title: Laws as Assets: a Possible Solution to the Time Consistency Problem

abstract: This paper presents a new solution to the time-consistency problem that appears capable of enforcing ex ante policy in a variety of settings in which other enforcement mechanisms do not work. The solution involves formulating a law, institution, or agreement that specifies the optimal ex ante policy and that can be sold by successive old generations to successive young generations. Each young generation pays for the law through the payment of taxes. Both old and young generations have an economic incentive to obey the law. For the old generation that owns the law, breaking the law makes the law valueless, and the generation suffers a capital loss. For the young generation the economic advantage of purchasing the existing law exceeds its cost as well as the economic gain from setting up the law.


title: A framework for large-scale evaluation of deep learning for EEG.

abstract: EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into appropriate actions. A recently emerging EEG decoding approach is deep learning with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many different architectures already published. Here we present a novel framework for the large-scale evaluation of different deep-learning architectures on different EEG datasets. This framework comprises (i) a collection of EEG datasets currently comprising 100 examples (recording sessions) from six different classification problems, (ii) a collection of different EEG decoding algorithms, and (iii) a wrapper linking the decoders to the data as well as handling structured documentation of all settings and (hyper-) parameters and statistics, designed to ensure transparency and reproducibility. As an applications example we used our framework by comparing three publicly available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow ConvNet, and EEGNet. We also show how our framework can be used to study similarities and differences in the performance of different decoding methods across tasks. We argue that the deep learning EEG framework as described here could help to tap the full potential of deep learning for BCI applications.


title: Neural Extractive Text Summarization with Syntactic Compression

abstract: Recent neural network approaches to summarization are largely either sentence-extractive, choosing a set of sentences as the summary, or abstractive, generating the summary from a seq2seq model. In this work, we present a neural model for single-document summarization based on joint extraction and compression. Following recent successful extractive models, we frame the summarization problem as a series of local decisions. Our model chooses sentences from the document and then decides which of a set of compression options to apply to each selected sentence. We compute this set of options using discrete compression rules based on syntactic constituency parses; however, our approach is modular and can flexibly use any available source of compressions. For learning, we construct oracle extractive-compressive summaries that reflect uncertainty over our model's decision sequence, then learn both of our components jointly with this supervision. Experimental results on the CNN/Daily Mail and New York Times datasets show that our model achieves the state-of-the-art performance on content selection evaluated by ROUGE. Moreover, human and manual evaluation show that our model's output generally remains grammatical.


title: Querying Distributed RDF Data Sources with SPARQL

abstract: Integrated access to multiple distributed and autonomous RDF data sources is a key challenge for many semantic web applications. As a reaction to this challenge, SPARQL, the W3C Recommendation for an RDF query language, supports querying of multiple RDF graphs. However, the current standard does not provide transparent query federation, which makes query formulation hard and lengthy. Furthermore, current implementations of SPARQL load all RDF graphs mentioned in a query to the local machine. This usually incurs a large overhead in network traffic, and sometimes is simply impossible for technical or legal reasons. To overcome these problems we present DARQ, an engine for federated SPARQL queries. DARQ provides transparent query access to multiple SPARQL services, i.e., it gives the user the impression to query one single RDF graph despite the real data being distributed on the web. A service description language enables the query engine to decompose a query into sub-queries, each of which can be answered by an individual service. DARQ also uses query rewriting and cost-based query optimization to speed-up query execution. Experiments show that these optimizations significantly improve query performance even when only a very limited amount of statistical information is available. DARQ is available under GPL License at http://darq.sf.net/.


title: Service modelling for the Internet of Things

abstract: The Internet of Things envisions a multitude of heterogeneous objects and interactions with the physical environment. The functionalities provided by these objects can be termed as ‘real-world services’ as they provide a near real-time state of the physical world. A structured, machine-processible approach to provision such real-world services is needed to make heterogeneous physical objects accessible on a large scale and to integrate them with the digital world. This paper presents a semantic modeling approach for different components in an IoT framework. It is also discussed how the model can be integrated into the IoT framework by using automated association mechanisms with physical entities and how the data can be discovered using semantic search and reasoning mechanisms.


title: Pneumatic Reel Actuator: Design, modeling, and implementation

abstract: We present the design, modeling, and implementation of a novel pneumatic actuator, the Pneumatic Reel Actuator (PRA). The PRA is highly extensible, lightweight, capable of operating in compression and tension, compliant, and inexpensive. An initial prototype of the PRA can reach extension ratios greater than 16:1, has a force-to-weight ratio over 28:1, reach speeds of 0.87 meters per second, and can be constructed with parts totaling less than $4 USD. We have developed a model describing the actuator and have conducted experiments characterizing the actuator's performance in regards to force, extension, pressure, and speed. We have implemented two parallel robotic applications in the form of a three degree of freedom robot arm and a tetrahedral robot.


title: RFID Light Bulb: Enabling Ubiquitous Deployment of Interactive RFID Systems

abstract: Radio-Frequency Identification (RFID) technology has the potential to provide inexpensive, wireless, battery-free connectivity and interactivity for objects that are traditionally not instrumented. However, these systems have not seen widespread deployment outside warehouses and supply chains, owing to the complexity of installing bulky RFID readers, antennas, and their supporting power and network infrastructure. In this work, we leverage advances in semiconductor optics, RF antenna design and system integration to create a hybrid RFID reader and smart LED lamp, in the form factor of a standard light bulb. This makes deploying RFID readers literally as easy as screwing in a light bulb. We explore the home-scale RFID interactions enabled by these smart bulbs, including infrastructure monitoring, localization and guided navigation, and standalone lighting effects.


title: Going wireless: behavior & practice of new mobile phone users

abstract: We report on the results of a study in which 19 new mobile phone users were closely tracked for the first six weeks after service acquisition. Results show that new users tend to rapidly modify their perceptions of social appropriateness around mobile phone use, that actual nature of use frequently differs from what users initially predict, and that comprehension of service-oriented technologies can be problematic. We describe instances and features of mobile telephony practice. When in use, mobile phones occupy multiple social spaces simultaneously, spaces with norms that sometimes conflict: the physical space of the mobile phone user and the virtual space of the conversation.


title: Network structure and patterns of information diversity on Twitter

abstract: Social media have great potential to support diverse information sharing, but there is widespread concern that platforms like Twitter do not result in communication between those who hold contradictory viewpoints. Because users can choose whom to follow, prior research suggests that social media users exist in “echo chambers” or become polarized. We seek evidence of this in a complete cross section of hyperlinks posted on Twitter, using previously validated measures of the political slant of news sources to study information diversity. Contrary to prediction, we find that the average account posts links to more politically moderate news sources than the ones they receive in their own feed. However, members of a tiny network core do exhibit cross-sectional evidence of polarization and are responsible for the majority of tweets received overall due to their popularity and activity, which could explain the widespread perception of polarization on social media.


title: The Employment Effects of Credit Market Disruptions: Firm-level Evidence from the 2008–9 Financial Crisis

abstract: This article investigates the effect of bank lending frictions on employment outcomes. I construct a new data set that combines information on banking relationships and employment at 2,000 nonfinancial firms during the 2008-9 crisis. The article first verifies empirically the importance of banking relationships, which imply a cost to borrowers who switch lenders. I then use the dispersion in lender health following the Lehman crisis as a source of exogenous variation in the availability of credit to borrowers. I find that credit matters. Firms that had precrisis relationships with less healthy lenders had a lower likelihood of obtaining a loan following the Lehman bankruptcy, paid a higher interest rate if they did borrow, and reduced employment by more compared to precrisis clients of healthier lenders. Consistent with frictions deriving from asymmetric information, the effects vary by firm type. Lender health has an economically and statistically significant effect on employment at small and medium firms, but the data cannot reject the hypothesis of no effect at the largest or most transparent firms. Abstracting from general equilibrium effects, I find that the withdrawal of credit accounts for between one-third and one-half of the employment decline at small and medium firms in the sample in the year following the Lehman bankruptcy. JEL Codes: E24, E44, G20. Copyright 2014, Oxford University Press.


title: Algorithms, Graph Theory, and Linear Equations in Laplacian Matrices

abstract: The Laplacian matrices of graphs are fundamental. In addition to facilitating the application of linear algebra to graph theory, they arise in many practical problems. In this talk we survey recent progress on the design of provably fast algorithms for solving linear equations in the Laplacian matrices of graphs. These algorithms motivate and rely upon fascinating primitives in graph theory, including low-stretch spanning trees, graph sparsifiers, ultra-sparsifiers, and local graph clustering. These are all connected by a definition of what it means for one graph to approximate another. While this definition is dictated by Numerical Linear Algebra, it proves useful and natural from a graph theoretic perspective. Mathematics Subject Classification (2010). Primary 68Q25; Secondary 65F08.


title: Understanding Membership Inferences on Well-Generalized Learning Models

abstract: Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model. Prior work has shown that the attack is feasible when the model is overfitted to its training data or when the adversary controls the training algorithm. However, when the model is not overfitted and the adversary does not control the training algorithm, the threat is not well understood. In this paper, we report a study that discovers overfitting to be a sufficient but not a necessary condition for an MIA to succeed. More specifically, we demonstrate that even a well-generalized model contains vulnerable instances subject to a new generalized MIA (GMIA). In GMIA, we use novel techniques for selecting vulnerable instances and detecting their subtle influences ignored by overfitting metrics. Specifically, we successfully identify individual records with high precision in real-world datasets by querying black-box machine learning models. Further we show that a vulnerable record can even be indirectly attacked by querying other related records and existing generalization techniques are found to be less effective in protecting the vulnerable instances. Our findings sharpen the understanding of the fundamental cause of the problem: the unique influences the training instance may have on the model.


title: Cross-lingual entity matching and infobox alignment in Wikipedia

abstract: Wikipedia has grown to a huge, multi-lingual source of encyclopedic knowledge. Apart from textual content, a large and ever-increasing number of articles feature so-called infoboxes, which provide factual information about the articles' subjects. As the different language versions evolve independently, they provide different information on the same topics. Correspondences between infobox attributes in different language editions can be leveraged for several use cases, such as automatic detection and resolution of inconsistencies in infobox data across language versions, or the automatic augmentation of infoboxes in one language with data from other language versions. We present an instance-based schema matching technique that exploits information overlap in infoboxes across different language editions. As a prerequisite we present a graph-based approach to identify articles in different languages representing the same real-world entity using (and correcting) the interlanguage links in Wikipedia. To account for the untyped nature of infobox schemas, we present a robust similarity measure that can reliably quantify the similarity of strings with mixed types of data. The qualitative evaluation on the basis of manually labeled attribute correspondences between infoboxes in four of the largest Wikipedia editions demonstrates the effectiveness of the proposed approach.


title: Metaheuristic and Evolutionary Methods for Feature Selection in Sentiment Analysis (a Comparative Study)

abstract: With the accelerated evolution of World Wide Web and the widespread of on-line collaborative tools, there is an increasing care towards automatic tools for Sentiment Analysis to provide a quantitative measure of “positivity” or “negativity” about opinions or social comments. But there are many challenges faced the sentiment analysis and evaluation process. These challenges become obstacles in analyzing the accurate meaning of sentiments and detecting the suitable sentiment polarity. The most important challenge is to identify and extract features we will use in our model. In this paper, we hand over an overview of the last spread out techniques for features selection in sentiment analysis based on metaheuristics and evolutionary algorithms as a quick reference guide in the choice of the most suitable methods for solving a specific problem in the sentiment analysis field more precisely in the feature selection stage.


title: Performance improvement of synchronous reluctance motors: A review

abstract: Synchronous Reluctance motors can replace the more commonly used, induction, switched reluctance and permanent magnet motors. They have been shown to outperform similarly dimensioned induction motors in efficiency, torque and power density. They are simple and cheap to construct and require simple control. Such a motor would be ideal for wide spread use in Africa, which currently faces a power deficit, considering that motors account for about 43% of global electric power consumption. The use of synchronous reluctance motors in Electric Vehicles may contribute to the production of more efficient, heavier load and longer range vehicles. This would accelerate adoption of Electric Vehicles and it would help wean the world off fossil fuels. The synchronous reluctance motor has disadvantages of high torque ripple and poor power factor. A radical redesign of the rotor geometry and simultaneous optimisation of the rotor and the stator may significantly improve its performance.


title: Credit Scoring Models Using Soft Computing Methods: A Survey

abstract: During the last fifteen years, soft computing methods have been successfully applied in building powerful and flexible credit scoring models and have been suggested to be a possible alternative to statistical methods. In this survey, the main soft computing methods applied in credit scoring models are presented and the advantages as well as the limitations of each method are outlined. The main modelling issues are discussed especially from the data mining point of view. The study concludes with a series of suggestions of other methods to be investigated for credit scoring modelling.


title: Optimizing Data Placement of MapReduce on Ceph-Based Framework under Load-Balancing Constraint

abstract: Ceph has been widely used as a distributed object store and file system due to its high availability, reliability and scalability. Strategies of data placements in Ceph composed of heterogeneous clusters can greatly affect the system performance and load balancing. For a given application, it is critical to find the optimal data placement in Ceph, such that the completion time of the application can be minimized under the load-balancing constraint. This paper presents a novel Ceph-based framework that integrally considers the load balancing and the heterogeneities, including the computational capacity and the network bandwidth. The presented framework is suitable for the applications based on the principle of moving computation rather than data across clusters, such as MapReduce. According to the Ceph-based framework and the properties of MapReduce, we formulate the Mixed Integer Linear Programming (MILP) to obtain the optimal data placement. However, because of the large computational complexity of MILP, we devise an efficient algorithm to obtain the near-optimal solutions. The experimental results show that the proposed algorithm can achieve up to 25.6% improvement on system performance, compared with the original strategy implemented in Ceph.


Developmental trends in face recognition


title: Building Recognition From Multi-Aspect High-Resolution InSAR Data in Urban Areas

abstract: The improved ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors suggests utilizing SAR data for the analysis of urban areas. The appearance of buildings in SAR or interferometric SAR (InSAR) data is characterized by the consequences of the inherent oblique scene illumination, such as layover, occlusion by radar shadow, and multipath signal propagation. Therefore, particularly in dense built-up areas, building reconstruction is often impossible from a single SAR or InSAR measurement alone. But, the reconstruction quality can be significantly improved by a combined analysis of multi-aspect data. In this paper, two approaches are proposed to detect and reconstruct buildings of different size from multi-aspect high-resolution InSAR data sets. Both approaches focus on the recognition of buildings supported by knowledge-based analysis considering the mentioned SAR-specific effects observed in urban areas. Building features are extracted independently for each direction from the magnitude and phase information of the interferometric data. Initial primitives are segmented and afterward projected from slant-range into the world coordinate system. From the fused set of primitives of both flight directions, building hypotheses are generated. The first approach exploits the frequently observed lines of bright double-bounce scattering, which are used for building reconstruction in residential districts. In the case of larger buildings, such as industrial halls, often additional features of roof and facade elements are visible. Therefore, in a second approach, extended buildings are extracted by grouping primitives of different kinds. The two approaches are demonstrated in an urban environment for an InSAR data set, which has spatial resolution of about 30 cm and was taken from two orthogonal flight directions.


title: Low-cost backdrivable motor control based on feed-forward/feed-back friction compensation

abstract: A low-cost but accurate and backdrivable motor control system running on a DC motor and a harmonic drive gear is proposed. It compensates internal friction of the gear and the counter-electromotive torque by combining a model-based feed-forward method and a disturbance observer using a cheap torque sensor. A complementary use of those techniques lowers requirements to their performances i.e. precision, bandwidth, etc., while it is equipped with a flexible property against the external torque. A 2-DOF servo controller is also built upon the system in order to simultaneously achieve smooth responses and robust convergences to the reference.


title: Chung,” Metrics for Software Adaptability

abstract: This paper discusses our initial work in developing metrics for software adaptability. In this paper we have developed several metrics for software adaptability. One of the advantages of the metrics that we have developed is that they are applicable at the architectural level. Since architecture development is the first stage of the design process, the extent to which the architecture is adaptable will determine the adaptability of the final software. Hence the metrics in this paper will help determine the extent to which the final software will be adaptable as well.


title: Aligning two sequences within a specified diagonal band

abstract: We describe an algorithm for aligning two sequences within a diagonal band that requires only O(NW) computation time and O(N) space, where N is the length of the shorter of the two sequences and W is the width of the band. The basic algorithm can be used to calculate either local or global alignment scores. Local alignments are produced by finding the beginning and end of a best local alignment in the band, and then applying the global alignment algorithm between those points. This algorithm has been incorporated into the FASTA program pacckage, where it has decreased the amount of memory required to calculate local alignments from O(NW) to O(N) and decreased the time required to calculate optimized scores for every sequence in a protein sequence database by 40%


title: Cloud computing-based map-matching for transportation data center

abstract: Propose a leapfrog method to improve the efficiency of map-matching algorithm.Use MapReduce to adapt the serial map-matching algorithm for cloud computing environment.Propose a privacy-aware map-matching model over hybrid clouds. Transportation data center has recently become a common practice of modern integrated transportation management in major cities of China. Being the convergence center of large-scale multi-source vehicle tracking data, it caused great challenge on GPS map-matching efficiency and privacy protection. In this paper, we propose a secure parallel map-matching system based on Cloud Computing technology to meet the demand of transportation data center. The main contributions are as follows: (1) we propose a leapfrog method to improve the efficiency of traditional serial map-matching algorithm on the increasingly common high sampling rate GPS data; (2) we adapt the serial leapfrog map-matching algorithm for cloud computing environment by reforming it in the MapReduce paradigm; (3) we propose a privacy-aware map-matching model over hybrid clouds to realize the sensitive GPS data protection. We implemented the proposed map-matching system in the hadoop platform and tested its performance with a large-scale vehicle tracking dataset, which exceeds 100 billion records. The experimental results show that our approach is highly efficient and effective on massive vehicle tracking data processing.


title: Future Internet of Things Architecture: Like Mankind Neural System or Social Organization Framework?

abstract: Internet of things (IoT) is fascinating; its future architecture is still under construction. Based on the analysis on the basic and essential characters of IoT, this paper deals with Future IoT architecture in two aspects: Unit IoT and Ubiquitous IoT. Focusing on a special application, the architecture of the Unit IoT is built from man like neural network (MLN) model and its modified model. Ubiquitous IoT refers to the global IoT or the integration of multiple Unit IoTs with "ubiquitous" characters, and its architecture employs social organization framework (SOF) model. The models for Future IoT are not only helpful to interpret the relationship between IoT and reality world, but also beneficial to the implementation of IoT in its current development milieu.


title: Circadian clocks, brain function, and development

abstract: Circadian clocks are temporal interfaces that organize biological systems and behavior to dynamic external environments. Components of the molecular clock are expressed throughout the brain and are centrally poised to play an important role in brain function. This paper focuses on key issues concerning the relationship among circadian clocks, brain function, and development, and discusses three topic areas: (1) sleep and its relationship to the circadian system; (2) systems development and psychopathology (spanning the prenatal period through late life); and (3) circadian factors and their application to neuropsychiatric disorders. We also explore circadian genetics and psychopathology and the selective pressures on the evolution of clocks. Last, a lively debate is presented on whether circadian factors are central to mood disorders. Emerging from research on circadian rhythms is a model of the interaction among genes, sleep, and the environment that converges on the circadian clock to influence susceptibility to developing psychopathology. This model may lend insight into effective treatments for mood disorders and inform development of new interventions.


title: Investigation Into the Polarization of Asymmetrical- Feed Triangular Microstrip Antennas and its Application to Reconfigurable Antennas

abstract: A single-feed equilateral-triangular microstrip antenna has been designed with truncated corners for diverse radiation polarizations. The proposed antenna is excited by a microstrip line placed parallel to the oblique side of an equilateral-triangular patch. The antenna exhibits linear polarization (LP), left-hand circular polarization, or right-hand circular polarization (RHCP) depending on whether the three identically sized corners of the triangular patch are cut. A novel reconfigurable microstrip antenna with selectable polarization is proposed based on this first antenna. This second antenna has a simple structure consisting of a corner-truncated triangular patch, a small triangular conductor, an asymmetrical microstrip feed line, and a biased PIN diode between the patch and the triangular conductor. It produces LP or RHCP depending on the bias voltage. The measured results show that this antenna has a low cross-polarization level when operated in the linear state and a good axial ratio in the circular state.


title: Introducing domain-specific language implementation using web service-oriented technologies

abstract: Several advantages have been documented that suggest Domain-Specific Languages DSLs have the potential to improve productivity, reliability, maintainability and portability in some specialized domains. However, several key challenges still remain. In particular, the extension and evolution of both DSL syntax and semantics still suffer due to the limitations related to the current state-of-the-art implementation techniques. Such techniques also lack interoperable capabilities among base languages and limited tool support. As changes of domain concepts are omnipresent and more base languages may support DSL implementation, the aforementioned limitations may be no longer tolerable, and hence a new implementation technique to DSL development is desired. This paper implements six DSL case studies representing imperative, declarative and hybrid categories to validate the feasibility of utilizing Service-Oriented Architecture SOA for DSL implementation. Such case studies also highlight that the advantages of SOA i.e., ease of evolution/extension, interoperability and tool support can be retained under the context of DSL development. The paper concludes with the discussion of additional findings, both positive and negative: the SOA-based approach improves modularization at the lexical, syntactical and semantic levels and delegates tokenization/parsing to the underlying WS-BPEL engine; yet, the usability, resource utilization, security, and flexibility of the SOA-based DSLs are degraded, which requires more future work in this unique area that spans SOA and DSLs.


title: The influence of aesthetics in usability testing: the case of dual-domain products

abstract: An experimental study examined whether the effects of aesthetic appeal on usability test outcomes are moderated by usage domain. The aesthetic appeal of a cell phone was experimentally manipulated in both home- and work-based usage domains. The two usage domains were modeled in a usability laboratory. 60 participants completed a series of typical cell phone user tasks. Dependent measures such as performance, perceived usability, and emotion were taken. The results showed that aesthetic appeal had a positive effect on perceived usability but a negative effect on performance. The effects of aesthetic appeal on usability test outcomes were not moderated by usage domain. The results of this study imply that it may be sufficient to test dual-domain products in only one of their usage domains.


title: An integrated architecture for autonomous vehicles simulation

abstract: Modeling and simulation tools are being increasingly acclaimed in the research field of autonomous vehicles systems, as they provide suitable test beds for the development and evaluation of such complex systems. However, these tools still do not account for some integration capabilities amongst several state-of-the-art Intelligent Transportation Systems, e.g. to study autonomous driving behaviors in human-steered urban traffic scenarios, which are crucial to the Future Urban Transport paradigm. In this paper we describe the modeling and implementation of an integration architecture of two types of simulators, namely a robotics and a traffic simulator. This integration should enable autonomous vehicles to be deployed in a rather realistic traffic flow as an agent entity (on the traffic simulator), at the same time it simulates all its sensors and actuators (on the robotics counterpart). Also, the statistical tools available in the traffic simulator will allow practitioners to infer what kind of advantages such a novel technology will bring to our everyday's lives. Furthermore, an architecture for the integration of the aforementioned simulators is proposed and implemented in the light of the most desired features of such software environments. To assess the usefulness of the platform architecture towards the expected realistic simulation facility, a comprehensive system evaluation is performed and critically reviewed, leveraging the feasibility of the integration. Further developments and future perspectives are also suggested.


title: A Comparative Study of online P2P Lending in the USA and

abstract: Peer-to-Peer (P2P) lending provides online users an innovative loaning and investment vehicle without the intermediation of financial institutions. However, the research on online P2P lending is still scarce. In this study we review relevant literature and conduct a comparative study of online P2P lending practices in the USA and China. We find that two categories of credit information, “hard” and “soft” information, may have profound influences on lending outcomes in both countries, but lenders in China is more reliable on “soft” information. This study provides valuable insights for future research and practices and enriches the understanding of online P2P lending across countries.


title: Optimal Action Extraction for Random Forests and Boosted Trees

abstract: Additive tree models (ATMs) are widely used for data mining and machine learning. Important examples of ATMs include random forest, adaboost (with decision trees as weak learners), and gradient boosted trees, and they are often referred to as the best off-the-shelf classifiers. Though capable of attaining high accuracy, ATMs are not well interpretable in the sense that they do not provide actionable knowledge for a given instance. This greatly limits the potential of ATMs on many applications such as medical prediction and business intelligence, where practitioners need suggestions on actions that can lead to desirable outcomes with minimum costs. To address this problem, we present a novel framework to post-process any ATM classifier to extract an optimal actionable plan that can change a given input to a desired class with a minimum cost. In particular, we prove the NP-hardness of the optimal action extraction problem for ATMs and formulate this problem in an integer linear programming formulation which can be efficiently solved by existing packages. We also empirically demonstrate the effectiveness of the proposed framework by conducting comprehensive experiments on challenging real-world datasets.


title: Survival of the unfittest : why the worst infrastructure gets built — and what we can do about it

abstract: The article first describes characteristics of major infrastructure projects. Second, it documents a much neglected topic in economics: that ex ante estimates of costs and benefits are often very different from actual ex post costs and benefits. For large infrastructure projects the consequence is cost overruns, benefit shortfalls, and the systematic underestimation of risks. Third, implications for cost-benefit analysis are described, including that such analysis is not to be trusted for major infrastructure projects. Fourth, the article uncovers the causes of this state of affairs in terms of perverse incentives that encourage promoters to underestimate costs and overestimate benefits in the business cases for their projects. But the projects that are made to look best on paper are the projects that amass the highest cost overruns and benefit shortfalls in reality. The article depicts this situation as "survival of the un-fittest." Fifth, the article sets out to explain how the problem may be solved, with a view to arriving at more efficient and more democratic projects, and avoiding the scandals that often accompany major infrastructure investments. Finally, the article identifies current trends in major infrastructure development. It is argued that a rapid increase in stimulus spending combined with more investments in emerging economies combined with more spending on information technology is catapulting infrastructure investment from the frying pan into the fire.


title: Compact Printed UWB Diversity Slot Antenna With 5.5-GHz Band-Notched Characteristics

abstract: A novel compact printed ultrawideband (UWB) slot antenna for MIMO/diversity applications is presented in this letter. The antenna consists of two modified coplanar waveguides (CPWs) feeding staircase-shaped radiating elements for orthogonal radiation patterns, where a rectangle stub is placed at 45° between the CPW to ensure high isolations. By etching two split-ring resonator (SRR) slots on the radiators respectively, the band-notched property is achieved. Results show that this antenna meets a 10-dB impedance bandwidth and 15 dB isolation from 2.5 to 12 GHz, with a notched band at 5.5 GHz. The measurements of the radiation patterns and envelope correlation coefficient (ECC) denote that the antenna is suitable for multiple-input-multiple-output (MIMO)/diversity systems. Furthermore, it has a compact size of 48 × 48 mm2, which has been significantly reduced, and it is a good candidate for portable devices.


title: How to Exploit the Digitalization Potential of Business Processes

abstract: Process improvement is the most value-adding activity in the business process management (BPM) lifecycle. Despite mature knowledge, many approaches have been criticized to lack guidance on how to put process improvement into practice. Given the variety of emerging digital technologies, organizations not only face a process improvement black box, but also high uncertainty regarding digital technologies. This paper thus proposes a method that supports organizations in exploiting the digitalization potential of their business processes. To achieve this, action design research and situational method engineering were adopted. Two design cycles involving practitioners (i.e., managers and BPM experts) and end-users (i.e., process owners and participants) were conducted. In the first cycle, the method’s alpha version was evaluated by interviewing practitioners from five organizations. In the second cycle, the beta version was evaluated via real-world case studies. In this paper, detailed results of one case study, which was conducted at a semiconductor manufacturer, are included.


Natural Language Inference with Definition Embedding Considering Context On the Fly


EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling


title: A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

abstract: The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT system effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML /DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.


title: Impact of Feature Selection Techniques for Tweet Sentiment Classification.

abstract: Sentiment analysis of tweets is a powerful application of mining social media sites that can be used for a variety of social sensing tasks. Common feature engineering techniques frequently result in a large numbers of features being generated to represent tweets. Many of these features may degrade classifier performance and increasing computational cost. Feature selection techniques can be used to select an optimal subset of features, reducing the computational cost of training a classifier, and potentially improving classification performance. Despite its benefits, feature selection has received little attention within the tweet sentiment domain. We study the impact of ten filter-based feature selection techniques on classification performance, using ten feature subset sizes and four different learners. Our experimental results demonstrate that feature selection can significantly improve classification performance in comparison to not using feature selection. Additionally, both choice of ranker and feature subset size significantly impact classifier performance. To the best of our knowledge, this is the first work which extensively studies feature selections effect on tweet sentiment classification.


title: MANAGING AND MINING UNCERTAIN DATA MANAGING AND MINING UNCERTAIN DATA Edited by

abstract: Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.


title: 100Gb/s ethernet chipsets in 65nm CMOS technology

abstract: This paper presents a complete design of 100GbE chipsets including gearbox TX/RX, LDD and TIA/LA arrays. Figure 7.3.1 shows the architecture, where 10×10Gb/s input data is serialized into 4×25Gb/s bit stream by a 10:4 serializer (i.e., gearbox TX). A 4-element LDD array subsequently drives 4 laser diodes, emitting 850nm light into 4 multimode fibers (MMFs). After traveling over 100m, these optical signals are captured and transformed into electrical domain by means of photo diodes (PDs) and a TIA/LA array. A 4:10 deserializer (gear-box RX) recovers the clock and data, and restores the data sequences into 10×10Gb/s outputs. In applications, gearbox TRX and optical frontends (i.e., LDD and TIA/LA arrays) may be separated by several inches in order to fulfill system-level integration.


title: The self system in reciprocal determinism.

abstract: A prefabricated T-shaped blank of flexible sheet material with a pair of screened openings spaced apart from one another in the longitudinal portion of the blank is foldable along spaced parallel fold lines into a fly trap structure composed of outer and inner arch-shaped walls with screened skylights therein, the inner screened skylight having a small exit port therein. The structure thus formed has a floor panel with approximately arch-shaped side wings foldable upward therefrom into end closures for the structure, these wings containing small entrance doors near their fold edges.


RE: Body Mass Index, PAM50 Subtype, and Outcomes in Node-Positive Breast Cancer: CALGB 9741.


title: Environment-Driven Lexicon Induction for High-Level Instructions

abstract: We focus on the task of interpreting complex natural language instructions to a robot, in which we must ground high-level commands such as microwave the cup to low-level actions such as grasping. Previous approaches that learn a lexicon during training have inadequate coverage at test time, and pure search strategies cannot handle the exponential search space. We propose a new hybrid approach that leverages the environment to induce new lexical entries at test time, even for new verbs. Our semantic parsing model jointly reasons about the text, logical forms, and environment over multi-stage instruction sequences. We introduce a new dataset and show that our approach is able to successfully ground new verbs such as distribute, mix, arrange to complex logical forms, each containing up to four predicates.


title: Magellan: Toward Building Entity Matching Management Systems

abstract: Entity matching (EM) has been a long-standing challenge in data management. Most current EM works focus only on developing matching algorithms. We argue that far more efforts should be devoted to building EM systems. We discuss the limitations of current EM systems, then present as a solution Magellan, a new kind of EM systems. Magellan is novel in four important aspects. (1) It provides how-to guides that tell users what to do in each EM scenario, step by step. (2) It provides tools to help users do these steps; the tools seek to cover the entire EM pipeline, not just matching and blocking as current EM systems do. (3) Tools are built on top of the data analysis and Big Data stacks in Python, allowing Magellan to borrow a rich set of capabilities in data cleaning, IE, visualization, learning, etc. (4) Magellan provides a powerful scripting environment to facilitate interactive experimentation and quick "patching" of the system. We describe research challenges raised by Magellan, then present extensive experiments with 44 students and users at several organizations that show the promise of the Magellan approach.


title: The "Parallel Vectors" operator-a vector field visualization primitive

abstract: In this paper we propose an elementary operation on a pair of vector fields as a building block for defining and computing global line-type features of vector or scalar fields. While usual feature definitions often are procedural and therefore implicit, our operator allows precise mathematical definitions. It can serve as a basis for comparing feature definitions and for reuse of algorithms and implementations. Applications focus on vortex core methods.


title: Optimization of the ultrasonic excitation stage

abstract: The ultrasonic transducer excitation circuits have been investigated. The system structure is presented where excitation circuit is planned to be used. The possible excitation solutions are presented and discussed. The result of impedance measurement of ultrasonic transducer has been used to derive the Butterworth-Van Dyke electrical model.Then this model was used for PSpice modeling of candidate topologies. The chosen circuit was simulated and experimentally investigated. In this article a structure of ultrasound generator is analyzed.


title: Decision making using fuzzy logic for stock trading

abstract: This paper present the use of fuzzy if-then rules for a decision support system in stock trading. The three following linguistic variables well be the input for the rule : view from the expert, earning — per-share and price to earnings ratio. The purpose of this rule is to assist investors making a decision on their shares. The investors need to make a right decision to gain a high profit in stock trading. Stock market is a complex environment. Therefore by using this Artificial Intelligence (AI) application which is Fuzzy Logic (FL) can make it simpler as well as giving benefits to investors. Most of the earlier research works show that FL can work within stocks environment.


title: Audio Signal Classification: History and Current Techniques

abstract: Audio signal classification (ASC) consists of extracting relevant features from a sound, and of using these features to identify into which of a set of classes the sound is most likely to fit. The feature extraction and grouping algorithms used can be quite diverse depending on the classification domain of the application. This paper presents background necessary to understand the general research domain of ASC, including signal processing, spectral analysis, psychoacoustics and auditory scene analysis. Also presented are the basic elements of classification systems. Perceptual and physical features are discussed, as well as clustering algorithms and analysis duration. Neural nets and hidden Markov models are discussed as they relate to ASC. These techniques are presented with an overview of the current state of the ASC research literature.


title: How Sustainable is COBIT 5? Insights from Theoretical Analysis and Empirical Survey Data

abstract: Increasing consideration of the concept of sustainability within information technology (IT) organizations and information systems (IS) management has lead to rising challenges regarding the application of existing, non- or partially sustainable IT process models. Although IT reference models exist within the scope of sustainable IS management, the integration of sustainability aspects into well-established IT reference models of IS management and IT governance still lacks theoretical foundation. The purpose of this paper is to explore the specificities of sustainability in the current COBIT 5 process reference model. Based on an argumentative-deductive analysis of COBIT 5, enriched with results of a survey, we examine the significance and specificities of sustainability in COBIT 5 from the user perspective. Our findings provide valuable insights referring to sustainability-related deficits of COBIT 5. Furthermore, this paper can serve as a theoretical basis for further research that eventually takes a sustainability-oriented adjustment of COBIT 5 into account.


title: Fixing the Infix: Unsupervised Discovery of Root-and-Pattern Morphology

abstract: We present an unsupervised and language-agnostic method for learning root-and-pattern morphology in Semitic languages. This form of morphology, abundant in Semitic languages, has not been handled in prior unsupervised approaches. We harness the syntactico-semantic information in distributed word representations to solve the long standing problem of root-and-pattern discovery in Semitic languages. Moreover, we construct an unsupervised root extractor based on the learned rules. We prove the validity of learned rules across Arabic, Hebrew, and Amharic, alongside showing that our root extractor compares favorably with a widely used, carefully engineered root extractor: ISRI.


title: Approximate image-based tree-modeling using particle flows

abstract: We present a method for producing 3D tree models from input photographs with only limited user intervention. An approximate voxel-based tree volume is estimated using image information. The density values of the voxels are used to produce initial positions for a set of particles. Performing a 3D flow simulation, the particles are traced downwards to the tree basis and are combined to form twigs and branches. If possible, the trunk and the first-order branches are determined in the input photographs and are used as attractors for particle simulation. The geometry of the tree skeleton is produced using botanical rules for branch thicknesses and branching angles. Finally, leaves are added. Different initial seeds for particle simulation lead to a variety, yet similar-looking branching structures for a single set of photographs.


title: A structural model of technology acceptance

abstract: Orientation: Enterprise resource systems have not always led to significant organisational enhancement and many projects in which these systems have been implemented turn out to be over budget, not on time and unsuccessful. Research purpose: The aim of this study was to test the technology acceptance model within a South African SAP® Enterprise Resource Planning user environment. Motivation for the study: No study could be traced in which the technology acceptance model has been evaluated in the South African context. Research approach, design and method: A cross-sectional survey design was used. The 23-item Technology Acceptance Model Questionnaire was deployed amongst SAP® Enterprise Resource Planning users ( N = 241). Main findings: The results confirmed significant paths from perceived usefulness of the information system to attitudes towards and behavioural intentions to use it. Furthermore, behavioural intention to use the system predicted actual use thereof. Perceived ease of use indirectly affected attitudes towards and behavioural intentions to use via perceived usefulness of the information system. Practical/managerial implications: Practitioners should build user confidence by ensuring the ease of use of a new system, providing relevant education, training and guidance and reiterating its usefulness and future added value to the user’s job and career. Contribution/value-add: This study contributes to scientific knowledge regarding the influence of individuals’ perceptions of information system usage on their attitudes, behavioural intentions and actual use of such a system.


title: End-to-End Saliency Mapping via Probability Distribution Prediction

abstract: Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection. Data-driven methods for training saliency models using eye-fixation data are increasingly popular, particularly with the introduction of large-scale datasets and deep architectures. However, current methods in this latter paradigm use loss functions designed for classification or regression tasks whereas saliency estimation is evaluated on topographical maps. In this work, we introduce a new saliency map model which formulates a map as a generalized Bernoulli distribution. We then train a deep architecture to predict such maps using novel loss functions which pair the softmax activation function with measures designed to compute distances between probability distributions. We show in extensive experiments the effectiveness of such loss functions over standard ones on four public benchmark datasets, and demonstrate improved performance over state-of-the-art saliency methods.


title: Declarative composition and peer-to-peer provisioning of dynamic Web services

abstract: The development of new services through the integration of existing ones has gained a considerable momentum as a means to create and streamline business-to-business collaborations. Unfortunately, as Web services are often autonomous and heterogeneous entities, connecting and coordinating them in order to build integrated services is a delicate and time-consuming task. In this paper, we describe the design and implementation of a system through which existing Web services can be declaratively composed, and the resulting composite services can be executed following a peer-to-peer paradigm, within a dynamic environment. This system provides tools for specifying composite services through. statecharts, data conversion rules, and provider selection, policies. These specifications are then translated into XML documents that can be interpreted by peer-to-peer inter-connected software components, in order to provision the composite service without requiring a central authority.


title: The MAXQ Method for Hierarchical Reinforcement Learning

abstract: This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decomposition of the value function. The MAXQ decomposition has both a procedural semantics—as a subroutine hierarchy—and a declarative semantics—as a representation of the value function of a hierarchical policy. MAXQ unifies and extends previous work on hierarchical reinforcement learning by Singh, Kaelbling, and Dayan and Hinton. Conditions under which the MAXQ decomposition can represent the optimal value function are derived. The paper defines a hierarchical Q learning algorithm, proves its convergence, and shows experimentally that it can learn much faster than ordinary “flat” Q learning. Finally, the paper discusses some interesting issues that arise in hierarchical reinforcement learning including the hierarchical credit assignment problem and non-hierarchical execution of the MAXQ hierarchy.


title: Data Mining: Analysis of student database using Classification Techniques

abstract: Data Analysis can be categorized into two forms. One is used for extracting models describing important classes; another is to predict future trends. Data classification can be used to generate models which are further used to predict the unknown classes. The accuracy of the models can be examined by checking the percentage of correctly classified instance. Lot of classification algorithms is available nowadays. One of the most commonly used algorithms is decision tree because of its simplicity of implementation and easier to understand when compared to other classification algorithms. J48 is the one of the effective classification method. In this paper, J48 algorithm is applied for analyzing student dataset which includes academic year, department, academic grade and job position


Graphical Models with Structured Factors, Neural Factors, and Approximation-aware Training


title: Interaction between Record Matching and Data Repairing

abstract: Central to a data cleaning system are record matching and data repairing. Matching aims to identify tuples that refer to the same real-world object, and repairing is to make a database consistent by fixing errors in the data by using integrity constraints. These are typically treated as separate processes in current data cleaning systems, based on heuristic solutions. This article studies a new problem in connection with data cleaning, namely the interaction between record matching and data repairing. We show that repairing can effectively help us identify matches, and vice versa. To capture the interaction, we provide a uniform framework that seamlessly unifies repairing and matching operations to clean a database based on integrity constraints, matching rules, and master data. We give a full treatment of fundamental problems associated with data cleaning via matching and repairing, including the static analyses of constraints and rules taken together, and the complexity, termination, and determinism analyses of data cleaning. We show that these problems are hard, ranging from NP-complete or coNP-complete, to PSPACE-complete. Nevertheless, we propose efficient algorithms to clean data via both matching and repairing. The algorithms find deterministic fixes and reliable fixes based on confidence and entropy analyses, respectively, which are more accurate than fixes generated by heuristics. Heuristic fixes are produced only when deterministic or reliable fixes are unavailable. We experimentally verify that our techniques can significantly improve the accuracy of record matching and data repairing that are taken as separate processes, using real-life and synthetic data.


title: Monoclonal antibody-induced cytokine-release syndrome.

abstract: Monoclonal antibodies (mAbs) are widely used in anti-inflammatory and tumor therapy. Although effective, mAbs can cause a variety of adverse effects. An important toxicity seen with a few mAbs is cytokine-release syndrome (CRS). These mAbs include: alemtuzumab, muromonab-CD3, rituximab, tosituzumab, CP-870,893, LO-CD2a/BTI-322 and TGN1412. By contrast, over 30 mAbs used clinically are not associated with CRS. In this review, the clinical aspects of CRS, the mAbs associated with CRS, the cytokines involved and putative mechanisms mediating cytokine release will be discussed. This will be followed by a discussion of the poor predictive value of studies in animals and the prospects for creating in vitro screens. Finally, approaches to decreasing the probability of CRS, decreasing the severity or treating CRS, should it occur, will be described.


title: Effect of β-Cyclodextrin Complexation on Solubility and Enzymatic Conversion of Naringin

abstract: In the present paper, the effect of β-cyclodextrin (β-CD) inclusion complexation on the solubility and enzymatic hydrolysis of naringin was investigated. The inclusion complex of naringin/β-CD at the molar ratio of 1:1 was obtained by the dropping method and was characterized by differential scanning calorimetry. The solubility of naringin complexes in water at 37 ± 0.1 °C was 15 times greater than that of free naringin. Snailase-involved hydrolysis conditions were tested for the bioconversion of naringin into naringenin using the univariate experimental design. Naringin can be transformed into naringenin by snailase-involved hydrolysis. The optimum conditions for enzymatic hydrolysis were determined as follows: pH 5.0, temperature 37 °C, ratio of snailase/substrate 0.8, substrate concentration 20 mg·mL−1, and reaction time 12 h. Under the optimum conditions, the transforming rate of naringenin from naringin for inclusion complexes and free naringin was 98.7% and 56.2% respectively, suggesting that β-CD complexation can improve the aqueous solubility and consequently the enzymatic hydrolysis rate of naringin.


title: Review of Heart Disease Prediction System Using Data Mining and Hybrid Intelligent Techniques

abstract: The Healthcare industry generally clinical diagnosis is done mostly by doctor's expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it has become important to categories the research outcomes and provides readers with an overview of the existing heart disease prediction techniques in each category. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. From the study it is observed that Hybrid Intelligent Algorithm improves the accuracy of the heart disease prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are summarized in this paper.


title: A design methodology for the realization of multi-decade baluns at microwave frequencies

abstract: A new methodology is presented for designing baluns exhibiting multi-decade bandwidths at microwave frequencies. Simulations show that resistors terminating the outer transmission line suppress the half-wavelength resonance and greatly extend the bandwidth. Using linear measurements at microwave frequencies, ferrite beads have been shown to behave as resistors with a small reactance, suitable for terminating the outer transmission line. This paper shows that ferrite beads can perform the dual role of improving magnetic coupling at low frequency and suppressing resonance at higher frequencies. The design methodology was applied to produce a balun that operates between 30MHz and 6GHz, displays less than 1dB of power loss up to 4.4GHz, and delivers an impedance transformation of 2∶1.


title: Recent Advances and Industrial Applications of Multilevel Converters

abstract: Multilevel converters have been under research and development for more than three decades and have found successful industrial application. However, this is still a technology under development, and many new contributions and new commercial topologies have been reported in the last few years. The aim of this paper is to group and review these recent contributions, in order to establish the current state of the art and trends of the technology, to provide readers with a comprehensive and insightful review of where multilevel converter technology stands and is heading. This paper first presents a brief overview of well-established multilevel converters strongly oriented to their current state in industrial applications to then center the discussion on the new converters that have made their way into the industry. In addition, new promising topologies are discussed. Recent advances made in modulation and control of multilevel converters are also addressed. A great part of this paper is devoted to show nontraditional applications powered by multilevel converters and how multilevel converters are becoming an enabling technology in many industrial sectors. Finally, some future trends and challenges in the further development of this technology are discussed to motivate future contributions that address open problems and explore new possibilities.


title: The ABCs of solute carriers: physiological, pathological and therapeutic implications of human membrane transport proteins

abstract: The Human Genome Organisation (HUGO) Nomenclature Committee Database provides a list of transporter families of the solute carrier (SLC) gene series (see http://www.gene.ucl.ac.uk/nomenclature/). Currently, it includes 43 families and 298 transporter genes. This special issue features mini-reviews on each of these SLC families written by the experts in each field. A WEB site has been established (http://www.pharmaconference.org/slctable.asp) that gives the latest updates for the SLC families and their members as well as relevant links to gene databases and reviews in the literature. A list of all currently known SLC families, a discussion of additional SLC families and family members as well as a brief summary of non-SLC transporter genes is included in this introduction.


title: Scene Reconstruction and Visualization From Community Photo Collections

abstract: There are billions of photographs on the Internet, representing an extremely large, rich, and nearly comprehensive visual record of virtually every famous place on Earth. Unfortunately, these massive community photo collections are almost completely unstructured, making it very difficult to use them for applications such as the virtual exploration of our world. Over the past several years, advances in computer vision have made it possible to automatically reconstruct 3-D geometry - including camera positions and scene models - from these large, diverse photo collections. Once the geometry is known, we can recover higher level information from the spatial distribution of photos, such as the most common viewpoints and paths through the scene. This paper reviews recent progress on these challenging computer vision problems, and describes how we can use the recovered structure to turn community photo collections into immersive, interactive 3-D experiences.


title: A cross-lingual dictionary for English Wikipedia concepts

abstract: We present a resource for automatically associating strings of text with English Wikipedia concepts. Our machinery is bi-directional, in the sense that it uses the same fundamental probabilistic methods to map strings to empirical distributions over Wikipedia articles as it does to map article URLs to distributions over short, language-independent strings of natural language text. For maximal interoperability, we release our resource as a set of flat line-based text files, lexicographically sorted and encoded with UTF-8. These files capture joint probability distributions underlying concepts (we use the terms article, concept and Wikipedia URL interchangeably) and associated snippets of text, as well as other features that can come in handy when working with Wikipedia articles and related information.


title: Cannabis use and symptoms of anxiety in adolescence and the moderating effect of the serotonin transporter gene

abstract: There is substantial evidence for the assumption that particularly heavy cannabis usett is associated with a variety of psychopathologic conditions. Little is known about the relationship between cannabis and anxiety. Prior studies have concluded that cannabis use alone is not sufficient for the development of long-term anxiety, and it has been suggested that cannabis is simply a risk factor that operates in conjunction with other risk factors. One such risk factor may be an individuals' genetic vulnerability. The present study examines the relationship between cannabis use and symptoms of anxiety by taking a developmental molecular-genetic perspective with a focus on a polymorphism involved in the regulation of serotonin. Specifically, we concentrated on changes in cannabis use and symptoms of anxiety over time and differences herein for individuals with and without the short allele of the 5-HTTLPR genotype. Data were from 1424 adolescents over a period of 5 years. We used different statistical analyses to test co-development of cannabis use and symptoms of anxiety throughout adolescence and the possible role of the 5-HTTLPR genotype in this process. Results from different analyses showed that cannabis use is associated with an increase in symptoms of anxiety, but only in carriers of the short allele of the 5-HTTLPR genotype, not in non-carriers. The findings of the present study show first evidence that the links between cannabis use and symptoms of anxiety are conditional on the individuals' genetic make-up.


title: Robust Subgraph Generation Improves Abstract Meaning Representation Parsing

abstract: The Abstract Meaning Representation (AMR) is a representation for open-domain rich semantics, with potential use in fields like event extraction and machine translation. Node generation, typically done using a simple dictionary lookup, is currently an important limiting factor in AMR parsing. We propose a small set of actions that derive AMR subgraphs by transformations on spans of text, which allows for more robust learning of this stage. Our set of construction actions generalize better than the previous approach, and can be learned with a simple classifier. We improve on the previous state-of-the-art result for AMR parsing, boosting end-to-end performance by 3 F$_1$ on both the LDC2013E117 and LDC2014T12 datasets.


Half Field Offense: An Environment for Multiagent Learning and Ad Hoc Teamwork


title: Adjustable dead-time control scheme for single-phase rectifier with auxiliary resonant snubber circuit

abstract: Auxiliary resonant snubber inverters (RSIs) have been proposed as a solution of demands for inverter, which are high efficiency and low EMI. In RSI, zero voltage switching (ZVS) on the inverter main switches is achieved by use of the auxiliary circuit; however, additional conduction loss increases simultaneously. The operating frequency of the auxiliary circuit in RSI can be reduced to a half of switching transition of a main switch when lossless snubber commutation, in which ZVS can be achieved without using auxiliary circuit by using output current, is used. As a result, additional conduction loss can be suppressed. However, in this scheme, ZVS turn-on on main switch is failed near zero output current when dead-time was constant. As a solution for this problem, adjustable dead-time control scheme for RSI has been proposed. Adjustable dead-time control has not been applied to a rectifier with auxiliary resonant snubber circuit — auxiliary resonant snubber rectifier (RSR)in the previous work. Thus, adjustable dead-time control is applied to RSR in this paper. This paper shows a control scheme for RSR. Moreover, the effectiveness of adjustable dead-time control by comparing between the adjustable and the fixed dead-time control is described.


title: Estimations of worldwide prevalence of chronic hepatitis B virus infection: a systematic review of data published between 1965 and 2013

abstract: Summary Background The quantification of the burden of disease attributable to hepatitis B virus (HBV) infection and the adaptation of prevention and control measures requires knowledge on its prevalence in the general population. For most countries such data are not routinely available. We estimated the national, regional, and global prevalence of chronic HBV infection. Methods For this systematic review and pooled analysis, we searched for data on prevalence of chronic HBV infection published between Jan 1, 1965, and Oct 23, 2013, in the databases Medline, Embase, CAB Abstracts (Global health), Popline, and Web of Science. We included studies reporting the hepatitis B surface antigen (HBsAg) serological marker of chronic HBV infection in non-high-risk groups and extracted data into a customised database. For each country, we calculated HBsAg prevalence estimates and 95% CIs weighted by study size. We extrapolated prevalence estimates to population sizes in 2010 to obtain the number of individuals with chronic HBV infection. Findings Of the 17 029 records screened, 1800 report on the prevalence of HBsAg covering 161 countries were included. HBsAg seroprevalence was 3·61% (95% CI 3·61–3·61) worldwide with highest endemicity in countries of the African region (total 8·83%, 8·82–8·83) and Western Pacific region (total 5·26%, 5·26–5·26). Within WHO regions, prevalence ranged from 0·20% (0·19–0·21; Mexico) to 13·55% (9·00–19·89; Haiti) in the Americas, to 0·48% (0·12–1·90; the Seychelles) to 22·38% (20·10–24·83; South Sudan) in the African region. We estimated that in 2010, globally, about 248 million individuals were HBsAg positive. Interpretation This first global assessment of country-level population prevalence of chronic HBV infection found a wide variation between countries and highlights the need for continued prevention and control strategies and the collection of reliable epidemiologic data using standardised methodology. Funding World Health Organization.


TL ; DR : Improving Abstractive Summarization Using LSTMs


title: Using LSTMs to learn physiological models of blood glucose behavior

abstract: For people with type 1 diabetes, good blood glucose control is essential to keeping serious disease complications at bay. This entails carefully monitoring blood glucose levels and taking corrective steps whenever they are too high or too low. If blood glucose levels could be accurately predicted, patients could take proactive steps to prevent blood glucose excursions from occurring. However, accurate predictions require complex physiological models of blood glucose behavior. Factors such as insulin boluses, carbohydrate intake, and exercise influence blood glucose in ways that are difficult to capture through manually engineered equations. In this paper, we describe a recursive neural network (RNN) approach that uses long short-term memory (LSTM) units to learn a physiological model of blood glucose. When trained on raw data from real patients, the LSTM networks (LSTMs) obtain results that are competitive with a previous state-of-the-art model based on manually engineered physiological equations. The RNN approach can incorporate arbitrary physiological parameters without the need for sophisticated manual engineering, thus holding the promise of further improvements in prediction accuracy.


title: 3d video generation and service based on a TOF depth sensor in MPEG-4 multimedia framework

abstract: In this paper, we present a new method to generate and serve 3D video represented by video-plus-depth using a time-of-flight (TOF) depth sensor. In practice, depth images captured by the depth sensor have critical problems, such as optical noise, unmatched boundaries with their corresponding color images, and depth flickering artifacts in the temporal domain. In this work, we enhance the noisy depth images by performing a series of processing steps including joint bilateral filtering with inner-edge selection, outer-boundary refinement by a robust image matting method, and temporal consistency based on motion estimation. Thereafter, the generated high-quality video-plus-depth is combined with computer graphics models in the MPEG-4 multimedia framework. Finally, the immersive video content is streamed to consumers to enjoy 3D view. Experimental results show that our method can minimize the inherent problems of depth images significantly and serve 3D video successfully in the MPEG-4 multimedia framework.


title: Multiband mobile phone antenna for GNSS application

abstract: In this article we have presented a new design of a monopole antenna mobile phone, which covers several bands with circular polarization. The proposed antenna generates five resonant frequencies. Our antenna has a bandwidth band that has bands of wireless communication systems such as GSM (880 960 MHz), DCS (1710 1880 MHz), PCS (1850 1990 MHz), UMTS (1920 2170 MHz), WiBro (2300 2390 MHz) and ISM (2400 2483 MHz) and GNSS, including COMPASS (1559.052 1591.788 MHz), GPS (1575.42 5MHz), GLONASS (1602 1615.5 MHz), Galileo (1189 1214 MHz). We discussed the various parameters of the antenna, such as return loss, radiation pattern and axial ratio AR.


The Ring of Gyges : Using Smart Contracts for Crime


title: A Physiological Profile Approach to Falls Risk Assessment and Prevention

abstract: The purpose of this perspective article is to describe the use of a physiological profile approach to falls risk assessment and prevention that has been developed by the Falls and Balance Research Group of the Prince of Wales Medical Research Institute, Sydney, Australia. The profile's use for people with a variety of factors that put them at risk for falls is discussed. The Physiological Profile Assessment (PPA) involves a series of simple tests of vision, peripheral sensation, muscle force, reaction time, and postural sway. The tests can be administered quickly, and all equipment needed is portable. The results can be used to differentiate people who are at risk for falls (“fallers”) from people who are not at risk for falls (“nonfallers”). A computer program using data from the PPA can be used to assess an individual's performance in relation to a normative database so that deficits can be targeted for intervention. The PPA provides valid and reliable measurements that can be used for assessing falls risk and evaluating the effectiveness of interventions and is suitable for use in a range of physical therapy and health care settings.


title: Behavior-based Robotics And The Reactive Paradigm A Survey

abstract: Behavior-based robots have come under the spotlight in recent years by making their presence known in diverse fields of human interest. Applications in behavior-based robotics have continued to grow in areas such as demining, search and rescue, office automation, health care, etc and continue to replace human beings in risky and menial tasks. With this inspiration, this paper investigates a variety of aspects in behavior-based robotics and the reactive paradigm in the context of their origins, concepts, applications and current research and is intended to provide a comprehensive overview about this area in robotics. The paper will review several central issues including a brief history of robotics, transition of robotic systems from hierarchical paradigm to reactive paradigm, biological/ethological inspiration for behavior-based robots, fundamentals of the reactive paradigm, architectures for controlling robotic behavior and the hybrid deliberative/reactive paradigm.


title: Associations between polymorphisms in IL-12A, IL-12B, IL-12Rβ1, IL-27 gene and serum levels of IL-12p40, IL-27p28 with esophageal cancer

abstract: PURPOSE ::: The aim of this study was to investigate whether IL-12A, IL-12B, IL-12Rβ1, and IL-27 gene polymorphisms and serum levels of IL-12, IL-27 are associated with esophageal cancer. ::: ::: ::: METHODS ::: We genotyped IL-12A gene rs568408, IL-12B gene rs3212227, IL-12Rβ1 gene 378 C/G, IL-27 gene rs153109, rs17855750, and rs181206 polymorphisms in a case-control study of 426 esophageal cancer patients and 432 health controls, using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), and serum IL-12p40 and IL-27p28 levels were measured by enzyme-linked immunosorbent assay. ::: ::: ::: RESULTS ::: Both serum IL-12p40 and IL-27p28 levels were significantly higher in controls than those in patients (P < 0.01). Rs568408 AG/AA, rs3212227 CC/AC, and IL-12Rβ1 378 GG/GC genotypes were associated with significantly increased risk of esophageal cancer (rs568408: χ(2) = 5.704, P = 0.017; rs3212227: χ(2) = 7.689, P = 0.006; IL-12Rβ1 378C/G: χ(2) = 5.206, P = 0.023). Moreover, rs3212227 CC/AC and 378 GG/GC genotypes were observed significantly associated with decreased serum IL-12p40 level in patients compare to other genotypes (rs3212227: t = 2.129, P = 0.034; IL-12Rβ1 378 C/G: t = 2.178, P = 0.030). Furthermore, frequency of rs3212227 CC/AC genotypes was significantly higher in patients with poor differentiation than those with AA genotype (χ(2) = 4.314, P = 0.035). ::: ::: ::: CONCLUSION ::: Our data suggest that the impaired production of IL-12p40 and IL-27p28 behaves as risk factors for esophageal cancer occurrence. IL-12B gene rs3212227 CC/AC and IL-12Rβ1 gene 378 GG/GC genotypes, which associated with decreased IL-12p40 level, may contribute to esophageal cancer susceptibility.


title: Remembering pictures: Pleasure and arousal in memory.

abstract: Incidental memory performance for pictures that varied along the affective dimensions of pleasantness and arousal was assessed. For both an immediate and delayed (1 year later) free-recall task, only the arousal dimension had a stable effect on memory performance: Pictures rated as highly arousing were remembered better than low-arousal stimuli. This effect was corroborated in a speeded recognition test, in which high-arousal materials encoded earlier in the experiment produced faster reaction times than their low-arousal counterparts. Pleasantness affected reaction time decisions only for pictures not encoded earlier. These results suggest that whereas both the dimensions of pleasantness and arousal are processed at initial encoding, long-term memory performance is mainly affected by arousal. Language: en


title: Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search

abstract: Policy search methods and, more broadly, reinforcement learning can enable robots to learn highly complex and general skills that may allow them to function amid the complexity and diversity of the real world. However, training a policy that generalizes well across a wide range of real-world conditions requires far greater quantity and diversity of experience than is practical to collect with a single robot. Fortunately, it is possible for multiple robots to share their experience with one another, and thereby, learn a policy collectively. In this work, we explore distributed and asynchronous policy learning as a means to achieve generalization and improved training times on challenging, real-world manipulation tasks. We propose a distributed and asynchronous version of guided policy search and use it to demonstrate collective policy learning on a vision-based door opening task using four robots. We describe how both policy learning and data collection can be conducted in parallel across multiple robots, and present a detailed empirical evaluation of our system. Our results indicate that distributed learning significantly improves training time, and that parallelizing policy learning and data collection substantially improves utilization. We also demonstrate that we can achieve substantial generalization on a challenging real-world door opening task.


The Benefits of Information Technology. ERIC Digest.


title: Optimum efficiency control of interior permanent magnet synchronous motors in drive trains of electric and hybrid vehicles

abstract: In automotive traction applications the interior permanent magnet synchronous motor (IPMSM) is preferentially chosen as traction drive due to its high torque and power densities. In drive trains of electrical vehicles (EV) and most hybrid electrical vehicles (HEV) the traction motor is operated in torque controlled mode. In a field-oriented control scheme an operation point selection strategy is required to choose appropriate current setpoints to generate the requested torque with high precision and optimal efficiency. In this paper the potential to improve the efficiency by operating a motor with minimum overall losses instead of minimum current per torque is investigated. The interaction between the motors loss characteristic, the impact of the voltage limit and the relevance of operation points for vehicle operation is explored theoretically and based on loss measurements. It is shown, that the losses can potentially be reduced at medium to high motor speeds with moderate torque requirements. This operation range corresponds to traveling with constant medium to high speed and slight acceleration and deceleration in electric vehicles. As well most operation points of the New European Driving Cycle (NEDC) and the Worldwide harmonized Light vehicles Test Procedure (WLTP) are in that operation range due to the moderate accelerations and decelerations in both driving cycles.


title: Decentralized SINR Balancing in Cognitive Radio Networks

abstract: This paper considers the downlink of a cognitive radio (CR) network formed by multiple primary and secondary transmitters, where each multiantenna transmitter serves a preknown set of single-antenna users. This paper assumes that the secondary and primary transmitters can simultaneously transmit their data over the same frequency bands to achieve high system spectrum efficiency. This paper considers the downlink balancing problem of maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of the secondary transmitters subject to both the total power constraint of the secondary transmitters and the maximum interference constraint at each primary user due to secondary transmissions. This paper proposes solving the problem using the alternating direction method of multipliers, which leads to a distributed implementation through limited information exchange across the coupled secondary transmitters. This paper additionally proposes a solution that guarantees feasibility at each iteration. Simulation results demonstrate that the proposed solution converges to the centralized solution in a reasonable number of iterations.


title: Deconstructing Building Blocks: Preschoolers' Spatial Assembly Performance Relates to Early Mathematical Skills

abstract: This study focuses on three main goals: First, 3-year-olds' spatial assembly skills are probed using interlocking block constructions (N = 102). A detailed scoring scheme provides insight into early spatial processing and offers information beyond a basic accuracy score. Second, the relation of spatial assembly to early mathematical skills was evaluated. Spatial skill independently predicted a significant amount of the variability in concurrent mathematical performance. Finally, the relation between spatial assembly skill and socioeconomic status (SES), gender, and parent-reported spatial language was examined. While children's performance did not differ by gender, lower SES children were already lagging behind higher SES children in block assembly. Furthermore, lower SES parents reported using significantly fewer spatial words with their children.


title: A pool of dreams: facebook, politics and the emergence of a social movement

abstract: In this paper we present insights from an empirical analysis of data from an emergent social movement primarily located on a Facebook page to contribute understanding of the conduct of everyday politics in social media and through this open up research agendas for HCI. The analysis focuses on how interactions and contributions facilitated the emergence of a collective with political will. We lay out an exploration of the intrinsic relationship between cultural memories, cultural expression and everyday politics and show how diverging voices co-constructed dynamic collectives capable of political action. We look at how interactions through the Facebook page challenge traditional ways for conceiving politics and the political. We outline possible research agendas in the field of everyday politics, which are sensitive to the everyday acts of resistance enclosed in the ordinary.


title: A Mobility Based Framework for Adaptive Clustering in Wireless Ad-Hoc Networks

abstract: This paper presents a novel framework for dynamically organizing mobile nodes in wireless ad hoc networks into clusters in which the probability of path availability can be bounded. The purpose of the (/spl alpha/, t) cluster is to help minimize the far-reaching effects of topological changes while balancing the need to support more optimal routing. A mobility model for ad hoc networks is developed and is used to derive expressions for the probability of path availability as a function of time. It is shown how this model provides the basis for dynamically grouping nodes into clusters using an efficient distributed clustering algorithm. Since the criteria for cluster organization depends directly upon path availability, the structure of the cluster topology is adaptive with respect to node mobility. Consequently, this framework supports an adaptive hybrid routing architecture that can be more responsive and effective when mobility rates are low and more efficient when mobility rates are high.


title: The Real-Time City? Big Data and Smart Urbanism

abstract: ‘Smart cities’ is a term that has gained traction in academia, business and government to describe cities that, on the one hand, are increasingly composed of and monitored by pervasive and ubiquitous computing and, on the other, whose economy and governance is being driven by innovation, creativity and entrepreneurship, enacted by smart people. This paper focuses on the former and, drawing on a number of examples, details how cities are being instrumented with digital devices and infrastructure that produce ‘big data’. Such data, smart city advocates argue enables real-time analysis of city life, new modes of urban governance, and provides the raw material for envisioning and enacting more efficient, sustainable, competitive, productive, open and transparent cities. The final section of the paper provides a critical reflection on the implications of big data and smart urbanism, examining five emerging concerns: the politics of big urban data, technocratic governance and city development, corporatisation of city governance and technological lock-ins, buggy, brittle and hackable cities, and the panoptic city.


title: Mediation of user models for enhanced personalization in recommender systems

abstract: Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models mediation. The work discusses the details of such a generic user modeling mediation framework. It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation. Specifically, four major types of mediation are presented: cross-user, cross-item, cross-context, and cross-representation. Finally, the work reports the application of the mediation framework and illustrates it with practical mediation scenarios. Evaluations of these scenarios demonstrate the potential benefits of user modeling data mediation, as in certain conditions it allows improving the quality of the recommendations provided to the users.


title: Outlier Detection using Evolutionary Computing

abstract: In this paper, we proposed Harmony Search and Differential Evolution based outlier detection for medium dimensional numerical datasets. The sparsity coefficient is taken as the objective function for finding outliers in the data. The upper limit of the number of dimensions for a dataset is fixed using the threshold suggested by Chebyshev's inequality. A t-test is conducted on the optimal sparsity coefficient for both methods over 30 simulations. At a 1% level of significance, the t-test confirmed that the Harmony Search based method is statistically more significant than Differential Evolution based one for all four datasets. Both methods outperformed the previous approaches.


title: Strain engineering of electrical conductivity in epitaxial thin Ba0.7Sr0.3TiO3 film heterostructures

abstract: Thin epitaxial films have a great potential to be used in real life applications, such as oxide-on-silicon. However, they often contain a large amount of defects, leading to an enhanced electrical conductivity. This could be desirable in some applications (i. e. memristors), but the mechanism is not fully understood. Here we report on the investigation of epitaxial barium strontium titanate thin films deposited on strontium titanate single crystal substrates (Ba 0.7 Sr 0.3 TiO 3 /SrRuO 3 //SrTiO 3 heterostructures) with a controlled epitaxial strain. The impedance analysis allowed us to propose a model, which explains changes in the temperature dependence of the conductivity based on the strain-dependent anisotropic change of electron/hole mobility.


title: Enjoyment of mediated fright and violence: A meta-analysis

abstract: In this meta-analysis, we synthesized data from published journal articles that investigated viewers' enjoyment of fright and violence. Given the limited research on this topic, this analysis was primarily a way of summarizing the current state of knowledge and developing directions for future research. The studies selected (a) examined frightening or violent media content; (b) used self-report measures of enjoyment or preference for such content (the dependent variable); and (c) included independent variables that were given theoretical consideration in the literature. The independent variables examined were negative affect and arousal during viewing, empathy, sensation seeking, aggressiveness, and the respondents' gender and age. The analysis confirmed that male viewers, individuals lower in empathy, and those higher in sensation seeking and aggressiveness reported more enjoyment of fright and violence. Some support emerged for Zillmann's (1980, 1996) model of suspense enjoyment. Overall, the results de...


title: Layout styles to improve CDM ESD robustness of integrated circuits in 65-nm CMOS process

abstract: Due to the thinner gate oxide in the nanoscale CMOS technology and the larger chip size in the system-on-chip (SoC) IC products, the charged-device-model (CDM) electrostatic discharge (ESD) has become the major ESD events to cause failures during IC manufacturing procedures. The effective ESD protection design against CDM ESD stresses should be implemented into the chip with layout optimization to improve its ESD robustness. In this work, the impacts of different layout styles of MOS devices on CDM ESD robustness were investigated in a 65-nm CMOS process. The experimental results can provide useful information to optimize the layout of integrated circuits against CDM ESD events.


title: www.elsevier.com/locate/patcog 3D face detection using curvature analysis

abstract: Face detection is a crucial preliminary in many applications. Most of the approaches to face detection have focused on the use of two-dimensional images. We present an innovative method that combines a feature-based approach with a holistic one for three-dimensional (3D) face detection. Salient face features, such as the eyes and nose, are detected through an analysis of the curvature of the surface. Each triplet consisting of a candidate nose and two candidate eyes is processed by a PCA-based classifier trained to discriminate between faces and non-faces. The method has been tested, with good results, on some 150 3D faces acquired by a laser range scanner.


title: Comparison study of 12kV n-type SiC IGBT with 10kV SiC MOSFET and 6.5kV Si IGBT based on 3L-NPC VSC applications

abstract: Silicon Carbide (SiC) devices and modules have been developed with high blocking voltages for Medium Voltage power electronics applications. Silicon devices do not exhibit higher blocking voltage capability due to its relatively low band gap energy compared to SiC counterparts. For the first time, 12kV SiC IGBTs have been fabricated. These devices exhibit excellent switching and static characteristics. A Three-level Neutral Point Clamped Voltage Source Converter (3L-NPC VSC) has been simulated with newly developed SiC IGBTs. This 3L-NPC Converter is used as a 7.2kV grid interface for the solid state transformer and STATCOM operation. Also a comparative study is carried out with 3L-NPC VSC simulated with 10kV SiC MOSFET and 6.5kV Silicon IGBT device data.


title: Approximating Unique Games Using Low Diameter Graph Decomposition

abstract: We design approximation algorithms for Unique Games when the constraint graph admits good low diameter graph decomposition. For the ${\sf Max2Lin}_k$ problem in $K_r$-minor free graphs, when there is an assignment satisfying $1-\varepsilon$ fraction of constraints, we present an algorithm that produces an assignment satisfying $1-O(r\varepsilon)$ fraction of constraints, with the approximation ratio independent of the alphabet size. A corollary is an improved approximation algorithm for the ${\sf MaxCut}$ problem for $K_r$-minor free graphs. For general Unique Games in $K_r$-minor free graphs, we provide another algorithm that produces an assignment satisfying $1-O(r \sqrt{\varepsilon})$ fraction of constraints. Our approach is to round a linear programming relaxation to find a minimum subset of edges that intersects all the inconsistent cycles. We show that it is possible to apply the low diameter graph decomposition technique on the constraint graph directly, rather than to work on the label extended graph as in previous algorithms for Unique Games. The same approach applies when the constraint graph is of genus $g$, and we get similar results with $r$ replaced by $\log g$ in the ${\sf Max2Lin}_k$ problem and by $\sqrt{\log g}$ in the general problem. The former result generalizes the result of Gupta-Talwar for Unique Games in the ${\sf Max2Lin}_k$ case, and the latter result generalizes the result of Trevisan for general Unique Games.