title: Neural Allocentric Intuitive Physics Prediction from Real Videos

abstract: Humans are able to make rich predictions about the future dynamics of physical objects from a glance. On the other hand, most existing computer vision approaches require strong assumptions about the underlying system, ad-hoc modeling, or annotated datasets, to carry out even simple predictions. To tackle this gap, we propose a new perspective on the problem of learning intuitive physics that is inspired by the spatial memory representation of objects and spaces in human brains, in particular the co-existence of egocentric and allocentric spatial representations. We present a generic framework that learns a layered representation of the physical world, using a cascade of invertible modules. In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture. Then, an allocentric viewpoint transformer removes viewpoint complexity by projecting images to a canonical view. Finally, a novel Recurrent Latent Variation Network (RLVN) architecture learns the dynamics of the objects interacting with the environment and predicts future motion, leveraging the availability of unlimited synthetic simulations. Predicted frames are then projected back to the original camera view and translated back to the real world domain. Experimental results show the ability of the framework to consistently and accurately predict several frames in the future and the ability to adapt to real images.


Deep Noise Tracking Network: A Hybrid Signal Processing/Deep Learning Approach to Speech Enhancement


title: AWE: Asymmetric Word Embedding for Textual Entailment

abstract: Textual entailment is a fundamental task in natural language processing. It refers to the directional relation between text fragments such that the "premise" can infer "hypothesis". In recent years deep learning methods have achieved great success in this task. Many of them have considered the inter-sentence word-word interactions between the premise-hypothesis pairs, however, few of them considered the "asymmetry" of these interactions. Different from paraphrase identification or sentence similarity evaluation, textual entailment is essentially determining a directional (asymmetric) relation between the premise and the hypothesis. In this paper, we propose a simple but effective way to enhance existing textual entailment algorithms by using asymmetric word embeddings. Experimental results on SciTail and SNLI datasets show that the learned asymmetric word embeddings could significantly improve the word-word interaction based textual entailment models. It is noteworthy that the proposed AWE-DeIsTe model can get 2.1% accuracy improvement over prior state-of-the-art on SciTail.


title: A Simple Ultra Wideband Printed Monopole Antenna With High Band Rejection and Wide Radiation Patterns

abstract: A compact ultra wideband antenna with strong notch-band rejections up to VSWR = 26, that is tunable over a wide frequency range from 3.55 GHz to 6.8 GHz, is presented. It has wide radiation patterns and yields a measured 10 dB return-loss bandwidth from 3 GHz to 10.5 GHz.


title: Interstellar communication. III. Optimal frequency to maximize data rate

abstract: The optimal frequency for interstellar communication, using"Earth 2017"technology, was derived in papers I and II of this series (arXiv:1706.03795, arXiv:1706.05570). The framework included models for the loss of photons from diffraction (free space), interstellar extinction, and atmospheric transmission. A major limit of current technology is the focusing of wavelengths $\lambda<300\,$nm (UV). When this technological constraint is dropped, a physical bound is found at $\lambda\approx1\,$nm ($E\approx\,$keV) for distances out to kpc. While shorter wavelengths may produce tighter beams and thus higher data rates, the physical limit comes from surface roughness of focusing devices at the atomic level. This limit can be surpassed by beam-forming with electromagnetic fields, e.g. using a free electron laser, but such methods are not energetically competitive. Current lasers are not yet cost efficient at nm wavelengths, with a gap of two orders of magnitude, but future technological progress may converge on the physical optimum. We recommend expanding SETI efforts towards targeted (at us) monochromatic (or narrow band) X-ray emission at 0.5-2 keV energies.


title: Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy

abstract: Background: The Internet is increasingly used as a medium for the delivery of interventions designed to promote health behavior change. However, reviews of these interventions to date have not systematically identified intervention characteristics and linked these to effectiveness. Objectives: The present review sought to capitalize on recently published coding frames for assessing use of theory and behavior change techniques to investigate which characteristics of Internet-based interventions best promote health behavior change. In addition, we wanted to develop a novel coding scheme for assessing mode of delivery in Internet-based interventions and also to link different modes to effect sizes. Methods: We conducted a computerized search of the databases indexed by ISI Web of Knowledge (including BIOSIS Previews and Medline) between 2000 and 2008. Studies were included if (1) the primary components of the intervention were delivered via the Internet, (2) participants were randomly assigned to conditions, and (3) a measure of behavior related to health was taken after the intervention. Results: We found 85 studies that satisfied the inclusion criteria, providing a total sample size of 43,236 participants. On average, interventions had a statistically small but significant effect on health-related behavior (d+ = 0.16, 95% CI 0.09-0.23). More extensive use of theory was associated with increases in effect size (P = .049), and, in particular, interventions based on the theory of planned behavior tended to have substantial effects on behavior (d+ = 0.36, 95% CI 0.15-0.56). Interventions that incorporated more behavior change techniques also tended to have larger effects compared to interventions that incorporated fewer techniques (P < .001). Finally, the effectiveness of Internet-based interventions was enhanced by the use of additional methods of communicating with participants, especially the use of short message service (SMS), or text, messages. Conclusions: The review provides a framework for the development of a science of Internet-based interventions, and our findings provide a rationale for investing in more intensive theory-based interventions that incorporate multiple behavior change techniques and modes of delivery. [J Med Internet Res 2010;12(1):e4]


title: A survey on behavior analysis in video surveillance for homeland security applications

abstract: Surveillance cameras are inexpensive and everywhere these days but the manpower required to monitor and analyze them is expensive. Consequently the videos from these cameras are usually monitored sparingly or not at all; they are often used merely as archive, to refer back to once an incident is known to have taken place. Surveillance cameras can be a far more useful tool if instead of passively recording footage, they can be used to detect events requiring attention as they happen, and take action in real time. This is the goal of automated visual surveillance: to obtain a description of what is happening in a monitored area, and then to take appropriate action based on that interpretation. Video surveillance for humans is one of the most active research topics in computer vision. It has a wide spectrum of promising homeland security applications. Video management and interpretation systems have become quite capable in recent years. This paper looks into how hardware and software can be put together to solve surveillance problems in an age of increased concern with public safety and security. In general, the framework of a video surveillance system includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, behavior understanding and description, and fusion of information from multiple cameras. Despite recent progress in computer vision and other related areas, there are still major technical challenges to be overcome before reliable automated video surveillance can be realized. This paper reviews developments and general strategies of stages involved in video surveillance, and analyzes the feasibility and challenges for combining motion analysis, behavior analysis, and standoff biometrics for identification of known suspects, anomaly detection, and behavior understanding.


title: Fast and Accurate Annotation of Short Texts with Wikipedia Pages

abstract: We address the problem of cross-referencing text fragments with Wikipedia pages, in a way that synonymy and polysemy issues are resolved accurately and efficiently. We take inspiration from a recent flow of work [Cucerzan 2007, Mihalcea and Csomai 2007, Milne and Witten 2008, Chakrabarti et al 2009], and extend their scenario from the annotation of long documents to the annotation of short texts, such as snippets of search-engine results, tweets, news, blogs, etc.. These short and poorly composed texts pose new challenges in terms of efficiency and effectiveness of the annotation process, that we address by designing and engineering TAGME, the first system that performs an accurate and on-the-fly annotation of these short textual fragments. A large set of experiments shows that TAGME outperforms state-of-the-art algorithms when they are adapted to work on short texts and it results fast and competitive on long texts.


title: A miniaturized dual-band meander line antenna for RF energy harvesting applications

abstract: A dual band electrically small meander line antenna is designed for RF energy harvesting applications. The antenna is designed to cover the most known wireless cellular networks such as GSM 900, GSM 1800 and UMTS. The dual band property is achieved by using two different radiators, each of them is responsible for covering one band and a lambda/4 transformer is used to match the antenna at 915 MHz. The antenna is fabricated on Rogers RO4350 board and the measured results had a good agreement with simulated ones. The fabricated antenna is compared to a reference antenna (a dipole antenna). Both antennas are tested with an RF energy harvesting kit and the maximum covered distance is recorded. The total size of the proposed antenna was 45 × 32.6 × 1.52 mm3.


title: A Survey on Ontology Reasoners and Comparison

abstract: Reasoner is a software that is used to derive new facts from the existing ontologies. Some of the popular reasoners developed in the last few years are: Pellet, RACER, FACT++, Snorocket, Hermit, CEL, ELK, SWRL-IQ, TrOWL and others. This survey describes the reasoners that can be used as plug-in for either protege or NeOn toolkit since these are most widely used ontology development tools. The current study describes the reasoners with their important features such as soundness, completeness, reasoning method, incremental classification etc. Finally this paper presents comparison of the reasoners with respect to their features.


title: Interference polarization in magnetic induction based Wireless Underground Sensor Networks

abstract: Wireless Underground Sensor Networks (WUSNs) present a variety of new research challenges. For WUSNs, the goal is to establish an efficient wireless communication in the underground medium. A magnetic induction (MI) based transmission technique was proposed to overcome the very harsh conditions of the soil environment. In this paper, we investigate the potential of the MI-WUSNs if, in contrast to some previous proposals, no relays are used. Our main focus is on the throughput of the bottleneck link of the network, which corresponds to the overall network capacity. In order to reduce the number of relevant interferers and maximize the network throughput, we exploit the polarization of the used magnetic antennas (coils) by optimizing their orientation. Additional optimization of the system parameters improves the channel capacity of the bottleneck link. In addition, we consider a special case of the network deployment in mines and tunnels and propose a frequency switching scheme for better propagation conditions.


title: PCB pad design for board to board SMP connector

abstract: The design of printed circuit board (PCB) pad for SMP connector is presented. A detailed design and parameterization study are performed using HFSS, a commercial finite element tool. The PCB pad for the vertical SMP connector and surface mounted SMP connector are optimized. According to the simulation result, the return loss and insertion loss for SMP-to-microstrip transitions are better than 26 dB and 0.12 dB respectively from DC to 6 GHz, and the return loss and insertion loss for SMP-to-coplanar waveguide (CPW) transitions are better than 10 dB and 0.7 dB respectively up to 26.5 GHz.


title: Novel topology for parallel connection of soft switching, high power, high frequency inverters

abstract: Inductive heating applications like pipe welding or steel strip annealing require electrical power ratings of several megawatts at frequencies up to 100 kHz and higher. The large power-frequency product represents a significant challenge for today's semiconductor technology. As the absolute maximum rating of a single-stage inverter is often far below rated power, several inverters or several devices have to be connected in parallel. This paper presents a novel topology, consisting of parallel-connected soft-switching high-frequency inverters. Distinctive features include flexible configurations, negligible shunt currents between inverters, and equally shared power among inverters. Furthermore, compared to a single-inverter system, no additional reactive components are necessary for connecting inverters in parallel and there is no need of a high-frequency transformer to adapt the impedance of the load to the inverters.


title: Application of Kinesio Taping method for newborn swallowing difficultly: A case report and literature review

abstract: Background: Preterm infants are at an increased risk of sucking problems, swallowing difficulty, and poor nourishment. During the neonatal period, the neurobehavioral organization of a preterm baby is poor compared with that of appropriate gestational age infants. Kinesio Taping has been widely used for edema control, joint protection, and proprioception training. With the help of augmentation of the sensory input for muscle facilitation and inhibition through tapping, the coordination of the target muscle groups can be improved. Until now, no research is available on the use of Kinesio Taping for the swallowing difficulty of infant. Methods: We reported a preterm infant suffering from brain edema at birth and swallowing difficultly until 40 weeks. The swallowing reflex was delayed. Moreover, lip closure and rooting reflex combined with the dysfunction grade of jaw movement were poor. We performed KT methods on the baby under the theory of the direction of the tape for facilitate or inhibit the muscle. Result: After the Kinesio Taping treatment, the sucking function was improved with good lip closure.One week later, the baby was discharged without the use of an oral gastric tube. Conclusion: Kinesio Taping contributed significantly to the improvement of impaired sucking and swallowing and could be implemented as a regular rehabilitative approach for infants suffering from these difficulties.


title: Hierarchical semantic parsing for object pose estimation in densely cluttered scenes

abstract: Densely cluttered scenes are composed of multiple objects which are in close contact and heavily occlude each other. Few existing 3D object recognition systems are capable of accurately predicting object poses in such scenarios. This is mainly due to the presence of objects with textureless surfaces, similar appearances and the difficulty of object instance segmentation. In this paper, we present a hierarchical semantic segmentation algorithm which partitions a densely cluttered scene into different object regions. A RANSAC-based registration method is subsequently applied to estimate 6-DoF object poses within each object class. Part of this algorithm includes a generalized pooling scheme used to construct robust and discriminative object representations from a convolutional architecture with multiple pooling domains. We also provide a new RGB-D dataset which serves as a benchmark for object pose estimation in densely cluttered scenes. This dataset contains five thousand scene frames and over twenty thousand labeled poses of ten common hand tools. We show that our method demonstrates improved performance of pose estimation on this new dataset compared with other state-of-the-art methods.


title: Minnorm training: an algorithm for training over-parameterized deep neural networks

abstract: In this work, we propose a new training method for finding minimum weight norm solutions in over-parameterized neural networks (NNs). This method seeks to improve training speed and generalization performance by framing NN training as a constrained optimization problem wherein the sum of the norm of the weights in each layer of the network is minimized, under the constraint of exactly fitting training data. It draws inspiration from support vector machines (SVMs), which are able to generalize well, despite often having an infinite number of free parameters in their primal form, and from recent theoretical generalization bounds on NNs which suggest that lower norm solutions generalize better. To solve this constrained optimization problem, our method employs Lagrange multipliers that act as integrators of error over training and identify `support vector'-like examples. The method can be implemented as a wrapper around gradient based methods and uses standard back-propagation of gradients from the NN for both regression and classification versions of the algorithm. We provide theoretical justifications for the effectiveness of this algorithm in comparison to early stopping and $L_2$-regularization using simple, analytically tractable settings. In particular, we show faster convergence to the max-margin hyperplane in a shallow network (compared to vanilla gradient descent); faster convergence to the minimum-norm solution in a linear chain (compared to $L_2$-regularization); and initialization-independent generalization performance in a deep linear network. Finally, using the MNIST dataset, we demonstrate that this algorithm can boost test accuracy and identify difficult examples in real-world datasets.


title: Getting the Drawing to Look Right as Well as to Be Right: The Interaction Between Production and Perception as a Mechanism of Development

abstract: Publisher Summary This chapter discusses interaction between production and perception as a mechanism of development. Denotation systems say what the marks in the picture stand for. In any kind of picture, the marks in the picture, or picture primitives, stand for, refer to, or denote corresponding objects or entities in the real world, or scene primitives. In line drawings, for example, lines commonly stand for the edges of objects; while in photographs the marks in the picture, which may be Benday dots in a newspaper photograph, points on a television screen or grains on a photographic film, stand for the intercepts of small bundles of light rays. Scene and picture primitives may be classified according to the number of dimensions of space into which they can be extended. 0-dimensional primitives like points have no extension. 1-dimensional primitives like lines or edges can be extended in only one dimension. 2-dimensional primitives like faces or regions can be extended in two dimensions. And, finally, 3-dimensional or volumetric primitives like spheres, cubes, pillow-shaped objects or flying saucers can be extended in three dimensions.


Ledgers and Law in the Blockchain


title: Dhatri - A Pervasive Cloud initiative for primary healthcare services

abstract: This paper introduces the concept entitled ‘Pervasive Cloud’ which is meant for ‘Accessing the Power of a Cloud through Pervasive Access of Mobile Communications’. ‘Dhatri’ is a Pervasive Cloud Based Healthcare Application targeted towards Healthcare needs specifically for Rural and Deep Rural Areas. Dhatri ‘inherits’ the powerful features of a typical Cloud Computing and enables the Pervasive Healthcare Access.


title: Hybrid Intelligent Systems Design -- A Review of a Decade of Research

abstract: The emerging need for Hybrid Intelligent Systems (HIS) is currently motivating important research and development work. The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. Soft Computing (SC) introduced by Lotfi Zadeh [1] is an innovative approach to construct computationally intelligent hybrid systems consisting of Artificial Neural Network (ANN), Fuzzy Logic (FL), approximate reasoning and derivative free optimization methods such as Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS). Most of these approaches, however, follow an ad hoc design methodology, further justified by success in certain application domains. Due to the lack of a common framework it remains often difficult to compare the various hybrid systems conceptually and evaluate their performance comparatively. It has been over a decade since HIS were first applied to solve complicated problems. In this paper, we first aim at classifying state--of--the--art intelligent systems, which have evolved over the past decade in the HIS community. Some theoretical concepts of ANN, FL and Global Optimization Algorithms (GOA) namely GA, SA and TS are also presented. We further attempt to summarize the work that has been done and present the current standing of our vision on HIS and future research directions.


title: Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification

abstract: In this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.


title: Learning grounded finite-state representations from unstructured demonstrations

abstract: Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting the scope of possible robot competencies. Thus, the primary limiting factor of robot capabilities is often not the physical attributes of the robot, but the limited time and skill of expert programmers. One way to deal with the vast number of situations and environments that robots face outside the laboratory is to provide users with simple methods for programming robots that do not require the skill of an expert. For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. Unfortunately, LfD often yields little knowledge about the world, and thus lacks robust generalization capabilities, especially for complex, multi-step tasks. We present a series of algorithms that draw from recent advances in Bayesian non-parametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction in demonstration data. The discovery of repeated structure provides critical insights into task invariants, features of importance, high-level task structure, and appropriate skills for the task. This culminates in the discovery of a finite-state representation of the task, composed of grounded skills that are flexible and reusable, providing robust generalization and transfer in complex, multi-step robotic tasks. These algorithms are tested and evaluated using a PR2 mobile manipulator, showing success on several complex real-world tasks, such as furniture assembly.


title: Towards Brazilian Portuguese automatic text simplification systems

abstract: In this paper we investigate the main linguistic phenomena that can make texts complex and how they could be simplified. We focus on a corpus analysis of simple account texts available on the web for Brazilian Portuguese and propose simplification strategies for this language. This study illustrates the need for text simplification to facilitate accessibility to information by poor literacy readers and potentially by people with other cognitive disabilities. It also highlights characteristics of simplification for Portuguese, which may differ from other languages. Such study consists of the first step towards building Brazilian Portuguese text simplification systems. One of the scenarios in which these systems could be used is that of reading electronic texts produced, e.g., by the Brazilian government or by relevant news agencies.


title: Cross-domain metric learning based on information theory

abstract: Supervised metric learning plays a substantial role in statistical classification. Conventional metric learning algorithms have limited utility when the training data and testing data are drawn from related but different domains (i.e., source domain and target domain). Although this issue has got some progress in feature-based transfer learning, most of the work in this area suffers from non-trivial optimization and pays little attention to preserving the discriminating information. In this paper, we propose a novel metric learning algorithm to transfer knowledge from the source domain to the target domain in an information-theoretic setting, where a shared Mahalanobis distance across two domains is learnt by combining three goals together: 1) reducing the distribution difference between different domains; 2) preserving the geometry of target domain data; 3) aligning the geometry of source domain data with its label information. Based on this combination, the learnt Mahalanobis distance effectively transfers the discriminating power and propagates standard classifiers across these two domains. More importantly, our proposed method has closed-form solution and can be efficiently optimized. Experiments in two real-world applications demonstrate the effectiveness of our proposed method.


title: The Tree-Drawing Test (Koch's Baum Test): A Useful Aid to Diagnose Cognitive Impairment

abstract: Objective. To study the Tree-Drawing Test in a group of demented patients and compare it with a group of mild cognitively impaired patients (MCI) and controls. Methods. Consecutive outpatients were classified as affected by dementia (Alzheimer's disease (AD), frontotemporal dementia (FTD), and vascular dementia (VD)) or by MCI. Patients and controls underwent the Tree-Drawing Test and MMSE. Results. 118 AD, 19 FTD, 46 VD, and 132 MCI patients and 90 controls were enrolled. AD patients draw trees globally smaller than other patients and controls. FTD patients draw trees with a wider space occupation than AD and MCI patients but smaller than controls as well as VD patients. Trees drawn by MCI patients are intermediate in size between AD patients and controls. The trunk-to-crown ratio of trees drawn by cognitive impaired patients is greater than controls while the tree size-relative-to-page space index is significantly smaller. The tree size-relative-to-page space index of trees drawn by AD patients is smaller than that of the other cognitively impaired patients. Tree height and the trunk-to-crown ratio are independent predictors of cognitive impairment. Conclusions. Trees drawn by cognitively impaired patients are different from those drawn by healthy subjects with a progressive differentiation from mild to more relevant degrees of cognitive impairment.


title: Charging algorithms of lithium-ion batteries: An overview

abstract: This paper presents the overview of charging algorithms for lithium-ion batteries, which include constant current-constant voltage (CC/CV), variants of the CC/CV, multistage constant current, pulse current and pulse voltage. The CC/CV charging algorithm is well developed and widely adopted in charging lithium-ion batteries. It is used as a benchmark to compare with other charging algorithms in terms of the charging time, the charging efficiency, the influences on battery life and other aspects, which can serve as a convenient reference for future work in developing a charger for lithium-ion battery.


title: A Blockchain Based New Secure Multi-Layer Network Model for Internet of Things

abstract: This paper presents a multi-layer secure IoT network model based on blockchain technology. The model reduces the difficulty of actual deployment of the blockchain technology by dividing the Internet of Things into a multi-level de-centric network and adopting the technology of block chain technology at all levels of the network, with the high security and credibility assurance of the blockchain technology retaining. It provides a wide-area networking solution of Internet of Things.


title: Coral Reefs Under Rapid Climate Change and Ocean Acidification

abstract: Atmospheric carbon dioxide concentration is expected to exceed 500 parts per million and global temperatures to rise by at least 2 degrees C by 2050 to 2100, values that significantly exceed those of at least the past 420,000 years during which most extant marine organisms evolved. Under conditions expected in the 21st century, global warming and ocean acidification will compromise carbonate accretion, with corals becoming increasingly rare on reef systems. The result will be less diverse reef communities and carbonate reef structures that fail to be maintained. Climate change also exacerbates local stresses from declining water quality and overexploitation of key species, driving reefs increasingly toward the tipping point for functional collapse. This review presents future scenarios for coral reefs that predict increasingly serious consequences for reef-associated fisheries, tourism, coastal protection, and people. As the International Year of the Reef 2008 begins, scaled-up management intervention and decisive action on global emissions are required if the loss of coral-dominated ecosystems is to be avoided.


title: A single-crystal silicon symmetrical and decoupled MEMS gyroscope on an insulating substrate

abstract: This paper presents a single-crystal silicon symmetrical and decoupled (SYMDEC) gyroscope implemented using the dissolved wafer microelectromechanical systems (MEMS) process on an insulating substrate. The symmetric structure allows matched resonant frequencies for the drive and sense vibration modes for high-rate sensitivity and low temperature-dependent drift, while the decoupled drive and sense modes prevents unstable operation due to mechanical coupling, achieving low bias-drift. The 12-15-/spl mu/m-thick single-crystal silicon structural layer with an aspect ratio of about 10 using DRIE patterning provides a high sense capacitance of 130 fF, while the insulating substrate provides a low parasitic capacitance of only 20 fF. A capacitive interface circuit fabricated in a 0.8-/spl mu/m CMOS process and having a sensitivity of 33 mV/fF is hybrid connected to the gyroscope. Drive and sense mode resonance frequencies of the gyroscope are measured to be 40.65 and 41.25 kHz, respectively, and their measured variations with temperature are +18.28 Hz//spl deg/C and +18.32 Hz//spl deg/C, respectively, in -40/spl deg/C to +85/spl deg/C temperature range. Initial tests show a rate resolution around 0.56 deg/s with slightly mismatched modes, which reveal that the gyroscope can provide a rate resolution of 0.030 deg/s in 50-Hz bandwidth at atmospheric pressure and 0.017 deg/s in 50-Hz bandwidth at vacuum operation with matched modes.


title: MAP inference in Large Factor Graphs with Reinforcement Learning

abstract: Large, relational factor graphs with structure defined by first-order logic or other languages give rise to notoriously difficult inference problems. Because unrolling the structure necessary to represent distributions over all hypotheses has exponential blow-up, solutions are often derived from MCMC. However, because of limitations in the design and parameterization of the jump function, these sampling-based methods suffer from local minima—the system must transition through lower-scoring configurations before arriving at a better MAP solution. This paper presents a new method of explicitly selecting fruitful downward jumps by leveraging reinforcement learning (RL) to model delayed reward with a log-linear function approximation of residual future score improvement. Our method provides dramatic empirical success, producing new state-of-the-art results on a complex joint model of ontology alignment, with a 48% reduction in error over state-of-the-art in that domain.


title: Where's the Theory for Software Engineering?

abstract: Darwin's theory of natural selection, Maxwell's equations, the theory of demand and supply; almost all established academic disciplines place great emphasis on what their core theory is. This is not, however, the case in software engineering. What is the reason behind the software engineering community's apparent indifference to a concept that is so important to so many others?


title: Bi-box Regression for Pedestrian Detection and Occlusion Estimation

abstract: Occlusions present a great challenge for pedestrian detection in practical applications. In this paper, we propose a novel approach to simultaneous pedestrian detection and occlusion estimation by regressing two bounding boxes to localize the full body as well as the visible part of a pedestrian respectively. For this purpose, we learn a deep convolutional neural network (CNN) consisting of two branches, one for full body estimation and the other for visible part estimation. The two branches are treated differently during training such that they are learned to produce complementary outputs which can be further fused to improve detection performance. The full body estimation branch is trained to regress full body regions for positive pedestrian proposals, while the visible part estimation branch is trained to regress visible part regions for both positive and negative pedestrian proposals. The visible part region of a negative pedestrian proposal is forced to shrink to its center. In addition, we introduce a new criterion for selecting positive training examples, which contributes largely to heavily occluded pedestrian detection. We validate the effectiveness of the proposed bi-box regression approach on the Caltech and CityPersons datasets. Experimental results show that our approach achieves promising performance for detecting both non-occluded and occluded pedestrians, especially heavily occluded ones.


title: Pose Proposal Networks

abstract: We propose a novel method to detect an unknown number of articulated 2D poses in real time. To decouple the runtime complexity of pixel-wise body part detectors from their convolutional neural network (CNN) feature map resolutions, our approach, called pose proposal networks, introduces a state-of-the-art single-shot object detection paradigm using grid-wise image feature maps in a bottom-up pose detection scenario. Body part proposals, which are represented as region proposals, and limbs are detected directly via a single-shot CNN. Specialized to such detections, a bottom-up greedy parsing step is probabilistically redesigned to take into account the global context. Experimental results on the MPII Multi-Person benchmark confirm that our method achieves 72.8% mAP comparable to state-of-the-art bottom-up approaches while its total runtime using a GeForce GTX1080Ti card reaches up to 5.6 ms (180 FPS), which exceeds the bottleneck runtimes that are observed in state-of-the-art approaches.


title: Auto-Tuned Publisher in a Pub/Sub System: Design and Performance Evaluation

abstract: Pub/sub systems form the underlying framework for many distributed applications including large social networking applications. In this paper, we consider the optimization of the end-to-end latency of a pub/sub system in which the publisher, the broker, and the subscriber are in different administrative domains. While general pub/sub systems provide reliability of message delivery, good end-to-end latency in a multi-domain environment requires that the pub/sub system adapts to workload changes and bottlenecks in the different sub-systems. This study is motivated by two applications. First, a pub/sub based Simple Lookup Service (sLS) that is used in perfSONAR to provide information about network performance in Research and Education (R&E) networks. Second, the pub/sub system that is used to distribute alerts generated in the data pipeline in the Zwicky Transient Factory (ZTF). In this multi-domain pub/sub performance study, we consider a publisher with a multi-threaded architecture that uses batching to coalesce messages over some variable polling period. We propose a control algorithm that auto-tunes the batch-processing parameters namely, the batch size and the polling interval, to the input message load and to broker-side congestion. Using a detailed simulation model, we demonstrate the performance of the control algorithm for different scenarios. We then study the performance using a real-trace obtained from the Simple Lookup Service (sLS). We show that the proposed algorithm quickly adapts to rapid changes in the workload and yields lower mean end-to-end delay performance when compared with delays in the current deployment.


title: CNN-based Fisheye Image Real-Time Semantic Segmentation

abstract: Semantic segmentation based on Convolutional Neural Networks (CNNs) has been proven as an efficient way of facing scene understanding for autonomous driving applications. Traditionally, environment information is acquired using narrow-angle pin-hole cameras, but autonomous vehicles need wider field of view to perceive the complex surrounding, especially in urban traffic scenes. Fisheye cameras have begun to play an increasingly role to cover this need. This paper presents a real-time CNN-based semantic segmentation solution for urban traffic images using fisheye cameras. We adapt our Efficient Residual Factorized CNN (ERFNet) architecture to handle distorted fish-eye images. A new fisheye image dataset for semantic segmentation from the existing CityScapes dataset is generated to train and evaluate our CNN. We also test a data augmentation suggestion for fisheye image proposed in [1]. Experiments show outstanding results of our proposal regarding other methods of the state of the art.


title: Mind Over Matter, The Development of The Mental Toughness Scale (MTS)

abstract: Mental Toughness (MT), which refers to an inner focus and commitment to rise above challenges when facing adversity, is viewed as one of the most important psychological attributes in determining success in sport. However, there is little consensus on key components of MT, and existing measures vary greatly while focusing on elite athletes. The purpose of this research was to develop a measure of MT for use with college athletes. Collegiate and noncollegiate athletes (N = 271) completed the original 54-item Mental Toughness Scale (MTS) in study 1. Factor analysis (PCA) results reduced the scale to an 11-item scale, with good reliability and validity as demonstrated by its positive correlations with self-esteem and flow. A second study of college basketball players (N = 143) was conducted to establish the psychometric properties of the MTS. Study 2 demonstrated convergent, divergent and criterion validity through correlations with related measures, and a CFA provided moderate support for the MTS as a one-dimensional measure of mental toughness in sport.


title: A Robust and Self-Paced BCI System Based on a Four Class SSVEP Paradigm: Algorithms and Protocols for a High-Transfer-Rate Direct Brain Communication

abstract: In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs) paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs) method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a screening, training, and testing phase aimed at collecting user-specific information regarding best stimulation frequencies, optimal sources identification, and overall system processing chain calibration in only a few minutes. The system, validated on 11 healthy/pathologic subjects, has proven to be reliable in terms of achievable communication speed (up to 70 bit/min) and very robust to false positive identifications.


title: Getting a grip on other minds: Mirror neurons, intention understanding, and cognitive empathy

abstract: Abstract We have previously shown that a right inferior frontal mirror neuron area for grasping responds differently to observed grasping actions embedded in contexts that suggest different intentions, such as drinking and cleaning (Iacoboni, Molnar-Szakacs, Gallese, Buccino, Mazziotta, & Rizzolatti, 2005). Information about intentions, however, may be conveyed also by the grasping action itself: for instance, people typically drink by grasping the handle of a cup with a precision grip. In this fMRI experiment, subjects watched precision grips and whole-hand prehensions embedded in a drinking or an eating context. Indeed, in the right inferior frontal mirror neuron area there was higher activity for observed precision grips in the drinking context. Signal changes in the right inferior frontal mirror neuron area were also significantly correlated with scores on Empathic Concern subscale of the Interpersonal Reactivity Index, a measure of emotional empathy. These data suggest that human mirror neuron areas ...


title: Structure from motion with wide circular field of view cameras

abstract: This paper presents a method for fully automatic and robust estimation of two-view geometry, autocalibration, and 3D metric reconstruction from point correspondences in images taken by cameras with wide circular field of view. We focus on cameras which have more than 180deg field of view and for which the standard perspective camera model is not sufficient, e.g., the cameras equipped with circular fish-eye lenses Nikon FC-E8 (183deg), Sigma 8 mm-f4-EX (180deg), or with curved conical mirrors. We assume a circular field of view and axially symmetric image projection to autocalibrate the cameras. Many wide field of view cameras can still be modeled by the central projection followed by a nonlinear image mapping. Examples are the above-mentioned fish-eye lenses and properly assembled catadioptric cameras with conical mirrors. We show that epipolar geometry of these cameras can be estimated from a small number of correspondences by solving a polynomial eigenvalue problem. This allows the use of efficient RANSAC robust estimation to find the image projection model, the epipolar geometry, and the selection of true point correspondences from tentative correspondences contaminated by mismatches. Real catadioptric cameras are often slightly noncentral. We show that the proposed autocalibration with approximate central models is usually good enough to get correct point correspondences which can be used with accurate noncentral models in a bundle adjustment to obtain accurate 3D scene reconstruction. Noncentral camera models are dealt with and results are shown for catadioptric cameras with parabolic and spherical mirrors


title: Synthesis and lymphatic transport of intestinal apolipoprotein A-IV in response to graded doses of triglyceride.

abstract: Factors regulating the intestinal synthesis and secretion of apoA-IV are incompletely understood. Although it is known that apoA-IV is stimulated by dietary lipid, it is not known whether graded doses of triglyceride elicit graded responses in apoA-IV synthesis and secretion. We used the chronic intestinal lymph-fistula rat to examine the effect of graded levels of intestinal triglyceride transport on secretion of apoA-IV into lymph and synthesis of apoA-IV in various regions of intestine. Rats were implanted with chronic duodenal and intestinal lymph duct cannulas and infused with lipid emulsions containing 5, 10, 20, 40, 80 mumol triolein (including [3H]triolein, 1.2 microCi), 8.7 mumol phosphatidylcholine, and 57 mumol sodium taurocholate in 3 ml phosphate-buffered saline (pH 6.4) at ml/h for 8 h. Lymph samples were collected for 1 h prior to and at 2, 4, 5, 6, 7, and 8 h after the start of lipid infusion. Lymphatic output of triglyceride, phospholipid, and apoA-IV was measured. Steady-state (8 h) content of radioactive lipid was also measured in the lumen and the wall of the gut. In separate studies rats were given infusions of either 5% glucose in saline ("fasting") or triolein emulsion (10, 20, 40, 80 mumol/h) for 8 h after which incorporation of [3H]leucine into apoA-IV in isolated, in situ loops of duodenum, proximal jejunum, mid-distal jejunum, and terminal ileum was measured. Lipid output increased dose-dependently in response to triolein infusion, with steady-state lipid transport achieved by 5 h after start of lipid infusion. Total recovery of 3H-labeled lipid was similar for all triolein doses. Increase in lymphatic apoA-IV output lagged that of triglyceride by 3-4 h, but by 8 h showed graded increases with triolein dose. ApoA-IV synthesis (apoA-IV radioactivity immunoprecipitated by an apoA-IV monospecific antibody and expressed as % of trichloroacetic acid-precipitable [3H]leucine radioactivity) in the duodenum and proximal jejunum showed a 2-fold increase (compared with fasting) in response to 10 mumol triolein/h, but no further increase at higher doses. However, apoA-IV synthesis in mid to distal jejunum increased dose-dependently.(ABSTRACT TRUNCATED AT 400 WORDS)


title: Relevance feature mapping for content-based multimedia information retrieval

abstract: This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.


title: Peer-to-Peer Protocol for Allocated Resources in Vehicular Cloud Based on V2V Communication

abstract: Intelligent transport systems (ITS) may take advantage of the mobile cloud, in other words, the vehicular cloud may provide a lot of services for assisting traffic, accident prevention, and content delivery, among others. However, search and allocation resources in the vehicular cloud has become challenging, due to vehicular network characteristics, and also to the necessity of attempting the QoS requirements of service independently of the external condition. Thus, one of the biggest obstacles in these environments is to allocate and share resources available in vehicles without the need for external infrastructure. With this challenge in mind, we propose a new protocol designed to facilitate resource sharing via mobile cloud in the vehicular network. Simulation results show that the proposed approach introduces a short search time of approximately 0.7 (ms) to query resources in one hop, and about 1.0 (ms) to seek resources in more than one hop. Furthermore, the proposed protocol enables a high availability of resources, about 95%.


title: Radar interferometry and its application to changes in the Earth's surface

abstract: Geophysical applications of radar interferometry to measure changes in the Earth's surface have exploded in the early 1990s. This new geodetic technique calculates the interference pattern caused by the difference in phase between two images acquired by a spaceborne synthetic aperture radar at two distinct times. The resulting interferogram is a contour map of the change in distance between the ground and the radar instrument. These maps provide an unsurpassed spatial sampling density (∼100 pixels km−2), a competitive precision (∼1 cm), and a useful observation cadence (1 pass month−1). They record movements in the crust, perturbations in the atmosphere, dielectric modifications in the soil, and relief in the topography. They are also sensitive to technical effects, such as relative variations in the radar's trajectory or variations in its frequency standard. We describe how all these phenomena contribute to an interferogram. Then a practical summary explains the techniques for calculating and manipulating interferograms from various radar instruments, including the four satellites currently in orbit: ERS-1, ERS-2, JERS-1, and RADARSAT. The next chapter suggests some guidelines for interpreting an interferogram as a geophysical measurement: respecting the limits of the technique, assessing its uncertainty, recognizing artifacts, and discriminating different types of signal. We then review the geophysical applications published to date, most of which study deformation related to earthquakes, volcanoes, and glaciers using ERS-1 data. We also show examples of monitoring natural hazards and environmental alterations related to landslides, subsidence, and agriculture. In addition, we consider subtler geophysical signals such as postseismic relaxation, tidal loading of coastal areas, and interseismic strain accumulation. We conclude with our perspectives on the future of radar interferometry. The objective of the review is for the reader to develop the physical understanding necessary to calculate an interferogram and the geophysical intuition necessary to interpret it.


title: Lane detection using Fourier-based line detector

abstract: In this paper a new approach to lane marker detection problem is introduced as a significant complement for a semi/fully autonomous driver assistance system. The method incorporates advance line detection using multilayer fractional Fourier transform (MLFRFT) and the state-of-the-art advance lane detector (ALD). Experimental results have shown a considerable reduction in computational complexity.


title: Towards Development of a Low Cost Early Fire Detection System Using Wireless Sensor Network and Machine Vision

abstract: Fire is one of the most prominent threat to safety of human and property, in both domestic and industrial setups. Efficiently combating a fire threat, usually, depends on how early the fire is detected. This paper reports work for development of a low cost wireless sensor-based system for surveillance and early fire detection, using machine vision technique. The system consists of an on-board camera node, capable of transmitting videos over wireless network to a remote host computer that runs an image processing based fire detection algorithm. The system is standalone and portable with the capability of transmitting videos to virtually anywhere in the world. Prototype of the system has been successfully tested, performing video streaming alongwith segmentation of fire regions using HSI features of the retrieved images. Future work will inlcude automatic fire detection and alarm generation alongwith the extension of the system on multiple and widely scattered transmission nodes.


title: Random number generators: good ones are hard to find

abstract: Practical and theoretical issues are presented concerning the design, implementation, and use of a good, minimal standard random number generator that will port to virtually all systems.


title: Fast single image fog removal using the adaptive Wiener filter

abstract: We present in this paper a fast single image defogging method that uses a novel approach to refining the estimate of amount of fog in an image with the Locally Adaptive Wiener Filter. We provide a solution for estimating noise parameters for the filter when the observation and noise are correlated by decorrelating with a naively estimated defogged image. We demonstrate our method is 50 to 100 times faster than existing fast single image defogging methods and that our proposed method subjectively performs as well as the Spectral Matting smoothed Dark Channel Prior method.


title: Adaptive Manifold Learning

abstract: Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization. In this paper, we develop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the local neighborhood sizes when imposing a connectivity structure on the given set of high-dimensional data points and 2) the adaptive bias reduction in the local low-dimensional embedding by accounting for the variations in the curvature of the manifold as well as its interplay with the sampling density of the data set. We demonstrate the effectiveness of our methods for improving the performance of manifold learning algorithms using both synthetic and real-world data sets.


title: Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations

abstract: Pretraining with expert demonstrations have been found useful in speeding up the training process of deep reinforcement learning algorithms since less online simulation data is required. Some people use supervised learning to speed up the process of feature learning, others pretrain the policies by imitating expert demonstrations. However, these methods are unstable and not suitable for actor-critic reinforcement learning algorithms. Also, some existing methods rely on the global optimum assumption, which is not true in most scenarios. In this paper, we employ expert demonstrations in a actor-critic reinforcement learning framework, and meanwhile ensure that the performance is not affected by the fact that expert demonstrations are not global optimal. We theoretically derive a method for computing policy gradients and value estimators with only expert demonstrations. Our method is theoretically plausible for actor-critic reinforcement learning algorithms that pretrains both policy and value functions. We apply our method to two of the typical actor-critic reinforcement learning algorithms, DDPG and ACER, and demonstrate with experiments that our method not only outperforms the RL algorithms without pretraining process, but also is more simulation efficient.


title: Controllable smoke animation with guiding objects

abstract: This article addresses the problem of controlling the density and dynamics of smoke (a gas phenomenon) so that the synthetic appearance of the smoke (gas) resembles a still or moving object. Both the smoke region and the target object are represented as implicit functions. As a part of the target implicit function, a shape transformation is generated between an initial smoke region and the target object. In order to match the smoke surface with the target surface, we impose carefully designed velocity constraints on the smoke boundary during a dynamic fluid simulation. The velocity constraints are derived from an iterative functional minimization procedure for shape matching. The dynamics of the smoke is formulated using a novel compressible fluid model which can effectively absorb the discontinuities in the velocity field caused by imposed velocity constraints while reproducing realistic smoke appearances. As a result, a smoke region can evolve into a regular object and follow the motion of the object, while maintaining its smoke appearance.


title: Support vector machines classification with a very large-scale taxonomy

abstract: Very large-scale classification taxonomies typically have hundreds of thousands of categories, deep hierarchies, and skewed category distribution over documents. However, it is still an open question whether the state-of-the-art technologies in automated text categorization can scale to (and perform well on) such large taxonomies. In this paper, we report the first evaluation of Support Vector Machines (SVMs) in web-page classification over the full taxonomy of the Yahoo! categories. Our accomplishments include: 1) a data analysis on the Yahoo! taxonomy; 2) the development of a scalable system for large-scale text categorization; 3) theoretical analysis and experimental evaluation of SVMs in hierarchical and non-hierarchical settings for classification; 4) an investigation of threshold tuning algorithms with respect to time complexity and their effect on the classification accuracy of SVMs. We found that, in terms of scalability, the hierarchical use of SVMs is efficient enough for very large-scale classification; however, in terms of effectiveness, the performance of SVMs over the Yahoo! Directory is still far from satisfactory, which indicates that more substantial investigation is needed.


title: Investing in the foundation of sustainable development: pathways to scale up for early childhood development

abstract: Building on long-term benefits of early intervention (Paper 2 of this Series) and increasing commitment to early childhood development (Paper 1 of this Series), scaled up support for the youngest children is essential to improving health, human capital, and wellbeing across the life course. In this third paper, new analyses show that the burden of poor development is higher than estimated, taking into account additional risk factors. National programmes are needed. Greater political prioritisation is core to scale-up, as are policies that afford families time and financial resources to provide nurturing care for young children. Effective and feasible programmes to support early child development are now available. All sectors, particularly education, and social and child protection, must play a role to meet the holistic needs of young children. However, health provides a critical starting point for scaling up, given its reach to pregnant women, families, and young children. Starting at conception, interventions to promote nurturing care can feasibly build on existing health and nutrition services at limited additional cost. Failure to scale up has severe personal and social consequences. Children at elevated risk for compromised development due to stunting and poverty are likely to forgo about a quarter of average adult income per year, and the cost of inaction to gross domestic product can be double what some countries currently spend on health. Services and interventions to support early childhood development are essential to realising the vision of the Sustainable Development Goals.


title: Saliency detection in images using graph-based rarity, spatial compactness and background prior

abstract: Bottom-up saliency detection techniques extract salient regions in an image while free-viewing the image. We have approached the problem with three different low-level cues- graph based rarity, spatial compactness and background prior. First, the image is broken into similar colored patches, called superpixels. To measure rarity we represent the image as a graph with superpixels as node and exponential color difference as the edge weights between the nodes. Eigenvectors of the Laplacian of the graph are then used, similar to spectral clustering (Ng et al., 2001). Each superpixel is associated with a descriptor formed from these eigenvectors and rarity or uniqueness of the superpixels are found using these descriptors. Spatial compactness is computed by combining disparity in color and spatial distance between superpixels. Concept of background prior is implemented by finding the weighted Mahalanobis distance of the superpixels from the statistically modeled mean background color. These cues in combination gives the proposed saliency map. Experimental results demonstrate that our method outperforms many of the recent state-of-the-art methods both in terms of accuracy and speed.


Learning strategies: a synthesis and conceptual model


title: Cognitive Cars: A New Frontier for ADAS Research

abstract: This paper provides a survey of recent works on cognitive cars with a focus on driver-oriented intelligent vehicle motion control. The main objective here is to clarify the goals and guidelines for future development in the area of advanced driver-assistance systems (ADASs). Two major research directions are investigated and discussed in detail: (1) stimuli-decisions-actions, which focuses on the driver side, and (2) perception enhancement-action-suggestion-function-delegation, which emphasizes the ADAS side. This paper addresses the important achievements and major difficulties of each direction and discusses how to combine the two directions into a single integrated system to obtain safety and comfort while driving. Other related topics, including driver training and infrastructure design, are also studied.


title: DL-Lite: Tractable Description Logics for Ontologies

abstract: We propose a new Description Logic, called DL-Lite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts, and checking satisfiability of the whole knowledge base, but also answering complex queries (in particular, conjunctive queries) over the set of instances maintained in secondary storage. We show that in DL-Lite the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is polynomial in the size of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial data complexity for query answering over DL knowledge bases. A notable feature of our logic is to allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring TBox reasoning is independent of the ABox, and the part of the process requiring access to the ABox can be carried out by an SQL engine, thus taking advantage of the query optimization strategies provided by current DBMSs.


title: Dynamoth: A Scalable Pub/Sub Middleware for Latency-Constrained Applications in the Cloud

abstract: This paper presents Dynamoth, a dynamic, scalable, channel-based pub/sub middleware targeted at large scale, distributed and latency constrained systems. Our approach provides a software layer that balances the load generated by a high number of publishers, subscribers and messages across multiple, standard pub/sub servers that can be deployed in the Cloud. In order to optimize Cloud infrastructure usage, pub/sub servers can be added or removed as needed. Balancing takes into account the live characteristics of each channel and is done in an hierarchical manner across channels (macro) as well as within individual channels (micro) to maintain acceptable performance and low latencies despite highly varying conditions. Load monitoring is performed in an unintrusive way, and rebalancing employs a lazy approach in order to minimize its temporal impact on performance while ensuring successful and timely delivery of all messages. Extensive real-world experiments that illustrate the practicality of the approach within a massively multiplayer game setting are presented. Results indicate that with a given number of servers, Dynamoth was able to handle 60% more simultaneous clients than the consistent hashing approach, and that it was properly able to deal with highly varying conditions in the context of large workloads.


title: The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study.

abstract: Over the last decade, research into ‘addictive technological behaviors’ has substantially increased. Research has also demonstrated strong associations between addictive use of technology and comorbid psychiatric disorders. In the present study, 23,533 adults (mean age 35.8 years, ranging from 16 to 88 years) participated in an online cross-sectional survey examining whether demographic variables, symptoms of Attention Deficit/Hyperactivity Disorder (ADHD), Obsessive-Compulsive Disorder (OCD), anxiety, and depression could explain variance in addictive use (i.e., compulsive and excessive use associated with negative outcomes) of two types of modern online technologies: social media and video games. Psychometrically robust instruments were utilized. Correlations between symptoms of addictive technology use and mental disorder symptoms were all positive and significant, including the interrelationship between the two addictive technological behaviors. Age appeared to be inversely related to the addictive use of these technologies. Being male was significantly associated with addictive use of video games, whereas being female was significantly associated with addictive use of social media. Being single was positively related to both addictive social networking and video gaming. Hierarchical regression analyses showed that demographic factors explained between 11% and 12% of the variance in addictive technology use. The mental health variables explained between 7% and 15% of the variance. The study significantly adds to our understanding of mental health symptoms and their role in addictive use of modern technology. Clinical implications, strengths, and limitations are discussed.


title: Wiselib: A Generic Algorithm Library for Heterogeneous Sensor Networks

abstract: One unfortunate consequence of the success story of wireless sensor networks (WSNs) in separate research communities is an ever-growing gap between theory and practice. Even though there is a increasing number of algorithmic methods for WSNs, the vast majority has never been tried in practice; conversely, many practical challenges are still awaiting efficient algorithmic solutions. The main cause for this discrepancy is the fact that programming sensor nodes still happens at a very technical level. We remedy the situation by introducing Wiselib, our algorithm library that allows for simple implementations of algorithms onto a large variety of hardware and software. This is achieved by employing advanced C++ techniques such as templates and inline functions, allowing to write generic code that is resolved and bound at compile time, resulting in virtually no memory or computation overhead at run time. ::: ::: The Wiselib runs on different host operating systems, such as Contiki, iSense OS, and ScatterWeb. Furthermore, it runs on virtual nodes simulated by Shawn. For any algorithm, the Wiselib provides data structures that suit the specific properties of the target platform. Algorithm code does not contain any platform-specific specializations, allowing a single implementation to run natively on heterogeneous networks. ::: ::: In this paper, we describe the building blocks of the Wiselib, and analyze the overhead. We demonstrate the effectiveness of our approach by showing how routing algorithms can be implemented. We also report on results from experiments with real sensor-node hardware.


title: Optimal information security investment in a Healthcare Information Exchange: An economic analysis

abstract: Abstract The complexity of the problem, the increasing security breaches, and the regulatory and financial consequences of breached patient data highlight the fact that security of electronic patient information in Healthcare Information Exchanges (HIEs) is an organizational imperative and a research priority. This study applies classical economic decision analysis techniques and models the HIE based on its network characteristics to offer key insights into the issue of determining the optimal level of information security investment. We find that for an organization in a HIE, only security events with the potential loss reaching some critical value are worth protecting, and organizations would only spend a fraction of the intrinsic security risk on protection measures. Even when business benefit from security investment exists, organizations in a HIE tend to invest based on risk reduction alone. The implications of such decisions made at the node level and the resulting built-in moral hazard at the HIE level is discussed.


MoXi: real-time ink dispersion in absorbent paper


title: The Relationship Between Consumers’ Tendencies to Buy Compulsively and Their Motivations to Shop and Buy on the Internet

abstract: This paper presents the results of a survey of customers of an Internet clothing retailer examining how consumers’ preferences to shop and buy on the Internet rather than at bricks-and-mortar stores differ depending on their compulsive buying tendencies. Using shopping motivations such as seeking product and information variety, the ability to buy unobserved, avoiding social interactions, and experiencing positive feelings during shopping and buying, we find a positive linear relationship between a tendency to buy compulsively and Internet shopping and buying motivations. The research demonstrates that the items used to measure these motivations can also be used to identify buyers who have a tendency to buy compulsively. The paper also offers important retailing, managerial and public policy implications of the findings.


title: Low Cost Planar Waveguide Technology-Based Dielectric Resonator Antenna (DRA) for Millimeter-Wave Applications: Analysis, Design, and Fabrication

abstract: A compact, low cost and high radiation efficiency antenna structure, planar waveguide, substrate integrated waveguide (SIW), dielectric resonator antennas (DRA) is presented in this paper. Since SIW is a high Q- waveguide and DRA is a low loss radiator, then SIW-DRA forms an excellent antenna system with high radiation efficiency at millimeter-waveband, where the conductor loss dominates. The impact of different antenna parameters on the antenna performance is studied. Experimental data for SIW-DRA, based on two different slot orientations, at millimeter-wave band are introduced and compared to the simulated HFSS results to validate our proposed antenna model. A good agreement is obtained. The measured gain for SIW-DRA single element showed a broadside gain of 5.51 dB,-19 dB maximum cross polarized radiation level, and overall calculated (simulated using HFSS) radiation efficiency of greater than 95%.


title: Design, implementation, and evaluation of an under-actuated miniature biped climbing robot

abstract: The design, implementation, and evaluation of a miniature biped robot for urban reconnaissance are presented. Design specifications for mobility, space requirement weight, sensing, and control are defined. A revolute hip joint is selected based on its enhanced mobility and capability to function in reasonably confined spaces. Small size dictates minimal weight, which is achieved by an under actuated joint structure, providing steering at only one foot, minimizing sensors, and structural optimization. The smart robotic foot supports the robot on a variety of smooth surfaces and provides feedback when a firm grip is established. Adaptable control strategies and dithering are implemented in lieu of minimal sensors and uncertainty created by backlash, gravity, and compliance in the suction feet. The robot is evaluated while performing tasks on surfaces with a variety of inclinations.


title: Cross-lingual Transfer for Unsupervised Dependency Parsing Without Parallel Data

abstract: Cross-lingual transfer has been shown to produce good results for dependency parsing of resource-poor languages. Although this avoids the need for a target language treebank, most approaches have still used large parallel corpora. However, parallel data is scarce for low-resource languages, and we report a new method that does not need parallel data. Our method learns syntactic word embeddings that generalise over the syntactic contexts of a bilingual vocabulary, and incorporates these into a neural network parser. We show empirical improvements over a baseline delexicalised parser on both the CoNLL and Universal Dependency Treebank datasets. We analyse the importance of the source languages, and show that combining multiple source-languages leads to a substantial improvement.


title: Comprehensive steady state analysis of bidirectional dual active bridge DC/DC converter using triple phase shift control

abstract: Several papers have been published recently on TPS control of dual active bridge (DAB) converter, however, no complete study of the converter operation behaviour exists, that takes into account all switching modes in both charging and discharging (bidirectional) power transfer. In this paper, six switching modes and their complements with opposite power transfer direction are defined with their operational constraints. Exact expressions for power transferred are derived with no fundamental frequency assumptions and range of power transfer for each mode is also defined to characterize mode limitations. Detailed constraints for zero voltage switching (ZVS) are also obtained. A new definition for converter reactive power consumption is introduced. This is based on calculation of inductor apparent power which avoids fundamental frequency approximations as well as the vague negative (back flowing) power definitions in recent papers. All known DAB phase shift modulation techniques including conventional, dual and extended phase shift, represent special cases from triple phase shift, therefore the presented analysis provides a generalised theory for all phase shift based modulation techniques.


title: Emerging Technologies to Conserve Biodiversity

abstract: Technologies to identify individual animals, follow their movements, identify and locate animal and plant species, and assess the status of their habitats remotely have become better, faster, and cheaper as threats to the survival of species are increasing. New technologies alone do not save species, and new data create new problems. For example, improving technologies alone cannot prevent poaching: solutions require providing appropriate tools to the right people. Habitat loss is another driver: the challenge here is to connect existing sophisticated remote sensing with species occurrence data to predict where species remain. Other challenges include assembling a wider public to crowdsource data, managing the massive quantities of data generated, and developing solutions to rapidly emerging threats.


title: Data mining for quality control: Burr detection in the drilling process

abstract: Drilling process is one of the most important operations in aeronautic industry. It is performed on the wings of the aeroplanes and its main problem lies with the burr generation. At present moment, there is a visual inspection and manual burr elimination task subsequent to the drilling and previous to the riveting to ensure the quality of the product. These operations increase the cost and the resources required during the process. The article shows the use of data mining techniques to obtain a reliable model to detect the generation of burr during high speed drilling in dry conditions on aluminium Al 7075-T6. It makes possible to eliminate the unproductive operations in order to optimize the process and reduce economic cost. Furthermore, this model should be able to be implemented later in a monitoring system to detect automatically and on-line when the generated burr is out of tolerance limits or not. The article explains the whole process of data analysis from the data preparation to the evaluation and selection of the final model.


title: Fingerprint image enhancement using GWT and DMF

abstract: Fingerprint image enhancement is an essential preprocessing step in fingerprint recognition applications. In this paper we introduce an approach that extracts simultaneously orientation and frequency of local ridge in the fingerprint image by Gabor wavelet filter bank and use them in Gabor Filtering of image. Furthermore, we describes a robust approach to fingerprint image enhancement, which is based on integration of Gabor Filters and Directional Median Filter(DMF). In fact, Gaussian-distributed noises are reduced effectively by Gabor Filters and impulse noises by DMF. the proposed DMF not only can finish its original tasks, it can also join broken fingerprint ridges, fill out the holes of fingerprint images, smooth irregular ridges as well as remove some annoying small artifacts between ridges. Experimental results show our method to be superior to those described in the literature.


title: Fully Integrated CMOS Power Amplifier With Efficiency Enhancement at Power Back-Off

abstract: This paper presents a new approach for power amplifier design using deep submicron CMOS technologies. A transformer based voltage combiner is proposed to combine power generated from several low-voltage CMOS amplifiers. Unlike other voltage combining transformers, the architecture presented in this paper provides greater flexibility to access and control the individual amplifiers in a voltage combined amplifier. In this work, this voltage combining transformer has been utilized to control output power and improve average efficiency at power back-off. This technique does not degrade instantaneous efficiency at peak power and maintains voltage gain with power back-off. A 1.2 V, 2.4 GHz fully integrated CMOS power amplifier prototype was implemented with thin-oxide transistors in a 0.13 mum RF-CMOS process to demonstrate the concept. Neither off-chip components nor bondwires are used for output matching. The power amplifier transmits 24 dBm power with 25% drain efficiency at 1 dB compression point. When driven into saturation, it transmits 27 dBm peak power with 32% drain efficiency. At power back-off, efficiency is greatly improved in the prototype which employs average efficiency enhancement circuitry.


title: Towards the Industrialization of Data Migration: Concepts and Patterns for Standard Software Implementation Projects

abstract: When a bank replaces its core-banking information system, the bank must migrate data like accounts from the old into the new system. Migrating data is necessary but not a catalyst for new business opportunities. The consequence is cost pressure to be addressed by an efficient software development process together with an industrialization of the development. Industrialization requires defining the deliverables. Therefore, our data migration architecture extends the ETL process by migration objectives to be reached in each step. Industrialization also means standardizing the implementation, e.g. with patterns. We present data migration patterns describing the typical transformations found in the data migration application domain. Finally, testing is an important issue because test-case based testing cannot guarantee that not a single customer gets lost. Reconciliation can do so by checking whether each object in the old and new system has a counterpart in the other system.


title: A Hierarchical Neural-Network-Based Document Representation Approach for Text Classification

abstract: Document representation is widely used in practical application, for example, sentiment classification, text retrieval, and text classification. Previous work is mainly based on the statistics and the neural networks, which suffer from data sparsity and model interpretability, respectively. In this paper, we propose a general framework for document representation with a hierarchical architecture. In particular, we incorporate the hierarchical architecture into three traditional neural-network models for document representation, resulting in three hierarchical neural representation models for document classification, that is, TextHFT, TextHRNN, and TextHCNN. Our comprehensive experimental results on two public datasets, that is, Yelp 2016 and Amazon Reviews (Electronics), show that our proposals with hierarchical architecture outperform the corresponding neural-network models for document classification, resulting in a significant improvement ranging from 4.65% to 35.08% in terms of accuracy with a comparable (or substantially less) expense of time consumption. In addition, we find that the long documents benefit more from the hierarchical architecture than the short ones as the improvement in terms of accuracy on long documents is greater than that on short documents.


title: Automatic Question Generation from Sentences

abstract: Question Generation (QG) and Question Answering (QA) are some of the many chal- lenges for natural language understanding and interfaces. As humans need to ask good questions, the potential benefits from automated QG systems may assist them in meeting useful inquiry needs. In this pa- per, we consider an automatic Sentence-to-Question generation task, where given a sentence, the Question Generation (QG) system generates a set of questions for which the sentence contains, implies, or needs answers. To facilitate the question generation task, we build elementary sentences from the input complex sentences using a syntactic parser. A named entity recognizer and a part of speech tagger are applied on each of these sentences to encode necessary information. We classify the sentences based on their subject, verb, object and preposition for determining the possible type of questions to be generated. We use the TREC-2007 (Question Answering Track) dataset for our experiments and evaluation. Mots-cles : Generation de questions, Analyseur syntaxique, Phrases elementaires, POS Tagging.


title: Multi-viewer gesture-based interaction for omni-directional video

abstract: Omni-directional video (ODV) is a novel medium that offers viewers a 360o panoramic recording. This type of content will become more common within our living rooms in the near future, seeing that immersive displaying technologies such as 3D television are on the rise. However, little attention has been given to how to interact with ODV content. We present a gesture elicitation study in which we asked users to perform mid-air gestures that they consider to be appropriate for ODV interaction, both for individual as well as collocated settings. We are interested in the gesture variations and adaptations that come forth from individual and collocated usage. To this end, we gathered quantitative and qualitative data by means of observations, motion capture, questionnaires and interviews. This data resulted in a user-defined gesture set for ODV, alongside an in-depth analysis of the variation in gestures we observed during the study.


title: Improved polar decoder based on deep learning

abstract: Deep learning recently shows strong competitiveness to improve polar code decoding. However, suffering from prohibitive training and computation complexity, the conventional deep neural network (DNN) is only possible for very short code length. In this paper, the main problems of deep learning in decoding are well solved. We first present the multiple scaled belief propagation (BP) algorithm, aiming at obtaining faster convergence and better performance. Based on this, deep neural network decoder (NND) with low complexity and latency, is proposed for any code length. The training only requires a small set of zero codewords. Besides, its computation complexity is close to the original BP. Experiment results show that the proposed (64,32) NND with 5 iterations achieves even lower bit error rate (BER) than the 30-iteration conventional BP and (512, 256) NND also outperforms conventional BP decoder with same iterations. The hardware architecture of basic computation block is given and folding technique is also considered, saving about 50% hardware cost.


title: zdelta: An Efficient Delta Compression Tool

abstract: In this report we describe a tool for delta compression, i.e., the efficient encoding of a given data set in relation to another one. Its possible applications include archiving multiple versions of data, distribution of software updates, delta compression of backup files, or compression at the file system level. The compressor, called zdelta, could be viewed as a modification of the zlib compression library [4] with some additional ideas inspired by the vdelta/vcdiff tool of Vo [5]. We also present experimental results comparing zdelta to other delta compression tools. Additional information about zdelta, including source code and updates, is available at http://cis.poly.edu/zdelta/. This project was supported by a grant from Intel Corporation. Torsten Suel was also supported by NSF CAREER Award NSF CCR-0093400.


title: A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples

abstract: Deep neural networks have been shown to suffer from a surprising weakness: their classification outputs can be changed by small, non-random perturbations of their inputs. This adversarial example phenomenon has been explained as originating from deep networks being"too linear"(Goodfellow et al., 2014). We show here that the linear explanation of adversarial examples presents a number of limitations: the formal argument is not convincing, linear classifiers do not always suffer from the phenomenon, and when they do their adversarial examples are different from the ones affecting deep networks. We propose a new perspective on the phenomenon. We argue that adversarial examples exist when the classification boundary lies close to the submanifold of sampled data, and present a mathematical analysis of this new perspective in the linear case. We define the notion of adversarial strength and show that it can be reduced to the deviation angle between the classifier considered and the nearest centroid classifier. Then, we show that the adversarial strength can be made arbitrarily high independently of the classification performance due to a mechanism that we call boundary tilting. This result leads us to defining a new taxonomy of adversarial examples. Finally, we show that the adversarial strength observed in practice is directly dependent on the level of regularisation used and the strongest adversarial examples, symptomatic of overfitting, can be avoided by using a proper level of regularisation.


title: The role of eyebrows in face recognition

abstract: A fundamental challenge in face recognition lies in determining which facial characteristics are important in the identification of faces. Several studies have indicated the significance of certain facial features in this regard, particularly internal ones such as the eyes and mouth. Surprisingly, however, one rather prominent facial feature has received little attention in this domain: the eyebrows. Past work has examined the role of eyebrows in emotional expression and nonverbal communication, as well as in facial aesthetics and sexual dimorphism. However, it has not been made clear whether the eyebrows play an important role in the identification of faces. Here, we report experimental results which suggest that for face recognition the eyebrows may be at least as influential as the eyes. Specifically, we find that the absence of eyebrows in familiar faces leads to a very large and significant disruption in recognition performance. In fact, a significantly greater decrement in face recognition is observ...


Towards intrusion detection in wireless sensor networks


title: A metapopulation model for malaria with transmission-blocking partial immunity in hosts

abstract: A metapopulation malaria model is proposed using SI and SIRS models for the vectors and hosts, respectively. Recovered hosts are partially immune to the disease and while they cannot directly become infectious again, they can still transmit the parasite to vectors. The basic reproduction number \({\mathcal{R}_0}\) is shown to govern the local stability of the disease free equilibrium but not the global behavior of the system because of the potential occurrence of a backward bifurcation. Using type reproduction numbers, we identify the reservoirs of infection and evaluate the effect of control measures. Applications to the spread to non-endemic areas and the interaction between rural and urban areas are given.


title: Visualizing the Hidden Activity of Artificial Neural Networks

abstract: In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles.


title: Is Cancer Information Exchanged on Social Media Scientifically Accurate?

abstract: Cancer patients and their caregivers are increasingly using social media as a platform to share cancer experiences, connect with support, and exchange cancer-related information. Yet, little is known about the nature and scientific accuracy of cancer-related information exchanged on social media. We conducted a content analysis of 12 months of data from 18 publically available Facebook Pages hosted by parents of children with acute lymphoblastic leukemia (N = 15,852 posts) and extracted all exchanges of medically-oriented cancer information. We systematically coded for themes in the nature of cancer-related information exchanged on personal Facebook Pages and two oncology experts independently evaluated the scientific accuracy of each post. Of the 15,852 total posts, 171 posts contained medically-oriented cancer information. The most frequent type of cancer information exchanged was information related to treatment protocols and health services use (35%) followed by information related to side effects and late effects (26%), medication (16%), medical caregiving strategies (13%), alternative and complementary therapies (8%), and other (2%). Overall, 67% of all cancer information exchanged was deemed medically/scientifically accurate, 19% was not medically/scientifically accurate, and 14% described unproven treatment modalities. These findings highlight the potential utility of social media as a cancer-related resource, but also indicate that providers should focus on recommending reliable, evidence-based sources to patients and caregivers.


title: Technology Frame Disruption during Short-Term Agile ISD Projects

abstract: Effective collaboration between clients and development teams is vitally important to all Agile ISD methods, enabling the key benefit of Agile approaches which is the ability to react quickly to changing requirements. The process by which this collaboration develops is particularly consequential for short-term Agile projects where we cannot assume that there will be sufficient time for initial "kinks" to be worked out. We develop a process model, based on extended concepts of technology frames and technology frame disruption, to explore the extent to which client framing of software projects is resistant to change in a highly collaborative environment. We report on survey and interview data from fourteen Agile ISD project clients, and discuss continuing research. We find that novice clients' frames focus on business-facing technology characteristics, but do not focus on project considerations, most notably the need to understand the capabilities of the team, and the technical constraints of the technology.


title: Battery-free wireless identification and sensing

abstract: The Wireless Identification and Sensing Platform (WISP) project explores an approach to provide power for sensor networks, based on passive radio-frequency-identification technology. In traditional passive RFID systems, ambient high-power readers interrogate battery-free devices, called tags, that modulate the interrogating signal to communicate a unique identifier to the reader. The WISP project aims to augment RFID tags with sensors so that tags can also send sensed data to the readers. We call these augmented tags wisps. Basing wisps on RFID has some immediate advantages. RFID tags communicate to ambient readers over distances of up to eight meters. Solutions compatible with RFID standards might therefore find quicker acceptance and see faster improvement than other solutions.


title: Semantic Streams: A Framework for Composable Semantic Interpretation of Sensor Data

abstract: We present a framework called Semantic Streams that allows users to pose declarative queries over semantic interpretations of sensor data. For example, instead of querying raw magnetometer data, the user queries whether vehicles are cars or trucks; the system decides which sensor data and which operations to use to infer the type of vehicle. The user can also place constraints on values such as the the amount of energy consumed or the confidence with which the vehicles are classified. We demonstrate how this system can be used on a network of video, magnetometer, and infrared break beam sensors deployed in a parking garage with three simultaneous and independent users.


title: Causal Interventions for Fairness

abstract: Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help private companies comply with non-discrimination laws or avoid negative publicity, we believe it is often too little, too late. By the time the training data is collected, individuals in disadvantaged groups have already suffered from discrimination and lost opportunities due to factors out of their control. In the present work we focus instead on interventions such as a new public policy, and in particular, how to maximize their positive effects while improving the fairness of the overall system. We use causal methods to model the effects of interventions, allowing for potential interference--each individual's outcome may depend on who else receives the intervention. We demonstrate this with an example of allocating a budget of teaching resources using a dataset of schools in New York City.


title: Fast Distributed Deep Learning via Worker-adaptive Batch Sizing

abstract: Deep neural network models are usually trained in cluster environments, where the model parameters are iteratively refined by multiple worker machines in parallel. One key challenge in this regard is the presence of stragglers, which significantly degrades the learning performance. In this paper, we propose to eliminate stragglers by adapting each worker's training load to its processing capability; that is, slower workers receive a smaller batch of data to process. Following this idea, we develop a new synchronization scheme called LB-BSP (Load-balanced BSP). It works by coordinately setting the batch size of each worker so that they can finish batch processing at around the same time. A prerequisite for deciding the workers' batch sizes is to know their processing speeds before each iteration starts. For the best prediction accuracy, we adopt NARX, an extended recurrent neural network that accounts for both the historical speeds and the driving factors such as CPU and memory in prediction. We have implemented LB-BSP for both TensorFlow and MXNet. EC2 experiments against popular benchmarks show that LB-BSP can effectively accelerate the training of deep models, with up to 2x speedup.