Robots

abstract: In this paper, we investigate the problem of inter-robot collision avoidance in multiple mobile robot formation control. Two methodologies are utilized, namely Virtual Robot tracking by [Jongusuk and Mita, 2001] and l-l control by [Desai et al., 1998] to establish formation and avoid collision among robots. We point out that the framework in Virtual Robot tracking is potentially subject to collision among robots. This drawback is overcome in our design by incorporating a different reactive scheme in the incident possibility of collision. To prove the advantages of our framework, we demonstrate in simulation the case of three robots moving in formation and avoiding inter-robot collisions.


title: Multiscale Hierarchical Convolutional Networks

abstract: Deep neural network algorithms are difficult to analyze because they lack structure allowing to understand the properties of underlying transforms and invariants. Multiscale hierarchical convolutional networks are structured deep convolutional networks where layers are indexed by progressively higher dimensional attributes, which are learned from training data. Each new layer is computed with multidimensional convolutions along spatial and attribute variables. We introduce an efficient implementation of such networks where the dimensionality is progressively reduced by averaging intermediate layers along attribute indices. Hierarchical networks are tested on CIFAR image data bases where they obtain comparable precisions to state of the art networks, with much fewer parameters. We study some properties of the attributes learned from these databases.


title: Low-Rank Matrix Completion in the Presence of High Coherence

abstract: Prevalent matrix completion methods capture only the low-rank property which gives merely a constraint that the data points lie on some low-dimensional subspace, but generally ignore the extra structures (beyond low-rank) that specify in more detail how the data points lie on the subspace. Whenever the data points are not uniformly distributed on the low-dimensional subspace, the row-coherence of the target matrix to recover could be considerably high and, accordingly, prevalent methods might fail even if the target matrix is fairly low-rank. To relieve this challenge, we suggest to consider a model termed low-rank factor decomposition (LRFD), which imposes an additional restriction that the data points must be represented as linear, compressive combinations of the bases in a given dictionary. We show that LRFD can effectively mitigate the challenges of high row-coherence, provided that its dictionary is configured properly. Namely, it is mathematically proven that if the dictionary is well-conditioned and low-rank, then LRFD can weaken the dependence on the row-coherence. In particular, if the dictionary itself is low-rank, then the dependence on the row-coherence can be entirely removed. Subsequently, we devise two practical algorithms to obtain proper dictionaries in unsupervised environments: one uses the existing matrix completion methods to construct the dictionary in LRFD, and the other tries to learn a proper dictionary from the data given. Experiments on randomly generated matrices and motion datasets show superior performance of our proposed algorithms.


title: Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer

abstract: While genetic mutation is a hallmark of cancer, many cancers also acquire epigenetic alterations during tumorigenesis including aberrant DNA hypermethylation of tumor suppressors, as well as changes in chromatin modifications as caused by genetic mutations of the chromatin-modifying machinery. However, the extent of epigenetic alterations in cancer cells has not been fully characterized. Here, we describe complete methylome maps at single nucleotide resolution of a low-passage breast cancer cell line and primary human mammary epithelial cells. We find widespread DNA hypomethylation in the cancer cell, primarily at partially methylated domains (PMDs) in normal breast cells. Unexpectedly, genes within these regions are largely silenced in cancer cells. The loss of DNA methylation in these regions is accompanied by formation of repressive chromatin, with a significant fraction displaying allelic DNA methylation where one allele is DNA methylated while the other allele is occupied by histone modifications H3K9me3 or H3K27me3. Our results show a mutually exclusive relationship between DNA methylation and H3K9me3 or H3K27me3. These results suggest that global DNA hypomethylation in breast cancer is tightly linked to the formation of repressive chromatin domains and gene silencing, thus identifying a potential epigenetic pathway for gene regulation in cancer cells.


title: 16 Transdisciplinarity and Digital Humanities: Lessons Learned from Developing Text-Mining Tools for Textual Analysis

abstract: This peer-reviewed chapter advances social science research on text mining and data mining, which are key artificial intelligence technologies applied in the digital humanities. The chapter provides a detailed documentation of an interdisciplinary project conducted by a team consisting of social scientists, linguists and software engineers to develop a set of bespoke text-mining tools for researchers in the humanities. Through looking at the user-participatory development processes of the text-mining tools, this chapter aims to improve our understandings of digital humanities in the context of scholarly research and, from a pragmatist perspective, to highlight its trans-disciplinary potential. The paper both analyses and produces an empirical account of interdisciplinary research practices across the social sciences and humanities. It concludes with a discussion of some methodological and socio-technical challenges of the 'digital humanity' emerging in this shift towards trans-disciplinarity, particularly focusing on the topic of 'interpretative flexibility'. ::: ::: The edited collection, which is interdisciplinary in nature, develops knowledge of how the application of new computational techniques and visualisation technologies in the arts and humanities is resulting in fresh approaches and methodologies for the study of new and traditional corpora. It includes articles from internationally significant scholars such as N. Katherine Hayles and Lev Manovich. ::: ::: The realisation of this piece has benefited from discussion at the 2009 Media, Communication and Cultural Studies Association (MeCCSA) conference at Bradford, 14–16 January 2009, and the Computational Turn Workshop at Swansea on 9 March 2010, where an earlier version of this paper was presented.


Obstacle detection using stereo vision for self-driving cars


title: A Short Survey on Online and Offline Methods for Search Quality Evaluation

abstract: Evaluation has always been the cornerstone of scientific development. Scientists come up with hypotheses (models) to explain physical phenomena, and validate these models by comparing their output to observations in nature. A scientific field consists then merely by a collection of hypotheses that could not been disproved (yet) when compared to nature. Evaluation plays the exact key role in the field of information retrieval. Researchers and practitioners develop models to explain the relation between an information need expressed by a person and information contained in available resources, and test these models by comparing their outcomes to collections of observations.


title: Mid-level Deep Pattern Mining

abstract: Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activation extracted from the first fully-connected layer of a CNN have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern (See Fig. 1), surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.


title: Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network

abstract: This paper focuses on the problems existing in intrusion detection using neural network, including redundant information, large amount of data, long-time training, easy to fall into the local optimal. An intrusion detection method using deep belief network (DBN) and probabilistic neural network (PNN) is proposed. First, the raw data are converted to low-dimensional data while retaining the essential attributes of the raw data by using the nonlinear learning ability of DBN. Second, to obtain the best learning performance, particle swarm optimization algorithm is used to optimize the number of hidden-layer nodes per layer. Next, PNN is used to classify the low-dimensional data. Finally, the KDD CUP 1999 dataset is employed to test the performance of the method mentioned above. The experiment result shows that the method performs better than the traditional PNN, PCA-PNN and unoptimized DBN-PNN


title: Quantifying Facial Age by Posterior of Age Comparisons

abstract: We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.


title: A full ka-band microstrip-to-waveguide transition using tapered CPS probe and V-antenna

abstract: A novel Ka-band microstrip-to-waveguide transition is presented in this paper. This transition is composed of a tapered coplanar strip (CPS) and a V-antenna which are inserted from E-plane center of a rectangular waveguide end. The mode transformation between waveguide and microstrip is achieved by electric field structural conversion in these two components. The impedance matching was also considered in the CPS design. The transition has characteristics of in-line structure, integration feasibility and compatibility in monolithic microwave integrated circuit (MMIC) technology. The Ka-band back-to-back transition fabricated on RT-duriod 5880 substrate demonstrates more than 85% bandwidth with overall measured insertion loss less than 1.5dB and return loss better than 15dB.


title: Synthetic Data Generation Capabilties for Testing Data Mining Tools

abstract: Recently, due to commercial success of data mining tools, there has been much attention to extracting hidden information from large databases to predict security problems and terrorist threats. The security applications are somewhat more complicated than commercial applications due to (i) lack of sufficient specific knowledge on what to look for, (ii) R&D labs developing these tools are not able to easily obtain sensitive information due to security, privacy or cost issues. Tools developed for security applications require substantially more testing and revisions in order to prevent costly errors. This paper describes a platform for the generation of realistic synthetic data that can facilitate the development and testing of data mining tools. The original applications for this platform were people information and credit card transaction data sets. In this paper, we introduce a new shipping container application that can support the testing of data mining tools developed for port security.


title: MOTAG: Moving Target Defense against Internet Denial of Service Attacks

abstract: Distributed Denial of Service (DDoS) attacks still pose a significant threat to critical infrastructure and Internet services alike. In this paper, we propose MOTAG, a moving target defense mechanism that secures service access for authenticated clients against flooding DDoS attacks. MOTAG employs a group of dynamic packet indirection proxies to relay data traffic between legitimate clients and the protected servers. Our design can effectively inhibit external attackers' attempts to directly bombard the network infrastructure. As a result, attackers will have to collude with malicious insiders in locating secret proxies and then initiating attacks. However, MOTAG can isolate insider attacks from innocent clients by continuously "moving" secret proxies to new network locations while shuffling client-to-proxy assignments. We develop a greedy shuffling algorithm to minimize the number of proxy re- allocations (shuffles) while maximizing attack isolation. Simulations are used to investigate MOTAG's effectiveness on protecting services of different scales against intensified DDoS attacks.


title: Robust Zero Placement for Motion Control of Lightly Damped Systems

abstract: A unified tuning rule for the motion profiles is newly proposed to reduce the residual vibration. For several motion profiles, including trapezoidal velocity profile, S-curve, and asymmetric S-curve, the characteristics and tuning rules are analytically formulated using the time- and Laplace-domain approaches. By adjusting the tuning parameters to place the zeros of the motion profile on a vibratory pole of the system, the residual vibration can be minimized. In particular, the placement of multiple zeros significantly improves the robust performance against modeling errors, owing to robust pole-zero cancellation. By experiments, the efficacy of the proposed approach is validated.


title: On-road vehicle detection using evolutionary Gabor filter optimization

abstract: Robust and reliable vehicle detection from images acquired by a moving vehicle is an important problem with numerous applications including driver assistance systems and self-guided vehicles. Our focus in this paper is on improving the performance of on-road vehicle detection by employing a set of Gabor filters specifically optimized for the task of vehicle detection. This is essentially a kind of feature selection, a critical issue when designing any pattern classification system. Specifically, we propose a systematic and general evolutionary Gabor filter optimization (EGFO) approach for optimizing the parameters of a set of Gabor filters in the context of vehicle detection. The objective is to build a set of filters that are capable of responding stronger to features present in vehicles than to nonvehicles, therefore improving class discrimination. The EGFO approach unifies filter design with filter selection by integrating genetic algorithms (GAs) with an incremental clustering approach. Filter design is performed using GAs, a global optimization approach that encodes the Gabor filter parameters in a chromosome and uses genetic operators to optimize them. Filter selection is performed by grouping filters having similar characteristics in the parameter space using an incremental clustering approach. This step eliminates redundant filters, yielding a more compact optimized set of filters. The resulting filters have been evaluated using an application-oriented fitness criterion based on support vector machines. We have tested the proposed framework on real data collected in Dearborn, MI, in summer and fall 2001, using Ford's proprietary low-light camera.


title: A Social – Cognitive Model of Trait and State Levels of Gratitude

abstract: Three studies tested a new model of gratitude, which specified the generative mechanisms linking individual differences (trait gratitude) and objective situations with the amount of gratitude people experience after receiving aid (state gratitude). In Study 1, all participants (N = 253) read identical vignettes describing a situation in which they received help. People higher in trait gratitude made more positive beneficial appraisals (seeing the help as more valuable, more costly to provide, and more altruistically intended), which fully mediated the relationship between trait and state levels of gratitude. Study 2 (N = 113) replicated the findings using a daily process study in which participants reported on real events each day for up to14 days. In Study 3, participants (N = 200) read vignettes experimentally manipulating objective situations to be either high or low in benefit. Benefit appraisals were shown to have a causal effect on state gratitude and to mediate the relationship between different prosocial situations and state gratitude. The 3 studies demonstrate the critical role of benefit appraisals in linking state gratitude with trait gratitude and the objective situation.


title: My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013

abstract: Deep Learning has attracted significant attention in recent years. Here I present a brief overview of my first Deep Learner of 1991, and its historic context, with a timeline of Deep Learning highlights.


title: Zebrafish tracking using convolutional neural networks

abstract: Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.


Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension


title: A New Dual-Channel Resonant Gate Drive Circuit for Low Gate Drive Loss and Low Switching Loss

abstract: At high-frequency applications, the gate drive loss of the power metal oxide semiconductor field-effect transistor (MOSFET) becomes quite significant. A new dual-channel low side resonant gate drive circuit is proposed in this paper. The proposed drive circuit can provide two symmetrical drive signals for driving two MOSFETs. It charges and discharges the MOSFET gate capacitor with a constant current source. Both gate drive loss and, more importantly, switching loss can be reduced significantly. The proposed resonant gate drive circuit can be used to drive the synchronous MOSFETs in a current doubler or full-wave rectifier configuration. It can also be used to drive the primary MOSFETs in push-pull converters. Analysis, computer simulation, and experimental results show that significant power loss reduction is achieved by the proposed circuit.


title: Harnessing ISA diversity: design of a heterogeneous-ISA chip multiprocessor

abstract: Heterogeneous multicore architectures have the potential for high performance and energy efficiency. These architectures may be composed of small power-efficient cores, large high-performance cores, and/or specialized cores that accelerate the performance of a particular class of computation. Architects have explored multiple dimensions of heterogeneity, both in terms of micro-architecture and specialization. While early work constrained the cores to share a single ISA, this work shows that allowing heterogeneous ISAs further extends the effectiveness of such architectures This work exploits the diversity offered by three modern ISAs: Thumb, x86-64, and Alpha. This architecture has the potential to outperform the best single-ISA heterogeneous architecture by as much as 21%, with 23% energy savings and a reduction of 32% in Energy Delay Product.


title: General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline

abstract: This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platform as "Freesound General-Purpose Audio Tagging Challenge". The goal of the task is to build an audio tagging system that can recognize the category of an audio clip from a subset of 41 diverse categories drawn from the AudioSet Ontology. We present the task, the dataset prepared for the competition, and a baseline system.


title: Efficient itinerary planning with category constraints

abstract: We propose a more realistic approach to trip planning for tourist applications by adding category information to points of interest (POIs). This makes it easier for tourists to formulate their preferences by stating constraints on categories rather than individual POIs. However, solving this problem is not just a matter of extending existing algorithms. In our approach we exploit the fact that POIs are usually not evenly distributed but tend to appear in clusters. We develop a group of efficient algorithms based on clustering with guaranteed theoretical bounds. We also evaluate our algorithms experimentally, using real-world data sets, showing that in practice the results are better than the theoretical guarantees and very close to the optimal solution.