title: Bandwidth extension in CMOS with optimized on-chip inductors

abstract: We present a technique for enhancing the bandwidth of gigahertz broad-band circuitry by using optimized on-chip spiral inductors as shunt-peaking elements. The series resistance of the on-chip inductor is incorporated as part of the load resistance to permit a large inductance to be realized with minimum area and capacitance. Simple, accurate inductance expressions are used in a lumped circuit inductor model to allow the passive and active components in the circuit to be simultaneously optimized. A quick and efficient global optimization method, based on geometric programming, is discussed. The bandwidth extension technique is applied in the implementation of a 2.125-Gbaud preamplifier that employs a common-gate input stage followed by a cascoded common-source stage. On-chip shunt peaking is introduced at the dominant pole to improve the overall system performance, including a 40% increase in the transimpedance. This implementation achieves a 1.6-k/spl Omega/ transimpedance and a 0.6-/spl mu/A input-referred current noise, while operating with a photodiode capacitance of 0.6 pF. A fully differential topology ensures good substrate and supply noise immunity. The amplifier, implemented in a triple-metal, single-poly, 14-GHz f/sub Tmax/, 0.5-/spl mu/m CMOS process, dissipates 225 mW, of which 110 mW is consumed by the 50-/spl Omega/ output driver stage. The optimized on-chip inductors consume only 15% of the total area of 0.6 mm/sup 2/.


title: Adaptive Ensembling of Semi-Supervised Clustering Solutions

abstract: Conventional semi-supervised clustering approaches have several shortcomings, such as (1) not fully utilizing all useful must-link and cannot-link constraints, (2) not considering how to deal with high dimensional data with noise, and (3) not fully addressing the need to use an adaptive process to further improve the performance of the algorithm. In this paper, we first propose the transitive closure based constraint propagation approach, which makes use of the transitive closure operator and the affinity propagation to address the first limitation. Then, the random subspace based semi-supervised clustering ensemble framework with a set of proposed confidence factors is designed to address the second limitation and provide more stable, robust, and accurate results. Next, the adaptive semi-supervised clustering ensemble framework is proposed to address the third limitation, which adopts a newly designed adaptive process to search for the optimal subspace set. Finally, we adopt a set of nonparametric tests to compare different semi-supervised clustering ensemble approaches over multiple datasets. The experimental results on 20 real high dimensional cancer datasets with noisy genes and 10 datasets from UCI datasets and KEEL datasets show that (1) The proposed approaches work well on most of the real-world datasets. (2) It outperforms other state-of-the-art approaches on 12 out of 20 cancer datasets, and 8 out of 10 UCI machine learning datasets.


title: Time-Frequency Training OFDM with High Spectral Efficiency and Reliable Performance in High Speed Environments

abstract: Orthogonal frequency division multiplexing (OFDM) is widely recognized as the key technology for the next generation broadband wireless communication (BWC) systems. Besides high spectral efficiency, reliable performance over fast fading channels is becoming more and more important for OFDM-based BWC systems, especially when high speed cars, trains and subways are playing an increasingly indispensable role in our daily life. The time domain synchronous OFDM (TDS-OFDM) has higher spectral efficiency than the standard cyclic prefix OFDM (CP-OFDM), but suffers from severe performance loss over high speed mobile channels since the required iterative interference cancellation between the training sequence (TS) and the OFDM data block. In this paper, a fundamentally distinct OFDM-based transmission scheme called time-frequency training OFDM (TFT-OFDM) is proposed, whereby every TFT-OFDM symbol has training information both in the time and frequency domains. Unlike TDS-OFDM or CP-OFDM where the channel estimation is solely dependent on either time-domain TS or frequency-domain pilots, the joint time-frequency channel estimation for TFT-OFDM utilizes the time-domain TS without interference cancellation to merely acquire the path delay information of the channel, while the path coefficients are estimated by using the frequency-domain grouped pilots. The redundant grouped pilots only occupy about 3% of the total subcarriers, thus TFT-OFDM still has much higher spectral efficiency than CP-OFDM by about 8.5% in typical applications. Simulation results also demonstrate that TFT-OFDM outperforms CP-OFDM and TDS-OFDM in high speed mobile environments.


title: Deep visual-semantic alignments for generating image descriptions

abstract: We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks (RNN) over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions outperform retrieval baselines on both full images and on a new dataset of region-level annotations. Finally, we conduct large-scale analysis of our RNN language model on the Visual Genome dataset of 4.1 million captions and highlight the differences between image and region-level caption statistics.


title: Level Shifter Design for Low Power Applications

abstract: With scaling of Vt sub-threshold leakage power is increasing and expected to become significant part of total power consumption In present work three new configurations of level shifters for low power application in 0.35{\mu}m technology have been presented. The proposed circuits utilize the merits of stacking technique with smaller leakage current and reduction in leakage power. Conventional level shifter has been improved by addition of three NMOS transistors, which shows total power consumption of 402.2264pW as compared to 0.49833nW with existing circuit. Single supply level shifter has been modified with addition of two NMOS transistors that gives total power consumption of 108.641pW as compared to 31.06nW. Another circuit, contention mitigated level shifter (CMLS) with three additional transistors shows total power consumption of 396.75pW as compared to 0.4937354nW. Three proposed circuit's shows better performance in terms of power consumption with a little conciliation in delay. Output level of 3.3V has been obtained with input pulse of 1.6V for all proposed circuits.


title: A 5.8 GHz phase- and self-injection-locked CMOS radar sensor chip for vital sign detector miniaturization

abstract: A 5.8 GHz phase- and self-injection-locked radar sensor chip is designed and implemented for remote vital sign detector module miniaturization using TSMC 65nm CMOS technology. The chip size is only 0.52mm × 0.58mm. Favorable features including single-antenna operation, on-chip dc level calibration, and RF power and frequency controls are realized. Stable detections of vital signs within the 3 m range are obtained with −8 dBm radiated power and 10 mW power consumption.


title: From Seed AI to Technological Singularity via Recursively Self-Improving Software

abstract: Software capable of improving itself has been a dream of computer scientists since the inception of the field. In this work we provide definitions for Recursively Self-Improving software, survey different types of self-improving software, review the relevant literature, analyze limits on computation restricting recursive self-improvement and introduce RSI Convergence Theory which aims to predict general behavior of RSI systems. Finally, we address security implications from self-improving intelligent software.


title: The integrative review: updated methodology.

abstract: AIM ::: The aim of this paper is to distinguish the integrative review method from other review methods and to propose methodological strategies specific to the integrative review method to enhance the rigour of the process. ::: ::: ::: BACKGROUND ::: Recent evidence-based practice initiatives have increased the need for and the production of all types of reviews of the literature (integrative reviews, systematic reviews, meta-analyses, and qualitative reviews). The integrative review method is the only approach that allows for the combination of diverse methodologies (for example, experimental and non-experimental research), and has the potential to play a greater role in evidence-based practice for nursing. With respect to the integrative review method, strategies to enhance data collection and extraction have been developed; however, methods of analysis, synthesis, and conclusion drawing remain poorly formulated. ::: ::: ::: DISCUSSION ::: A modified framework for research reviews is presented to address issues specific to the integrative review method. Issues related to specifying the review purpose, searching the literature, evaluating data from primary sources, analysing data, and presenting the results are discussed. Data analysis methods of qualitative research are proposed as strategies that enhance the rigour of combining diverse methodologies as well as empirical and theoretical sources in an integrative review. ::: ::: ::: CONCLUSION ::: An updated integrative review method has the potential to allow for diverse primary research methods to become a greater part of evidence-based practice initiatives.


title: Biopsy Needle Detection in Transrectal Ultrasound

abstract: Using the fusion of pre-operative MRI and real time intra-procedural transrectal ultrasound (TRUS) to guide prostate biopsy has been shown as a very promising approach to yield better clinical outcome than the routinely performed TRUS only guided biopsy. In several situations of the MRI/TRUS fusion guided biopsy, it is important to know the exact location of the deployed biopsy needle, which is imaged in the TRUS video. In this paper, we present a method to automatically detect and segment the biopsy needle in TRUS. To achieve this goal, we propose to combine information from multiple resources, including ultrasound probe stability, TRUS video background model, and the prior knowledge of needle orientation and position. The proposed algorithm was tested on TRUS video sequences which have in total more than 25,000 frames. The needle deployments were successfully detected and segmented in the sequences with high accuracy and low false-positive detection rate.


title: Evidence Base Update for Autism Spectrum Disorder.

abstract: This evidence base update examines the level of empirical support for interventions for children with autism spectrum disorder (ASD) younger than 5 years old. It focuses on research published since a previous review in this journal (Rogers & Vismara, 2008). We identified psychological or behavioral interventions that had been manualized and evaluated in either (a) experimental or quasi-experimental group studies or (b) systematic reviews of single-subject studies. We extracted data from all studies that met these criteria and were published after the previous review. Interventions were categorized across two dimensions. First, primary theoretical principles included applied behavior analysis (ABA), developmental social-pragmatic (DSP), or both. Second, practice elements included scope (comprehensive or focused), modality (individual intervention with the child, parent training, or classrooms), and intervention targets (e.g., spoken language or alternative and augmentative communication). We classified two...


title: Building a Fine-Grained Entity Typing System Overnight for a New X (X = Language, Domain, Genre)

abstract: Recent research has shown great progress on fine-grained entity typing. Most existing methods require pre-defining a set of types and training a multi-class classifier from a large labeled data set based on multi-level linguistic features. They are thus limited to certain domains, genres and languages. In this paper, we propose a novel unsupervised entity typing framework by combining symbolic and distributional semantics. We start from learning general embeddings for each entity mention, compose the embeddings of specific contexts using linguistic structures, link the mention to knowledge bases and learn its related knowledge representations. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework doesn't rely on any annotated data, predefined typing schema, or hand-crafted features, therefore it can be quickly adapted to a new domain, genre and language. Furthermore, it has great flexibility at incorporating linguistic structures (e.g., Abstract Meaning Representation (AMR), dependency relations) to improve specific context representation. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.


title: Automatic fruit recognition and counting from multiple images

abstract: In our post-genomic world, where we are deluged with genetic information, the bottleneck to scientific progress is often phenotyping, i.e. measuring the observable characteristics of living organisms, such as counting the number of fruits on a plant. Image analysis is one route to automation. In this paper we present a method for recognising and counting fruits from images in cluttered greenhouses. The plants are 3-m high peppers with fruits of complex shapes and varying colours similar to the plant canopy. Our calibration and validation datasets each consist of over 28,000 colour images of over 1000 experimental plants. We describe a new two-step method to locate and count pepper fruits: the first step is to find fruits in a single image using a bag-of-words model, and the second is to aggregate estimates from multiple images using a novel statistical approach to cluster repeated, incomplete observations. We demonstrate that image analysis can potentially yield a good correlation with manual measurement (94.6%) and our proposed method achieves a correlation of 74.2% without any linear adjustment for a large dataset.


Interdisciplinary Communities and Research Issues in Music Information Retrieval


title: An elementary social information foraging model

abstract: User interfaces and information systems have become increasingly social in recent years, aimed at supporting the decentralized, cooperative production and use of content. A theory that predicts the impact of interface and interaction designs on such factors as participation rates and knowledge discovery is likely to be useful. This paper reviews a variety of observed phenomena in social information foraging and sketches a framework extending Information Foraging Theory towards making predictions about the effects of diversity, interference, and cost-of-effort on performance time, participation rates, and utility of discoveries.


title: Pathophysiological and neurochemical mechanisms of postoperative nausea and vomiting.

abstract: Clinical research shows that postoperative nausea and vomiting (PONV) is caused primarily by the use of inhalational anesthesia and opioid analgesics. PONV is also increased by several risk predictors, including a young age, female sex, lack of smoking, and a history of motion sickness. Genetic studies are beginning to shed light on the variability in patient experiences of PONV by assessing polymorphisms of gene targets known to play roles in emesis (serotonin type 3, 5-HT3; opioid; muscarinic; and dopamine type 2, D2, receptors) and the metabolism of antiemetic drugs (e.g., ondansetron). Significant numbers of clinical trials have produced valuable information on pharmacological targets important for controlling PONV (e.g., 5-HT3 and D2), leading to the current multi-modal approach to inhibit multiple sites in this complex neural system. Despite these significant advances, there is still a lack of fundamental knowledge of the mechanisms that drive the hindbrain central pattern generator (emesis) and forebrain pathways (nausea) that produce PONV, particularly the responses to inhalational anesthesia. This gap in knowledge has limited the development of novel effective therapies of PONV. The current review presents the state of knowledge on the biological mechanisms responsible for PONV, summarizing both preclinical and clinical evidence. Finally, potential ways to advance the research of PONV and more recent developments on the study of postdischarge nausea and vomiting (PDNV) are discussed.


title: You Are What You Eat: Learning User Tastes for Rating Prediction

abstract: Poor nutrition is one of the major causes of ill-health and death in the western world and is caused by a variety of factors including lack of nutritional understanding and preponderance towards eating convenience foods. We wish to build systems which can recommend nutritious meal plans to users, however a crucial pre-requisite is to be able to recommend recipes that people will like. In this work we investigate key factors contributing to how recipes are rated by analysing the results of a longitudinal study (n=124) in order to understand how best to approach the recommendation problem. We identify a number of important contextual factors which can influence the choice of rating. Based on this analysis, we construct several recipe recommendation models that are able to leverage understanding of user's likes and dislikes in terms of ingredients and combinations of ingredients and in terms of nutritional content. Via experiment over our dataset we are able to show that these models can significantly outperform a number of competitive baselines.


title: Monitoring dopants by Raman scattering in an electrochemically top-gated graphene transistor

abstract: The recent discovery of graphene has led to many advances in two-dimensional physics and devices. The graphene devices fabricated so far have relied on SiO(2) back gating. Electrochemical top gating is widely used for polymer transistors, and has also been successfully applied to carbon nanotubes. Here we demonstrate a top-gated graphene transistor that is able to reach doping levels of up to 5x1013 cm-2, which is much higher than those previously reported. Such high doping levels are possible because the nanometre-thick Debye layer in the solid polymer electrolyte gate provides a much higher gate capacitance than the commonly used SiO(2) back gate, which is usually about 300 nm thick. In situ Raman measurements monitor the doping. The G peak stiffens and sharpens for both electron and hole doping, but the 2D peak shows a different response to holes and electrons. The ratio of the intensities of the G and 2D peaks shows a strong dependence on doping, making it a sensitive parameter to monitor the doping.


title: When to use which heuristic: A rational solution to the strategy selection problem.

abstract: The human mind appears to be equipped with a toolbox full of cognitive strategies, but how do people decide when to use which strategy? We leverage rational metareasoning to derive a rational solution to this problem and apply it to decision making under uncertainty. The resulting theory reconciles the two poles of the debate about human rationality by proposing that people gradually learn to make rational use of fallible heuristics. We evaluate this theory against empirical data and existing accounts of strategy selection (i.e. SSL and RELACS). Our results suggest that while SSL and RELACS can explain people’s ability to adapt to homogeneous environments in which all decision problems are of the same type, rational metareasoning can additionally explain people’s ability to adapt to heterogeneous environments and flexibly switch strategies from one decision to the next.


Managing energy consumption costs in desktop PCs and LAN switches with proxying , split TCP connections , and scaling of link speed


title: Avoiding honeypot detection in peer-to-peer botnets

abstract: A botnet is group of compromised computers that are controlled by a botmaster, who uses them to perform illegal activities. Centralized and P2P (Peer-to-Peer) botnets are the most commonly used botnet types. Honeypots have been used in many systems as computer defense. They are used to attract botmasters to add them in their botnets; to become spies in exposing botnet attacker behaviors. In recent research works, improved mechanisms for honeypot detection have been proposed. Such mechanisms would enable bot masters to distinguish honeypots from real bots, making it more difficult for honeypots to join botnets. This paper presents a new method that can be used by security defenders to overcome the authentication procedure used by the advanced two-stage reconnaissance worm (ATSRW). The presented method utilizes the peer list information sent by an infected host during the ATSRW authentication process and uses a combination of IP address spoofing and fake TCP three-way handshake. The paper provides an analytical study on the performance and the success probability of the presented method. We show that the presented method provide a higher chance for honeypots to join botnets despite security measures taken by botmasters.


title: Introduction to the R package TDA

abstract: We present a short tutorial and introduction to using the R package TDA, which provides some tools for Topological Data Analysis. In particular, it includes implementations of functions that, given some data, provide topological information about the underlying space, such as the distance function, the distance to a measure, the kNN density estimator, the kernel density estimator, and the kernel distance. The salient topological features of the sublevel sets (or superlevel sets) of these functions can be quantified with persistent homology. We provide an R interface for the efficient algorithms of the C++ libraries GUDHI, Dionysus and PHAT, including a function for the persistent homology of the Rips filtration, and one for the persistent homology of sublevel sets (or superlevel sets) of arbitrary functions evaluated over a grid of points. The significance of the features in the resulting persistence diagrams can be analyzed with functions that implement recently developed statistical methods. The R package TDA also includes the implementation of an algorithm for density clustering, which allows us to identify the spatial organization of the probability mass associated to a density function and visualize it by means of a dendrogram, the cluster tree.


Fake News Headline Classification using Neural Networks with Attention


title: A convolutional neural network approach for face verification

abstract: In this paper, we present a convolutional neural network (CNN) approach for the face verification task. We propose a “Siamese” architecture of two CNNs, with each CNN reduced to only four layers by fusing convolutional and subsampling layers. Network training is performed using the stochastic gradient descent algorithm with annealed global learning rate. Generalization ability of network is investigated via unique pairing of face images, and testing is done on AT&T face database. Experimental work shows that the proposed CNN system can classify a pair of 46×46 pixel face images in 0.6 milliseconds, which is significantly faster compared to equivalent network architecture with cascade of convolutional and subsampling layers. The verification accuracy achieved is 3.33% EER (equal error rate). Learning converges within 20 epochs, and the proposed technique can verify a test subject unseen in training. This work shows the viability of the “Siamese” CNN for face verification applications, and further improvements to the architecture are under construction to enhance its performance.


title: Long distance biotic dispersal of tropical seagrass seeds by marine mega-herbivores

abstract: Terrestrial plants use an array of animals as vectors for dispersal, however little is known of biotic dispersal of marine angiosperms such as seagrasses. Our study in the Great Barrier Reef confirms for the first time that dugongs (Dugong dugon) and green sea turtles (Chelonia mydas) assist seagrass dispersal. We demonstrate that these marine mega-herbivores consume and pass in faecal matter viable seeds for at least three seagrass species (Zostera muelleri, Halodule uninervis and Halophila decipiens). One to two seagrass seeds per g DW of faecal matter were found during the peak of the seagrass reproductive season (September to December), with viability on excretion of 9.13% ± 4.61% (SE). Using population estimates for these mega-herbivores, and data on digestion time (hrs), average daily movement (km h) and numbers of viable seagrass seeds excreted (per g DW), we calculated potential seagrass seed dispersal distances. Dugongs and green sea turtle populations within this region can disperse >500,000 viable seagrass seeds daily, with a maximum dispersal distance of approximately 650 km. Biotic dispersal of tropical seagrass seeds by dugongs and green sea turtles provides a large-scale mechanism that enhances connectivity among seagrass meadows, and aids in resilience and recovery of these coastal habitats.


title: Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability

abstract: We present a numerical algorithm for nonnegative matrix factorization (NMF) problems under noisy separability. An NMF problem under separability can be stated as one of finding all vertices of the convex hull of data points. The research interest of this paper is to find the vectors as close to the vertices as possible in a situation in which noise is added to the data points. Our algorithm is designed to capture the shape of the convex hull of data points by using its enclosing ellipsoid. We show that the algorithm has correctness and robustness properties from theoretical and practical perspectives; correctness here means that if the data points do not contain any noise, the algorithm can find the vertices of their convex hull; robustness means that if the data points contain noise, the algorithm can find the near-vertices. Finally, we apply the algorithm to document clustering, and report the experimental results.


title: Web-scale knowledge extraction from semi-structured tables

abstract: A wealth of knowledge is encoded in the form of tables on the World Wide Web. We propose a classification algorithm and a rich feature set for automatically recognizing layout tables and attribute/value tables. We report the frequencies of these table types over a large analysis of the Web and propose open challenges for extracting from attribute/value tables semantic triples (knowledge). We then describe a solution to a key problem in extracting semantic triples: protagonist detection, i.e., finding the subject of the table that often is not present in the table itself. In 79% of our Web tables, our method finds the correct protagonist in its top three returned candidates.


title: Contextualize Your Listening: The Playlist as Recommendation Engine

abstract: It is not hyperbole to note that a revolution has occurred in the way that we as a society distribute data and information. This revolution has come about through the confluence of Web-related technologies and the approaching universal adoption of internet connectivity. Add to this mix the normalised use of lossy compression in digital music and the increase in digital music download and streaming services; the result is an environment where nearly anyone can listen to nearly any piece of music nearly anywhere. This is in many respects the pinnacle in music access and availability. Yet, a listener is now faced with a ::: dilemma of choice. Without being familiar with the ever-expanding millions of songs available, how does a listener know what to listen to? If a near-complete collection of recorded music is available what does one listen to next? While the world of music distribution underwent a revolution, the ubiquitous access and availability it created brought new problems in recommendation and discovery. ::: ::: In this thesis, a solution to these problems of recommendation and discovery is presented. We begin with an introduction to the core concepts around the playlist (i.e. sequential ordering of musical works). Next, we examine the history of the playlist as a recommendation technique, starting from before the invention of audio recording and moving through to modern automatic methods. This leads to an awareness that the creation of suitable playlists requires a high degree of knowledge of the relation between songs in a collection (e.g. song similarity). To better inform our base of knowledge of the relationships between songs we explore the use of social network analysis in combination with content-based music information retrieval. In an effort to show the promise of this more complex relational space, a fully automatic interactive radio system is proposed, using audio-content and social network data as a backbone. The implementation of the system is detailed. The creation of this system presents another problem in the area of evaluation. To that end, a novel distance metric between playlists is specified and tested. We then conclude with a discussion of what has been shown and what future work remains.


title: Modeling and path tracking for articulated steering vehicles

abstract: This paper presents an integrated articulated steering vehicle's (ASV) dynamics model, which provides a simulation platform for research on path tracking control of ASV. The active speed limiting control is designed according to the sideslip constraints to improve the lateral stability of the vehicle during path tracking. The weighted PID controller is applied to control the steering torque based on the path tracking error. The model and the effect of the path-tracking controller are validated through the Matlab/Simulink simulation.


Analysis and Design of Block Cipher Constructions


title: Aligning temporal data by sentinel events: discovering patterns in electronic health records

abstract: Electronic Health Records (EHRs) and other temporal databases contain hidden patterns that reveal important cause-and-effect phenomena. Finding these patterns is a challenge when using traditional query languages and tabular displays. We present an interactive visual tool that complements query formulation by providing operations to align, rank and filter the results, and to visualize estimates of the intervals of validity of the data. Display of patient histories aligned on sentinel events (such as a first heart attack) enables users to spot precursor, co-occurring, and aftereffect events. A controlled study demonstrates the benefits of providing alignment (with a 61% speed improvement for complex tasks). A qualitative study and interviews with medical professionals demonstrates that the interface can be learned quickly and seems to address their needs.


Sentence Correction using Recurrent Neural Networks Gene


title: Learning to detect tables in document images using line and text information

abstract: Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to readers in a structured manner. It is still a challenging problem due to the variety of table structures and the complexity of document layout. This paper presents a hybrid method consisting of three fundamental steps to detect table zones: classification of the regions, detection of the tables that constitute intersecting horizontal and vertical lines, and identification of the tables made up by only parallel lines. Experiments on the UW-III dataset show that the obtained results are very promising.


title: Parallel meta-blocking for scaling entity resolution over big heterogeneous data

abstract: Entity resolution constitutes a crucial task for many applications, but has an inherently quadratic complexity. In order to enable entity resolution to scale to large volumes of data, blocking is typically employed: it clusters similar entities into (overlapping) blocks so that it suffices to perform comparisons only within each block. To further increase efficiency, Meta-blocking is being used to clean the overlapping blocks from unnecessary comparisons, increasing precision by orders of magnitude at a small cost in recall. Despite its high time efficiency though, using Meta-blocking in practice to solve entity resolution problem on very large datasets is still challenging: applying it to 7.4million entities takes (almost) 8 full days on a modern high-end server.In this paper, we introduce scalable algorithms for Meta-blocking, exploiting the MapReduce framework. Specifically, we describe a strategy for parallel execution that explicitly targets the core concept of Meta-blocking, the blocking graph. Furthermore, we propose two more advanced strategies, aiming to reduce the overhead of data exchange. The comparison-based strategy creates the blocking graph implicitly, while the entity-based strategy is independent of the blocking graph, employing fewer MapReduce jobs with a more elaborate processing. We also introduce a load balancing algorithm that distributes the computationally intensive workload evenly among the available compute nodes. Our experimental analysis verifies the feasibility and superiority of our advanced strategies, and demonstrates their scalability to very large datasets. HighlightsWe adapt Meta-blocking to the MapReduce paradigm through 3 alternative parallelization strategies: an edge-based strategy that explicitly builds the blocking graph, a comparison-based strategy that uses the blocking graph implicitly, as a conceptual model, and an entity-based strategy that is independent of the blocking graph. We also provide concrete implementations for all weighting schemes that are used in Meta-blocking.We present a load balancing technique that deals with skewness in the input block collection, splitting it into partitions of the same computational cost.We verify the scalability of our techniques through a thorough experimental evaluation over the four largest, real datasets that have been applied to Meta-blocking. The data and the implementation of our techniques are publicly available.


title: A non-photorealistic lighting model for automatic technical illustration

abstract: Phong-shaded 3D imagery does not provide geometric information of the same richness as human-drawn technical illustrations. A non-photorealistic lighting model is presented that attempts to narrow this gap. The model is based on practice in traditional technical illustration, where the lighting model uses both luminance and changes in hue to indicate surface orientation, reserving extreme lights and darks for edge lines and highlights. The lighting model allows shading to occur only in mid-tones so that edge lines and highlights remain visually prominent. In addition, we show how this lighting model is modified when portraying models of metal objects. These illustration methods give a clearer picture of shape, structure, and material composition than traditional computer graphics methods. CR Categories: I.3.0 [Computer Graphics]: General; I.3.6 [Computer Graphics]: Methodology and Techniques.


title: Video Description with Spatial-Temporal Attention

abstract: Temporal attention has been widely used in video description to adaptively focus on important frames. However, most existing methods based on temporal attention suffer from the problems of recognition error and detail missing, because only coarse frame-level global features are employed. Inspired by recent successful work in image description using spatial attention, we propose a spatial-temporal attention (STAT) method to address such problems. In particular, first, we take advantage of object-level local features to address the problem of detail missing. Second, the STAT method further selects relevant local features by spatial attention and then attend to important frames by temporal attention to recognize related semantics. The proposed two-stage attention mechanism can recognize the salient objects more precisely with high recall and automatically focus on the most relevant spatial-temporal segments given the sentence context. Extensive experiments on two well-known benchmarks suggest that STAT method outperforms the state-of-the-art methods on MSVD with BLEU4 score 0.511, and achieves superior BLEU4 score 0.374 on MSR-VTT-10K. Compared to the method without local features, the relative improvements derived from our STAT method are 10.1% and 0.8% respectively on two benchmarks. Compared to the method using only temporal attention, the relative improvements derived from our STAT method are 18.3% and 9.0% respectively on two benchmarks.


Model of the Role of Organizational Orientations and Machiavellianism on Nonverbal Immediacy Behavior and Job Satisfaction


title: Principle and Performance of Semi-Persistent Scheduling for VoIP in LTE System

abstract: This paper presents an effective scheduling scheme called semi-persistent scheduling for VoIP service in LTE system. The main challenges of effectively supporting VoIP service in LTE system are 1) the tight delay requirement combined with the frequent arrival of small packets of VoIP traffic and 2) the scarcity of radio resources along with control channel restriction in LTE system. Simulation results show that semi-persistent scheduling can support high system capacity while at the same time guaranteeing the QoS requirements such as packet delay and packet loss rate of VoIP. Furthermore, semi- persistent scheduling requires less control signaling overhead which is very important for efficient resources utilization in a practical system.


title: Social Presence, Trust, and Social Commerce Purchase Intention: an Empirical Research.

abstract: Lacking the presence of human and social elements is claimed one major weakness that is hindering the growth of e-commerce. The emergence of social commerce might help ameliorate this situation. Social commerce is a new evolution of e-commerce that combines the commercial and social activities by deploying social technologies into e-commerce sites. Social commerce reintroduces the social aspect of shopping to e-commerce, increasing the degree of social presences in online environment. Drawing upon the social presence theory, this study theorizes the nature of social aspect in online SC marketplace by proposing a set of three social presence variables. These variables are then hypothesized to have positive impacts on trusting beliefs which in turn result in online purchase behaviors. The research model is examined via data collected from a typical e-commerce site in China. Our findings suggest that social presence factors grounded in social technologies contribute significantly to the building of the trustworthy online exchanging relationships. In doing so, this paper confirms the positive role of social aspect in shaping online purchase behaviors, providing a theoretical evidence for the fusion of social and commercial activities. Finally, this paper introduces a new perspective of e-commerce and calls more attention to this new phenomenon of social commerce. Social commerce increases the degree of social presences in online environment.This study proposes a multi-dimensional model of social presence.The social presence factors are found to have positive impacts on trust in sellers.The model delineates a full picture of online buyer behaviors in social commerce.


title: Abstract 2005 Special Issue A systems approach to appraisal mechanisms in emotion

abstract: While artificial neural networks are regularly employed in modeling the perception of facial and vocal emotion expression as well as in automatic expression decoding by artificial agents, this approach is yet to be extended to the modeling of emotion elicitation and differentiation. In part, this may be due to the dominance of discrete and dimensional emotion models, which have not encouraged computational modeling. This situation has changed with the advent of appraisal theories of emotion and a number of attempts to develop rule-based models can be found in the literature. However, most of these models operate at a high level of conceptual abstraction and rarely include the underlying neural architecture. In this contribution, an appraisal-based emotion theory, the Component Process Model (CPM), is described that seems particularly suited to modeling with the help of artificial neural network approaches. This is due to its high degree of specificity in postulating underlying mechanisms including efferent physiological and behavioral manifestations as well as to the possibility of linking the theoretical assumptions to underlying neural architectures and dynamic processes. This paper provides a brief overview of the model, suggests constraints imposed by neural circuits, and provides examples on how the temporal unfolding of emotion can be conceptualized and experimentally tested. In addition, it is shown that the specific characteristics of emotion episodes can be profitably explored with the help of non-linear dynamic systems theory.


title: The Role of Uncertainty, Awareness, and Trust in Visual Analytics

abstract: Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in addition to the ones inherent in the data, and these propagated and compounded uncertainties can result in impaired decision making. The user's confidence or trust in the results depends on the extent of user's awareness of the underlying uncertainties generated on the system side. This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how human's perceptual and cognitive biases influence the user's awareness of such uncertainties, and how this affects the user's trust building. The knowledge generation model for visual analytics is used to provide a terminology and framework to discuss the consequences of these aspects in knowledge construction and though examples, machine uncertainty is compared to human trust measures with provenance. Furthermore, guidelines for the design of uncertainty-aware systems are presented that can aid the user in better decision making.


title: The Expected Performance Curve: a New Assessment Measure for Person Authentication

abstract: Keywords: learning Reference EPFL-REPORT-82993 URL: http://publications.idiap.ch/downloads/reports/2003/rr03-84.pdf Record created on 2006-03-10, modified on 2017-05-10


title: peHash: A Novel Approach to Fast Malware Clustering

abstract: Data collection is not a big issue anymore with available honeypot software and setups. However malware collections gathered from these honeypot systems often suffer from massive sample counts, data analysis systems like sandboxes cannot cope with. Sophisticated self-modifying malware is able to generate new polymorphic instances of itself with different message digest sums for each infection attempt, thus resulting in many different samples stored for the same specimen. Scaling analysis systems that are fed by databases that rely on sample uniqueness based on message digests is only feasible to a certain extent. ::: ::: In this paper we introduce a non cryptographic, fast to calculate hash function for binaries in the Portable Executable format that transforms structural information about a sample into a hash value. Grouping binaries by hash values calculated with the new function allows for detection of multiple instances of the same polymorphic specimen as well as samples that are broken e.g. due to transfer errors. ::: ::: Practical evaluation on different malware sets shows that the new function allows for a significant reduction of sample counts.


title: A multidimensional empirical study on refactoring activity

abstract: In this paper we present an empirical study on the refactoring activity in three well-known projects. We have studied five research questions that explore the different types of refactorings applied to different types of sources, the individual contribution of team members on refactoring activities, the alignment of refactoring activity with release dates and testing periods, and the motivation behind the applied refactorings. The studied projects have a history of 12, 7, and 6 years, respectively. We have found that there is very little variation in the types of refactorings applied on test code, since the majority of the refactorings focus on the reorganization and renaming of classes. Additionally, we have identified that the refactoring decision making and application is often performed by individual refactoring "managers". We have found a strong alignment between refactoring activity and release dates. Moreover, we found that the development teams apply a considerable amount of refactorings during testing periods. Finally, we have also found that in addition to code smell resolution the main drivers for applying refactorings are the introduction of extension points, and the resolution of backward compatibility issues.


title: Approximating the Covariance Matrix of GMMs with Low-rank Perturbations

abstract: Covariance matrices capture correlations that are invaluable in modeling real-life datasets. Using all d2 elements of the covariance (in d dimensions) is costly and could result in over-fitting; and the simple diagonal approximation can be over-restrictive. In this work, we present a new model, the low-rank Gaussian mixture model (LRGMM), for modeling data which can be extended to identifying partitions or overlapping clusters. The curse of dimensionality that arises in calculating the covariance matrices of the GMM is countered by using low-rank perturbed diagonal matrices. The efficiency is comparable to the diagonal approximation, yet one can capture correlations among the dimensions. Our experiments reveal the LRGMM to be an efficient and highly applicable tool for working with large high-dimensional datasets.


3d city modeling from high-resolution satellite images


title: Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory

abstract: Reverse engineering the brain is proving difficult, perhaps impossible. While many believe that this is just a matter of time and effort, a different approach might help. Here, we describe a very simple idea which explains the power of the brain as well as its structure, exploiting complex dynamics rather than abstracting it away. Just as a Turing Machine is a Universal Digital Computer operating in a world of symbols, we propose that the brain is a Universal Dynamical Systems Modeller, evolved bottom-up (itself using nested networks of interconnected, self-organised dynamical systems) to prosper in a world of dynamical systems. ::: Recent progress in Applied Mathematics has produced startling evidence of what happens when abstract Dynamical Systems interact. Key latent information describing system A can be extracted by system B from very simple signals, and signals can be used by one system to control and manipulate others. Using these facts, we show how a region of the neocortex uses its dynamics to intrinsically "compute" about the external and internal world. ::: Building on an existing "static" model of cortical computation (Hawkins' Hierarchical Temporal Memory - HTM), we describe how a region of neocortex can be viewed as a network of components which together form a Dynamical Systems modelling module, connected via sensory and motor pathways to the external world, and forming part of a larger dynamical network in the brain. ::: Empirical modelling and simulations of Dynamical HTM are possible with simple extensions and combinations of currently existing open source software. We list a number of relevant projects.


title: A new 2D static hand gesture colour image dataset for ASL gestures

abstract: It usually takes a fusion of image processing and machine learning algorithms in order to build a fully-functioning computer vision system for hand gesture recognition. Fortunately, the complexity of developing such a system could be alleviated by treating the system as a collection of multiple sub-systems working together, in such a way that they can be dealt with in isolation. Machine learning need to feed on thousands of exemplars (e.g. images, features) to automatically establish some recognisable patterns for all possible classes (e.g. hand gestures) that applies to the problem domain. A good number of exemplars helps, but it is also important to note that the efficacy of these exemplars depends on the variability of illumination conditions, hand postures, angles of rotation, scaling and on the number of volunteers from whom the hand gesture images were taken. These exemplars are usually subjected to image processing first, to reduce the presence of noise and extract the important features from the images. These features serve as inputs to the machine learning system. Different sub-systems are integrated together to form a complete computer vision system for gesture recognition. The main contribution of this work is on the production of the exemplars. We discuss how a dataset of standard American Sign Language (ASL) hand gestures containing 2425 images from 5 individuals, with variations in lighting conditions and hand postures is generated with the aid of image processing techniques. A minor contribution is given in the form of a specific feature extraction method called moment invariants, for which the computation method and the values are furnished with the dataset.


title: Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

abstract: This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production.


title: Development of an Oral Compound Pickering Emulsion Composed of Nanocrystals of Poorly Soluble Ingredient and Volatile Oils from Traditional Chinese Medicine

abstract: In this study, an oral drug nanocrystals self-stabilized Pickering emulsion (NSSPE), which used nanocrystals of a poorly soluble ingredient from Puerariae Radix called puerarin as solid particle stabilizers and Ligusticum chuanxiong essential oil since the main oil phase had been developed to improve the oral bioavailability of puerarin. The appearance of emulsions, size and zeta potential of droplets, and content of puerarin in emulsified layer during a storage of six months at 4, 25, and 40 °C were investigated. The centrifugation stability at 4000× g was also studied. The micro-structure of emulsion droplets was characterized by a scanning electron micrograph (SEM), confocal laser scanning microscopy (CLSM), a fluorescence microscope (FM), and differential scanning calorimetry (DSC). The in vivo oral bioavailability of puerarin NSSPE was investigated in rats. Results showed that appearances of puerarin NSSPE kept stable after centrifugation at 4000× g for 15 min or storage for six months at 4, 25, and 40 °C. SEM, CLSM, FM, and DSC showed that the puerarin NSSPE had a stable core-shell structure of emulsion droplets formed by the adsorption of puerarin nanocrystals on the surface of oil droplets of mixed oil of Ligusticum chuanxiong essential oil and Labrafil M 1944 CS (9:1, v/v). The relative bioavailability of puerarin NSSPE to puerarin coarse powder suspension, nanocrystal suspension, and surfactant emulsion were 262.43%, 155.92%, and 223.65%, respectively. All these results indicated that puerarin nanocrystals could stabilize Pickering emulsion of Ligusticum chuanxiong essential oil without any other stabilizers and Pickering emulsion could improve the oral bioavailability of puerarin, which suggests that the drug nanocrystal self-stabilized Pickering emulsion as a promising oral drug delivery system for Traditional Chinese Medicine containing poorly soluble ingredients and volatile oils.


title: Finite Automata and Their Decision Proble’ms#

abstract: Finite automata are considered in this paper as instruments for classifying finite tapes. Each one-tape automaton defines a set of tapes, a two-tape automaton defines a set of pairs of tapes, et cetera. The structure of the defined sets is studied. Various generalizations of the notion of an automaton are introduced and their relation to the classical automata is determined. Some decision problems concerning automata are shown to be solvable by effective algorithms; others turn out to be unsolvable by algorithms.


title: Gender differences in psychosocial functioning of adolescents with symptoms of anxiety and depression: longitudinal findings from the Nord-Trøndelag Health Study

abstract: PURPOSE ::: To explore longitudinally gender differences in the associations between psychosocial functioning, subjective well-being and self-esteem among adolescents with and without symptoms of anxiety and depression. ::: ::: ::: METHODS ::: Data were obtained from a major population-based Norwegian study, the Nord-Trøndelag Health Study, in which 1,092 boys and 1,262 girls (86% of all invited) completed an extensive self-report questionnaire at baseline (mean age 14.4 years) and at follow-up (mean age 18.4 years). ::: ::: ::: RESULTS ::: Gender was a moderator variable in the associations between symptoms of anxiety and depression and impairment, meaning that boys' functioning was impaired to a larger extent than girls' functioning. A statistically significant interaction effect between gender and symptoms of anxiety and depression was found at follow-up in terms of subjective well-being (p < 0.05), self-esteem (p < 0.05), academic problems (p < 0.01), behaviour problems (p < 0.01) and frequency of meeting friends (p < 0.001). Onset of symptoms between baseline and follow-up was associated with less frequent meetings with friends among boys, but not among girls. After remission of symptoms, boys still had more behaviour and academic problems, less frequently met friends and reported lower subjective well-being and self-esteem than boys who had no symptoms at both time points. No similar differences were found among the girls. ::: ::: ::: CONCLUSION ::: Previous and ongoing symptoms of anxiety and depression had more negative consequences for boys than for girls. These findings may contribute to improved assessment and intervention methods tailored differently for each gender.


title: Design Optimization of a Low-Profile UWB Body-of-Revolution Monopole Antenna

abstract: This paper presents a systematic design approach for low-profile UWB body-of-revolution (BoR) monopole antennas with specified radiation objectives and size constraints. The proposed method combines a random walk scheme, the genetic algorithm, and a BoR moment method analysis for antenna shape optimization. A weighted global cost function, which minimizes the difference between potential optimal points and a utopia point (optimal design combining 3 different objectives) within the criterion space, is adapted. A 24'' wide and 6'' tall aperture was designed operating from low VHF frequencies up to 2 GHz. This optimized antenna shape reaches -15 dBi gain at 41 MHz on a ground plane and is only λ/12 in aperture width and λ/50 in height at this frequency. The same antenna achieves VSWR <; 3 from 210 MHz up to at least 2 GHz. Concurrently, it maintains a realized gain of ~5 dBi with moderate oscillations across the band of interest. A resistive treatment was further applied at the top antenna rim to improve matching and pattern stability. Measurements are provided for validation of the design. Of importance is that the optimized aperture delivers a larger impedance bandwidth as well as more uniform gain and pattern when compared to a previously published inverted-hat antenna of the same size.


title: Strategies for Creating a Community of Inquiry through Online Asynchronous Discussions

abstract: Asynchronous discussions are often utilized in online courses to provide a venue for students to openly communicate and build shared understanding, and for instructors to skillfully facilitate the process. While discussions can be invaluable toward creating and sustaining an online community of inquiry (CoI), they are not effective if not optimally designed. It is the authors' position that it is helpful to identify research-proven online discussion strategies and conceptualize them into the CoI framework, which has been extensively studied and validated. This framework posits that there are three interrelated presences – social, cognitive, and teaching – that must be perceived by members in order to facilitate a successful educational experience. Classifying strategies within this framework may guide instructors to purposefully select and employ methods that encourage productive, efficient, and meaningful discussions. Strategies, such as providing prompt but modest feedback, peer facilitation, protocol discussion prompts, and providing audio feedback, were found to support multiple presences in a review of the literature. Based on these findings, it is argued that educators need to employ discussion strategies that integrate all three presences in order to support an effective online CoI.


title: The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations

abstract: In this paper, we describe the acquisition and contents of a large-scale Chinese face database: the CAS-PEAL face database. The goals of creating the CAS-PEAL face database include the following: 1) providing the worldwide researchers of face recognition with different sources of variations, particularly pose, expression, accessories, and lighting (PEAL), and exhaustive ground-truth information in one uniform database; 2) advancing the state-of-the-art face recognition technologies aiming at practical applications by using off-the-shelf imaging equipment and by designing normal face variations in the database; and 3) providing a large-scale face database of Mongolian. Currently, the CAS-PEAL face database contains 99 594 images of 1040 individuals (595 males and 445 females). A total of nine cameras are mounted horizontally on an arc arm to simultaneously capture images across different poses. Each subject is asked to look straight ahead, up, and down to obtain 27 images in three shots. Five facial expressions, six accessories, and 15 lighting changes are also included in the database. A selected subset of the database (CAS-PEAL-R1, containing 30 863 images of the 1040 subjects) is available to other researchers now. We discuss the evaluation protocol based on the CAS-PEAL-R1 database and present the performance of four algorithms as a baseline to do the following: 1) elementarily assess the difficulty of the database for face recognition algorithms; 2) preference evaluation results for researchers using the database; and 3) identify the strengths and weaknesses of the commonly used algorithms.


title: Thermal degradation mechanisms of epoxy composites filled with tin particles

abstract: This article reports an evaluation study of the thermal degradation mechanisms of electrically insulating and conducting epoxy/Sn composites by using solid-state kinetic approaches and structural characterizations. Comparison of the thermoanalytical data of epoxy/Sn composites with pure epoxy shows that the addition of tin in epoxy catalyzes the thermal degradation of epoxy and the catalytic ability of tin depends upon its contents in epoxy. Kinetic modeling of the phenomena elaborates the thermal behaviors of epoxy/Sn composites in terms of the comparison of their activation parameters and reaction models. Friedman's differential and Arshad–Maaroufi's generalized linear integral isoconversional methods are used to obtain the variation in activation energies with the advancement of reaction. Advanced reaction model determination methodology is effectively employed to evaluate the reaction mechanisms of epoxy/Sn composites. Kinetic analysis suggests that tin increases the thermal degradation rate of epoxy by lowering the activation energy barrier of reaction. It is worth noticing that the parameters of the probable reaction model, i.e., Sestak Berggren have been found nearly the same for pure epoxy and epoxy/Sn composites, revealing weak epoxy–tin interactions in the composites. The mechanistic information obtained by kinetic analysis fairly agrees with the scanning electron microscopy and X-ray diffraction results. POLYM. COMPOS., 2015. © 2015 Society of Plastics Engineers


title: GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

abstract: Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. ::: In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. We examine the contextual relationship between these units and their surroundings by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in a scene. We provide open source interpretation tools to help researchers and practitioners better understand their GAN models.


title: A Note on Modeling Retweet Cascades on Twitter

abstract: Information cascades on social networks, such as retweet cascades on Twitter, have been often viewed as an epidemiological process, with the associated notion of virality to capture popular cascades that spread across the network. The notion of structural virality or average path length has been posited as a measure of global spread. ::: ::: In this paper, we argue that this simple epidemiological view, though analytically compelling, is not the entire story. We first show empirically that the classical SIR diffusion process on the Twitter graph, even with the best possible distribution of infectiousness parameter, cannot explain the nature of observed retweet cascades on Twitter. More specifically, rather than spreading further from the source as the SIR model would predict, many cascades that have several retweets from direct followers, die out quickly beyond that. ::: ::: We show that our empirical observations can be reconciled if we take interests of users and tweets into account. In particular, we consider a model where users have multi-dimensional interests, and connect to other users based on similarity in interests. Tweets are correspondingly labeled with interests, and propagate only in the subgraph of interested users via the SIR process. In this model, interests can be either narrow or broad, with the narrowest interest corresponding to a star graph on the interested users, with the root being the source of the tweet, and the broadest interest spanning the whole graph. We show that if tweets are generated using such a mix of interests, coupled with a varying infectiousness parameter, then we can qualitatively explain our observation that cascades die out much more quickly than is predicted by the SIR model. In the same breath, this model also explains how cascades can have large size, but low "structural virality" or average path length.


title: A High Temperature Frequency Signal Conditioning Unit for Aeronautical Rotating Systems

abstract: This paper covers the development and testing of a high temperature signal conditioning unit capable of sensing wide dynamic range signals from rotating sensors. The unit includes a current peak detector block, a digital pulse generator, ESD (electrostatic discharge) protection and generates an output current proportional to the peak value of the applied input signal. The digital data from the unit can be directly utilized by the FADEC (Full Authority Digital Electronic Control) system or the EHMS (Engine Health Monitoring System) on an aeroengine. The mixed signal unit presented in this paper was designed and fabricated on 1 $\mu\text{m}$ silicon-on-insulator (SOI) CMOS (complementary metal oxide semiconductor) technology and has been shown to function up to 235 $^{\circ}$ Celsius with very high linearity of the output signal. This is the highest operating temperature of an integrated signal conditioning unit developed for a civil aircraft. The mixed signal unit was mounted on a high temperature hybrid module and helps reducing the weight of the aeroengine by several kilograms.


title: An Infrastructure Modelling Tool for Cloud Provisioning

abstract: Cloud computing offers computing, network, and storage capabilities through services that abstract the capabilities of the underlying hardware. Currently, a variety of tools exist that manage the infrastructure provisioning and use scripts to define the final state of the hardware to be deployed in the cloud. However, there are major challenges that need to be addressed to automate the infrastructure management so that they are effectively used in initiatives such as DevOps. In particular, the management of Infrastructure as a Code (IaC) is one of the most important technical challenges to support activities such as the integration, deployment, and continuous delivery of applications. To address this problem, we present a support for the management of DevOps tools, through the definition of a Domain Specific Language (DSL) based on the concept of Infrastructure as a Code, and a tool that supports this language allowing to model the final state of a provisioning infrastructure in the cloud and generating the provisioning scripts for the Amazon Web Services (AWS) platform. The proposed tool reduces the work for development and operations personnel and facilitates their communication.


title: Conflict monitoring and anterior cingulate cortex: An update

abstract: One hypothesis concerning the human dorsal anterior cingulate cortex (ACC) is that it functions, in part, to signal the occurrence of conflicts in information processing, thereby triggering compensatory adjustments in cognitive control. Since this idea was first proposed, a great deal of relevant empirical evidence has accrued. This evidence has largely corroborated the conflict-monitoring hypothesis, and some very recent work has provided striking new support for the theory. At the same time, other findings have posed specific challenges, especially concerning the way the theory addresses the processing of errors. Recent research has also begun to shed light on the larger function of the ACC, suggesting some new possibilities concerning how conflict monitoring might fit into the cingulate's overall role in cognition and action.


title: Dialkylaluminum Complexes with Chelating Anilido-Imine Ligands: Synthesis, Structures, and Luminescent Properties

abstract: Syntheses of three new chelating anilido-imine conjugated ligands, ortho-C6H4(NHAr‘)(CHNAr‘ ‘) [Ar‘ = Ar‘ ‘ = 2,6-Me2C6H3 (2b); Ar‘ = p-MeC6H4, Ar‘ ‘ = 2,6-iPr2C6H3 (2c); Ar‘ = Ar‘ ‘ = p-MeC6H4 (2d)], as well as their four-coordinate dialkyl aluminum complexes, ortho-C6H4(NAr‘)(CHNAr‘ ‘)AlMe2 [Ar‘ = Ar‘ ‘ = 2,6-iPr2C6H3 (3a); Ar‘ = Ar‘ ‘ = 2,6-Me2C6H3 (3b); Ar‘ = p-MeC6H4, Ar‘ ‘ = 2,6-iPr2C6H3 (3c); Ar‘ = Ar‘ ‘ = p-MeC6H4 (3d)], are described. Complexes 3a−d were synthesized from the reaction of AlMe3 with the corresponding ligand at room temperature by alkane elimination. All complexes were characterized by 1H and 13C NMR spectroscopy, and the molecular structures of complexes 3a, 3c, and 3d were determined by X-ray crystallography. Luminescent properties of the ligands 2a−d and the complexes 3a−d in solution were studied, and it was found that emission maxima of 3a−d are in the range 519−582 nm, depending on the substituents on the aryl rings of their ligands. Complexes 3a−d exhibit intense photolumines...


title: Exposure to environmental tobacco smoke and health effects among hospitality workers in Sweden--before and after the implementation of a smoke-free law.

abstract: Objectives This study attempted to identify changes in exposure to environmental tobacco smoke, as well as symptoms and attitudes among hospitality workers after the introduction of extended smoke-free workplace legislation. ::: ::: Methods A total of 37 volunteers working in bingo halls and casinos (gaming workers) and 54 bars and restaurant employees (other workers) in nine Swedish communities participated in the study. Altogether 71 of 91 persons (14 daily smokers and 57 nonsmokers) participated in both the preban baseline survey and the follow-up 12 months after the ban. Exposure to environmental tobacco smoke, smoking habits, respiratory and sensory symptoms, and attitudes towards the ban were recorded, and spirometry was carried out. ::: ::: Results The frequency of reported respiratory and sensory symptoms was approximately halved among the nonsmokers in both occupational groups after the introduction of the ban. Initially 87% had exposure to environmental tobacco smoke that was over the nicotine cut-off level chosen to identify possible health risk (<0.5 µg/m 3 ), while, after the ban, it was only 22%, a relative risk of 0.25 (95% confidence interval 0.15–0.41). The risk decreased in both occupational groups, but gaming workers experienced the highest preban exposure levels. Attitudes towards the legislation were largely positive, particularly after the ban. However, there was no notable change in lung function, and there was no notable reduction in the number of cigarettes consumed by smokers. ::: ::: Conclusions The introduction of smoke-free legislation was associated with a substantial reduction in respiratory and sensory symptoms, as well as reduced exposure to environmental tobacco smoke at work, particularly among gaming workers.


title: Regulation of somatodendritic dopamine release in the ventral tegmental area by opioids and GABA: an in vivo microdialysis study

abstract: Microdialysis of the ventral tegmental area in conscious rats was used to evaluate the influence of opioids and GABA agonists on extracellular levels of GABA and somatodendritically released dopamine. The administration of morphine through the dialysis probe elicited significant, dose-dependent increases in the levels of extracellular dopamine and significantly reduced the extracellular concentration of GABA. In contrast, a dose-dependent decrease in somatodendritic extracellular dopamine was produced following the administration of the GABAB agonist baclofen. The increase in dopamine levels elicited by morphine (100 microM) was completely blocked by either baclofen (100 microM) coadministration or peripheral injection of naloxone (2 mg/kg, i.p.). Application of the GABAA agonist muscimol produced a significant increase in both extracellular levels of dopamine and locomotor activity. The present results, together with other electrophysiological, neurochemical, and behavioral data, support a hypothesis that stimulation of mu-opioid or GABAA receptors inhibits the activity of GABAergic afferents to dopamine neurons, thereby removing tonic inhibitory regulation, whereas stimulation of GABAB receptors directly inhibits dopamine neurons.


title: A Gait Monitoring System Based on Air Pressure Sensors Embedded in a Shoe

abstract: Measurement of ground contact forces (GCFs) provides necessary information to detect human gait phases. In this paper, a new analysis method of the GCF signals is discussed for detection of the gait phases. Human gaits are complicated, and the gait phases cannot be exactly distinguished by comparing sensor outputs to a threshold. This paper proposes a method by fuzzy logic for detecting the gait phases continuously and smoothly. The smooth and continuous detection of the gait phases enables a full use of information obtained from GCF sensors. For advanced rehabilitation systems, this paper also introduces a higher level algorithm that quantitatively monitors the amount of abnormalities in a human gait. The abnormalities detected by the proposed method include an improper GCF pattern as well as an incorrect sequence of the gait phases. To realize the monitoring algorithm, the gait phases are analyzed as a vector and the abnormalities are detected by simple kinematic equations. The proposed methods are implemented by using signals from sensor-embedded shoes called smart shoes. Each smart shoe has four GCF sensors installed between the cushion pad and the sole. The GCF sensor applies an air pressure sensor connected to an air bladder. A gait monitoring system that integrates the proposed methods is shown in this paper and verified for both normal and abnormal gaits.


title: A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

abstract: We propose to use a meta-learning objective that maximizes the speed of transfer on a modified distribution to learn how to modularize acquired knowledge. In particular, we focus on how to factor a joint distribution into appropriate conditionals, consistent with the causal directions. We explain when this can work, using the assumption that the changes in distributions are localized (e.g. to one of the marginals, for example due to an intervention on one of the variables). We prove that under this assumption of localized changes in causal mechanisms, the correct causal graph will tend to have only a few of its parameters with non-zero gradient, i.e. that need to be adapted (those of the modified variables). We argue and observe experimentally that this leads to faster adaptation, and use this property to define a meta-learning surrogate score which, in addition to a continuous parametrization of graphs, would favour correct causal graphs. Finally, motivated by the AI agent point of view (e.g. of a robot discovering its environment autonomously), we consider how the same objective can discover the causal variables themselves, as a transformation of observed low-level variables with no causal meaning. Experiments in the two-variable case validate the proposed ideas and theoretical results.


title: A time improvement technique of lead-acid battery balancing system

abstract: In the application of lead-acid series batteries, the voltage imbalance of each battery should be considered. Therefore, additional balancer circuits must be integrated into the battery. An active battery balancing circuit with an auxiliary storage can employ a sequential battery imbalance detection algorithm by comparing the voltage of a battery and auxiliary storage. The system is being in balance if the battery voltage imbalance is less than 10mV/cell. In this paper, a new algorithm is proposed so that the battery voltage balancing time can be improved. The battery balancing system is based on the LTC3305 working principle. The simulation verifies that the proposed algorithm can achieve permitted battery voltage imbalance faster than that of the previous algorithm.


title: A Frequency-Reconfigurable Quasi-Yagi Dipole Antenna

abstract: A frequency reconfigurable quasi-Yagi dipole antenna is presented in this paper. Simulation results show that a 35% continuously tuning impedance bandwidth, from 1.85 to 2.5GHz, is obtained. Loaded with varactor diodes, the effective electrical lengths of the driver and director dipoles are varied by changing the biasing voltages. The length adjustable director allows the endfire pattern with relatively high gain being maintained over each tuning sub-band. Across the tuning bandwidth, the predicted gain variation is from 5.8dBi to 7.9dBi. The H-plane cross-polarization level is below −17dB while this value is below −32dB in the E-plane. The front-to-back ratio is better than 12dB.


title: Dynamic Spectral Envelope Modeling for Timbre Analysis of Musical Instrument Sounds

abstract: We present a computational model of musical instrument sounds that focuses on capturing the dynamic behavior of the spectral envelope. A set of spectro-temporal envelopes belonging to different notes of each instrument are extracted by means of sinusoidal modeling and subsequent frequency interpolation, before being subjected to principal component analysis. The prototypical evolution of the envelopes in the obtained reduced-dimensional space is modeled as a nonstationary Gaussian Process. This results in a compact representation in the form of a set of prototype curves in feature space, or equivalently of prototype spectro-temporal envelopes in the time-frequency domain. Finally, the obtained models are successfully evaluated in the context of two music content analysis tasks: classification of instrument samples and detection of instruments in monaural polyphonic mixtures.


title: Standard operation procedures for conducting the on-the-road driving test, and measurement of the standard deviation of lateral position (SDLP)

abstract: This review discusses the methodology of the standardized on-the-road driving test and standard operation procedures to conduct the test and analyze the data. The on-the-road driving test has proven to be a sensitive and reliable method to examine driving ability after administration of central nervous system (CNS) drugs. The test is performed on a public highway in normal traffic. Subjects are instructed to drive with a steady lateral position and constant speed. Its primary parameter, the standard deviation of lateral position (SDLP), ie, an index of ‘weaving’, is a stable measure of driving performance with high test–retest reliability. SDLP differences from placebo are dose-dependent, and do not depend on the subject’s baseline driving skills (placebo SDLP). It is important that standard operation procedures are applied to conduct the test and analyze the data in order to allow comparisons between studies from different sites.


title: MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding

abstract: Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. As a result, conventional network embedding techniques cannot work on such HINs. Recently, metapath-based approaches have been proposed to characterize relationships in HINs, but they are ineffective in capturing rich contexts and semantics between nodes for embedding learning, mainly because (1) metapath is a rather strict single path node-node relationship descriptor, which is unable to accommodate variance in relationships, and (2) only a small portion of paths can match the metapath, resulting in sparse context information for embedding learning. In this paper, we advocate a new metagraph concept to capture richer structural contexts and semantics between distant nodes. A metagraph contains multiple paths between nodes, each describing one type of relationships, so the augmentation of multiple metapaths provides an effective way to capture rich contexts and semantic relations between nodes. This greatly boosts the ability of metapath-based embedding techniques in handling very sparse HINs. We propose a new embedding learning algorithm, namely MetaGraph2Vec, which uses metagraph to guide the generation of random walks and to learn latent embeddings of multi-typed HIN nodes. Experimental results show that MetaGraph2Vec is able to outperform the state-of-the-art baselines in various heterogeneous network mining tasks such as node classification, node clustering, and similarity search.


title: Multi-Armed Bandits for Intelligent Tutoring Systems

abstract: We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. ::: ::: The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children.


Automatic Counting and Classification of Microplastic Particles


title: A Deep Policy Inference Q-Network for Multi-Agent Systems

abstract: We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We focus on one challenging issue in such systems---modeling agents with varying strategies---and propose to employ "policy features" learned from raw observations (e.g., raw images) of collaborators and opponents by inferring their policies. DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learning models. We further propose an enhanced version of DPIQN, called deep recurrent policy inference Q-network (DRPIQN), for handling partial observability. Both DPIQN and DRPIQN are trained by an adaptive training procedure, which adjusts the network's attention to learn the policy features and its own Q-values at different phases of the training process. We present a comprehensive analysis of DPIQN and DRPIQN, and highlight their effectiveness and generalizability in various multi-agent settings. Our models are evaluated in a classic soccer game involving both competitive and collaborative scenarios. Experimental results performed on 1 vs. 1 and 2 vs. 2 games show that DPIQN and DRPIQN demonstrate superior performance to the baseline DQN and deep recurrent Q-network (DRQN) models. We also explore scenarios in which collaborators or opponents dynamically change their policies, and show that DPIQN and DRPIQN do lead to better overall performance in terms of stability and mean scores.


title: Generative Adversarial Networks for Extreme Learned Image Compression

abstract: We present a learned image compression system based on GANs, operating at extremely low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi-scale discriminator, which we train jointly for a generative learned compression objective. The model synthesizes details it cannot afford to store, obtaining visually pleasing results at bitrates where previous methods fail and show strong artifacts. Furthermore, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, proportionally reducing the storage cost. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.


On some problems of population genetics and evolution


title: Dynamic ad layout revenue optimization for display advertising

abstract: Display advertising has been growing rapidly in recent years, with revenue generated from display ads placed on spaces allocated on publisher's web pages. Traditionally, the design and layout of ad spaces on a web page are predetermined and fixed for the publisher. The objective of this work is to investigate the revenue opportunities of changing the ad layout dynamically for the publisher. A dynamic ad layout revenue optimization framework is developed for display advertising, in terms of both guaranteed and non-guaranteed advertising. The system automatically selects the ad layout template with the highest potential revenue yield for each single web page presented to the user. Forecasting algorithms are developed to predict the revenue of each ad opportunity. Two objectives are explored for the forecasting algorithms of ad layout optimization, the expected revenue and actual revenue. Promising results are obtained in offline simulation on real data collected from a Yahoo! property. The dynamic ad layout optimization system is further tested on real-time traffic and a significant revenue gain is observed compared with a static ad layout serving method.


title: Need for Resource Management in IoT

abstract: Internet of Things (IoT) is a technical revolution which represents the future of communications and computing. In IoT many devices are connected through network and they communicate by the Internet and send data or secret information on the web. The next huge step in the IoT field is to recognize a virtual computing platform that provides access to heterogeneous group of device present in our living environment. It is nonlinear connection between resource information of smart device or client and quality of services (Qos) are built to manage the universal or limited network resource management. KeywordsManagement, Internet of Things, CoAP


title: Corticosteroid-induced neuropsychiatric disorders: review and contrast with neuropsychiatric lupus

abstract: The aim of this review is to analyze the available literature regarding the neuropsychiatric (NP) disturbances associated with corticosteroid (CS) therapy; to determine the nature, severity, and frequency of these NP symptoms; and to identify the various risk factors involved in the development of CS-induced NP disturbances. We searched the available literature since the advent of corticosteroid therapy (1950) utilizing the PubMed database (www.pubmed.gov). Primary articles were identified, and they and their pertinent references were reviewed. Due to potential confusion between NP manifestations of CS therapy and central nervous system (CNS) involvement of systemic lupus erythematosus (SLE), a condition often treated with CS, a brief review of NP manifestations of SLE was also performed. The presentation of CS-induced neuropsychiatric disorders (CIPD) can be quite varied with depression, hypomania, and overt psychosis being the most common manifestations. CIPD can also include bipolar affective changes, delirium, panic attacks, agoraphobia, obsessive–compulsive disorder, anxiety, insomnia, restlessness, fatigue, catatonia, reversible dementia-like cognitive changes, impaired memory, and concentration. No factors have been identified that allow for the accurate prediction of development of CIPD. A dose-dependent relationship (increased risk when the daily prednisone-equivalent dose is ≥40 mg) has been observed in most cases of CIPD, although there have been case reports with lower doses, alternate-day therapy, and even inhaled CS. Women are more commonly affected with most symptoms occurring in the first 6 weeks of starting treatment. SLE has been the only specific illness that has been linked to a greater risk of CIPD and the NP manifestations of SLE may mimic those of CIPD, with most occurring in the first year of diagnosis. Antiribosomal P, antineuronal, or antiphospholipid antibodies are frequently seen in patients with SLE developing CIPD. Imaging and EEG abnormalities, the coexistence of non-CNS manifestations of SLE, and the presence of serious disturbances in memory and concentration are more suggestive of NP–SLE than CIPD. Although NP symptoms associated with the use of CS generally resolve with discontinuation of the medication, prophylaxis with lithium, and treatment with antidepressants, anticonvulsants and electroconvulsive therapy for severe mania and depression have been reported with successful outcomes. A greater understanding of the underlying mechanism of CIPD, risk factors involved, treatment options, and the distinguishing features from NP–SLE will ultimately lead to more directed therapy for such patients.


title: Search Intelligence: Deep Learning For Dominant Category Prediction

abstract: Deep Neural Networks, and specifically fully-connected convolutional neural networks are achieving remarkable results across a wide variety of domains. They have been trained to achieve state-of-the-art performance when applied to problems such as speech recognition, image classification, natural language processing and bioinformatics. Most of these deep learning models when applied to classification employ the softmax activation function for prediction and aim to minimize cross-entropy loss. In this paper, we have proposed a supervised model for dominant category prediction to improve search recall across all eBay classifieds platforms. The dominant category label for each query in the last 90 days is first calculated by summing the total number of collaborative clicks among all categories. The category having the highest number of collaborative clicks for the given query will be considered its dominant category. Second, each query is transformed to a numeric vector by mapping each unique word in the query document to a unique integer value; all padded to equal length based on the maximum document length within the pre-defined vocabulary size. A fully-connected deep convolutional neural network (CNN) is then applied for classification. The proposed model achieves very high classification accuracy compared to other state-of-the-art machine learning techniques.


Machine vision: a survey


title: Metallic metasurface for high efficiency optical phase control in transmission mode.

abstract: Existing metasurfaces for high efficiency optical phase control in transmission mode are all based on dielectric materials. Metallic metasurfaces for optical phase control in transmission mode never achieved efficiency above 40%. In this paper, we theoretically demonstrate that metallic metasurface constructed by thick nanoparticles can realize high efficiency (above 85%) phase control in optical wavelength range. We investigated the resonant properties of thick nanoparticle arrays and found that bulk magnetic resonance can be formed by antiparallel dipole electric resonances on thick nanoparticles' sidewalls. In addition, lateral Fabry-Perot (FP) resonance can be generated in the cavity constituted by adjacent thick nanoparticles. Both of the two resonances exhibit high transmission with near-zero reflection. What's more, the lateral FP resonance can be utilized to manipulate transmitted phase with high efficiency by adjusting the length of thick nanoparticles. The method proposed here may induce a series of new metasurfaces based on thick nanoparticles for various applications.


title: Stator fault modeling in induction motors

abstract: In this paper a model of an induction motor affected by stator faults is presented. Two different types of faults are considered, these are; disconnection of a supply phase, and inter-turn and turn-turn short circuits inside the stator. The output of the derived model is compared to real measurements from a specially designed induction motor. With this motor it is possible to simulate both terminal disconnections, inter-turn and turn-turn short circuits. The results show good agreement between the measurements and the simulated signals obtained from the model. In the tests focus is on the phase currents and the star point voltage as these signals are often used for fault detection


title: Data Protection Compliance Regulations and Implications for Smart Factories of the Future

abstract: Context-aware systems in intelligent environments digest large amounts of data and personal information to gain situational awareness as a way to assist individuals with their daily activities, enhance their experiences and adapt to their needs and intention, whenever and wherever they are. Large amounts of data drive these environments, motivating the adoption of big data and cloud technologies. A similar digital transformation is taking place in the Factory of the Future and Industry 4.0, two paradigms on creating smart products through smart processes and procedures. As the attack surface for security and privacy threats grows, it is no surprise that new regulations and directives will be put in place to protect the privacy of individuals. In this paper, we discuss the foundational principles of Privacy by Design and key obligations of the upcoming EU General Data Protection Regulation (GDPR), and highlight how they impact the design and development of context-aware intelligent environments. We provide technical guidelines for better compliance with these regulatory frameworks.


title: Court Reconstruction for Camera Calibration in Broadcast Basketball Videos

abstract: We introduce a technique of calibrating camera motions in basketball videos. Our method particularly transforms player positions to standard basketball court coordinates and enables applications such as tactical analysis and semantic basketball video retrieval. To achieve a robust calibration, we reconstruct the panoramic basketball court from a video, followed by warping the panoramic court to a standard one. As opposed to previous approaches, which individually detect the court lines and corners of each video frame, our technique considers all video frames simultaneously to achieve calibration; hence, it is robust to illumination changes and player occlusions. To demonstrate the feasibility of our technique, we present a stroke-based system that allows users to retrieve basketball videos. Our system tracks player trajectories from broadcast basketball videos. It then rectifies the trajectories to a standard basketball court by using our camera calibration method. Consequently, users can apply stroke queries to indicate how the players move in gameplay during retrieval. The main advantage of this interface is an explicit query of basketball videos so that unwanted outcomes can be prevented. We show the results in Figs. 1, 7, 9, 10 and our accompanying video to exhibit the feasibility of our technique.