title: Believe it or not: how much can we rely on published data on potential drug targets?

abstract: 1. This indicates the limitations of the predictivity of disease models and also that the validity of the targets being investigated is frequently questionable, which is a crucial issue to address if success rates in clinical trials are to be improved. Candidate drug targets in industry are derived from various sources, including inhouse target identification campaigns, inlicensing and public sourcing, in particular based on reports published in the literature and presented at conferences. During the transfer of projects from an academic to a company setting, the focus changes from ‘interesting’ to ‘feasible/marketable’, and the financial costs of pursuing a full-blown drug discovery and development programme for a particular target could ultimately be hundreds of millions of Euros. Even in the earlier stages, investments in activities such as high-throughput screening programmes are substantial, and thus the validity of published data on potential targets is crucial for companies when deciding to start novel projects. To mitigate some of the risks of such investments ultimately being wasted, most pharmaceutical companies run in-house target validation programmes. However, validation projects that were started in our company based on exciting published data have often resulted in disillusionment when key data could not be reproduced. Talking to scientists, both in academia and in industry, there seems to be a general impression that many


title: Multimedia features for click prediction of new ads in display advertising

abstract: Non-guaranteed display advertising (NGD) is a multi-billion dollar business that has been growing rapidly in recent years. Advertisers in NGD sell a large portion of their ad campaigns using performance dependent pricing models such as cost-per-click (CPC) and cost-per-action (CPA). An accurate prediction of the probability that users click on ads is a crucial task in NGD advertising because this value is required to compute the expected revenue. State-of-the-art prediction algorithms rely heavily on historical information collected for advertisers, users and publishers. Click prediction of new ads in the system is a challenging task due to the lack of such historical data. The objective of this paper is to mitigate this problem by integrating multimedia features extracted from display ads into the click prediction models. Multimedia features can help us capture the attractiveness of the ads with similar contents or aesthetics. In this paper we evaluate the use of numerous multimedia features (in addition to commonly used user, advertiser and publisher features) for the purposes of improving click prediction in ads with no history. We provide analytical results generated over billions of samples and demonstrate that adding multimedia features can significantly improve the accuracy of click prediction for new ads, compared to a state-of-the-art baseline model.


title: Insiders Behaving Badly: Addressing Bad Actors and Their Actions

abstract: We present a framework for describing insiders and their actions based on the organization, the environment, the system, and the individual. Using several real examples of unwelcome insider action (hard drive removal, stolen intellectual property, tax fraud, and proliferation of e-mail responses), we show how the taxonomy helps in understanding how each situation arose and could have been addressed. The differentiation among types of threats suggests how effective responses to insider threats might be shaped, what choices exist for each type of threat, and the implications of each. Future work will consider appropriate strategies to address each type of insider threat in terms of detection, prevention, mitigation, remediation, and punishment.


title: X/Ka-Band Dual-Polarized Digital Beamforming Synthetic Aperture Radar

abstract: This paper presents a digital beamforming (DBF) synthetic aperture radar (SAR) for future spaceborne earth observation systems. The objective of the DBF-SAR system is to achieve a low cost, lightweight, low-power consumption, and dual-band (X/Ka) dual-polarized module for the next-generation spaceborne SAR system in Europe. The architectures and modules of the proposed DBF-SAR system are designed according to a realistic mission scenario, which is compatible with the future small/microsatellites platforms. This system fills an important gap in the conception of the future DBF-SAR, facilitating a high level of integration and complexity reduction. The proposed system is considered not only the first demonstrator of a receive-only spaceborne DBF system, but also the first X/Ka-band dual-polarized SAR system with shared aperture. This paper presents a description of the proposed instrument hardware and first experimental validations. The concept and design of the DBF multistatic SAR system are discussed and presented first, followed by the design of subsystems such as DBF networks, microwave integrated circuit, and antennas. Simulated and measured results of the subsystems are presented, demonstrating that the proposed SAR instrument architecture is well-suited for the future SAR applications.


title: A Study of Vertical Thin Poly-Si Channel Transfer Gate Structured CMOS Image Sensors

abstract: In this letter, the image characteristics of CMOS image sensor (CIS) pixels using a vertical thin poly-Si channel (VTPC) transfer gate (TG) are established for the first time. The study of three-dimensional (3D) structures in the image sensor field has been started by 3D Flash memories. By adopting the poly-Si channel fabrication concept of 3D NAND flash memories-appropriately modified to fit the requirements of a TG in CIS pixel applications-the VTPC structure effectively suppresses the grain boundary effect. The VTPC-TG performance improves as the poly-Si channel becomes thinner. The possibility of implementing 3D pixel-based CIS is confirmed by applying the fabricated VTPC-TG to a mass-produced 1.12-$\mu \text{m}$ BSI product, and using it to capture 5-Mpixel images.


Different size multilingual phone inventories and context-dependent acoustic models for language identification.


title: Interactive Creation of Virtual Worlds Using Procedural Sketching

abstract: Procedural modelling is an attractive alternative to cut down the costs of manual content creation for virtual worlds. We discuss our declarative modelling approach to the creation of 3D virtual worlds, which integrates a variety of procedural techniques in order to enable a non-specialist user to interactively create a complete 3D virtual world in minutes. In particular, we introduce procedural sketching, a novel paradigm which allows designers to quickly specify and see the effects of their procedural modelling operations, and describe its main features as implemented in our prototype system SketchaWorld. Two main interaction modes are described, for specifying the landscape and terrain features, respectively. Our approach automatically fits all generated terrain features with their surroundings, for example by smoothing out rough terrain for roads, or creating a bridge to cross a river. It is concluded that this approach provides designers with the productivity gain of procedural methods, while still allowing for fine user control and actively supporting iterative modelling.


Region Filling and Object Removal by Exemplar based Image Inpainting


title: Cutaneous solitary neural hamartoma: report of an unusual case.

abstract: Abstract: Cutaneous hamartomas are tumor-like proliferations of tissue indigenous to the organ but arranged abnormally. There are examples in the literature of cutaneous hamartomas composed of a variety of different components. To our knowledge, there is no previous report of such cutaneous solitary neural hamartoma. Our case occurred in a 51-year-old man with pain and paresthesia in the right shoulder associated with a nodule that was surgically removed. There was no history of trauma, other skin nodules, neurofibromatosis, or tuberous sclerosis. Histologically, there was an unencapsulated nodule, composed of mature nerve bundles noted abnormally high within the papillary dermis, extending to the reticular dermis with periadnexal distribution. Immunohistochemically, the nerve bundles were positive for S-100, including the smaller nerve twigs, and the perineurium was highlighted by epithelial membrane antigen, reminiscent of normal peripheral nerves. Although, neural components including mature nerve bundles have been described in various cutaneous hamartomas, this represents a peculiar case of a cutaneous mature peripheral nerve hamartoma. Whether this is related to other entities of cutaneous hamartomas (ie, neurofollicular hamartoma, folliculosebaceous cystic hamartoma) is not yet apparent, although it is probably a unique entity.


title: Radio Tomographic Imaging with Wireless Networks

abstract: Radio Tomographic Imaging (RTI) is an emerging technology for imaging the attenuation caused by physical objects in wireless networks. This paper presents a linear model for using received signal strength (RSS) measurements to obtain images of moving objects. Noise models are investigated based on real measurements of a deployed RTI system. Mean-squared error (MSE) bounds on image accuracy are derived, which are used to calculate the accuracy of an RTI system for a given node geometry. The ill-posedness of RTI is discussed, and Tikhonov regularization is used to derive an image estimator. Experimental results of an RTI experiment with 28 nodes deployed around a 441 square foot area are presented.


title: A hybrid clustering technique combining a novel genetic algorithm with K-Means

abstract: Many existing clustering techniques including K-Means require a user input on the number of clusters. It is often extremely difficult for a user to accurately estimate the number of clusters in a data set. The genetic algorithms (GAs) generally determine the number of clusters automatically. However, they typically choose the genes and the number of genes randomly. If we can identify the right genes in the initial population then GAs have better possibility to produce a high quality clustering result than the case when we randomly choose the genes. We propose a novel GA based clustering technique that is capable of automatically finding the right number of clusters and identifying the right genes through a novel initial population selection approach. With the help of our novel fitness function, and gene rearrangement operation it produces high quality cluster centers. The centers are then fed into K-Means as initial seeds in order to produce an even higher quality clustering solution by allowing the initial seeds to readjust as needed. Our experimental results indicate a statistically significant superiority (according to the sign test analysis) of our technique over five recent techniques on twenty natural data sets used in this study based on six evaluation criteria.


title: Real-Time Vehicle Recognition and Improved Traffic Congestion Resolution

abstract: An intelligent traffic management system (E-Traffic Warden) is proposed, using image processing techniques along with smart traffic control algorithm. Traffic recognition was achieved using cascade classifier for vehicle recognition utilizing Open CV and Visual Studio C/C++. The classifier was trained on 700 positive samples and 1140 negative samples. The results show that the accuracy of vehicle detection is approximately 93 percent. The count of vehicles at all approaches of intersection is used to estimate traffic. Traffic build up is then avoided or resolved by passing the extracted data to traffic control algorithm. The control algorithm shows approximately 86% improvement over Fixed-Delay controller in worst case scenarios.


title: Domain-Adaptive Conversational Agent with Two-Stage Dialogue Management

abstract: The conversational agent understands and provides users with proper information based on natural language Conventional agents based on pattern matching have much restriction to manage various types of real dialogues and to improve the answering performance For the effective construction of conversational agents, we propose a domain-adaptive conversational agent that infers the user's intention with two-stage inference and incrementally improves the answering performance through a learning dialogue We can confirm the usefulness of the proposed method with examples and usability tests.


title: An Electronic Market-Maker

abstract: This paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order arrival or price process, is assumed. Instead, the agent learns from realtime market experience and develops explicit market-making strategies, achieving multiple objectives including the maximizing of profits and minimization of the bid-ask spread. The simulation results show initial success in bringing learning techniques to building marketmaking algorithms. This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and in the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. This research was sponsored by grants from: Office of Naval Research under contract No. N00014-93-1-3085, Office of Naval Research (DARPA) under contract No. N00014-00-1-0907, National Science Foundation (ITR) under contract No. IIS-0085836, National Science Foundation (KDI) under contract No. DMS-9872936, and National Science Foundation under contract No. IIS-9800032 This research was partially funded by the Center for e-Business (MIT). Additional support was provided by: Central Research Institute of Electric Power Industry, Eastman Kodak Company, DaimlerChrysler AG, Compaq, Honda R&D Co., Ltd., Komatsu Ltd., Merrill-Lynch, NEC Fund, Nippon Telegraph & Telephone, Siemens Corporate Research, Inc., and The Whitaker Foundation.


title: The Sense of Embodiment in Virtual Reality

abstract: What does it feel like to own, to control, and to be inside a body? The multidimensional nature of this experience together with the continuous presence of one's biological body, render both theoretical and experimental approaches problematic. Nevertheless, exploitation of immersive virtual reality has allowed a reframing of this question to whether it is possible to experience the same sensations towards a virtual body inside an immersive virtual environment as toward the biological body, and if so, to what extent. The current paper addresses these issues by referring to the Sense of Embodiment (SoE). Due to the conceptual confusion around this sense, we provide a working definition which states that SoE consists of three subcomponents: the sense of self-location, the sense of agency, and the sense of body ownership. Under this proposed structure, measures and experimental manipulations reported in the literature are reviewed and related challenges are outlined. Finally, future experimental studies are proposed to overcome those challenges, toward deepening the concept of SoE and enhancing it in virtual applications.


title: Impression Management 2.0

abstract: Investigated the relationship between self-reported (offline) personality traits and (online) self-presentation in social networking profiles on the Internet. Social networking sites like MySpace, Facebook, and StudiVZ are popular means of communicating personality. Recent theoretical and empirical considerations of homepages and Web 2.0 platforms show that impression management is a major motive for actively participating in social networking sites. 58 users (mean age 22.98 years) of the German Web 2.0 site, StudiVZ.net, completed online questionnaires assessing extraversion (NEO Personality Inventory, NEO-FFI), self-esteem (Rosenberg Self-Esteem Scale, RSES), and efficacy of self-presentation, and their profiles were subject to a content analysis. Findings reveal a slight influence of extraversion on self-presentation, whereas there was no significant effect for self-esteem. Moveover, analyses of respondents' profiles showed that self-efficacy with regard to impression management is strongly related to the number of virtual friends, the level of profile detail, and the style of the personal photo.


title: A continuous and objective evaluation of emotional experience with interactive play environments

abstract: Researchers are using emerging technologies to develop novel play environments, while established computer and console game markets continue to grow rapidly. Even so, evaluating the success of interactive play environments is still an open research challenge. Both subjective and objective techniques fall short due to limited evaluative bandwidth; there remains no corollary in play environments to task performance with productivity systems. This paper presents a method of modeling user emotional state, based on a user's physiology, for users interacting with play technologies. Modeled emotions are powerful because they capture usability and playability through metrics relevant to ludic experience; account for user emotion; are quantitative and objective; and are represented continuously over a session. Furthermore, our modeled emotions show the same trends as reported emotions for fun, boredom, and excitement; however, the modeled emotions revealed differences between three play conditions, while the differences between the subjective reports failed to reach significance.


title: Design of an FMCW radar baseband signal processing system for automotive application

abstract: For a typical FMCW automotive radar system, a new design of baseband signal processing architecture and algorithms is proposed to overcome the ghost targets and overlapping problems in the multi-target detection scenario. To satisfy the short measurement time constraint without increasing the RF front-end loading, a three-segment waveform with different slopes is utilized. By introducing a new pairing mechanism and a spatial filter design algorithm, the proposed detection architecture not only provides high accuracy and reliability, but also requires low pairing time and computational loading. This proposed baseband signal processing architecture and algorithms balance the performance and complexity, and are suitable to be implemented in a real automotive radar system. Field measurement results demonstrate that the proposed automotive radar signal processing system can perform well in a realistic application scenario.


title: Cellulose- and xylan-degrading thermophilic anaerobic bacteria from biocompost.

abstract: Nine thermophilic cellulolytic clostridial isolates and four other noncellulolytic bacterial isolates were isolated from self-heated biocompost via preliminary enrichment culture on microcrystalline cellulose. All cellulolytic isolates grew vigorously on cellulose, with the formation of either ethanol and acetate or acetate and formate as principal fermentation products as well as lactate and glycerol as minor products. In addition, two out of nine cellulolytic strains were able to utilize xylan and pretreated wood with roughly the same efficiency as for cellulose. The major products of xylan fermentation were acetate and formate, with minor contributions of lactate and ethanol. Phylogenetic analyses of 16S rRNA and glycosyl hydrolase family 48 (GH48) gene sequences revealed that two xylan-utilizing isolates were related to a Clostridium clariflavum strain and represent a distinct novel branch within the GH48 family. Both isolates possessed high cellulase and xylanase activity induced independently by either cellulose or xylan. Enzymatic activity decayed after growth cessation, with more-rapid disappearance of cellulase activity than of xylanase activity. A mixture of xylan and cellulose was utilized simultaneously, with a significant synergistic effect observed as a reduction of lag phase in cellulose degradation.


title: Pollution control using Internet of Things (IoT)

abstract: The change of ecosystem and the surrounding atmosphere by different forms of pollutants (Chemicals and energies) are called environmental pollution. Pollution affects the climate most. The issue of environmental pollution and climate change has become an international concern due to their affects to the physical and biological entities of the environment. In this paper, the main objective is to find the pollutants in nature with the help of the Internet of Things (IoT). The UVI-01 sensor is an ultraviolet light sensor. The UV Sensor outputs an analog signal of the amount of UV light it detects. The 2 in one Temperature and PH sensor is used to monitor the Quality of water, interns of monitoring the level of water, the temperature of the water and its surrounding, the turbidity of the water (how clean the water is) as well as the PH levels of the Water. Therefore, this system monitors all of these result and finally when all data are collected, it sends the information or data to the cloud.


Automating weather forecasts based on convolutional networks


title: Sznajd2: A Community-Aware Opinion Dynamics Model

abstract: It is well known that social networks are composed of many communities of nodes, where the nodes of the same community are highly connected, and few links are between the nodes of different communities. We observe scenarios in both real-world networks as well as computer networks that opinions of nodes in the networks can be aware of the existence of communities and take them into account during opinion formation. Based on the observations, we propose the first community-aware opinion dynamics model called Sznajd^2 by applying the famous Sznajd model on the inter-community level and intra-community level. We then briefly introduce coupled fully connected networks (CFCN), analyze our model theoretically on it, and reveal that when interconnectivity parameter v > 0.172, nodes in the networks are surely to reach consensus on opinions along time, and when v0.31 at least, which is nearly two times big than our model.


title: Synchronisation of Partial Multi-Matchings via Non-negative Factorisations

abstract: Abstract In this work we study permutation synchronisation for the challenging case of partial permutations, which plays an important role for the problem of matching multiple objects (e.g. images or shapes). The term synchronisation refers to the property that the set of pairwise matchings is cycle-consistent, i.e. in the full matching case all compositions of pairwise matchings over cycles must be equal to the identity. Motivated by clustering and matrix factorisation perspectives of cycle-consistency, we derive an algorithm to tackle the permutation synchronisation problem based on non-negative factorisations. In order to deal with the inherent non-convexity of the permutation synchronisation problem, we use an initialisation procedure based on a novel rotation scheme applied to the solution of the spectral relaxation. Moreover, this rotation scheme facilitates a convenient Euclidean projection to obtain a binary solution after solving our relaxed problem. In contrast to state-of-the-art methods, our approach is guaranteed to produce cycle-consistent results. We experimentally demonstrate the efficacy of our method and show that it achieves better results compared to existing methods.


title: The roles of emojis in mobile phone notifications

abstract: The texts in mobile messages are not always easy to decipher since tone and body language is removed from the context. Emojis offer an attractive way to express emotions to avoid misunderstandings of message tone. In this paper we shed the light on the roles of Emojis in phone notification, we conducted an in-situ study to gather phone notification data. We outline the relationship between Emojis and various social network applications including WhatsApp, Facebook and Twitter. Early results allow us to draw several conclusions in relation to number, position, type and sentimental value of Emojis. It turns out that most popular Emojis in one social app is not as popular in the others. Emojis sentimental polarity in Twitter is high and overall number of Emojis is less than Facebook. The sentimental value of Emojis is more meaningful when there are multiple Emoji in one notification.


title: Federated Identity and Access Management for the Internet of Things

abstract: We examine the use of Federated Identity andAccess Management (FIAM) approaches for the Internet ofThings (IoT). We look at specific challenges that devices, sensorsand actuators have, and look for approaches to address them.OAuth is a widely deployed protocol -- built on top of HTTP -- forapplying FIAM to Web systems. We explore the use of OAuth forIoT systems that instead use the lightweight MQTT 3.1 protocol.In order to evaluate this area, we built a prototype that usesOAuth 2.0 to enable access control to information distributed viaMQTT. We evaluate the results of this prototyping activity, andassess the strengths and weaknesses of this approach, and thebenefits of using the FIAM approaches with IoT and Machine toMachine (M2M) scenarios. Finally we outline areas for furtherresearch.


title: Experimental software engineering: a report on the state of the art

abstract: The goal of this session is to make the software engineering community aware of the opportunities that exist to pursue such an experimental approach. In the remainder of the essay, we describe an emerging model for empirical work and the language for discussing it. We then focus on the current state of experimental software engineering, the road blocks barring effective progress, and what developers and researchers can do to remove them.


title: Privacy-preserving social network publication against friendship attacks

abstract: Due to the rich information in graph data, the technique for privacy protection in published social networks is still in its infancy, as compared to the protection in relational databases. In this paper we identify a new type of attack called a friendship attack. In a friendship attack, an adversary utilizes the degrees of two vertices connected by an edge to re-identify related victims in a published social network data set. To protect against such attacks, we introduce the concept of k2-degree anonymity, which limits the probability of a vertex being re-identified to 1/k. For the k2-degree anonymization problem, we propose an Integer Programming formulation to find optimal solutions in small-scale networks. We also present an efficient heuristic approach for anonymizing large-scale social networks against friendship attacks. The experimental results demonstrate that the proposed approaches can preserve much of the characteristics of social networks.


title: Assessing the maturity of capstone engineering project courses

abstract: One of the critical issues facing engineering education today is assessment. Engineering departments are asked to determine their program goals then how students meet the outcomes specified. This assessment is driven by the requirement that data be collected that guides changes to the program based on a determination of the difference between goal and outcome. Assessment offers a new approach to modifying engineering education. The major question to be asked in how well engineering educators will meet the challenges posed. Design projects offer a particular challenging area for assessment. Capstone design projects, are an integral part of engineering education at most institutions. The capstone course is typically taken during the final year of study and involves a one or two semester project, typically involving groups. As engineering programs move to adopt assessment there is an increased need to develop methods to assist faculty in understanding the project course, the outcomes actually realized by the project course, and how to structure improvements. The goal of our project is to provide a framework for determining the capability of an engineering project course.


title: Study on Face Detection Algorithm Based on Skin Color Segmentation and AdaBoost Algorithm

abstract: Because skin color detection had high false detecting rate in color image with complex background and the AdaBoost face detection algorithm does not work well in multi-pose and multi-face images. In this paper, a new face detection algorithm is introduced combined the skin color segmentation and the AdaBoost algorithm. The simulated experiment results show that this algorithm mentioned in this paper, not only reduces the false detecting rate and missing rate, but also obviously improves the detection speed. And the algorithm can be applied to more complex background face detection which has multi-pose and multi-face of large images.


title: A universal image quality index

abstract: We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematically defined and no human visual system model is explicitly employed, our experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error. Demonstrative images and an efficient MATLAB implementation of the algorithm are available online at http://anchovy.ece.utexas.edu//spl sim/zwang/research/quality_index/demo.html.


title: Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors

abstract: The recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed that robots can adapt to damage in a matter of a few trials. The success of this algorithm relies on two components: prior knowledge acquired through simulation with an intact robot, and Bayesian optimization (BO) that operates on-line, on the damaged robot. While IT&E leads to fast damage recovery, it does not incorporate any safety constraints that prevent the robot from attempting harmful behaviors. In this work, we address this limitation by replacing the BO component with a constrained BO procedure. We evaluate our approach on a simulated damaged humanoid robot that needs to crawl as fast as possible, while performing as few unsafe trials as possible. We compare our new"safety-aware IT&E"algorithm to IT&E and a multi-objective version of IT&E in which the safety constraints are dealt as separate objectives. Our results show that our algorithm outperforms the other approaches, both in crawling speed within the safe regions and number of unsafe trials.


title: Design and Characterization of Screen-Printed Textile Electrodes for ECG Monitoring

abstract: This study was conducted to develop a screen-printed approach for the fabrication of textile electrodes and analyze their characteristic features for use as electrocardiography (ECG) signal sensors. The electrodes were obtained by printing poly(3,4-ethylenedioxythiophene) doped with poly(4-styrenesulfonate) organic bio-compatible polymer-based ink on a finished fabric using a polyester mesh with 43 lines/cm. The physical–chemical properties of the polymer solution were varied to obtain a simple and reproducible fabrication process and a good electrode performance simultaneously. The electrode properties were first assessed by a benchtop measurement instrument and then on human subjects. Several tests under different conditions, for instance, by adding liquid and solid electrolytes, revealed that the electrodes possess the majority of characteristics required by the ANSI/AAMI EC12:2000 standard for gelled ECG electrodes. Furthermore, the benchtop measurements demonstrated that these electrodes preserve their electrical properties and functionality even after several washing cycles (performed under mild conditions), while they suffered physical stretching. While using some electrolytes, the skin contact impedance and the features extracted from the physiological signals were found to be highly similar to those obtained with disposable gelled Ag/AgCl electrodes ( $\rho >0.99$ ). Through the proposed fabrication process, it is possible to change the electrical properties of a specific region of a finished fabric or a garment with an excellent geometrical resolution. This opportunity, along with the obtained results in terms of the electrical characteristics as ECG electrodes, indicates that the proposed approach could possibly be adopted in the future for the development of smart garments that could comfortably detect ECG signals.


title: The Computable News project: Research in the Newsroom

abstract: We report on a four year academic research project to build a natural language processing platform in support of a large media company. The Computable News platform processes news stories, producing a layer of structured data that can be used to build rich applications. We describe the underlying platform and the research tasks that we explored building it. The platform supports a wide range of prototype applications designed to support different newsroom functions. We hope that this qualitative review provides some insight into the challenges involved in this type of project.


title: Block-Based Neural Networks for Personalized ECG Signal Classification

abstract: This paper presents evolvable block-based neural networks (BbNNs) for personalized ECG heartbeat pattern classification. A BbNN consists of a 2-D array of modular component NNs with flexible structures and internal configurations that can be implemented using reconfigurable digital hardware such as field-programmable gate arrays (FPGAs). Signal flow between the blocks determines the internal configuration of a block as well as the overall structure of the BbNN. Network structure and the weights are optimized using local gradient-based search and evolutionary operators with the rates changing adaptively according to their effectiveness in the previous evolution period. Such adaptive operator rate update scheme ensures higher fitness on average compared to predetermined fixed operator rates. The Hermite transform coefficients and the time interval between two neighboring R-peaks of ECG signals are used as inputs to the BbNN. A BbNN optimized with the proposed evolutionary algorithm (EA) makes a personalized heartbeat pattern classifier that copes with changing operating environments caused by individual difference and time-varying characteristics of ECG signals. Simulation results using the Massachusetts Institute of Technology/Beth Israel Hospital (MIT-BIH) arrhythmia database demonstrate high average detection accuracies of ventricular ectopic beats (98.1%) and supraventricular ectopic beats (96.6%) patterns for heartbeat monitoring, being a significant improvement over previously reported electrocardiogram (ECG) classification results.


title: Role of Metal Contacts in Designing High-Performance Monolayer n-Type WSe2Field Effect Transistors

abstract: This work presents a systematic study toward the design and first demonstration of high-performance n-type monolayer tungsten diselenide (WSe2) field effect transistors (FET) by selecting the contact metal based on understanding the physics of contact between metal and monolayer WSe2. Device measurements supported by ab initio density functional theory (DFT) calculations indicate that the d-orbitals of the contact metal play a key role in forming low resistance ohmic contacts with monolayer WSe2. On the basis of this understanding, indium (In) leads to small ohmic contact resistance with WSe2 and consequently, back-gated In–WSe2 FETs attained a record ON-current of 210 μA/μm, which is the highest value achieved in any monolayer transition-metal dichalcogenide- (TMD) based FET to date. An electron mobility of 142 cm2/V·s (with an ON/OFF current ratio exceeding 106) is also achieved with In–WSe2 FETs at room temperature. This is the highest electron mobility reported for any back gated monolayer TMD materia...


title: Constraint Reasoning and Kernel Clustering for Pattern Decomposition With Scaling

abstract: Motivated by an important and challenging task encountered in material discovery, we consider the problem of finding K basis patterns of numbers that jointly compose N observed patterns while enforcing additional spatial and scaling constraints. We propose a Constraint Programming (CP) model which captures the exact problem structure yet fails to scale in the presence of noisy data about the patterns. We alleviate this issue by employing Machine Learning (ML) techniques, namely kernel methods and clustering, to decompose the problem into smaller ones based on a global data-driven view, and then stitch the partial solutions together using a global CP model. Combining the complementary strengths of CP and ML techniques yields a more accurate and scalable method than the few found in the literature for this complex problem.


title: A strategy-aware technique for learning behaviors from discrete human feedback

abstract: This paper introduces two novel algorithms for learning behaviors from human-provided rewards. The primary novelty of these algorithms is that instead of treating the feedback as a numeric reward signal, they interpret feedback as a form of discrete communication that depends on both the behavior the trainer is trying to teach and the teaching strategy used by the trainer. For example, some human trainers use a lack of feedback to indicate whether actions are correct or incorrect, and interpreting this lack of feedback accurately can significantly improve learning speed. Results from user studies show that humans use a variety of training strategies in practice and both algorithms can learn a contextual bandit task faster than algorithms that treat the feed-back as numeric. Simulated trainers are also employed to evaluate the algorithms in both contextual bandit and sequential decision-making tasks with similar results.


title: Verbal Redundancy in Multimedia Learning Environments: A Meta-Analysis

abstract: Verbal redundancy arises from the concurrent presentation of text and verbatim speech. To inform theories of multimedia learning that guide the design of educational materials, a meta-analysis was conducted to investigate the effects of spoken-only, written-only, and spoken--written presentations on learning retention and transfer. After an extensive search for experimental studies meeting specified inclusion criteria, data from 57 independent studies were extracted. Most of the research participants were postsecondary students. Overall, this meta-analysis revealed that outcomes comparing spoken--written and written-only presentations did not differ, but students who learned from spoken--written presentations outperformed those who learned from spoken-only presentations. This effect was dependent on learners' prior knowledge, pacing of presentation, and inclusion of animation or diagrams. Specifically, the advantages of spoken--written presentations over spoken-only presentations were found for low prior knowledge learners, system-paced learning materials, and picture-free materials. In comparison with verbatim, spoken--written presentations, presentations displaying key terms extracted from spoken narrations were associated with better learning outcomes and accounted for much of the advantage of spoken--written over spoken-only presentations. These findings have significant implications for the design of multimedia materials.


title: The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems

abstract: Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multi agent systems. We examine some of the factors that can influence the dynamics of the learning process in such a setting. We first distinguish reinforcement learners that are unaware of (or ignore) the presence of other agents from those that explicitly attempt to learn the value of joint actions and the strategies of their counterparts. We study (a simple form of) Q-leaming in cooperative multi agent systems under these two perspectives, focusing on the influence of that game structure and exploration strategies on convergence to (optimal and suboptimal) Nash equilibria. We then propose alternative optimistic exploration strategies that increase the likelihood of convergence to an optimal equilibrium.


title: Random features for large-scale kernel machines

abstract: To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shift-invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classification and regression tasks linear machine learning algorithms applied to these features outperform state-of-the-art large-scale kernel machines.


title: Image decomposition via the combination of sparse representations and a variational approach

abstract: The separation of image content into semantic parts plays a vital role in applications such as compression, enhancement, restoration, and more. In recent years, several pioneering works suggested such a separation be based on variational formulation and others using independent component analysis and sparsity. This paper presents a novel method for separating images into texture and piecewise smooth (cartoon) parts, exploiting both the variational and the sparsity mechanisms. The method combines the basis pursuit denoising (BPDN) algorithm and the total-variation (TV) regularization scheme. The basic idea presented in this paper is the use of two appropriate dictionaries, one for the representation of textures and the other for the natural scene parts assumed to be piecewise smooth. Both dictionaries are chosen such that they lead to sparse representations over one type of image-content (either texture or piecewise smooth). The use of the BPDN with the two amalgamed dictionaries leads to the desired separation, along with noise removal as a by-product. As the need to choose proper dictionaries is generally hard, a TV regularization is employed to better direct the separation process and reduce ringing artifacts. We present a highly efficient numerical scheme to solve the combined optimization problem posed by our model and to show several experimental results that validate the algorithm's performance.


title: The Cross-Coupled Pair?Part III [A Circuit for All Seasons]

abstract: In this article, we study applications of the cross-coupled pair (XCP) in analog and RF circuits. The XCP can serve as a negative resistance or a negative impedance converter in small-signal operation, or a regenerative circuit in large-signal operation.


title: Cross lingual text classification by mining multilingual topics from wikipedia

abstract: This paper investigates how to effectively do cross lingual text classification by leveraging a large scale and multilingual knowledge base, Wikipedia. Based on the observation that each Wikipedia concept is described by documents of different languages, we adapt existing topic modeling algorithms for mining multilingual topics from this knowledge base. The extracted topics have multiple types of representations, with each type corresponding to one language. In this work, we regard such topics extracted from Wikipedia documents as universal-topics, since each topic corresponds with same semantic information of different languages. Thus new documents of different languages can be represented in a space using a group of universal-topics. We use these universal-topics to do cross lingual text classification. Given the training data labeled for one language, we can train a text classifier to classify the documents of another language by mapping all documents of both languages into the universal-topic space. This approach does not require any additional linguistic resources, like bilingual dictionaries, machine translation tools, or labeling data for the target language. The evaluation results indicate that our topic modeling approach is effective for building cross lingual text classifier.


title: Autonomic mix-aware provisioning for non-stationary data center workloads

abstract: Online Internet applications see dynamic workloads that fluctuate over multiple time scales. This paper argues that the non-stationarity in Internet application workloads, which causes the request mix to change over time, can have a significant impact on the overall processing demands imposed on data center servers. We propose a novel mix-aware dynamic provisioning technique that handles both the non-stationarity in the workload as well as changes in request volumes when allocating server capacity in Internet data centers. Our technique employs the k-means clustering algorithm to automatically determine the workload mix and a queuing model to predict the server capacity for a given workload mix. We implement a prototype provisioning system that incorporates our technique and experimentally evaluate its efficacy on a laboratory Linux data center running the TPC-W web benchmark. Our results show that our k-means clustering technique accurately captures workload mix changes in Internet applications. We also demonstrate that mix-aware dynamic provisioning eliminates SLA violations due to under-provisioning with non-stationary web workloads, and that it offers a better resource usage by reducing over-provisioning when compared to a baseline provisioning approach that only reacts to workload volume changes. We also present a case study of our provisioning approach on Amazon's EC2 cloud platform.


title: Improving Reliability of Islanded Distribution Systems With Distributed Renewable Energy Resources

abstract: An optimization problem called “the feeder addition problem” is defined to determine potential locations for adding interties between feeders in a legacy radial distribution system to improve the reliability in the islanded mode of operation - a desired feature under the Smart Grid Initiative. Numerical bounds are established on the growth of the feeder addition problem. The feeder addition problem in a popular test system is handled using two different optimization methods to balance cost and reliability for utility applications. To approximate the power output of the distributed generation sources considered, an empirical equation incorporating the capacity factors of renewable energy-based distributed generation sources and results of an industry survey is used. Both optimization methods are shown to improve the system reliability for cases where the distributed generation output exceeded feeder demand.


title: Memristor modeling in MATLAB® &Simulink®

abstract: The article deals with various ways of memristor modeling and simulation in the MATLAB® & Simulink® environment. Recently used and published mathematical memristor model serves as a base, regarding all known features of its behavior. Three different approaches in the MATLAB & Simulink system are used for the differential and other equations formulation. The first one employs the standard system core offer for the Ordinary Differential Equations solutions (ODE) in the form of an m-file. The second approach is the model construction in Simulink environment. The third approach employs so-called physical modeling using the built-in Simscape™ system. The output data are the basic memristor characteristics and appropriate time courses. The features of all models are discussed, especially regarding the computer simulation. Possible problems that may occur during modeling are pointed.


title: MEAD - A Platform for Multidocument Multilingual Text Summarization

abstract: Abstract This paper describes the functionality of MEAD, a comprehensive, public domain, open source, multidocument multilingual summarization environment that has been thus far downloaded by more than 500 organizations. MEAD has been used in a variety of summarization applications ranging from summarization for mobile devices to Web page summarization within a search engine and to novelty detection.


title: Conducted EMI analysis and filter design for MHz active clamp flyback front-end converter

abstract: High frequency is the major catalyst for size reduction in the advancement of power conversion technology. It is essential to understand the EMI characteristic of high frequency converter since the EMI filter typically occupies one third of total system volume. This paper presents an insight view of CM/DM noise transformation in Flyback converter for low power adapter applications. The so-called mixed-mode noise is DM noise transformed from CM noise due to unbalanced impedance when the diode-bridge is off. The transformer shielding technique can effectively reduce the CM noise, and therefore it can significantly reduce the magnitude of CM transformed noise. A 65W (19V/3.3A) prototype of active clamp flyback front-end converter operating around 1MHz with GaN devices is developed to verify the analysis. A single stage EMI filter is designed accordingly with much smaller size compared with industry practice.


title: A computational approach to relative aesthetics

abstract: Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, we formulate a novel problem of ranking images with respect to their aesthetic quality. We construct a new dataset of image pairs with relative labels by carefully selecting images from the popular AVA dataset. Unlike in aesthetics classification, there is no single threshold which would determine the ranking order of the images across our entire dataset. We propose a deep neural network based approach that is trained on image pairs by incorporating principles from relative learning. Results show that such relative training procedure allows our network to rank the images with a higher accuracy than a state-of-art network trained on the same set of images using binary labels.


title: Migrating to internet-based e-commerce: Factors affecting e-commerce adoption and migration at the firm level

abstract: Web technology has enabled e-commerce. However, in our review of the literature, we found little research on how firms can better position themselves when adopting e-commerce for revenue generation. Drawing upon technology diffusion theory, we developed a conceptual model for assessing e-commerce adoption and migration, incorporating six factors unique to e-commerce. A series of propositions were then developed. Survey data of 1036 firms in a broad range of industries were collected and used to test our model. Our analysis based on multi-nominal logistic regression demonstrated that technology integration, web functionalities, web spending, and partner usage were significant adoption predictors. The model showed that these variables could successfully differentiate non-adopters from adopters. Further, the migration model demonstrated that web functionalities, web spending, and integration of externally oriented inter-organizational systems tend to be the most influential drivers in firms' migration toward e-commerce, while firm size, partner usage, electronic data interchange (EDI) usage, and perceived obstacles were found to negatively affect e-commerce migration. This suggests that large firms, as well as those that have been relying on outsourcing or EDI, tended to be slow to migrate to the internet platform.


title: Accurate Multiple View 3D Reconstruction Using Patch-Based Stereo for Large-Scale Scenes

abstract: In this paper, we propose a depth-map merging based multiple view stereo method for large-scale scenes which takes both accuracy and efficiency into account. In the proposed method, an efficient patch-based stereo matching process is used to generate depth-map at each image with acceptable errors, followed by a depth-map refinement process to enforce consistency over neighboring views. Compared to state-of-the-art methods, the proposed method can reconstruct quite accurate and dense point clouds with high computational efficiency. Besides, the proposed method could be easily parallelized at image level, i.e., each depth-map is computed individually, which makes it suitable for large-scale scene reconstruction with high resolution images. The accuracy and efficiency of the proposed method are evaluated quantitatively on benchmark data and qualitatively on large data sets.


title: Metronomic chemotherapy: new rationale for new directions

abstract: Metronomic chemotherapy is the chronic administration of chemotherapeutic agents at relatively low, minimally toxic doses, and with no prolonged drug-free breaks. This type of chemotherapy inhibits tumor growth primarily through anti-angiogenic mechanisms. The latest clinical trials of metronomic chemotherapy in adult and pediatric cancer patients are discussed and the authors highlight the research efforts that need to be made to facilitate the optimal development of metronomic chemotherapy in the clinic.


title: pVM: persistent virtual memory for efficient capacity scaling and object storage

abstract: Next-generation byte-addressable nonvolatile memories (NVMs), such as phase change memory (PCM) and Memristors, promise fast data storage, and more importantly, address DRAM scalability issues. State-of-the-art OS mechanisms for NVMs have focused on improving the block-based virtual file system (VFS) to manage both persistence and the memory capacity scaling needs of applications. However, using the VFS for capacity scaling has several limitations, such as the lack of automatic memory capacity scaling across DRAM and NVM, inefficient use of the processor cache and TLB, and high page access costs. These limitations reduce application performance and also impact applications that use NVM for persistent object storage with flat namespaces, such as photo stores, NoSQL databases, and others. To address such limitations, we propose persistent virtual memory (pVM), a system software abstraction that provides applications with (1) automatic OS-level memory capacity scaling, (2) flexible memory placement policies across NVM, and (3) fast object storage. pVM extends the OS virtual memory (VM) instead of building on the VFS and abstracts NVM as a NUMA node with support for NVM-based memory placement mechanisms. pVM inherits benefits from the cache and TLB-efficient VM subsystem and augments these further by distinguishing between persistent and nonpersistent capacity use of NVM. Additionally, pVM achieves fast persistent storage by further extending the VM subsystem with consistent and durable OS-level persistent metadata. Our evaluation of pVM with memory capacity-intensive applications shows a 2.5x speedup and up to 80% lower TLB and cache misses compared to VFS-based systems. pVM's object store provides 2x higher throughput compared to the block-based approach of the state-of-the art solution and up to a 4x reduction in the time spent in the OS.


title: Context-Based Chinese Word Segmentation using SVM Machine-Learning Algorithm without Dictionary Support

abstract: This paper presents a new machine-learning Chinese word segmentation (CWS) approach, which defines CWS as a break-point classification problem; the break point is the boundary of two subsequent words. Further, this paper exploits a support vector machine (SVM) classifier, which learns the segmentation rules of the Chinese language from a context model of break points in a corpus. Additionally, we have designed an effective feature set for building the context model, and a systematic approach for creating the positive and negative samples used for training the classifier. Unlike the traditional approach, which requires the assistance of large-scale known information sources such as dictionaries or linguistic tagging, the proposed approach selects the most frequent words in the corpus as the learning sources. In this way, CWS is able to execute in any novel corpus without proper assistance sources. According to our experimental results, the proposed approach can achieve a competitive result compared with the Chinese knowledge and information processing (CKIP) system from Academia Sinica.


title: Cross-lingual Models of Word Embeddings: An Empirical Comparison

abstract: Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature. We perform an extensive evaluation of four popular approaches of inducing cross-lingual embeddings, each requiring a different form of supervision, on four typographically different language pairs. Our evaluation setup spans four different tasks, including intrinsic evaluation on mono-lingual and cross-lingual similarity, and extrinsic evaluation on downstream semantic and syntactic applications. We show that models which require expensive cross-lingual knowledge almost always perform better, but cheaply supervised models often prove competitive on certain tasks.


Two-body linear guidance matrices


title: An Inventory of Preposition Relations

abstract: We describe an inventory of semantic relations that are expressed by prepositions. We define these relations by building on the word sense disambiguation task for prepositions and propose a mapping from preposition senses to the relation labels by collapsing semantically related senses across prepositions.


title: Bayesian Network Structure Learning and Inference Methods for Handwriting

abstract: Probabilistic models of characteristics of handwritten words are useful in forensic document examination since they can be used to answer queries such as: determine the rarity of a given style of writing of the word, find the probability of observing those characteristics in a representative database of given size, etc. The task considered here is to use a training set of samples of a word written by a representative population of individuals (with each individual's writing of the word being described by a fixed set of discrete categorical variables), to construct directed probabilistic graphical models (Bayesian networks or BNs) and then use such models to answer probabilistic queries. However, since the BN structure learning problem is NP-hard, we propose an approximate method and analyze its performance and complexity. The proposed algorithm uses a local measure of deviance from independence (chi-squared tests between pairs of variables) and a global score (log-loss). The method builds the BN structure incrementally, by adding directed edges with high deviance and choosing the edge direction to minimize log-loss. The method is evaluated with samples of the word and obtained from a representative population of the United States with descriptive characteristic sets that are different for cursive writing and for hand-printing. For several samples obtained from the BN, the probability of random correspondence (PRC) is inferred. A measure of the discriminatory power of the characteristic set (conditional PRC) is also determined. The computational complexity of determining the probability of finding a similar one to a given sample, within a tolerance, in a database of given size, is discussed.


title: Postural Stability of Biped Robots and the Foot-Rotation Indicator (FRI) Point

abstract: The focus of this paper is the problem of foot rotation in biped robots during the single-support phase. Foot rotation is an indication of postural instability, which should be carefully treated in a dynamically stable walk and avoided altogether in a statically stable walk. We introduce the foot-rotation indicator (FRI) point, which is a point on the foot/ground-contact surface where the net ground-reaction force would have to act to keep the foot stationary. To ensure no foot rotation, the FRI point must remain within the convex hull of the foot-support area. In contrast with the ground projection of the center of mass (GCoM), which is a static criterion, the FRI point incorporates robot dynamics. As opposed to the center of pressure (CoP)—better known as the zero-moment point (ZMP) in the robotics literature—which may not leave the support area, the FRI point may leave the area. In fact, the position of the FRI point outside the footprint indicates the direction of the impending rotation and the magnitude of rotational moment acting on the foot. Owing to these important properties, the FRI point helps not only to monitor the state of postural stability of a biped robot during the entire gait cycle, but indicates the severity of instability of the gait as well. In response to a recent need, the paper also resolves the misconceptions surrounding the CoP/ZMP equivalence.


title: "GrabCut": interactive foreground extraction using iterated graph cuts

abstract: The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.


Planning for Resilience in Oregon ’ s Coastal Drinking Water Systems


title: Learning to Detect A Salient Object

abstract: We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient object from the image background. We propose a set of novel features including multi-scale contrast, center-surround histogram, and color spatial distribution to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. We also constructed a large image database containing tens of thousands of carefully labeled images by multiple users. To our knowledge, it is the first large image database for quantitative evaluation of visual attention algorithms. We validate our approach on this image database, which is public available with this paper.


title: God and the Great Failure: Poetry from the Irish Famine

abstract: In the years from 1845 to 1849, the potato crop in Ireland was afflicted by recurrent disease, effectively obliterating the staple diet for much of the population. Historians disagree about the number of deaths and other details, but several million people died; many emigrated, often dying on the journey; Cecil Woodham-Smith's Tire Great Hunger. Ireland 1845-9 is a good introduction to the situation. The crop failure itself was aggravated by homelessness; inability to pay rent gave a pretext for eviction of tenants. Governmental attitudes were affected by notions of Progress and the need to reform the Irish land system, by the belief that the Irish had been most irresponsible in their population growth, by a conviction that Providence intended such sufferings.


title: Bacteriocins: Evolution, Ecology, and Application

abstract: Microbes produce an extraordinary array of microbial defense systems. These include classical antibiotics, metabolic by-products, lytic agents, numerous types of protein exotoxins, and bacteriocins. The abundance and diversity of this potent arsenal of weapons are clear. Less clear are their evolutionary origins and the role they play in mediating microbial interactions. The goal of this review is to explore what we know about the evolution and ecology of the most abundant and diverse family of microbial defense systems: the bacteriocins. We summarize current knowledge of how such extraordinary protein diversity arose and is maintained in microbial populations and what role these toxins play in mediating microbial population-level and community-level dynamics. In the latter half of this review we focus on the potential role bacteriocins may play in addressing human health concerns and the current role they serve in food preservation.


title: From prediction error to incentive salience: mesolimbic computation of reward motivation

abstract: Reward contains separable psychological components of learning, incentive motivation and pleasure. Most computational models have focused only on the learning component of reward, but the motivational component is equally important in reward circuitry, and even more directly controls behavior. Modeling the motivational component requires recognition of additional control factors besides learning. Here I discuss how mesocorticolimbic mechanisms generate the motivation component of incentive salience. Incentive salience takes Pavlovian learning and memory as one input and as an equally important input takes neurobiological state factors (e.g. drug states, appetite states, satiety states) that can vary independently of learning. Neurobiological state changes can produce unlearned fluctuations or even reversals in the ability of a previously learned reward cue to trigger motivation. Such fluctuations in cue-triggered motivation can dramatically depart from all previously learned values about the associated reward outcome. Thus, one consequence of the difference between incentive salience and learning can be to decouple cue-triggered motivation of the moment from previously learned values of how good the associated reward has been in the past. Another consequence can be to produce irrationally strong motivation urges that are not justified by any memories of previous reward values (and without distorting associative predictions of future reward value). Such irrationally strong motivation may be especially problematic in addiction. To understand these phenomena, future models of mesocorticolimbic reward function should address the neurobiological state factors that participate to control generation of incentive salience.


title: Signaling-Embedded Preamble Design for Flexible Optical Transport Networks

abstract: Coherent optical orthogonal frequency division multiplexing (CO-OFDM) is a promising technique for future elastic optical transport networks. In CO- OFDM systems, the preamble is usually used for timing and frequency synchronization, and dedicated pilots are adopted to carry signaling for flexible system configurations. In this paper, we propose a judicious signalingembedded preamble design to simultaneously achieve the exact timing synchronization based on the ideal Delta-like timing metric, the accurate frequency synchronization with sufficient estimation range, as well as reliable signaling transmission. This is achieved by designing the preamble in the frequency, i.e., two identical training sequences with a specific distance occupy the even subcarriers of the preamble, whereby the system signaling is carried by the combination of different training sequences and different distances between two sequences. Such frequencydomain design would result in the time- domain preamble having conjugate symmetric property and two repetitive parts. The former property is used to produce the ideal Delta- like correlation function for exact timing synchronization, while the latter one is used for accurate frequency synchronization. Simulation results also show that the improved signaling detection performance can be achieved.


title: About the size of Google Scholar: playing the numbers

abstract: The emergence of academic search engines (Google Scholar and Microsoft Academic Search essentially) has revived and increased the interest in the size of the academic web, since their aspiration is to index the entirety of current academic knowledge. The search engine functionality and human search patterns lead us to believe, sometimes, that what you see in the search engine's results page is all that really exists. And, even when this is not true, we wonder which information is missing and why. The main objective of this working paper is to calculate the size of Google Scholar at present (May 2014). To do this, we present, apply and discuss up to 4 empirical methods: Khabsa&Giles's method, an estimate based on empirical data, and estimates based on direct queries and absurd queries. The results, despite providing disparate values, place the estimated size of Google Scholar in about 160 million documents. However, the fact that all methods show great inconsistencies, limitations and uncertainties, makes us wonder why Google does not simply provide this information to the scientific community if the company really knows this figure.


title: Decrease alpha waves in depression: An electroencephalogram(EEG) study

abstract: There was no affirm study that differentiates the brainwaves between depression subjects and healthy subjects until today. Hyperactivity and low activity of the brain activities in various parts and regions could be evaluated and studied though the brainwaves measured from the electroencephalogram (EEG). The losing ability of the brain to transmit signals and information that caused a person to be depressed makes a person influences their normal daily activities. Depression is a mood disorder which may affect our daily work, sleep, eating habits and general health. Therefore, it is a general term and commonly infected by anyone. The aim of this study was to determine the differences of alpha waves between normal and depression groups. Throughout the research, depression screening measurements of Patient Health Questionnaire-9 (PHQ-9) and Depression Anxiety Stress Scale-21 (DASS-21) were taken into accounts in order to identify the normal and depression groups. A total of 4 normal subjects and 4 depressed subjects participated in this study. A 32 channels EEG was used to detect the difference of alpha waves in depression and normal groups. The alpha waves in depression group were found out to be lower compared to the normal group in both close eyes and open eyes conditions. The T-Test statistical analysis shown that there were significant differences in the alpha-1 waves in close eyes condition and alpha 1 and alpha-2 waves in the open eyes condition for depression group. In addition, the frontal lobe, parietal lobe, occipital lobe and temporal lobe had found out to have much lower alpha waves in the depression group compared to the normal group. In short, by measuring the alpha waves of a person using EEG may be a biomarker in differentiating a healthy or depressed person in the future.


title: Palmprint verification using binary orientation co-occurrence vector

abstract: The development of accurate and robust palmprint verification algorithms is a critical issue in automatic palmprint authentication systems. Among various palmprint verification approaches, the orientation based coding methods, such as competitive code (CompCode), palmprint orientation code (POC) and robust line orientation code (RLOC), are state-of-the-art ones. They extract and code the locally dominant orientation as features and could match the input palmprint in real-time and with high accuracy. However, using only one dominant orientation to represent a local region may lose some valuable information because there are cross lines in the palmprint. In this paper, we propose a novel feature extraction algorithm, namely binary orientation co-occurrence vector (BOCV), to represent multiple orientations for a local region. The BOCV can better describe the local orientation features and it is more robust to image rotation. Our experimental results on the public palmprint database show that the proposed BOCV outperforms the CompCode, POC and RLOC by reducing the equal error rate (EER) significantly.


title: Mixture-Model Adaptation for SMT

abstract: We describe a mixture-model approach to adapting a Statistical Machine Translation System for new domains, using weights that depend on text distances to mixture components. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and translation model adaptation; different methods of assigning weights; and granularity of the source unit being adapted to. The best methods achieve gains of approximately one BLEU percentage point over a state-of-the art non-adapted baseline system.


title: High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data

abstract: The Entity Disambiguation and Linking (EDL) task matches entity mentions in text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase id. It plays a critical role in the construction of a high quality information network, and can be further leveraged for a variety of information retrieval and NLP tasks such as text categorization and document tagging. EDL is a complex and challenging problem due to ambiguity of the mentions and real world text being multi-lingual. Moreover, EDL systems need to have high throughput and should be lightweight in order to scale to large datasets and run on off-the-shelf machines. More importantly, these systems need to be able to extract and disambiguate dense annotations from the data in order to enable an Information Retrieval or Extraction task running on the data to be more efficient and accurate. In order to address all these challenges, we present the Lithium EDL system and algorithm - a high-throughput, lightweight, language-agnostic EDL system that extracts and correctly disambiguates 75% more entities than state-of-the-art EDL systems and is significantly faster than them.


title: A Two-Stage Resonant Inverter With Control of the Phase Angle and Magnitude of the Output Voltage

abstract: A high-efficiency two-stage resonant inverter with effective control of both the magnitude and phase angle of the output voltage was proposed in this paper for high-frequency ac (HFAC) power-distribution applications, where a number of resonant inverters need to be paralleled. In order to parallel multiple resonant inverters of the same operation frequency, each inverter module needs independent control of the phase angle and magnitude of the output voltage. It is also desirable that the output voltage has very low total harmonics distortion, as well as high efficiency over wide input and load ranges. The proposed resonant inverter consists of two stages. The first stage is a two-switch dc/dc converter with zero-voltage transition, and the second stage is a half-bridge resonant dc/ac inverter with fixed duty ratio. A series-parallel resonant tank is used to achieve high waveform quality of the output voltage. The magnitude of the output voltage is regulated through the duty-ratio control of the first stage with pulsewidth modulation. The phase angle of the output voltage is regulated through a pulse-phase-modulation control of the second stage. The proposed resonant inverter has the advantages of better waveform quality, wide range of input and load variations for soft-switching, and independent control of the phase angle and magnitude of the output voltage, making it an attractive candidate for applications where a number of resonant inverters need to be placed in parallel to the HFAC bus and a number of distributed loads are connected to the HFAC bus. The performance is verified with both simulation and experiments.


title: Dbpedia and the live extraction of structured data from wikipedia. Program: electronic library and information systems

abstract: Purpose – DBpedia extracts structured information from Wikipedia, interlinks it with other knowledge bases and freely publishes the results on the web using Linked Data and SPARQL. However, the DBpedia release process is heavyweight and releases are sometimes based on several months old data. DBpedia‐Live solves this problem by providing a live synchronization method based on the update stream of Wikipedia. This paper seeks to address these issues.Design/methodology/approach – Wikipedia provides DBpedia with a continuous stream of updates, i.e. a stream of articles, which were recently updated. DBpedia‐Live processes that stream on the fly to obtain RDF data and stores the extracted data back to DBpedia. DBpedia‐Live publishes the newly added/deleted triples in files, in order to enable synchronization between the DBpedia endpoint and other DBpedia mirrors.Findings – During the realization of DBpedia‐Live the authors learned that it is crucial to process Wikipedia updates in a priority queue. Recently‐upd...


Computer-Based Clinical Decision Support System for Prediction of Heart Diseases Using Naïve Bayes Algorithm


Entropy analysis of physical unclonable functions


title: Application of spiking neural networks and the bees algorithm to control chart pattern recognition

abstract: Statistical process control (SPC) is a method for improving the quality of products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types of charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application of SNN techniques to control chart pattern recognition. It is designed to present an analysis of the existing learning algorithms of SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation of neural networks. This thesis focuses on the architecture and the learning procedure of the network. Four new learning algorithms arc presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim of the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies.


title: Video stabilization with moving object detecting and tracking for aerial video surveillance

abstract: Aerial surveillance system provides a large amount of data compared with traditional surveillance system. But, it usually suffers from undesired motion of cameras, which presents new challenges. These challenges must be overcome before such video can be widely used. In this paper, we present a novel video stabilization and moving object detection system based on camera motion estimation. We use local feature extraction and matching to estimate global motion and we demonstrate that Scale Invariant Feature Transform (SIFT) keypoints are suitable for the stabilization task. After estimating the global camera motion parameters using affine transformation, we detect moving object by Kalman filtering. For motion smoothing, we use a median filter to retain the desired motion. Finally, motion compensation is carried out to obtain a stabilized video sequence. A number of aerial video examples demonstrate the effectiveness of our proposed system. We use the software Virtual Dub with the Deshaker-Plugin for test purposes. For objective evaluation, we use Interframe Transformation Fidelity for video stabilization tasks and Detection Ratio for moving object detection task.


title: On a generalized Levinthal's paradox: The role of long- and short range interactions in complex bio-molecular reactions, including protein and DNA folding.

abstract: The current protein folding literature is reviewed. Two main approaches to the problem of folding were selected for this review: geometrical and biophysical. The geometrical approach allows the formulation of topological restrictions on folding, that are usually not taken into account in the construction of physical models. In particular, the topological constraints do not allow the known funnel-like energy landscape modeling, although most common methods of resolving the paradox are based on this method. The very paradox is based on the fact that complex molecules must reach their native conformations (complexes that result from reactions) in an exponentially long time, which clearly contradicts the observed experimental data. In this respect we considered the complexity of the reactions between ligands and proteins. On this general basis, the folding-reaction paradox was reformulated and generalized. We conclude that prospects for solving the paradox should be associated with incorporating a topology aspect in biophysical models of protein folding, through the construction of hybrid models. However, such models should explicitly include long-range force fields and local cell biological conditions, such as structured water complexes and photon/phonon/soliton waves, ordered in discrete frequency bands. In this framework, collective and coherent oscillations in, and between, macromolecules are instrumental in inducing intra- and intercellular resonance, serving as an integral guiding network of life communication: the electrome aspect of the cell. Yet, to identify the actual mechanisms underlying the bonds between molecules (atoms), it will be necessary to perform dedicated experiments to more definitely solve the particular time paradox.


title: Knowledge Graph Embeddings with node2vec for Item Recommendation

abstract: In the past years, knowledge graphs have proven to be beneficial for recommender systems, efficiently addressing paramount issues such as new items and data sparsity. Graph embeddings algorithms have shown to be able to automatically learn high quality feature vectors from graph structures, enabling vector-based measures of node relatedness. In this paper, we show how node2vec can be used to generate item recommendations by learning knowledge graph embeddings. We apply node2vec on a knowledge graph built from the MovieLens 1M dataset and DBpedia and use the node relatedness to generate item recommendations. The results show that node2vec consistently outperforms a set of collaborative filtering baselines on an array of relevant metrics.


title: Integrating psychological approaches to entrepreneurship: the Entrepreneurial Personality System (EPS)

abstract: Understanding the psychological nature and development of the individual entrepreneur is at the core of contemporary entrepreneurship research. Since the individual functions as a totality of his or her single characteristics (involving the interplay of biological, psychosocial, and context-related levels), a person-oriented approach focusing on intraindividual dynamics seems to be particularly fruitful to infer realistic implications for practice such as entrepreneurship education and promotion. Applying a person-oriented perspective, this paper integrates existing psychological approaches to entrepreneurship and presents a new, person-oriented model of entrepreneurship, the Entrepreneurial Personality System (EPS). In the empirical part, this model guided us to bridge two separate research streams dealing with entrepreneurial personality: research on broad traits like the Big Five and research on specific traits like risk-taking, self-efficacy, and internal locus of control. We examine a gravity effect of broad traits, as assumed in the EPS framework, by analyzing large personality data sets from three countries. The results reveal a consistent gravity effect of an intraindividual entrepreneurial Big Five profile on the more malleable psychological factors. Implications for entrepreneurship research and practice are discussed.


Deep Active Learning for Civil Infrastructure Defect Detection and Classification


Towards a Definition of Knowledge Graphs


title: Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations

abstract: Travel websites and online booking platforms represent today’s major sources for customers when gathering information before a trip. In particular, community-provided customer reviews and ratings of various tourism services represent a valuable source of information for trip planning. With respect to customer ratings, many modern travel and tourism platforms—in contrast to several other e-commerce domains—allow customers to rate objects along multiple dimensions and thus to provide more fine-granular post-trip feedback on the booked accommodation or travel package. In this paper, we first show how this multi-criteria rating information can help to obtain a better understanding of factors driving customer satisfaction for different segments. For this purpose, we performed a Penalty-Reward contrast analysis on a data set from a major tourism platform, which reveals that customer segments significantly differ in the way the formation of overall satisfaction can be explained. Beyond the pure identification of segment-specific satisfaction factors, we furthermore show how this fine-granular rating information can be exploited to improve the accuracy of rating-based recommender systems. In particular, we propose to utilize user- and object-specific factor relevance weights which can be learned through linear regression. An empirical evaluation on datasets from different domains finally shows that our method helps us to predict the customer preferences more accurately and thus to develop better online recommendation services.


abstract: A single-layer CPW-fed active patch antenna oscillator at 2.76 GHz is presented. The patch antenna acts both as a resonator and a radiator. Electromagnetic coupling is utilized for providing the appropriate closed-loop positive feedback. The active antenna is built around a commercially available GaAs MMIC on a low-permittivity /spl epsiv//sub r/=2.33 Duroid 5870 substrate. The measured effective isotropic radiated power (EIRP) is 20 dBm, whereas the front-to-back ratio is about 12 dB with the cross-polarized fields better than -15 db. The measured phase noise is -87.5 dBc/Hz at a 100-kHz offset away from the carrier. The structure only requires a single substrate, is compatible with uniplanar technology, and results in a low-component count.


title: Interpreting"altmetrics": viewing acts on social media through the lens of citation and social theories

abstract: More than 30 years after Cronin's seminal paper on "the need for a theory of citing" (Cronin, 1981), the metrics community is once again in need of a new theory, this time one for so-called "altmetrics". Altmetrics, short for alternative (to citation) metrics -- and as such a misnomer -- refers to a new group of metrics based (largely) on social media events relating to scholarly communication. As current definitions of altmetrics are shaped and limited by active platforms, technical possibilities, and business models of aggregators such as Altmetric.com, ImpactStory, PLOS, and Plum Analytics, and as such constantly changing, this work refrains from defining an umbrella term for these very heterogeneous new metrics. Instead a framework is presented that describes acts leading to (online) events on which the metrics are based. These activities occur in the context of social media, such as discussing on Twitter or saving to Mendeley, as well as downloading and citing. The framework groups various types of acts into three categories -- accessing, appraising, and applying -- and provides examples of actions that lead to visibility and traceability online. To improve the understanding of the acts, which result in online events from which metrics are collected, select citation and social theories are used to interpret the phenomena being measured. Citation theories are used because the new metrics based on these events are supposed to replace or complement citations as indicators of impact. Social theories, on the other hand, are discussed because there is an inherent social aspect to the measurements.


Urban Agriculture For Sustainable Poverty Alleviation and Food Security


title: Electro-Enzymatic Sensor for Non-Invasive Glucose Measurement

abstract: In this article, a cost effective and simple electro-enzymatic sensor to measure glucose concentration non-invasively is proposed. Measurement of blood glucose concentration for early diabetes detection is one of the major health concerns. Usually, blood or other biofluid is collected by puncturing the finger or fore arm (invasive) which is a painful process. Hence, blood glucose measurement by non-invasive method is more desirable. Here, we propose a first step towards our final goal of non-invasive glucose measurement from human tears using contact lenses.


title: StrikeAPose: revealing mid-air gestures on public displays

abstract: We investigate how to reveal an initial mid-air gesture on interactive public displays. This initial gesture can serve as gesture registration for advanced operations. We propose three strategies to reveal the initial gesture: spatial division, temporal division and integration. Spatial division permanently shows the gesture on a dedicated screen area. Temporal division interrupts the application to reveal the gesture. Integration embeds gesture hints directly in the application. We also propose a novel initial gesture called Teapot to illustrate our strategies. We report on a laboratory and field study. Our main findings are: A large percentage of all users execute the gesture, especially with spatial division (56%). Users intuitively discover a gesture vocabulary by exploring variations of the Teapot gesture by themselves, as well as by imitating and extending other users' variations.


title: The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory

abstract: Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the optimization backbone for training several classic models, from regression to neural networks. Given the recent practical focus on distributed machine learning, significant work has been dedicated to the convergence properties of this algorithm under the inconsistent and noisy updates arising from execution in a distributed environment. However, surprisingly, the convergence properties of this classic algorithm in the standard shared-memory model are still not well-understood. In this work, we address this gap, and provide new convergence bounds for lock-free concurrent stochastic gradient descent, executing in the classic asynchronous shared memory model, against a strong adaptive adversary. Our results give improved upper and lower bounds on the "price of asynchrony'' when executing the fundamental SGD algorithm in a concurrent setting. They show that this classic optimization tool can converge faster and with a wider range of parameters than previously known under asynchronous iterations. At the same time, we exhibit a fundamental trade-off between the maximum delay in the system and the rate at which SGD can converge, which governs the set of parameters under which this algorithm can still work efficiently.


title: Enrichment of a mixed bacterial culture with a high polyhydroxyalkanoate storage capacity.

abstract: Polyhydroxyalkanoates (PHAs) are microbial storage polymers that attract interest as bioplastics. PHAs can be produced with open mixed cultures if a suitable enrichment step based on the ecological role of PHA is used. An acetate-fed sequencing batch reactor operated with 1 day biomass residence time and with feast-famine cycles of 12 h was used to enrich a mixed culture of PHA producers. In subsequent fed-batch experiments under growth limiting conditions, the enriched mixed culture produced PHA up to a cellular content of 89 wt % within 7.6 h (average rate of 1.2 g/g/h). The PHA produced from acetate was the homopolymer polyhydroxybutyrate. The culture was dominated by a Gammaproteobacterium that showed little similarity on 16S rRNA level with known bacteria (<90% sequence similarity). The mixed culture process for PHA production does not require aseptic conditions. Waste streams rather than pure substrates could be used as raw materials.


The lumigraph


title: Power analysis and sample size estimation for RNA-Seq differential expression.

abstract: It is crucial for researchers to optimize RNA-seq experimental designs for differential expression detection. Currently, the field lacks general methods to estimate power and sample size for RNA-Seq in complex experimental designs, under the assumption of the negative binomial distribution. We simulate RNA-Seq count data based on parameters estimated from six widely different public data sets (including cell line comparison, tissue comparison, and cancer data sets) and calculate the statistical power in paired and unpaired sample experiments. We comprehensively compare five differential expression analysis packages (DESeq, edgeR, DESeq2, sSeq, and EBSeq) and evaluate their performance by power, receiver operator characteristic (ROC) curves, and other metrics including areas under the curve (AUC), Matthews correlation coefficient (MCC), and F-measures. DESeq2 and edgeR tend to give the best performance in general. Increasing sample size or sequencing depth increases power; however, increasing sample size is more potent than sequencing depth to increase power, especially when the sequencing depth reaches 20 million reads. Long intergenic noncoding RNAs (lincRNA) yields lower power relative to the protein coding mRNAs, given their lower expression level in the same RNA-Seq experiment. On the other hand, paired-sample RNA-Seq significantly enhances the statistical power, confirming the importance of considering the multifactor experimental design. Finally, a local optimal power is achievable for a given budget constraint, and the dominant contributing factor is sample size rather than the sequencing depth. In conclusion, we provide a power analysis tool (http://www2.hawaii.edu/~lgarmire/RNASeqPowerCalculator.htm) that captures the dispersion in the data and can serve as a practical reference under the budget constraint of RNA-Seq experiments.


title: Calculating source line level energy information for Android applications

abstract: The popularity of mobile apps continues to grow as developers take advantage of the sensors and data available on mobile devices. However, the increased functionality comes with a higher energy cost, which can cause a problem for users on battery constrained mobile devices. To improve the energy consumption of mobile apps, developers need detailed information about the energy consumption of their applications. Existing techniques have drawbacks that limit their usefulness or provide information at too high of a level of granularity, such as components or methods. Our approach is able to calculate source line level energy consumption information. It does this by combining hardware-based power measurements with program analysis and statistical modeling. Our empirical evaluation of the approach shows that it is fast and accurate.


title: Surgical checklists: a systematic review of impacts and implementation

abstract: BACKGROUND ::: Surgical complications represent a significant cause of morbidity and mortality with the rate of major complications after inpatient surgery estimated at 3-17% in industrialised countries. The purpose of this review was to summarise experience with surgical checklist use and efficacy for improving patient safety. ::: ::: ::: METHODS ::: A search of four databases (MEDLINE, CINAHL, EMBASE and the Cochrane Database of Controlled Trials) was conducted from 1 January 2000 to 26 October 2012. Articles describing actual use of the WHO checklist, the Surgical Patient Safety System (SURPASS) checklist, a wrong-site surgery checklist or an anaesthesia equipment checklist were eligible for inclusion (this manuscript summarises all but the anaesthesia equipment checklists, which are described in the Agency for Healthcare Research and Quality publication). ::: ::: ::: RESULTS ::: We included a total of 33 studies. We report a variety of outcomes including avoidance of adverse events, facilitators and barriers to implementation. Checklists have been adopted in a wide variety of settings and represent a promising strategy for improving the culture of patient safety and perioperative care in a wide variety of settings. Surgical checklists were associated with increased detection of potential safety hazards, decreased surgical complications and improved communication among operating staff. Strategies for successful checklist implementation included enlisting institutional leaders as local champions, incorporating staff feedback for checklist adaptation and avoiding redundancies with existing systems for collecting information. ::: ::: ::: CONCLUSIONS ::: Surgical checklists represent a relatively simple and promising strategy for addressing surgical patient safety worldwide. Further studies are needed to evaluate to what degree checklists improve clinical outcomes and whether improvements may be more pronounced in particular settings.