abstract: Background: Arithmetical skills are essential to the effective exercise of citizenship in a numerate society. How these skills are acquired, or fail to be acquired, is of great importance not only to individual children but to the organisation of formal education and its role in society. Method: The evidence on the normal and abnormal developmental progression of arithmetical abilities is reviewed; in particular, evidence for arithmetical ability arising from innate specific cognitive skills (innate numerosity) vs. general cognitive abilities (the Piagetian view) is compared. Results: These include evidence from infancy research, neuropsychological studies of developmental dyscalculia, neuroimaging and genetics. The development of arithmetical abilities can be described in terms of the idea of numerosity ‐ the number of objects in a set. Early arithmetic is usually thought of as the effects on numerosity of operations on sets such as set union. The child’s concept of numerosity appears to be innate, as infants, even in the first week of life, seem to discriminate visual arrays on the basis of numerosity. Development can be seen in terms of an increasingly sophisticated understanding of numerosity and its implications, and in increasing skill in manipulating numerosities. The impairment in the capacity to learn arithmetic ‐ dyscalculia ‐ can be interpreted in many cases as a deficit in the concept in the child’s concept of numerosity. The neuroanatomical bases of arithmetical development and other outstanding issues are discussed. Conclusions: The evidence broadly supports the idea of an innate specific capacity for acquiring arithmetical skills, but the effects of the content of learning, and the timing of learning in the course of development, requires further investigation. Keywords: Arithmetic, cognitive development, dyscalculia, numerosity, number, infants, child. ‘A child, at birth, is a candidate for humanity; it cannot become human in isolation.’ (Pieron, 1959)


title: Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews

abstract: This paper offers an approach for governments to harness the information contained in social media in order to make public inspections and disclosure more efficient. As a case study, we turn to restaurant hygiene inspections – which are done for restaurants throughout the United States and in most of the world and are a frequently cited example of public inspections and disclosure. We present the first empirical study that shows the viability of statistical models that learn the mapping between textual signals in restaurant reviews and the hygiene inspection records from the Department of Public Health. The learned model achieves over 82% accuracy in discriminating severe offenders from places with no violation, and provides insights into salient cues in reviews that are indicative of the restaurant’s sanitary conditions. Our study suggests that public disclosure policy can be improved by mining public opinions from social media to target inspections and to provide alternative forms of disclosure to customers.


title: Photorealistic Scene Reconstruction by Voxel Coloring

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


title: AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions

abstract: This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1.58M action labels with multiple labels per person occurring frequently. The key characteristics of our dataset are: (1) the definition of atomic visual actions, rather than composite actions; (2) precise spatio-temporal annotations with possibly multiple annotations for each person; (3) exhaustive annotation of these atomic actions over 15-minute video clips; (4) people temporally linked across consecutive segments; and (5) using movies to gather a varied set of action representations. This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. We will release the dataset publicly. ::: AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. To benchmark this, we present a novel approach for action localization that builds upon the current state-of-the-art methods, and demonstrates better performance on JHMDB and UCF101-24 categories. While setting a new state of the art on existing datasets, the overall results on AVA are low at 15.6% mAP, underscoring the need for developing new approaches for video understanding.


title: Detecting Silicone Mask-Based Presentation Attack via Deep Dictionary Learning

abstract: In movies, film stars portray another identity or obfuscate their identity with the help of silicone/latex masks. Such realistic masks are now easily available and are used for entertainment purposes. However, their usage in criminal activities to deceive law enforcement and automatic face recognition systems is also plausible. Therefore, it is important to guard biometrics systems against such realistic presentation attacks. This paper introduces the first-of-its-kind silicone mask attack database which contains 130 real and attacked videos to facilitate research in developing presentation attack detection algorithms for this challenging scenario. Along with silicone mask, there are several other presentation attack instruments that are explored in literature. The next contribution of this research is a novel multilevel deep dictionary learning-based presentation attack detection algorithm that can discern different kinds of attacks. An efficient greedy layer by layer training approach is formulated to learn the deep dictionaries followed by SVM to classify an input sample as genuine or attacked. Experimental are performed on the proposed SMAD database, some samples with real world silicone mask attacks, and four existing presentation attack databases, namely, replay-attack, CASIA-FASD, 3DMAD, and UVAD. The results show that the proposed algorithm yields better performance compared with state-of-the-art algorithms, in both intra-database and cross-database experiments.


title: 3D Gestural Interaction: The State of the Field

abstract: 3D gestural interaction provides a powerful and natural way to interact with computers using the hands and body for a variety of different applications including video games, training and simulation, and medicine. However, accurately recognizing 3D gestures so that they can be reliably used in these applications poses many different research challenges. In this paper, we examine the state of the field of 3D gestural interfaces by presenting the latest strategies on how to collect the raw 3D gesture data from the user and how to accurately analyze this raw data to correctly recognize 3D gestures users perform. In addition, we examine the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction. Finally, we present ideas for future research in this thriving and active research area.


title: A gm-C Ramp Generator for Voltage Feedforward Control of DC-DC Switching Regulators

abstract: A gm-C programmable-slope sawtooth generator for voltage feedforward control of DC-DC switchers is presented. Optimal feedforward is obtained by making the ramp amplitude proportional to the power input voltage PVIN. The input transistors of the generator operate on triode region, so that its small-signal transconductance depends linearly on their drain-source voltage, which is a scaled replica of PVIN, whereas showing better performance with respect to a standard differential pair in saturation. A saw-tooth waveform whose amplitude scales directly with PVIN is then available across the integrating capacitor. As part of a PWM-controlled synchronous buck regulator, the ramp generator is designed according to a SmartMOS process. A range of SPECTREcopy simulations attests the usefulness of the proposed circuit. Design parameters are 3V les PVIN les9V, 0 les Iload les 2A, 200KHz les fSW les 1MHz and VDD = 2.5V. Compatible with low-voltage requirements, the generator can operate at even lower VDD supply voltages. Following a transition on PVIN from 3V to 6V, a reduction of 76% and 50% on line regulation occurs as compared to PWM approaches with a fixed ramp and with voltage feedforward using an integrator with a differential pair in saturation, respectively.


title: Natural Gradient Works Efficiently in Learning

abstract: When a parameter space has a certain underlying structure, the ordinary gradient of a function does not represent its steepest direction, but the natural gradient does. Information geometry is used for calculating the natural gradients in the parameter space of perceptrons, the space of matrices (for blind source separation), and the space of linear dynamical systems (for blind source deconvolution). The dynamical behavior of natural gradient online learning is analyzed and is proved to be Fisher efficient, implying that it has asymptotically the same performance as the optimal batch estimation of parameters. This suggests that the plateau phenomenon, which appears in the backpropagation learning algorithm of multilayer perceptrons, might disappear or might not be so serious when the natural gradient is used. An adaptive method of updating the learning rate is proposed and analyzed.


title: Mindfulness DOI 10.1007/s12671-012-0158-6 REVIEW The Role of Mindfulness and Loving-Kindness Meditation in Cultivating Self-Compassion and Other-Focused Concern in Health Care Professionals

abstract: Therapists and other health professionals might benefit from interventions that increase their self-compassion and other-focused concern since these may strengthen their relationships with clients, reduce the chances of empathetic distress fatigue and burnout and increase their well-being. This article aimed to review the effectiveness of mindfulness-based interventions (MBIs) and loving-kindness mediation (LKM) in cultivating clinicians’ self-compassion and other-focused concern. Despite methodological limitations, the studies reviewed offer some support to the hypothesis that MBIs can increase self-compassion in health professionals, but provide a more mixed picture with regard to MBIs’ affect on other-focused concern. The latter finding may in part be due to ceiling effects; therefore future research, employing more sensitive measures, would be beneficial. Turning to LKM, there is encouraging preliminary evidence from non-clinician samples that LKM, or courses including LKM and related practices, can increase self-compassion and other-focused concern. As well as extending the LKM evidence base to health professionals and using more robust, large-scale designs, future research could usefully seek to identify the characteristics of people who find LKM challenging and the supports necessary to teach them LKM safely.


title: Junior 3: A test platform for Advanced Driver Assistance Systems

abstract: Advanced Driver Assistance Systems (ADAS) are becoming more common in today's vehicles with more complex functions being implemented in production vehicles. On the research side, significant technology development in hardware and software has been spurred in recent years by the DARPA Grand Challenge and Urban Challenge in 2005 and 2007 respectively. This paper describes ‘Junior 3’, a vehicle built at Volkswagen Group of America's Electronics Research Lab, based on the knowledge gained from the DARPA Challenges and applied to ADAS functionalities. Specifically we focussed on object detection and vehicle positioning capabilities with close to production grade sensors and describe an ‘Autonomous Valet Parking’ and ‘Object Tracking’ application with this system.


title: Semi-Supervised Learning for Imbalanced Sentiment Classification

abstract: Various semi-supervised learning methods have been proposed recently to solve the long-standing shortage problem of manually labeled data in sentiment classification. However, most existing studies assume the balance between negative and positive samples in both the labeled and unlabeled data, which may not be true in reality. In this paper, we investigate a more common case of semi-supervised learning for imbalanced sentiment classification. In particular, various random subspaces are dynamically generated to deal with the imbalanced class distribution problem. Evaluation across four domains shows the effectiveness of our approach.


title: Combining self-supervised learning and imitation for vision-based rope manipulation

abstract: Manipulation of deformable objects, such as ropes and cloth, is an important but challenging problem in robotics. We present a learning-based system where a robot takes as input a sequence of images of a human manipulating a rope from an initial to goal configuration, and outputs a sequence of actions that can reproduce the human demonstration, using only monocular images as input. To perform this task, the robot learns a pixel-level inverse dynamics model of rope manipulation directly from images in a self-supervised manner, using about 60K interactions with the rope collected autonomously by the robot. The human demonstration provides a high-level plan of what to do and the low-level inverse model is used to execute the plan. We show that by combining the high and low-level plans, the robot can successfully manipulate a rope into a variety of target shapes using only a sequence of human-provided images for direction.


title: A power supply circuit for gate driver of GaN-based flying capacitor multi-level converters

abstract: Recently, flying capacitor multi-level (FCML) converters with GaN power switches have received increased attention due to their potential to achieve high efficiency and high power density. However, one of the major challenges is providing power to the gate drive circuits, most of which are not ground referenced. The most common existing method utilizes isolated DC/DC converters, which are bulky, expensive and energy inefficient. This work introduces two new techniques that leverage inherent properties of the FCML topology and the GaN switches to provide gate drive power to the floating switches with minimum hardware requirements. The two methods, termed cascaded bootstrap and gate driven charge pump, are analyzed in details and their performance evaluated. A new power supply circuit combining the two techniques can help shrink the size of the converter, and further increase the converter's power density. Experimental results show that the proposed circuit is up to 5× more power efficient than conventional solutions, at 1/10 of the cost.


title: Ontologies-Based Platform for Sociocultural Knowledge Management

abstract: In this paper, we present a sociocultural platform aiming at persevering and capitalizing sociocultural events in Senegal. This platform relies on Semantic Web technologies. First, we discuss the two ontologies we provided to support our platform: an upper-level sociocultural ontology and a human time ontology (HuTO). To build our upper-level ontology, we proposed a methodology based on the theory of Russian psychologist Lev Vygotsky called “Vygotskian Framework”. We also present how the upper-level ontology can be matched in the linked open data cloud. On the other hand, we present the HuTO of which major contributions are (i) the modeling of non-convex intervals (repetitive interval) like every Monday, (ii) representation of deictic temporal expressions which form specific relations with time speech and (iii) qualitative temporal notions which are temporal notions relative to a culture or a geographical position. Finally, we discuss the platform designed on top of Semantic MediaWiki to apply our scientific contributions. Indeed, the platform allows Senegalese communities to share and co-construct their sociocultural knowledge.


title: Reverse draw solute permeation in forward osmosis: modeling and experiments.

abstract: Osmotically driven membrane processes are an emerging set of technologies that show promise in water and wastewater treatment, desalination, and power generation. The effective operation of these systems requires that the reverse flux of draw solute from the draw solution into the feed solution be minimized. A model was developed that describes the reverse permeation of draw solution across an asymmetric membrane in forward osmosis operation. Experiments were carried out to validate the model predictions with a highly soluble salt (NaCl) as a draw solution and a cellulose acetate membrane designed for forward osmosis. Using independently determined membrane transport coefficients, strong agreement between the model predictions and experimental results was observed. Further analysis shows that the reverse flux selectivity, the ratio of the forward water flux to the reverse solute flux, is a key parameter in the design of osmotically driven membrane processes. The model predictions and experiments demonstra...


title: Compact Microstrip Wilkinson Power Dividers With Harmonic Suppression and Arbitrary Power Division Ratios

abstract: This paper describes a new Wilkinson power divider on a single-layer microstrip line that can reduce the occupied area, suppress the harmonic components, and/or provide the arbitrary power division ratios. It consists of two-section transmission lines, two inductors, and one isolation resistor. Four different designs have been conducted to investigate the capabilities of the structure. In addition, a compact divider along with harmonic suppression and a practical divider with a large power-dividing ratio has been constructed and measured. The simulation and measurement results are in good agreement with each other. This indicates that the structure can effectively be used as a power divider for miniaturized or arbitrary power division ratio applications.


title: Transconjunctival approach vs subciliary skin-muscle flap approach for orbital fracture repair.

abstract: OBJECTIVE ::: To compare the transcutaneous and transconjunctival approaches for repair of orbital rim and floor fractures. ::: ::: ::: DESIGN ::: We conducted a retrospective study of the occurrence of eyelid retraction following the repair of 63 orbital fracture, 27 with the subciliary skin-muscle flap approach and 36 with the transconjunctival preseptal approach. ::: ::: ::: SETTING ::: Academic tertiary referral medical center. ::: ::: ::: PARTICIPANTS ::: Fifty-nine patients underwent 63 orbital explorations. ::: ::: ::: INTERVENTIONS ::: Of the 27 transcutaneous explorations, 24 were done early within the first 2 weeks of injury and three were performed for correction of late posttraumatic enophthalmos. Of the 36 transconjunctival explorations, 25 were done early and 11 were performed for correction of late posttraumatic enophthalmos. ::: ::: ::: OUTCOME MEASURE ::: Clinically noted complications. ::: ::: ::: RESULTS ::: We found a 12% rate of transient ectropion and a 28% rate of permanent scleral show with the subciliary skin-muscle flap approach compared with no transient ectropion and a 3% rate of permanent scleral show with the transconjunctival approach. ::: ::: ::: CONCLUSIONS ::: We believe that the transconjunctival approach provides excellent exposure with less risk of postoperative eyelid retraction and ectropion.


A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP


title: Quantification of Lung Damage in an Elastase-Induced Mouse Model of Emphysema

abstract: Objective. To define the sensitivity of microcomputed tomography- (micro-CT-) derived descriptors for the quantification of lung damage caused by elastase instillation. Materials and Methods. The lungs of 30 elastase treated and 30 control A/J mice were analyzed 1, 6, 12, and 24 hours and 7 and 17 days after elastase instillation using (i) breath-hold-gated micro-CT, (ii) pulmonary function tests (PFTs), (iii) RT-PCR for RNA cytokine expression, and (iv) histomorphometry. For the latter, an automatic, parallel software toolset was implemented that computes the airspace enlargement descriptors: mean linear intercept (L(m)) and weighted means of airspace diameters (D(0), D(1), and D(2)). A Support Vector Classifier was trained and tested based on three nonhistological descriptors using D(2) as ground truth. Results. D(2) detected statistically significant differences (P < 0.01) between the groups at all time points. Furthermore, D(2) at 1 hour (24 hours) was significantly lower (P < 0.01) than D(2) at 24 hours (7 days). The classifier trained on the micro-CT-derived descriptors achieves an area under the curve (AUC) of 0.95 well above the others (PFTS AUC = 0.71; cytokine AUC = 0.88). Conclusion. Micro-CT-derived descriptors are more sensitive than the other methods compared, to detect in vivo early signs of the disease.


title: A Game-Theoretic Framework for Bandwidth Attacks and Statistical Defenses

abstract: We introduce a game-theoretic framework for reasoning about bandwidth attacks, a common form of distributed denial of service (DDoS) attacks. In particular, our traffic injection game models the attacker as a rational but limited-resource entity who uses limited knowledge of traffic patterns to launch IP spoofing based bandwidth attacks on a server. We model the defender as a coarse-grained, relative volume based statistical filter. We analyze the effectiveness of the defender against the attacker by analyzing the payoffs of various strategies in the traffic injection game. Furthermore, we analyze how these payoffs change in the presence of random noise. Our results show that there is potential for using statistical methods for creating defense mechanisms that can detect a DDoS attack and that even when an attacker has a priori knowledge of the expected traffic volume for the dimension and divisions employed in the attack, the attack traffic can still be exposed to the defender.


title: Output-capacitorless CMOS LDO regulator based on high slew-rate current-mode transconductance amplifier

abstract: A low quiescent current output-capacitorless CMOS LDO regulator based on a high slew-rate current-mode transconductance amplifier (CTA) as an error amplifier is presented. Load transient characteristic of the proposed LDO is improved even at low quiescent currents, by using a local common-mode feedback (LCMFB) in the proposed CTA. This provides an increase in the order of transfer characteristic of the circuit, thereby enhancing the slew-rate at the gate of pass transistor. The proposed CTA-based LDO topology has been designed and post-layout simulated in HSPICE, in a 0.18 μm CMOS process to supply a load current between 0-100 mA. Postlayout simulation results reveal that the proposed LDO is stable without any internal compensation strategy and with on-chip output capacitor or lumped parasitic capacitances at the output node between 10-100 pF.


title: LTE-A enhanced Inter-cell Interference Coordination (eICIC) with Pico cell adaptive antenna

abstract: This paper presents a study of the mutual interference between a Macro cell and a Pico cell within LTE-A (Long Term Evolution — Advanced) framework. It is assumed that the Macro cell and the Pico cell share the same frequency channel and only the downlink (DL) performance is studied. In this paper, we introduce the idea that a Pico cell located within the coverage area of a Macro cell be equipped with an adaptive antenna. The Pico cell examines the interference environment around it and adjusts the antenna radiation pattern to equalize the Signal to Interference Ratio (SIR) along its circular coverage range. We compare this new approach with the existing known scheme of the Pico-cell Range Extension (PRE). The performance of enhanced Inter-cell Interference Coordination (eICIC) for LTE-A is analyzed with the Macro cell muted for different Almost Blank Sub-frame (ABS) patterns. The operation of time domain eICIC is united with Pico cell adaptive smart antenna in order to further enhance eICIC. The variable antenna gain expression is derived. Simulations are performed using 3GPP (3rd Generation Partnership Project) guidelines to note the dissimilarity between the PRE technology and the proposed Pico adaptive antenna technology. The overall network performance is evaluated for different loading levels at Macro and Pico cells and user distributions. Also the Pico cell edge user performance is analyzed with eICIC and eICIC combined with Pico cell smart antenna.


title: Neighbor-weighted K-nearest neighbor for unbalanced text corpus

abstract: Text categorization or classification is the automated assigning of text documents to pre-defined classes based on their contents. Many of classification algorithms usually assume that the training examples are evenly distributed among different classes. However, unbalanced data sets often appear in many practical applications. In order to deal with uneven text sets, we propose the neighbor-weighted K-nearest neighbor algorithm, i.e. NWKNN. The experimental results indicate that our algorithm NWKNN achieves significant classification performance improvement on imbalanced corpora.


title: Can a “poor” verification system be a “good” identification system? A preliminary study

abstract: The matching accuracy of a biometric system is typically quantified through measures such as the False Match Rate (FMR), False Non-match Rate (FNMR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) curve and Cumulative Match Characteristic (CMC) curve. In this work, we analyze the relationship between the ROC and CMC curves, which are two measures commonly used to describe the performance of verification and identification systems, respectively. We establish that it is possible for a biometric system to exhibit “good” verification performance and “poor” identification performance (and vice versa) by demonstrating the conditions required to produce such outcomes. Experimental analysis using synthetically generated match scores confirms our hypothesis that the ROC or CMC alone cannot completely characterize biometric system performance.


title: Are Databases Fit for Hybrid Workloads on GPUs? A Storage Engine's Perspective

abstract: Employing special-purpose processors (e.g., GPUs) in database systems has been studied throughout the last decade. Research on heterogeneous database systems that use both general-and special-purpose processors has addressed either transaction-or analytic processing, but not the combination of them. Support for hybrid transaction-and analytic processing (HTAP) has been studied exclusively for CPU-only systems. In this paper we ask the question whether current systems are ready for HTAP workload management with cooperating generaland special-purpose processors. For this, we take the perspective of the backbone of database systems: the storage engine. We propose a unified terminology and a comprehensive taxonomy to compare state-of-the-art engines from both domains. We show similarities and differences, and determine a necessary set of features for engines supporting HTAP workload on CPUs and GPUs. Answering our research question, our findings yield a resolute: not yet.


title: Low-Voltage Embedded NAND-ROM Macros Using Data-Aware Sensing Reference Scheme for VDDmin, Speed and Power Improvement

abstract: Density-prioritized memories such as NAND-ROM require a longer single-ended bitline (BL) sensing scheme to maintain high cell array efficiency and therefore suffer from a reduced BL sensing margin at ultra-low supply voltages (VDD) for read-0. This is the result of 1) reduced BL discharge driving current due to limited cell size and driving voltage; and 2) a larger BL false drop noise for read-1. This study proposes a data-aware sensing reference (DASR) scheme, capable of maintaining sensing margins for both read-0 and read-1 under given timing constraints. The key mechanism involved in maintaining the sensing margin is the adaptive changing of the reference voltage (VREF), such that the sensing headroom or potential range (VBL-VREF) for read-1 and read-0 overlap, as in differential BL sensing. Two 256 Kb DASR NAND-ROM macros, with and without code-inversion schemes, were fabricated using a 90 nm bulk CMOS logic process. The 256 Kb DASR NAND-ROM macros are functional down to 0.25 V. DASR also increases the access speed by 66.7% at 0.31 V VDD, compared with the conventional approach without the proposed DASR scheme.


title: On the Estimation of Discount Parameters for Language Model Smoothing

abstract: The goal of statistical language modeling is to find probability estimates for arbitrary word sequences. To obtain non-zero values, the probability distributions found in the training data need to be smoothed. In the widely-used Kneser-Ney family of smoothing algorithms, this is achieved by absolute discounting. The discount parameters can be computed directly using some approximation formulas minimizing the leaving-one-out log-likelihood of the training data. In this work, we outline several shortcomings of the standard estimators for the discount parameters. We propose an efficient method for computing the discount values on heldout data and analyze the resulting parameter estimates. Experiments on large English and French corpora show consistent improvements in perplexity and word error rate over the baseline method. At the same time, this approach can be used for language model pruning, leading to slightly better results than standard pruning algorithms.


title: Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

abstract: This paper is the result of a literature study carried out by the authors. It is a review of the different attempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation. Likewise, we show the experimental results obtained with different standard examples using combination of crossover and mutation operators in relation with path representation.


title: SGX-Step: A Practical Attack Framework for Precise Enclave Execution Control

abstract: Protected module architectures such as Intel SGX hold the promise of protecting sensitive computations from a potentially compromised operating system. Recent research convincingly demonstrated, however, that SGX's strengthened adversary model also gives rise to to a new class of powerful, low-noise side-channel attacks leveraging first-rate control over hardware. These attacks commonly rely on frequent enclave preemptions to obtain fine-grained side-channel observations. A maximal temporal resolution is achieved when the victim state is measured after every instruction. Current state-of-the-art enclave execution control schemes, however, do not generally achieve such instruction-level granularity. This paper presents SGX-Step, an open-source Linux kernel framework that allows an untrusted host process to configure APIC timer interrupts and track page table entries directly from user space. We contribute and evaluate an improved approach to single-step enclaved execution at instruction-level granularity, and we show how SGX-Step enables several new or improved attacks. Finally, we discuss its implications for the design of effective defense mechanisms.


VEGF Polymorphisms Related to Higher Serum Levels of Protein Identify Patients with Hepatocellular Carcinoma


title: Should we reject the expertise hypothesis?

abstract: On the basis of a review of the literature and the results of three experiments with dog experts, Robbins and McKone [Robbins, R. A., & McKone, E. (2006). No face-like processing for objects-of-expertise in three behavioural tasks, Cognition] argue that there is little or no evidence supporting an expertise account of the differences in configural processing that are typically observed between faces and non-face objects. In the spirit of a debate that has become overly polarized, we believe that R&M often emphasized relatively unimportant controversial issues at the expense of bigger, more important questions. We also feel that some of R&M’s arguments are rooted in methodological confusions that should be clarified because they have implications beyond this specific debate. In this commentary, we first clarify issues surrounding the proper statistical analysis of the composite paradigm, a methodology that is commonly used to assess configural and holistic effects in both face and non-face objects. We then discuss several theoretical issues that we feel are central to the debate regarding accounts of face-specificity. We also briefly review positive evidence for the correlation between measures of behavioral expertise and neural markers of face-selectivity. Unlike R&M, we believe the positive evidence for expertise effects, both behavioral and neural, greatly outweighs evidence stemming from null results and that it clearly motivates the importance of future work on the role of experience in the specialization of visual cortex.


title: Realistic rendering of bokeh effect based on optical aberrations

abstract: Bokeh effect is an important characteristic for realistic image synthesis. However, existing bokeh rendering methods are incapable of simulating realistic bokeh effects due to not taking into account optical characteristics of real lenses, especially optical aberrations. In this paper, a novel realistic bokeh rendering method, based on an accurate camera lens model and distributed ray tracing, is presented. An optical analysis of the relationship between bokeh and optical aberrations, including spherical aberration, coma, astigmatism and field curvature, is firstly introduced. Based on this analysis, a physically-based camera lens model, which takes detailed lens prescription as input, is then introduced for accurately modeling the aberrations. The position and diameter of the entrance and exit pupils are calculated by tracing rays inside the lens for achieving efficient ray sampling, and a general sequential ray tracing algorithm is proposed to better combine with bidirectional ray tracing. Furthermore, correct integration between the lens model and bidirectional ray tracing is also analyzed. The rendering results demonstrate a variety of realistic bokeh effects caused by the aberrations.


Data Semantics


title: Incremental View Maintenance over Array Data

abstract: Science applications are producing an ever-increasing volume of multi-dimensional data that are mainly processed with distributed array databases. These raw arrays are ``cooked'' into derived data products using complex pipelines that are time-consuming. As a result, derived data products are released infrequently and become stale soon thereafter. In this paper, we introduce materialized array views as a database construct for scientific data products. We model the ``cooking'' process as incremental view maintenance with batch updates and give a three-stage heuristic that finds effective update plans. Moreover, the heuristic repartitions the array and the view continuously based on a window of past updates as a side-effect of view maintenance without overhead. We design an analytical cost model for integrating materialized array views in queries. A thorough experimental evaluation confirms that the proposed techniques are able to incrementally maintain a real astronomical data product in a production environment.


Implementing E-Public Service in North Sumatera: Prospects and Challenges


THEORY OF INVENTORY MANAGEMENT BASED ON DEMAND


title: On the negativity of negation

abstract: Natural language negation is persistently negative in the pragmatic sense, and emphatic and attenuating negative polarity items modulate this effect in systematic ways. I use large corpora of informal texts with meta-data approximating features of the context to characterize this pragmatic negativity, and I attempt to explain it in terms of the ways in which negative sentences engage the questions under discussion. The discussion highlights some of the ways in which quantitative corpus methods can be used to achieve novel results in linguistic pragmatics.


title: Effects of Age and Sex on Space Occupation by European Roe Deer

abstract: The effects of age and sex on annual home-range characteristics of European roe deer ( Capreolus capreolus ) were investigated by means of radiotracking individuals from a high-density population within a deciduous forest. Subadult males often used more diffuse home ranges than did adults and subadult females, whereas old males exhibited the most concentrated pattern of space occupation. Subadult males also showed a higher dispersal than did subadult females. Such results contrast with those obtained at low densities and suggest that population density influences the process of range acquisition in a sex-dependent way, probably because of the retention of territoriality by adult males at high densities.


title: Intelligent Guardrails: An IoT Application for Vehicle Traffic Congestion Reduction in Smart City

abstract: This paper discusses vehicle traffic congestion which leads to air pollution, driver frustration, and costs billions of dollars annually in fuel consumption. Finding a proper solution to vehicle congestion is a considerable challenge due to the dynamic and unpredictable nature of the network topology of vehicular environments, especially in urban areas. Recent advances in sensing, communication and computing technologies enables us to gather real-time data about traffic condition of the roads and mitigate the traffic congestion via various ways such as Vehicle Traffic Routing Systems (VTRSs), electronic toll collection system (ETCS), and intelligent traffic light signals (TLSs). Regarding this issue, an innovative technology, called Intelligent Guardrails (IGs), is presented in this paper. IGs takes advantages of Internet of Things (IoT) and vehicular networks to provide a solution for vehicle traffic congestion in large cities. IGs senses the roads' traffic condition and uses this information to set the capacity of the roads dynamically.


title: Interactive Code Review for Systematic Changes

abstract: Developers often inspect a diff patch during peer code reviews. Diff patches show low-level program differences per file without summarizing systematic changes---similar, related changes to multiple contexts. We present C ritics , an interactive approach for inspecting systematic changes. When a developer specifies code change within a diff patch, C ritics allows developers to customize the change template by iteratively generalizing change content and context. By matching a generalized template against the codebase, it summarizes similar changes and detects potential mistakes. We evaluated C ritics using two methods. First, we conducted a user study at Salesforce.com, where professional engineers used C ritics to investigate diff patches authored by their own team. After using C ritics , all six participants indicated that they would like C ritics to be integrated into their current code review environment. This also attests to the fact that C ritics scales to an industry-scale project and can be easily adopted by professional engineers. Second, we conducted a user study where twelve participants reviewed diff patches using C ritics and Eclipse diff. The results show that human subjects using C ritics answer questions about systematic changes 47.3% more correctly with 31.9% saving in time during code review tasks, in comparison to the baseline use of Eclipse diff. These results show that C ritics should improve developer productivity in inspecting systematic changes during peer code reviews.


title: CMOS millimeter wave phase shifter based on tunable transmission lines

abstract: This paper presents a tunable transmission line (t-line) structure, featuring independent control of line inductance and capacitance. The t-line provides variable delay while maintaining relatively constant characteristic impedance using direct digital control through FET switches. As an application of this original structure, a 60 GHz RF-phase shifter for phased-array applications is implemented in a 32 nm SOI process attaining state-of-the-art performance. Measured data from two phase shifter variants at 60 GHz showed phase changes of 175° and 185°, S21 losses of 3.5-7.1 dB and 6.1-7.6 dB, RMS phase errors of 2° and 3.2°, and areas of 0.073 mm2 and 0.099 mm2 respectively.


title: New initiative: the naturalness of software

abstract: This paper describes a new research consortium, studying the Naturalness of Software. This initiative is supported by a pair of grants by the US National Science Foundation, totaling $2,600,000: the first, exploratory ("EAGER") grant of $600,000 helped kickstart an inter-disciplinary effort, and demonstrate feasibility; a follow-on full grant of $2,000,000 was recently awarded. The initiative is led by the author, who is at UC Davis, and includes investigators from Iowa State University and Carnegie-Mellon University (Language Technologies Institute).


title: A New High-Efficiency Single-Phase Transformerless PV Inverter Topology

abstract: There is a strong trend in the photovoltaic inverter technology to use transformerless topologies in order to acquire higher efficiencies combining with very low ground leakage current. In this paper, a new topology, based on the H-bridge with a new ac bypass circuit consisting of a diode rectifier and a switch with clamping to the dc midpoint, is proposed. The topology is simulated and experimentally validated, and a comparison with other existing topologies is performed. High conversion efficiency and low leakage current are demonstrated.


title: Generating Paths with WFC

abstract: Motion plans are often randomly generated for minor game NPCs. Repetitive or regular movements, however, require non-trivial programming effort and/or integration with a pathing system. We here describe an example-based approach to path generation that requires little or no additional programming effort. Our work modifies the Wave Function Collapse (WFC) algorithm, adapting it to produce pathing plans similar to an input sketch. We show how simple sketch modifications control path characteristics, and demonstrate feasibility through a usable Unity implementation.


title: Language Guided Fashion Image Manipulation with Feature-wise Transformations

abstract: Developing techniques for editing an outfit image through natural sentences and accordingly generating new outfits has promising applications for art, fashion and design. However, it is considered as a certainly challenging task since image manipulation should be carried out only on the relevant parts of the image while keeping the remaining sections untouched. Moreover, this manipulation process should generate an image that is as realistic as possible. In this work, we propose FiLMedGAN, which leverages feature-wise linear modulation (FiLM) to relate and transform visual features with natural language representations without using extra spatial information. Our experiments demonstrate that this approach, when combined with skip connections and total variation regularization, produces more plausible results than the baseline work, and has a better localization capability when generating new outfits consistent with the target description.


title: Automatic eyeglasses removal from face images

abstract: In this paper, we present an intelligent image editing and face synthesis system that automatically removes eyeglasses from an input frontal face image. Although conventional image editing tools can be used to remove eyeglasses by pixel-level editing, filling in the deleted eyeglasses region with the right content is a difficult problem. Our approach works at the object level where the eyeglasses are automatically located, removed as one piece, and the void region filled. Our system consists of three parts: eyeglasses detection, eyeglasses localization, and eyeglasses removal. First, an eye region detector, trained offline, is used to approximately locate the region of eyes, thus the region of eyeglasses. A Markov-chain Monte Carlo method is then used to accurately locate key points on the eyeglasses frame by searching for the global optimum of the posterior. Subsequently, a novel sample-based approach is used to synthesize the face image without the eyeglasses. Specifically, we adopt a statistical analysis and synthesis approach to learn the mapping between pairs of face images with and without eyeglasses from a database. Extensive experiments demonstrate that our system effectively removes eyeglasses.


title: Robust feature selection and robust PCA for internet traffic anomaly detection

abstract: Robust statistics is a branch of statistics which includes statistical methods capable of dealing adequately with the presence of outliers. In this paper, we propose an anomaly detection method that combines a feature selection algorithm and an outlier detection method, which makes extensive use of robust statistics. Feature selection is based on a mutual information metric for which we have developed a robust estimator; it also includes a novel and automatic procedure for determining the number of relevant features. Outlier detection is based on robust Principal Component Analysis (PCA) which, opposite to classical PCA, is not sensitive to outliers and precludes the necessity of training using a reliably labeled dataset, a strong advantage from the operational point of view. To evaluate our method we designed a network scenario capable of producing a perfect ground-truth under real (but controlled) traffic conditions. Results show the significant improvements of our method over the corresponding classical ones. Moreover, despite being a largely overlooked issue in the context of anomaly detection, feature selection is found to be an important preprocessing step, allowing adaption to different network conditions and inducing significant performance gains.


title: On being the right scale: sizing large collections of 3D models

abstract: We address the problem of recovering reliable sizes for a collection of models defined using scales with unknown correspondence to physical units. Our algorithmic approach provides absolute size estimates for 3D models by combining category-based size priors and size observations from 3D scenes. Our approach handles un-observed 3D models without any user intervention. It also scales to large public 3D model databases and is appropriate for handling the open-world problem of rapidly expanding collections of 3D models. We use two datasets from online 3D model repositories to evaluate against both human judgments of size and ground truth physical sizes of 3D models, and find that an algorithmic approach can predict sizes more accurately than people.


title: Adults and children processing music: an fMRI study. Neuroimage 25

abstract: Abstract The present study investigates the functional neuroanatomy of music perception with functional magnetic resonance imaging (fMRI). Three different subject groups were investigated to examine developmental aspects and effects of musical training: 10-year-old children with varying degrees of musical training, adults without formal musical training (nonmusicians), and adult musicians. Subjects made judgements on sequences that ended on chords that were music-syntactically either regular or irregular. In adults, irregular chords activated the inferior frontal gyrus, orbital frontolateral cortex, the anterior insula, ventrolateral premotor cortex, anterior and posterior areas of the superior temporal gyrus, the superior temporal sulcus, and the supramarginal gyrus. These structures presumably form different networks mediating cognitive aspects of music processing (such as processing of musical syntax and musical meaning, as well as auditory working memory), and possibly emotional aspects of music processing. In the right hemisphere, the activation pattern of children was similar to that of adults. In the left hemisphere, adults showed larger activations than children in prefrontal areas, in the supramarginal gyrus, and in temporal areas. In both adults and children, musical training was correlated with stronger activations in the frontal operculum and the anterior portion of the superior temporal gyrus.


title: On the jitter and entropy of the oscillator-based random source

abstract: True Random Number Generator (TRNG) is a research hotspot recently because it is the main part of many security chips. Random source is considered to be a pillar of TRNG. There are many types of TRNG and oscillator-based random source is widely used on chip. The design of it exploits the random cycle-to-cycle time drift(jitter) in clock of low frequency to produce random bit sequence. Entropy is a basic metric of randomness. In order to increase the randomness of sequence we have to increase the jitter in clock. In this work we propose a method to find the relationship between jitter and the entropy of a bit sequence. First we calculate the possibility of a single bit under the assumption of the normal distribution. Then we give the algorithm of L-bit entropy in the worst case. Finally, we generate a series of bit sequence with different jitters and calculate the entropy with the test of AIS31. Moreover, the paper will also analyze the threshold of the ratio of jitter and period to get a bit sequence with high randomness. From our theory and experimental results, if the ratio is greater than 9.16, the raw sequence has adequate randomness. This threshold is useful to the designers of TRNG.


title: Multiplexing gains achieved in pools of baseband computation units in 4G cellular networks

abstract: The tremendous increase of mobile user traffic load within the last few years forces us to efficiently use the wireless network and processing resources. Cloud computing and virtualization techniques offer an exciting opportunity to considerably reduce operation costs and provide flexible and dynamic systems. In this paper we present a simulation study for a cloud base station, which concentrates baseband processing functions of multiple radio sites. There we focus on the multiplexing-gains induced from user load and traffic heterogeneity. Our simulation results show that the data traffic influences the variance of the compute resource utilization, which in consequence leads to significant multiplexing gains if multiple sectors are aggregated into one single cloud base station. In addition, the spatial user distribution has a high impact on the compute resource load. These findings should be taken into account for the assessment of multiplexing gains in real networks.


title: Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification

abstract: We propose Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) to address the problem of person re-identification on multi-cameras. Re-identifications on different cameras are considered as related tasks, which allows the shared information among different tasks to be explored to improve the re-identification accuracy. The MTL-LORAE framework integrates low-level features with mid-level attributes as the descriptions for persons. To improve the accuracy of such description, we introduce the low-rank attribute embedding, which maps original binary attributes into a continuous space utilizing the correlative relationship between each pair of attributes. In this way, inaccurate attributes are rectified and missing attributes are recovered. The resulting objective function is constructed with an attribute embedding error and a quadratic loss concerning class labels. It is solved by an alternating optimization strategy. The proposed MTL-LORAE is tested on four datasets and is validated to outperform the existing methods with significant margins.


title: End-to-End Detection and Re-identification Integrated Net for Person Search

abstract: This paper proposes a pedestrian detection and re-identification (re-id) integrated net (I-Net) in an end-to-end learning framework. The I-Net is used in real-world video surveillance scenarios, where the target person needs to be searched in the whole scene videos, and the annotations of pedestrian bounding boxes are unavailable. Comparing to the successful OIM method [31] for joint detection and re-id, we have three distinct contributions. First, we implement a Siamese architecture instead of one stream for an end-to-end training strategy. Second, a novel on-line pairing loss (OLP) with a feature dictionary restricts the positive pairs. Third, hard example priority softmax loss (HEP) with little computation cast is proposed to deal with the online hard example mining. We show our results on CUHK-SYSU and PRW datasets. Our method narrows the gap between detection and re-identification, and achieves a superior performance.


title: A method to define an Enterprise Architecture using the Zachman Framework

abstract: The proliferation of IT and its consequent dispersion is an enterprise reality, however, most organizations do not have adequate tools and/or methodologies that enable the management and coordination of their Information Systems. The Zachman Framework provides a structured way for any organization to acquire the necessary knowledge about itself with respect to the Enterprise Architecture. Zachman proposes a logical structure for classifying and organizing the descriptive representations of an enterprise, in different dimensions, and each dimension can be perceived in different perspectives.In this paper, we propose a method for achieving an Enterprise Architecture Framework, based on the Zachman Framework Business and IS perspectives, that defines the several artifacts for each cell, and a method which defines the sequence of filling up each cell in a top-down and incremental approach. We also present a tool developed for the purpose of supporting the Zachman Framework concepts. The tool: (i) behaves as an information repository for the framework's concepts; (ii) produces the proposed artifacts that represent each cell contents, (iii) allows multi-dimensional analysis among cell's elements, which is concerned with perspectives (rows) and/or dimensions (columns) dependency; and (iv) finally, evaluate the integrity, dependency and, business and information systems alignment level, through the answers defined for each framework dimension.


title: Mining Electronic Health Records (EHRs): A Survey

abstract: The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes, and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this article, we provide a structured and comprehensive overview of data mining techniques for modeling EHRs. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.


title: Multi-scale object candidates for generic object tracking in street scenes

abstract: Most vision based systems for object tracking in urban environments focus on a limited number of important object categories such as cars or pedestrians, for which powerful detectors are available. However, practical driving scenarios contain many additional objects of interest, for which suitable detectors either do not yet exist or would be cumbersome to obtain. In this paper we propose a more general tracking approach which does not follow the often used tracking-by-detection principle. Instead, we investigate how far we can get by tracking unknown, generic objects in challenging street scenes. As such, we do not restrict ourselves to only tracking the most common categories, but are able to handle a large variety of static and moving objects. We evaluate our approach on the KITTI dataset and show competitive results for the annotated classes, even though we are not restricted to them.


title: SDT: A Virus Classification Tool Based on Pairwise Sequence Alignment and Identity Calculation

abstract: The perpetually increasing rate at which viral full-genome sequences are being determined is creating a pressing demand for computational tools that will aid the objective classification of these genome sequences. Taxonomic classification approaches that are based on pairwise genetic identity measures are potentially highly automatable and are progressively gaining favour with the International Committee on Taxonomy of Viruses (ICTV). There are, however, various issues with the calculation of such measures that could potentially undermine the accuracy and consistency with which they can be applied to virus classification. Firstly, pairwise sequence identities computed based on multiple sequence alignments rather than on multiple independent pairwise alignments can lead to the deflation of identity scores with increasing dataset sizes. Also, when gap-characters need to be introduced during sequence alignments to account for insertions and deletions, methodological variations in the way that these characters are introduced and handled during pairwise genetic identity calculations can cause high degrees of inconsistency in the way that different methods classify the same sets of sequences. Here we present Sequence Demarcation Tool (SDT), a free user-friendly computer program that aims to provide a robust and highly reproducible means of objectively using pairwise genetic identity calculations to classify any set of nucleotide or amino acid sequences. SDT can produce publication quality pairwise identity plots and colour-coded distance matrices to further aid the classification of sequences according to ICTV approved taxonomic demarcation criteria. Besides a graphical interface version of the program for Windows computers, command-line versions of the program are available for a variety of different operating systems (including a parallel version for cluster computing platforms).


Visual Literacy in Teaching and Learning: A Literature Perspective’, [Online], Electronic Journal for the Integration of Technology in


title: UNIK: unsupervised social network spam detection

abstract: Social network spam increases explosively with the rapid development and wide usage of various social networks on the Internet. To timely detect spam in large social network sites, it is desirable to discover unsupervised schemes that can save the training cost of supervised schemes. In this work, we first show several limitations of existing unsupervised detection schemes. The main reason behind the limitations is that existing schemes heavily rely on spamming patterns that are constantly changing to avoid detection. Motivated by our observations, we first propose a sybil defense based spam detection scheme SD2 that remarkably outperforms existing schemes by taking the social network relationship into consideration. In order to make it highly robust in facing an increased level of spam attacks, we further design an unsupervised spam detection scheme, called UNIK. Instead of detecting spammers directly, UNIK works by deliberately removing non-spammers from the network, leveraging both the social graph and the user-link graph. The underpinning of UNIK is that while spammers constantly change their patterns to evade detection, non-spammers do not have to do so and thus have a relatively non-volatile pattern. UNIK has comparable performance to SD2 when it is applied to a large social network site, and outperforms SD2 significantly when the level of spam attacks increases. Based on detection results of UNIK, we further analyze several identified spam campaigns in this social network site. The result shows that different spammer clusters demonstrate distinct characteristics, implying the volatility of spamming patterns and the ability of UNIK to automatically extract spam signatures.


title: Cyclops: miniature robotic reconnaissance system

abstract: Miniature mobile robots are desirable for military applications because of their ability to function covertly in highly constrained environments. However, work on such tiny robotic systems has been hampered by the difficulty of packaging locomotion, sensing, computing and power subsystems into diminutive spaces. This paper presents the design and initial prototype of a miniature mobile robot designed to carry out surveillance and reconnaissance missions in an urban environment. This initial prototype is spherical in shape, with a diameter of 5.5 inches, and weighs only 4.5 pounds. A novel inertial steering system, along with the robot's robust control and communication systems, make it useful for covert video surveillance and reconnaissance in tightly constrained spaces.


title: Inhibition of Glut1 by WZB117 sensitizes radioresistant breast cancer cells to irradiation

abstract: Purpose ::: Breast cancer is the most common type of cancer with high incidence in women. Currently, identifying new therapies that selectively inhibit tumor growth without damaging normal tissue are a major challenge of cancer research. One of the features of cancer cells is that they do not consume more oxygen even under normal oxygen circumstances but prefer to aerobic glycolysis through the enhanced catabolism of glucose and glutamine. In this study, we investigate the mechanisms of the radioresistance in breast cancer cells.


title: Wordnet Enhanced Automatic Crossword Generation

abstract: We report on a system for automatically generating and displaying crosswords from a system manager supplied database of potential clues and corresponding words that index those clues. The system relies on the lexical relations encoded in WordNet to enhance the aesthetics of the resulting crossword by making it easier to automatically identify a grid that may be populated with words and clues that have a thematic focus. The system architecture is provided in overview, as is empirical evaluation.


title: Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation

abstract: Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour and time for annotation. In contrast, web images and their image-level labels are much easier and cheaper to obtain. In this work, we propose a novel method for weakly supervised semantic segmentation with only image-level labels. The method utilizes the internet to retrieve a large number of images and uses a large scale co-segmentation framework to generate masks for the retrieved images. We first retrieve images from search engines, e.g. Flickr and Google, using semantic class names as queries, e.g. class names in the dataset PASCAL VOC 2012. We then use high quality masks produced by co-segmentation on the retrieved images as well as the target dataset images with image level labels to train segmentation networks. We obtain an IoU score of 56.9 on test set of PASCAL VOC 2012, which reaches the state-of-the-art performance.


title: Distributionally Robust Stochastic Optimization with Wasserstein Distance

abstract: Distributionally robust stochastic optimization (DRSO) is an approach to optimization under uncertainty in which, instead of assuming that there is an underlying probability distribution that is known exactly, one hedges against a chosen set of distributions. In this paper we first point out that the set of distributions should be chosen to be appropriate for the application at hand, and that some of the choices that have been popular until recently are, for many applications, not good choices. We consider sets of distributions that are within a chosen Wasserstein distance from a nominal distribution, for example an empirical distribution resulting from available data. The paper argues that such a choice of sets has two advantages: (1) The resulting distributions hedged against are more reasonable than those resulting from other popular choices of sets. (2) The problem of determining the worst-case expectation over the resulting set of distributions has desirable tractability properties. We derive a dual reformulation of the corresponding DRSO problem and construct approximate worst-case distributions (or an exact worst-case distribution if it exists) explicitly via the first-order optimality conditions of the dual problem. Our contributions are five-fold. (i) We identify necessary and sufficient conditions for the existence of a worst-case distribution, which are naturally related to the growth rate of the objective function. (ii) We show that the worst-case distributions resulting from an appropriate Wasserstein distance have a concise structure and a clear interpretation. (iii) Using this structure, we show that data-driven DRSO problems can be approximated to any accuracy by robust optimization problems, and thereby many DRSO problems become tractable by using tools from robust optimization. (iv) To the best of our knowledge, our proof of strong duality is the first constructive proof for DRSO problems, and we show that the constructive proof technique is also useful in other contexts. (v) Our strong duality result holds in a very general setting, and we show that it can be applied to infinite dimensional process control problems and worst-case value-at-risk analysis.


Foundations of Modern Graph Query Languages


title: Sparse Communication for Distributed Gradient Descent

abstract: We make distributed stochastic gradient descent faster by exchanging sparse updates instead of dense updates. Gradient updates are positively skewed as most updates are near zero, so we map the 99% smallest updates (by absolute value) to zero then exchange sparse matrices. This method can be combined with quantization to further improve the compression. We explore different configurations and apply them to neural machine translation and MNIST image classification tasks. Most configurations work on MNIST, whereas different configurations reduce convergence rate on the more complex translation task. Our experiments show that we can achieve up to 49% speed up on MNIST and 22% on NMT without damaging the final accuracy or BLEU.


title: Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence

abstract: Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, CoFactor, which jointly decomposes the user-item interaction matrix and the item-item co-occurrence matrix with shared item latent factors. For each pair of items, the co-occurrence matrix encodes the number of users that have consumed both items. CoFactor is inspired by the recent success of word embedding models (e.g., word2vec) which can be interpreted as factorizing the word co-occurrence matrix. We show that this model significantly improves the performance over MF models on several datasets with little additional computational overhead. We provide qualitative results that explain how CoFactor improves the quality of the inferred factors and characterize the circumstances where it provides the most significant improvements.


title: Designing Random Graph Models Using Variational Autoencoders With Applications to Chemical Design

abstract: Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with graphs due to their unique characteristics--their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the nodes labels, and they come with a different number of nodes and edges. In this paper, we propose a variational autoencoder for graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. Moreover, the decoder is able to guarantee a set of local structural and functional properties in the generated graphs. Experiments reveal that our model is able to learn and mimic the generative process of several well-known random graph models and can be used to create new molecules more effectively than several state of the art methods.


title: Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

abstract: In this paper, we propose a vehicle type classification method using a semisupervised convolutional neural network from vehicle frontal-view images. In order to capture rich and discriminative information of vehicles, we introduce sparse Laplacian filter learning to obtain the filters of the network with large amounts of unlabeled data. Serving as the output layer of the network, the softmax classifier is trained by multitask learning with small amounts of labeled data. For a given vehicle image, the network can provide the probability of each type to which the vehicle belongs. Unlike traditional methods by using handcrafted visual features, our method is able to automatically learn good features for the classification task. The learned features are discriminative enough to work well in complex scenes. We build the challenging BIT-Vehicle dataset, including 9850 high-resolution vehicle frontal-view images. Experimental results on our own dataset and a public dataset demonstrate the effectiveness of the proposed method.


title: Cache content-selection policies for streaming video services

abstract: The majority of internet traffic is now dominated by streamed video content. As video quality continues to increase, the strain that streaming traffic places on the network infrastructure also increases. Caching content closer to users, e.g., using Content Distribution Networks, is a common solution to reduce the load on the network. A simple approach to selecting what to put in regional caches is to put the videos that are most popular globally across the entire customer base. However, this approach ignores distinct regional taste. In this paper we explore the question of how a video content provider could go about determining whether or not they should use a cache filling policy based solely upon global popularity or take into account regional tastes as well. We propose a model that captures the overlap between inter-regional and intra-regional preferences. We focus on movie content and derive a synthetic model that captures “taste” using matrix factorization, similarly to the method used in recommender systems. Our model enables us to widely explore the parameter space, and derive a set of metrics providers can use to determine whether populating caches according to regional of global tastes provides better cache performance.


title: Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets

abstract: Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime.


title: Design of an online EEG based neurofeedback game for enhancing attention and memory

abstract: Brain-Computer Interface (BCI) is an alternative communication and control channel between brain and computer which finds applications in neuroprosthetics, brain wave controlled computer games etc. This paper proposes an Electroencephalogram (EEG) based neurofeedback computer game that allows the player to control the game with the help of attention based brain signals. The proposed game protocol requires the player to memorize a set of numbers in a matrix, and to correctly fill the matrix using his attention. The attention level of the player is quantified using sample entropy features of EEG. The statistically significant performance improvement of five healthy subjects after playing a number of game sessions demonstrates the effectiveness of the proposed game in enhancing their concentration and memory skills.


title: Accounting for common method variance in cross-sectional research designs.

abstract: Cross-sectional studies of attitude-behavior relationships are vulnerable to the inflation of correlations by common method variance (CMV). Here, a model is presented that allows partial correlation analysis to adjust the observed correlations for CMV contamination and determine if conclusions about the statistical and practical significance of a predictor have been influenced by the presence of CMV. This method also suggests procedures for designing questionnaires to increase the precision of this adjustment. Many theories used in applied psychology are based on the premise that behavior is a function of individuals' attitudes, beliefs, or perceptions of the situations in which they find themselves. For practical reasons, tests of theories such as Vroom's (1964) valence-instrumentality-expectancy theory and Fishbein and Ajzen's (1975) theory of reasoned action often have crosssectional rather than longitudinal designs. This means that individuals' reports of their internal states are collected at the same time as their reports of their past behavior related to those internal states. Consequently, the possibility arises that method variance (MV) has inflated the observed correlations between these two types of variables artifactually. Indeed, Feldman and Lynch (1988) have argued that the behavioral self-reports could be significantly correlated with job dimensions that are completely meaningless to the respondents if they are asked «o report their behavior and then provide ratings of job characteristics related to that behavior. More recent studies suggest that the MV problem is not this severe but does require further study (Crampton & Wagner, 1994; Harrison, McLaughlin, & Coalter, 1996). Many researchers have contended that MV problems can be solved by using multiple methods of measurement, and some have advocated analysis of multitrait-multimethod (MTMM) matrices by means of confirmatory factor analysis (Millsap, 1990; Mitchell, 1985; Podsakoff & Organ, 1986; Williams, Cote, & Buckley, 1989). However, MTMM matrix designs require at least twice as many measures as a conventional design. This forces the researcher either to limit the scope of a study (i.e., the number of constructs to be measured) or to accept a reduced response rate


title: Nature and autonomy: An organizational view of social and neurobiological aspects of self-regulation in behavior and development

abstract: The concepts of self-regulation and autonomy are examined within an organizational framework. We begin by retracing the historical origins of the organizational viewpoint in early debates within the field of biology between vitalists and reductionists, from which the construct of self-regulation emerged. We then consider human autonomy as an evolved behavioral, developmental, and experiential phenomenon that operates at both neurobiological and psychological levels and requires very specific supports within higher order social organizations. We contrast autonomy or true self-regulation with controlling regulation (a nonautonomous form of intentional behavior) in phenomenological and functional terms, and we relate the forms of regulation to the developmental processes of intrinsic motivation and internalization. Subsequently, we describe how self-regulation versus control may be characterized by distinct neurobiological underpinnings, and we speculate about some of the adaptive advantages that may underlie the evolution of autonomy. Throughout, we argue that disturbances of autonomy, which have both biological and psychological etiologies, are central to many forms of psychopathology and social alienation.


title: Rethinking NoCs for Spatial Neural Network Accelerators

abstract: Applications across image processing, speech recognition, and classification heavily rely on neural network-based algorithms that have demonstrated highly promising results in accuracy. However, such algorithms involve massive computations that are not manageable in general purpose processors. To cope with this challenge, spatial architecture-based accelerators, which consist of an array of hundreds of processing elements (PEs), have emerged. These accelerators achieve high throughput exploiting massive parallel computations over the PEs; however, most of them do not focus on on-chip data movement overhead, which increases with the degree of computational parallelism, and employ primitive networks-on-chip (NoC) such as buses, crossbars, and meshes. Such NoCs work for general purpose multicores, but lack scalability in area, power, latency, and throughput to use inside accelerators, as this work demonstrates. To this end, we propose a novel NoC generator that generates a network tailored for the traffic flows within a neural network, namely scatters, gathers and local communication, facilitating accelerator design. We build our NoC using an array of extremely lightweight microswitches that are energy- and area-efficient compared to traditional on-chip routers. We demonstrate the performance, area, and energy of our micro-switch based networks for convolutional neural network accelerators.


title: Learning a Contextual Multi-Thread Model for Movie/TV Scene Segmentation

abstract: Compared with general videos, movies and TV shows attract a significantly larger portion of people across time and contain very rich and interesting narrative patterns of shots and scenes. In this paper, we aim to recover the inherent structure of scenes and shots in such video narratives. The obtained structure could be useful for subsequent video analysis tasks such as tracking objects across cuts, action retrieval, as well as enriching user browsing and video editing interfaces. Recent research on this problem has mainly focused on combining multiple cues such as scripts, subtitles, sound, or human faces. However, considering that visual information is sufficient for human to identify scene boundaries and some cues are not always available, we are motivated to design a purely visual approach. Observing that dialog patterns occur frequently in a movie/TV show to form a scene, we propose a probabilistic framework to imitate the authoring process. The multi-thread shot model and contextual visual dynamics are embedded into a unified framework to capture the video hierarchy. We devise an efficient algorithm to jointly learn the parameters of the unified model. Experiments on two large datasets containing six movies and 24 episodes of Lost, a popular TV show with complex plot structures, are conducted. Comparative results show that, leveraging only visual cues, our method could successfully recover complicated shot threads and outperform several approaches. Moreover, our method is fast and advantageous for large-scale computation.


title: Spatio-temporal context analysis within video volumes for anomalous-event detection and localization

abstract: In this paper, we propose an anomaly-detection approach applied for video surveillance in crowded scenes. This approach is an unsupervised statistical learning framework based on analysis of spatio-temporal video-volume configuration within video cubes. It learns global activity patterns and local salient behavior patterns via clustering and sparse coding, respectively. Upon the composition-pattern dictionary learned from normal behavior, a sparse reconstruction cost criterion is designed to detect anomalies that occur in video both globally and locally. In addition, a multiple scale analysis is employed for obtaining accurate anomaly localization, considering scale variations of abnormal events. This approach is verified on publically available anomaly-detection datasets and compared with other existing work. The experiment results demonstrate that it not only detects various anomalies more efficiently, but also locates anomalous regions more accurately.


title: Infinite Hierarchical Hidden Markov Models

abstract: In this paper we present the Infinite Hierarchical Hidden Markov Model (IHHMM), a nonparametric generalization of Hierarchical Hidden Markov Models (HHMMs). HHMMs have been used for modeling sequential data in applications such as speech recognition, detecting topic transitions in video and extracting information from text. The IHHMM provides more flexible modeling of sequential data by allowing a potentially unbounded number of levels in the hierarchy, instead of requiring the specification of a fixed hierarchy depth. Inference and learning are performed eciently using Gibbs sampling and a modified forward-backtrack algorithm. We present encouraging results on toy sequences and English text data.


title: Measuring Home Networks with HomeNet Profiler

abstract: This paper designs HomeNet Profiler, a software that runs on any computer connected inside a home network, to collect a wide range of measurements about home networks including the set of devices, the set of services (with UPnP and Zeroconf), and the characteristics of theWiFi environment. To attract a larger number of users, HomeNet Profiler runs one-shot measurements upon user demand. We evaluate this design choice against periodic measurements taken from six home networks. Data collected from these six homes and with Home- Net Profiler in more than 1,600 homes in France shed light on the diversity of devices that connect to home networks and of the WiFi neighborhood across home networks.


title: Probabilistic Noise Identification and Data Cleaning

abstract: Real world data is never as perfect as we would like it to be and can often suffer from corruptions that may impact interpretations of the data, models created from the data, and decisions made based on the data. One approach to this problem is to identify and remove records that contain corruptions. Unfortunately, if only certain fields in a record have been corrupted then usable, uncorrupted data will be lost. We present LENS, an approach for identifying corrupted fields and using the remaining noncorrupted fields for subsequent modeling and analysis. Our approach uses the data to learn a probabilistic model containing three components: a generative model of the clean records, a generative model of the noise values, and a probabilistic model of the corruption process. We provide an algorithm for the unsupervised discovery of such models and empirically evaluate both its performance at detecting corrupted fields and, as one example application, the resulting improvement this gives to a classifier.


title: Data-Intensive Question Answering

abstract: Utilisation de la redondance des reponses elles-memes pour ameliorer le resultat final de la recherche d'information- redondance due a la tres grande quantite d'informations disponibles actuellement


title: Study on system latency reduction based on Shorten TTI

abstract: System latency is one key metric that guides LTE design. Shortening TTI by reducing subframe length along with backward compatibility design can bring significant latency gains for legacy LTE systems. In general, it is believed that the shorter TTI is, the more gain can be achieved. However, due to the hardware limitation as well as the implementation complexity, it is not easy for ultra-short TTI lengths to adapt to legacy LTE. Therefore, to maximize the latency gain with relative longer TTI length (slot level TTI), this paper proposes another method including reducing UL access delay and HARQ ACK/NACK RTT. It can be verified that combining slot TTI, this method can reduce 45.58% and 40.56% latency at low traffic load for cell edge and center users, higher than the 40.29% and 39.51% latency reduction brought by very short TTIs.


title: The epistemology of computer security

abstract: This paper studies computer security from first principles. The basic questions "Why?", "How do we know what we know?" and "What are the implications of what we believe?"


title: HACCP and water safety plans in Icelandic water supply: preliminary evaluation of experience.

abstract: Icelandic waterworks first began implementing hazard analysis and critical control points (HACCP) as a preventive approach for water safety management in 1997. Since then implementation has been ongoing and currently about 68% of the Icelandic population enjoy drinking water from waterworks with a water safety plan based on HACCP. Preliminary evaluation of the success of HACCP implementation was undertaken in association with some of the waterworks that had implemented HACCP. The evaluation revealed that compliance with drinking water quality standards improved considerably following the implementation of HACCP. In response to their findings, waterworks implemented a large number of corrective actions to improve water safety. The study revealed some limitations for some, but not all, waterworks in relation to inadequate external and internal auditing and a lack of oversight by health authorities. Future studies should entail a more comprehensive study of the experience with the use of HACCP with the purpose of developing tools to promote continuing success.


title: Diversity in the monotony? Habitat traits and management practices shape avian communities in intensive vineyards

abstract: Abstract Spreading of viticulture may pose serious threats to biodiversity and adequate policies targeted at decreasing its impact are urgently required. Current knowledge of viticulture effects on biodiversity is scarce and studies on bird communities in vineyards are even scarcer. We surveyed avian assemblages in Trentino vineyards (North-East Italy) in both breeding and wintering seasons to evaluate the effect of: i) landscape, ii) management and iii) topographic-climatic characteristics on birds and to derive implications for conservation. We calculated four community indexes (species richness, abundance, evenness and conservation index) and modelled their relative variation according to 18 environmental variables belonging to the three above-mentioned groups. Landscape models performed better than the others, except for winter evenness, for which management models were the most supported ones. Generally, models considering the three groups together explained more variation than models from an individual group. Landscape (and agricultural) heterogeneity, extent of marginal habitats, density of traditional elements (hedgerows, tree rows, isolated trees and rural buildings) all had positive effects, whereas vineyard cover had negative impact on the value of the four community indexes. Organic management had no apparent effect on avian communities. We detected a seasonal difference in the effects of environmental characteristics on bird communities, which suggested that local conservation efforts could be tuned according to the seasonal importance of vineyards in different regions. Key measures to promote biodiversity in vineyards include maintaining patches of residual habitats in the vineyard matrix and enhancing heterogeneity. Marginal features appeared particularly important in the homogeneous landscape of intensive vineyards to favour bird communities.


title: Hashing in the Zero Shot Framework with Domain Adaptation

abstract: Techniques to learn hash codes which can store and retrieve large dimensional multimedia data efficiently have attracted broad research interests in the recent years. With rapid explosion of newly emerged concepts and online data, existing supervised hashing algorithms suffer from the problem of scarcity of ground truth annotations due to the high cost of obtaining manual annotations. Therefore, we propose an algorithm to learn a hash function from training images belonging to seen classes which can efficiently encode images of unseen classes to binary codes. Specifically, we project the image features from visual space and semantic features from semantic space into a common Hamming subspace. Earlier works to generate hash codes have tried to relax the discrete constraints on hash codes and solve the continuous optimization problem. However, it often leads to quantization errors. In this work, we use the max-margin classifier to learn an efficient hash function. To address the concern of domain-shift which may arise due to the introduction of new classes, we also introduce an unsupervised domain adaptation model in the proposed hashing framework. Results on the three datasets show the advantage of using domain adaptation in learning a high-quality hash function and superiority of our method for the task of image retrieval performance as compared to several state-of-the-art hashing methods.


title: 3D reconstruction based on SIFT and Harris feature points

abstract: This paper presents a new 3D reconstruction method using feature points extracted by the SIFT and Harris corner detector. Since the SIFT feature points can be detected stably and relatively accurately, the proposed algorithm first uses the SIFT matching points to calculate the fundamental matrix. On the other hand many of the feature points detected by the SIFT are not what we need for reconstruction, so by combining the SIFT feature points with the Harris corners it is possible to obtain more vivid and detailed 3D information. Experiments have been conducted to validate the proposed method.


title: Rapid retraining on speech data with LSTM recurrent networks

abstract: A system that could be quickly retrained on different corpora would be of great benefit to speech recognition. Recurrent Neural Networks (RNNs) are able to transfer knowledge by simply storing and then retraining their weights. In this report, we partition the TIDIGITS database into utterances spoken by men, women, boys and girls, and successively retrain a Long Short Term Memory (LSTM) RNN on them. We find that the network rapidly adapts to new subsets of the data, and achieves greater accuracy than when trained on them from scratch. This would be useful for applications requiring either cross corpus adaptation or continually expanding datasets.


title: On conjunctive queries containing inequalities

abstract: Conjunctive queries are generalized so that inequality comparisons can be made between elements of the query. Algorithms for containment and equivalence of such “inequality queries” are given, under the assumption that the data domains are dense and totally ordered. In general, containment does not imply the existence of homomorphisms (containment mappings), but the homomorphism property does exist for subclasses of inequality queries. A minimization algorithm is defined using the equivalence algorithm. It is first shown that the constants appearing in a query can be divided into “essential” and “nonessential” subgroups. The minimum query can be nondeterministically guessed using only the essential constants of the original query.


title: Stackless KD-Tree Traversal for High Performance GPU Ray Tracing

abstract: Significant advances have been achieved for realtime ray tracing recently, but realtime performance for complex scenes still requires large computational resources not yet available from the CPUs in standard PCs. Incidentally, most of these PCs also contain modern GPUs that do offer much larger raw compute power. However, limitations in the programming and memory model have so far kept the performance of GPU ray tracers well below that of their CPU counterparts. In this paper we present a novel packet ray traversal implementation that completely eliminates the need for maintaining a stack during kd-tree traversal and that reduces the number of traversal steps per ray. While CPUs benefit moderately from the stackless approach, it improves GPU performance significantly. We achieve a peak performance of over 16 million rays per second for reasonably complex scenes, including complex shading and secondary rays. Several examples show that with this new technique GPUs can actually outperform equivalent CPU based ray tracers.


title: Direct Dopaminergic Projections from the SNc Modulate Visuomotor Transformation in the Lamprey Tectum

abstract: Summary Dopamine neurons in the SNc play a pivotal role in modulating motor behavior via striatum. Here, we show that the same dopamine neuron that targets striatum also sends a direct branch to the optic tectum (superior colliculus). Whenever SNc neurons are activated, both targets will therefore be affected. Visual stimuli (looming or bars) activate the dopamine neurons coding saliency and also elicit distinct motor responses mediated via tectum (eye, orienting or evasive), which are modulated by the dopamine input. Whole-cell recordings from tectal projection neurons and interneurons show that dopamine, released by SNc stimulation, increases or decreases the excitability depending on whether they express the dopamine D1 or the D2 receptor. SNc thus exerts its effects on the visuomotor system through a combined effect directly on tectum and also via striatum. This direct SNc modulation will occur regardless of striatum and represents a novel mode of motor control.


title: Automating Image Morphing Using Structural Similarity on a Halfway Domain

abstract: The main challenge in achieving good image morphs is to create a map that aligns corresponding image elements. Our aim is to help automate this often tedious task. We compute the map by optimizing the compatibility of corresponding warped image neighborhoods using an adaptation of structural similarity. The optimization is regularized by a thin-plate spline and may be guided by a few user-drawn points. We parameterize the map over a halfway domain and show that this representation offers many benefits. The map is able to treat the image pair symmetrically, model simple occlusions continuously, span partially overlapping images, and define extrapolated correspondences. Moreover, it enables direct evaluation of the morph in a pixel shader without mesh rasterization. We improve the morphs by optimizing quadratic motion paths and by seamlessly extending content beyond the image boundaries. We parallelize the algorithm on a GPU to achieve a responsive interface and demonstrate challenging morphs obtained with little effort.


title: Foundations of an Alternative Approach to Reification in RDF

abstract: This document defines extensions of the RDF data model and of the SPARQL query language that capture an alternative approach to represent statement-level metadata. While this alternative approach is backwards compatible with RDF reification as defined by the RDF standard, the approach aims to address usability and data management shortcomings of RDF reification. One of the great advantages of the proposed approach is that it clarifies a means to (i) understand sparse matrices, the property graph model, hypergraphs, and other data structures with an emphasis on link attributes, (ii) map such data onto RDF, and (iii) query such data using SPARQL. Further, the proposal greatly expands both the freedom that database designers enjoy when creating physical indexing schemes and query plans for graph data annotated with link attributes and the interoperability of those database solutions.