title: Fast Trajectory Planning and Robust Trajectory Tracking for Pedestrian Avoidance

abstract: This paper presents an integrated design method for pedestrian avoidance by considering the interaction between trajectory planning and trajectory tracking. This method aims to reduce the need for control calibration by properly considering plant uncertainties and tire force limits at the design stage. Two phases of pedestrian avoidance—trajectory planning and trajectory tracking—are designed in an integrated manner. The available tire force is distributed to the feedforward part, which is used to generate the nominal trajectory in trajectory planning phase, and to the feedback part, which is used for trajectory tracking. The trajectory planning problem is solved not by searching through a continuous spectrum of steering/braking actions, but by examining a limited set of “motion primitives,” or motion templates that can be adopted in sequence to avoid the pedestrian. An emergency rapid random tree (RRT) methodology is proposed to quickly identify a feasible solution. Subsequently, in order to guarantee accuracy and provide safety margin in trajectory tracking with presence of model uncertainties and exogenous disturbance, a simplified LQR-based funnel algorithm is proposed. Simulation results provide insight into how pedestrian collisions can be avoided under given initial vehicle and pedestrian states.


title: See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification

abstract: Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises. In this paper, we propose Weakly Supervised Data Augmentation Network (WS-DAN) to explore the potential of data augmentation. Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning. Next, we augment the image guided by these attention maps, including attention cropping and attention dropping. The proposed WS-DAN improves the classification accuracy in two folds. In the first stage, images can be seen better since more discriminative parts' features will be extracted. In the second stage, attention regions provide accurate location of object, which ensures our model to look at the object closer and further improve the performance. Comprehensive experiments in common fine-grained visual classification datasets show that our WS-DAN surpasses the state-of-the-art methods, which demonstrates its effectiveness.


title: Variable Input Delay CMOS Logic Design for Low Dynamic Power Circuits

abstract: A gate that oers dier ent delays for dier ent input-output paths through it, is known as a variable input delay (VID) gate. The upper bound on this differential delay capability is specie d by the parameter \ub". These gates can be used to minimize the active power of a digital CMOS circuit by a path balancing and glitch ltering techniques discussed in recent publications. In this paper, we describe transistor sizing methods for three types of VID gates that satisfy given delay requirements. The three ways to obtain the differential delays are capacitance manipulation, nMOS transistor insertion, and CMOS transmission gate insertion. We also describe techniques for calculating the ub of each VID gate type. Finally, we outline an algorithm for quick determination of the transistor sizes for a gate for a given load capacitance.


title: LXC Container Migration in Cloudlets under Multipath TCP

abstract: The growing popularity of mobile devices and Internet of Things (IoT) has inspired the advent of the Cloudlet concept—a "small data center" close to users at the edge. It is believed that the Quality of Experience (QoE) of end users would greatly improve if they can access required resources within a one-hop distance from Cloudlet servers. Over the years, many researchers have proposed using virtual machines (VMs) as such service-provisioning servers. However, seeing the potentiality of containers—a lightweight virtualization tool, this paper adopts LXC containers as Cloudlet platforms. To facilitate container migration between Cloudlets, CRIU (Checkpoint/Restore in Userspace) has been chosen as the migration tool. Since the migration process goes through the Wide Area Network (WAN), which may experience congestion or network failures, this paper adopts the MPTCP (Multipath TCP) protocol to address the challenge. The multiple subflows established within a MPTCP connection can improve the resilience of the migration process and reduce migration time. We have conducted a number of experiments to validate the proposed approach. The experimental results show that LXC containers are suitable candidates for the problem and MPTCP protocol is effective in enhancing the migration process.


title: Hallmarks of Cancer: The Next Generation

abstract: The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list—reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.


title: CINET 2.0: A CyberInfrastructure for Network Science

abstract: Analysis of structural properties and dynamics of networks is currently a central topic in many disciplines including Social Sciences, Biology and Business. CINET, a cyber infrastructure for such studies, introduced the concept of supporting network analysis as a service. The basic idea is to allow experts in various disciplines to focus on obtaining domain-specific insights from the results of network analyses instead of worrying about programming details and allocation of computational resources needed to carry out the analyses. A basic version of CINET was released in May 2012. This paper discusses CINET 2.0, a significantly enhanced version that supports complex network analyses through a web portal. CINET 2.0 has already been used for teaching courses related to Network Science at several US universities. In this paper, we discuss how CINET 2.0 significantly extends CINET 1.0 through enhancements to some components and the addition of new components.


Baluns , and Detuning Elements in MRI RF coils


title: The Neural Bases of Emotion Regulation: Reappraisal and Suppression of Negative Emotion

abstract: Background Emotion regulation strategies are thought to differ in when and how they influence the emotion-generative process. However, no study to date has directly probed the neural bases of two contrasting (e.g., cognitive versus behavioral) emotion regulation strategies. This study used functional magnetic resonance imaging (fMRI) to examine cognitive reappraisal (a cognitive strategy thought to have its impact early in the emotion-generative process) and expressive suppression (a behavioral strategy thought to have its impact later in the emotion-generative process). Methods Seventeen women viewed 15 sec neutral and negative emotion-eliciting films under four conditions—watch-neutral, watch-negative, reappraise-negative, and suppress-negative—while providing emotion experience ratings and having their facial expressions videotaped. Results Reappraisal resulted in early (0–4.5 sec) prefrontal cortex (PFC) responses, decreased negative emotion experience, and decreased amygdala and insular responses. Suppression produced late (10.5–15 sec) PFC responses, decreased negative emotion behavior and experience, but increased amygdala and insular responses. Conclusions These findings demonstrate the differential efficacy of reappraisal and suppression on emotional experience, facial behavior, and neural response and highlight intriguing differences in the temporal dynamics of these two emotion regulation strategies.


title: Digitalisation and the regulation of work: theoretical issues and normative challenges

abstract: This paper presents an introductory overview of the main issues that the digitalisation of industrial enterprises known as Industry 4.0 raises for social sciences. First, it will show that this technological transition—which, however, is unfinished and is seen to be in continuity with the so-called “third industrial revolution”—cannot be interpreted with reference to a deterministic approach. It can be analysed more usefully as a range of decisions affecting the industrial policies of national states, the conception and design of machines, their adoption in production processes and finally their use by operators. Second, certain aspects of Industry 4.0 of special concern in terms of organisation of work processes will be analysed on various scales (from inter-company transactions to the specific tasks of individual workers). Finally, we will explain various hypotheses that social and economic research is developing with regard to the new technologies’ controversial effects on employment.


title: A single-input dual-output Boost converter with PI controller

abstract: In this paper, the proposed SIMO (Single input multiple outputs) DC-DC converter based on coupled inductor. The required controllable high DC voltage and intermediate DC voltage with high voltage gain from low input voltage sources, like renewable energy, can be achieved easily from the proposed converter. The high voltage DC bus can be used as the leading power for a DC load and intermediate voltage DC output terminals can charge supplementary power sources like battery modules. This converter operates simply with one power switch. It incorporates the techniques of voltage clamping (VC) and zero current switching (ZCS). The simulation result in PSIM software shows that the aims of high efficiency, high voltage gain, several output voltages with unlike levels, are achieved.


title: Trichoderma gamsii(NFCCI 2177): A newly isolated endophytic, psychrotolerant, plant growth promoting, and antagonistic fungal strain

abstract: An endophytic fungus has been isolated from the lateral roots of lentil (Lens esculenta Moench), growing under mountain ecosystem of Indian Himalayan Region (IHR). While the fungus was observed as fast growing with white scanty mycelium turning to turmeric brown in 5 days of incubation at 25 °C, it also produced a unique odor. The fungus exhibited growth between 4 and 30 °C (optimum 25 °C) and tolerated pH between 2.0 and 13.5 (optimum 4-6). Based on phenotypic (colony morphology and microscopy) and genotypic (18S rRNA analysis) characters, the fungus was identified as Trichoderma gamsii (99% similarity). The fungus was evaluated for its plant growth promotion and biocontrol properties. The fungus was found to be positive for phosphate solubilization, chitinase activity, and production of ammonia and salicylic acid, while the results for production of IAA, HCN, and siderophores were negative. Out of the seven phytopathogenic fungi tested, it showed antagonism against six. Bioassays conducted under green house using four test crops (two cereals and two legumes) showed its potential in plant growth promotion. The fungus has potential to be developed as a bioformulation for application under mountain ecosystem.


title: Sensorless control of the BLDC motors from near-zero to high speeds

abstract: This work presents the theory and implementation of a novel sensorless control technique for the brushless DC (BLDC) motor. The proposed new sensorless drive method solves the problem of the sensorless BLDC motor drives at very low speeds. It provides a highly accurate and robust sensorless operation from near zero to high speeds. For this purpose, an approach, a new flux linkage function is defined, that is speed-independent. The validity of the proposed method is verified through both simulation and experimental results and discussion.


title: Fully Automatic Multi-Organ Segmentation Based on Multi-Boost Learning and Statistical Shape Model Search

abstract: In this paper, an automatic multi-organ segmentation based on multi-boost learning and statistical shape model search was proposed. First, simple but robust Multi-Boost Classier was trained to hierarchically locate and pre-segment multiple organs. To ensure the generalization ability of the classierrelative


title: Structural similarity image quality reliability: Determining parameters and window size

abstract: The need to obtain objective values of the quality of distorted images with respect to the original is fundamental in multimedia and image processing applications. It is generally required that this value correlates well with the human vision system (HVS). In spite of the properties and the general use of the mean square error (MSE) measurement, this has a poor correlation with HSV, which has led to the development of methods such as structural similarity (SSIM). This metric improves the correlation with respect to the classic MSE and PSNR (peak signal to noise ratio). However, its behavior depends on the values assigned to constants and on the windows size selected. These values are usually assigned arbitrarily and there have been no studies on how they affect the SSIM. In this work, we have analyzed empirically the most appropriate values for the different constants used in the SSIM equations. We have also analyzed the importance of window size in the calculation of MSSIM, and propose a method for determining the window size based on image complexity. Using the values selected and the window size defined, the correlation between SSIM and DMOS (differential mean opinion score) is significantly improved by around 17% with respect to the values commonly used.


title: Application of Data Mining Methods and Techniques for Diabetes Diagnosis

abstract: Medical professionals need a reliable prediction methodology to diagnose Diabetes. Data mining is the process of analysing data from different perspectives and summarizing it into useful information. The main goal of data mining is to discover new patterns for the users and to interpret the data patterns to provide meaningful and useful information for the users. Data mining is applied to find useful patterns to help in the important tasks of medical diagnosis and treatment. This project aims for mining the relationship in Diabetes data for efficient classification. The data mining methods and techniques will be explored to identify the suitable methods and techniques for efficient classification of Diabetes dataset and in mining useful patterns.


An Implementation of a Sub-nanosecond UWB Pulse Generator


title: A Novel Design of Rotor Contour for Variable Reluctance Resolver by Injecting Auxiliary Air-Gap Permeance Harmonics

abstract: This paper proposes a novel rotor contour design for variable reluctance (VR) resolvers by injecting auxiliary air-gap permeance harmonics. Based on the resolver model with nonoverlapping tooth-coil windings, the influence of air-gap length function is first investigated by finite element (FE) method, and the detection accuracy of designs with higher values of fundamental wave factor may deteriorate due to the increasing third order of output voltage harmonics. Further, the origins of the third harmonics are investigated by analytical derivation and FE analyses of output voltages. Furthermore, it is proved that the voltage harmonics and the detection accuracy are significantly improved by injecting auxiliary air-gap permeance harmonics in the design of rotor contour. In addition, the proposed design can also be employed to eliminate voltage tooth harmonics in a conventional VR resolver topology. Finally, VR resolver prototypes with the conventional and the proposed rotors are fabricated and tested respectively to verify the analyses.


title: Dependency-Based Semantic Parsing for Concept-Level Text Analysis

abstract: Concept-level text analysis is superior to word-level analysis as it preserves the semantics associated with multi-word expressions. It offers a better understanding of text and helps to significantly increase the accuracy of many text mining tasks. Concept extraction from text is a key step in concept-level text analysis. In this paper, we propose a ConceptNet-based semantic parser that deconstructs natural language text into concepts based on the dependency relation between clauses. Our approach is domain-independent and is able to extract concepts from heterogeneous text. Through this parsing technique, 92.21% accuracy was obtained on a dataset of 3,204 concepts. We also show experimental results on three different text analysis tasks, on which the proposed framework outperformed state-of-the-art parsing techniques.


title: Is Twitter a Generalizable Public Sphere?: A Comparison of 2016 Presidential Campaign Issue Importance among General and Twitter Publics

abstract: News media often cite Twitter and other social media metrics as measures of public opinion. This study draws on a quota sample (N=420) of adult American Twitter users to determine the representativeness of the Twitter public in relation to the U.S. general population around 14 issues related to the 2016 presidential campaign, and considers implications for news media coverage of the Twitterverse and other social media populations as representations of the greater public sphere.


title: Synchrophasor Measurement Technology in Power Systems: Panorama and State-of-the-Art

abstract: Phasor measurement units (PMUs) are rapidly being deployed in electric power networks across the globe. Wide-area measurement system (WAMS), which builds upon PMUs and fast communication links, is consequently emerging as an advanced monitoring and control infrastructure. Rapid adaptation of such devices and technologies has led the researchers to investigate multitude of challenges and pursue opportunities in synchrophasor measurement technology, PMU structural design, PMU placement, miscellaneous applications of PMU from local perspectives, and various WAMS functionalities from the system perspective. Relevant research articles appeared in the IEEE and IET publications from 1983 through 2014 are rigorously surveyed in this paper to represent a panorama of research progress lines. This bibliography will aid academic researchers and practicing engineers in adopting appropriate topics and will stimulate utilities toward development and implementation of software packages.


title: Exploiting fully convolutional neural networks for fast road detection

abstract: Road detection is a crucial task in autonomous navigation systems. It is responsible for delimiting the road area and hence the free and valid space for maneuvers. In this paper, we consider the visual road detection problem where, given an image, the objective is to classify every of its pixels into road or non-road. We address this task by proposing a convolutional neural network architecture. We are especially interested in a model that takes advantage of a large contextual window while maintaining a fast inference. We achieve this by using a Network-in-Network (NiN) architecture and by converting the model into a fully convolutional network after training. Experiments have been conducted to evaluate the effects of different contextual window sizes (the amount of contextual information) and also to evaluate the NiN aspect of the proposed architecture. Finally, we evaluated our approach using the KITTI road detection benchmark achieving results in line with other state-of-the-art methods while maintaining real-time inference. The benchmark results also reveal that the inference time of our approach is unique at this level of accuracy, being two orders of magnitude faster than other methods with similar performance.


title: Who is Spreading Rumours about Vaccines?: Influential User Impact Modelling in Social Networks

abstract: Vaccine hesitancy, traditionally linked to issues of trust, misinformation and prior beliefs, has been increasingly fuelled by influential groups on social media (SM) and the Internet. Analysis of news media and social networks (SN) accessible in real-time provides a new opportunity for detecting changes in public confidence in vaccines. However, different concerns are important in different regions, and reasons for hesitancy and the role of opinion leaders vary between sub-controversies in the broader vaccination debates. It is therefore important for public health professionals to gain an overview of the emerging debates in cyberspace, identify influential users and rumours, and assess their impact in order to know how to respond. The VAC Medi+Board project aims to visualise the diffusion of rumours through SN and assess the impact of key individuals. We include, as a case study, discussions during winter 2015-16 pertaining to the alleged side-effects of the HPV vaccine.


title: A Systematic Literature Review: Code Bad Smells in Java Source Code

abstract: Code smell is an indication of a software designing problem. The presence of code smells can have a severe impact on the software quality. Smells basically refers to the structure of the code which violates few of the design principals and so has negative effect on the quality of the software. Larger the source code, more is its presence. Software needs to be reliable, robust and easily maintainable so that it can minimize the cost of its development as well as maintenance. Smells may increase the chances of failure of the system during maintenance. A SLR has been performed based on the search of digital libraries that includes the publications since 1999 to 2016. 60 research papers are deeply analyzed that are most relevant. The objective of this paper is to provide an extensive overview of existing research in the field of bad smells, identify the detection techniques and correlation between the detection techniques, in addition to find the name of the code smells that need more attention in detection approaches. This SLR identified that code clone (code smell) receives most research attention. Our findings also show that very few papers report on the impact of code bad smells. Most of the papers focused on the detection techniques and tools. A significant correlation between detection techniques has been calculated. There are four code smells that are not yet detected are Primitive Obsession, Inappropriate Intimacy, Incomplete library class and Comments.


title: Why Users Disintermediate Peer-to-Peer Marketplaces

abstract: This paper reports on a study of the prevalence of and possible reasons for peer-to-peer transaction marketplace (P2PM) users turning to out-of-market (OOM) transactions after finding transaction partners within a P2P system. We surveyed 97 P2PM users and interviewed 22 of 58 who reported going OOM. We did not find any evidence of predisposing personality factors for OOM activity; instead, it seems to be a rational response to circumstances, with a variety of situationally rational motivations at play, such as liking the transaction partner and trusting that good quality repeat transactions will occur in the future.


title: Capsular contracture after breast reconstruction with the lateral thoracodorsal flap

abstract: Early postoperative manipulation of breast implants in patients who underwent breast reconstruction utilizing a lateral thoracodorsal flap is described. This manipulation did not reduce the rate of firm capsular contracture after the reconstruction with the flap, but only postponed its development. Open capsulotomies, however, yielded stationary soft breasts to a statistically significant extent. The patient's acceptance of the reconstructed breast as a natural part of her own body was high. It was not influenced by the occurrence of firm capsular contracture.


title: Protective role for interleukin-5 during chronic Toxoplasma gondii infection.

abstract: To investigate the role of interleukin-5 (IL-5) during Toxoplasma gondii infection, IL-5 knockout (KO) mice and C57BL/6 control mice were infected intraperitoneally with ME49 cysts and the course of infection was monitored. The mortality rate during chronic infection was significantly greater in IL-5-deficient animals, and consistent with this finding, the KO mice harbored a greater number of brain cysts and tachyzoites than did their wild-type counterparts. Although the IL-5 KO animals did not succumb until late during infection, increased susceptibility, as measured by accelerated weight loss, was detectable during the acute stages of infection. The amounts of total immunoglobulin (Ig), IgM, and IgG2b were comparable in both strains, while the amount of IgG1 was much smaller in IL-5 KO mice. Spleen cell production of IL-12 in response to T. gondii antigen was approximately threefold lower in the KO strain, and this decrease correlated with a selective loss of B lymphocytes during culture. A link between the presence of B cells and augmented IL-12 production was established by the finding that after removal of B cells with monoclonal antibody and complement, wild-type- and KO-derived cells produced equivalent levels of IL-12 in response to T. gondii antigen. These results demonstrate a protective role of IL-5 against T. gondii infection and suggest that IL-5 may play a role in the production of IL-12.


title: Terna's approach for on-line monitoring system: Intelligent management of assets in a large scale infrastructures

abstract: The paper deals with the on-line monitoring system integrated by the Italian TSO (Terna S.p.A.) in the HV substations. Arose from the long experience achieved, the latest integrated system is the SMOAT (acronym for Monitoring System for HV, in Italian). Infrastructure introduced by the online monitoring system, integrated into the Terna's existing expert systems (PSE, MBI) are also presented as well as the possible upgrade following the IoT (Internet of Thing) criteria.


title: Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles

abstract: Abstract A 1D electrochemical, lumped thermal model is used to explore pulse power limitations and thermal behavior of a 6 Ah, 72 cell, 276 V nominal Li-ion hybrid-electric vehicle (HEV) battery pack. Depleted/saturated active material Li surface concentrations in the negative/positive electrodes consistently cause end of high-rate (∼25 C) pulse discharge at the 2.7 V cell −1 minimum limit, indicating solid-state diffusion is the limiting mechanism. The 3.9 V cell −1 maximum limit, meant to protect the negative electrode from lithium deposition side reaction during charge, is overly conservative for high-rate (∼15 C) pulse charges initiated from states-of-charge (SOCs) less than 100%. Two-second maximum pulse charge rate from the 50% SOC initial condition can be increased by as much as 50% without risk of lithium deposition. Controlled to minimum/maximum voltage limits, the pack meets partnership for next generation vehicles (PNGV) power assist mode pulse power goals (at operating temperatures >16 °C), but falls short of the available energy goal. In a vehicle simulation, the pack generates heat at a 320 W rate on a US06 driving cycle at 25 °C, with more heat generated at lower temperatures. Less aggressive FUDS and HWFET cycles generate 6–12 times less heat. Contact resistance ohmic heating dominates all other mechanisms, followed by electrolyte phase ohmic heating. Reaction and electronic phase ohmic heats are negligible. A convective heat transfer coefficient of h = 10.1 W m −2 K −1 maintains cell temperature at or below the 52 °C PNGV operating limit under aggressive US06 driving.


title: A new nonlinear guidance logic for trajectory tracking

abstract: A new nonlinear guidance logic, that has demonstrated superior performance in guiding unmanned air vehicles (UAVs) on curved trajectories, is presented. The logic approximates a proportional-derivative controller when following a straight line path, but the logic also contains an element of anticipatory control enabling tight tracking when following curved paths. The method uses inertial speed in the computation of commanded lateral acceleration and adds adaptive capability to the change of vehicle speed due to external disturbances, such as wind. Flight tests using two small UAVs showed that each aircraft was controlled to within 1.6 meters RMS when following circular paths. The logic was ultimately used for air rendezvous of the two aircraft, bringing them in close proximity to within 12 meters of separation, with 1.4 meters RMS relative position errors.


title: The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

abstract: Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR) are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.


title: StarCraft II: A New Challenge for Reinforcement Learning

abstract: This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for reinforcement learning, representing a more difficult class of problems than considered in most prior work. It is a multi-agent problem with multiple players interacting; there is imperfect information due to a partially observed map; it has a large action space involving the selection and control of hundreds of units; it has a large state space that must be observed solely from raw input feature planes; and it has delayed credit assignment requiring long-term strategies over thousands of steps. We describe the observation, action, and reward specification for the StarCraft II domain and provide an open source Python-based interface for communicating with the game engine. In addition to the main game maps, we provide a suite of mini-games focusing on different elements of StarCraft II gameplay. For the main game maps, we also provide an accompanying dataset of game replay data from human expert players. We give initial baseline results for neural networks trained from this data to predict game outcomes and player actions. Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain. On the mini-games, these agents learn to achieve a level of play that is comparable to a novice player. However, when trained on the main game, these agents are unable to make significant progress. Thus, SC2LE offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures.


title: Lane-Level Localization and Mapping in GNSS-Challenged Environments by Fusing Lidar Data and Cellular Pseudoranges

abstract: A method for achieving lane-level localization in global navigation satellite system (GNSS)-challenged environments is presented. The proposed method uses the pseudoranges drawn from unknown ambient cellular towers as an exclusive aiding source for a vehicle-mounted light detection and ranging (lidar) sensor. The following scenario is considered. A vehicle aiding its lidar with GNSS signals enters an environment where these signals become unusable. The vehicle is equipped with a receiver capable of producing pseudoranges to unknown cellular towers in its environment. These pseudoranges are fused through an extended Kalman filter to aid the lidar odometry, while estimating the vehicle's own state (3-D position and orientation) simultaneously with the position of the cellular towers and the difference between the receiver's and cellular towers’ clock error states (bias and drift). The proposed method is computationally efficient and is demonstrated to achieve lane-level accuracy in different environments. Simulation and experimental results with the proposed method are presented illustrating a close match between the vehicle's true trajectory and estimated using the cellular-aided lidar odometry over a 1 km trajectory. The proposed method yielded a 68% reduction in the 2-D position root mean-squared error (RMSE) over lidar odometry-only.


title: 3D Reconstruction Based on Pseudo-Linearization and Errors-in-Variables Model

abstract: This paper proposes a general approach of 3D reconstruction, a major problem arising in computer vision and virtual reality, based on a combination of Pseudo-Linearization and Errors-in-Variables model. The proposed approach concerns a bunch of corrupted measurements under nonlinear constraints, and optimizes the estimation by taking errors into account. Furthermore, we set a synthetic projective model and adopt a standard deviation-expectation criterion to evaluate the performance or our method applied in 3D reconstruction. Also, some test images are picked from an image database to give this method a chance to demonstrate its performance in our experiments. Finally, as a successful application, this method is used in a calibration-free augmented reality system.


title: Bayesian Modeling of Temporal Coherence in Videos for Entity Discovery and Summarization

abstract: A video is understood by users in terms of entities present in it. Entity Discovery is the task of building appearance model for each entity (e.g., a person), and finding all its occurrences in the video. We represent a video as a sequence of tracklets, each spanning 10-20 frames, and associated with one entity. We pose Entity Discovery as tracklet clustering, and approach it by leveraging Temporal Coherence (TC): the property that temporally neighboring tracklets are likely to be associated with the same entity. Our major contributions are the first Bayesian nonparametric models for TC at tracklet-level. We extend Chinese Restaurant Process (CRP) to TC-CRP, and further to Temporally Coherent Chinese Restaurant Franchise (TC-CRF) to jointly model entities and temporal segments using mixture components and sparse distributions. For discovering persons in TV serial videos without meta-data like scripts, these methods show considerable improvement over state-of-the-art approaches to tracklet clustering in terms of clustering accuracy, cluster purity and entity coverage. The proposed methods can perform online tracklet clustering on streaming videos unlike existing approaches, and can automatically reject false tracklets. Finally we discuss entity-driven video summarization- where temporal segments of the video are selected based on the discovered entities, to create a semantically meaningful summary.


Exploring the Usability of Augmented Reality Interaction Techniques During Children's Early Elementary-School Years


title: Object-Based Selective Materialization for Efficient Implementation

abstract: With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and OLAP of spatial data. In this paper, we study methods for spatial OLAP, by integrating nonspatial OLAP methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for the computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies being proposed, including approximation and selective materialization of the spatial objects resulting from spatial OLAP operations. The focus of our study is on a method for spatial cube construction, called object-based selective materialization, which is different from cuboid-based selective materialization (proposed in previous studies of nonspatial data cube construction). Rather than using a cuboid as an atomic structure during the selective materialization, we explore granularity on a much finer level: that of a single cell of a cuboid. Several algorithms are proposed for object-based selective materialization of spatial data cubes, and a performance study has demonstrated the effectiveness of these techniques.


title: Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation

abstract: Convolutional neural network (CNN) has achieved the state-of-the-art performance in many different visual tasks. Learned from a large-scale training data set, CNN features are much more discriminative and accurate than the handcrafted features. Moreover, CNN features are also transferable among different domains. On the other hand, traditional dictionary-based features (such as BoW and spatial pyramid matching) contain much more local discriminative and structural information, which is implicitly embedded in the images. To further improve the performance, in this paper, we propose to combine CNN with dictionary-based models for scene recognition and visual domain adaptation (DA). Specifically, based on the well-tuned CNN models (e.g., AlexNet and VGG Net), two dictionary-based representations are further constructed, namely, mid-level local representation (MLR) and convolutional Fisher vector (CFV) representation. In MLR, an efficient two-stage clustering method, i.e., weighted spatial and feature space spectral clustering on the parts of a single image followed by clustering all representative parts of all images, is used to generate a class-mixture or a class-specific part dictionary. After that, the part dictionary is used to operate with the multiscale image inputs for generating mid-level representation. In CFV, a multiscale and scale-proportional Gaussian mixture model training strategy is utilized to generate Fisher vectors based on the last convolutional layer of CNN. By integrating the complementary information of MLR, CFV, and the CNN features of the fully connected layer, the state-of-the-art performance can be achieved on scene recognition and DA problems. An interested finding is that our proposed hybrid representation (from VGG net trained on ImageNet) is also complementary to GoogLeNet and/or VGG-11 (trained on Place205) greatly.


title: Supraventricular Tachycardia Classification in the 12-Lead ECG Using Atrial Waves Detection and a Clinically Based Tree Scheme

abstract: Specific supraventricular tachycardia (SVT) classification using surface ECG is considered a challenging task, since the atrial electrical activity (AEA) waves, which are a crucial element for obtaining diagnosis, are frequently hidden. In this paper, we present a fully automated SVT classification method that embeds our recently developed hidden AEA detector in a clinically based tree scheme. The process begins with initial noise removal and QRS detection. Then, ventricular features are extracted. According to these features, an initial AEA-wave search window is defined and a single AEA-wave is detected. Using a synthetic Gaussian signal and a linear combination of 12-lead ECG signals, all AEA-waves are detected. In accord with the atrial and ventricular information found, classification to atrial fibrillation, atrial flutter, atrioventricular nodal reentry tachycardia, atrioventricular reentry tachycardia, or sinus rhythm is performed in the framework of a clinically oriented decision tree. A study was performed to evaluate the classification from 68 patients (26 were used for the classifier's design, 42 were used for its validation). Average sensitivity of 83.21% [95% confidence interval (CI): 79.33–86.49%], average specificity of 95.80% (95% CI: 94.73–96.67%), and average accuracy of 93.29% (95% CI: 92.13–94.28%) were achieved compared to the definite diagnosis. In conclusion, the presented method may serve as a valuable decision support tool, allowing accurate detection of SVTs using noninvasive means.


title: Search for a Herbal Medicine: Anti-asthmatic Activity of MethanolicExtract of Curcuma longa

abstract: Ethnopharmacological evidence tells us that rhizomes of Curcuma longa L. (Zingiberaceae) have an imposing array of pharmacological activities and has been used traditionally to treat inflammatory conditions since ages. Curcumin is one of the major constituent in Curcuma longa L., being accountable for its biological actions. Asthma is fundamentally the consequence of inflammatory process involving the respiratory system. In this present work we attempt to unravel the anti-asthmatic property of curcumin through changes brought about in some novel bio-molecular parameters that play a decisive role in the pathophysiology of inflammation and thereby in asthma. Short (15 days) and long (60 days) term studies were carried by dividing rats into 5 groups; each group consisted of 3 male and 3 female rats. Rats of all groups excluding the control (saline treated) group were sensitized with ovalbumin (OVA) and apart from the control and the sensitized control (i.e. only OVA treated) groups, remaining three groups received standard drug (i.e. ketotifen and beclomethasone), curcumin (100 mg/kg) and (200 mg/kg) correspondingly. Broncho alveolar lavage fluid (BALF) and lung tissue homogenate were obtained following sacrifice of animals after 15 days (short term) and 60 days (long term) of treatment and then nitrate, malonyl dialdehyde (MDA), myeloperoxidase (MPO) levels and percentage of intact and granulated mast cells were determined. On treatment with OVA, nitrate, MDA and MPO levels were elevated, whereas the percentage of intact mast cells was decreased in comparison to control levels. A considerable decrease in the nitrite, MDA and MPO levels were observed in the Curcumin-II (200 mg/kg) treated group when compared to that treated with Curcumin-I (100 mg/kg). Curcumin appreciably refurbished the distorted bio-molecular parameters in asthma. But, the precise molecular machinery by which curcumin decreases the levels of malonyl


title: On Log-Structured Merge for Solid-State Drives

abstract: Log-structure merge (LSM) is an increasingly prevalent approach to indexing, especially for modern writeheavy workloads. LSM organizes data in levels with geometrically increasing sizes. Records enter the top level, whenever a level fills up, it is merged down into the next level. Hence, the index is updated only through merges and records are never updated inplace. While originally conceived to avoid slow random accesses of hard drives, LSM also turns out to be especially suited to solidstate drives, or any block-based storage with expensive writes. We study how to further reduce writes in LSM. Traditionally, LSM always merges an overflowing level fully into the next. We investigate in depth how partial merges save writes and prove bounds on their effectiveness. We propose new algorithms that make provably good decisions on whether to perform a partial merge, and if yes, which part of a level to merge. We also show how to further reduce writes by reusing data blocks during merges. Overall, our approach offers better worst-case guarantees and better practical performance than existing LSM variants.


title: Learning locomotion skills using DeepRL: does the choice of action space matter?

abstract: The use of deep reinforcement learning allows for high-dimensional state descriptors, but little is known about how the choice of action representation impacts learning and the resulting performance. We compare the impact of four different action parameterizations (torques, muscle-activations, target joint angles, and target joint-angle velocities) in terms of learning time, policy robustness, motion quality, and policy query rates. Our results are evaluated on a gait-cycle imitation task for multiple planar articulated figures and multiple gaits. We demonstrate that the local feedback provided by higher-level action parameterizations can significantly impact the learning, robustness, and motion quality of the resulting policies.


title: Clustering validity assessment: finding the optimal partitioning of a data set

abstract: Clustering is a mostly unsupervised procedure and the majority of clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set. As a consequence, in most applications the resulting clustering scheme requires some sort of evaluation regarding its validity. In this paper we present a clustering validity procedure, which evaluates the results of clustering algorithms on data sets. We define a validity index, S Dbw, based on well-defined clustering criteria enabling the selection of optimal input parameter values for a clustering algorithm that result in the best partitioning of a data set. We evaluate the reliability of our index both theoretically and experimentally, considering three representative clustering algorithms run on synthetic and real data sets. We also carried out an evaluation study to compare S Dbw performance with other known validity indices. Our approach performed favorably in all cases, even those in which other indices failed to indicate the correct partitions in a data set.


title: Accurate nonrigid 3D human body surface reconstruction using commodity depth sensors

abstract: In the last decade, 3D modeling techniques enjoyed a booming development in both hardware and software. High-end hardware generates high fidelity results, but the cost is prohibitive, whereas consumer-level devices generate plausible results for entertainment purposes but are not appropriate for medical uses. We present a cost-effective and easy-to-use 3D body reconstruction system using consumer-grade depth sensors, which provides reconstructed body shapes with a high degree of accuracy and reliability appropriate for medical applications. Our surface registration framework integrates the articulated motion assumption, global loop closure constraint, and a general as-rigid-as-possible deformation model. To enhance the reconstruction quality, we propose a novel approach to accurately infer skeletal joints from anatomical data using multimodality registration. We further propose a supervised predictive model to infer the skeletal joints for arbitrary subjects independent from anatomical data reference. A rigorous validation test has been conducted on real subjects to evaluate the reconstruction accuracy and repeatability. Our system has the potential to make accurate body surface scanning systems readily available for medical professionals and the general public. The system can be used to obtain additional health data derived from 3D body shapes, such as the percentage of body fat.


title: Polarization resolved second harmonic microscopy

abstract: Second harmonic (SH) microscopy has proven to be a powerful imaging modality over the past years due to its intrinsic advantages as a multiphoton process with endogenous contrast specificity, which allows pinhole-less optical sectioning, non-invasive observation, deep tissue penetration, and the possibility of easier signal detection at visible wavelengths. Depending on the relative orientation between the polarization of the incoming light and the second-order susceptibility of non-centrosymmetric structures, SH microscopy provides the unique capacity to probe the absolute molecular structure of a broad variety of biological tissues without the necessity for additional labeling. In addition, SH microscopy, when working with polarimetry, provides clear and in-depth insights on the details of molecular orientation and structural symmetry. In this review, the working principles of the polarization resolving techniques and the corresponding implements of SH microscopy are elucidated, with focus on Stokes vector based polarimetry. An overview of the advancements on SH anisotropy measurements are also presented. Specifically, the recent progresses on the following three topics in polarization resolved SH microscopy will be elucidated, which include Stokes vector resolving for imaging molecular structure and orientation, 3-D structural chirality by SH circular dichroism, and correlation with fluorescence lifetime imaging (FLIM) for in vivo wound healing diagnosis. The potentials and challenges for future researches in exploring complex biological tissues are also discussed.


title: Adapted Deep Embeddings: A Synthesis of Methods for $k$-Shot Inductive Transfer Learning

abstract: The focus in machine learning has branched beyond training classifiers on a single task to investigating how previously acquired knowledge in a source domain can be leveraged to facilitate learning in a related target domain, known as inductive transfer learning. Three active lines of research have independently explored transfer learning using neural networks. In weight transfer, a model trained on the source domain is used as an initialization point for a network to be trained on the target domain. In deep metric learning, the source domain is used to construct an embedding that captures class structure in both the source and target domains. In few-shot learning, the focus is on generalizing well in the target domain based on a limited number of labeled examples. We compare state-of-the-art methods from these three paradigms and also explore hybrid adapted-embedding methods that use limited target-domain data to fine tune embeddings constructed from source-domain data. We conduct a systematic comparison of methods in a variety of domains, varying the number of labeled instances available in the target domain ($k$), as well as the number of target-domain classes. We reach three principal conclusions: (1) Deep embeddings are far superior, compared to weight transfer, as a starting point for inter-domain transfer or model re-use (2) Our hybrid methods robustly outperform every few-shot learning and every deep metric learning method previously proposed, with a mean error reduction of 34% over state-of-the-art. (3) Among loss functions for discovering embeddings, the histogram loss (Ustinova&Lempitsky, 2016) is most robust. We hope our results will motivate a unification of research in weight transfer, deep metric learning, and few-shot learning.


title: Brain–Gut Microbiome Interactions and Functional Bowel Disorders

abstract: Alterations in the bidirectional interactions between the intestine and the nervous system have important roles in the pathogenesis of irritable bowel syndrome (IBS). A body of largely preclinical evidence suggests that the gut microbiota can modulate these interactions. A small and poorly defined role for dysbiosis in the development of IBS symptoms has been established through characterization of altered intestinal microbiota in IBS patients and reported improvement of subjective symptoms after its manipulation with prebiotics, probiotics, or antibiotics. It remains to be determined whether IBS symptoms are caused by alterations in brain signaling from the intestine to the microbiota or primary disruption of the microbiota, and whether they are involved in altered interactions between the brain and intestine during development. We review the potential mechanisms involved in the pathogenesis of IBS in different groups of patients. Studies are needed to better characterize alterations to the intestinal microbiome in large cohorts of well-phenotyped patients, and to correlate intestinal metabolites with specific abnormalities in gut–brain interactions.


title: Optimal Target Assignment and Path Finding for Teams of Agents

abstract: We study the TAPF (combined target-assignment and path-finding) problem for teams of agents in known terrain, which generalizes both the anonymous and non-anonymous multi-agent path-finding problems. Each of the teams is given the same number of targets as there are agents in the team. Each agent has to move to exactly one target given to its team such that all targets are visited. The TAPF problem is to first assign agents to targets and then plan collision-free paths for the agents to their targets in a way such that the makespan is minimized. We present the CBM (Conflict-Based Min-Cost-Flow) algorithm, a hierarchical algorithm that solves TAPF instances optimally by combining ideas from anonymous and non-anonymous multi-agent path-finding algorithms. On the low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to assign all agents in a single team to targets and plan their paths. On the high level, CBM uses conflict-based search to resolve collisions among agents in different teams. Theoretically, we prove that CBM is correct, complete and optimal. Experimentally, we show the scalability of CBM to TAPF instances with dozens of teams and hundreds of agents and adapt it to a simulated warehouse system.


title: A secure cloud framework to share EHRs using modified CP-ABE and the attribute bloom filter

abstract: In recent years, the Internet of Things (IoT), cloud computing, and wireless body area networks (WBANs) have converged and become popular due to their potential to improve quality of life. This convergence has greatly promoted the industrialization of e-healthcare. With the flourishing of the e-healthcare industry, full electronic health records (EHRs) are expected to promote preventative health services as well as global health. However, the outsourcing of EHRs to third-party servers, like the cloud, involves many challenges, including securing health information and preserving privacy. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising scheme for storing and sharing information in third-party servers. This scheme enables patients and doctors to encrypt or decrypt their information using access policies defined by attributes. In this scheme, the access policy is tied with the ciphertext in the form of plaintext, which may risk leaking personal patient information. Earlier protocols only partially hide the attribute values in the access policies but leave the attribute names unprotected. To address these security issues, we propose a secure cloud framework using modified CP-ABE and an attribute Bloom filter (ABF). In modified CP-ABE, we can hide the entire attribute, including values, in the access policies. The ABFs assist in data decryption by evaluating the presence of an attribute in the access policy and pointing to its position. Security analysis and performance evaluation demonstrate the efficiency and effectiveness of the proposed framework. Finally, the proposed framework is explored to verify its feasibility.


title: Low RF-Complexity Technologies to Enable Millimeter-Wave MIMO with Large Antenna Array for 5G Wireless Communications

abstract: Millimeter-wave (mmWave) MIMO with large antenna array has attracted considerable interests from academic and industry communities, as it can provide larger bandwidth and higher spectrum efficiency. However, with hundreds of antennas, the number of radio frequency (RF) chains required by mmWave MIMO is also huge, leading to unaffordable hardware cost and power consumption in practice. In this paper, we investigate low RF-complexity technologies to solve this bottleneck. We first review the evolution of low RF-complexity technologies from microwave frequencies to mmWave frequencies. Then, we discuss two promising low RF-complexity technologies for mmWave MIMO systems in detail, i.e., phased array based hybrid precoding (PAHP) and lens array based hybrid precoding (LAHP), including their principles, advantages, challenges, and recent results. We compare the performance of these two technologies to draw some insights about how they can be deployed in practice. Finally, we conclude this paper and point out some future research directions in this area.


title: IoT Mashup as a Service: Cloud-Based Mashup Service for the Internet of Things

abstract: Mashup, a way to compose a new service from existing services, is expected to play a great role in internet of things (IoT) environment. Many recent researches suggest that mashup in IoT environment is possible with existing web mashup technology if each thing exposes its functionalities as a web service. However, this approach may have limitations in dealing with many heterogeneous devices and computation scalability in the presence of large number of things involved. In this paper, we propose a cloud-based IoT mashup service model, called IoT Mashup as a Service (IoTMaaS), to overcome heterogeneity of devices by following the model driven architecture principles and computational scalability based on cloud computing paradigm. We also design a cloud platform on which IoTMaaS be executed in harmony with stakeholders such as end users, device manufacturers, and cloud computing providers. We proved the concept of the architecture by implementing a prototype platform with an vacant room detection application.


title: An Overlap-Save Frequency-Domain Implementation of the Delayless Subband ANC Algorithm

abstract: An efficient active noise control algorithm based on the delay less subband adaptive filter was previously proposed by Park (ldquoA delayless subband active noise control system for wideband noise control, IEEE transactions on speech and audio processing, vol. 9, no. 8, pp. 892-897, November 2001), whereby the computational complexity is reduced significantly by using a short impulse response filter to model the secondary path transfer function in a subband-decomposed form. In this correspondence, an overlap-save frequency-domain implementation of the algorithm is proposed. Simulations show that the proposed implementation has better performance than the original.


title: The Furhat Back-Projected Humanoid Head-Lip Reading, Gaze And Multi-Party Interaction

abstract: In this paper, we present Furhat - a back-projected human-like robot head using state-of-the art facial animation. Three experiments are presented where we investigate how the head might facilitate human - robot face-to-face interaction. First, we investigate how the animated lips increase the intelligibility of the spoken output, and compare this to an animated agent presented on a flat screen, as well as to a human face. Second, we investigate the accuracy of the perception of Furhat's gaze in a setting typical for situated interaction, where Furhat and a human are sitting around a table. The accuracy of the perception of Furhat's gaze is measured depending on eye design, head movement and viewing angle. Third, we investigate the turn-taking accuracy of Furhat in a multi-party interactive setting, as compared to an animated agent on a flat screen. We conclude with some observations from a public setting at a museum, where Furhat interacted with thousands of visitors in a multi-party interaction.


title: A Comparison of Retrieval Models using Term Dependencies

abstract: A number of retrieval models incorporating term dependencies have recently been introduced. Most of these modify existing "bag-of-words" retrieval models by including features based on the proximity of pairs of terms (or bi-terms). Although these term dependency models have been shown to be significantly more effective than the bag-of-words models, there have been no previous systematic comparisons between the different approaches that have been proposed. In this paper, we compare the effectiveness of recent bi-term dependency models over a range of TREC collections, for both short (title) and long (description) queries. To ensure the reproducibility of our study, all experiments are performed on widely available TREC collections, and all tuned retrieval model parameters are made public. These comparisons show that the weighted sequential dependence model is at least as effective as, and often significantly better than, any other model across this range of collections and queries. We observe that dependency features are much more valuable in improving the performance of longer queries than for shorter queries. We then examine the effectiveness of dependence models that incorporate proximity features involving more than two terms. The results show that these features can improve effectiveness, but not consistently, over the available data sets.


title: The disabled throwing shoulder: spectrum of pathology part III: the SICK scapula, scapular dyskinesis, the kinetic chain, and rehabilitation

abstract: We use the acronym SICK to refer to the findings one sees in this syndrome (Scapular malposition, Inferior medial border prominence, Coracoid pain and malposition, and dysKinesis of scapular movement). This recently recognized overuse muscular fatigue syndrome is yet another cause of shoulder pain in the throwing athlete who presents with dead arm complaints. 1 The hallmark feature of this syndrome is asymmetric malposition of the scapula in the dominant throwing shoulder, which usually appears on examination as if one shoulder is lower than the other. This statically observable position is suggestive of underlying muscle activation alterations that produce altered kinematics of the scapula upon dynamic use. The altered kinematics fall into 3 clinically recognizable patterns of scapular dyskinesis, 2 of which are most commonly associated with labral pathology: type I, inferior medial scapular border prominence, and type II, medial scapular border prominence. The type III pattern, which is associated with impingement and rotator cuff lesions rather than labral lesions, displays prominence of the superomedial border of the scapula. In the SICK scapula syndrome, scapular asymmetry is measured statically, but actively produces scapular dyskinesis as the shoulder goes through the throwing cycle. The malpositioned dyskinetic scapula, in turn, dynamically produces altered kinematics of the glenohumeral and acromioclavicular joints and the muscles that insert on the scapula. Because of these complex interrelationships, scapular dyskinesis, including the SICK scapula syndrome, can cause a spectrum of clinical complaints originating from any or all of these anatomic locations. A thrower with this syndrome presents with an apparent “dropped” scapula in his dominant symptomatic shoulder compared with the contralateral shoulder’s scapular position. In actuality, the scapula initially protracts, rotating about a horizontal axis, with the upper scapula rotating anteroinferiorly. However, the clinical appearance, with the arms relaxed in adduction at the side, is that the involved scapula is lower than the scapula on the uninvolved side (Figs 1 and 2). Viewing from behind, the inferior medial scapular border appears very prominent, with the superior medial border and acromion less prominent. When viewed from the front, this tilting (protraction) of the scapula makes the shoulder appear to be lower than the opposite side. The pectoralis minor tightens as the coracoid tilts inferiorly and shifts laterally away from the midline, and its insertion at the coracoid becomes very tender.


title: Business Intelligence and Analytics: Research Directions

abstract: Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving products and services, achieving better operational efficiency, and fostering customer relationships. In this article, we will categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics. The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications. For each research direction, we will also determine a few important questions to be addressed in future research.


title: Pivot Approach for Extracting Paraphrase Patterns from Bilingual Corpora

abstract: Paraphrase patterns are useful in paraphrase recognition and generation. In this paper, we present a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the English paraphrase patterns are extracted using the sentences in a foreign language as pivots. We propose a loglinear model to compute the paraphrase likelihood of two patterns and exploit feature functions based on maximum likelihood estimation (MLE) and lexical weighting (LW). Using the presented method, we extract over 1,000,000 pairs of paraphrase patterns from 2M bilingual sentence pairs, the precision of which exceeds 67%. The evaluation results show that: (1) The pivot approach is effective in extracting paraphrase patterns, which significantly outperforms the conventional method DIRT. Especially, the log-linear model with the proposed feature functions achieves high performance. (2) The coverage of the extracted paraphrase patterns is high, which is above 84%. (3) The extracted paraphrase patterns can be classified into 5 types, which are useful in various applications.


Use of Additive Waxup and Direct Intraoral Mock-up for Enamel Preservation with


title: Deep Face Recognition.

abstract: The goal of this paper is face recognition – from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets. We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve comparable state of the art results on the standard LFW and YTF face benchmarks.


title: Configuration and performance testing of IEC 61850 GOOSE

abstract: The IEC 61850 standard part 8-1 proposes Generic Object Oriented Substation Event (GOOSE) message for time critical applications over the Ethernet network. In order to cover the wide range of applications and achieve flexibility in implementation, GOOSE messages are kept generic in the standard. However, this flexibility leads to configuration problem achieving multi-vendor interoperability. Therefore, some efforts have been carried out in this work to present a systematic GOOSE configuration approach, as well as, verification and performance testing of the GOOSE. First part of this paper configuration of Ethernet switched network, including IEEE 1588 based time synchronization, Rapid Spanning Tree Protocol (RSTP), and IEEE 802.1Q based Quality of Services (QoS). In the second part, the paper leads to step-by-step configuration process comprising IEC 61850 data modeling, datasets of GOOSE within individual IEDs, and system integration of GOOSE. Finally, the verification of configured GOOSE messages is presented using network analyzer tools, and performance testing time delay) over the network is carried out for various network scenarios.


title: Tagless staged interpreters for typed languages

abstract: Multi-stage programming languages provide a convenient notation for explicitly staging programs. Staging a definitional interpreter for a domain specific language is one way of deriving an implementation that is both readable and efficient. In an untyped setting, staging an interpreter "removes a complete layer of interpretive overhead", just like partial evaluation. In a typed setting however, Hindley-Milner type systems do not allow us to exploit typing information in the language being interpreted. In practice, this can mean a slowdown cost by a factor of three or mor.Previously, both type specialization and tag elimination were applied to this problem. In this paper we propose an alternative approach, namely, expressing the definitional interpreter in a dependently typed programming language. We report on our experience with the issues that arise in writing such an interpreter and in designing such a language. .To demonstrate the soundness of combining staging and dependent types in a general sense, we formalize our language (called Meta-D) and prove its type safety. To formalize Meta-D, we extend Shao, Saha, Trifonov and Papaspyrou's λH language to a multi-level setting. Building on λH allows us to demonstrate type safety in a setting where the type language contains all the calculus of inductive constructions, but without having to repeat the work needed for establishing the soundness of that system.


An Efficient Representation for Surface Details


title: Data gathering tours for mobile robots

abstract: We study a path planning problem which arises when multiple robots are used to gather data from stationary devices with wireless communication capabilities. Each device has a given communication range, and stores a fixed amount of data. The objective of the robots is to gather the data from these devices and to upload it to a base-station/gateway. We introduce a new optimization problem called the Data Gathering Problem (DGP). In DGP, the objective is to compute a tour for each robot in such a way that minimizes the time to collect data from all devices. In order to download the data from a device, a robot must visit a point within the communication range of the device. Then, it spends a fixed amount of time to download the data. Thus, the time to complete a tour depends on not only the travel time but also the time to download the data, and the number of devices visited along the tour. First, we study a special case of DGP where the robots' motion is restricted to a curve which contains the base station at one end. Next, we study the 2D version. We show that two existing algorithms for variants of the Traveling Salesperson Problem can be combined and adapted to obtain a constant factor approximation to DGP. Afterwards, we present an improvement for sparse deployments. We also present simulations which shed light on the utility of data gathering using mobile robots.


title: Developing a team performance framework for the intensive care unit*

abstract: OBJECTIVE ::: There is a growing literature on the relationship between teamwork and patient outcomes in intensive care, providing new insights into the skills required for effective team performance. The purpose of this review is to consolidate the most robust findings from this research into an intensive care unit (ICU) team performance framework. ::: ::: ::: DATA SOURCES ::: Studies investigating teamwork within the ICU using PubMed, Science Direct, and Web of Knowledge databases. ::: ::: ::: STUDY SELECTION ::: Studies investigating the relationship between aspects of teamwork and ICU outcomes, or studies testing factors that are found to influence team working in the ICU. ::: ::: ::: DATA EXTRACTION ::: Teamwork behaviors associated with patient or staff-related outcomes in the ICU were identified. ::: ::: ::: DATA SYNTHESIS ::: Teamwork behaviors were grouped according to the team process categories of "team communication," "team leadership," "team coordination," and "team decision making." A prototype framework explaining the team performance in the ICU was developed using these categories. The purpose of the framework is to consolidate the existing ICU teamwork literature and to guide the development and testing of interventions for improving teamwork. ::: ::: ::: CONCLUSIONS ::: Effective teamwork is shown as crucial for providing optimal patient care in the ICU. In particular, team leadership seems vital for guiding the way in which ICU team members interact and coordinate with others.


Various Strategies for Optimizing Traffic Lights : A Review


title: Analyzing mRNA Expression Using Single mRNA Resolution Fluorescent In Situ Hybridization

abstract: As the product of transcription and the blueprint for translation, mRNA is the main intermediate product of the gene expression pathway. The ability to accurately determine mRNA levels is, therefore, a major requirement when studying gene expression. mRNA is also a target of different regulatory steps, occurring in different subcellular compartments. To understand the different steps of gene expression regulation, it is therefore essential to analyze mRNA in the context of a single cell, maintaining spatial information. Here, we describe a stepwise protocol for fluorescent in situ hybridization (FISH) that allows detection of individual mRNAs in single yeast cells. This method allows quantitative analysis of mRNA expression in single cells, permitting “absolute” quantification by simply counting mRNAs. It further allows us to study many aspects of mRNA metabolism, from transcription to processing, localization, and mRNA degradation.


title: Integrating Classical Encryption with Modern Technique

abstract: Alphabetical ciphers are being used since centuries for inducing confusion in messages, but there are some drawbacks that are associated with Classical alphabetic techniques like concealment of key and plaintext. Here in this paper we will suggest an encryption technique that is a blend of both classical encryption as well as modern technique, this hybrid technique will be superior in terms of security than average Classical ciphers.


title: Neural bases of prospective memory: A meta-analysis and the “Attention to Delayed Intention” (AtoDI) model

abstract: Abstract Remembering to realize delayed intentions is a multi-phase process, labelled as prospective memory (PM), and involves a plurality of neural networks. The present study utilized the activation likelihood estimation method of meta-analysis to provide a complete overview of the brain regions that are consistently activated in each PM phase. We formulated the ‘Attention to Delayed Intention’ (AtoDI) model to explain the neural dissociation found between intention maintenance and retrieval phases. The dorsal frontoparietal network is involved mainly in the maintenance phase and seems to mediate the strategic monitoring processes, such as the allocation of top-down attention both towards external stimuli, to monitor for the occurrence of the PM cues, and to internal memory contents, to maintain the intention active in memory. The ventral frontoparietal network is recruited in the retrieval phase and might subserve the bottom-up attention captured externally by the PM cues and, internally, by the intention stored in memory. Together with other brain regions (i.e., insula and posterior cingulate cortex), the ventral frontoparietal network would support the spontaneous retrieval processes. The functional contribution of the anterior prefrontal cortex is discussed extensively for each PM phase.


title: Machine learning approaches in medical image analysis: From detection to diagnosis

abstract: Abstract Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.


title: Systematic literature review on user interfaces of autonomous cars: Liabilities and responsibilities

abstract: There is a growing interest in developing user interfaces of self-driving cars or automated vehicles which are also known as driverless cars. The literature on Self-driving cars has steadily been growing. However, there has not been any effort to systematically select, review, and synthesize the literature on this topic. A systematic literature review is conducted that reports different perspectives of autonomous cars like liabilities and responsibilities of these Self-driving cars in case of accidents, ethics related to decision making, potentials and drawbacks of using Self-driving cars, people's perspective about them and challenges faced by them. Data has been extracted from different papers to identify various aspects of self-driving cars as discussed above. More recent and current findings are considered to deal with these research issues. Review's and findings presented in this paper are expected to help researchers and practitioners to understand the Self-driving cars from various perspectives.


title: Human gesture recognition using Kinect camera

abstract: In this paper, we propose a comparison of human gesture recognition using data mining classification methods in video streaming. In particular, we are interested in a specific stream of vector of twenty body-joint positions which are representative of the human body captured by Kinect camera. The recognized gesture patterns of the study are stand, sit down, and lie down. Classification methods chosen for comparison study are backpropagation neural network, support vector machine, decision tree, and naive Bayes. Experimental results have shown that the backpropagation neural network method outperforms other classification methods and can achieve recognition with 100% accuracy. Moreover, the average accuracy of all classification methods used in this study is 93.72%, which confirms the high potential of using the Kinect camera in human body recognition applications. Our future work will use the knowledge obtained from these classifiers in time series analysis of gesture sequence for detecting fall motion in a smart home system.


title: Peer Support Services for Individuals With Serious Mental Illnesses: Assessing the Evidence

abstract: Providers of mental health services have shown increasing interest in hiring peer specialists to provide a range of services and supports to persons with severe mental illnesses. In fact, many states are including supports offered by certified peer support specialists as Medicaid-reimbursable services. This literature review found encouraging evidence for the effectiveness of peer support services, but more rigorous studies are needed to pinpoint the contributions of peers, the authors note.


title: Teaching Cybersecurity with DeterLab

abstract: The DETER project aims to advance cybersecurity research and education. Over the past seven years, the project has focused on improving and redefining the methods, technology, and infrastructure for developing cyberdefense technology. The project's research results are put into practice by DeterLab, a public, free-for-use experimental facility available to researchers and educators worldwide. Educators can use DeterLab's exercises to teach cybersecurity technology and practices. This use of DeterLab provides valuable feedback on DETER innovations and helps grow the pool of cybersecurity innovators and cyberdefenders.


title: Efforts to overcome vegetarian-induced dissonance among meat eaters

abstract: Abstract Meat eaters face dissonance whether it results from inconsistency (“I eat meat; I don’t like to hurt animals”), aversive consequences (“I eat meat; eating meat harms animals”), or threats to self image (“I eat meat; compassionate people don’t hurt animals”). The present work proposes that there are a number of strategies that omnivores adopt to reduce this dissonance including avoidance, dissociation, perceived behavioral change, denial of animal pain, denial of animal mind, pro-meat justifications, reducing perceived choice, and actual behavioral change. The presence of vegetarians was speculated to cause meat eating to be a scrutinized behavior, remind meat eaters of their discomfort, and undermine the effectiveness of these strategies. It was therefore hypothesized that exposure to a description of a vegetarian would lead omnivores to embrace dissonance-reducing strategies. Supporting this hypothesis, participants who read a vignette about a vegetarian denied animal mind more than participants who read about a gluten-free individual. It was also hypothesized that omnivores would be sensitive to individual differences between vegetarians and would demonstrate using dissonance-reducing strategies more when the situation failed to provide cognitions consonant with eating meat or to reduce dissonant cognitions. Four experiments supported this prediction and found that authentic vegetarians, vegetarians freely making the decision to abandon meat, consistent vegetarians, and anticipating moral reproach from vegetarians produced greater endorsement of dissonance-reducing strategies than their counterpart conditions.


title: Speech Recognition Using Factorial Hidden Markov Models for Separation in the Feature Space

abstract: This paper proposes an algorithm for the recognition and separation of speech signals in non-stationary noise, such as another speaker. We present a method to combine hidden Markov models (HMMs) trained for the speech and noise into a factorial HMM to model the mixture signal. Robustness is obtained by separating the speech and noise signals in a feature domain, which discards unnecessary information. We use mel-cepstral coefficients (MFCCs) as features, and estimate the distribution of mixture MFCCs from the distributions of the target speech and noise. A decoding algorithm is proposed for finding the state transition paths and estimating gains for the speech and noise from a mixture signal. Simulations were carried out using speech material where two speakers were mixed at various levels, and even for high noise level (9 dB above the speech level), the method produced relatively good (60% word recognition accuracy) results. Audio demonstrations are available at www.cs.tut.fi/˜tuomasv. IndexTerms: speech recognition, speech separation, factorial hidden Markov model.


title: Deep attractor network for single-microphone speaker separation

abstract: Despite the overwhelming success of deep learning in various speech processing tasks, the problem of separating simultaneous speakers in a mixture remains challenging. Two major difficulties in such systems are the arbitrary source permutation and unknown number of sources in the mixture. We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source. Attractor points in this study are created by finding the centroids of the sources in the embedding space, which are subsequently used to determine the similarity of each bin in the mixture to each source. The network is then trained to minimize the reconstruction error of each source by optimizing the embeddings. The proposed model is different from prior works in that it implements an end-to-end training, and it does not depend on the number of sources in the mixture. Two strategies are explored in the test time, K-means and fixed attractor points, where the latter requires no post-processing and can be implemented in real-time. We evaluated our system on Wall Street Journal dataset and show 5.49% improvement over the previous state-of-the-art methods.


title: A Survey: Embedded Systems Supporting By Different Operating Systems

abstract: In these days embedded system have an important role in different Fields and applications like Network embedded system , Real-time embedded systems which supports the mission-critical domains, mostly having the time constraints, Stand-alone systems which includes the network router etc. A great deployment in the processors made for completing the demanding needs of the users. There is also a large-scale deployment occurs in sensor networks for providing the advance facilities, for handled such type of embedded systems a specific operating system must provide. This paper presents some software infrastructures that have the ability of supporting such types of embedded systems.


title: Comparison study of non-orthogonal multiple access schemes for 5G

abstract: With the development of mobile Internet and Internet of things (IoT), the 5th generation (5G) wireless communications will foresee explosive increase in mobile traffic. To address challenges in 5G such as higher spectral efficiency, massive connectivity, and lower latency, some non-orthogonal multiple access (NOMA) schemes have been recently actively investigated, including power-domain NOMA, multiple access with low-density spreading (LDS), sparse code multiple access (SCMA), multiuser shared access (MUSA), pattern division multiple access (PDMA), etc. Different from conventional orthogonal multiple access (OMA) schemes, NOMA can realize overloading by introducing some controllable interferences at the cost of slightly increased receiver complexity, which can achieve significant gains in spectral efficiency and accommodate much more users. In this paper, we will discuss basic principles and key features of three typical NOMA schemes, i.e., SCMA, MUSA, and PDMA. What's more, their performance in terms of uplink bit error rate (BER) will be compared. Simulation results show that in typical Rayleigh fading channels, SCMA has the best performance, while the BER performance of MUSA and PDMA are very close to each other. In addition, we also analyze the performance of PDMA using the same factor graph as SCMA, which indicates that the performance gain of SCMA over PDMA comes from both the difference of factor graph and the codebook optimization.


title: Using Context to Improve Emotion Detection in Spoken Dialog Systems

abstract: Most research that explores the emotional state of users of spoken dialog systems does not fully utilize the contextual nature that the dialog structure provides. This paper reports results of machine learning experiments designed to automatically classify the emotional state of user turns using a corpus of 5,690 dialogs collected with the “How May I Help You SM ” spoken dialog system. We show that augmenting standard lexical and prosodic features with contextual features that exploit the structure of spoken dialog and track user stateincreases classification accuracy by 2.6%.


title: Probabilistic Demand Forecasting at Scale

abstract: We present a platform built on large-scale, data-centric machine learning (ML) approaches, whose particular focus is demand forecasting in retail. At its core, this platform enables the training and application of probabilistic demand forecasting models, and provides convenient abstractions and support functionality for forecasting problems. The platform comprises of a complex end-to-end machine learning system built on Apache Spark, which includes data preprocessing, feature engineering, distributed learning, as well as evaluation, experimentation and ensembling. Furthermore, it meets the demands of a production system and scales to large catalogues containing millions of items. ::: ::: We describe the challenges of building such a platform and discuss our design decisions. We detail aspects on several levels of the system, such as a set of general distributed learning schemes, our machinery for ensembling predictions, and a high-level dataflow abstraction for modeling complex ML pipelines. To the best of our knowledge, we are not aware of prior work on real-world demand forecasting systems which rivals our approach in terms of scalability.


title: Towards Practical Differentially Private Convex Optimization

abstract: Building useful predictive models often involves learning from sensitive data. Training models with differential privacy can guarantee the privacy of such sensitive data. For convex optimization tasks, several differentially private algorithms are known, but none has yet been deployed in practice. In this work, we make two major contributions towards practical differentially private convex optimization. First, we present Approximate Minima Perturbation, a novel algorithm that can leverage any off-the-shelf optimizer. We show that it can be employed without any hyperparameter tuning, thus making it an attractive technique for practical deployment. Second, we perform an extensive empirical evaluation of the state-of-the-art algorithms for differentially private convex optimization, on a range of publicly available benchmark datasets, and real-world datasets obtained through an industrial collaboration. We release open-source implementations of all the differentially private convex optimization algorithms considered, and benchmarks on as many as nine public datasets, four of which are high-dimensional.


title: Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction

abstract: Internet of Things technology provides a competent and structured approach to improve health and wellbeing of mankind. One of the feasible ways to offer healthcare services based on IoT is to monitor human's health in real-time using ubiquitous health monitoring systems which have the ability to acquire bio-signals from sensor nodes and send the data to the gateway via a particular wireless communication protocol. The real-time data is then transmitted to a remote cloud server for real-time processing, visualization, and diagnosis. In this paper, we enhance such a health monitoring system by exploiting the concept of fog computing at smart gateways providing advanced techniques and services such as embedded data mining, distributed storage, and notification service at the edge of network. Particularly, we choose Electrocardiogram (ECG) feature extraction as the case study as it plays an important role in diagnosis of many cardiac diseases. ECG signals are analyzed in smart gateways with features extracted including heart rate, P wave and T wave via a flexible template based on a lightweight wavelet transform mechanism. Our experimental results reveal that fog computing helps achieving more than 90% bandwidth efficiency and offering low-latency real time response at the edge of the network.


title: How software engineers use documentation: the state of the practice

abstract: Software engineering is a human task, and as such we must study what software engineers do and think. Understanding the normative practice of software engineering is the first step toward developing realistic solutions to better facilitate the engineering process. We conducted three studies using several data-gathering approaches to elucidate the patterns by which software engineers (SEs) use and update documentation. Our objective is to more accurately comprehend and model documentation use, usefulness, and maintenance, thus enabling better decision making and tool design by developers and project managers. Our results confirm the widely held belief that SEs typically do not update documentation as timely or completely as software process personnel and managers advocate. However, the results also reveal that out-of-date software documentation remains useful in many circumstances.


title: 21 REVIEW Androgen regulation of satellite cell function

abstract: Androgen treatment can enhance the size and strength of muscle. However, the mechanisms of androgen action in skeletal muscle are poorly understood. This review discusses potential mechanisms by which androgens regulate satellite cell activation and function. Studies have demonstrated that androgen administration increases satellite cell numbers in animals and humans in a dose–dependent manner. Moreover, androgens increase androgen receptor levels in satellite cells. In vitro, the results are contradictory as to whether androgens regulate satellite cell proliferation or differentiation. IGF-I is one major target of androgen action in satellite cells. In addition, the possibility of non-genomic actions of androgens on satellite cells is discussed. In summary, this review focuses on exploring potential mechanisms through which androgens regulate satellite cells, by analyzing developments from research in this area.


title: Helicobacter pylori persistence: biology and disease

abstract: Helicobacter pylori are bacteria that have coevolved with humans to be transmitted from person to person and to persistently colonize the stomach. Their population structure is a model for the ecology of the indigenous microbiota. A well-choreographed equilibrium between bacterial effectors and host responses permits microbial persistence and health of the host but confers risk of serious diseases, including peptic ulceration and gastric neoplasia.


title: Current-mode bi-directional single-inductor three-port DC-DC converter for portable systems with PV power harvesting

abstract: This work targets low-power portable electronic applications with PV power harvesting. A bidirectional dc-dc converter is required to interface the battery with the load and PV module. A new current-mode scheme is applied to the single-inductor three-port converter, where the peak and valley inductor current are controlled by two separate digital regulation loops. The controller is shown to regulate both the PV voltage and the load voltage in the presence of load and irradiance steps. In solar deficit mode, energy is transferred from the battery to the load indirectly through the PV node, eliminating the two input switches used in conventional dual-input, dual-output converters. The scheme is demonstrated on a digitally controlled 1 W harvester prototype.


title: Global and Local Consistent Age Generative Adversarial Networks

abstract: Age progression/regression is a challenging task due to the complicated and non-linear transformation in human aging process. Many researches have shown that both global and local facial features are essential for face representation, but previous GAN based methods mainly focused on the global feature in age synthesis. To utilize both global and local facial information, we propose a Global and Local Consistent Age Generative Adversarial Network (GLCA-GAN). In our generator, a global network learns the whole facial structure and simulates the aging trend of the whole face, while three crucial facial patches are progressed or regressed by three local networks aiming at imitating subtle changes of crucial facial subregions. To preserve most of the details in age-attribute-irrelevant areas, our generator learns the residual face. Moreover, we employ an identity preserving loss to better preserve the identity information, as well as age preserving loss to enhance the accuracy of age synthesis. A pixel loss is also adopted to preserve detailed facial information of the input face. Our proposed method is evaluated on three face aging datasets, i.e., CACD dataset, Morph dataset and FG-NET dataset. Experimental results show appealing performance of the proposed method by comparing with the state-of-the-art.


Multi-directional Recurrent Neural Networks : A Novel Method for Estimating Missing Data


title: Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality

abstract: The field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics. One unvisited assumption, however, is the reliance of GEC evaluation on error-coded corpora, which contain specific labeled corrections. We examine current practices and show that GEC’s reliance on such corpora unnaturally constrains annotation and automatic evaluation, resulting in (a) sentences that do not sound acceptable to native speakers and (b) system rankings that do not correlate with human judgments. In light of this, we propose an alternate approach that jettisons costly error coding in favor of unannotated, whole-sentence rewrites. We compare the performance of existing metrics over different gold-standard annotations, and show that automatic evaluation with our new annotation scheme has very strong correlation with expert rankings (ρ = 0.82). As a result, we advocate for a fundamental and necessary shift in the goal of GEC, from correcting small, labeled error types, to producing text that has native fluency.


title: Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images

abstract: Recent advances in deep learning and distributed representations of images and text have resulted in the emergence of several neural architectures for cross-modal retrieval tasks, such as searching collections of images in response to textual queries and assigning textual descriptions to images. However, the multi-modal retrieval scenario, when a query can be either a text or an image and the goal is to retrieve both a textual fragment and an image, which should be considered as an atomic unit, has been significantly less studied. In this paper, we propose a gated neural architecture to project image and keyword queries as well as multi-modal retrieval units into the same low-dimensional embedding space and perform semantic matching in this space. The proposed architecture is trained to minimize structured hinge loss and can be applied to both cross- and multi-modal retrieval. Experimental results for six different cross- and multi-modal retrieval tasks obtained on publicly available datasets indicate superior retrieval accuracy of the proposed architecture in comparison to the state-of-art baselines.


Von Willebrand Factor, ADAMTS13 and D-Dimer Are Correlated with Different Levels of Nephropathy in Type 1 Diabetes Mellitus