query: Deep Reasoning with Multi-scale Context for Salient Object Detection

positive: Visual saliency based on multiscale deep features Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. We then propose a refinement method to enhance the spatial coherence of our saliency results. Finally, aggregating multiple saliency maps computed for different levels of image segmentation can further boost the performance, yielding saliency maps better than those generated from a single segmentation. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F-Measure by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively on these two datasets.

positive: Deep Saliency with Encoded Low Level Distance Map and High Level Features Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features. Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework. The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map. The low level distance map is then encoded using a convolutional neural network(CNN) with multiple 1 1 convolutional and ReLU layers. We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region. Our experiments show that our method can further improve the performance of state-of-the-art deep learning-based saliency detection methods.

positive: ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2. Comprehensive ablation experiments verify that our model is the stateof-the-art in terms of speed and accuracy tradeoff.

positive: Very Deep Convolutional Networks for Large-Scale Image Recognition In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.

positive: Symbiotic Segmentation and Part Localization for Fine-Grained Categorization We propose a new method for the task of fine-grained visual categorization. The method builds a model of the base-level category that can be fitted to images, producing high-quality foreground segmentation and mid-level part localizations. The model can be learnt from the typical datasets available for fine-grained categorization, where the only annotation provided is a loose bounding box around the instance (e.g. bird) in each image. Both segmentation and part localizations are then used to encode the image content into a highly-discriminative visual signature. The model is symbiotic in that part discovery/localization is helped by segmentation and, conversely, the segmentation is helped by the detection (e.g. part layout). Our model builds on top of the part-based object category detector of Felzenszwalb et al., and also on the powerful Grab Cut segmentation algorithm of Rother et al., and adds a simple spatial saliency coupling between them. In our evaluation, the model improves the categorization accuracy over the state-of-the-art. It also improves over what can be achieved with an analogous system that runs segmentation and part-localization independently.

negative: Overhead line fault detection using GSM technology In this paper, a novel technique for the fault detection, classification & protection of transmission lines is proposed. The proposed system uses different protective equipment's, voltage sense section, microcontroller section, LED display section & GSM (global system for mobile communication) module. The faults like all series (1LO, 2LO, 3LO etc.) & shunt faults (L-G, L-L, L-L-G, L-L-L, L-L-L-G) get detected & classified according to characteristics condition of current & voltage at the occurrence of fault in the three phase overhead lines. The sensed signals are given to microcontroller for detection & classification of faults. Also wireless mobile communication technique i.e. GSM is used simultaneously to send message to responsible person on mobile. Type of fault gets displayed on fault display section. Simultaneously fault gets interrupted using protective devices. (fuse, circuit breakers etc.)

negative: Complete 3D Scene Parsing from an RGBD Image One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the layout of orthogonal walls and the extent of objects, modeled with CAD-like 3D shapes. We parse both the visible and occluded portions of the scene and all observable objects, producing a complete 3D parse. Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the well-known challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scenes. We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and spatial consistency. We use support inference to aid interpretation and propose a retrieval scheme that uses convolutional neural networks to classify regions and retrieve objects with similar shapes. We demonstrate the performance of our method on our newly annotated NYUd v2 dataset (Silberman et al., in: Computer vision-ECCV, 2012, pp 746–760, 2012) with detailed 3D shapes.

negative: CertiCoq : A verified compiler for Coq CertiCoq is a mechanically verified, optimizing compiler for Coq that bridges the gap between certified high-level programs and their translation to machine language. We outline its design as well as the main foundational and engineering challenges involved in building and certifying a compiler for Coq in Coq.

negative: Compaction Management in Distributed Key-Value Datastores Compactions are a vital maintenance mechanism used by datastores based on the log-structured merge-tree to counter the continuous buildup of data files under update-intensive workloads. While compactions help keep read latencies in check over the long run, this comes at the cost of significantly degraded read performance over the course of the compaction itself. In this paper, we offer an in-depth analysis of compaction-related performance overheads and propose techniques for their mitigation. We offload large, expensive compactions to a dedicated compaction server to allow the datastore server to better utilize its resources towards serving the actual workload. Moreover, since the newly compacted data is already cached in the compaction server’s main memory, we fetch this data over the network directly into the datastore server’s local cache, thereby avoiding the performance penalty of reading it back from the filesystem. In fact, pre-fetching the compacted data from the remote cache prior to switching the workload over to it can eliminate local cache misses altogether. Therefore, we implement a smarter warmup algorithm that ensures that all incoming read requests are served from the datastore server’s local cache even as it is warming up. We have integrated our solution into HBase, and using the YCSB and TPC-C benchmarks, we show that our approach significantly mitigates compaction-related performance problems. We also demonstrate the scalability of our solution by distributing compactions across multiple compaction servers.

negative: Massively Parallel A* Search on a GPU A* search is a fundamental topic in artificial intelligence. Recently, the general purpose computation on graphics processing units (GPGPU) has been widely used to accelerate numerous computational tasks. In this paper, we propose the first parallel variant of the A* search algorithm such that the search process of an agent can be accelerated by a single GPU processor in a massively parallel fashion. Our experiments have demonstrated that the GPU-accelerated A* search is efficient in solving multiple real-world search tasks, including combinatorial optimization problems, pathfinding and game solving. Compared to the traditional sequential CPU-based A* implementation, our GPU-based A* algorithm can achieve a significant speedup by up to 45x on largescale search problems.

negative: Portable and Efficient Auto-vectorized Bytecode: a Look at the Interaction between Static and JIT Compilers Heterogeneity is a confirmed trend of computing systems. Bytecode formats and just-in-time compilers have been proposed to deal with the diversity of the platforms. By hiding architectural details and giving software developers a unified view of the machine, they help improve portability and manage the complexity of large software. Independently, careful exploitation of SIMD instructions has become crucial for the performance of many applications. However, auto-vectorizing compilers need detailed information about the architectural features of the processor to generate efficient code. We propose to reconcile the use of architecture neutral bytecode formats with the need to generate highly efficient vectorized native code. We make three contributions. 1) We show that vectorized bytecode is a viable approach that can deliver portable performance in the presence of SIMD extensions, while incurring only minor penalty when SIMD is not supported. In other words, the information that a loop can be vectorized is the vectorized loop itself. 2) We analyze the interaction between the static and just-in-time compilers and we derive conditions to deliver performance. 3) We add vectorization capabilities to the CLI port of the GCC compiler.

negative: Real-time 3D Walking Pattern Generation for a Biped Robot with Telescopic Legs Meltran V, a new biped robot with telescopic legs, is introduced. For 3D walking control of the robot we analyze the dynamics of a three-dimensional inverted pendulum in which motion is constrained to move along an arbitrarily de ned plane. From this analysis we obtain simple linear dynamics, the ThreeDimensional Linear Inverted Pendulum Mode (3DLIPM). Using a real-time control method based on 3DLIPM, the Meltran V robot successfully demonstrated 3D dynamic walking without the use of any prepared trajectories.

negative: Review on cyber-physical systems Cyber-physical systems U+0028 CPS U+0029 are complex systems with organic integration and in-depth collaboration of computation, communications and control U+0028 3C U+0029 technology. Subject to the theory and technology of existing network systems and physical systems, the development of CPS is facing enormous challenges. This paper first introduces the concept and characteristics of CPS and analyzes the present situation of CPS researches. Then the development of CPS is discussed from perspectives of system model, information processing technology and software design. At last it analyzes the main obstacles and key researches in developing CPS.

negative: {\Omega}-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): a novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image; second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation; and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA; four-chamber, 4C; two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained on a cohort of patients with hypertrophic cardiomyopathy (HCM, N = 42) and healthy control subjects (N = 21). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude that this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally.

negative: Two cases of hymenal scars occurred by child rape Children who have been raped some years back may have hymenal scars. However, medical professionals are not accustomed in assessing these scars because of the lack of experience in performing physical examinations of the external genitalia of children who suffered from rape some years back. Moreover, the importance of physical examination of the victim's external genitalia is sometimes overlooked. Two cases of rape victims with hymenal scars who visited Daegu Child Sexual Abuse Response Center several years after their first sexual abuse along with a literature review are presented here.

negative: Distributed Power Control for the Downlink of Multi-Cell NOMA Systems This paper investigates the power control problem for the downlink of a multi-cell non-orthogonal multiple access system. The problem, called P-OPT, aims to minimize the total transmit power of all the base stations subject to the data rate requirements of the users. The feasibility and optimality properties of P-OPT are characterized through a related optimization problem, called Q-OPT, which is constituted by some relevant power control subproblems. First, we characterize the feasibility of Q-OPT and prove the uniqueness of its optimal solution. Next, we prove that the feasibility of P-OPT can be characterized by the Perron-Frobenius eigenvalues of the matrices arising from the power control subproblems. Subsequently, the relationship between the optimal solutions to P-OPT and that to Q-OPT is presented, which motivates us to obtain the optimal solution to P-OPT through solving the corresponding Q-OPT. Furthermore, a distributed algorithm to solve Q-OPT is designed, and the underlying iteration is shown to be a standard interference function. According to Yates’s power control framework, the algorithm always converges to the optimal solution if exists. Numerical results validate the convergence of the distributed algorithm and quantify the improvement of our proposed method over fractional transmit power control and orthogonal multiple access schemes in terms of power consumption and outage probability.

negative: MANUFACTURE OF ARBITRARY CROSS-SECTION COMPOSITE HONEYCOMB CORES BASED ON ORIGAMI TECHNIQUES In recent years, as space structures have become large and require higher accuracy, composite honeycombs, which can reduce weight and have low thermal expansion, are in increasing demand. As observed in the design of antenna reflectors and rocket bodies, both flat and 3D-shaped cores are used in this field. However, these special honeycombs have high manufacturing costs and limited applications. This study illustrates a new strategy to fabricate arbitrary cross-section honeycombs with applications of advanced composite materials. These types of honeycombs are usually manufactured from normal flat honeycombs by curving or carving, but the proposed method enables us to construct objective shaped honeycombs directly. The authors first introduce the concept of the kirigami honeycomb, which is made from single flat sheets and has periodical slits resembling origami. In previous studies, honeycombs having various shapes were made using this method, and were realized by only changing folding line diagrams (FLDs). In this study, these 3D kirigami honeycombs are generalized by numerical parameters and fabricated using a newly proposed FLD design method, which enables us to draw the FLD of arbitrary cross-section honeycombs. Next, the authors describe a method of applying this technique to advanced composite materials. Applying the partially soft composite techniques, folding lines are materialized by silicon rubber hinges on carbon fiber reinforced plastic. Complex FLD patterns are then printed using masks on carbon fabrics. Finally, these foldable composites that are cured in corrugated shapes in autoclaves are folded into honeycomb shapes, and some typical samples are shown with their FLDs. INTRODUCTION In the construction of aerospace components, lightweight, rigid, and strong honeycomb sandwich panels are required. In recent years, the use of composite materials has drastically increased in this field. In the case of sandwich panels, carbon fiber reinforced plastic (CFRP) face sheets are typically combined with an aluminum honeycomb. Currently, space structures are increasing in size and require greater degrees of accuracy; hence, the use of composites as a core material is a natural progression. It is promising for further reducing the weight of the body of satellites and enhancing the accuracy of their on-board equipment because of their low coefficients of thermal expansion (CTE). Furthermore, use of the same material for both the face sheet and the core prevents certain problems that would otherwise arise when combining materials that have different CTEs, such as CFRP-face aluminum honeycomb [1]. Various types of composite honeycombs [2-4] are commercially available, in addition to aluminum or nomex honeycombs. A comparable product is the CFRP honeycomb that has recently been used in antenna reflectors for high frequencies [5,6]. Other composites such as Kevlar honeycombs are also manufactured and used in floor panels of the latest airplane model. Quartz fiber, which has superior electrical properties has found the application in radome. 1 Copyright © 2013 by ASME DETC2013-12743 Proceedings of the ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE 2013 August 4-7, 2013, Portland, Oregon, USA Many studies on various types of core configurations that do not include hexagonal honeycombs have been reported. The main examples include lattice materials consisting of webs and struts [7-9]. Several configurations have already been proposed in this research area: octet-truss, the 2D and 3D Kagome structure, and the tetrahedral lattice. Square composite honeycombs, which are expected to show high in-plane stretching rigidity, have also been researched [10]. These square honeycombs are fabricated by assembling slotted rectangular composite sheets. The Z-fiber and X-Cor are truss structures that are fabricated by angled carbon fiber rods embedded in polymeric foam, and they are typical examples of composite sandwich panels [11] that have potential commercial applications. However, these composite core materials are not regularly used in sandwich construction. Compared to standard aluminum or nomex honeycombs, their manufacturing costs are very high and they have limited applications. Another problem is difficulty of machining. In the manufacture of complex-shaped parts, the cores must have some degree of curvature. For aluminum honeycombs, this can be done using a contour cutter, a 3-D tracer, and numerically controlled machines. However, burrs and buckling of cell walls present a difficult problem for surface accuracy. It is clear that the machining of composite cores requires more expensive and sophisticated systems. Realizing curvatures in honeycombs is also difficult because they deform a saddle shape when bent. It requires special cell shapes such as flexcore [2] or cells having auxetic behavior [12,13] for a large and accurate curvature. This study proposes a novel method to construct arbitrary cross-section composite honeycombs. The basic idea originates from the fold-made paper honeycombs proposed by Nojima and Saito in 2007 [14], in which they attempted to apply origami and kirigami techniques to the creation of sandwich structures. Origami is the traditional Japanese art of paper folding and has received widespread attention from artists, architects, and mathematicians. Kirigami is a variation of origami. While traditional origami prohibits the cutting of paper, it is permissible in kirigami. Kirigami artists create remarkable patterns on paper using a combination of cutting and folding. Figure 1 shows the basic concept of kirigami (or origami) honeycombs. The advantage of this method is that it can be extended to manufacture 3D (non-flat) honeycomb. This is achieved only by changing the folding line patterns. Some paper samples and their folding line diagrams (FLDs) are shown in Fig. 2. However, these previous studies [14] have not included non-convex-shaped cross-sections. Because of geometrical restrictions, their FLDs cannot be drawn on single sheets of flat paper. Generalized FLD design methods have also remained a challenge. In this study, these 3D kirigami honeycombs are generalized by numerical parameters, and a new FLD design method is devised. This study also includes non-convex-shaped crosssections that have not been possible to realize in previous studies. The outline of the paper is as follows. First, the design method of the FLD is devised. This involves the calculation of the position of the folding lines and slits from given crosssectional shapes. The second part describes the condition pertaining to foldability and propose a modified method for unfoldable cross-sections. This approach enabled us to fold arbitrary cross-section honeycombs (including non-convex honeycombs) by folding single sheets. The third part describes a method of applying this technique to advanced composite materials. As an exapmle of kirigami composite honeycombs, a wingbox was already manufactured in autoclave-cured woven Kevlar fabric using the FLD around an airfoil profile[15]. In Ref.15, a dashed line cutting is used on a folding line. This study proposes a new method to introduce folding lines on CFRP; Silicon rubber is used as the matrix for hinge areas. Finally, these foldable composites that are cured in corrugated shapes in autoclaves are folded into honeycomb shapes, and some typical samples are shown with their FLDs. Fig. 1 Concept of kirigami honeycomb core. (a) Basic folding lines diagram. Thick lines: Slits. Fine lines: mountain folding lines. Dashed lines: valley folding lines. (b)–(d) The folding process for realizing a honeycomb shape. Fig. 2 Examples of 3D folded honeycombs and their folding line diagrams (FLDs). Upper: Tapered honeycomb. Lower: Convex curved honeycomb. Black lines and areas: Slits or cutouts. Gray lines: Folding lines. (a) (d) c'

negative: A second order accurate projection method for the incompressible Navier-Stokes equations on non-graded adaptive grids We present an unconditionally stable second order accurate projection method for the incompressible Navier–Stokes equations on non-graded adaptive Cartesian grids. We employ quadtree and octree data structures as an efficient means to represent the grid. We use the supra-convergent Poisson solver of [C.-H. Min, F. Gibou, H. Ceniceros, A supra-convergent finite difference scheme for the variable coefficient Poisson equation on fully adaptive grids, CAM report 05-29, J. Comput. Phys. (in press)], a second order accurate semi-Lagrangian method to update the momentum equation, an unconditionally stable backward difference scheme to treat the diffusion term and a new method that guarantees the stability of the projection step on highly non-graded grids. We sample all the variables at the grid nodes, producing a scheme that is straightforward to implement. We propose two and three-dimensional examples to demonstrate second order accuracy for the velocity field and the divergence free condition in the L and L1 norms. 2006 Elsevier Inc. All rights reserved.

negative: Software-based resolver-to-digital converter using the PLL tracking algorithm In this paper, an all-digital resolver-to-digital conversion (RDC) system using phase-locked loop (PLL) tracking algorithm is proposed. Excitation signal is obtained from PWM module of a TMS320F2812 digital signal processor (DSP). According to the excitation signal, the resolver outputs differential sine and cosine signals, which are sampled by a 16-bit peripheral AD7606 module. The rotor angle position is estimated with the PLL tracking algorithm based on DSP. The proposed RDC system is applied to a servo synchronous motor with a resolver coupled to the shaft, and the electrical error of the resolver is ±10'. Finally, responding simulations and experiments are carried out. The results demonstrate that the RDC system has the features with simple hardware structure and low cost, and high-precision resolver-to-digital conversion is achieved.

negative: Adopting the Cognitive Complexity Measure for Business Process Models Business process models, often modelled using graphical languages like UML, serve as a base for communication between the stakeholders in the software development process. To fulfil this purpose, they should be easy to understand and easy to maintain. For this reason, it is useful to have measures that can give us some information about understandability, analyzability and maintainability of a business process model. Shao and Wang (2003) have proposed a cognitive complexity measure. It can be used to estimate the comprehension effort for understanding software. This paper discusses how these research results can be extended in order to analyze the cognitive complexity of graphical business process models

negative: Challenges in Exploiting Conversational Memory in Human-Agent Interaction In human interactions, language is used to project and maintain a social identity over time. The way people speak with others and revisit language across repeated interactions helps to create rapport and develop a feeling of coordination between conversational partners. Memory of past conversations is the main mechanism that allows us to exploit and explore ways of speaking, given knowledge acquired in previous encounters. As such, we introduce an agent that uses its conversational memory to revisit shared history with users to maintain a coherent social relationship over time. In this paper, we describe the dialog management mechanisms to achieve these goals when applied to a robot that engages in social chit-chat. In a study lasting 14 days with 28 users, totaling 474 interactions, we find that it is difficult to leverage the shared history with individual users and to also accommodate to expected conversational coordination patterns. We discuss the implications of this finding for long-term human-agent interaction. In particular, we highlight the importance of topic modeling and signaling explicit recall of previous episodes. Moreover, the way that users contribute to interactions requires additional adaptation, indicating a significant challenge for language interaction designers.

negative: Are leader stereotypes masculine? A meta-analysis of three research paradigms. This meta-analysis examined the extent to which stereotypes of leaders are culturally masculine. The primary studies fit into 1 of 3 paradigms: (a) In Schein's (1973) think manager-think male paradigm, 40 studies with 51 effect sizes compared the similarity of male and leader stereotypes and the similarity of female and leader stereotypes; (b) in Powell and Butterfield's (1979) agency-communion paradigm, 22 studies with 47 effect sizes compared stereotypes of leaders' agency and communion; and (c) in Shinar's (1975) masculinity-femininity paradigm, 7 studies with 101 effect sizes represented stereotypes of leadership-related occupations on a single masculinity-femininity dimension. Analyses implemented appropriate random and mixed effects models. All 3 paradigms demonstrated overall masculinity of leader stereotypes: (a) In the think manager-think male paradigm, intraclass correlation = .25 for the women-leaders similarity and intraclass correlation = .62 for the men-leaders similarity; (b) in the agency-communion paradigm, g = 1.55, indicating greater agency than communion; and (c) in the masculinity-femininity paradigm, g = 0.92, indicating greater masculinity than the androgynous scale midpoint. Subgroup and meta-regression analyses indicated that this masculine construal of leadership has decreased over time and was greater for male than female research participants. In addition, stereotypes portrayed leaders as less masculine in educational organizations than in other domains and in moderate- than in high-status leader roles. This article considers the relation of these findings to Eagly and Karau's (2002) role congruity theory, which proposed contextual influences on the incongruity between stereotypes of women and leaders. The implications for prejudice against women leaders are also considered.

negative: Design and Characterization of Miniaturized Patch Antennas Loaded With Complementary Split-Ring Resonators An investigation into the design of compact patch antennas loaded with complementary split-ring resonators (CSRRs) and reactive impedance surface (RIS) is presented in this study. The CSRR is incorporated on the patch as a shunt LC resonator providing a low resonance frequency and the RIS is realized using the two-dimensional metallic patches printed on a metal-grounded substrate. Both the meta-resonator (CSRR) and the meta-surface (RIS) are able to miniaturize the antenna size. By changing the configuration of the CSRRs, multi-band operation with varied polarization states can be obtained. An equivalent circuit has been developed for the CSRR-loaded patch antennas to illustrate their working principles. Six antennas with different features are designed and compared, including a circularly-polarized antenna, which validate their versatility for practical applications. These antennas are fabricated and tested. The measured results are in good agreement with the simulation.

negative: Semi-Supervised Neural Machine Translation with Language Models Training neural machine translation models is notoriously slow and requires abundant parallel corpora and computational power. In this work we propose an approach of transferring knowledge from separately trained language models to translation systems, also we investigate several techniques to improve translation quality when there is a lack of parallel data and computational resources. Our method is based on fusion between translation system and language model, and initialization of translation system with weights from pretrained language models. We show that this technique gives +2.2 BLEU score on En–Fr pair of WMT europarl-7 dataset and allows us to reach 20.7 BLEU on 20k parallel sentences in less than 8 hours of training on a single NVIDIA GeForce GTX 1080 Ti. We specifically note, that for this advance we use nothing but monolingual corpora for source and target languages. Significant part of presented results was obtained during DeepHack.Babel hackathon on low-resource machine translation organized by iPavlov Lab.

negative: Non-specific effects of methylphenidate (Ritalin) on cognitive ability and decision-making of ADHD and healthy adults The effect of a single dose of methylphenidate (MPH) on cognitive measures and decision-making processes was assessed in a sample of adults with ADHD and in a control sample. Thirty-two adults satisfying DSM-IV criteria for ADHD and 26 healthy controls performed several cognitive tasks. Half of the participants received MPH prior to performing the tasks, and the other half received placebo in a randomized, double-blind manner. The average digit-span test score was higher in the groups receiving MPH compared to the groups receiving placebo, while diagnosis did not have an effect upon scores. In decision-making tasks, however, MPH did not have an effect upon performance, whereas in one of the tasks the average proportion of risky choices was higher in ADHD adults compared to controls. Our data therefore demonstrates that (a) MPH is capable of enhancing specific aspects of cognitive performance and (b) this enhancement is not specific to ADHD.

negative: Evaluating Window Joins over Unbounded Streams We investigate algorithms for evaluating sliding window joins over pairs of unbounded streams. We introduce a unittime-basis cost model to analyze the expected performance of these algorithms. Using this cost model, we propose strategies for maximizing the efficiency of processing joins in three scenarios. First, we consider the case where one stream is much faster than the other. We show that asymmetric combinations of join algorithms, (e.g., hash join on one input, nested-loops join on the other) can outperform symmetric join algorithm implementations. Second, we investigate the case where system resources are insufficient to keep up with the input streams. We show that we can maximize the number of join result tuples produced in this case by properly allocating computing resources across the two input streams. Finally, we investigate strategies for maximizing the number of result tuples produced when memory is limited, and show that proper memory allocation across the two input streams can result in significantly lower resource usage and/or more result tuples produced.

negative: Reconfigurable Multiband Antenna Designs for Wireless Communication Devices New designs for compact reconfigurable antennas are introduced for mobile communication devices. The uniqueness of the antenna designs are that they allow various groups of their operating frequency bands to be selected electronically. In particular, each group of frequency bands, or mode, can be made to serve several different communication systems simultaneously. These systems may include various combinations of GSM, DCS, PCS, UMTS, Bluetooth, and wireless local-area network (LAN). Therefore, by electronically selecting different antenna modes, a variety of communication systems can be conveniently served by only one antenna. One advantage is that through the different operational modes, the total antenna volume can be reused, and therefore the overall antenna can be made compact. In these designs, the selection of the different modes is achieved by either i) switching different feeding locations of the antenna (switched feed) or ii) switching or breaking of the antenna's connection to the ground (switched ground). This paper demonstrates these two designs. For the first design of switched feed, it can support GSM, DCS, PCS, and UMTS. In the second design, the antenna makes use of a switched-ground technique, which can cover GSM, DCS, PCS, UMTS, Bluetooth, and 2.4 GHz wireless LAN. The designs are investigated when ideal switches and also various realistic active switches based on PIN diodes, GaAs field effect transistor, and MEMs configurations. The designs are verified through both numerical simulations and measurement of an experimental prototype. The results confirm good performance of the two multiband reconfigurable antenna designs.

negative: An Efficient Multiplication Algorithm Using Nikhilam Method Multiplication is one of the most important operation in computer arithmetic. Many integer operations such as squaring, division and computing reciprocal require same order of time as multiplication whereas some other operations such as computing GCD and residue operation require at most a factor of logn time more than multiplication. We propose an integer multiplication algorithm using Nikhilam method of Vedic mathematics which can be used to multiply two binary numbers efficiently.

negative: Comparison of eSports and Traditional Sports Consumption Motives With recognition of the need for studying eSports in this interactive digital communication era, this study explored 14 motivational factors affecting the time spent on eSports gaming. Using a sample of 515 college students and athletic event attendees, we further compared eSports game patterns to their non-eSport or traditional sport involvements (game participation, game attendance, sports viewership, sports readership, sports listenership, Internet usage specific to sports, and purchase of team merchandise). Multiple regression results indicated that competition and skill had a statistically significant impact on the time spent on eSports games while peer pressure had marginal significance. Related to the overall findings, developing tailored messages that drives consumption behaviors of target audiences to specific eSports games will provide a better chance for marketers to fulfill their strategic goals of increased purchasing and larger market shares. Understanding that the interest in competition and skill are critical to eSports gamers may influence marketers to focus on creating games and opportunities for gamers to compete against each other and give tangible rewards to the winner. The use of peer pressure may be another motivational factor for playing. Consequently, those marketing dollars could be spent more on the interactive nature of game design. The subsequent analysis on cross-validation check suggests that the results of the regression analysis could be generalized.

negative: Protocols for Public Key Cryptosystems New Cryptographic protocols which take full advantage of the unique properties of public key cryptosystems are now evolving. Several protocols for public key distribution and for digital signatures are briefly compared with each other and with the conventional alternative.

negative: Retargeting Semantically-Rich Photos Semantically-rich photos contain a rich variety of semantic objects (e.g., pedestrians and bicycles). Retargeting these photos is a challenging task since each semantic object has fixed geometric characteristics. Shrinking these objects simultaneously during retargeting is prone to distortion. In this paper, we propose to retarget semantically-rich photos by detecting photo semantics from image tags, which are predicted by a multi-label SVM. The key technique is a generative model termed latent stability discovery (LSD). It can robustly localize various semantic objects in a photo by making use of the predicted noisy image tags. Based on LSD, a feature fusion algorithm is proposed to detect salient regions at both the low-level and high-level. These salient regions are linked into a path sequentially to simulate human visual perception . Finally, we learn the prior distribution of such paths from aesthetically pleasing training photos. The prior enforces the path of a retargeted photo to be maximally similar to those from the training photos. In the experiment, we collect 217 1600 ×1200 photos, each containing over seven salient objects. Comprehensive user studies demonstrate the competitiveness of our method.

negative: Holistic Multi-modal Memory Network for Movie Question Answering Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data sources. Existing approaches only employ partial interactions among data sources in one attention hop. In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop. In addition, it takes answer choices into consideration during the context retrieval stage. Therefore, the proposed framework effectively integrates multi-modal context, question, and answer information, which leads to more informative context retrieved for question answering. Our HMMN framework achieves state-of-the-art accuracy on MovieQA dataset. Extensive ablation studies show the importance of holistic reasoning and contributions of different attention strategies.

negative: Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors.

negative: Deep Continuous Fusion for Multi-sensor 3D Object Detection In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.

negative: Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

negative: ConTagNet: Exploiting User Context for Image Tag Recommendation In recent years, deep convolutional neural networks have shown great success in single-label image classification. However, images usually have multiple labels associated with them which may correspond to different objects or actions present in the image. In addition, a user assigns tags to a photo not merely based on the visual content but also the context in which the photo has been captured. Inspired by this, we propose a deep neural network which can predict multiple tags for an image based on the content as well as the context in which the image is captured. The proposed model can be trained end-to-end and solves a multi-label classification problem. We evaluate the model on a dataset of 1,965,232 images which is drawn from the YFCC100M dataset provided by the organizers of Yahoo-Flickr Grand Challenge. We observe a significant improvement in the prediction accuracy after integrating user-context and the proposed model performs very well in the Grand Challenge.

negative: Going deeper with convolutions We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.