title: Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) are well established models capable of achieving state-of-the-art classification accuracy for various computer vision tasks. However, they are becoming increasingly larger, using millions of parameters, while they are restricted to handling images of fixed size. In this paper, a quantization-based approach, inspired from the well-known Bag-of-Features model, is proposed to overcome these limitations. The proposed approach, called Convolutional BoF (CBoF), uses RBF neurons to quantize the information extracted from the convolutional layers and it is able to natively classify images of various sizes as well as to significantly reduce the number of parameters in the network. In contrast to other global pooling operators and CNN compression techniques the proposed method utilizes a trainable pooling layer that it is end-to-end differentiable, allowing the network to be trained using regular back-propagation and to achieve greater distribution shift invariance than competitive methods. The ability of the proposed method to reduce the parameters of the network and increase the classification accuracy over other state-of-the-art techniques is demonstrated using three image datasets.


title: Methodological developments in searching for studies for systematic reviews: past, present and future?

abstract: The Cochrane Collaboration was established in 1993, following the opening of the UK Cochrane Centre in 1992, at a time when searching for studies for inclusion in systematic reviews was not well-developed. Review authors largely conducted their own searches or depended on medical librarians, who often possessed limited awareness and experience of systematic reviews. Guidance on the conduct and reporting of searches was limited. When work began to identify reports of randomized controlled trials (RCTs) for inclusion in Cochrane Reviews in 1992, there were only approximately 20,000 reports indexed as RCTs in MEDLINE and none indexed as RCTs in Embase. No search filters had been developed with the aim of identifying all RCTs in MEDLINE or other major databases. This presented The Cochrane Collaboration with a considerable challenge in identifying relevant studies. Over time, the number of studies indexed as RCTs in the major databases has grown considerably and the Cochrane Central Register of Controlled Trials (CENTRAL) has become the best single source of published controlled trials, with approximately 700,000 records, including records identified by the Collaboration from Embase and MEDLINE. Search filters for various study types, including systematic reviews and the Cochrane Highly Sensitive Search Strategies for RCTs, have been developed. There have been considerable advances in the evidence base for methodological aspects of information retrieval. The Cochrane Handbook for Systematic Reviews of Interventions now provides detailed guidance on the conduct and reporting of searches. Initiatives across The Cochrane Collaboration to improve the quality inter alia of information retrieval include: the recently introduced Methodological Expectations for Cochrane Intervention Reviews (MECIR) programme, which stipulates 'mandatory’ and 'highly desirable’ standards for various aspects of review conduct and reporting including searching, the development of Standard Training Materials for Cochrane Reviews and work on peer review of electronic search strategies. Almost all Cochrane Review Groups and some Cochrane Centres and Fields now have a Trials Search Co-ordinator responsible for study identification and medical librarians and other information specialists are increasingly experienced in searching for studies for systematic reviews. Prospective registration of clinical trials is increasing and searching trials registers is now mandatory for Cochrane Reviews, where relevant. Portals such as the WHO International Clinical Trials Registry Platform (ICTRP) are likely to become increasingly attractive, given concerns about the number of trials which may not be registered and/or published. The importance of access to information from regulatory and reimbursement agencies is likely to increase. Cross-database searching, gateways or portals and improved access to full-text databases will impact on how searches are conducted and reported, as will services such as Google Scholar, Scopus and Web of Science. Technologies such as textual analysis, semantic analysis, text mining and data linkage will have a major impact on the search process but efficient and effective updating of reviews may remain a challenge. In twenty years’ time, we envisage that the impact of universal social networking, as well as national and international legislation, will mean that all trials involving humans will be registered at inception and detailed trial results will be routinely available to all. Challenges will remain, however, to ensure the discoverability of relevant information in diverse and often complex sources and the availability of metadata to provide the most efficient access to information. We envisage an ongoing role for information professionals as experts in identifying new resources, researching efficient ways to link or mine them for relevant data and managing their content for the efficient production of systematic reviews.


title: Vehicle Pose and Shape Estimation Through Multiple Monocular Vision

abstract: In this paper, we present a method to estimate a vehicle's pose and shape from off-board multi-view images. These images are taken from monocular cameras with small overlaps. We utilize state-of-the-art Convolutional Neural Networks (CNNs) to extract vehicles' semantic keypoints and introduce a Cross Projection Optimization (CPO) method to estimate the 3D pose. During the iterative CPO process, an adaptive shape adjustment method named Hierarchical Wireframe Constraint (HWC) is implemented to estimate the shape. Our approach is evaluated under both simulated and real-world scenes for performance verification. It's shown that our algorithm outperforms other existing monocular and stereo methods for vehicles' pose and shape estimation. This approach provides a new and robust solution for off-board visual vehicle localization and tracking, which can be applied to massive surveillance camera networks for intelligent transportation.


title: Detect to Track and Track to Detect

abstract: Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this paper we propose a ConvNet architecture that jointly performs detection and tracking, solving the task in a simple and effective way. Our contributions are threefold: (i) we set up a ConvNet architecture for simultaneous detection and tracking, using a multi-task objective for frame-based object detection and across-frame track regression; (ii) we introduce correlation features that represent object co-occurrences across time to aid the ConvNet during tracking; and (iii) we link the frame level detections based on our across-frame tracklets to produce high accuracy detections at the video level. Our ConvNet architecture for spatiotemporal object detection is evaluated on the large-scale ImageNet VID dataset where it achieves state-of-the-art results. Our approach provides better single model performance than the winning method of the last ImageNet challenge while being conceptually much simpler. Finally, we show that by increasing the temporal stride we can dramatically increase the tracker speed.


title: Evaluation of skyline algorithms in PostgreSQL

abstract: In this paper, we present our work on evaluating the skyline algorithms BNL, SFS, and a variant of LESS in PostgreSQL. It is well known that the performance of skyline queries is sensitive to a number of parameters. From extensive experiments on skyline implementations we have discovered several rules, which are remarkably simple and useful, but hard to obtain from theoretical investigation. Our findings are beneficial for developing heuristics for the skyline query optimization, and in the meantime, provide some insight for a deeper understanding of the skyline query characteristics.


title: Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation

abstract: This paper proposes a tag-based statistical framework to solve math word problems with understanding and reasoning. It analyzes the body and question texts into their associated tag-based logic forms, and then performs inference on them. Comparing to those rule-based approaches, the proposed statistical approach alleviates rules coverage and ambiguity resolution problems, and our tag-based approach also provides the flexibility of handling various kinds of related questions with the same body logic form. On the other hand, comparing to those purely statistical approaches, the proposed approach is more robust to the irrelevant information and could more accurately provide the answer. The major contributions of our work are: (1) proposing a tag-based logic representation such that the system is less sensitive to the irrelevant information and could provide answer more precisely; (2) proposing a unified statistical framework for performing reasoning from the given text.


title: Implicit Entity Linking in Tweets

abstract: Over the years, Twitter has become one of the largest communication platforms providing key data to various applications such as brand monitoring, trend detection, among others. Entity linking is one of the major tasks in natural language understanding from tweets and it associates entity mentions in text to corresponding entries in knowledge bases in order to provide unambiguous interpretation and additional context. State-of-the-art techniques have focused on linking explicitly mentioned entities in tweets with reasonable success. However, we argue that in addition to explicit mentions --- i.e. 'The movie Gravity was more expensive than the mars orbiter mission' --- entities movie Gravity can also be mentioned implicitly --- i.e. 'This new space movie is crazy. you must watch it!.' This paper introduces the problem of implicit entity linking in tweets. We propose an approach that models the entities by exploiting their factual and contextual knowledge. We demonstrate how to use these models to perform implicit entity linking on a ground truth dataset with 397 tweets from two domains, namely, Movie and Book. Specifically, we show: 1 the importance of linking implicit entities and its value addition to the standard entity linking task, and 2 the importance of exploiting contextual knowledge associated with an entity for linking their implicit mentions. We also make the ground truth dataset publicly available to foster the research in this new research areacity.


title: Learning a Text-Video Embedding from Incomplete and Heterogeneous Data

abstract: Joint understanding of video and language is an active research area with many applications. Prior work in this domain typically relies on learning text-video embeddings. One difficulty with this approach, however, is the lack of large-scale annotated video-caption datasets for training. To address this issue, we aim at learning text-video embeddings from heterogeneous data sources. To this end, we propose a Mixture-of-Embedding-Experts (MEE) model with ability to handle missing input modalities during training. As a result, our framework can learn improved text-video embeddings simultaneously from image and video datasets. We also show the generalization of MEE to other input modalities such as face descriptors. We evaluate our method on the task of video retrieval and report results for the MPII Movie Description and MSR-VTT datasets. The proposed MEE model demonstrates significant improvements and outperforms previously reported methods on both text-to-video and video-to-text retrieval tasks. Code is available at: https://github.com/antoine77340/Mixture-of-Embedding-Experts


title: Dual-polarized antenna with orthomode transducer for 60 GHz communications

abstract: A design procedure for a millimeter wave dual-polarized antenna is presented. A square waveguide carrying orthogonal polarizations is extended to an overmoded cavity which is integrated with a 2×2 slot fed patch antenna layer fabricated on a thin circuit board. The antenna array operates in the license-free 60 GHz band with a 10 dB impedance bandwidth of 13%. The radiating structure is fed through an orthomode transducer which makes it possible for the two independent linear polarizations to be accessible at waveguide feeds.


title: Laplacian-Steered Neural Style Transfer

abstract: Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to synthesize a new image that retains the high-level structure of a content image, rendered in the low-level texture of a style image. This is achieved by constraining the new image to have high-level CNN features similar to the content image, and lower-level CNN features similar to the style image. However in the traditional optimization objective, low-level features of the content image are absent, and the low-level features of the style image dominate the low-level detail structures of the new image. Hence in the synthesized image, many details of the content image are lost, and a lot of inconsistent and unpleasing artifacts appear. As a remedy, we propose to steer image synthesis with a novel loss function: the Laplacian loss. The Laplacian matrix ("Laplacian" in short), produced by a Laplacian operator, is widely used in computer vision to detect edges and contours. The Laplacian loss measures the difference of the Laplacians, and correspondingly the difference of the detail structures, between the content image and a new image. It is flexible and compatible with the traditional style transfer constraints. By incorporating the Laplacian loss, we obtain a new optimization objective for neural style transfer named Lapstyle. Minimizing this objective will produce a stylized image that better preserves the detail structures of the content image and eliminates the artifacts. Experiments show that Lapstyle produces more appealing stylized images with less artifacts, without compromising their "stylishness".


title: Dissolution Testing for Generic Drugs: An FDA Perspective

abstract: In vitro dissolution testing is an important tool used for development and approval of generic dosage forms. The objective of this article is to summarize how dissolution testing is used for the approval of safe and effective generic drug products in the United States (US). Dissolution testing is routinely used for stability and quality control purposes for both oral and non-oral dosage forms. The dissolution method should be developed using an appropriate validated method depending on the dosage form. There are several ways in which dissolution testing plays a pivotal role in regulatory decision-making. It may be used to waive in vivo bioequivalence (BE) study requirements, as BE documentation for Scale Up and Post Approval Changes (SUPAC), and to predict the potential for a modified-release (MR) drug product to dose-dump if co-administered with alcoholic beverages. Thus, in vitro dissolution testing plays a major role in FDA’s efforts to reduce the regulatory burden and unnecessary human studies in generic drug development without sacrificing the quality of the drug products.


title: Notes on the development of a mother-reared orang-utan: The first six months

abstract: Reproductive behavior is a topic of extreme importance in the literature of primate behavior. This report concerns the mother-infant interaction system in a captiveborn, mother-reared infant orang-utan during the first six months of its life. Of particular interest are the sexual behaviors directed by the mother toward the infant, and the regular stimulation of the infant's genitals. The behaviors described herein are compared to other mother-infant pairs of this and other anthropoid species.


title: Subtle cues affect generosity in an anonymous economic

abstract: Abstract Models indicate that opportunities for reputation formation can play an important role in sustaining cooperation and prosocial behavior. Results from experimental economic games support this conclusion, as manipulating reputational opportunities affects prosocial behavior. Noting that some prosocial behavior remains even in anonymous noniterated games, some investigators argue that humans possess a propensity for prosociality independent of reputation management. However, decision-making processes often employ both explicit propositional knowledge and intuitive or affective judgments elicited by tacit cues. Manipulating game parameters alters explicit information employed in overt strategizing but leaves intact cues that may affect intuitive judgments relevant to reputation formation. To explore how subtle cues of observability impact prosocial behavior, we conducted five dictator games, manipulating both auditory cues of the presence of others (via the use of sound-deadening earmuffs) and visual cues (via the presentation of stylized eyespots). Although earmuffs appeared to reduce generosity, this effect was not significant. However, as predicted, eyespots substantially increased generosity, despite no differences in actual anonymity; when using a computer displaying eyespots, almost twice as many participants gave money to their partners compared with the controls. Investigations of prosocial behavior must consider both overt information about game parameters and subtle cues influencing intuitive judgments.


Radiotherapy for postoperative recurrent squamous cell carcinoma in head and neck


title: Conditional Image Synthesis With Auxiliary Classifier GANs

abstract: Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.


title: Describing and Understanding Neighborhood Characteristics through Online Social Media

abstract: Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e. tags) that specifically characterizes a region (e.g. neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledge.


title: Efficient Lane and Vehicle Detection with Integrated Synergies (ELVIS)

abstract: On-road vehicle detection and lane detection are integral parts of most advanced driver assistance systems (ADAS). In this paper, we introduce an integrated approach called Efficient Lane and Vehicle detection with Integrated Synergies (ELVIS), that exploits the inherent synergies between lane and on-road vehicle detection to improve the overall computational efficiency without compromising on the robustness of both the tasks. Detailed evaluations show that the vehicle detection component of ELVIS shows at least 50% lesser false alarms with equal or better detection rates, and reducing the computational costs by over 90% as compared to state-of-the-art vehicle detection methods. Similarly, the lane detection component shows more reliable lane feature extraction with average computation costs that are at least 35% lesser than existing techniques.


title: Direct Volume Editing

abstract: In this work we present basic methodology for interactive volume editing on GPUs, and we demonstrate the use of these methods to achieve a number of different effects. We present fast techniques to modify the appearance and structure of volumetric scalar fields given on Cartesian grids. Similar to 2D circular brushes as used in surface painting we present 3D spherical brushes for intuitive coloring of particular structures in such fields. This paint metaphor is extended to allow the user to change the data itself, and the use of this functionality for interactive structure isolation, hole filling, and artefact removal is demonstrated. Building on previous work in the field we introduce high-resolution selection volumes, which can be seen as a resolution-based focus+context metaphor. By utilizing such volumes we present a novel approach to interactive volume editing at sub-voxel accuracy. Finally, we introduce a fast technique to paste textures onto iso-surfaces in a 3D scalar field. Since the texture resolution is independent of the volume resolution, this technique allows structure-aligned textures containing appearance properties or textual information to be used for volume augmentation and annotation.


Convolutional Neural Networks Arise From Ising Models and Restricted Boltzmann Machines


title: Unsupervised Domain Adaptation for 3D Keypoint Prediction from a Single Depth Scan.

abstract: In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan/image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency provides effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general-purpose domain adaptation techniques.


title: Industrial application development exploiting IoT vision and model driven programming

abstract: In recent years there has been a huge discussion about the IoT (Internet of Things) concept, and even more about IoT within industrial environment. The real IoT is a network of devices with local intelligence (sensors, lights, gas pumps, HVAC systems), which shares access & control mechanisms to push and pull status and command information from the networked world. However, there are still some issues, limiting the IoT diffusion: the devices involved are heterogeneous, with proprietary system of chips, protocols and interface; furthermore, there is a lack of development toolkits, enabling developers to create and evaluate IoT prototypes in simple and flexible manner. The ebbits [1] platform addressed those issues, taking advantages from the IoT vision and providing a middleware infrastructure for the integration of heterogeneous industrial devices and sensors, transforming them into web services and thus enabling their seamless integration into existing legacy systems. This paper will introduce the platform and its software architecture, describing features like semantic devices interoperability and entity virtualization. Furthermore, the paper will describe an innovative, IoT oriented, model driven development toolkit. This toolkit leverages on the semantic discovery service, allowing to dynamically selecting and locating available resources or devices, and provides a flexible instrument, including a graphical interface, that enables developers to compose mashup applications.


title: On the Equivalence Between Deep NADE and Generative Stochastic Networks

abstract: Neural Autoregressive Distribution Estimators (NADEs) have recently been shown as successful alternatives for modeling high dimensional multimodal distributions. One issue associated with NADEs is that they rely on a particular order of factorization for $P(\mathbf{x})$. This issue has been recently addressed by a variant of NADE called Orderless NADEs and its deeper version, Deep Orderless NADE. Orderless NADEs are trained based on a criterion that stochastically maximizes $P(\mathbf{x})$ with all possible orders of factorizations. Unfortunately, ancestral sampling from deep NADE is very expensive, corresponding to running through a neural net separately predicting each of the visible variables given some others. This work makes a connection between this criterion and the training criterion for Generative Stochastic Networks (GSNs). It shows that training NADEs in this way also trains a GSN, which defines a Markov chain associated with the NADE model. Based on this connection, we show an alternative way to sample from a trained Orderless NADE that allows to trade-off computing time and quality of the samples: a 3 to 10-fold speedup (taking into account the waste due to correlations between consecutive samples of the chain) can be obtained without noticeably reducing the quality of the samples. This is achieved using a novel sampling procedure for GSNs called annealed GSN sampling, similar to tempering methods that combines fast mixing (obtained thanks to steps at high noise levels) with accurate samples (obtained thanks to steps at low noise levels).


SUMMARIZATION : AN OVERVIEW ∗


title: Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings

abstract: This paper is the first attempt to learn the policy of an inquiry dialog system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks represent dialog states and dialog acts with logical formulae. In order to make learning inquiry dialog policies more effective, we introduce a logical formula embedding framework based on a recursive neural network. The results of experiments to evaluate the effect of 1) the DRL and 2) the logical formula embedding framework show that the combination of the two are as effective or even better than existing rule-based methods for inquiry dialog policies.


title: Control of cascaded H-bridge multilevel inverter with individual MPPT for grid-connected photovoltaic generators

abstract: A single-phase cascaded H-bridge multilevel inverter for a grid-connected photovoltaic (PV) system with nonactive power compensation is presented in this paper. To maximize the solar energy extraction of each PV string, an individual maximum power point tracking (MPPT) control scheme is applied, which allows the independent control of each dc-link voltage. A generalized nonactive power theory is applied to generate the nonactive current reference. Within the inverter's capability, the local consumption of nonactive power is provided to realize power factor correction. A single-phase modular cascaded multilevel inverter prototype has been built. Each H-bridge is connected to a 195 W solar panel. Simulation and experimental results are presented to validate the proposed ideas.


title: Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

abstract: In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper’s hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.


title: EMARS: Efficient Management and Allocation of Resources in Serverless

abstract: We introduce EMARS, an efficient resource management system for serverless cloud computing frameworks with the goal to enhance resource (focus on memory) allocation among containers. We have built our prototype on top of an open-source serverless platform, OpenLambda. It is based upon application workloads and serverless functions' memory needs. As a background motivation we analyzed the latencies and memory requirements of functions running on AWS lambda. The memory limits also lead to variations in number of containers spawned on OpenLambda. We use memory limit settings to propose a model of predictive efficient memory management.


title: Thyroid disease and the heart

abstract: The cardiovascular signs and symptoms of thyroid disease are some of the most profound and clinically relevant findings that accompany both hyperthyroidism and hypothyroidism. On the basis of the understanding of the cellular mechanisms of thyroid hormone action on the heart and cardiovascular system, it is possible to explain the changes in cardiac output, cardiac contractility, blood pressure, vascular resistance, and rhythm disturbances that result from thyroid dysfunction. The importance of the recognition of the effects of thyroid disease on the heart also derives from the observation that restoration of normal thyroid function most often reverses the abnormal cardiovascular hemodynamics. In the present review, we discuss the appropriate thyroid function tests to establish a suspected diagnosis as well as the treatment modalities necessary to restore patients to a euthyroid state. We also review the alterations in thyroid hormone metabolism that accompany chronic congestive heart failure and the approach to the management of patients with amiodarone-induced alterations in thyroid function tests.


title: That cloud game: dreaming (and doing) innovative game design

abstract: This paper describes the application of a methodology for game genre and player experience innovation called "play-centric design." This method is shown in context as the primary design methodology for an experimental play project, Cloud, created by students from the USC School of Cinema-Television's Interactive Media Division. The application of the play-centric design method throughout the project is detailed and explored as a potential model for innovating in the realm of overall player experience as opposed to traditional research focus on technology or subject matter innovation.


title: Effective and efficient Semantic Table Interpretation using TableMiner

abstract: This article introduces TableMiner+, a Semantic Table Interpretation method that annotates Web tables in a both effective and efficient way. Built on our previous work TableMiner, the extended version advances state-of-the-art in several ways. First, it improves annotation accuracy by making innovative use of various types of contextual information both inside and outside tables as features for inference. Second, it reduces computational overheads by adopting an incremental, bootstrapping approach that starts by creating preliminary and partial annotations of a table using ‘sample’ data in the table, then using the outcome as ‘seed’ to guide interpretation of remaining contents. This is then followed by a message passing process that iteratively refines results on the entire table to create the final optimal annotations. Third, it is able to handle all annotation tasks of Semantic Table Interpretation (e.g., annotating a column, or entity cells) while state-of-the-art methods are limited in different ways. We also compile the largest dataset known to date and extensively evaluate TableMiner+ against four baselines and two re-implemented (near-identical, as adaptations are needed due to the use of different knowledge bases) state-of-the-art methods. TableMiner+ consistently outperforms all models under all experimental settings. On the two most diverse datasets covering multiple domains and various table schemata, it achieves improvement in F1 by between 1 and 42 percentage points depending on specific annotation tasks. It also significantly reduces computational overheads in terms of wall-clock time when compared against classic methods that ‘exhaustively’ process the entire table content to build features for inference. As a concrete example, compared against a method based on joint inference implemented with parallel computation, the non-parallel implementation of TableMiner+ achieves significant improvement in learning accuracy and almost orders of magnitude of savings in wall-clock time.


title: Functional Gradient Motion Planning in Reproducing Kernel Hilbert Spaces

abstract: We introduce a functional gradient descent trajectory optimization algorithm for robot motion planning in Reproducing Kernel Hilbert Spaces (RKHSs). Functional gradient algorithms are a popular choice for motion planning in complex many-degree-of-freedom robots, since they (in theory) work by directly optimizing within a space of continuous trajectories to avoid obstacles while maintaining geometric properties such as smoothness. However, in practice, functional gradient algorithms typically commit to a fixed, finite parameterization of trajectories, often as a list of waypoints. Such a parameterization can lose much of the benefit of reasoning in a continuous trajectory space: e.g., it can require taking an inconveniently small step size and large number of iterations to maintain smoothness. Our work generalizes functional gradient trajectory optimization by formulating it as minimization of a cost functional in an RKHS. This generalization lets us represent trajectories as linear combinations of kernel functions, without any need for waypoints. As a result, we are able to take larger steps and achieve a locally optimal trajectory in just a few iterations. Depending on the selection of kernel, we can directly optimize in spaces of trajectories that are inherently smooth in velocity, jerk, curvature, etc., and that have a low-dimensional, adaptively chosen parameterization. Our experiments illustrate the effectiveness of the planner for different kernels, including Gaussian RBFs, Laplacian RBFs, and B-splines, as compared to the standard discretized waypoint representation.


title: CONCAVE HULL: A K-NEAREST NEIGHBOURS APPROACH FOR THE COMPUTATION OF THE REGION OCCUPIED BY A SET OF POINTS

abstract: This paper describes an algorithm to compute the envelope of a set of points in a plane, which generates convex or non-convex hulls that represent the area occupied by the given points. The proposed algorithm is based on a k-nearest neighbours approach, where the value of k, the only algorithm parameter, is used to control the “smoothness” of the final solution. The obtained results show that this algorithm is able to deal with arbitrary sets of points, and that the time to compute the polygons increases approximately linearly with the number of points.


title: Mate-selection and the Dark Triad: Facilitating a short-term mating strategy and creating a volatile environment

abstract: The current study (N = 242) seeks to establish the relationship between traits known collectively as the Dark Triad – narcissism, psychopathy, Machiavellianism – and mating standards and preferences. Using a budget-allocation task, we correlated scores on the Dark Triad traits with mate preferences for a longterm and short-term mate. Men scoring high on the Dark Triad may be more indiscriminate than most when selecting for short-term mates in order to widen their prospects. Furthermore, those high on the Dark Triad – psychopathy in particular – tend to select for mates based on self-interest, assortative mating, or a predilection for volatile environments. We assessed these correlations when controlling for the Big Five and the sex of the participant. We also tested for moderation by the sex of the participant and mating context. Ramifications and future directions are considered. 2011 Published by Elsevier Ltd.


Goodness of tone (GOT) for non-native Mandarin tone recognition.


Adapting Event Embedding for Implicit Discourse Relation Recognition


title: Non-universal usability?: a survey of how usability is understood by Chinese and Danish users

abstract: Most research assumes that usability is understood similarly by users in different cultures, implying that the notion of usability, its aspects, and their interrelations are constant across cultures. The present study shows that this is not the case for a sample of 412 users from China and Denmark, who differ in how they understand and prioritize different aspects of usability. Chinese users appear to be more concerned with visual appearance, satisfaction, and fun than Danish users; Danish users prioritize effectiveness, efficiency, and lack of frustration higher than Chinese users. The results suggest that culture influences perceptions of usability. We discuss implications for usability research and for usability practice.


title: A Domain-Specific Language and Compiler for Stencil Computations on Short-Vector SIMD and GPU Architectures

abstract: Stencil computations are an integral part of applications in a number of scientific computing domains, such as image processing and partial differential equations. We describe a domain-specific language for regular stencil computations, that allows specification of the computations in a concise manner. We describe a multi-target compiler for this DSL, that generates optimized code for multi-core processors with short-vector SIMD engines, as well as GPUs. The hardware differences between these two types of architecture prompt different optimization strategies for the compiler. A data layout transformation along with split tiling is used for multi-core CPUs, while overlapped tiling is used for GPUs. We evaluate our domain-specific compiler for a number of benchmarks on CPU and GPU platforms.


title: Undoing the Damage of Dataset Bias

abstract: The presence of bias in existing object recognition datasets is now well-known in the computer vision community. While it remains in question whether creating an unbiased dataset is possible given limited resources, in this work we propose a discriminative framework that directly exploits dataset bias during training. In particular, our model learns two sets of weights: (1) bias vectors associated with each individual dataset, and (2) visual world weights that are common to all datasets, which are learned by undoing the associated bias from each dataset. The visual world weights are expected to be our best possible approximation to the object model trained on an unbiased dataset, and thus tend to have good generalization ability. We demonstrate the effectiveness of our model by applying the learned weights to a novel, unseen dataset, and report superior results for both classification and detection tasks compared to a classical SVM that does not account for the presence of bias. Overall, we find that it is beneficial to explicitly account for bias when combining multiple datasets.


title: Exploring the Millennium Run - Scalable Rendering of Large-Scale Cosmological Datasets

abstract: In this paper we investigate scalability limitations in the visualization of large-scale particle-based cosmological simulations, and we present methods to reduce these limitations on current PC architectures. To minimize the amount of data to be streamed from disk to the graphics subsystem, we propose a visually continuous level-of-detail (LOD) particle representation based on a hierarchical quantization scheme for particle coordinates and rules for generating coarse particle distributions. Given the maximal world space error per level, our LOD selection technique guarantees a sub-pixel screen space error during rendering. A brick-based page-tree allows to further reduce the number of disk seek operations to be performed. Additional particle quantities like density, velocity dispersion, and radius are compressed at no visible loss using vector quantization of logarithmically encoded floating point values. By fine-grain view-frustum culling and presence acceleration in a geometry shader the required geometry throughput on the GPU can be significantly reduced. We validate the quality and scalability of our method by presenting visualizations of a particle-based cosmological dark-matter simulation exceeding 10 billion elements.


title: Parametrization and smooth approximation of surface triangulations

abstract: A method based on graph theory is investigated for creating global parametrizations for surface triangulations for the purpose of smooth surface fitting. The parametrizations, which are planar triangulations, are the solutions of linear systems based on convex combinations. A particular parametrization, called shape-preserving, is found to lead to visually smooth surface approximations.


title: Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

abstract: More than a decade has passed since research on automatic recognition of emotion from speech has become a new field of research in line with its 'big brothers' speech and speaker recognition. This article attempts to provide a short overview on where we are today, how we got there and what this can reveal us on where to go next and how we could arrive there. In a first part, we address the basic phenomenon reflecting the last fifteen years, commenting on databases, modelling and annotation, the unit of analysis and prototypicality. We then shift to automatic processing including discussions on features, classification, robustness, evaluation, and implementation and system integration. From there we go to the first comparative challenge on emotion recognition from speech - the INTERSPEECH 2009 Emotion Challenge, organised by (part of) the authors, including the description of the Challenge's database, Sub-Challenges, participants and their approaches, the winners, and the fusion of results to the actual learnt lessons before we finally address the ever-lasting problems and future promising attempts.


title: Relative Camera Pose Estimation Using Convolutional Neural Networks

abstract: This paper presents a convolutional neural network based approach for estimating the relative pose between two cameras. The proposed network takes RGB images from both cameras as input and directly produces the relative rotation and translation as output. The system is trained in an end-to-end manner utilising transfer learning from a large scale classification dataset. The introduced approach is compared with widely used local feature based methods (SURF, ORB) and the results indicate a clear improvement over the baseline. In addition, a variant of the proposed architecture containing a spatial pyramid pooling (SPP) layer is evaluated and shown to further improve the performance.


title: 3D reconstruction of mirror-type objects using efficient ray coding

abstract: Mirror-type specular objects are difficult to reconstruct: they do not possess their own appearance and the reflections from environment are view-dependent. In this paper, we present a novel computational imaging solution for reconstructing the mirror-type specular objects. Specifically, we adopt a two-layer liquid crystal display (LCD) setup to encode the illumination directions. We devise an efficient ray coding scheme by only considering the useful rays. To recover the mirror-type surface, we derive a normal integration scheme under the perspective camera model. Since the resulting surface is determined up to a scale, we develop a single view approach to resolve the scale ambiguity. To acquire the object surface as completely as possible, we further develop a multiple-surface fusion algorithm to combine the surfaces recovered from different viewpoints. Both synthetic and real experiments demonstrate that our approach is reliable on recovering small to medium scale mirror-type objects.


title: Broadband Millimeter-Wave Propagation Measurements and Models Using Adaptive-Beam Antennas for Outdoor Urban Cellular Communications

abstract: The spectrum crunch currently experienced by mobile cellular carriers makes the underutilized millimeter-wave frequency spectrum a sensible choice for next-generation cellular communications, particularly when considering the recent advances in low cost sub-terahertz/millimeter-wave complementary metal–oxide semiconductor circuitry. To date, however, little is known on how to design or deploy practical millimeter-wave cellular systems. In this paper, measurements for outdoor cellular channels at 38 GHz were made in an urban environment with a broadband (800-MHz RF passband bandwidth) sliding correlator channel sounder. Extensive angle of arrival, path loss, and multipath time delay spread measurements were conducted for steerable beam antennas of differing gains and beamwidths for a wide variety of transmitter and receiver locations. Coverage outages and the likelihood of outage with steerable antennas were also measured to determine how random receiver locations with differing antenna gains and link budgets could perform in future cellular systems. This paper provides measurements and models that may be used to design future fifth-generation millimeter-wave cellular networks and gives insight into antenna beam steering algorithms for these systems.


title: COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images

abstract: This paper describes the COCO-Text dataset. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recognition. The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images. The dataset is based on the MS COCO dataset, which contains images of complex everyday scenes. The images were not collected with text in mind and thus contain a broad variety of text instances. To reflect the diversity of text in natural scenes, we annotate text with (a) location in terms of a bounding box, (b) fine-grained classification into machine printed text and handwritten text, (c) classification into legible and illegible text, (d) script of the text and (e) transcriptions of legible text. The dataset contains over 173k text annotations in over 63k images. We provide a statistical analysis of the accuracy of our annotations. In addition, we present an analysis of three leading state-of-the-art photo Optical Character Recognition (OCR) approaches on our dataset. While scene text detection and recognition enjoys strong advances in recent years, we identify significant shortcomings motivating future work.


title: Direct geometrico-static problem of under-constrained cable-driven parallel robots with three cables

abstract: This paper studies under-constrained cable-driven parallel robots with three cables. A major challenge in the study of these manipulators is the intrinsic coupling between kinematics and statics, which must be tackled simultaneously. In this contribution, a general elimination procedure is provided that solves the direct geometrico-static problem, which consists in determining the platform posture and the cable tensions when the cable lengths are assigned. The problem is proven to have up to 156 complex solutions.


A Comparative Analysis of Personality-Based Music Recommender Systems


title: FPGA realization of Particle Swarm Optimization algorithm using floating point arithmetic

abstract: Attraction towards Particle Swarm Optimization (PSO) algorithm has grown rapidly in the recent times due to being a simple and efficient optimization technique able to solve many continuous multimodal and multidimensional problems. In this paper, we have attempted to realize the PSO algorithm in Xilinx™ Vertex V FPGA (Field Programmable Gate Array). Standard benchmark functions such as Sphere, Rastrigin and Rosenbrock are considered to investigate the efficiency of the implementation. We have studied the effect of diverse swarm size to evaluate the performance of the PSO algorithm. IEEE 754 double precision floating point format is used to implement arithmetic modules. The results obtained in this work are compared with those reported in literature. Synthesis and simulation results demonstrate that FPGA implementation proposed in this paper converges faster compared to earlier reported work.


title: Calculating Completeness of Agile Scope in Scaled Agile Development

abstract: Flexible nature of scope definition in agile makes it difficult or impossible to measure its completeness and quality. The aim of this paper is to highlight the important ingredients of scope definition for agile projects and to present a method for agile projects in order to measure the quality and completeness of their scope definitions. The proposed method considers the elements that are retrieved as a result of the systematic literature review. An industrial survey is conducted to validate and prioritize these elements. Elements are then assigned weights according to their importance in scope definition to build a scorecard for calculating the score of user stories present in the product backlog. The proposed method is able to identify the clear and complete user stories that can better be implemented in the coming iteration. Formal experiments are performed for the evaluation of the proposed method, and it suggests that the method is useful for experts in order to quantify the completeness and quality of scope definition of an agile software project.


title: Phase Retrieval by Linear Algebra

abstract: The null vector method, based on a simple linear algebraic concept, is proposed as a solution to the phase retrieval problem. ::: In the case with complex Gaussian random measurement matrices, a non-asymptotic error bound is derived, yielding an asymptotic regime of accurate approximation comparable to that for the spectral vector method.


Education and Creativity Education and Creativity


title: Runoff Analysis for a Small Watershed of Tono Area Japan by Back Propagation Artificial Neural Network with Seasonal Data

abstract: The rainfall runoff (R-R) process was studied for two small sub-basins having different sizes in a mountainous catchment of Tono area Japan. The runoff and other meterological data have been collected in this catchment for the last 14 years. The major objective of this study was to construct numerical models for these sub-basins to predict runoff after 1/2 and 1 h. The effects of season and the size of the catchment on R-R process were also investigated. The hydrogeological conditions of the catchment were studied prior to the analyses. The data obtained for summer (rainy) and winter (dry) seasons were treated separately in order to study the seasonal effects on the model development. The back propagation artificial neural network technique (BPANN) and the multivariate autoregressive and moving average models (ARMA) were adopted for the analysis. It was found that for very small catchments the seasonal effects are dominant and therefore separate models should be developed for each season to obtain better forecasting estimates. It was also found that the predictions by BPANN models were better than multivariate ARMA models for intense rains having complex R-R relationships in summer. On the other hand, both the modelling techniques yielded almost similar results for smaller rains in winter. It was also found clearly that the accuracy of prediction decreased with the increase of the time period for prediction.


title: 19q13.11 deletion syndrome: a novel clinically recognisable genetic condition identified by array comparative genomic hybridisation.

abstract: BACKGROUND ::: Deletions of chromosome 19 have rarely been reported, with the exception of some patients with deletion 19q13.2 and Blackfan-Diamond syndrome due to haploinsufficiency of the RPS19 gene. Such a paucity of patients might be due to the difficulty in detecting a small rearrangement on this chromosome that lacks a distinct banding pattern. Array comparative genomic hybridisation (CGH) has become a powerful tool for the detection of microdeletions and microduplications at high resolution in patients with syndromic mental retardation. ::: ::: ::: METHODS AND RESULTS ::: Using array CGH, this study identified three interstitial overlapping 19q13.11 deletions, defining a minimal critical region of 2.87 Mb, associated with a clinically recognisable syndrome. The three patients share several major features including: pre- and postnatal growth retardation with slender habitus, severe postnatal feeding difficulties, microcephaly, hypospadias, signs of ectodermal dysplasia, and cutis aplasia over the posterior occiput. Interestingly, these clinical features have also been described in a previously reported patient with a 19q12q13.1 deletion. No recurrent breakpoints were identified in our patients, suggesting that no-allelic homologous recombination mechanism is not involved in these rearrangements. ::: ::: ::: CONCLUSIONS ::: Based on these results, the authors suggest that this chromosomal abnormality may represent a novel clinically recognisable microdeletion syndrome caused by haploinsufficiency of dosage sensitive genes in the 19q13.11 region.


title: Emotion Effects on Timing: Attention versus Pacemaker Accounts

abstract: Emotions change our perception of time. In the past, this has been attributed primarily to emotions speeding up an “internal clock” thereby increasing subjective time estimates. Here we probed this account using an S1/S2 temporal discrimination paradigm. Participants were presented with a stimulus (S1) followed by a brief delay and then a second stimulus (S2) and indicated whether S2 was shorter or longer in duration than S1. We manipulated participants' emotions by presenting a task-irrelevant picture following S1 and preceding S2. Participants were more likely to judge S2 as shorter than S1 when the intervening picture was emotional as compared to neutral. This effect held independent of S1 and S2 modality (Visual: Exps. 1, 2, & 3; Auditory: Exp. 4) and intervening picture valence (Negative: Exps. 1, 2 & 4; Positive: Exp. 3). Moreover, it was replicated in a temporal reproduction paradigm (Exp. 5) where a timing stimulus was preceded by an emotional or neutral picture and participants were asked to reproduce the duration of the timing stimulus. Taken together, these findings indicate that emotional experiences may decrease temporal estimates and thus raise questions about the suitability of internal clock speed explanations of emotion effects on timing. Moreover, they highlight attentional mechanisms as a viable alternative.


title: The determinants of customer interactions with internet-enabled e-banking services

abstract: This paper empirically explores the major considerations associated with Internet-enabled e-banking systems and systematically measures the determinants of customer interactions with e-banking services. The results suggest that perceived usefulness, ease of use, security, convenience and responsiveness to service requests significantly explain the variation in customer interactions. Exploratory factor analysis and reliability test indicate that these constructs are relevant and reliable. Confirmatory factor analysis confirms that they possess significant convergent and discriminatory validities. Both perceived usefulness and perceived ease of use have significant impacts on customer interactions with Internet e-banking services. Perceived security, responsiveness and convenience also represent the primary avenues influencing customer interactions. In particular, stringent security control is critical to Internet e-banking operations. Prompt responses to service requests can encourage customers to use Internet e-banking services. The findings have managerial implications for enhancing extant Internet e-banking operations and developing viable Internet e-banking services.


title: Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network

abstract: Fine-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category. The task is challenging due to the facts that (1) training images with ground-truth labels are difficult to obtain, and (2) variations among different subordinate categories are subtle. It is well established that characterizing features of different subordinate categories are located on local parts of object instances. In fact, careful part annotations are available in many fine-grained categorization datasets. However, manually annotating object parts requires expertise, which is also difficult to generalize to new fine-grained categorization tasks. In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for use of fine-grained categorization. A vanilla PartNet builds on top of a base subnetwork two parallel streams of upper network layers, which respectively compute scores of classification probabilities (over subordinate categories) and detection probabilities (over a specified number of discriminative part detectors) for local regions of interest (RoIs). The image-level prediction is obtained by aggregating element-wise products of these region-level probabilities. To generate a diverse set of RoIs as inputs of PartNet, we propose a simple Discretized Part Proposals module (DPP) that directly targets for proposing candidates of discriminative local parts, with no bridging via object-level proposals. Experiments on the benchmark CUB-200-2011 and Oxford Flower 102 datasets show the efficacy of our proposed method for both discriminative part detection and fine-grained categorization. In particular, we achieve the new state-of-the-art performance on CUB-200-2011 dataset when ground-truth part annotations are not available.


title: Building Block Converter Module for Universal (AC-DC, DC-AC, DC-DC) Fully Modular Power Conversion Architecture

abstract: This paper presents the configuration and control methods for a building block module suitable for application in a fully modular, AC-DC, DC-AC and DC-DC power conversion architecture, where the modules can be connected in any combination of series and parallel connections and support bidirectional power flow. The advantages of the proposed configuration, which is a combination of AC PWM full bridge stage and a dual active bridge based isolated DC-DC stage, are explained. The control implementations and average models of the building block module are discussed in detail. Analytical, simulation and experimental results are presented that confirm the feasibility of the fully modular power conversion approach with the proposed building block module.


title: Automated classification of glaucoma stages using higher order cumulant features

abstract: Glaucoma is a group of disease often causing visual impairment without any prior symptoms. It is usually caused due to high intra ocular pressure (IOP) which can result in blindness by damaging the optic nerve. Hence, diagnosing the glaucoma in the early stage can prevent the vision loss. This paper proposes a novel automated glaucoma diagnosis system using higher order spectra (HOS) cumulants extracted from Radon transform (RT) applied on digital fundus images. In this work, the images are classified into three classes: normal, mild glaucoma and moderate/severe glaucoma. The 3rd order HOS cumulant features are subjected to linear discriminant analysis (LDA) to reduce the number of features and then these clinically significant linear discriminant (LD) features are fed to the support vector machine (SVM) and Naive Bayesian (NB) classifiers for automated diagnosis. This work is validated using 272 fundus images with 100 normal, 72 mild glaucoma and 100 moderate/severe glaucoma images using ten-fold cross validation method. The proposed system can detect the early glaucoma stage with an average accuracy of 84.72%, and the three classes with an average accuracy of 92.65%, sensitivity of 100% and specificity of 92% using NB classifier. This automated system can be used during the mass screening of glaucoma.


title: Deep Lexical Semantics

abstract: The link between words and the world is made easier if we have conceptualized the world in a way that language indicates. In the effort I will describe, we have constructed a number of core formal theories, trying to capture the abstract structure that underlies language and enable literal and metaphorical readings to be seen as specializations of the abstract structures. In the core theories, we have axiomatized composite entities (or things made out of other things), the figure-ground relation, scalar notions (of which space, time and number are specializations), change of state, causality, and the structure of complex events and processes. These theories explicate the basic predicates in terms of which the most common word senses need to be defined or characterized. We are now encoding axioms that link the word senses to the core theories, focusing on 450 of words senses in Core WordNet that are primarily concerned with events and their structure. This may be thought of as a kind of "advanced lexical decomposition", where the "primitives" into which words are "decomposed" are elements in coherently worked-out theories.


Privacy Preserving Data Mining


title: Output Constraint Transfer for Kernelized Correlation Filter in Tracking

abstract: Kernelized Correlation Filter (KCF) is one of the state-of-the-art object trackers. However, it does not reasonably model the distribution of correlation response during tracking process, which might cause the drifting problem, especially when targets undergo significant appearance changes due to occlusion, camera shaking, and/or deformation. In this paper, we propose an Output Constraint Transfer (OCT) method that by modeling the distribution of correlation response in a Bayesian optimization framework is able to mitigate the drifting problem. OCT builds upon the reasonable assumption that the correlation response to the target image follows a Gaussian distribution, which we exploit to select training samples and reduce model uncertainty. OCT is rooted in a new theory which transfers data distribution to a constraint of the optimized variable, leading to an efficient framework to calculate correlation filters. Extensive experiments on a commonly used tracking benchmark show that the proposed method significantly improves KCF, and achieves better performance than other state-of-the-art trackers. To encourage further developments, the source code is made available this https URL


title: Together alone: motivations for live-tweeting a television series

abstract: In this paper, we explore motivations for live-tweeting across a season of a television show. Using the third season of Downton Abbey as a case study, we followed 2,234 live-tweeters from the show's premiere episode to its finale, finding that nearly a third of users returned each week to tweet. Semi-structured interviews with 11 diverse live-tweeters revealed that the decision to live-tweet is dependent upon a variety of personal considerations and social conventions forming around this emerging TV viewing practice. This includes the desire to feel connected to a larger community that is interested in the show. Participants actively sought to protect the user experience of others by following good live-tweeting "etiquette", including limiting their number of posts and censoring content that might spoil the show for others. Over time, live-tweeting helped users build and maintain a network of fellow Downton Abbey viewers with shared interests.


title: A lambda-calculus foundation for universal probabilistic programming

abstract: We develop the operational semantics of an untyped probabilistic lambda-calculus with continuous distributions, as a foundation for universal probabilistic programming languages such as Church, Anglican, and Venture. Our first contribution is to adapt the classic operational semantics of lambda-calculus to a continuous setting via creating a measure space on terms and defining step-indexed approximations. We prove equivalence of big-step and small-step formulations of this distribution-based semantics. To move closer to inference techniques, we also define the sampling-based semantics of a term as a function from a trace of random samples to a value. We show that the distribution induced by integrating over all traces equals the distribution-based semantics. Our second contribution is to formalize the implementation technique of trace Markov chain Monte Carlo (MCMC) for our calculus and to show its correctness. A key step is defining sufficient conditions for the distribution induced by trace MCMC to converge to the distribution-based semantics. To the best of our knowledge, this is the first rigorous correctness proof for trace MCMC for a higher-order functional language.


title: Autonomous vision-guided approach for the analysis and grading of vertical suspension tests during Hammersmith Infant Neurological Examination (HINE)

abstract: Computer vision assisted diagnostic systems are gaining popularity in different healthcare applications. This paper presents a video analysis and pattern recognition framework for the automatic grading of vertical suspension tests on infants during the Hammersmith Infant Neurological Examination (HINE). The proposed vision-guided pipeline applies a color-based skin region segmentation procedure followed by the localization of body parts before feature extraction and classification. After constrained localization of lower body parts, a stick-diagram representation is used for extracting novel features that correspond to the motion dynamic characteristics of the infant's leg movements during HINE. This set of pose features generated from such a representation includes knee angles and distances between knees and hills. Finally, a time-series representation of the feature vector is used to train a Hidden Markov Model (HMM) for classifying the grades of the HINE tests into three predefined categories. Experiments are carried out by testing the proposed framework on a large number of vertical suspension test videos recorded at a Neuro-development clinic. The automatic grading results obtained from the proposed method matches the scores of experts at an accuracy of 74%.


title: A high-performance 640-kW 10.000-rpm Halbach-array PM slotless motor with active magnetic bearings. Part I: Preliminary and detailed design

abstract: The ongoing gas industry expansion calls for quick and strong advances in turbomachinery-coupled highspeed electric motors. Very high efficiency, dynamic performance and reliability are among the main requirements to be met at competitive costs by electric machine suppliers. This paper reports on an industrial R&D project aimed at finding the best compromise between performance targets and production cost reduction. The development of a 640-kW 10.000-rpm PM motor prototype with slotless stator and Halbach-array magnetically-levitated PM rotor is discussed, including in particular the basic technology selection and design optimization processes. In a companion paper (Part II), prototype manufacturing and testing will be reported to assess the technology and design solutions adopted.


title: Archimetrix: Improved Software Architecture Recovery in the Presence of Design Deficiencies

abstract: Maintaining software systems requires up-to-date models of these systems to systematically plan, analyse and execute the necessary reengineering steps. Often, no or only outdated models of such systems exist. Thus, a reverse engineering step is needed that recovers the system's components, subsystems and connectors. However, reverse engineering methods are severely impacted by design deficiencies in the system's code base, e.g., they lead to wrong component structures. Several approaches exist today for the reverse engineering of component-based systems, however, none of them explicitly integrates a systematic design deficiency removal into the process to improve the quality of the reverse engineered architecture. Therefore, in our Archimetrix approach, we propose to regard the most relevant deficiencies with respect to the reverse engineered component-based architecture and support reengineers by presenting the architectural consequences of removing a given deficiency. We validate our approach on the Common Component Modeling Example and show that we are able to identify relevant deficiencies and that their removal leads to an improved reengineered architecture.


title: Radiation of High-Gain Cavity-Backed Slot Antennas Through a Two-Layer Superstrate

abstract: The radiation of cavity-backed slot (CBS) antennas through a two-layer superstrate is studied in this paper. A novel transmission-line model is proposed to explain the gain-enhancement effect of the superstrate on cavity-backed slot antennas. Experimental result showed that a single cavity-backed slot antenna can achieve a high gain of 16.7 dBi through a properly designed superstrate, and this gain-enhancement property is valid for array configurations, as well. A rotationally arranged four-slot array, covered by the superstrate, is also investigated, to demonstrate that good circular polarization and high gain can be obtained from the superstrate-covered slot antennas.


title: A MUSIC-based method for SSVEP signal processing

abstract: The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100 %. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.


title: A 3-10GHz Broadband CMOS T/R Switch for UWB Applications

abstract: A 3-10 GHz broadband CMOS T/R switch for ultra-wideband (UWB) transceiver is presented. The broadband CMOS T/R switch is fabricated based on the 0.18 mu 1P6M standard CMOS process. On-chip measurement of the CMOS T/R switch is performed. The insertion loss of the proposed CMOS T/R Switch is about 3.1plusmn1.3dB. The return losses at both input and output terminals are higher than 14 dB. It is also characterized with 25-34dB isolation and 18-20 dBm input P1dB. The broadband CMOS T/R switch shows highly linear phase and group delay of 20plusmn10 ps from 10MHz to 15GHz. It can be easily integrated with other CMOS RFICs to form on-chip transceivers for various UWB applications


title: Double-talk robust VSS-NLMS algorithm for under-modeling acoustic echo cancellation

abstract: Most of the adaptive algorithms used for acoustic echo cancellation (AEC) are designed assuming an exact modeling scenario (i.e., the acoustic echo path and the adaptive filter have the same length) and a single-talk context (i.e., the near-end speech is absent). In real-world AEC applications, the adaptive filter works most likely in an under-modeling situation, i.e., its length is smaller than the length of the acoustic impulse response, so that the under-modeling noise is present. Also, the double-talk case is almost inherent, so that a double-talk detector (DTD) is usually involved. Both aspects influence and limit the algorithm's performance. Taking into account these two practical issues, a double-talk robust variable step size normalized least-mean- square (VSS-NLMS) algorithm is proposed in this paper. This algorithm is nonparametric in the sense that it does not require any information about the acoustic environment, so that it is robust and easy to control in practice.


title: LightlySupervised Attribute Extraction for Web Search

abstract: Web search engines can greatly benefit from knowledge about attributes of entities present in search queries. In this paper, we introduce lightly-supervised methods for extracting entity attributes from natural language text. Using these methods, we are able to extract large numbers of attributes of different entities at fairly high precision from a large natural language corpus. We compare our methods against a previously proposed pattern-based relation extractor, showing that the new methods give considerable improvements over that baseline. We also demonstrate that query expansion using extracted attributes improves retrieval performance on underspecified information-seeking queries. 1 Attributes in Web Search Web search engines receive numerous queries requesting information, often focused on a specific entity, such as a person, place or organization. These queries are sometimes general requests, such as “bio of George Bush,” or specific requests, such as “new york mayor.” Accurately identifying the entity (new york) or related attributes (mayor) can improve search results in several ways [1]. For example, knowledge of attributes and entities can identify a query as being a factual request [1, 2]. Query expansion using known attributes of the entity can also improve results [3]. Additionally, an engine could suggest alternative queries based on attributes. If a user searches for just “Craig Ferguson” and “shows” is a known attribute of the entity ”Craig Ferguson”, then an alternative query suggestion could be “Craig Ferguson shows” which may guide the user to more informative results. The widely explored technique of pseudo relevance feedback can also benefit from a known list of entities and attributes [4]. Some view entity and attribute extraction as a primary building block for the automatic creation of large scale knowledge bases aimed at addressing these issues [1]. The first step towards improving search results with attributes is to create lists of entities and attributes. Towards that end, we propose new algorithms that, beginning with a small seed set of entities and attributes, learn to extract new entities and attributes from a large corpus of text. We adopt a bootstrapping approach, where the inputs for our learning algorithms are a large unlabeled corpus and the small seed set containing an entity type of interest, such as seed pairs automatically extracted from query logs [1]. The seed pairs are matched against the corpus to create training instances for the learning algorithms. The algorithms exploit a wide range of instance features to alleviate the effects of noise and sparseness. The algorithms produce a large list of entities and associated attributes, which can be directly applied towards improving web search. This paper proceeds as follows. We begin with some background on attribute extraction and web search applications. We then outline our extraction algorithms. Some examples and evaluations of extracted attributes and entities follow.


title: The evicted-address filter: a unified mechanism to address both cache pollution and thrashing

abstract: Off-chip main memory has long been a bottleneck for system performance. With increasing memory pressure due to multiple onchip cores, effective cache utilization is important. In a system with limited cache space, we would ideally like to prevent 1) cache pollution, i.e., blocks with low reuse evicting blocks with high reuse from the cache, and 2) cache thrashing, i.e., blocks with high reuse evicting each other from the cache.


title: Divergent Responses to Identical Problems: Businessmen and the Smoke Nuisance in Germany and the United States, 1880–1917

abstract: This article counters a common misconception that business was universally opposed to air pollution control at the beginning of the twentieth century. In comparing the reaction of German and American businessmen to smoke abatement efforts before World War I, it shows that behavior was primarily shaped by national culture, rather than by a general desire to “externalize costs.” German smoke abatement did not meet significant resistance from industrialists, with regulation being based on a general consensus of all parties involved—a process which turned out to be as much a chance for abatement as it was an impediment for reforms. The American business community was split into two factions: those opposed to smoke abatement because they feared additional costs and the intrusion of factories by officials, and others, frequently organized in Chambers of Commerce or similar civic associations, who took a broader perspective and argued that the economic prospects of their city were at stake. The ultimate success of the latter group was largely due to changes in strategy, which allowed businessmen to develop a more positive attitude toward smoke abatement while simultaneously increasing the effectiveness of regulation. Business, therefore, should not be viewed as an inevitably “negative force” in environmental regulation.


title: Analysis of Android Applications' Permissions

abstract: We developed an architecture that automatically searches for and downloads Android applications from the Android Market. Furthermore, we created a detailed mapping of Android application programming interface (API) calls to the required permission(s), if any, for each call. We then performed an analysis of 141,372 Android applications to determine if they have the appropriate set of permissions based on the static analysis of the APK bytecode of each application. Our findings indicate that the majority of mobile software developers are notusing the correct permission set and that they either over-specify or under-specify their security requirements.


title: Shallow Networks for High-Accuracy Road Object-Detection

abstract: The ability to automatically detect other vehicles on the road is vital to the safety of partially-autonomous and fully-autonomous vehicles. Most of the high-accuracy techniques for this task are based on R-CNN or one of its faster variants. In the research community, much emphasis has been applied to using 3D vision or complex R-CNN variants to achieve higher accuracy. However, are there more straightforward modifications that could deliver higher accuracy? Yes. We show that increasing input image resolution (i.e. upsampling) offers up to 12 percentage-points higher accuracy compared to an off-the-shelf baseline. We also find situations where earlier/shallower layers of CNN provide higher accuracy than later/deeper layers. We further show that shallow models and upsampled images yield competitive accuracy. Our findings contrast with the current trend towards deeper and larger models to achieve high accuracy in domain specific detection tasks.


title: To TUI or not to TUI: Evaluating performance and preference in tangible vs. graphical user interfaces

abstract: Tangible user interfaces (TUIs) are often compared to graphical user interfaces (GUIs). However, the existing literature is unable to demonstrate clear advantages for either interface, as empirical studies yielded different findings, sometimes even contradicting ones. The current study set out to conduct an in-depth analysis of the strengths and weaknesses of both interfaces, based on a comparison between similar TUI and GUI versions of a modeling and simulation system called ''FlowBlocks''. Results showed most users preferred the TUI version over the GUI version. This is a surprising finding, considering both versions were equivalent in regard to most performance parameters, and the TUI version was even perceived as inferior to the GUI version in regard to usability. Interviews with users revealed this preference stemmed from high levels of stimulation and enjoyment, derived from three TUI properties: physical interaction, rich feedback, and high levels of realism. Potential underlying mechanisms for these findings and practical implications are discussed.


title: Estrogen and colorectal cancer incidence and mortality.

abstract: BACKGROUND ::: The preponderance of observational studies describe an association between the use of estrogen alone and a lower incidence of colorectal cancer. In contrast, no difference in the incidence of colorectal cancer was seen in the Women's Health Initiative (WHI) randomized, placebo-controlled trial with estrogen alone after a mean intervention of 7.1 years and cumulative follow-up of 13.2 years. This study extends these findings by providing detailed analyses of the effects of estrogen alone on the histology, grade, and stage of colorectal cancer, relevant subgroups, and deaths from and after colorectal cancer. ::: ::: ::: METHODS ::: The WHI study was a randomized, double-blind, placebo-controlled trial involving 10,739 postmenopausal women with prior hysterectomy. Participants were assigned to conjugated equine estrogen at 0.625 mg/d (n = 5279) or a matching placebo (n = 5409). Rates of colorectal cancer diagnoses and deaths from and after colorectal cancer were assessed throughout the study. ::: ::: ::: RESULTS ::: Colorectal cancer rates in the estrogen-alone and placebo groups were comparable: 0.14% and 0.12% per year, respectively (hazard ratio [HR], 1.13; 95% confidence interval [CI], 0.83-1.58; P = .43). Bowel screening examinations were comparable between the 2 groups throughout the study. The grade, stage, and location of colorectal cancer did not differ between the randomization groups. There were more colorectal cancer deaths in the estrogen-alone group (34 [0.05%] vs 24 [0.03%]; HR, 1.46, 95% CI, 0.86-2.46; P = .16), but the difference was not statistically significant. The colorectal cancer incidence was higher for participants with a history of colon polyp removal in the estrogen-alone group (0.23% vs 0.02%; HR, 13.47; nominal 95% CI, 1.76-103.0; P < .001). ::: ::: ::: CONCLUSIONS ::: The use of estrogen alone in postmenopausal women with prior hysterectomy does not influence the incidence of colorectal cancer or deaths from or after colorectal cancer. A possibly higher risk of colorectal cancer in women with prior colon polyp removal who use estrogen alone requires confirmation.


title: Generating performance portable code using rewrite rules: from high-level functional expressions to high-performance OpenCL code

abstract: Computers have become increasingly complex with the emergence of heterogeneous hardware combining multicore CPUs and GPUs. These parallel systems exhibit tremendous computational power at the cost of increased programming effort resulting in a tension between performance and code portability. Typically, code is either tuned in a low-level imperative language using hardware-specific optimizations to achieve maximum performance or is written in a high-level, possibly functional, language to achieve portability at the expense of performance. We propose a novel approach aiming to combine high-level programming, code portability, and high-performance. Starting from a high-level functional expression we apply a simple set of rewrite rules to transform it into a low-level functional representation, close to the OpenCL programming model, from which OpenCL code is generated. Our rewrite rules define a space of possible implementations which we automatically explore to generate hardware-specific OpenCL implementations. We formalize our system with a core dependently-typed lambda-calculus along with a denotational semantics which we use to prove the correctness of the rewrite rules. We test our design in practice by implementing a compiler which generates high performance imperative OpenCL code. Our experiments show that we can automatically derive hardware-specific implementations from simple functional high-level algorithmic expressions offering performance on a par with highly tuned code for multicore CPUs and GPUs written by experts.


title: Simultaneous Modeling Of Spectrum, Pitch And Duration In HMM-Based Speech Synthesis

abstract: In this paper, we describe an HMM-based speech synthesis system in which spectrum, pitch and state duration are modeled simultaneously in a unified framework of HMM. In the system, pitch and state duration are modeled by multi-space probability distribution HMMs and multi-dimensional Gaussian distributions, respectively. The distributions for spectral parameter, pitch parameter and the state duration are clustered independently by using a decision-tree based context clustering technique. Synthetic speech is generated by using an speech parameter generation algorithm from HMM and a mel-cepstrum based vocoding technique. Through informal listening tests, we have confirmed that the proposed system successfully synthesizes natural-sounding speech which resembles the speaker in the training database.


title: An electric energy consumer characterization framework based on data mining techniques

abstract: This paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of unsupervised and supervised learning techniques. Two main modules compose this framework: the load profiling module and the classification module. The load profiling module creates a set of consumer classes using a clustering operation and the representative load profiles for each class. The classification module uses this knowledge to build a classification model able to assign different consumers to the existing classes. The quality of this framework is illustrated with a case study concerning a real database of LV consumers from the Portuguese distribution company.


title: NOBLE – Flexible concept recognition for large-scale biomedical natural language processing

abstract: Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system’s matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator. We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE’s performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems. NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.


Machine vision system for a reverse vending machine


title: Online Off-policy Prediction

abstract: This paper investigates the problem of online prediction learning, where learning proceeds continuously as the agent interacts with an environment. The predictions made by the agent are contingent on a particular way of behaving, represented as a value function. However, the behavior used to select actions and generate the behavior data might be different from the one used to define the predictions, and thus the samples are generated off-policy. The ability to learn behavior-contingent predictions online and off-policy has long been advocated as a key capability of predictive-knowledge learning systems but remained an open algorithmic challenge for decades. The issue lies with the temporal difference (TD) learning update at the heart of most prediction algorithms: combining bootstrapping, off-policy sampling and function approximation may cause the value estimate to diverge. A breakthrough came with the development of a new objective function that admitted stochastic gradient descent variants of TD. Since then, many sound online off-policy prediction algorithms have been developed, but there has been limited empirical work investigating the relative merits of all the variants. This paper aims to fill these empirical gaps and provide clarity on the key ideas behind each method. We summarize the large body of literature on off-policy learning, focusing on 1- methods that use computation linear in the number of features and are convergent under off-policy sampling, and 2- other methods which have proven useful with non-fixed, nonlinear function approximation. We provide an empirical study of off-policy prediction methods in two challenging microworlds. We report each method's parameter sensitivity, empirical convergence rate, and final performance, providing new insights that should enable practitioners to successfully extend these new methods to large-scale applications.[Abridged abstract]


Zexe: Enabling Decentralized Private Computation.


title: A Deep Neural Network-Driven Feature Learning Method for Multi-view Facial Expression Recognition

abstract: In this paper, a novel deep neural network (DNN)-driven feature learning method is proposed and applied to multi-view facial expression recognition (FER). In this method, scale invariant feature transform (SIFT) features corresponding to a set of landmark points are first extracted from each facial image. Then, a feature matrix consisting of the extracted SIFT feature vectors is used as input data and sent to a well-designed DNN model for learning optimal discriminative features for expression classification. The proposed DNN model employs several layers to characterize the corresponding relationship between the SIFT feature vectors and their corresponding high-level semantic information. By training the DNN model, we are able to learn a set of optimal features that are well suitable for classifying the facial expressions across different facial views. To evaluate the effectiveness of the proposed method, two nonfrontal facial expression databases, namely BU-3DFE and Multi-PIE, are respectively used to testify our method and the experimental results show that our algorithm outperforms the state-of-the-art methods.


title: The Impact of Quantum Computing on Present Cryptography

abstract: The aim of this paper is to elucidate the implications ::: of quantum computing in present cryptography and ::: to introduce the reader to basic post-quantum algorithms. In ::: particular the reader can delve into the following subjects: present ::: cryptographic schemes (symmetric and asymmetric), differences ::: between quantum and classical computing, challenges in quantum ::: computing, quantum algorithms (Shor’s and Grover’s), public key ::: encryption schemes affected, symmetric schemes affected, the impact ::: on hash functions, and post quantum cryptography. Specifically, ::: the section of Post-Quantum Cryptography deals with ::: different quantum key distribution methods and mathematicalbased ::: solutions, such as the BB84 protocol, lattice-based cryptography, ::: multivariate-based cryptography, hash-based signatures ::: and code-based cryptography.


title: Optimising ring oscillator-based true random number generators concept on FPGA

abstract: Random Number Generators are widely used in cryptographic applications (symmetric and asymmetric encryption, digital certificate generation, etc.) and other related areas. True Random Number Generators (TRNG) are based on physical non-deterministic phenomenon. Most of these generators are based on a large number of interconnected high frequency ring oscillators (RO), which are most commonly implemented in FPGA. This paper describes an optimized solution which increases the speed and the complexity of these ring oscillator-based TRNG. The proposed solution combines a personalized approach on the Ring Oscillator scheme, based on preprocessed raw data and a well-known one, based on the desynchronization of a large number of free running ROs, therefore minimizing the resource used within the FPGA. The paper covers the most known statistical testes (provided by the National Institute of Standards and Technology - NIST) applied on a large amount of data and proves the stability of a generator by providing positive experimental results that were obtained from measurements using different operating frequencies.


title: What Do Small Businesses Do?

abstract: In this paper, we show that most small business owners are very different from the entrepreneurs that economic models and policy makers often have in mind. Using new data that samples early stage entrepreneurs just prior to business start up, we show that few small businesses intend to bring a new idea to market. Instead, most intend to provide an existing service to an existing market. Further, we find that most small businesses have little desire to grow big or to innovate in any observable way. We show that such behavior is consistent with the industry characteristics of the majority of small businesses, which are concentrated among skilled craftsmen, lawyers, real estate agents, doctors, small shopkeepers, and restaurateurs. Lastly, we show non pecuniary benefits (being one's own boss, having flexibility of hours, etc.) play a first-order role in the business formation decision. We then discuss how our findings suggest that the importance of entrepreneurial talent, entrepreneurial luck, and financial frictions in explaining the firm size distribution may be overstated. We conclude by discussing the potential policy implications of our findings.


title: Reconfigurable Waveguide Filter with Variable Bandwidth and Center Frequency

abstract: This paper presents a center frequency and bandwidth reconfiguration mechanism for waveguide microwave bandpass filters. The filters are designed to be used in the output section of a satellite communication system. While the center frequency of a bandpass filter can be tuned by changing the lengths of the filter resonators, the coupling irises as practical realizations of the impedance inverters cannot easily be adapted to a new value. The novel approach chosen in this paper is to replace the impedance inverters by a novel type of coupling structure. This coupling structure allows adjustment of the coupling by changing the length of a transmission line, which is equivalent to changing the resonant frequency of a coupling resonator. Design equations are presented to transform an impedance inverter into the proposed coupling structure. A synthesis procedure for bandwidth reconfigurable filters is proposed. The feasibility of coupling adaptation is proven based on a full-wave model of the coupling structure. The synthesis process is applied to a four-pole Chebyshev sidewall-coupled circular waveguide filter with 54- and 72-MHz bandwidth settings. The filter is built up and measured. The measured results prove that the filter can be tuned to both bandwidths over a center frequency range of 200 MHz with excellent return loss and insertion loss.