title: Non-local sparse models for image restoration

abstract: We propose in this paper to unify two different approaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has proven to be very effective in image reconstruction and classification tasks. On the other hand, explicitly exploiting the self-similarities of natural images has led to the successful non-local means approach to image restoration. We propose simultaneous sparse coding as a framework for combining these two approaches in a natural manner. This is achieved by jointly decomposing groups of similar signals on subsets of the learned dictionary. Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.


Some Digital Communication Fundamentals for Physicists and Others ∗ Add berkeley.edu


title: DeepCyTOF: Automated Cell Classification of Mass Cytometry Data by Deep Learning and Domain Adaptation

abstract: Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance, and interpretation of data generated in large studies. However, most current tools are less suitable for routine use where analysis must be standardized, reproducible, interpretable, and comparable. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps untenable for larger studies. The subjectivity of manual gating introduces variability into the data and impacts reproducibility and comparability of results, particularly in multi-center studies. The FlowCAP consortium was formed to address these issues and it aims to boost user confidence in the viability of automated gating methods. We introduce DeepCyTOF, a standardization approach for gating based on a multi-autoencoder neural network. DeepCyTOF requires labeled cells from only a single sample. It is based on domain adaptation principles and is a generalization of previous work that allows us to calibrate between a source domain distribution (reference sample) and multiple target domain distributions (target samples) in a supervised manner. We apply DeepCyTOF to two CyTOF datasets generated from primary immune blood cells: (i) 14 subjects with a history of infection with West Nile virus (WNV), and (ii) 34 healthy subjects of different ages. Each blood sample was labeled with 42 antibody markers, 12 of which were used in our analysis, at baseline and three different stimuli (PMA/ionomycin, tumor cell line K562, and infection with WNV). In each of these datasets we manually gated a single baseline reference sample to automatically gate the remaining uncalibrated samples. We show that DeepCyTOF cell classification is highly concordant with cell classification obtained by individual manual gating of each sample with over 99% concordance. Additionally, we apply a stacked autoencoder, which is one of the building blocks of DeepCyTOF, to cytometry datasets used in the 4th challenge of the FlowCAP-I competition and demonstrate that it over performs relative to all gating methods introduced in this competition. We conclude that stacked autoencoders combined with a domain adaptation procedure offers a powerful computational approach for semi-automated gating of CyTOF and flow cytometry data.


title: A Novel Technique for Mapping the Vascularity of Labia Minora Prior to Labiaplasty : Cold Light Illumination Neuartiges Verfahren zur Darstellung der Vaskularität der inneren Schamlippen vor der Labioplastik : Einsatz von Kaltlichtbeleuchtung

abstract: Objective Aim of the study was to assess the feasibility of a novel technique to determine the vascularity of labia minora prior to labiaplasty. Methods A cold light source employed in laparoscopic procedures was used to illuminate the outer surface of the labia minora as described in this prospective descriptive study. Results Of the patients, 46.1% (n = 41) had upper third prominence, 36% (n = 32) had middle third prominence, and 18% (n = 16) had lower third prominence according to the Banwell classification of morphologies. Right labia minora width was 0 - 2 cm in 51.7% (n = 46), 2 - 4 cm in 47.2% (n = 42) and > 4 cm in 1.1% (n = 1) of cases. Left labia minora width was 0 - 2 cm in 52.8% (n = 47), 2 - 4 cm in 41.6% (n = 37) and > 4 cm in 5.6% (n = 5) of cases. The incidence of Anterior 2 and Posterior 1 vessels in the different morphologies were statistically significantly different (p = 0.007, p = 0.018). The Anterior 2 vessel was higher in the lower morphology group whereas the Posterior 1 vessel was higher in the upper morphology group. A central vessel was observed in 93.3% (n = 83) of patients in the left labium minus and the right labium minus. The incidence of the Posterior 1 vessel was significantly higher in the left labium minus than in the right labium minus (p = 0.021). Discussion This novel technique to assess labial vascularity using a cold light source could be very useful to reduce dehiscence by avoiding excessive resection of highly vascularized tissue. Most units can easily access a cold light source, which can be used to assess labial vascularity prior to labiaplasty.


title: Question Generation from SQL Queries Improves Neural Semantic Parsing

abstract: We study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-of-the-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further improves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.


title: Visual Abstraction of Large Scale Geospatial Origin-Destination Movement Data

abstract: A variety of human movement datasets are represented in an Origin-Destination(OD) form, such as taxi trips, mobile phone locations, etc. As a commonly-used method to visualize OD data, flow map always fails to discover patterns of human mobility, due to massive intersections and occlusions of lines on a 2D geographical map. A large number of techniques have been proposed to reduce visual clutter of flow maps, such as filtering, clustering and edge bundling, but the correlations of OD flows are often neglected, which makes the simplified OD flow map present little semantic information. In this paper, a characterization of OD flows is established based on an analogy between OD flows and natural language processing (NPL) terms. Then, an iterative multi-objective sampling scheme is designed to select OD flows in a vectorized representation space. To enhance the readability of sampled OD flows, a set of meaningful visual encodings are designed to present the interactions of OD flows. We design and implement a visual exploration system that supports visual inspection and quantitative evaluation from a variety of perspectives. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in reducing the visual clutter and enhancing correlations of OD flows.


title: WRES: A Novel 3 DoF WRist ExoSkeleton With Tendon-Driven Differential Transmission for Neuro-Rehabilitation and Teleoperation

abstract: In this letter, the authors proposed a new wrist exoskeleton designed to provide kinesthetic feedback to the wrist user's joints for rehabilitative, teleoperation, and virtual environment interaction purposes. The design process focused on the need to use the interface as the end-effector of a whole bimanual upper limb exoskeleton system, composed of two exoskeleton arms with 7 DoF each and two hand exoskeletons for all the fingers. The guideline of the design pointed to reach a tradeoff between high transparency and low weight. In addition, both the compactness and mass distribution have played an important role in the design process due to the need to perform bimanual task and interaction. The proposed device was designed adopting a tendon-cable transmission for all the three joints. A differential transmission solution has been adopted to actuate the flexion/extension and radial/ulnar deviation joints, which allows to achieve lower inertia and higher compactness than a serial transmission. A first prototype has been build and characterized with several experimental tests showing its suitability for both teleoperation and rehabilitative therapy. Finally, the wrist device has been integrated with both the arm and the hand exoskeleton to prove the requirement observance.


title: Decision-making and addiction (part I): impaired activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences

abstract: Abstract Some substance dependent individuals (SDI) suffer from a decision-making impairment akin to that seen in neurological patients with lesions of the ventromedial (VM) prefrontal cortex. The somatic-marker hypothesis posits that decision-making is a process that depends on emotion and that deficits in emotional signaling will lead to poor decision-making. In this study, we tested the hypothesis that SDI who perform disadvantageously on a decision-making instrument, the gambling task (GT), have a deficit in the somatic signals that help guide their decision in the advantageous direction. Since deficits in decision-making/somatic markers can also result from dysfunctional amygdala, we asked indirectly (i.e. via tests sensitive to VM or amygdala dysfunction) whether such a deficit in SDI is restricted to VM dysfunction or includes the amygdala. Using the GT, and skin conductance response (SCR) as an index of somatic state activation, we studied groups of SDI ( n =46), normal controls ( n =49), and VM patients ( n =10). A subgroup of SDI showed defective performance on the GT coupled with impaired anticipatory SCR, but normal SCR to punishment, and normal acquisition of conditioned SCR to an aversive loud sound. This supports the hypothesis that the poor decision-making in some SDI is associated with defective somatic state activation that is linked to a dysfunctional VM cortex. Thus, the dysfunctional VM cortex underlying the “myopia” for the future in some SDI may be one of the principle mechanisms underlying the transition from casual substance taking to compulsive and uncontrollable behavior.


title: Learning by doing and learning through play: an exploration of interactivity in virtual environments for children

abstract: The development of interactive, participatory, multisensoryenvironments that combine the physical with the virtual comes as anatural continuation to the computer game industrys constant racefor more exciting user experiences. Specialized theme parks andvarious other leisure and entertainment centers worldwide areembracing the interactive promise that games have made usersexpect. This is not a trend limited to the entertainment domain;non-formal learning environments for children are also followingthis path, backed up by a theoretical notion of play as a coreactivity in a childs development. In this article we explore acentral thread in learning, play, as well as an essentialcharacteristic of virtual reality environments: interactivity. Acritical review of examples of immersive virtual reality worldscreated for children, with particular attention given to the roleand nature of interactivity, is attempted. Interactivity isexamined in relation to learning, play, narrative, and tocharacteristics inherent in virtual reality, such as immersion,presence, and the creation of illusion.


title: Breast reduction for asymmetrical hypertrophy.

abstract: Design details of reduction of asymmetrical hypertrophy of the breasts are discussed. The concepts apply to nearly any breast reduction technique, although here they are presented in the context of the inferior segment method, which we favor. Although most of these points may seem self-evident to the experienced plastic surgeon, there has been little written about the details.


title: Real-time bag of words, approximately

abstract: We start from the state-of-the-art Bag of Words pipeline that in the 2008 benchmarks of TRECvid and PASCAL yielded the best performance scores. We have contributed to that pipeline, which now forms the basis to compare various fast alternatives for all of its components: (i) For descriptor extraction we propose a fast algorithm to densely sample SIFT and SURF, and we compare several variants of these descriptors. (ii) For descriptor projection we compare a k-means visual vocabulary with a Random Forest. As a preprojection step we experiment with PCA on the descriptors to decrease projection time. (iii) For classification we use Support Vector Machines and compare the x2 kernel with the RBF kernel. Our results lead to a 10-fold speed increase without any loss of accuracy and to a 30-fold speed increase with 17% loss of accuracy, where the latter system does real-time classification at 26 images per second.


title: Supply Chain Forecasting Model Using Computational Intelligence Techniques

abstract: Nowadays, supply chain is more complex with the advance of technology. One of the important tasks in supply chain management is product demand forecasting as it provide initial figure for various plan to work on, for example, production, inventory, personnel. The complexity of supply chain makes the traditional techniques become less appropriate. This paper propose an applica- tion of Artificial neural network (ANN) and Support vector regression (SVR) to forecast product demand. A case study of an arm coil of a hard disk from a hard disk drive part manufacturing was used to demonstrate the proposed techniques.


title: An efficient CNN-based classification on G-protein Coupled Receptors using TF-IDF and N-gram

abstract: Protein sequence classification is increasingly crucial in the current “biological information sciences” epoch, where researchers hammer at functional genomics and proteomics technologies for predicting the function of large-scale new proteins. This has sparked interest in the methods which do not rely on traditional sequence alignment, but prefer machine learning approaches. In this paper, we present a Convolutional Neural Network (CNN) based method to perform the classification on the different levels of G-protein Coupled Receptors (GPCRs). The method is implemented in conjunction with an improved feature extraction method and TF-IDF feature weighting strategy. Experimental results indicate that the proposed method makes significant improvements over previous methods, which attains an accuracy of up to 98.34%, 98.13% and 96.47% in the classification of family level, subfamily level I and II, respectively. In comparison to the other well-known classification methods for GPCRs, the classification error rate of the proposed method is reduced by of at least 55.14% (family level), 72.86% (level I) and 52.63% (Level II).


title: Software-Defined Networking Paradigms in Wireless Networks: A Survey

abstract: Software-defined networking (SDN) has generated tremendous interest from both academia and industry. SDN aims at simplifying network management while enabling researchers to experiment with network protocols on deployed networks. This article is a distillation of the state of the art of SDN in the context of wireless networks. We present an overview of the major design trends and highlight key differences between them.


Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric


title: CarrotDB: a genomic and transcriptomic database for carrot

abstract: Carrot (Daucus carota L.) is an economically important vegetable worldwide and is the largest source of carotenoids and provitamin A in the human diet. Given the importance of this vegetable to humans, research and breeding communities on carrot should obtain useful genomic and transcriptomic information. The first whole-genome sequences of ‘DC-27’ carrot were de novo assembled and analyzed. Transcriptomic sequences of 14 carrot genotypes were downloaded from the Sequence Read Archive (SRA) database of National Center for Biotechnology Information (NCBI) and mapped to the whole-genome sequence before assembly. Based on these data sets, the first Web-based genomic and transcriptomic database for D. carota (CarrotDB) was developed (database homepage: http://apiaceae.njau.edu.cn/car rotdb). CarrotDB offers the tools of Genome Map and Basic Local Alignment Search Tool. Using these tools, users can search certain target genes and simple sequence repeats along with designed primers of ‘DC-27’. Assembled transcriptomic sequences along with fragments per kilobase of transcript sequence per millions base pairs sequenced information (FPKM) information of 14 carrot genotypes are also provided. Users can download de novo assembled whole-genome sequences, putative gene sequences and putative protein sequences of ‘DC27’. Users can also download transcriptome sequence assemblies of 14 carrot genotypes along with their FPKM information. A total of 2826 transcription factor (TF) genes classified into 57 families were identified in the entire genome sequences. These TF genes were embedded in CarrotDB as an interface. The ‘GERMPLASM’ part of CarrotDB also offers taproot photos of 45 carrot genotypes and a table containing accession numbers, names, countries of origin and colors of cortex, phloem and xylem parts of taproots corresponding to each carrot genotype. CarrotDB will be continuously updated with new information. Database URL: http://apiaceae.njau.edu.cn/carrotdb/


title: Combination of Machine Scores for Automatic Grading of Pronunciation Quality

abstract: This work is part of an effort aimed at developing computer-based systems for language instruction; we address the task of grading the pronunciation quality of the speech of a student of a foreign language. The automatic grading system uses SRI's DecipherTM continuous speech recognition system to generate phonetic segmentations. Based on these segmentations and probabilistic models we produce different pronunciation scores for individual or groups of sentences that can be used as predictors of the pronunciation quality. Different types of these machine scores can be combined to obtain a better prediction of the overall pronunciation quality. In this paper we review some of the best-performing machine scores and discuss the application of several methods based on linear and nonlinear mapping and combination of individual machine scores to predict the pronunciation quality grade that a human expert would have given. We evaluate these methods in a database that consists of pronunciation-quality-graded speech from American students speaking French. With predictors based on spectral match and on durational characteristics, we find that the combination of scores improved the prediction of the human grades and that nonlinear mapping and combination methods performed better than linear ones. Characteristics of the different nonlinear methods studied are discussed.


title: Too Much of a Good Thing? The Relationship Between Number of Friends and Interpersonal Impressions on Facebook

abstract: A central feature of the online social networking system, Facebook, is the connection to and links among friends. The sum of the number of one’s friends is a feature displayed on users’ profiles as a vestige of the friend connections a user has accrued. In contrast to offline social networks, individuals in online network systems frequently accrue friends numbering several hundred. The uncertain meaning of friend status in these systems raises questions about whether and how sociometric popularity conveys attractiveness in non-traditional, non-linear ways. An experiment examined the relationship between the number of friends a Facebook profile featured and observers’ ratings of attractiveness and extraversion. A curvilinear effect of sociometric popularity and social attractiveness emerged, as did a quartic relationship between friend count and perceived extraversion. These results suggest that an overabundance of friend connections raises doubts about Facebook users’ popularity and desirability. doi:10.1111/j.1083-6101.2008.00409.x New forms of computer-mediated-communication (CMC) are raising questions, about the relationship between communication activities and interpersonal judgments. Communication technology has evolved beyond the means by which senders had more or less complete control over the impression-related information that receivers could observe. With the advent of new social technologies, users no longer have to rely on an individual’s self-composed emails, chat statements, or personal web pages to garner impressions about a subject. Users employ strategies unique to CMC including browsing archived transcripts of discussions and chats, surfing personal and institutional web sites, or using search engines to uncover a variety of


title: A multi-modulus divider with high sensitivity and extended division range in 0.18 μm BiCMOS

abstract: This work presents an integer-N divider based on multi-modulus frequency dividers (MMDs) which covers a continuous integer-N divider ratio range of 8 to 127 and was built using current mode logic (CML). A new powerful and simple modulus extension for MMDs optimized for fast CML implementations is demonstrated. The proposed divider is a hybrid composition of emitter coupled logic (ECL) stages and source coupled logic (SCL) stages. Along an input frequency range of 1 GHz to 7.5 GHz the needed input voltage level is less than 45 mV PP which is equivalent to −20 dBm at a 100Ω load. The maximum operating input frequency is 8.1 GHz while the divider consumes only 18.4 mW. The circuit was implemented in a low cost 180 nm BiCMOS process and is an essential part of a phase looked loop circuit (PLL). Together with the controllable current biasing circuitry it occupies an area of less than 0.09 mm2.


title: From information security to . business security ?

abstract: This short opinion paper argues that information security, the discipline responsible for protecting a company's information assets against business risks, has now become such a crucial component of good Corporate Governance, that it should rather be called Business Security instead of Information Security.


title: Deep learning for source camera identification on mobile devices

abstract: Abstract In the present paper, we propose a source camera identification (SCI) method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layers where each layer is composed by a set of high pass filters which are applied all over the input image. This convolution process provides the unique ability to extract features automatically from data and to learn from those features. Our proposal describes a CNN architecture which is able to infer the noise pattern of mobile camera sensors (also known as camera fingerprint) with the aim at detecting and identifying not only the mobile device used to capture an image (with a 98% of accuracy), but also from which embedded camera the image was captured. More specifically, we provide an extensive analysis on the proposed architecture considering different configurations. The experiment has been carried out using the images captured from different mobile device cameras (MICHE-I Dataset) and the obtained results have proved the robustness of the proposed method.


title: Capture Commonsense Knowledge for Sentiment Analysis

abstract: This paper presents a new distributed representation based approach to capture commonsense knowledge, which is a very important kind of knowledge for affective computing and sentiment analysis. Currently, we focus on meanings of verbs and related dependency relations. Built upon such dependency relations, a set of vector space models are developed to capture the meaning of verbs and transfer them to related words. To demonstrate the capability of this data-driven approach, we do not incorporate any human knowledge. The results show that we can achieve superior performance on a subset of Winograd Schema challenge dataset, compared with other existing pronoun co-reference and embedding models. Besides that, we also use an example to demonstrate that our model can be potentially used in further sentiment analysis tasks.


title: A language-based intrusion detection approach for automotive embedded networks

abstract: The increase in connectivity and complexity of modern automotive networks presents new opportunities for potential hackers trying to take over a vehicle. To protect the automotive networks from such attacks, security mechanisms, such as firewalls or secure authentication protocols may be included. However, should an attacker succeed in bypassing such measures and gain access to the internal network, these security mechanisms become unable to report about the attacks ensuing such a breach, occurring from the internal network. To complement these preventive security mechanisms, we present a non intrusive network-based intrusion detection approach fit for vehicular networks, such as the widely used CAN. Leveraging the high predictability of embedded automotive systems, we use language theory to elaborate a set of attack signatures derived from behavioural models of the automotive calculators in order to detect a malicious sequence of messages transiting through the internal network.


title: A study of normative and informational social influences upon individual judgment.

abstract: Several modifications of the Asch experiment in which the S judges the length of lines in the company of a group of “stooges” who carry out the experimenter's instructions are described. These include a face-to-face situation, an anonymous situation, and a group situation, with self-commitment, publ


title: Economy-Wide Estimates of the Implications of Climate Change

abstract: The economy-wide implications of sea level rise in 2050 are estimated using a static computable general equilibrium model. Overall, general equilibrium effects increase the costs of sea level rise, but not necessarily in every sector or region. In the absence of coastal protection, economies that rely most on agriculture are hit hardest. Although energy is substituted for land, overall energy consumption falls with the shrinking economy, hurting energy exporters. With full coastal protection, GDP increases, particularly in regions that do a lot of dike building, but utility falls, least in regions that build a lot of dikes and export energy. Energy prices rise and energy consumption falls. The costs of full protection exceed the costs of losing land.


title: Collaborative Diffusion on the GPU for Path-Finding in Games

abstract: Exploiting the powerful processing power available on the GPU in many machines, we investigate the performance of parallelised versions of pathfinding algorithms in typical game environments. We describe a parallel implementation of a collaborative diffusion algorithm that is shown to find short paths in real-time across a range of graph sizes and provide a comparison to the well known Dijkstra and A* algorithms. Although some trade-off of cost vs path-length is observed under specific environmental conditions, results show that it is a viable contender for pathfinding in typical real-time game scenarios, freeing up CPU computation for other aspects of game AI.


title: Fully automatic lesion boundary detection in ultrasound breast images

abstract: We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of ::: the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and ::: in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to pre-process ::: the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued ::: thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point ::: of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the ::: inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding ::: segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the ::: initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model). ::: We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final ::: boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing state-of- ::: the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean ::: technique proposed by Yap et. al., in proving the proposed method's robustness and accuracy.


title: Thermostable DNA Immobilization and Temperature Effects on Surface Hybridization

abstract: Monolayer films of nucleic acids on solid supports are encountered in a range of diagnostic and bioanalytical applications. These applications often rely on elevated temperatures to improve performance; moreover, studies at elevated temperatures can provide fundamental information on layer organization and functionality. To support such applications, this study compares thermostability of oligonucleotide monolayers immobilized to gold by first coating the gold with a nanometer-thick film (an “anchor layer”) of a polymercaptosiloxane, to which DNA oligonucleotides are subsequently tethered through maleimide–thiol conjugation, with thermostability of monolayers formed via widely used attachment through a terminal thiol moiety on the DNA. The temperature range covered is from 25 to 90 °C. After confirming stability of immobilization and, more importantly, retention of hybridization activity even under the harshest conditions investigated, these thermostable films are used to demonstrate measurements of (1) r...


Speech Representation Models for Speech Synthesis and Multimodal Speech Recognition


title: Hands on the wheel: A Dataset for Driver Hand Detection and Tracking

abstract: The ability to detect, localize and track the hands is crucial in many applications requiring the understanding of the person behavior, attitude and interactions. In particular, this is true for the automotive context, in which hand analysis allows to predict preparatory movements for maneuvers or to investigate the driver's attention level. Moreover, due to the recent diffusion of cameras inside new car cockpits, it is feasible to use hand gestures to develop new Human-Car Interaction systems, more user-friendly and safe. In this paper, we propose a new dataset, called Turms, that consists of infrared images of driver's hands, collected from the back of the steering wheel, an innovative point of view. The Leap Motion device has been selected for the recordings, thanks to its stereo capabilities and the wide view-angle. Besides, we introduce a method to detect the presence and the location of driver's hands on the steering wheel, during driving activity tasks.


title: Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience

abstract: In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in our perception of the world, the origin of the concept of space remains largely mysterious. For example in the context of artificial perception, this issue is usually circumvented by having engineers pre-define the spatial structure of the problem the agent has to face. We here show that the structure of space can be autonomously discovered by a naive agent in the form of sensorimotor regularities, that correspond to so called compensable sensory experiences: these are experiences that can be generated either by the agent or its environment. By detecting such compensable experiences the agent can infer the topological and metric structure of the external space in which its body is moving. We propose a theoretical description of the nature of these regularities and illustrate the approach on a simulated robotic arm equipped with an eye-like sensor, and which interacts with an object. Finally we show how these regularities can be used to build an internal representation of the sensor's external spatial configuration.


Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health.


title: Depth-aware convolutional neural networks for accurate 3D pose estimation in RGB-D images

abstract: Most recent approaches to 3D pose estimation from RGB-D images address the problem in a two-stage pipeline. First, they learn a classifier-typically a random forest-to predict the position of each input pixel on the object surface. These estimates are then used to define an energy function that is minimized w.r.t. the object pose. In this paper, we focus on the first stage of the problem and propose a novel classifier based on a depth-aware Convolutional Neural Network. This classifier is able to learn a scale-adaptive regression model that yields very accurate pixel-level predictions, allowing to finally estimate the pose using a simple RANSAC-based scheme, with no need to optimize complex ad hoc energy functions. Our experiments on publicly available datasets show that our approach achieves remarkable improvements over state-of-the-art methods.


title: Modularity-based Dynamic Community Detection.

abstract: In this project, we designed a modularity-based dynamic community detection method, which keeps tracking the community structure of a dynamic network with very low computing complexity in updating the community structure while the network changing dynamically, and we implemented our algorithm using Java. Furthermore, we generated random dynamic networks that have the community structures with different parameters as our experiment dataset. Extensive experiments have been taken to evaluate our method in terms of accuracy and efficiency. The experiment result shows that, comparing with Louvain algorithm, our method could greatly enhance the efficiency without jeopardizing much of the accuracy. Although we got a pretty good results from our experiments, we only implemented our method on our own generated dataset. For future work, we might want to test our method on real public dataset. In the real public dynamic networks, there might be more specific dynamic change cases other than the cases covered by our method, so that we might need to dig into more specific other dynamic cases and find out the solutions.


title: Using Mobile LiDAR Data for Rapidly Updating Road Markings

abstract: Updating road markings is one of the routine tasks of transportation agencies. Compared with traditional road inventory mapping techniques, vehicle-borne mobile light detection and ranging (LiDAR) systems can undertake the job safely and efficiently. However, current hurdles include software and computing challenges when handling huge volumes of highly dense and irregularly distributed 3-D mobile LiDAR point clouds. This paper presents the development and implementation aspects of an automated object extraction strategy for rapid and accurate road marking inventory. The proposed road marking extraction method is based on 2-D georeferenced feature (GRF) images, which are interpolated from 3-D road surface points through a modified inverse distance weighted (IDW) interpolation. Weighted neighboring difference histogram (WNDH)-based dynamic thresholding and multiscale tensor voting (MSTV) are proposed to segment and extract road markings from the noisy corrupted GRF images. The results obtained using 3-D point clouds acquired by a RIEGL VMX-450 mobile LiDAR system in a subtropical urban environment are encouraging.


title: Analysis and Optimization of Engines for Dynamically Typed Languages

abstract: Dynamically typed programming languages have become very popular in the recent years. These languages ease the task of the programmer but introduce significant overheads since assumptions about the types of variables have to be constantly validated at run time. Java Script is a widely used dynamically typed language that has gained significant popularity in recent years. In this paper, we provide a detailed analysis of the two main sources of overhead in the Java Script execution. The first one is the runtime overhead needed for dynamic compilation and housekeeping activities (i.e. Garbage collector, compilation, etc.). The second one is the additional checks and guards introduced by the dynamic nature of Java Script. Then, we propose three new HW/SW optimizations that reduce this latter type of overhead. We show that these two types of overhead represent 35% and 25% respectively of the total execution time on average for a representative workload, and the proposed optimizations provide a 6% average speedup.


title: China's language input system in the digital age affects children's reading development

abstract: Written Chinese as a logographic system was developed over 3,000 y ago. Historically, Chinese children have learned to read by learning to associate the visuo-graphic properties of Chinese characters with lexical meaning, typically through handwriting. In recent years, however, many Chinese children have learned to use electronic communication devices based on the pinyin input method, which associates phonemes and English letters with characters. When children use pinyin to key in letters, their spelling no longer depends on reproducing the visuo-graphic properties of characters that are indispensable to Chinese reading, and, thus, typing in pinyin may conflict with the traditional learning processes for written Chinese. We therefore tested character reading ability and pinyin use by primary school children in three Chinese cites: Beijing (n = 466), Guangzhou (n = 477), and Jining (n = 4,908). Children with severe reading difficulty are defined as those who were normal in nonverbal IQ but two grades (i.e., 2 y) behind in character-reading achievement. We found that the overall incidence rate of severe reading difficulty appears to be much higher than ever reported on Chinese reading. Crucially, we found that children’s reading scores were significantly negatively correlated with their use of the pinyin input method, suggesting that pinyin typing on e-devices hinders Chinese reading development. The Chinese language has survived the technological challenges of the digital era, but the benefits of communicating digitally may come with a cost in proficient learning of written Chinese.


title: Do We Have to Prohibit the Use of Mobile Phones in Classrooms

abstract: People take their mobile phones everywhere they go. In Saudi Arabia, the mobile penetration is very high and students use their phones for different reasons in the classroom. The use of mobile devices in classroom triggers an alert of the impact it might have on students’ learning. This study investigates the association between the use of mobile phones during classroom and the learners’ performance and satisfaction. Results showed that students get distracted, and that this diversion of their attention is reflected in their academic success. However, this is not applicable for all. Some students received high scores even though they declared using mobile phones in classroom, which triggers a request for a deeper study.


title: A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals

abstract: Compressive sensing (CS) is a promising technology for realizing low-power and cost-effective wireless sensor nodes (WSNs) in pervasive health systems for 24/7 health monitoring. Due to the high computational complexity (CC) of the reconstruction algorithms, software solutions cannot fulfill the energy efficiency needs for real-time processing. In this paper, we present a 12—237 kS/s 12.8 mW sparse-approximation (SA) engine chip that enables the energy-efficient data aggregation of compressively sampled physiological signals on mobile platforms. The SA engine chip integrated in 40 nm CMOS can support the simultaneous reconstruction of over 200 channels of physiological signals while consuming $ of a smartphone’s power budget. Such energy-efficient reconstruction enables two-to-three times energy saving at the sensor nodes in a CS-based health monitoring system as compared to traditional Nyquist-based systems, while providing timely feedback and bringing signal intelligence closer to the user.


title: An integral automation of industrial fruit and vegetable sorting by machine vision

abstract: Intensive fruit and vegetable sorting is a common task in productive regions. In order to meet the market standards, produce is classified according to quality levels that depend on maturity degree, weight, size, density, skin defects, etc. Probably the most important of these tasks involve automatic visual inspection. A distributed and scalable system for sorting automation is presented, that addresses all aspects of the quality classification mentioned above. The main characteristics of the system are: it can control from 1 to 10 conveyor belts; its maximum performance is 15 fruits per second per belt; apart from weight sensors, it combines infrared, color and ultraviolet images; some fruit defect modules are available to take account of the presence of a given defect.


title: Hierarchical structured learning for indoor autonomous navigation of Quadcopter

abstract: Autonomous navigation of generic monocular quadcopter in the indoor environment requires sophisticated approaches for perception, planning and control. This paper presents a system which enables a miniature quadcopter with a frontal monocular camera to autonomously navigate and explore the unknown indoor environment. Initially, the system estimates dense depth map of the environment from a single video frame using our proposed novel supervised Hierarchical Structured Learning (hsl) technique, which yields both high accuracy levels and better generalization. The proposed hsl approach discretizes the overall depth range into multiple sets. It structures these sets hierarchically and recursively through partitioning the set of classes into two subsets with subsets representing apportioned depth range of the parent set, forming a binary tree. The binary classification method is applied to each internal node of binary tree separately using Support Vector Machine (svm). Whereas, the depth estimation of each pixel of the image starts from the root node in top-down approach, classifying repetitively till it reaches any of the leaf node representing its estimated depth. The generated depth map is provided as an input to Convolutional Neural Network (cnn), which generates flight planning commands. Finally, trajectory planning and control module employs a convex programming technique to generate collision-free minimum time trajectory which follows these flight planning commands and produces appropriate control inputs for the quadcopter. The results convey unequivocally the advantages of depth perception by hsl, while repeatable flights of successful nature in typical indoor corridors confirm the efficacy of the pipeline.


IDIOM-INDEPENDENT HARMONIC PATTERN RECOGNITION BASED ON A NOVEL CHORD TRANSITION REPRESENTATION


title: Double-sided cooling and thermo-electrical management of power transients for silicon chips on DCB-substrates for converter applications: Design, technology and test

abstract: This paper deals with the system design, technology and test of a novel concept of integrating Si and SiC power dies along with thermo-electric coolers in order to thermally manage transients occurring during operation. The concept features double-sided cooling as well as new materials and joining technologies to integrate the dies such as transient liquid phase bonding/soldering and sintering. Coupled-field simulations are used to predict thermal performance and are verified by especially designed test stands to very good agreement. This paper is the second in a series of publications on the ongoing work.


title: On2Vec: Embedding-based Relation Prediction for Ontology Population

abstract: Populating ontology graphs represents a long-standing problem for the Semantic Web community. Recent advances in translation-based graph embedding methods for populating instance-level knowledge graphs lead to promising new approaching for the ontology population problem. However, unlike instance-level graphs, the majority of relation facts in ontology graphs come with comprehensive semantic relations, which often include the properties of transitivity and symmetry, as well as hierarchical relations. These comprehensive relations are often too complex for existing graph embedding methods, and direct application of such methods is not feasible. Hence, we propose On2Vec, a novel translation-based graph embedding method for ontology population. On2Vec integrates two model components that effectively characterize comprehensive relation facts in ontology graphs. The first is the Component-specific Model that encodes concepts and relations into low-dimensional embedding spaces without a loss of relational properties; the second is the Hierarchy Model that performs focused learning of hierarchical relation facts. Experiments on several well-known ontology graphs demonstrate the promising capabilities of On2Vec in predicting and verifying new relation facts. These promising results also make possible significant improvements in related methods.


Pattern-based Information Extraction and Diagnostics


title: Unusual association between cheilitis glandularis and actinic cheilitis.

abstract: Objective: our aim is to report the simultaneous occurence of cheilitis glandularis and actinic cheilitis on the lower lip of a middle-aged female patient. Case Report: the patient presented clinical features compatible with these two lesions, confirmed by histopathological exam. Conclusion: the importance of the present case is the rare concomitant occurrence of both conditions, with special concern towards the malignancy potential related to both diseases


title: Bayesian inference of T Tauri star properties using multi-wavelength survey photometry

abstract: There are many pertinent open issues in the area of star and planet formation. Large statistical samples of young stars across star-forming regions are needed to trigger a breakthrough in our understanding, but most optical studies are based on a wide variety of spectrographs and analysis methods, which introduces large biases. Here we show how graphical Bayesian networks can be employed to construct a hierarchical probabilistic model which allows pre-main sequence ages, masses, accretion rates, and extinctions to be estimated using two widely available photometric survey databases (IPHAS r/i/Halpha and 2MASS J-band magnitudes.) Because our approach does not rely on spectroscopy, it can easily be applied to homogeneously study the large number of clusters for which Gaia will yield membership lists. We explain how the analysis is carried out using the Markov Chain Monte Carlo (MCMC) method and provide Python source code. We then demonstrate its use on 587 known low-mass members of the star-forming region NGC 2264 (Cone Nebula), arriving at a median age of 3.0 Myr, an accretion fraction of 20+/-2% and a median accretion rate of 10^-8.4 Msol/yr. The Bayesian analysis formulated in this work delivers results which are in agreement with spectroscopic studies already in the literature, but achieves this with great efficiency by depending only on photometry. It is a significant step forward from previous photometric studies, because the probabilistic approach ensures that nuisance parameters, such as extinction and distance, are fully included in the analysis with a clear picture on any degeneracies.


title: Implementation of digital IQ imbalance compensation in OFDM WLAN receivers

abstract: Although the maximum transmission speed in IEEE 802.11a WLAN is 54 Mbps, the real throughput is actually limited to 20/spl sim/30 Mbps. Except for the main effect from multi-path, we should also consider some non-ideal effects from imperfect hardware design, such as the IQ imbalance from direct conversion in RF front-end. IQ imbalance is not apparent in lower-order QAM modulation. However, in higher-order QAM modulation, it will become serious interference. In this paper, an IQ imbalance compensation circuit in IEEE802.11a baseband receiver is proposed. A low complexity time-domain compensation algorithm is used to replace the traditional high-order equalizer. MATLAB is used to simulate the whole transceiver including the channel model. After system verification, we use Verilog to implement the IQ imbalance compensation circuit with UMC 0.18 /spl mu/m CMOS 1p6m technology. Post-layout simulation results show that this scheme contributes to a very robust and easily implemented OFDM WLAN receiver.


title: Resonant Immittance Converter Topologies

abstract: An immittance converter (IC), in general, is a two-port network, in which input impedance is proportional to the load admittance connected across the output terminals and is useful in transforming a voltage source into a current source and vice versa. In this paper, a family of lumped-element resonant IC (RIC) topologies is identified by investigating the transmission parameters of various topological structures of electrical networks. In all, 24 RIC topologies have been identified with three and four reactive elements. The operating point and the design condition, under which these topologies exhibit immittance conversion characteristics, are derived. The suitability of these topologies in terms of absorbing parasitic components and providing inherent dc blocking to the transformer is examined. The analysis and design is illustrated with a newly identified four-element RIC topology as a constant-current power supply and validated with experimental results on a 250-W 105-kHz prototype converter.


title: Introduction to Automata Theory, Languages, and Computation

abstract: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity. The authors present the theory in a concise and straightforward manner, with an eye out for the practical applications. Exercises at the end of each chapter, including some that have been solved, help readers confirm and enhance their understanding of the material. This book is appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.


title: Digital government evolution: From transformation to contextualization

abstract: Abstract The Digital Government landscape is continuously changing to reflect how governments are trying to find innovative digital solutions to social, economic, political and other pressures, and how they transform themselves in the process. Understanding and predicting such changes is important for policymakers, government executives, researchers and all those who prepare, make, implement or evaluate Digital Government decisions. This article argues that the concept of Digital Government evolves toward more complexity and greater contextualization and specialization, similar to evolution-like processes that lead to changes in cultures and societies. To this end, the article presents a four-stage Digital Government Evolution Model comprising Digitization (Technology in Government), Transformation (Electronic Government), Engagement (Electronic Governance) and Contextualization (Policy-Driven Electronic Governance) stages; provides some evidence in support of this model drawing upon the study of the Digital Government literature published in Government Information Quarterly between 1992 and 2014; and presents a Digital Government Stage Analysis Framework to explain the evolution. As the article consolidates a representative body of the Digital Government literature, it could be also used for defining and integrating future research in the area.


title: WELDABILITY OF Ti6Al4V TO AISI 2205 WITH A NICKEL INTERLAYER USING FRICTION WELDING PREIZKU(ANJE VARIVOSTI PRI VARJENJU S TRENJEM Ti6Al4V IN AISI 2205 Z VMESNO PLASTJO NIKLJA

abstract: The aim of this study was to friction weld dissimilar metals, i.e., Ti6Al4V to duplex stainless steel, with and without a nickel interlayer using a new method. The metallographic examinations of the weld were carried out and the strength of the joints was determined with tensile tests. The experimental results indicate that the Ti6Al4V and duplex stainless steel could be joined with a nickel interlayer. The highest tensile strength (605 MPa) was obtained and the tensile strength of the joint was significantly increased with an increase of the rotation speed and the friction pressure.


title: Service Quality and Customer Satisfaction: A Case Study of Hotel Industry in Vietnam

abstract: The purpose of this study is to empirically examine the relationship between service quality and customer satisfaction in Vietnamese hotels, survey questionnaire was constructed with 23 service quality items covering 5 service quality dimensions based on SERVQUAL model. Data were collected from 432 guests of 33 three-star hotels in Vietnam in 2013. Analysis results indicate that Reliability, Responsiveness, Assurance, and Empathy significantly impact on the customer satisfaction. The study implies that service quality plays an important role as a driver for higher customer satisfaction level in hotel service. Managers would focus on Empathy, Reliability, Responsiveness, and Assurance to achieve high degree of customer satisfaction which leads to customer loyalty and business profit.


title: Markov random walk under constraint for discovering overlapping communities in complex networks

abstract: Detection of overlapping communities in complex networks has motivated recent research in the relevant fields. Aiming this problem, we propose a Markov dynamics based algorithm, called UEOC, which means, 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge on the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and was compared with a set of competing algorithms. Experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities.


title: Performance comparison between buck and boost CRM PFC converter

abstract: This paper presents a detailed performance comparison (including efficiency, EMC performance and component electrical stress) between boost and buck type PFC under critical conduction mode (CRM). In universal input (90–265Vac) applications, the CRM buck PFC has around 1% higher efficiency compared to its counterpart at low-line (90Vac) condition. Due to the low voltage swing of switch, buck PFC has a better CM EMI performance than boost PFC. It seems that the buck PFC is more attractive in low power applications which only need to meet the IEC61000-3-2 Class D standard based on the comparison. The experimental results from two 100-W prototypes are also presented for side by side comparison.


title: Application-Transparent Near-Memory Processing Architecture with Memory Channel Network

abstract: The physical memory capacity of servers is expected to increase drastically with deployment of the forthcoming non-volatile memory technologies. This is a welcomed improvement for emerging data-intensive applications. For such servers to be cost-effective, nonetheless, we must cost-effectively increase compute throughput and memory bandwidth commensurate with the increase in memory capacity without compromising application readiness. Tackling this challenge, we present Memory Channel Network (MCN) architecture in this paper. Specifically, first, we propose an MCN DIMM, an extension of a buffered DIMM where a small but capable processor called MCN processor is integrated with a buffer device on the DIMM for near-memory processing. Second, we implement device drivers to give the host and MCN processors in a server an illusion that they are independent heterogeneous nodes connected through an Ethernet link. These allow the host and MCN processors in a server to run a given data-intensive application together based on popular distributed computing frameworks such as MPI and Spark without any change in the host processor hardware and its application software, while offering the benefits of high-bandwidth and low-latency communications between the host and the MCN processors over memory channels. As such, MCN can serve as an application-transparent framework which can seamlessly unify near-memory processing within a server and distributed computing across such servers for data-intensive applications. Our simulation running the full software stack shows that a server with 8 MCN DIMMs offers 4.56X higher throughput and consume 47.5% less energy than a cluster with 9 conventional nodes connected through Ethernet links, as it facilitates up to 8.17X higher aggregate DRAM bandwidth utilization. Lastly, we demonstrate the feasibility of MCN with an IBM POWER8 system and an experimental buffered DIMM.


title: 3D Point Cloud Learning for Large-scale Environment Analysis and Place Recognition

abstract: In this paper, we develop a new deep neural network which can extract discriminative and generalizable global descriptors from the raw 3D point cloud. Specifically, two novel modules, Adaptive Local Feature Extraction and Graph-based Neighborhood Aggregation, are designed and integrated into our network. This contributes to extract the local features adequately, reveal the spatial distribution of the point cloud, and find out the local structure and neighborhood relations of each part in a large-scale point cloud with an end-to-end manner. Furthermore, we utilize the network output for point cloud based analysis and retrieval tasks to achieve large-scale place recognition and environmental analysis. We tested our approach on the Oxford RobotCar dataset. The results for place recognition increased the existing state-of-the-art result (PointNetVLAD) from 81.01% to 94.92%. Moreover, we present an application to analyze the large-scale environment by evaluating the uniqueness of each location in the map, which can be applied to localization and loop-closure tasks, which are crucial for robotics and self-driving applications.


title: Anomaly Detection in IaaS Clouds

abstract: Security is still a major concern in Cloud computing, especially the detection of nefarious use or abuse of cloud instances. One reason for this, is the ever-growing complexity and dynamic of the underlying system design and architecture. To be able to detect misuse of cloud instances, this work presents an anomaly detection system for Infrastructure as a Service Clouds. It is based on Cloud customers' usage behaviour analysis. Neural networks are used to analyse and learn the normal usage behaviour of Cloud customers, to then detect anomalies which could originate from a cloud security incident caused by an overtaken virtual machine. It increases transparency for Cloud customers about the security of their Cloud instances and supports the Cloud provider to detect misuse of their infrastructure. A simulation environment and an anomaly detection prototype get presented. Experiments validate the effectiveness of the proposed system.


title: Persistent homology and partial similarity of shapes

abstract: Persistent homology provides shapes descriptors called persistence diagrams. We use persistence diagrams to address the problem of shape comparison based on partial similarity. We show that two shapes having a common sub-part in general present a common persistence sub-diagram. Hence, the partial Hausdorff distance between persistence diagrams measures partial similarity between shapes. The approach is supported by experiments on 2D and 3D data sets.


title: Saliency-Based Spatiotemporal Attention for Video Captioning

abstract: Most existing video captioning methods ignore the visual saliency information in videos. We suppose that using saliency information can be helpful to generate more accurate video captions. Therefore, we propose a saliency-based spatiotemporal attention mechanism, and integrate it with the encoder-decoder framework of the classical video captioning model. Especially, we design a residual block which can use the saliency information to properly extract the visual feature of video frames. We evaluate our method on MSVD dataset and the results show that exploiting the visual saliency information can improve the performance of video captioning. Specifically, when compared with the traditional temporal attention method, our saliency-based temporal attention model can improve the METEOR and CIDEr metrics by 3.4% and 22.5% respectively. While by using the full saliency-based spatiotemporal attention mechanism, we can further improve the METEOR and CIDEr by 4.5% and 23.1 % respectively.


title: A study to support agile methods more effectively through traceability

abstract: Traceability is recognized to be important for supporting agile development processes. However, after analyzing many of the existing traceability approaches it can be concluded that they strongly depend on traditional development process characteristics. Within this paper it is justified that this is a drawback to support adequately agile processes. As it is discussed, some concepts do not have the same semantics for traditional and agile methodologies. This paper proposes three features that traceability models should support to be less dependent on a specific development process: (1) user-definable traceability links, (2) roles, and (3) linkage rules. To present how these features can be applied, an emerging traceability metamodel (TmM) will be used within this paper. TmM supports the definition of traceability methodologies adapted to the needs of each project. As it is shown, after introducing these three features into traceability models, two main advantages are obtained: 1) the support they can provide to agile process stakeholders is significantly more extensive, and 2) it will be possible to achieve a higher degree of automation. In this sense it will be feasible to have a methodical trace acquisition and maintenance process adapted to agile processes.


title: Open Source OPC UA PubSub Over TSN for Realtime Industrial Communication

abstract: OPC UA is a client-server communication protocol for industrial use cases without hard realtime requirements. The new PubSub extension of OPC UA adds the possibility of many-to-many communication based on the Publish / Subscribe paradigm. In conjunction with the upcoming Time-Sensitive Networking (TSN) extensions of Ethernet, OPC UA Pub Sub aims to also cover time-deterministic connectivity. This poses requirements to OPC UA implementations that have traditionally not been regarded. We propose an approach to combine non-realtime OPCUA servers with realtime OPC UA Pub Sub where both can access a shared information model without the loss of realtime guarantees for the publisher. As a result, the publisher can be run inside a (hardware-triggered) interrupt to ensure short delays and small jitter. An open source implementation of OPC UA Pub Sub is provided based on the open62541 SDK. This is also the basis for measurements used to evaluate the potential of the technology.


Word Embeddings for Code-Mixed Language Processing


Cross-Pair Text Representations for Answer Sentence Selection


title: Randomised Introduction of 2-CDA as Intensification during Consolidation for Children with High-risk AML – Results from Study AML-BFM 2004

abstract: BACKGROUND ::: The outcome in children and adolescents with high-risk (HR) acute myeloid leukemia (AML) is still unsatisfactory. Therefore, in study AML-BFM 2004 we aimed to improve outcome of HR-patients by adding moderately dosed 2-Chloro-2-Deoxyadenosine (2-CDA) to the respective consolidation treatment backbone without increasing toxicity. The aim was to improve prognosis especially in FAB M4/M5/MLL patients, who represent the largest subgroup of HR patients. ::: ::: ::: PATIENTS AND METHODS ::: In total, 343 children and adolescents with HR-AML were randomized to receive or not 2-CDA (6 mg/m²/d, days 1, 3) in combination with cytarabine/idarubicine (AI=500 mg/m² cytarabine 5 days continuous infusion plus 7 mg/m²/d idarubicin, days 3 and 5). ::: ::: ::: RESULTS ::: RESULTS for patients of the AI/2-CDA arm (n=168) vs. the AI-arm (n=175) were similar: 5-year overall survival 68±4 vs. 72±4%, plogrank=0.38, event-free survival 53±4 vs. 49±4%, plogrank=0.77; cumulative incidence of relapse at 5 years: 35±4 vs. 37±4%, p(Gray)=0.89. RESULTS in patients with MLL rearrangement or FAB M4/M5 were also similar in the treatment groups. In addition, toxicities did not differ between the two arms. ::: ::: ::: CONCLUSION ::: We conclude that additional, moderate dose 2-CDA does not improve prognosis in HR-patients when given during consolidation treatment. Its effect might be too low in this multidrug regimen, where the strongest effects are achieved during induction, or the chosen dose of 2-CDA might have been too low.


title: Kylin: An efficient and scalable graph data processing system

abstract: We introduce Kylin, an efficient and scalable graph data processing system. Kylin is based on bulk synchronization processing(BSP) model to process graph data. Although there have been some BSP-based graph processing systems, Kylin is different from these systems in two-fold. First, Kylin cooperates with HBase to achieve scalable data manipulation. Second, We propose three techniques to optimize the performance of Kylin. The proposed techniques are pull messaging, lazy vertex loading and vertex-weighted partitioning. We demonstrate Kylin outperforms other BSP-based systems, i.e. Hama and Giraph, in the experiments.


title: The Use of Partial Least Squares Path Modeling in International Marketing

abstract: In order to determine the status quo of PLS path modeling in international marketing research, we conducted an exhaustive literature review. An evaluation of double-blind reviewed journals through important academic publishing databases (e.g., ABI/Inform, Elsevier ScienceDirect, Emerald Insight, Google Scholar, PsycINFO, Swetswise) revealed that more than 30 academic articles in the domain of international marketing (in a broad sense) used PLS path modeling as means of statistical analysis. We assessed what the main motivation for the use of PLS was in respect of each article. Moreover, we checked for applications of PLS in combination with one or more additional methods, and whether the main reason for conducting any additional method(s) was mentioned.


title: Student Effort, Consistency, and Online Performance

abstract: This paper examines how student effort, consistency, motivation, and marginal learning, influence student grades in an online course. We use data from eleven Microeconomics courses taught online for a total of 212 students. Our findings show that consistency, or less time variation, is a statistically significant explanatory variable, whereas effort, or total minutes spent online, is not. Other independent variables include GPA and the difference between a pre-test and a post-test. The GPA is used as a measure of motivation, and the difference between a posttest and pre-test as marginal learning. As expected, the level of motivation is found statistically significant at a 99% confidence level, and marginal learning is also significant at a 95% level.


title: Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation

abstract: Neural sequence generation is commonly approached by using maximum- likelihood (ML) estimation or reinforcement learning (RL). However, it is known that they have their own shortcomings; ML presents training/testing discrepancy, whereas RL suffers from sample inefficiency. We point out that it is difficult to resolve all of the shortcomings simultaneously because of a tradeoff between ML and RL. In order to counteract these problems, we propose an objective function for sequence generation using α-divergence, which leads to an ML-RL integrated method that exploits better parts of ML and RL. We demonstrate that the proposed objective function generalizes ML and RL objective functions because it includes both as its special cases (ML corresponds to α → 0 and RL to α → 1). We provide a proposition stating that the difference between the RL objective function and the proposed one monotonically decreases with increasing α. Experimental results on machine translation tasks show that minimizing the proposed objective function achieves better sequence generation performance than ML-based methods.


title: Generalized Linear Models

abstract: The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation. A generalization of the analysis of variance is given for these models using log- likelihoods. These generalized linear models are illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables) and gamma (variance components).


title: High-fat diet enhances stemness and tumorigenicity of intestinal progenitors

abstract: Little is known about how pro-obesity diets regulate tissue stem and progenitor cell function. Here we show that high-fat diet (HFD)-induced obesity augments the numbers and function of Lgr5(+) intestinal stem cells of the mammalian intestine. Mechanistically, a HFD induces a robust peroxisome proliferator-activated receptor delta (PPAR-δ) signature in intestinal stem cells and progenitor cells (non-intestinal stem cells), and pharmacological activation of PPAR-δ recapitulates the effects of a HFD on these cells. Like a HFD, ex vivo treatment of intestinal organoid cultures with fatty acid constituents of the HFD enhances the self-renewal potential of these organoid bodies in a PPAR-δ-dependent manner. Notably, HFD- and agonist-activated PPAR-δ signalling endow organoid-initiating capacity to progenitors, and enforced PPAR-δ signalling permits these progenitors to form in vivo tumours after loss of the tumour suppressor Apc. These findings highlight how diet-modulated PPAR-δ activation alters not only the function of intestinal stem and progenitor cells, but also their capacity to initiate tumours.


title: Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians

abstract: We present a Hamiltonian Monte Carlo algorithm to sample from multivariate Gaussian distributions in which the target space is constrained by linear and quadratic inequalities or products thereof. The Hamiltonian equations of motion can be integrated exactly and there are no parameters to tune. The algorithm mixes faster and is more efficient than Gibbs sampling. The runtime depends on the number and shape of the constraints but the algorithm is highly parallelizable. In many cases, we can exploit special structure in the covariance matrices of the untruncated Gaussian to further speed up the runtime. A simple extension of the algorithm permits sampling from distributions whose log-density is piecewise quadratic, as in the “Bayesian Lasso” model.


title: Automatic clustering using an improved artificial bee colony optimization for customer segmentation

abstract: In cluster analysis, determining number of clusters is an important issue because information about the most appropriate number of clusters do not exist in the real-world problems. Automatic clustering is a clustering approach which is able to automatically find the most suitable number of clusters as well as divide the instances into the corresponding clusters. This study proposes a novel automatic clustering algorithm using a hybrid of improved artificial bee colony optimization algorithm and K-means algorithm (iABC). The proposed iABC algorithm improves the onlooker bee exploration scheme by directing their movements to a better location. Instead of using a random neighborhood location, the improved onlooker bee considers the data centroid to find a better initial centroid for the K-means algorithm. To increase efficiency of the improvement, the updating process is only applied on the worst cluster centroid. The proposed iABC algorithm is verified using some benchmark datasets. The computational result indicates that the proposed iABC algorithm outperforms the original ABC algorithm for automatic clustering problem. Furthermore, the proposed iABC algorithm is utilized to solve the customer segmentation problem. The result reveals that the iABC algorithm has better and more stable result than original ABC algorithm.


title: Demagnetization Fault Detection in Axial Flux PM Machines by Using Sensing Coils and an Analytical Model

abstract: The paper studies demagnetization detection in axial flux permanent magnet synchronous machines of the yokeless and segmented armature (YASA) type. In YASA machines, asymmetrical defects can occur: the demagnetization defects in the permanent magnets of both rotors differ from each other. Many techniques can indicate that there is a defect, even if it is an asymmetrical defect, but cannot detect which rotor has the defect, i.e., exactly locate the asymmetrical defect. In this paper, a method is proposed to detect and locate asymmetrical demagnetization defects in AFPMSMs. The method is useful for real-time condition monitoring of demagnetization defects, or as virtual temperature sensor for the magnets. A sensing coil is placed in each air gap. An analytical model is constructed, which uses the demagnetization defect and the three-phase current as input and calculates the sensing coil back-EMF. Sensing coils back-EMF data are combined with the data from the analytical model to solve an electromagnetic inverse problem. The results are the demagnetization amount and location of the demagnetization defect. The method is validated with the experimental data.


title: Can Gender-Fair Language Reduce Gender Stereotyping and Discrimination?

abstract: Gender-fair language (GFL) aims at reducing gender stereotyping and discrimination. Two principle strategies have been employed to make languages gender-fair and to treat women and men symmetrically: neutralization and feminization. Neutralization is achieved, for example, by replacing male-masculine forms (policeman) with gender-unmarked forms (police officer), whereas feminization relies on the use of feminine forms to make female referents visible (i.e., the applicant… he or she instead of the applicant… he). By integrating research on (1) language structures, (2) language policies, and (3) individual language behavior, we provide a critical review of how GFL contributes to the reduction of gender stereotyping and discrimination. Our review provides a basis for future research and for scientifically based policy-making.


title: Evolving Opponents for Interesting Interactive Computer Games

abstract: In this paper we introduce experiments on neuro-evolution mechanisms applied to predator/prey multi-character computer games. Our test-bed is a modified version of the well-known Pac-Man game. By viewing the game from the predators’ (i.e. opponents’) perspective, we attempt off-line to evolve neural-controlled opponents capable of playing effectively against computer-guided fixed strategy players. However, emergent near-optimal behaviors make the game less interesting to play. We therefore discuss the criteria that make a game interesting and, furthermore, we introduce a generic measure of predator/prey computer games’ interest. Given this measure, we present an evolutionary mechanism for opponents that keep learning from a player while playing against it (i.e. on-line) and we demonstrate its efficiency and robustness in increasing and maintaining the game’s interest. Computer game opponents following this on-line learning approach show high adaptability to changing player strategies which provides evidence for the approach’s effectiveness against human players.


title: Fingerprint Recognition Using Minutia Score Matching

abstract: The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person's life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae from the thinned image. The false matching ratio is better compared to the existing algorithm.


title: Cross-Modal Multistep Fusion Network With Co-Attention for Visual Question Answering

abstract: Visual question answering (VQA) is receiving increasing attention from researchers in both the computer vision and natural language processing fields. There are two key components in the VQA task: feature extraction and multi-modal fusion. For feature extraction, we introduce a novel co-attention scheme by combining Sentence-guide Word Attention (SWA) and Question-guide Image Attention in a unified framework. To be specific, the textual attention SWA relies on the semantics of the whole question sentence to calculate contributions of different question words for text representation. For the multi-modal fusion, we propose a “Cross-modal Multistep Fusion (CMF)” network to generate multistep features and achieve multiple interactions for two modalities, rather than focusing on modeling complex interactions between two modals like most current feature fusion methods. To avoid the linear increase of the computational cost, we share the parameters for each step in the CMF. Extensive experiments demonstrate that the proposed method can achieve competitive or better performance than the state of the art.


title: Integrating Blockchain, Smart Contract-Tokens, and IoT to Design a Food Traceability Solution

abstract: Information asymmetry exists amongst stakeholders in the current food supply chain. Lack of standardization in data format, lack of regulations, and siloed, legacy information systems exasperate the problem. Global agriculture trade is increasing creating a greater need for traceability in the global supply chain. This paper introduces Harvest Network, a theoretical end-to-end, vis a vie “farm-to-fork”, food traceability application integrating the Ethereum blockchain and IoT devices exchanging GS1 message standards. The goal is to create a distributed ledger accessible for all stakeholders in the supply chain. Our design effort creates a basic framework (artefact) for building a prototype or simulation using existing technologies and protocols [1]. The next step is for industry practitioners and researchers to apply AGILE methods for creating working prototypes and advanced projects that bring about greater transparency.


title: One-Class SVMs for Document Classification

abstract: We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schoelkopf et al. and a somewhat different version of one-class SVM based on identifying "outlier" data as representative of the second-class. We report on experiments with different kernels for both of these implementations and with different representations of the data, including binary vectors, tf-idf representation and a modification called "Hadamard" representation. Then we compared it with one-class versions of the algorithms prototype (Rocchio), nearest neighbor, naive Bayes, and finally a natural one-class neural network classification method based on "bottleneck" compression generated filters.The SVM approach as represented by Schoelkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable. However, the SVM methods turned out to be quite sensitive to the choice of representation and kernel in ways which are not well understood; therefore, for the time being leaving the neural network approach as the most robust.


title: ErgoAssist: An assistance system to maintain ergonomie guidelines at workplaces

abstract: We propose an assistance system which observes a process involving humans and automatically takes measures in situations where ergonomic guidelines are violated. The system uses a Microsoft Kinect Sensor to capture a depth map of the scene. Using this sensory information, the system extracts working pose and assesses ergonomics in the current situation. If an ergonomically critical situation occurs, the system flexibly adjusts the height of a workpiece during a process in order to enable the worker to adapt a more comfortable pose. First experiments show the potential of the method but also reveal the remaining technological challenge in capturing the pose. As future work, we plan to extend our existing pose capturing method for the 3D case. Moreover, we plan to conduct studies in order to analyse the acceptance of such a system in production environment.


title: Export, Migration, and Costs of Market Entry: Evidence from Central European Firms

abstract: In this article I analyze the export behavior of firms located in different Central European countries (Austria, Hungary, Czech Republic and Slovakia) with respect to migration. Ever since the seminal article by Gould (1994) on immigrant links to their home country and due to empirical research following his contribution, it is a well established result that immigrants from a particular country spur exports to and imports from that destination. Chaney (2008) shows that a decrease in fixed costs of exporting increases the number of exporters (extensive margin), whereas a reduction in variable costs also increases the volume exported by each exporting firm (intensive margin). Empirical contributions using firm-level data focus on various aspects influencing costs of exporting (like spillover effects of nearby firms (Sinani and Hobdari, 2010; Silvente and GemA©nez, 2007) or financial factors (Berman and HA©ricourt, 2010)), but leave out the issue of migration. I combine detailed information coming from a questionnaire conducted among 8,300 firms on the export behavior to different countries with regional data on migration from the European Labor Force Survey (LFS). I find evidence that both the propensity to export and â€“ to a much smaller extent â€“ the volume of sales of exporting firms to a particular destination is higher for firms located in regions with a larger number of immigrants from that country. I conclude that migrants mainly reduce fixed costs of exporting.


title: 0.35V, 4.1μW, 39MHz crystal oscillator in 40nm CMOS

abstract: A design methodology for sub-0.5V crystal oscillators is shown to realize an all-sub-0.5V ultra low power RF transceiver for wireless sensor networks. To reduce the minimum operating voltage (VDDmin) of the crystal oscillator, both the optimization of the gate width of the CMOS inverter in the crystal oscillator and the reduction in gate length by CMOS technology scaling are required. In accordance with the developed design methodology, a 39MHz crystal oscillator is designed and fabricated in a 40nm CMOS. The measured power consumption is 4.1μW at 0.35V and 39MHz, and the power supply voltage is the lowest among the previously reported crystal oscillators.


title: A Compact Gysel Power Divider With Unequal Power-Dividing Ratio Using One Resistor

abstract: A novel compact Gysel power divider is proposed and investigated in this paper. The proposed power divider consists of two transmission lines, a pair of coupled lines, and only one grounded resistor for unequal power-dividing ratio. Comparing to the conventional Gysel power divider, the new power divider saves its space by eliminating the 180 ° electrical length transmission line and reduces complicity by using one grounded resistor. The flexibility in choosing the value of the resistor provides favorable freedom in circuit realization. In addition, analytic design formulas of the proposed power divider, for both equal and unequal ratio cases, are given. For the equal radio case, two grounded resistors are required. Two prototype power dividers are simulated, fabricated, and measured. Both prototypes operate at 2 GHz, but with different power divisions, one for power division ratio k = 1 and the other for k = 2. There are good correlation between the measured results and those of the theoretically designed, justifying the circuit configuration and the design theory.


title: Heat Pipe Integrated in Direct Bonded Copper (DBC) Technology for the Cooling of Power Electronics Packaging

abstract: Thermal dissipation in power electronics systems is becoming an extremely important issue with the continuous growth of power density in their components. The primary cause of failure in this equipment is excessive temperatures in the critical components, such as semiconductors and transformers. This problem is particularly important in power electronic systems for space applications. These systems are usually housed in completely sealed enclosures for safety reasons. The effective management of heat removal from a sealed enclosure poses a major thermal-design challenge since the cooling of these systems primarily rely on natural convection. In this context, the presented paper treats the heat pipes as effective heat transfer devices that can be used to raise the thermal conductive path in order to spread a concentrated heat source over a larger surface area. As a result, the high heat flux at the heat source can be reduced to a smaller and manageable level that can be dissipated through conventional cooling methods. The objective of our work is to describe the feasibility of a cooling system with miniature heat pipes embedded in a direct bonded copper (DBC) structure. The advantage of this type of heat pipe is the possibility for implementation of the component layout on the heat pipe itself, which eliminates the existence of a thermal interface between the device and the cooling system


title: Learning a visuomotor controller for real world robotic grasping using simulated depth images

abstract: We want to build robots that are useful in unstructured real world applications, such as doing work in the household. Grasping in particular is an important skill in this domain, yet it remains a challenge. One of the key hurdles is handling unexpected changes or motion in the objects being grasped and kinematic noise or other errors in the robot. This paper proposes an approach to learning a closed-loop controller for robotic grasping that dynamically guides the gripper to the object. We use a wrist-mounted sensor to acquire depth images in front of the gripper and train a convolutional neural network to learn a distance function to true grasps for grasp configurations over an image. The training sensor data is generated in simulation, a major advantage over previous work that uses real robot experience, which is costly to obtain. Despite being trained in simulation, our approach works well on real noisy sensor images. We compare our controller in simulated and real robot experiments to a strong baseline for grasp pose detection, and find that our approach significantly outperforms the baseline in the presence of kinematic noise, perceptual errors and disturbances of the object during grasping.


title: Digital Advertising: An Information Scientist’s Perspective

abstract: Digital online advertising is a form of promotion that uses the Internet and Web for the express purpose of delivering marketing messages to attract customers. Examples of online advertising include text ads that appear on search engine results pages, banner ads, in-text ads, or Rich Media ads that appear on regular web pages, portals, or applications. Over the past 15 years online advertising, a $65 billion industry worldwide in 2009, has been pivotal to the success of the Web. That being said, the field of advertising has been equally revolutionized by the Internet, Web, and more recently, by the emergence of the social web, and mobile devices. This success has arisen largely from the transformation of the advertising industry from a low-tech, human intensive, “Mad Men” way of doing work to highly optimized, quantitative, mathematical, computer- and data-centric processes that enable highly targeted, personalized, performance-based advertising. This chapter provides a clear and detailed overview of the technologies and business models that are transforming the field of online advertising primarily from statistical machine learning and information science perspectives.


title: A general framework for brain-computer interface design

abstract: The Brain-Computer Interface (BCI) research community has acknowledged that researchers are experiencing difficulties when they try to compare the BCI techniques described in the literature. In response to this situation, the community has stressed the need for objective methods to compare BCI technologies. Suggested improvements have included the development and use of benchmark applications and standard data sets. However, as a young, multidisciplinary research field, the BCI community lacks a common vocabulary. As a result, this deficiency leads to poor intergroup communication, which hinders the development of the desired methods of comparison. One of the principle reasons for the lack of common vocabulary is the absence of a common functional model of a BCI System. This paper proposes a new functional model for BCI System design. The model supports many features that facilitate the comparison of BCI technologies with other BCI and non-BCI user interface technologies. From this model, taxonomy for BCI System design is developed. Together the model and taxonomy are considered a general framework for BCI System design. The representational power of the proposed framework was evaluated by applying it to a set of existing BCI technologies. The framework could effectively describe all of the BCI System designs tested.


title: A Novel Monostable Multivibrator Circuit

abstract: This article represents a novel monostable multivibrator circuit in which width and height of the pulse output can be independent electronically adjusted. In addition, the pulse width of output is not restricted to be wider or narrower than that of input pulse as conventionally employed. Namely, there is no restriction on the width of input and output pulse, the circuit's implementation is thus more convenient. The proposed circuit is composed of three OTAs, two resistors and one capacitor, basically constructed a simple pulse generator (Schmitt-trigger circuit). Therewith the compactness of the circuit, it is thus suitable to be fabricated for integrated circuit. The results of circuit simulation is accordance well with theoretical analysis.


title: Integrating formal verification into an advanced computer architecture course

abstract: This paper presents a sequence of three projects on design and formal verification of pipelined and superscalar processors: 1) a single-issue, five-stage DLX (an academic processor used widely for teaching pipelined execution and defined by Hennessy and Patterson in the first edition of their graduate textbook); 2) an extension of the DLX with exceptions and branch prediction; and 3) a dual-issue superscalar DLX. The projects were integrated into two editions of an advanced computer architecture course that was offered at the Georgia Institute of Technology, Atlanta, in the summer and fall 2002 and was taught to 67 students (25 of whom were undergraduates) in a way that required them to have no prior knowledge of formal methods. Preparatory homework problems included an exercise on design and formal verification of a staggered Arithmetic Logic Unit (ALU), pipelined in the style of the integer ALUs in the Intel Pentium 4. The processors were designed and formally verified with a tool flow that was used to formally verify the M/spl middot/CORE processor at Motorola and detected bugs.


title: Development of low-loss broad-band planar baluns using multilayered organic thin films

abstract: This paper presents the design and development of a low-loss and wide-band width multilayered Marchand balun. The balun has been implemented using printed circuit board materials with integrated multilayered organic thin films. We have designed the top layer transmission lines on twin-thickness organic thin films to achieve low loss and wide bandwidth for the balun. The experimental results demonstrate that the balun achieves less than 0.5-0.7 dB insertion loss throughout the 4-20-GHz operation bandwidth. The phase and amplitude imbalances are less than 5/spl deg/ and 0.5 dB, respectively. Time-domain measurement results demonstrate that the balun has negligible dispersion.


Pseudo Quantum Steganography with “ Color Barcode ” in M-band Wavelet Domain


title: Comprehending the pronouns her, him, and his: Implications for theories of referential processing

abstract: Abstract The research investigated how comprehenders use structural information during co-reference resolution of the pronouns her , him , and his . In Experiment 1, participants read sentences containing the pronoun her in object position, functioning as SPEC or NP and preceded by one of three types of NPs in subject position (e.g., “Mary/Mark/They saw her friend/yesterday…”). In Experiment 2, additional participants read similar sentences, containing the unambiguous pronouns him or his . The results of both experiments showed that the type of NP occurring in subject position influenced processing on both SPEC and NP pronouns. These results were inconsistent with claims that comprehenders consider only those antecedents that are structurally available as antecedents, following Chomsky’s (1981) Binding Theory ( Clifton, Kennison, & Albrecht, 1997 ; Nicol & Swinney, 1989 ). In Experiment 3, subject type did not influence processing of NP pronouns when the context contained a highly salient, structurally available antecedent. Implications for theories of referential processing are discussed.


title: Deep Video Generation, Prediction and Completion of Human Action Sequences

abstract: Current video generation/prediction/completion results are limited, due to the severe ill-posedness inherent in these three problems. In this paper, we focus on human action videos, and propose a general, two-stage deep framework to generate human action videos with no constraints or arbitrary number of constraints, which uniformly addresses the three problems: video generation given no input frames, video prediction given the first few frames, and video completion given the first and last frames. To solve video generation from scratch, we build a two-stage framework where we first train a deep generative model that generates human pose sequences from random noise, and then train a skeleton-to-image network to synthesize human action videos given the human pose sequences generated. To solve video prediction and completion, we exploit our trained model and conduct optimization over the latent space to generate videos that best suit the given input frame constraints. With our novel method, we sidestep the original ill-posed problems and produce for the first time high-quality video generation/prediction/completion results of much longer duration. We present quantitative and qualitative evaluations to show that our approach outperforms state-of-the-art methods in all three tasks.


title: The group Lasso for logistic regression

abstract: The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic regression models and present an efficient algorithm, that is especially suitable for high dimensional problems, which can also be applied to generalized linear models to solve the corresponding convex optimization problem. The group lasso estimator for logistic regression is shown to be statistically consistent even if the number of predictors is much larger than sample size but with sparse true underlying structure. We further use a two-stage procedure which aims for sparser models than the group lasso, leading to improved prediction performance for some cases. Moreover, owing to the two-stage nature, the estimates can be constructed to be hierarchical. The methods are used on simulated and real data sets about splice site detection in DNA sequences. Copyright 2008 Royal Statistical Society.