Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities

Motivation: Deep learning architectures have recently demonstrated their power in predicting DNAand RNA-binding specificities. Existing methods fall into three classes: Some are based on Convolutional Neural Networks (CNNs), others use Recurrent Neural Networks (RNNs), and others rely on hybrid architectures combining CNNs and RNNs. However, based on existing studies it is still unclear which deep learning architecture is achieving the best performance. Thus an in-depth analysis and evaluation of the different methods is needed to fully evaluate their relative. Results: In this study, We present a systematic exploration of various deep learning architectures for predicting DNAand RNA-binding specificities. For this purpose, we present deepRAM, an end-to-end deep learning tool that provides an implementation of novel and previously proposed architectures; its fully automatic model selection procedure allows us to perform a fair and unbiased comparison of deep learning architectures. We find that an architecture that uses k-mer embedding to represent the sequence, a convolutional layer and a recurrent layer, outperforms all other methods in terms of model accuracy. Our work provides guidelines that will assist the practitioner in choosing the best architecture for the task at hand, and provides some insights on the differences between the models learned by convolutional and recurrent networks. In particular, we find that although recurrent networks improve model accuracy, this comes at the expense of a loss in the interpretability of the features learned by the model. Availability and implementation: The source code for deepRAM is available at https://github.com/MedChaabane/deepRAM Contact: asa@cs.colostate.edu Supplementary information: Supplementary data are available at Bioinformatics online.


Educating the Disagreeable Extravert : Narcissism , the Big Five Personality Traits , and Achievement Goal Orientation

Despite the fact that longitudinal data have been compiled over the past 30 years among undergraduate students in higher education settings regarding narcissism, the literature is devoid of empirical investigations that explore the relationships between narcissism and learning. Because the data suggest that narcissism scores are increasing each year among this population, an exploration of the relationship between narcissism and learning is timely and warranted. Sampling from university undergraduate students, this study uses the Narcissistic Personality Inventory, the Big Five Inventory, and the Achievement Goal Questionnaire to verify the known relationships between narcissism and the Big Five personality traits of extraversion and agreeableness; to verify the known relationships between the Big Five personality traits of extraversion and agreeableness and goal orientation; and to explore a previously undocumented empirical relationship between narcissism and performance goal orientation. Results of this exploratory study indicate that while narcissism does contribute to a performance goal orientation, it is not a substantial variable in determining achievement goal orientation in general. The study addresses the implications and limitations of this research in addition to areas for additional investigation.


Altitude optimization of Airborne Wind Energy systems: A Bayesian Optimization approach

This study presents a data-driven approach for optimizing the operating altitude of Airborne Wind Energy (AWE) systems to maximize net energy production. Determining the optimal operating altitude of an AWE system is challenging, as the wind speed constantly varies with both time and altitude. Furthermore, without expensive auxiliary equipment, the wind speed is only measurable at the AWE system's operating altitude. The work presented in this paper shows how tools from machine learning can be blended with real-time control to optimize the AWE system's operating altitude efficiently, without the use of auxiliary wind profiling equipment. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is applied to the real-time control of an AWE system. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent operating altitude. In the AWE application, conventional Bayesian Optimization is extended to handle the time-varying nature of the wind shear profile (wind speed vs. time). Using real wind data, our method is validated against three baseline approaches. Our simulation results show that the Bayesian Optimization method is successful in dramatically increasing power production over these baselines.


Collecting Large, Richly Annotated Facial-Expression Databases from Movies

Two large facial-expression databases depicting challenging real-world conditions were constructed using a semi-automatic approach via a recommender system based on subtitles.


HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Dynamic mode decomposition of numerical and experimental data Peter Schmid


Traffic monitoring and accident detection at intersections

Among the most important research in Intelligent Transportation Systems (ITS) is the development of systems that automatically monitor traffic flow at intersections. Rather than being based on global flow analysis as is currently done, these automatic monitoring systems should be based on local analysis of the behavior of each vehicle at the intersection. The systems should be able to identify each vehicle and track its behavior, and to recognize situations or events that are likely to result from a chain of such behavior. The most difficult problem associated with vehicle tracking is the occlusion effect among vehicles. In order to solve this problem we have developed an algorithm, referred to as spatio-temporal Markov random field (MRF), for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the – image axes as well as the time axes. Vehicles, of course, are of various shapes and they move in random fashion, thereby leading to full or partial occlusion at intersections. Despite these complications, our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%–96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.


Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition

Most successful deep learning algorithms for action recognition extend models designed for image-based tasks such as object recognition to video. Such extensions are typically trained for actions on single video frames or very short clips, and then their predictions from sliding-windows over the video sequence are pooled for recognizing the action at the sequence level. Usually this pooling step uses the first-order statistics of frame-level action predictions. In this paper, we explore the advantages of using higherorder correlations, specifically, we introduce Higher-order Kernel (HOK) descriptors generated from the late fusion of CNN classifier scores from all the frames in a sequence. To generate these descriptors, we use the idea of kernel linearization. Specifically, a similarity kernel matrix, which captures the temporal evolution of deep classifier scores, is first linearized into kernel feature maps. The HOK descriptors are then generated from the higher-order cooccurrences of these feature maps, and are then used as input to a video-level classifier. We provide experiments on two fine-grained action recognition datasets, and show that our scheme leads to state-of-the-art results.


Pattern codification strategies in structured light systems

Coded structured light is considered one of the most reliable techniques for recovering the surface of objects. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. The decoded points can be triangulated and 3D information is obtained. We present an overview of the existing techniques, as well as a new and de nitive classi cation of patterns for structured light sensors. We have implemented a set of representative techniques in this eld and present some comparative results. The advantages and constraints of the di5erent patterns are also discussed. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.


Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming

This paper presents a color structured light technique for recovering object shape from one or more images. The technique works by projecting a pattern of stripes of alternating colors and matching the projected color transitions with observed edges in the image. The correspondence problem is solved using a novel, multi-pass dynamic programming algorithm that eliminates global smoothness assumptions and strict ordering constraints present in previous formulations. The resulting approach is suitable for generating both highspeed scans of moving objects when projecting a single stripe pattern and high-resolution scans of static scenes using a short sequence of time-shifted stripe patterns. In the latter case, spacetime analysis is used at each sensor pixel to obtain inter-frame depth localization. Results are demonstrated for a variety of complex scenes.


Recent progress in coded structured light as a technique to solve the correspondence problem: a survey

We present a survey of the most significant techniques, used in the last few years, concerning the coded structured light methods employed to get 3D information. In fact, depth perception is one of the most important subjects in computer vision. Stereovision is an attractive and widely used method, but, it is rather limited to make 3D surface maps, due to the correspondence problem. The correspondence problem can be improved using a method based on structured light concept, projecting a given pattern on the measuring surfaces. However, some relations between the projected pattern and the reflected one must be solved. This relationship can be directly found codifying the projected light, so that, each imaged region of the projected pattern carries the needed information to solve the correspondence problem. ( 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. Pattern projection Correspondence problem Active stereo Depth perception Range data Computer vision.


A low cost 3D scanner based on structured light

Automatic 3D acquisition devices (often called 3D scanners) allow to build highly accurate models of real 3D objects in a costand time-effective manner. We have experimented this technology in a particular application context: the acquisition of Cultural Heritage artefacts. Specific needs of this domain are: medium-high accuracy, easy of use, affordable cost of the scanning device, self-registered acquisition of shape and color data, and finally operational safety for both the operator and the scanned artefacts. According to these requirements, we designed a low-cost 3D scanner based on structured light which adopts a new, versatile colored stripe pattern approach. We present the scanner architecture, the software technologies adopted, and the first results of its use in a project regarding the 3D acquisition of an archeological statue.


Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transformation

The Hough transformation employing a normal line-to-point parameterization is widely applied in digital image processing for feature detection. In this paper, we demonstrate how this same transformation can be adapted to classify defect signatures on semiconductor wafers as an aid to visual defect metrology. Given a rectilinear grid of die centers on a wafer, we demonstrate an efficient and effective procedure for classifying defect clusters composed of lines at angles of 0deg, 45deg, 90deg, and 135deg from the horizontal, as well as adjacent compositions of such lines. Included are defect clusters representing stripes, scratches at arbitrary angles, and center and edge defects. The principle advantage of the procedure over current industrial practice is that it can be fully automated to screen wafers for further engineering analysis.


The Columbia-GWU System at the 2016 TAC KBP BeSt Evaluation

We present the components of the ColumbiaGWU contribution to the 2016 TAC KBP BeSt Evaluation.


Enhancing information retrieval and resource discovery from data using the Semantic Web

Data are everywhere. Often the sheer quantities of data that is stored or archived in repositories or digital libraries make it difficult to navigate data; information in the data is obscured, particularly where we have Big Data. Traditionally, we record metadata on our data items to assist data classification and some information retrieval. The Semantic Web enables us to further unlock and enrich our data, by exploring how different data are related or connected. Using ontologies and Linked Data we can declare, navigate and discover semantic relationships. Relationships exist both locally, within the data, and globally, such that we can enhance our data with information retrieved from a wider context. To illustrate how the application of Semantic Web technologies aids data discovery and information retrieval, I discuss two case studies: (1) Sharing Ancient Wisdoms (SAWS), a Dynamic Library of information on selected ancient wisdom literature; and (2) the DEFRA DTC archive, a repository of data about freshwater quality in the UK.


Combining equilibrium , resampling , and analysts ’ views in portfolio optimization

Portfolio optimization methodologies play a central role in strategic asset allocation (SAA), where it is desirable to have portfolios that are efficient, diversified, and stable. Since the development of the traditional mean-variance approach of Markowitz (1952), many improvements have been made to overcome problems such as lack of diversification and strong sensitivity of optimal portfolio weights to expected returns.


Selective Transfer Learning for Cross Domain Recommendation

Collaborative filtering (CF) aims to predict users’ ratings on items according to historical user-item preference data. In many realworld applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show that by transferring knowledge from some manually selected source domains, the data sparseness problem could be mitigated. However for most cases, parts of source domain data are not consistent with the observations in the target domain, which may misguide the target domain model building. In this paper, we propose a novel criterion based on empirical prediction error and its variance to better capture the consistency across domains in CF settings. Consequently, we embed this criterion into a boosting framework to perform selective knowledge transfer. Comparing to several state-of-the-art methods, we show that our proposed selective transfer learning framework can significantly improve the accuracy of rating prediction tasks on several real-world recommendation tasks.


A recent development of antenna-in-package for 5G millimeter-wave applications (Invited paper)

Millimeter-wave technology brings a new paradigm of wireless communication in various areas including mobile devices, automotive, IoT (Internet of Things), medical, military and many others. In this paper, we present our recently developed antenna in a package and system for 5G (Fifth-Generation) millimeter-wave applications. The antenna is configured to be an array to compensate path loss and maintain data link budget. The antenna is designed with a multi-layer laminate substrate and is integrated with a 5G mmW RFIC. 5G NR (New Radio) wireless connection is demonstrated with a RFIC transceiver at 28 GHz band. Interoperability test is conducted to ensure compatibility with the 3GPP standard. Finally, indoor and outdoor field test including beamforming and beamtracking are presented to show realistic system-level throughput performance.


Dilated convolutions for image classification and object localization

Yu et al.[1] showed that dilated convolutions are very effective in dense prediction problems such as semantic segmentation. In this work, we propose a new ResNet[2] based convolutional neural network model using dilated convolutions and show that this model can achieve lower error rate for image classification than ResNet with reduction of the number of the parameters of the network by 94% and that this model has high ability to localize objects despite being trained on image-level labels. We evaluated this model on ImageNet[5] which has 50 class labels randomly selected from 1000 class labels.


Evaluation of a 900 V SiC MOSFET in a 13.56 MHz 2 kW resonant inverter for wireless power transfer

This paper demonstrates the performance of a 900 V silicon carbide (SiC) power MOSFET operating at 13.56 MHz in a 2 kW resonant inverter targeted for wireless power transfer (WPT) systems. Operating at multi-MHz switching frequency leads to smaller passive components and has the potential to improve power density and reduce weight for weight-sensitive applications. In addition, the advent of wide band gap (WBG) power semiconductors with higher breakdown voltages than silicon (Si) allows the design of high-power (2 kW or more) inverters even when operating at MHz frequencies. Previous work described the design and implementation of a class Φ2 inverter using an enhancement mode gallium nitride (eGaN) FET at output power of 1.3 kW and switching at 13.56 MHz [1]. While eGaN FETs have promising electrical characteristics for high-frequency operation, SiC MOSFETs are becoming available with higher temperature ratings, higher breakdown voltages and higher avalanche ratings to operate at tens of MHz switching frequency. In this work, we study and evaluate the electrical characteristics of a 900 V, 7L D2PAK SiC MOSFET and demonstrate the design of a 2 kW single-ended resonant inverter operating at 13.56 MHz.


Incremental tessellation of trimmed parametric surfaces

This paper presents an incremental approach for tessellating trimmed parametric surfaces. A database is built to keep the tessellation of a CAD model. With the topological information provided, the database is efficiently updated when the model is modified. This enhances the tessellation process as only a slight change of the existing tessellation is required. The tessellation which involves the insertion and removal of trimlines is performed completely in the parametric space. The topological information also facilitates the triangles classification and the merging of boundaries between surfaces. The algorithm is particularly suitable for the generation of a valid triangulation of a complex CAD model for many downstream applications, e.g. in rapid prototyping (RP). q 2000 Elsevier Science Ltd. All rights reserved.


Cannabinoids in the Treatment of Epilepsy.

Despite the availability of more than 20 different antiseizure drugs and the provision of appropriate medical therapy, 30% of people with epilepsy continue to have seizures.1,2 The approval of many new antiseizure drugs during the past two decades, including several with novel mechanisms of action, has not substantially reduced the proportion of patients with medically refractory disease.1 The safety and side-effect profile of antiseizure drugs has improved, but side effects related to the central nervous system are common and affect quality of life.3 Patients need new treatments that control seizures and have fewer side effects. This treatment gap has led patients and families to seek alternative treatments. Cannabis-based treatment for epilepsy has recently received prominent attention in the lay press4 and in social media, with reports of dramatic improvements in seizure control in children with severe epilepsy. In response, many states have legalized cannabis for the treatment of epilepsy (and other medical conditions) in children and adults (for a list of medical marijuana laws according to state, see www . ncsl . org/ research/ health/ state-medical-marijuana-laws . aspx). Cannabis has been used medicinally for millennia and was used in the treatment of epilepsy as early as 1800 b.c.e. in Sumeria.5 Victorian-era neurologists used Indian hemp to treat epilepsy and reported dramatic success.5,6 The use of cannabis therapy for the treatment of epilepsy diminished with the introduction of phenobarbital (1912) and phenytoin (1937) and the passage of the Marijuana Tax Act (1937). The discovery of an endogenous cannabinoid-signaling system in the 1990s7 rekindled interest in therapies derived from constituents of cannabis for nervous system disorders such as epilepsy (see ClinicalTrials.gov numbers, NCT02091375, NCT02224690, NCT02324673, NCT02318537, and NCT02318563). This review addresses the current preclinical and clinical data that suggest that compounds found in cannabis have efficacy against seizures. The pharmacokinetic properties of cannabinoids and related safety and regulatory issues that may affect clinical use are also discussed, as are the distinct challenges of conducting rigorous clinical trials of these compounds. More than 545 distinct compounds have been isolated from cannabis species; the most abundant are the cannabinoids, a family of molecules that have a 21-carbon terpenophenolic skeleton and includes numerous metabolites.8 The best studied of these cannabinoids (termed “phytocannabinoids” if derived from the plant) are Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol and their metabolites. (See Fig. 1 for the structure of Δ9-THC, cannabidiol, and one other cannabinoid, cannabidivarin, as well as their targets in the central nervous system, and their actions.) Most of the psychoactive effects of cannabis are mediated by Δ9-THC. Many of the noncannabinoid molecules in cannabis plants may have biologic activity. This review focuses on cannabinoids, since other cannabis-derived compounds have been less well studied. From the Department of Neurology, New York University Langone School of Medicine, New York. Address reprint requests to Dr. Friedman at the Department of Neurology, NYU Langone School of Medicine, 223 E. 34th St., New York, NY 10016, or at daniel . friedman@ nyumc . org.


SharesSkew: An algorithm to handle skew for joins in MapReduce

In this paper, we investigate the problem of computing a multiway join in one round of MapReduce when the data may be skewed. We optimize on communication cost, i.e., the amount of data that is transferred from the mappers to the reducers. We identify join attributes values that appear very frequently, Heavy Hitters (HH). We distribute HH valued records to reducers avoiding skew by using an adaptation of the Shares [3] algorithm to achieve minimum communication cost. Our algorithm is implemented for experimentation and is offered as open source software. Furthermore, we investigate a class of multiway joins for which a simpler variant of the algorithm can handle skew. We offer closed forms for computing the parameters of the algorithm for chain and symmetric joins.


Noninvasive prediction of glucose by near-infrared diffuse reflectance spectroscopy.

BACKGROUND
Self-monitoring of blood glucose by diabetics is crucial in the reduction of complications related to diabetes. Current monitoring techniques are invasive and painful, and discourage regular use. The aim of this study was to demonstrate the use of near-infrared (NIR) diffuse reflectance over the 1050-2450 nm wavelength range for noninvasive monitoring of blood glucose.

METHODS
Two approaches were used to develop calibration models for predicting the concentration of blood glucose. In the first approach, seven diabetic subjects were studied over a 35-day period with random collection of NIR spectra. Corresponding blood samples were collected for analyte analysis during the collection of each NIR spectrum. The second approach involved three nondiabetic subjects and the use of oral glucose tolerance tests (OGTTs) over multiple days to cause fluctuations in blood glucose concentrations. Twenty NIR spectra were collected over the 3.5-h test, with 16 corresponding blood specimens taken for analyte analysis.

RESULTS
Statistically valid calibration models were developed on three of the seven diabetic subjects. The mean standard error of prediction through cross-validation was 1.41 mmol/L (25 mg/dL). The results from the OGTT testing of three nondiabetic subjects yielded a mean standard error of calibration of 1.1 mmol/L (20 mg/dL). Validation of the calibration model with an independent test set produced a mean standard error of prediction equivalent to 1.03 mmol/L (19 mg/dL).

CONCLUSIONS
These data provide preliminary evidence and allow cautious optimism that NIR diffuse reflectance spectroscopy using the 1050-2450 nm wavelength range can be used to predict blood glucose concentrations noninvasively. Substantial research is still required to validate whether this technology is a viable tool for long-term home diagnostic use by diabetics.


Selective stiffening of soft actuators based on jamming

The ability to selectively stiffen otherwise compliant soft actuators increases their versatility and dexterity. We investigate granular jamming and layer jamming as two possible methods to achieve stiffening with PneuFlex actuators, a type of soft continuum actuator. The paper details five designs of jamming compartments that can be attached to an actuator. We evaluate the stiffening of the five different prototypes, achieving an up to 8-fold increase in stiffness. The strength of the most effective prototype based on layer jamming is also validated in the context of pushing buttons, resulting in an 2.23-fold increase in pushing force.


Two-dimensional Disjunctively Constrained Knapsack Problem: Heuristic and exact approaches


Multiple extended object tracking using Gaussian processes

The goal of multi-object tracking is to estimate the number of objects and their states recursively over time. In the presence of extended objects, i.e. objects with an extent that is not negligible in comparison to sensor resolution and which give rise to multiple measurements per time step, the tracking task is even more challenging: To fully utilize all available information and to achieve accurate estimation results, measurement models that are able to represent the object shape as well as algorithms that tackle the aggravated track-to-measurements association problem are necessary. In this work, the Gaussian process measurement model is integrated in the recently developed labeled multi-Bernoulli filter for extended objects. Thus, the ability of Gaussian processes to estimate and represent a wide range of free-from shapes is combined with a principled approach to the multiple extended objects tracking problem. The filter performance is demonstrated for simulated and experimental vehicle tracking using a laser scanner. For this particular application, a new, approximately axis-symmetric covariance function is additionally introduced.


The Networked Sensory Landscape: Capturing and Experiencing Ecological Change Across Scales

What role will ubiquitous sensing play in our understanding and experience of ecology in the future? What opportunities are created by weaving a continuously sampling, geographically dense web of sensors into the natural environment, from the ground up? In this article, we explore these questions holistically, and present our work on an environmental sensor network designed to support a diverse array of applications, interpretations, and artistic expressions, from primary ecological research to musical composition. Over the past four years, we have been incorporating our ubiquitous sensing framework into the design and implementation of a large-scale wetland restoration, creating a broad canvas for creative exploration at the landscape scale. The projects we present here span the development and wide deployment of custom sensor node hardware, novel web services for providing real-time sensor data to end user applications, public-facing user interfaces for open-ended exploration of the data, as well as more radical UI modalities, through unmanned aerial vehicles, virtual and augmented reality, and wearable devices for sensory augmentation. From this work, we distill the Networked Sensory Landscape, a vision for the intersection of ubiquitous computing and environmental restoration. Sensor network technologies and novel approaches to interaction promise to reshape presence, opening up sensorial connections to ecological processes across spatial and temporal scales.


Contact System Design to Improve Energy Efficiency in Copper Electrowinning Processes

To improve energy efficiency in copper electrowinning, different technologies have been developed. These include electrode positioning capping boards and 3-D grids, electrode spacers, and segmented intercell bars. This paper introduces a design concept to avoid electrode open circuits and reduce contact resistances. The design is based on a female tooth shape for the contacts on the intercell bar. This leads to improved electrode alignment, reduced contact resistances, easier contact cleaning, and ensured electrical contact for the electrodes. It results in lower operational temperature for the electrodes, reduced plant housekeeping, increased lifespan for capping boards, and higher rate of grade A copper production. The comparative results presented should be a useful guideline for any type of intercell bar. Improvements in production levels and energy efficiency should reach 0.5% and 3%, respectively. A 3-D finite-element-based analysis and industrial measurements are used to verify the results.


ADHD comorbidity findings from the MTA study: comparing comorbid subgroups.

OBJECTIVES
Previous research has been inconclusive whether attention-deficit/hyperactivity disorder (ADHD), when comorbid with disruptive disorders (oppositional defiant disorder [ODD] or conduct disorder [CD]), with the internalizing disorders (anxiety and/or depression), or with both, should constitute separate clinical entities. Determination of the clinical significance of potential ADHD + internalizing disorder or ADHD + ODD/CD syndromes could yield better diagnostic decision-making, treatment planning, and treatment outcomes.

METHOD
Drawing upon cross-sectional and longitudinal information from 579 children (aged 7-9.9 years) with ADHD participating in the NIMH Collaborative Multisite Multimodal Treatment Study of Children With Attention-Deficit/Hyperactivity Disorder (MTA), investigators applied validational criteria to compare ADHD subjects with and without comorbid internalizing disorders and ODD/CD.

RESULTS
Substantial evidence of main effects of internalizing and externalizing comorbid disorders was found. Moderate evidence of interactions of parent-reported anxiety and ODD/CD status were noted on response to treatment, indicating that children with ADHD and anxiety disorders (but no ODD/CD) were likely to respond equally well to the MTA behavioral and medication treatments. Children with ADHD-only or ADHD with ODD/CD (but without anxiety disorders) responded best to MTA medication treatments (with or without behavioral treatments), while children with multiple comorbid disorders (anxiety and ODD/CD) responded optimally to combined (medication and behavioral) treatments.

CONCLUSIONS
Findings indicate that three clinical profiles, ADHD co-occurring with internalizing disorders (principally parent-reported anxiety disorders) absent any concurrent disruptive disorder (ADHD + ANX), ADHD co-occurring with ODD/CD but no anxiety (ADHD + ODD/CD), and ADHD with both anxiety and ODD/CD (ADHD + ANX + ODD/CD) may be sufficiently distinct to warrant classification as ADHD subtypes different from "pure" ADHD with neither comorbidity. Future clinical, etiological, and genetics research should explore the merits of these three ADHD classification options.


A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence

Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization. However, many interesting optimization objectives are neither submodular nor factorizable into an integer linear program. We address this issue and present a general optimization framework where any function of input documents and a system summary can be plugged in. Our framework includes two kinds of summarizers – one based on genetic algorithms, the other using a swarm intelligence approach. In our experimental evaluation, we investigate the optimization of two information-theoretic summary evaluation metrics and find that our framework yields competitive results compared to several strong summarization baselines. Our comparative analysis of the genetic and swarm summarizers reveals interesting complementary properties.


SQL server column store indexes

The SQL Server 11 release (code named "Denali") introduces a new data warehouse query acceleration feature based on a new index type called a column store index. The new index type combined with new query operators processing batches of rows greatly improves data warehouse query performance: in some cases by hundreds of times and routinely a tenfold speedup for a broad range of decision support queries. Column store indexes are fully integrated with the rest of the system, including query processing and optimization. This paper gives an overview of the design and implementation of column store indexes including enhancements to query processing and query optimization to take full advantage of the new indexes. The resulting performance improvements are illustrated by a number of example queries.


A new weighted SVDD algorithm for outlier detection

Traditional Support Vector Domain Description (SVDD) and some improved SVDD algorithms are suboptimal in detecting some complex outliers. To solve the difficulty, we proposed a novel adaptive weighted SVDD (AW-SVDD) algorithm. Firstly, a weighting is computed for each data point based on their spatial position distribution and density distribution in a training dataset. The weighting can be used to measure the degree of the data point to be an outlier. Then the weighted data are trained by traditional SVDD. Lastly, a sphere shaped data description can be obtained for the training data. Experimental results demonstrate that AW-SVDD can overcome the interference from some complex outliers objectively, therefore the algorithm has a better performance than traditional SVDD and other improved SVDD algorithms.


IRIS: a Chat-oriented Dialogue System based on the Vector Space Model

This system demonstration paper presents IRIS (Informal Response Interactive System), a chat-oriented dialogue system based on the vector space model framework. The system belongs to the class of examplebased dialogue systems and builds its chat capabilities on a dual search strategy over a large collection of dialogue samples. Additional strategies allowing for system adaptation and learning implemented over the same vector model space framework are also described and discussed.


Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning

Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforcement learning and Markov decision processes (MDPs). We extend the usual notion of action in this framework to include options—closed-loop policies for taking action over a period of time. Examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as muscle twitches and joint torques. Overall, we show that options enable temporally abstract knowledge and action to be included in the reinforcement learning framework in a natural and general way. In particular, we show that options may be used interchangeably with primitive actions in planning methods such as dynamic programming and in learning methods such as Q-learning. Formally, a set of options defined over an MDP constitutes a semi-Markov decision process (SMDP), and the theory of SMDPs provides the foundation for the theory of options. However, the most interesting issues concern the interplay between the underlying MDP and the SMDP and are thus beyond SMDP theory. We present results for three such cases: (1) we show that the results of planning with options can be used during execution to interrupt options and thereby perform even better than planned, (2) we introduce new intra-option methods that are able to learn about an option from fragments of its execution, and (3) we propose a notion of subgoal that can be used to improve the options themselves. All of these results have precursors in the existing literature; the contribution of this paper is to establish them in a simpler and more general setting with fewer changes to the existing reinforcement learning framework. In particular, we show that these results can be obtained without committing to (or ruling out) any particular approach to state abstraction, hierarchy, function approximation, or the macroutility problem. ! 1999 Published by Elsevier Science B.V. All rights reserved. ∗ Corresponding author. 0004-3702/99/$ – see front matter ! 1999 Published by Elsevier Science B.V. All rights reserved. PII: S0004-3702(99)00052 -1 182 R.S. Sutton et al. / Artificial Intelligence 112 (1999) 181–211


The Design and Implementation of the Clouds Distributed Operating System

Clouds is a native operating system for a distributed environment. The Clouds operating system is built on top of a kernel called Ra. Ra ís a second generation kernel derived from our experience with the first version of the Clouds operating system. Rø is a minimal, flexible kernel that provides a framework for implementing a variety of distributed operating systems. This paper presents the Clouds paradigm and a brief overview of its first implementation. We then present the details of the Rø kernel, the rationale for its design, and the system services that constitute the Clouds operating system. This work was funded by NSF grant CCR-8619886. @ Computing Systems, Vol. 3 . No. I . Winter 1990 11


Simulation of wide area measurement system with optimal phasor measurement unit location

Traditional supervisory control and data acquisition (SCADA) measurements have been providing information regarding different power system parameters for decades. These measurements are typically taken once every 4 to 10 seconds offering a steady state view of the power system behaviour. However, for monitoring and control of large grid only steady state information may not be sufficient. Phasor measurement units (PMUs) provide dynamic view of the grid. Since PMUs are costly device, optimal placement of PMUs is a thrust areas of research in power system planning. This has been already investigated in previous research findings by different optimization technique. This paper extensively corroborates a simulation model of a wide area network with optimal PMU location. The purpose is to experimentally determine the ability of PMU to observe the entire network from its optimal location. The proposed methodology is implemented and investigated on IEEE six bus system using MATLAB/Simulink.


Eigenvector-centrality - a node-centrality?

Networks of social relations can be represented by graphs and socioor adjacency-matrices and their structure can be analyzed using different concepts, one of them called centrality. We will provide a new formalization of a “node-centrality” which leads to some properties a measure of centrality has to satisfy. These properties allow to test given measures, for example measures based on degree, closeness, betweenness or Bonacich’s eigenvector-centrality. It turns out that it depends on normalization whether eigenvector-centrality does satisfy the expected properties or not. © 2000 Elsevier Science B.V. All rights reserved.


Leveraging Tokens in a Natural Language Query for

Natural Language Interface to Database (NLIDB) systems convert a Natural Language (NL) query into a Structured Query Language (SQL) and then use the SQL query to retrieve information from a database. The main advantage with this function of an NLIDB system is that it makes information retrieval much easier and more importantly, it also allows non-technical users to query a database. In this work, we present an effective usage of tokens in an NL query to address various problems in an NLIDB system. The conversion of an NL query to SQL query is framed as a token classification problem, using which we unveil a novel method of mapping an NL query to an SQL query. Our approach reasonably addresses domain dependency which is one of the major drawbacks of NLIDB systems. Concepts Identification is a major component of NLIDB systems (Gupta et al., 2012; Srirampur et al., 2014). We improve Concepts Identification (Srirampur et al., 2014) by making use of Stanford Parser Dependencies. Our approach is more robust than previously proposed methods. In addition, we also discuss how Concepts Identification can be applied to address Ellipsis in a dialogue process. In addition to providing results to a user, it is essential to provide a relevant and a compact set of results. We propose a new problem of generating a compact set of results to a user query. At a higher level, user-system interactions are modeled based on patterns frequently observed between a user’s current query and his previous queries, while interacting with a system. Using these models, we propose a novel method of system prompting to help a user obtain a smaller and a relevant set of results. In addition to providing a compact and a relevant set of results, it is imperative that answers of an NLIDB system are comprehensible even by the people who are less familiar with a common language like English. NLIDB systems use Natural Language Generation (NLG) modules to provide answers in the form of sentences. It is important to make sure that an answer generated by an NLG module is very simple to understand. This brings in the problem of text simplification, wherein, one of the most crucial and initial sub-problems is Complex Word Identification (CWI). We address the problem of CWI by distinguishing words as simple and complex. A plethora of classifiers were explored to identify the complex words. This information helps us in improving the simplification of an NLIDB system’s final output to a user. To summarize, in addition to addressing problems within an NLIDB system, this work touches postNLIDB problems like results processing. All the proposed issues are tackled using tokens in an NL query as the basic and a driving unit of force.


FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge

The field of Automatic Facial Expression Analysis has grown rapidly in recent years. However, despite progress in new approaches as well as benchmarking efforts, most evaluations still focus on either posed expressions, near-frontal recordings, or both. This makes it hard to tell how existing expression recognition approaches perform under conditions where faces appear in a wide range of poses (or camera views), displaying ecologically valid expressions. The main obstacle for assessing this is the availability of suitable data, and the challenge proposed here addresses this limitation. The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views. In this paper we present the third challenge in automatic recognition of facial expressions, to be held in conjunction with the 12th IEEE conference on Face and Gesture Recognition, May 2017, in Washington, United States. Two sub-challenges are defined: the detection of AU occurrence, and the estimation of AU intensity. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for both sub-challenges.


Efficient Real-Time Auditing and Proof of Violation for Cloud Storage Systems

In this paper we study how to develop an efficient real-time auditing and proof of violation (POV) scheme for cloud storage systems. A POV scheme enables a user or a service provider to produce cryptographic proofs which can be used to prove either the occurrence of the violation of properties or the innocence of the service provider. POV schemes are solutions for obtaining mutual nonrepudiation between users and the service provider in the cloud. After each file operation, a real-time auditing should be performed so that the violation of the service provider can be found instantly. Existing solutions need to cache the hash values of files in client devices and thus the overhead for storing and synchronizing hash values in client devices which share files is huge. We propose a novel scheme in which client devices do not need to cache any hash values of files. A small portion called slice of a binary hash tree is transferred to the client device for real-time auditing and it can support POV whenever the audit does not pass. Experimental results are presented that demonstrate the feasibility of the proposed scheme and show that our scheme outperforms previous work by one to two order of magnitude. Service providers of cloud storage can use the proposed scheme to provide a mutual nonrepudiation guarantee in their service-level agreements.


High-accuracy differential tracking of low-cost GPS receivers

In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This paper presents an approach that uses GPS to derive relative location information for multiple receivers. Nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. The system has been implemented using a network of Android phones equipped with a custom Bluetooth headset and integrated GPS chip to provide raw measurement data. Our evaluation shows that centimeter-scale tracking accuracy at an update rate of 1 Hz is possible under various conditions with the presented technique. This is more than an order of magnitude more accurate than simply taking the difference of reported absolute node coordinates or other simplistic approaches due to the presence of uncorrelated measurement errors.


Automatic drift calibration for EOG-based gaze input interface

A drift calibration technique for DC-coupled EOG (electrooculogram) systems is proposed. It assumes a non-linear relationship between EOG and eye angle and estimates the absolute eye angle by the EOG differences during saccade. Drift is calibrated every saccade without user's explicit action, so it is especially suitable for long-term gaze input interfaces. An experiment confirms that it can estimate horizontal absolute eye angle with an error of about 5° in addition to accurate eye movement.


Towards Continuous Sign Language Recognition with Deep Learning

Humans communicate with each other using abstract signs and symbols. While the cooperation between humans and machines can be a powerful tool for solving complex or difficult tasks, the communication must be at the abstract enough level that is both natural to the humans and understandable to the machines. Our paper focuses on natural language and in particular on sign language recognition. The approach described here combines heuristics for segmentation of the video stream by identifying the epenthesis with stacked LSTMs for automatic classification of the derived segments. This approach segments continuous stream of video data with the accuracy of over 80% and reaches accuracies of over 95% on segmented sign recognition. We compare results in terms of the number of signs being recognised and the utility of various features used for the recognition. We aim to integrate the models into a single continuous sign language recognition system and to learn policies for specific domains that would map perception of a robot to its action. This will improve the accuracy of understanding the common task within the shared activity between a human and a machine. Such understanding, in turn, will foster meaningful cooperation.


Data-driven segmentation and labeling of freehand sketches

We present a data-driven approach to derive part-level segmentation and labeling of free-hand sketches, which depict single objects with multiple parts. Our method performs segmentation and labeling simultaneously, by inferring a structure that best fits the input sketch, through selecting and connecting 3D components in the database. The problem is formulated using Mixed Integer Programming, which optimizes over both the local fitness of the selected components and the global plausibility of the connected structure. Evaluations show that our algorithm is significantly better than the straightforward approaches based on direct retrieval or part assembly, and can effectively handle challenging variations in the sketch.


Photorealistic Scene Reconstruction by Voxel Coloring

A novel scene reconstruction technique is presented, different from previous approaches in its ability to cope with large changes in visibility and its modeling of intrinsic scene color and texture information. The method avoids image correspondence problems by working in a discretized scene space whose voxels are traversed in a fixed visibility ordering. This strategy takes full account of occlusions and allows the input cameras to be far apart and widely distributed about the environment. The algorithm identifies a special set of invariant voxels which together form a spatial and photometric reconstruction of the scene, fully consistent with the input images. The approach is evaluated with images from both inward-facing and outward-facing cameras.


Performance of Two 18-Story Steel Moment-Frame Buildings in Southern California During Two Large Simulated San Andreas Earthquakes

Using state-of-the-art computational tools in seismology and structural engineering, validated using data from the Mw=6.7 January 1994 Northridge earthquake, we determine the damage to two 18-story steel moment-frame buildings, one existing and one new, located in southern California due to ground motions from two hypothetical magnitude 7.9 earthquakes on the San Andreas Fault. The new building has the same configuration as the existing building but has been redesigned to current building code standards. Two cases are considered: rupture initiating at Parkfield and propagating from north to south, and rupture propagating from south to north and terminating at Parkfield. Severe damage occurs in these buildings at many locations in the region in the north-to-south rupture scenario. Peak velocities of 1 m.s−1 and 2 m.s−1 occur in the Los Angeles Basin and San Fernando Valley, respectively, while the corresponding peak displacements are about 1 m and 2 m, respectively. Peak interstory drifts in the two buildings exceed 0.10 and 0.06 in many areas of the San Fernando Valley and the Los Angeles Basin, respectively. The redesigned building performs significantly better than the existing building; however, its improved design based on the 1997 Uniform Building Code is still not adequate to prevent serious damage. The results from the south-to-north scenario are not as alarming, although damage is serious enough to cause significant business interruption and compromise life safety. DOI: 10.1193/1.2360698


Blockchain and Artificial Intelligence

It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate. Both technologies have distinct degree of technological complexity and multi-dimensional business implications. However, a common misunderstanding about blockchain concept, in particular, is that “a blockchain is decentralized and therefore no one controls it”. But the underlying development of a blockchain system is still attributed to a cluster of core developers. Take smart contract as an example, it is essentially a collection of codes (or functions) and data (or states) that are programmed and deployed on a blockchain (say, Ethereum) by different human programmers. It is thus, unfortunately, less likely to be free of loopholes and flaws. In this article, through a brief overview about how artificial intelligence could be used to deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we to emphasize that the blockchain implementation can be assisted or enhanced via various AI techniques. The alliance of AI and blockchain is expected to create numerous possibilities.


Simulation of bulk current injection test using integrated circuit immunity macro model and electromagnetic analysis

This paper provides a technique to predict bulk current injection (BCI) test results. In order to define the threshold of failure, the direct power injection based integrated circuit (IC) immunity macro model for conducted immunity is adopted. Injected radio frequency disturbance that reaches to an integrated circuit is calculated by using electromagnetic analysis with a high accuracy injection probe model. 3D model of equipment under test can provide the terminal voltage of IC which reference is the ground terminal of IC, not BCI test setups reference ground plane. The proposed method is applied to BCI tests and the simulated results have good correlation with experimental results.


Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation

The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain. This is partially due to the small amount of annotated artistic data, which is not even comparable to that of natural images captured by cameras. In this paper, we propose a semantic-aware architecture which can translate artworks to photo-realistic visualizations, thus reducing the gap between visual features of artistic and realistic data. Our architecture can generate natural images by retrieving and learning details from real photos through a similarity matching strategy which leverages a weakly-supervised semantic understanding of the scene. Experimental results show that the proposed technique leads to increased realism and to a reduction in domain shift, which improves the performance of pre-trained architectures for classification, detection, and segmentation. Code will be made publicly available.


Round Robin Classification

In this paper, we discuss round robin classification (aka pairwise classification), a technique for handling multi-class problems with binary classifiers by learning one classifier for each pair of classes. We present an empirical evaluation of the method, implemented as a wrapper around the Ripper rule learning algorithm, on 20 multi-class datasets from the UCI database repository. Our results show that the technique is very likely to improve Ripper’s classification accuracy without having a high risk of decreasing it. More importantly, we give a general theoretical analysis of the complexity of the approach and show that its run-time complexity is below that of the commonly used one-against-all technique. These theoretical results are not restricted to rule learning but are also of interest to other communities where pairwise classification has recently received some attention. Furthermore, we investigate its properties as a general ensemble technique and show that round robin classification with C5.0 may improve C5.0’s performance on multi-class problems. However, this improvement does not reach the performance increase of boosting, and a combination of boosting and round robin classification does not produce any gain over conventional boosting. Finally, we show that the performance of round robin classification can be further improved by a straight-forward integration with bagging.


QGrid: Q-learning based routing protocol for vehicular ad hoc networks

In Vehicular Ad Hoc Networks (VANETs), moving vehicles are considered as mobile nodes in the network and they are connected to each other via wireless links when they are within the communication radius of each other. Efficient message delivery in VANETs is still a very challenging research issue. In this paper, a Q-learning based routing protocol (i.e., QGrid) is introduced to help to improve the message delivery from mobile vehicles to a specific location. QGrid considers both macroscopic and microscopic aspects when making the routing decision, while the traditional routing methods focus on computing meeting information between different vehicles. QGrid divides the region into different grids. The macroscopic aspect determines the optimal next-hop grid and the microscopic aspect determines the specific vehicle in the optimal next-hop grid to be selected as next-hop vehicle. QGrid computes the Q-values of different movements between neighboring grids for a given destination via Q-learning. Each vehicle stores Q-value table learned offline, then selects optimal next-hop grid by querying Q-value table. Inside the selected next-hop grid, we either greedily select the nearest neighboring vehicle to the destination or select the neighboring vehicle with highest probability of moving to the optimal next-hop grid predicted by the two-order Markov chain. The performance of QGrid is evaluated by using real life trajectory GPS data of Shanghai taxies. Simulation comparison among QGrid and other existing position-based routing protocols confirms the advantages of proposed QGrid routing protocol for VANETs.


Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach

Business analytics has evolved from being a novelty used by a select few, to an accepted facet of conducting business. Recommender systems form a cri tical component of the business analytics toolkit and, by enabling firms to effectively target custom ers with products and services, are helping alter t h ecommerce landscape. A variety of methods exist for providing recommendations, with collaborative filtering, matrix factorization, and association ru le based methods being the most common. In this pap er, we propose a method to improve the quality of recom mendations made using association rules. This is accomplished by combining rules when possible, and stands apart from existing rule-combination methods in that it is strongly grounded in probabil ity theory. Combining rules requires the identifica t on of the best combination of rules from the many comb inations that might exist, and we use a maximumlikelihood framework to compare alternative combina tions. As it is impractical to apply the maximum likelihood framework directly in real time, we show that this problem can equivalently be represented as a set partitioning problem by translating it into an information theoretic context – the best solution corresponds to the set of rules that leads to the h ighest sum of mutual information associated with th e rules. Through a variety of experiments that evalua te the quality of recommendations made using the proposed approach, we show that (i) a greedy heuris tic u ed to solve the maximum likelihood estimation problem is very effective, providing results compar able to those from using the optimal set partitioni ng solution, (ii) the recommendations made by our appr oach are more accurate than those made by a variety of state-of-the-art benchmarks, including collabora tive filtering and matrix factorization, and (iii) the recommendations can be made in a fraction of a seco nd on a desktop computer, making it practical to us e in real-world applications.


Smart digital door lock for the home automation

In this paper, we propose a smart digital door lock system for home automation. A digital door lock system is equipment that uses the digital information such as a secret code, semi-conductors, smart card, and finger prints as the method for authentication instead of the legacy key system. In our proposed system, a ZigBee module is embedded in digital door lock and the door lock acts as a central main controller of the overall home automation system. Technically, our proposed system is the network of sensor nodes and actuators with digital door lock as base station. A door lock system proposed here consists of RFID reader for user authentication, touch LCD, motor module for opening and closing of the door, sensor modules for detecting the condition inside the house, communication module, and control module for controlling other modules. Sensor nodes for environment sensing are deployed at appropriate places at home. Status of individual ZigBee module can be monitored and controlled by the centralized controller, digital door lock. As the door lock is the first and last thing people come across in entering and leaving the home respectively, the home automation function in digital door lock system enables user to conveniently control and monitor home environment and condition all at once before entering or leaving the house. Furthermore, it also allows users to remotely monitor the condition inside the house through Internet or any other public network. The biggest advantage of our proposed system over existing ones is that it can be easily installed when and where necessary without requirement of any infrastructures and proper planning.


Automatic Driver Assistance System-Glare Free High Beam Control

At present automobiles upgrades with different technologies for assisting and providing safety for the occupant. Due to this a system known as Automatic Driver Assistance System (ADAS) proposed many systems which aim to help the driver in controlling the vehicle and to drive safely during night time. One of the systems of ADAS is Glare Free High Beam (GFHB) control. This provides the information of the oncoming vehicles and preceding vehicles based on camera, which slices out the high beam and shades spot when it detects the object by avoiding the glare and provides the driver with greater visibility. It makes use of the various sensors, actuator, image processor and microcontroller to detect an object. In this paper, unit level testing is done for different cases in Embedded C, simulated in Visual Studio and results are displayed using GUI.


Correction of the retracted alar base.

Alar base retraction is a common yet difficult problem faced by the rhinoplasty surgeon. It may be caused by weakened, overresected lateral crura, vestibular lining deficiencies, or congenital alar malpositioning. Methods of correction include soft tissue manipulation, auricular composite grafting, and cartilage grafting. We present the senior author's graded approach to alar retraction using auricular composite grafting, alar rim grafting, and lateral crural strut graft placement with caudal lateral crural repositioning.


You Talking to Me? A Corpus and Algorithm for Conversation Disentanglement

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat (IRC) dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. This is, to our knowledge, the first such corpus for internet chat. We propose a graph-theoretic model for disentanglement, using discourse-based features which have not been previously applied to this task. The model’s predicted disentanglements are highly correlated with manual annotations.


Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning

Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.


Crowdsourcing Annotations for Visual Object Detection

A large number of images with ground truth object bounding boxes are critical for learning object detectors, which is a fundamental task in compute vision. In this paper, we study strategies to crowd-source bounding box annotations. The core challenge of building such a system is to effectively control the data quality with minimal cost. Our key observation is that drawing a bounding box is significantly more difficult and time consuming than giving answers to multiple choice questions. Thus quality control through additional verification tasks is more cost effective than consensus based algorithms. In particular, we present a system that consists of three simple sub-tasks — a drawing task, a quality verification task and a coverage verification task. Experimental results demonstrate that our system is scalable, accurate, and cost-effective.


Multilevel Converters-A New Breed of Power Converters

AbstructMultilevel voltage source converters are emerging as a new breed of power converter options for high-power applications. The multilevel voltage source converters typically synthesize the staircase voltage wave from several levels of dc capacitor voltages. One of the major limitations of the multilevel converters is the voltage unbalance between different levels. The techniques to balance the voltage between different levels normally involve voltage clamping or capacitor charge control. There are several ways of implementing voltage balance in multilevel converters. Without considering the traditional magnetic coupled converters, this paper presents three recently developed multilevel voltage source converters: 1) diode-clamp, 2) flyingcapacitors, and 3) cascaded-inverters with separate dc sources. The operating principle, features, constraints, and potential applications of these converters will be discussed.


A generalized multilevel inverter topology with self voltage balancing

Multilevel power converters that provide more than two levels of voltage to achieve smoother and less distorted ac-to-dc, dc-to-ac, and dc-to-dc power conversion, have attracted many contributors. This paper presents a generalized multilevel inverter (converter) topology with self voltage balancing. The existing multilevel inverters such as diode-clamped and capacitor-clamped multilevel inverters can be derived from the generalized inverter topology. Moreover, the generalized multilevel inverter topology provides a true multilevel structure that can balance each dc voltage level automatically without any assistance from other circuits, thus, in principle, providing a complete and true multilevel topology that embraces the existing multilevel inverters. From this generalized multilevel inverter topology, several new multilevel inverter structures can be derived. Some application examples of the generalized multilevel converter will be given.


Multilevel Converters as a Utility Interface for Renewable Energy Systems

Electric power production in the 21 Century will see dramatic changes in both the physical infrastructure and the control and information infrastructure. A shift will take place from a relatively few large, concentrated generation centers and the transmission of electricity over mostly a high voltage ac grid (Fig. 1) to a more diverse and dispersed generation infrastructure that also has a higher percentage of dc transmission lines (Fig. 2) [1]. In the United States, generation capacity has not kept up with power demands, as over the last decade the reserve margins declined from 22% in 1990 to 16% in 1997. This declining margin trend is expected to continue over the next decade partly because of the uncertainty of how the future deregulated electrical environment will function. Less reserve margins will lead to less peaking capacity on highdemand days and more volatile energy prices [2]. This change in a physical infrastructure combined with a more deregulated electric power industry will result in more parties generating power – or distributed generation. Some of the distributed generation power sources that are expected to increase greatly their market share of the total power produced in the United States and abroad include renewable energy sources such as photovoltaics, wind, low-head hydro, and geothermal [3]. Fuel cell technology is also nearing the development point where it could start to supply a significant share of the power needs [4]. The advent of high power electronic modules has also encouraged the use of more dc transmission and made the prospects of interfacing dc power sources such as fuel cells and photovoltaics more easily attainable. A modular, scalable power electronics technology that is ideal for these types of utility applications is the transformerless multilevel converter [5].


Cascaded DC-DC converter connection of photovoltaic modules

New residential scale photovoltaic (PV) arrays are commonly connected to the grid by a single dc-ac inverter connected to a series string of pv panels, or many small dc-ac inverters which connect one or two panels directly to the ac grid. This paper proposes an alternative topology of nonisolated per-panel dc-dc converters connected in series to create a high voltage string connected to a simplified dc-ac inverter. This offers the advantages of a "converter-per-panel" approach without the cost or efficiency penalties of individual dc-ac grid connected inverters. Buck, boost, buck-boost, and Cu/spl acute/k converters are considered as possible dc-dc converters that can be cascaded. Matlab simulations are used to compare the efficiency of each topology as well as evaluating the benefits of increasing cost and complexity. The buck and then boost converters are shown to be the most efficient topologies for a given cost, with the buck best suited for long strings and the boost for short strings. While flexible in voltage ranges, buck-boost, and Cu/spl acute/k converters are always at an efficiency or alternatively cost disadvantage.


An FPGA-based AES-CCM Crypto Core For IEEE 802.11i Architecture

The widespread adoption of IEEE 802.11 wireless networks has brought its security paradigm under active research. One of the important research areas in this field is the realization of fast and secure implementations of cryptographic algorithms. Under this work, such an implementation has been done for Advanced Encryption Standard (AES) on fast, efficient and low power Field Programmable Gate Arrays (FPGAs) whereby computational intensive cryptographic processes are offloaded from the main processor thus results in achieving highspeed secure wireless connectivity. The dedicated resources of Spartan-3 FPGAs have been effectively utilized to develop wider logic function which minimizes the critical paths by confining logic to single Configurable Logic Block (CLB), thus improving the performance, density and power consumption of the design. The resultant design consumes only 4 Block RAMs and 487 Slices to fit both AES cores and its key scheduling.


Learning an Intrinsic Image Decomposer Using Synthesized RGB-D Dataset

Intrinsic image decomposition refers to recover the albedo and shading from images, which is an ill-posed problem in signal processing. As realistic labeled data are severely lacking, it is difficult to apply learning methods in this issue. In this letter, we propose using a synthesized dataset to facilitate the solving of this problem. A physically based renderer is used to generate color images and their underlying ground-truth albedo and shading from three-dimensional models. Additionally, we render a Kinect-like noisy depth map for each instance. We utilize this synthetic dataset to train a deep neural network for intrinsic image decomposition and further fine-tune it for real-world images. Our model supports both RGB and RGB-D as input, and it employs both high-level and low-level features to avoid blurry outputs. Experimental results verify the effectiveness of our model on realistic images.


Survey: Finite-state technology in natural language processing

In this survey, we will discuss current uses of finite-state information in several statistical natural language processing tasks. To this end, we will review standard approaches in tokenization, part-of-speech tagging, and parsing, and illustrate the utility of finite-state information and technology in these areas. The particular problems were chosen to allow a natural progression from simple prediction to structured prediction. We aim for a sufficiently formal presentation suitable for readers with a background in automata theory that allows to appreciate the contribution of finite-state approaches, but we will not discuss practical issues outside the core ideas. We provide instructive examples and pointers into the relevant literature for all constructions. We close with an outlook on finite-state technology in statistical machine translation.


Design and performance analysis of a high-speed air-cored axial-flux permanent-magnet generator with circular magnets and coils

Air-cored, axial flux permanent-magnet (AFPM) machines have magnetic and mechanical characteristics considered to be ideal for compact high speed electric power generation applications. To date, research on high speed AFPM machines is primarily from a mechanical perspective, with emphasis on higher power level applications such as in hybrid vehicles. Recently, there has been an increasing interest in man-portable mobile power sources with a power envelope of around 1kW, for mission critical applications. In this paper, a high speed air-cored surface mounted AFPM machine with circular magnets and coils is proposed. Comprehensive theoretical analysis and three-dimensional (3-D) electromagnetic finite element analysis (FEA) are developed to evaluate the performance of the machine. In addition, the rotor's mechanical stresses developed by high rotational speed are evaluated to ensure mechanical integrity. Finally, a prototype machine is developed for validation. Both the experimental and predicted results demonstrate that the proposed generator possesses distinct advantages over other systems, such as high efficiency, high power factor, and a simple and robust structure that offers a high degree of technology readiness.


PeerFlow: Secure Load Balancing in Tor

We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidthscanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.


Sentiment Analysis on Twitter Data

Abstract – Now-a-days social networking sites are at the boom, so large amount of data is generated. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we will discuss about a paradigm to extract the sentiment from a famous micro blogging service, Twitter, where users post their opinions for everything. In this paper, we will discuss the existing analysis of twitter dataset with data mining approach such as use of Sentiment analysis algorithm using machine learning algorithms. An approach is introduced that automatically classifies the sentiments of Tweets taken from Twitter dataset as in [1]. These messages or tweets are classified as positive, negative or neutral with respect to a query term. This is very useful for the companies who want to know the feedback about their product brands or the customers who want to search the opinion from others about product before purchase. We will use machine learning algorithms for classifying the sentiment of Twitter messages using distant supervision which is discussed in [8]. The training data consists of Twitter messages with emoticons, acronyms which are used as noisy labels discussed in [4]. We examine sentiment analysis on Twitter data. The contributions of this survey paper are: (1) we use Parts Of Speech (POS)-specific prior polarity features. (2) We also use a tree kernel to prevent the need for monotonous feature engineering.


Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System

An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.


Implementation of a Cognitive Radio Front-End Using Rotatable Controlled Reconfigurable Antennas

This communication presents a new antenna system designed for cognitive radio applications. The antenna structure consists of a UWB antenna and a frequency reconfigurable antenna system. The UWB antenna scans the channel to discover “white space” frequency bands while tuning the reconfigurable section to communicate within these bands. The frequency agility is achieved via a rotational motion of the antenna patch. The rotation is controlled by a stepper motor mounted on the back of the antenna structure. The motor's rotational motion is controlled by LABVIEW on a computer connected to the motor through its parallel port. The computer's parallel port is connected to a NPN Darlington array that is used to drive the stepper motor. The antenna has been simulated with the driving motor being taken into consideration. A good agreement is found between the simulated and the measured antenna radiation properties.


A new ultra-wideband, ultra-short monocycle pulse generator with reduced ringing

We introduce a new ultra-wideband (UWB), ultra-short, step recovery diode monocycle pulse generator. This pulse generator uses a simple RC high-pass filter as a differentiator to generate the monocycle pulse directly. The pulse-shaping network employs a resistive circuit to achieve UWB matching and substantial removal of the pulse ringing, and rectifying and switching diodes to further suppress the ringing. An ultra-short monocycle pulse of 300-ps pulse duration, -17-dB ringing level, and good symmetry has been demonstrated. Good agreement between the measured and calculated results was achieved.


EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks

In recent years, there has been a growing interest in wireless sensor networks. One of the major issues in wireless sensor network is developing an energy-efficient clustering protocol. Hierarchical clustering algorithms are very important in increasing the network’s life time. Each clustering algorithm is composed of two phases, the setup phase and steady state phase. The hot point in these algorithms is the cluster head selection. In this paper, we study the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are hierarchically clustered. We assume that a percentage of the population of sensor nodes is equipped with the additional energy resources. We also assume that the sensor nodes are randomly distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. Homogeneous clustering protocols assume that all the sensor nodes are equipped with the same amount of energy and as a result, they cannot take the advantage of the presence of node heterogeneity. Adapting this approach, we introduce an energy efficient heterogeneous clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to the residual energy in each node. Finally, the simulation results demonstrate that our proposed heterogeneous clustering approach is more effective in prolonging the network lifetime compared with LEACH. 2008 Elsevier B.V. All rights reserved.


Online Adaptive Passive-Aggressive Methods for Non-Negative Matrix Factorization and Its Applications

This paper aims to investigate efficient and scalable machine learning algorithms for resolving Non-negative Matrix Factorization (NMF), which is important for many real-world applications, particularly for collaborative filtering and recommender systems. Unlike traditional batch learning methods, a recently proposed online learning technique named "NN-PA" tackles NMF by applying the popular Passive-Aggressive (PA) online learning, and found promising results. Despite its simplicity and high efficiency, NN-PA falls short in at least two critical limitations: (i) it only exploits the first-order information and thus may converge slowly especially at the beginning of online learning tasks; (ii) it is sensitive to some key parameters which are often difficult to be tuned manually, particularly in a practical online learning system. In this work, we present a novel family of online Adaptive Passive-Aggressive (APA) learning algorithms for NMF, named "NN-APA", which overcomes two critical limitations of NN-PA by (i) exploiting second-order information to enhance PA in making more informative updates at each iteration; and (ii) achieving the parameter auto-selection by exploring the idea of online learning with expert advice in deciding the optimal combination of the key parameters in NMF. We theoretically analyze the regret bounds of the proposed method and show its advantage over the state-of-the-art NN-PA method, and further validate the efficacy and scalability of the proposed technique through an extensive set of experiments on a variety of large-scale real recommender systems datasets.


Network-based wireless location: challenges faced in developing techniques for accurate wireless location information

Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety feature of cellular systems in response to an order issued by the Federal Communications Commission (FCC) in 1996. The FCC mandate aims to solve a serious public safety problem caused by the fact that, at present, a large proportion of all 911 calls originate from mobile phones, the location of which cannot be determined with the existing technology. However, many difficulties intrinsic to the wireless environment make meeting the FCC objective challenging. These challenges include channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions. In addition to emergency services, there are many other applications for wireless location technology, including monitoring and tracking for security reasons, location sensitive billing, fraud protection, asset tracking, fleet management, intelligent transportation systems, mobile yellow pages, and even cellular system design and management. This article provides an overview of wireless location challenges and techniques with a special focus on network-based technologies and applications.


Microstrip Antennas

Microstrip antennas have been one of the niost innovative topics in antenna theory and design in recent years, and are increasingly finding application in a wide range of modern microwave systems. This paper begins with a brief overview of the basic characteristics of microstrip antennas, and then concentrates on the most significant developments in microstrip antenna technology that have been made in the last several years. Emphasis is on new antenna configurations for improved electrical performance and manufacturability, and advances in the analytical modeling of microstrip antennas and arrays.


Environmental impacts of polyvinyl chloride (PVC) production process

The increasing demand for plastics in the world leads to pressures on the environment generated by the consumption of raw materials based on fossil fuels and the need for plastic waste reduction and absorption to diminish their environmental impacts. In this paper we evaluated the environmental impacts induced by the production of polyvinyl chloride (PVC), one of the most widely used plastics in the manufacture of various goods. The impacts were calculated based on Life Cycle Assessment methodology. The negative environmental impacts of PVC production may be reduced by recycling, a sustainable, effective, currently available alternative of waste management. Our study showed that the use of recyclable materials, such as organic biodegradable waste as raw materials instead of crude oil can lead to significant reduction of environmental impacts.


On Networking of Internet of Things: Explorations and Challenges

Internet of Things (IoT), as the trend of future networks, begins to be used in many aspects of daily life. It is of great significance to recognize the networking problem behind developing IoT. In this paper, we first analyze and point out the key problem of IoT from the perspective of networking: how to interconnect large-scale heterogeneous network elements and exchange data efficiently. Combining our on-going works, we present some research progresses on three main aspects: 1) the basic model of IoT architecture; 2) the internetworking model; and 3) the sensor-networking mode. Finally, we discuss two remaining challenges in this area.


A Systematic Review of Service Level Management in the Cloud

Cloud computing make it possible to flexibly procure, scale, and release computational resources on demand in response to workload changes. Stakeholders in business and academia are increasingly exploring cloud deployment options for their critical applications. One open problem is that service level agreements (SLAs) in the cloud ecosystem are yet to mature to a state where critical applications can be reliably deployed in clouds. This article systematically surveys the landscape of SLA-based cloud research to understand the state of the art and identify open problems. The survey is particularly aimed at the resource allocation phase of the SLA life cycle while highlighting implications on other phases. Results indicate that (i) minimal number of SLA parameters are accounted for in most studies; (ii) heuristics, policies, and optimisation are the most commonly used techniques for resource allocation; and (iii) the monitor-analysis-plan-execute (MAPE) architecture style is predominant in autonomic cloud systems. The results contribute to the fundamentals of engineering cloud SLA and their autonomic management, motivating further research and industrial-oriented solutions.


Logo Retrieval Using Logo Proposals and Adaptive Weighted Pooling

This letter presents a novel approach for logo retrieval. Considering the fact that logo only occupies a small portion of an image, we apply Faster R-CNN to detect logo proposals first, and then use a two-step pooling strategy with adaptive weight to obtain an accurate global signature. The adaptive weighted pooling method can effectively balance the recall and precision of proposals by incorporating the probability of each proposal being a logo. Experimental results show that the proposed method interprets the similarity between query and database image more accurately and achieves state of the art performance.


Monolithically Integrated Si Photonics Transmitters in 0.25 um BiCMOS Platform for High-Speed Optical Communications

Monolithically integrated electro-optical transmitters fabricated in a 0.25 μm SiGe:C BiCMOS electronic-photonic integrated circuit (EPIC) technology with fT =190 GHz are presented. The modules are based on the co-design and integration of a segmented depletion-type Si Mach-Zehnder modulator and multichannel driver amplifiers. Two driving approaches and their performance trade-offs are discussed: a linear one, with direct interface to an external digital-to-analog converter (DAC), as well as a more power efficient implementation featuring integrated 4-bit DAC functionality. High extinction ratios at high speed are demonstrated, enabled by the high breakdown voltages of the HBT transistors. The modules support several modulation formats among which pulse-amplitude-modulation (PAM)−4 eye diagrams up to 37 GBd are shown, for the first time.


IT Outsourcing Strategies: Universalistic, Contingency, and Configurational Explanations of Success

F on individual outsourcing decisions in IT research has often yielded contradictory findings and recommendations. To address these contradictions, we investigate a holistic, configurational approach with the prevailing universalistic or contingency perspectives in exploring the effects of IT outsourcing strategies on outsourcing success. Based on residual rights theory, we begin by identifying three dimensions of IT outsourcing strategies: degree of integration, allocation of control, and performance period. We then develop a model of fit-as-gestalt, drawing from literatures on strategy, governance, interorganizational relationships, and outsourcing. Next, based on data from 311 firms in South Korea, we test universalistic and contingency perspectives in explaining the relationship between IT outsourcing strategies and outsourcing success. We then identify three congruent patterns, or gestalts, of IT outsourcing strategies. We term these strategies independent, arm’s-length, and embedded strategies. To establish the predictive validity of these gestalts and the viability of a configurational perspective, we then explore the effects of these congruent gestalts vis-à-vis noncongruent patterns on three dimensions of outsourcing success: strategic competence, cost efficiency, and technology catalysis. We also contrast the effects of each of the three gestalts on each of the three dimensions of outsourcing success. Our findings indicate the superiority of the configurational approach over universalistic and contingency perspectives in explaining outsourcing success.


An ontology for representing financial headline news

This paper presents the development of an ontology to represent financial headline news. This ontology is developed using the On-To-Knowledge methodology where the focus is on the design steps of the Knowledge Meta Process. This development is part of an ongoing project which aims to design a virtual stock market simulator based on multi-agent systems. The proposed ontology has 31 concepts and includes 201 attributes. The testing results conducted on reliable headline news show that 99% of these headline news can be properly represented by the attributes of the right category in the ontology. Unreliable headline news characterized by news having uncertainties, incompleteness, ill-definition, or imprecision cannot be represented by the proposed ontology. Approaches for representing these unreliable headline


The real-time video stabilization for the rescue robot

When rescue robots navigate in a rough terrain, significant vibration of the video occurs unavoidably and a video stabilization system is proposed in this paper to reduce the disturbance on the visual system of the present rescue robots. The Kalman filter is applied to estimate the motion vector of the robot and the false estimation occurrence can be thus greatly reduced by applying analysis of correlation and variance of the motion vector estimation. With a hierarchical searching algorithm implemented on the TI DSP 6437, the frame rate can be improved from 10 fps to 28 fps to realize the real-time video stabilization and furthermore, the cooperative mission of multiple rescue robots has been achieved by applying the developed visual servo technique. The proposed virtual bounded motion control algorithm further leads the robots approaching the target precisely with cooperation of multiple rescue robots. The demo video is available at: http://lab816.cn.nctu.edu.tw/DemoVideo/. video stabilization


Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units

We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map, combining the intuitions of dropout and zoneout while respecting neuron values. This connection suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks.


Device-to-Device Communication Underlaying a Finite Cellular Network Region

Underlay in-band device-to-device (D2D) communication can improve the spectrum efficiency of cellular networks. However, the coexistence of D2D and cellular users causes inter-cell and intra-cell interference. The former can be effectively managed through inter-cell interference coordination and, therefore, is not considered in this paper. Instead, we focus on the intra-cell interference and propose a D2D mode selection scheme to manage it inside a finite cellular network region. The potential D2D users are controlled by the base station (BS) to operate in D2D mode based on the average interference generated to the BS. Using stochastic geometry, we study the outage probability experienced at the BS and a D2D receiver, and spectrum reuse ratio, which quantifies the average fraction of successfully transmitting D2D users. The analysis shows that the outage probability at the D2D receiver varies for different locations. In addition, without impairing the performance at the BS, if the path-loss exponent on the cellular link is slightly lower than that on the D2D link, the spectrum reuse ratio can have negligible decrease, while the D2D users’ average number of successful transmissions increases with increasing D2D node density. This indicates that an increasing level of D2D communication can be beneficial in future networks.


BLAT--the BLAST-like alignment tool.

Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments. A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences. BLAT's speed stems from an index of all nonoverlapping K-mers in the genome. This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly. BLAT has several major stages. It uses the index to find regions in the genome likely to be homologous to the query sequence. It performs an alignment between homologous regions. It stitches together these aligned regions (often exons) into larger alignments (typically genes). Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible. This paper describes how BLAT was optimized. Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches. BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications. http://genome.ucsc.edu hosts a web-based BLAT server for the human genome.