title: GeeBot: A Robotic Platform for Refugee Integration

abstract: The refugee crisis is one of society's leading challenges. After a journey for survival, refugees and host institutions face barriers that hinder the integration process. To design solutions, we interviewed two groups: host institutions and past refugees. We identified critical issues, from legal concerns, like unfamiliarity of their Refugee Status, to grocery shopping. Our envisioned solution is GeeBot, a low-cost egg-shaped robot that institutions would lend to arriving families for eighteen months. GeeBot will be a translator with teaching functions, an information provider, and an active promoter of interaction between native and refugee populations.


title: The sound of sarcasm

abstract: The present study was conducted to identify possible acoustic cues of sarcasm. Native English speakers produced a variety of simple utterances to convey four different attitudes: sarcasm, humour, sincerity, and neutrality. Following validation by a separate naive group of native English speakers, the recorded speech was subjected to acoustic analyses for the following features: mean fundamental frequency (F0), F0 standard deviation, F0 range, mean amplitude, amplitude range, speech rate, harmonics-to-noise ratio (HNR, to probe for voice quality changes), and one-third octave spectral values (to probe resonance changes). The results of analyses indicated that sarcasm was reliably characterized by a number of prosodic cues, although one acoustic feature appeared particularly robust in sarcastic utterances: overall reductions in mean F0 relative to all other target attitudes. Sarcasm was also reliably distinguished from sincerity by overall reductions in HNR and in F0 standard deviation. In certain linguistic contexts, sarcasm could be differentiated from sincerity and humour through changes in resonance and reductions in both speech rate and F0 range. Results also suggested a role of language used by speakers in conveying sarcasm and sincerity. It was concluded that sarcasm in speech can be characterized by a specific pattern of prosodic cues in addition to textual cues, and that these acoustic characteristics can be influenced by language used by the speaker.


title: Beyond frontal faces: Improving Person Recognition using multiple cues

abstract: We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset, consisting of over 60000 instances of 2000 individuals collected from public Flickr photo albums. With only about half of the person images containing a frontal face, the recognition task is very challenging due to the large variations in pose, clothing, camera viewpoint, image resolution and illumination. We propose the Pose Invariant PErson Recognition (PIPER) method, which accumulates the cues of poselet-level person recognizers trained by deep convolutional networks to discount for the pose variations, combined with a face recognizer and a global recognizer. Experiments on three different settings confirm that in our unconstrained setup PIPER significantly improves on the performance of DeepFace, which is one of the best face recognizers as measured on the LFW dataset.


title: Diabetic neuropathic pain: Physiopathology and treatment

abstract: Diabetic neuropathy is a common complication of both type 1 and type 2 diabetes, which affects over 90% of the diabetic patients. Although pain is one of the main symptoms of diabetic neuropathy, its pathophysiological mechanisms are not yet fully known. It is widely accepted that the toxic effects of hyperglycemia play an important role in the development of this complication, but several other hypotheses have been postulated. The management of diabetic neuropathic pain consists basically in excluding other causes of painful peripheral neuropathy, improving glycemic control as a prophylactic therapy and using medications to alleviate pain. First line drugs for pain relief include anticonvulsants, such as pregabalin and gabapentin and antidepressants, especially those that act to inhibit the reuptake of serotonin and noradrenaline. In addition, there is experimental and clinical evidence that opioids can be helpful in pain control, mainly if associated with first line drugs. Other agents, including for topical application, such as capsaicin cream and lidocaine patches, have also been proposed to be useful as adjuvants in the control of diabetic neuropathic pain, but the clinical evidence is insufficient to support their use. In conclusion, a better understanding of the mechanisms underlying diabetic neuropathic pain will contribute to the search of new therapies, but also to the improvement of the guidelines to optimize pain control with the drugs currently available.


title: An Open Source Linked Data Framework for Publishing Environmental Data under the UK Location Strategy

abstract: Linked data offers a novel and more flexible means of sharing complex geospatial datasets by breaking away from the traditional domainspecific technologies used for accessing and integrating geospatial data with heterogeneous sources and disparate formats. In 2010, the UK Cabinet Office released a set of draft guidelines for exposing geospatial data as linked-data in support of the UK Open Data initiative. These draft guidelines have been proposed under the UK Location Strategy in specific recognition of the importance of geospatial data, and also with a view to promote linked-data within the EU INSPIRE community. This paper presents a customisable opensource linked-data framework developed by the GeoTOD-II project that implements these guidelines. The framework provides an efficient means for exposing both existing and new data sources in the linked-data form. We also attempt to articulate and address a number of issues and hidden assumptions with these guidelines identified during the development of the framework.


title: Extraction,layout analysis and classification of diagrams in PDF documents

abstract: Diagrams are a critical part of virtually all scientificand technical documents. Analyzing diagrams will beimportant for building comprehensive document retrievalsystems. This paper focuses on the extraction andclassification of diagrams from PDF documents. Westudy diagrams available in vector (not raster) format inonline research papers.PDF files are parsed and their vector graphicscomponents installed in a spatial index. Subdiagrams arefound by analyzing white space gaps. A set of statistics isgenerated for each diagram, e.g., the number ofhorizontal lines and vertical lines. The statistics form afeature vector description of the diagram. The vectorsare used in a kernel-based machine learning system(Support Vector Machine). Separating a set of bargraphs from non-bar-graphs gathered from 20,000biology research papers gave a classification accuracy of91.7%. The approach is directly applicable to diagramsvectorized from images.


title: Coordinated Active Power Curtailment of Grid Connected PV Inverters for Overvoltage Prevention

abstract: Overvoltages in low voltage (LV) feeders with high penetration of photovoltaics (PV) are usually prevented by limiting the feeder's PV capacity to very conservative values, even if the critical periods rarely occur. This paper discusses the use of droop-based active power curtailment techniques for overvoltage prevention in radial LV feeders as a means for increasing the installed PV capacity and energy yield. Two schemes are proposed and tested in a typical 240-V/75-kVA Canadian suburban distribution feeder with 12 houses with roof-top PV systems. In the first scheme, all PV inverters have the same droop coefficients. In the second, the droop coefficients are different so as to share the total active power curtailed among all PV inverters/houses. Simulation results demonstrate the effectiveness of the proposed schemes and that the option of sharing the power curtailment among all customers comes at the cost of an overall higher amount of power curtailed.


title: Influence of Site-Specific Sulfonation on Acrylic Graft Copolymer Morphology

abstract: Novel selectively sulfonated graft copolymers, poly(methyl methacrylate)-g-(poly(sulfonic acid styrene)-b-poly(tert-butylstyrene)), poly(methyl methacrylate)-g-(poly(tert-butylstyrene)-b-poly(styrenesulfonic acid)), and the corresponding sodium sulfonate salts were successfully synthesized. The weight-average molecular weights (Mw) of the graft copolymers were ∼200 000 g/mol and the polydispersity ranged from 1.86 to 2.22. The graft copolymers contained ∼9−10 branches on average and 4 wt % of sulfonic acid or sodium sulfonate blocks adjacent to the backbone or at the branch terminus. The mobility of the sulfonated blocks located at the branch terminus enabled the sulfonated blocks to more readily interact and form ionic aggregates. The glass transition temperatures of the sulfonated graft copolymer with sulfonated blocks at the branch terminus were higher than values for copolymers with sulfonated blocks adjacent to the backbone. This behavior was attributed to the sulfonated blocks at the branch terminus...


title: Plant classification using convolutional neural networks

abstract: Application of the benefits of modern computing technology to improve the efficiency of agricultural fields is inevitable with growing concerns about increasing world population and limited food resources. Computing technology is crucial not only to industries related to food production but also to environmentalists and other related authorities. It is expected to increase the productivity, contribute to a better understanding of the relationship between environmental factors and healthy crops, reduce the labor costs for farmers and increase the operation speed and accuracy. Implementing machine learning methods such as deep neural networks on agricultural data has gained immense attention in recent years. One of the most important problems is automatic classification of plant species based on their types. Automatic plant type identification process could offer a great help for application of pesticides, fertilization and harvesting of different species on-time in order to improve the production processes of food and drug industries. In this paper, we propose a Convolutional Neural Network (CNN) architecture to classify the type of plants from the image sequences collected from smart agro-stations. First challenges introduced by illumination changes and deblurring are eliminated with some preprocessing steps. Following the preprocessing step, Convolutional Neural Network architecture is employed to extract the features of images. The construction of the CNN architecture and the depth of CNN are crucial points that should be emphasized since they affect the recognition capability of the architecture of neural networks. In order to evaluate the performance of the approach proposed in this paper, the results obtained through CNN model are compared with those obtained by employing SVM classifier with different kernels, as well as feature descriptors such as LBP and GIST. The performance of the approach is tested on dataset collected through a government supported project, TARBIL, for which over 1200 agro-stations are placed throughout Turkey. The experimental results on TARBIL dataset confirm that the proposed method is quite effective.


title: Matlab simulation of lithium ion cell using electrochemical single particle model

abstract: Battery modelling is a significant component of advanced Battery Management Systems (BMSs). The full electrochemical model of a battery can represent high precision battery behavior during its operation. However, the high computational requirement to solve the coupled nonlinear partial differential equations (PDEs) that define these models limits their applicability in an online BMS, especially for a battery pack containing hundreds of cells. Therefore, a reduced SPM-Three parameter model is proposed in this paper to efficiently model a lithium ion cell with high accuracy in a specific range of cell operation. The reduced model is implemented in a Simulink block for developing an advanced battery modelling tool that can be applied to a wide variety of battery applications.


title: Robust methods for partial least squares regression

abstract: Partial least squares regression (PLSR) is a linear regression technique developed to deal with high-dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm, this being the leading PLSR algorithm because of its speed and efficiency. Because SIMPLS is based on the empirical cross-covariance matrix between the response variables and the regressors and on linear least squares regression, the results are affected by abnormal observations in the data set. Two robust methods, RSIMCD and RSIMPLS, are constructed from a robust covariance matrix for high-dimensional data and robust linear regression. We introduce robust RMSECV and RMSEP values for model calibration and model validation. Diagnostic plots are constructed to visualize and classify the outliers. Several simulation results and the analysis of real data sets show the effectiveness and robustness of the new approaches. Because RSIMPLS is roughly twice as fast as RSIMCD, it stands out as the overall best method. Copyright © 2003 John Wiley & Sons, Ltd.


title: Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching

abstract: Deep text matching approaches have been widely studied for many applications including question answering and information retrieval systems. To deal with a domain that has insufficient labeled data, these approaches can be used in a Transfer Learning (TL) setting to leverage labeled data from a resource-rich source domain. To achieve better performance, source domain data selection is essential in this process to prevent the "negative transfer" problem. However, the emerging deep transfer models do not fit well with most existing data selection methods, because the data selection policy and the transfer learning model are not jointly trained, leading to sub-optimal training efficiency. In this paper, we propose a novel reinforced data selector to select high-quality source domain data to help the TL model. Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector. We build the reinforced data selector based on the actor-critic framework and integrate it to a DNN based transfer learning model, resulting in a Reinforced Transfer Learning (RTL) method. We perform a thorough experimental evaluation on two major tasks for text matching, namely, paraphrase identification and natural language inference. Experimental results show the proposed RTL can significantly improve the performance of the TL model. We further investigate different settings of states, rewards, and policy optimization methods to examine the robustness of our method. Last, we conduct a case study on the selected data and find our method is able to select source domain data whose Wasserstein distance is close to the target domain data. This is reasonable and intuitive as such source domain data can provide more transferability power to the model.


title: Advances in Multi-GPU Smoothed Particle Hydrodynamics Simulations

abstract: We present a multi-GPU version of GPUSPH, a CUDA implementation of fluid-dynamics models based on the smoothed particle hydrodynamics (SPH) numerical method. The SPH is a well-known Lagrangian model for the simulation of free-surface fluid flows; it exposes a high degree of parallelism and has already been successfully ported to GPU. We extend the GPU-based simulator to run simulations on multiple GPUs simultaneously, to obtain a gain in speed and overcome the memory limitations of using a single device. The computational domain is spatially split with minimal overlapping and shared volume slices are updated at every iteration of the simulation. Data transfers are asynchronous with computations, thus completely covering the overhead introduced by slice exchange. A simple yet effective load balancing policy preserves the performance in case of unbalanced simulations due to asymmetric fluid topologies. The obtained speedup factor (up to 4.5x for 6 GPUs) closely follows the expected one (5x for 6 GPUs) and it is possible to run simulations with a higher number of particles than would fit on a single device. We use the Karp-Flatt metric to formally estimate the overall efficiency of the parallelization.


title: Kinect-like depth denoising

abstract: Accuracy and stability of Kinect-like depth data is limited by its generating principle. In order to serve further applications with high quality depth, the preprocessing on depth data is essential. In this paper, we analyze the characteristics of the Kinect-like depth data by examing its generation principle and propose a spatial-temporal denoising algorithm taking into account its special properties. Both the intra-frame spatial correlation and the inter-frame temporal correlation are exploited to fill the depth hole and suppress the depth noise. Moreover, a divisive normalization approach is proposed to assist the noise filtering process. The 3D rendering results of the processed depth demonstrates that the lost depth is recovered in some hole regions and the noise is suppressed with depth features preserved.


title: Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

abstract: We propose a computer-aided detection (CAD) system which can detect small-sized (from 3mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass-Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1FPs/CT. A reduction to 2.5FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.


title: Graph-based Hypothesis Generation for Parallax-tolerant Image Stitching

abstract: The seam-driven approach has been proven fairly effective for parallax-tolerant image stitching, whose strategy is to search for an invisible seam from finite representative hypotheses of local alignment. In this paper, we propose a graph-based hypothesis generation and a seam-guided local alignment for improving the effectiveness and the efficiency of the seam-driven approach. The experiment demonstrates the significant reduction of number of hypotheses and the improved quality of naturalness of final stitching results, comparing to the state-of-the-art method SEAGULL.


title: MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices

abstract: We present a class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. We first make a simple analysis on the weakness of common mobile networks for face verification. The weakness has been well overcome by our specifically designed MobileFaceNets. Under the same experimental conditions, our MobileFaceNets achieve significantly superior accuracy as well as more than 2 times actual speedup over MobileNetV2. After trained by ArcFace loss on the refined MS-Celeb-1M, our single MobileFaceNet of 4.0MB size achieves 99.55% accuracy on LFW and 92.59% TAR@FAR1e-6 on MegaFace, which is even comparable to state-of-the-art big CNN models of hundreds MB size. The fastest one of MobileFaceNets has an actual inference time of 18 milliseconds on a mobile phone. For face verification, MobileFaceNets achieve significantly improved efficiency over previous state-of-the-art mobile CNNs.


title: A Micropower Chopper-Stabilized Operational Amplifier Using a SC Notch Filter With Synchronous Integration Inside the Continuous-Time Signal Path

abstract: A micropower chopper stabilized opamp is presented. The new topology incorporates a switched capacitor filter with synchronous integration inside the continuous time signal path virtually eliminating chopping noise. A three-stage amplifier with multipath nested Miller compensation is modified to incorporate chopping of the input stage, sinc filtering to notch any chopping ripple, and a compensation scheme to maintain an undistorted high-speed signal path. Characteristics of the amplifier presented include rail to rail input and output operating on supplies of 1.8 to 5.5 V over -40degC to 125degC. Quiescent supply current is 17 muA, input offset is 3 muV, input offset drift is 0.02 muV/degC, GBW is 350 kHz, and the chopping frequency is 125 kHz. Die area is 0.7 mm2 using a precision analog mixed-signal CMOS process combining low-noise 0.6-mum analog transistors with 0.3-mum digital CMOS capability


title: A study on transmission characteristic of signal line with solder-mount on a PCB

abstract: As a fundamental study of the high frequency printed circuit or wire-bonding, the transmission characteristics and magnetic near field intensity distribution for the modeled printed circuit board (PCB), which have the soldered thin wire line are investigated experimentally. The size of a modeled sample is 30/spl times/50 mm/sup 2/ using 1.53 mm glass-epoxy substrate. Basic signal line constructed as 50-ohm characteristic impedance. The soldered thin wire line of 100-micrometer diameter was fabricated at the center of the modeled PCB that simulates the bonding part. S-parameters, i.e. |S/sub 21/| and |S/sub 11/|, characteristic impedance measured by the time domain reflectometry and magnetic near field distribution measured by a shielded loop probe are discussed. The length of the thin wire and shape of the strip line and wire-bonding part have significant influence to the characteristic impedance, transmission property and magnetic distribution properties. These results offer the design ways and guidelines for the PCB and parts designing and wiring including solder mounted element and also wire bonding packaging at high frequency applications.


title: Long-Term Relations Among Prosocial-Media Use, Empathy, and Prosocial Behavior

abstract: Despite recent growth of research on the effects of prosocial media, processes underlying these effects are not well understood. Two studies explored theoretically relevant mediators and moderators of the effects of prosocial media on helping. Study 1 examined associations among prosocial- and violent-media use, empathy, and helping in samples from seven countries. Prosocial-media use was positively associated with helping. This effect was mediated by empathy and was similar across cultures. Study 2 explored longitudinal relations among prosocial-video-game use, violent-video-game use, empathy, and helping in a large sample of Singaporean children and adolescents measured three times across 2 years. Path analyses showed significant longitudinal effects of prosocial- and violent-video-game use on prosocial behavior through empathy. Latent-growth-curve modeling for the 2-year period revealed that change in video-game use significantly affected change in helping, and that this relationship was mediated by change in empathy.


title: High performance training of deep neural networks using pipelined hardware acceleration and distributed memory

abstract: Recently, Deep Neural Networks (DNNs) have made unprecedented progress in various tasks. However, there is a timely need to accelerate the training process in DNNs specifically for real-time applications that demand high performance, energy efficiency and compactness. Numerous algorithms have been proposed to improve the accuracy, however the network training process is computationally slow. In this paper, we present a scalable pipelined hardware architecture with distributed memories for a digital neuron to implement deep neural networks. We also explore various functions and algorithms as well as different memory topologies, to optimize the performance of our training architecture. The power, area, and delay of our proposed model are evaluated with respect to software implementation. Experimental results on the MNIST dataset demonstrate that compared with the software training, our proposed hardware-based approach for training process achieves 33X runtime reduction, 5X power reduction, and nearly 168X energy reduction.


Generation by Structure-aware Seq 2 seq Learning


title: Harmonic optimization for the multi-stage saturable magnetically controlled reactor using particle swarm optimization algorithm

abstract: Magnetically controlled reactors (MCRs) as shunt reactors have offered flexible ways regulating the reactive power in the power system. MCRs can be implemented in ultra high voltage power systems and are much more economical and operational than the thyristor controlled reactors (TCRs). However, one remarkable defect of the MCRs is that they will inject great harmonics into the power system. In this paper, the structure and the mathematical model of a harmonic free multi-stage saturable MCR (MSMCR) are proposed. This paper illustrated the use of the particle swarm optimization (PSO) algorithm to decide the areas of the stages based on the harmonics mathematical model of the MSMCR. Since the 3rd harmonic will not be injected to the power systems if delta connected symmetrically, the current harmonics injected to the power system can be limited to as low as 0.8% of the rated output current of the MSMCR, theoretically. A simulation is finally produced to verify the research in PSCAD/EMTDC.


title: Predicting the future relevance of research institutions - The winning solution of the KDD Cup 2016

abstract: The world's collective knowledge is evolving through research and new scientific discoveries. It is becoming increasingly difficult to objectively rank the impact research institutes have on global advancements. However, since the funding, governmental support, staff and students quality all mirror the projected quality of the institution, it becomes essential to measure the affiliation's rating in a transparent and widely accepted way. We propose and investigate several methods to rank affiliations based on the number of their accepted papers at future academic conferences. We carry out our investigation using publicly available datasets such as the Microsoft Academic Graph, a heterogeneous graph which contains various information about academic papers. We analyze several models, starting with a simple probabilities-based method and then gradually expand our training dataset, engineer many more features and use mixed models and gradient boosted decision trees models to improve our predictions.


title: Development of cecal distention in germ-free baby rats.

abstract: The weight of the full cecum was determined in normal-born, mother-fed, germ-free and conventional rats during the lactation period and shortly thereafter. Compared to conventional rats of the same age, the germfree animals showed an increase in cecal weight which started during the 2nd week of life. Even with mother's milk as a sole source of nourishment up to 25 days of age, the cecum of the germ-free animals at that time already had reached the degree of distention seen in older germ-free rats. Germ-free rats fed semisynthetic and practical diets in general show a five- to sevenfold increase in cecal weight, compared to conventional animals. It is concluded that the absence of stimulation by a microbial flora, rather than a nutritional deficiency due to sterilization of the diet is the prime etiological factor in this phenomenon.


title: Combining schema and instance information

abstract: Determining the correspondences among heterogeneous data sources, which is critical to integration of the data sources, is a complex and resource-consuming task that demands automated support. We propose an iterative procedure for detecting both schema-level and instance-level correspondences from heterogeneous data sources. Cluster analysis techniques are used first to identify similar schema elements (i.e., relations and attributes). Based on the identified schema-level correspondences, classification techniques are used to identify matching tuples. Statistical analysis techniques are then applied to a preliminary integrated data set to evaluate the relationships among schema elements more accurately. Improvement in schema-level correspondences triggers another iteration of an iterative procedure. We have performed empirical evaluation using real-world heterogeneous data sources and report in this paper some promising results (i.e., incremental improvement in identified correspondences) that demonstrate the utility of the proposed iterative procedure.


title: Challenges and opportunities in electronic textiles modeling and optimization

abstract: This paper addresses an emerging new field of research that combines the strengths and capabilities of electronics and textiles in one: electronic textiles, or e-textiles. E-textiles, also called Smart Fabrics, have not only "wearable" capabilities like any other garment, but also local monitoring and computation, as well as wireless communication capabilities. Sensors and simple computational elements are embedded in e-textiles, as well as built into yarns, with the goal of gathering sensitive information, monitoring vital statistics and sending them remotely (possibly over a wireless channel) for further processing. Possible applications include medical (infant or patient) monitoring, personal information processing systems, or remote monitoring of deployed personnel in military or space applications. We illustrate the challenges imposed by the dual textile/electronics technology on their modeling and optimization methodology.


title: Individual user characteristics and information visualization: connecting the dots through eye tracking

abstract: There is increasing evidence that users' characteristics such as cognitive abilities and personality have an impact on the effectiveness of information visualization techniques. This paper investigates the relationship between such characteristics and fine-grained user attention patterns. In particular, we present results from an eye tracking user study involving bar graphs and radar graphs, showing that a user's cognitive abilities such as perceptual speed and verbal working memory have a significant impact on gaze behavior, both in general and in relation to task difficulty and visualization type. These results are discussed in view of our long-term goal of designing information visualisation systems that can dynamically adapt to individual user characteristics.


title: Critical phenomena in complex networks

abstract: The combination of the compactness of networks, featuring small diameters, and their complex architectures results in a variety of critical effects dramatically different from those in cooperative systems on lattices. In the last few years, researchers have made important steps toward understanding the qualitatively new critical phenomena in complex networks. We review the results, concepts, and methods of this rapidly developing field. Here we mostly consider two closely related classes of these critical phenomena, namely structural phase transitions in the network architectures and transitions in cooperative models on networks as substrates. We also discuss systems where a network and interacting agents on it influence each other. We overview a wide range of critical phenomena in equilibrium and growing networks including the birth of the giant connected component, percolation, k-core percolation, phenomena near epidemic thresholds, condensation transitions, critical phenomena in spin models placed on networks, synchronization, and self-organized criticality effects in interacting systems on networks. We also discuss strong finite size effects in these systems and highlight open problems and perspectives.


title: Mining checkins from location-sharing services for client-independent IP geolocation

abstract: Accurately determining the geographic location of an Internet host is important for location-aware applications such as location-based advertising and network diagnostics. Despite their fast response time, widely used database-driven geolocation approaches provide only inaccurate locations. Delay measurement based approaches improve the estimation accuracy but still suffer from a limited precision (about 10 km) and a long response time (tens of seconds) to localize a single PC, which cannot meet the demand of precise and real-time geolocation for location- aware applications. In this paper, we propose a new geolocation approach, Checkin-Geo, which exploits geolocation resources fun- damentally different from existing database-driven (using DNS, Whois, etc.) or network delay measurement based approaches. In particular, we leverage the location data that users are willing to share in location-sharing services and logs of user logins from PCs for real-time and accurate geolocation. Experimental results show that compared to existing geolocation techniques, Checkin-Geo achieves 1) a median estimation error of 799 meters (an order of magnitude smaller than existing approaches), and 2) a negligible response time, which are promising for accurate location-aware applications.


title: High-Efficiency High-Power-Density LLC Converter With an Integrated Planar Matrix Transformer for High-Output Current Applications

abstract: Isolated high-output current DC/DC converters are critical for future data center power architecture. LLC converters with matrix transformer are suitable for these applications due to its high efficiency and high power density. Different matrix transformer structures are investigated in this paper. To improve the current design practice, a high-frequency transformer loss model is developed and a detailed design methodology is proposed. Moreover, a novel matrix transformer structure is proposed to integrate four elemental transformers into one magnetic core with simple four-layer print circuit board windings implementation and further reduced core loss. By pushing switching frequency up to megahertz with GaN devices, the proposed matrix transformer can achieve high efficiency, high power density, and automatic manufacturing for magnetic components. A 1-MHz 380 V/12 V 800-W LLC converter with GaN devices is demonstrated. The prototype achieves a peak efficiency of 97.6% and a power density of 900 W/in3.


title: A microcontroller-based ECG signal generator design utilizing microcontroller PWM output and experimental ECG data

abstract: Signal generators are commonly used in biomedical engineering studies. Biomedical circuits must often be tested using EEG, ECG etc. signals. The circuit tests are usually done by connecting electronic circuits to humans. It is a cumbersome process. Electronic biomedical signal sources or signal generators can be used to test biomedical measurement circuits at the early stage of the design processes. Such signal generators are expensive and are not always found in laboratories. In this study, a biomedical signal generator is designed using a cheap and easy- to-use Arduino Mega 2560 R3 microcontroller. SD card connected to the microcontroller is uploaded with an experimental ECG waveform. It is shown that the circuit is able to produce the desired ECG waveforms by sending through its PWM output and low-pass filter and performs well.


title: High-availability algorithms for distributed stream processing

abstract: Stream-processing systems are designed to support an emerging class of applications that require sophisticated and timely processing of high-volume data streams, often originating in distributed environments. Unlike traditional data-processing applications that require precise recovery for correctness, many stream-processing applications can tolerate and benefit from weaker recovery guarantees. In this paper, we study various recovery guarantees and pertinent recovery techniques that can meet the correctness and performance requirements of stream-processing applications. We discuss the design and algorithmic challenges associated with the proposed recovery techniques and describe how each can provide different guarantees with proper combinations of redundant processing, checkpointing, and remote logging. Using analysis and simulations, we quantify the cost of our recovery guarantees and examine the performance and applicability of the recovery techniques. We also analyze how the knowledge of query network properties can help decrease the cost of high availability.


title: CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset

abstract: People convey their emotional state in their face and voice. We present an audio-visual dataset uniquely suited for the study of multi-modal emotion expression and perception. The dataset consists of facial and vocal emotional expressions in sentences spoken in a range of basic emotional states (happy, sad, anger, fear, disgust, and neutral). 7,442 clips of 91 actors with diverse ethnicbackgrounds were rated by multiple raters in three modalities: audio, visual, and audio-visual. Categorical emotion labels andreal-value intensity values for the perceived emotion were collected using crowd-sourcing from 2,443 raters. The human recognition of intended emotion for the audio-only, visual-only, and audio-visual data are 40.9, 58.2 and 63.6 percent respectively. Recognition rates are highest for neutral, followed by happy, anger, disgust, fear, and sad. Average intensity levels of emotion are rated highest forvisual-only perception. The accurate recognition of disgust and fear requires simultaneous audio-visual cues, while anger andhappiness can be well recognized based on evidence from a single modality. The large dataset we introduce can be used to probe other questions concerning the audio-visual perception of emotion.


title: Class Steiner trees and VLSI-design

abstract: Abstract We consider a generalized version of the Steiner problem in graphs, motivated by the wire routing phase in physical VLSI design: given a connected, undirected distance graph with required classes of vertices and Steiner vertices, find a shortest connected subgraph containing at least one vertex of each required class. We show that this problem is NP-hard, even if there are no Steiner vertices and the graph is a tree. Moreover, the same complexity result holds if the input class Steiner graph additionally is embedded in a unit grid, if each vertex has degree at most three, and each class consists of no more than three vertices. For similar restricted versions, we prove MAX SNP-hardness and we show that there exists no polynomial-time approximation algorithm with a constant bound on the relative error, unless P = NP. We propose two efficient heuristics computing different approximate solutions in time O(¦E¦+¦V¦log¦V¦) and in time O(c(¦E¦+¦V¦log¦V¦)) , respectively, where E is the set of edges in the given graph, V is the set of vertices, and c is the number of classes. We present some promising implementation results. kw]Steiner Tree; Heuristic; Approximation complexity; MAX-SNP-hardness


title: Spatial alignment network for facial landmark localization

abstract: Facial Landmark Localization (FLL) on unconstrained images still remains challenging as they poses complex variation in face spatial structure and appearance. To address this problem, we propose a Spatial Alignment Network (SAN), which consist of two modules, like the transformation sub-network and the estimation sub-network. In the first module, we propose two methods to achieving spatial transformation, one is the handcrafted method which can ensure model stability and the other is the learning-based method which is efficient and flexible. In the second module, we add an attention layer in the deep CNN to enhance the importance of discriminative features and obtain more accurate results. Through extensive experiments, our model achieves good performance on several public challenging datasets.


title: Developing and Deploying Universal Diagnostic Platforms for One-Health Biosurveillance

abstract: Objective Our goal is to develop deployable strategies for infectious disease diagnosis at the point-of-care that are applicable to multiple hosts of infection - conforming to the global One Health Strategy for diagnostics. We aim to develop methods that do not require prior knowledge of the pathogen in question, and can facilitate rapid and effective decision-making and situational awareness. Introduction There is an urgent need for diagnostic strategies for infections which are host-independent, so as to effectively track zoonotic spread, monitor animal carriers of pathogens, and evaluate transmission dynamics. Infection of a host - pathogen or human- by an animal results in recognition by the immune response, which consequently causes release of inflammatory mediators. Many scientists have explored the use of cytokines as diagnostic indicators of disease, but the conserved nature of the immune response in humans and animals results in cross-reactivity among many pathogens, making evaluation of the results difficult, especially in high disease burden populations. Measuring the pathogen-specific signature, however, is advantageous - as it offers discrete identification of active infection, and discrimination from exposure. It also offers a universal strategy that can be applied to human and animal hosts of infection - allowing for One Health Biosurveillance. Achieving this, however, requires the development of a) tailored strategies for the measurement of biochemically disparate pathogen signatures in clinical samples and b) ultra-sensitive detection of such signatures in the host. The sensor team at Los Alamos National Laboratory is working on both of these aspects, and the development of One Health Diagnostic platforms, the focus of the work presented here. Methods Many of the biomarkers secreted by bacterial pathogens and recognized by innate immune receptors elicit host cytokine responses that are amphiphilic (largely glycolipids, lipoproteins or lipoglycans). Based on such, our team has developed tailored methods – membrane insertion and lipoprotein capture for the capture and detection of these greasy and stealthy molecules in infected blood. Sensitivity and specificity, assay optimization, alinearity associated with lipidic molecules and assay parameter development for biomarkers associated with bacterial pathogens will be presented. Considerations for clinical sampling for amphiphile detection in blood, and clinical study design will be demonstrated. Our team has developed an ultra-sensitive biosensor for detection of biomarkers in complex matrices based on the evanescent field properties of single mode planar optical waveguides. Detection of amphiphilic biomarkers in clinical samples, using the waveguide-based biosensor and tailored methods for the detection of such molecules has allowed for the diagnosis of infection in both human and animals hosts. Herein, we present two examples of the same; 1) diagnosis of tuberculosis in humans, cattle and badgers using pathogen-based diagnostic strategies; and 2) detection of Gram-negative Lipopolysacharides in beef and in patients with Salmonella -induced sepsis. Results We have developed tailored ultra-sensitive waveguide-biosensor assays for the detection of Lipoarabinomannan (and other biomarkers) from Mycobacteria and demonstrated feasibility in blinded clinical studies in humans and cattle, demonstrating one -health compatibility.We have also demonstrated detection of lipopolysacharides from eight different serotypes of Shiga-toxin carrying E. coli in beef, and Salmonella Typhimurium in pediatric patients using the same approach. Conclusions We have demonstrated clinical feasibility of a One Health Strategy for point-of-care diagnostics of bacterial infections in blood for tuberculosis and Gram-negative pathogens in clinical samples. The results will be demonstrated with several discussion points on the consideration of unconventional biochemistry of pathogens for diagnostics, factors influencing point-of-care deployment and integration of diagnostic platforms for biosurveillance. One Health Considerations and challenges therein will also merit discussion. References Sakamuri, R; et al. Accurate Tracking of Bovine Tuberculosis Biomarkers in Infected Cattle using a Novel Biomarker Capture Strategy, Analytical Sciences, 33(4): 457-460, 2017. Noormohammed A et al. Detection of Salmonella LPS in Patient blood by Lipoprotein Capture. Proc. SPIE 10072, Optical Diagnostics and Sensing XVII: Toward Point-of-Care Diagnostics, 100720A , 2017. doi:10.1117/12.2253506 Stromberg L et al. Membrane Insertion for the Detection of Lipopolysaccharides: Exploring the Dynamics of Amphiphile-in-Lipid Assays, PloS One, April 2016. Sakamuri RM et al. Association of lipoarabinomannan with high density lipoprotein in blood: implications for diagnostics.Tuberculosis (Edinb). 2013 May;93(3):301-7. doi: 10.1016/j.tube.2013.02.015. Epub 2013 Mar 16 Mukundan H et al . Understanding the interaction of Lipoarabinomannan with membrane mimetic architectures. Tuberculosis, 92(1) 32-47, 2012. Mukundan H et al. , Waveguide-based Sensors for Pathogen Detection, Invited Review, Sensors, 9(7), 5783-5809.


title: Concept Mask: Large-Scale Segmentation from Semantic Concepts

abstract: Existing works on semantic segmentation typically consider a small number of labels, ranging from tens to a few hundreds. With a large number of labels, training and evaluation of such task become extremely challenging due to correlation between labels and lack of datasets with complete annotations. We formulate semantic segmentation as a problem of image segmentation given a semantic concept, and propose a novel system which can potentially handle an unlimited number of concepts, including objects, parts, stuff, and attributes. We achieve this using a weakly and semi-supervised framework leveraging multiple datasets with different levels of supervision. We first train a deep neural network on a 6M stock image dataset with only image-level labels to learn visual-semantic embedding on 18K concepts. Then, we refine and extend the embedding network to predict an attention map, using a curated dataset with bounding box annotations on 750 concepts. Finally, we train an attention-driven class agnostic segmentation network using an 80-category fully annotated dataset. We perform extensive experiments to validate that the proposed system performs competitively to the state of the art on fully supervised concepts, and is capable of producing accurate segmentations for weakly learned and unseen concepts.


title: Compiling Bayesian Networks Using Variable Elimination

abstract: Compiling Bayesian networks has proven an effective approach for inference that can utilize both global and local network structure. In this paper, we define a new method of compiling based on variable elimination (VE) and Algebraic Decision Diagrams (ADDs). The approach is important for the following reasons. First, it exploits local structure much more effectively than previous techniques based on VE. Second, the approach allows any of the many VE variants to compute answers to multiple queries simultaneously. Third, the approach makes a large body of research into more structured representations of factors relevant in many more circumstances than it has been previously. Finally, experimental results demonstrate that VE can exploit local structure as effectively as state-of-the-art algorithms based on conditioning on the networks considered, and can sometimes lead to much faster compilation times.


title: The influence of size and coverage on test suite effectiveness

abstract: We study the relationship between three properties of test suites: size, structural coverage, and fault-finding effectiveness. In particular, we study the question of whether achieving high coverage leads directly to greater effectiveness, or only indirectly through forcing a test suite to be larger. Our experiments indicate that coverage is sometimes correlated with effectiveness when size is controlled for, and that using both size and coverage yields a more accurate prediction of effectiveness than size alone. This in turn suggests that both size and coverage are important to test suite effectiveness. Our experiments also indicate that no linear relationship exists among the three variables of size, coverage and effectiveness, but that a nonlinear relationship does exist.


title: Visual complexity of graphical user interfaces

abstract: Graphical User Interfaces (GUIs) of low visual complexity tend to have higher aesthetics, usability and accessibility, and result in higher user satisfaction. Despite a few authors recently used or studied visual complexity, the concept of visual complexity still needs to be better defined for the use in HCI research and GUI design, with its underlying aspects systematized and operationalized, and different measures validated. This paper reviews the aspects of GUI visual complexity and operationalizes four aspects with nine computation-based measures in total. Two user studies validated the measures on two types of stimuli - webpages (study 1, n = 55) and book pages (study 2, n = 150) - with two user groups, dyslexics (people with reading difficulties) and typical readers. The same complexity aspects could be expected to determine complexity perception for both GUI types, whereas different complexity aspects could be expected to determine complexity perception for dyslexics, relative to typical readers. However, the studies showed little to no difference between dyslexics and average readers, whereas web pages did differ from book pages in what aspects made them seem complex. It was not the intergroup differences, but the stimulus type that defined criteria to judge visual complexity. Future research and visual design could rely on the visual complexity aspects outlined in this paper.


title: Adolescent Development and the Regulation of Youth Crime

abstract: Summary Elizabeth Scott and Laurence Steinberg explore the dramatic changes in the law’s conception of young offenders between the end of the nineteenth century and the beginning of the twentyfirst. At the dawn of the juvenile court era, they note, most youths were tried and punished as if they were adults. Early juvenile court reformers argued strongly against such a view, believing that the justice system should offer young offenders treatment that would cure them of their antisocial ways. That rehabilitative model of juvenile justice held sway until a sharp upswing in youth violence at the end of the twentieth century led both public opinion and public policy toward a view that youths should be held to the same standard of criminal accountability as adults. Lawmakers seemed to lose sight of developmental differences between adolescents and adults. But Scott and Steinberg note that lawmakers and the public appear now to be rethinking their views once more. A justice system that operates on the principle of “adult time for adult crime” now seems to many to take too little note of age and immaturity in calculating criminal punishment. In 2005 the United States Supreme Court abolished the juvenile death penalty as cruel and unusual punishment, emphasizing that the immaturity of adolescents made them less culpable than adult criminals. In addition, state legislatures recently have repealed or moderated some of the punitive laws they recently enacted. Meanwhile, observe the authors, public anger has abated and attitudes toward young offenders have softened somewhat. In response to these changes, Scott and Steinberg argue that it is appropriate to reexamine juvenile justice policy and to devise a new model for the twenty-first century. In this article, they propose what they call a developmental model. They observe that substantial new scientific evi dence about adolescence and criminal activity by adolescents provides the building blocks for a new legal regime superior to today’s policy. They put adolescent offenders into an intermediate legal category—neither children, as they were seen in the early juvenile court era, nor adults, as they often are seen today. They observe that such an approach is not only more compatible than the current regime with basic principles of fairness at the heart of the criminal law, but also more likely to promote social welfare by reducing the social cost of juvenile crime.


title: Using Risk to Balance Agile and Plan- Driven Methods

abstract: Both agile and plan-driven approaches have situation-dependent shortcomings that, if not addressed, can lead to project failure. The challenge is to balance the two approaches to take advantage of their strengths in a given situation while compensating for their weaknesses. The authors present a risk-based approach for structuring projects to incorporate both agile and plan-driven approaches in proportion to a project's needs.


Learning Against Non-Stationary Agents with Opponent Modelling and Deep Reinforcement Learning.


Preliminary study of age, growth, and spawning activity of albacore in australia's eastern tuna & billfish fishery


title: (Leader/Randomization/Signature)-free Byzantine Consensus for Consortium Blockchains

abstract: This paper presents a new Byzantine consensus algorithm targeting consortium blockchains. To this end, it first revisits the consensus validity property by requiring that the decided value satisfies a predefined predicate, which does not systematically exclude a value proposed only by Byzantine processes, thereby generalizing the validity properties found in the literature. Then, the paper presents a simple and modular Byzantine consensus algorithm that relies neither on a leader, nor on signatures, nor on randomization. It features the fastest multivalued reduction to binary consensus we know of and a time optimal binary Byzantine consensus algorithm. The multivalued reduction runs multiple instances of binary consensus concurrently, which result in a bitmask that is then applied to a vector of multivalued proposals to filter out a valid proposed value that is decided. To ensure eventual decision deterministically, the underlying binary consensus algorithm assumes eventual synchrony.


Establishing confidence in the output of qualitative research synthesis: the ConQual approach


Creating a Novel Geolocation Corpus from Historical Texts


title: Security Aspects of the Extended Playfair Cipher

abstract: The well known multiple letter encryption cipher is the Playfair cipher. Here the digrams in the plaintext are treated as single units and converted into corresponding cipher text digrams. However because of the drawbacks inherent in the 5*5 Playfair cipher which adversely affects the security we proposed an 8*8 Playfair cipher and then coupled it with LFSR (Linear Feedback Shift Register) to make the traditional Playfair cipher at par with the advanced ciphers available like AES and DES. For details one can refer to [1]. Now for all practical applications, performance and speed are also prime concerns besides security. The LFSR not only enhances the security up to a considerable level by generating random sequences but also provides a much faster rate of encryption and decryption. This paper deals in with the security issues of the new proposed system. Various types of cryptography attacks have been taken under consideration and the resistance of the proposed cipher to all these attacks has been discussed. At the end we find that the proposed cipher is not vulnerable to attacks.


title: No country for old members: user lifecycle and linguistic change in online communities

abstract: Vibrant online communities are in constant flux. As members join and depart, the interactional norms evolve, stimulating further changes to the membership and its social dynamics. Linguistic change --- in the sense of innovation that becomes accepted as the norm --- is essential to this dynamic process: it both facilitates individual expression and fosters the emergence of a collective identity. We propose a framework for tracking linguistic change as it happens and for understanding how specific users react to these evolving norms. By applying this framework to two large online communities we show that users follow a determined two-stage lifecycle with respect to their susceptibility to linguistic change: a linguistically innovative learning phase in which users adopt the language of the community followed by a conservative phase in which users stop changing and the evolving community norms pass them by. Building on this observation, we show how this framework can be used to detect, early in a user's career, how long she will stay active in the community. Thus, this work has practical significance for those who design and maintain online communities. It also yields new theoretical insights into the evolution of linguistic norms and the complex interplay between community-level and individual-level linguistic change.


title: Reflections on the first man vs. machine no-limit Texas hold 'em competition

abstract: The first ever human vs. computer no-limit Texas hold 'em competition took place from April 24-May 8, 2015 at River's Casino in Pittsburgh, PA. In this article I present my thoughts on the competition design, agent architecture, and lessons learned.


title: Radio frequency identification: technologies, applications, and research issues

abstract: A radio frequency identification (RFID) system is a special kind of sensor network to identify an object or a person using radio frequency transmission. A typical RFID system includes transponders (tags) and interrogators (readers): tags are attached to objects/persons, and readers communicate with the tags in their transmission ranges via radio signals. RFID systems have been gaining more and more popularity in areas such as supply chain management, automated identification systems, and any place requiring identifications of products or people. RFID technology is better than barcode in many ways, and may totally replace barcode in the future if certain technologies can be achieved such as low cost and protection of personal privacy. This paper provides a technology survey of RFID systems and various RFID applications. We also discuss five critical research issues: cost control, energy efficiency, privacy issue, multiple readers' interference, and security issue. Copyright (c) 2006 John Wiley & Sons, Ltd.


PHYTOCHEMICALS PROPERTIES AND ANTIBACTERIAL ACTIVITY OF COMBINED LEAVES EXTRACT OF Senna siamea , Coffee senna and Citrus lemon


title: Crossed Dipole Loaded With Magneto-Electric Dipole for Wideband and Wide-Beam Circularly Polarized Radiation

abstract: A crossed dipole that is loaded with a magneto-electric dipole to produce the wideband and wide-beam circularly polarized radiation characteristics is proposed. The crossed dipole is incorporated with double-printed vacant-quarter rings to feed the antenna. The antenna is backed by a metallic cavity to provide a unidirectional radiation pattern with a wide axial-ratio (AR) beamwidth and a high front-to-back ratio. Experimental results showed that the prototype with an overall size of $120\times 120\times30.5~\hbox{mm}^3$ has a $\vert { S}_{11}\vert bandwidth of 1.274–2.360 GHz and a 3-dB AR bandwidth of 1.39–1.82 GHz. The antenna showed a right-hand circular polarization (CP) radiation with a very wide 3-dB AR beamwidth ( $>165^{\circ}$ ) and a high radiation efficiency ( $ > 94\%$ ) within the operational bandwidth.


title: A Time-Restricted Self-Attention Layer for ASR

abstract: Self-attention - an attention mechanism where the input and output sequence lengths are the same - has recently been successfully applied to machine translation, caption generation, and phoneme recognition. In this paper we apply a restricted self-attention mechanism (with multiple heads) to speech recognition. By “restricted” we mean that the mechanism at a particular frame only sees input from a limited number of frames to the left and right. Restricting the context makes it easier to encode the position of the input - we use a I-hot encoding of the frame offset. We try introducing attention layers into TDNN architectures, and replacing LSTM layers with attention layers in TDNN+LSTM architectures. We show experiments on a number of ASR setups. We observe improvements compared to the TDNN and TDNN+LSTM baselines. Attention layers are also faster than LSTM layers in test time, since they lack recurrence.


title: Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work

abstract: Educational robotics are increasingly appearing in educational settings, being considered a useful supporting tool for the development of cognitive skills, including Computational Thinking (CT), for students of all ages. Meanwhile, there is an overwhelming argument that CT will be a fundamental skill needed for all individuals by the middle of the twenty-first century and thus, should be cultivated in the early school years, as part of the child’s analytical thinking and as a principal component of Science-Technology-Engineering-Mathematics (STEM) education. This study reviews published literature at the intersection of CT and educational robotics, particularly focused on the use of educational robotics for advancing students’ CT skills in K-12. The reviewed articles reveal initial evidence suggesting that educational robotics can foster students’ cognitive and social skills. The paper discusses specific areas for further inquiry by learning researchers and learning practitioners. Such inquiry should start from a widely agreed definition of CT and validated measurement instruments for its assessment. A practical framework for the development of CT via robotics is next in demand, so as instructional designers and educators can implement it consistently and at scale.


title: Substrate Integrated Waveguide Leaky-Wave Antenna With Wide Bandwidth via Prism Coupling

abstract: New communications systems require high-speed data transfer and need high frequency, wideband, and directive antennas. Leaky-wave antennas are a desirable type of antennas for millimeter and submillimeter waves, since they can produce a high directive radiation with a single feeding. The latter is an enormous advantage to reducing the cost and losses at high frequency. Despite these advantages, their dispersive nature inherently produces a beam squint effect in their radiation patterns. Here, we propose the use of a lens that compensates for the dispersion of the leaky wave, making the overall antenna broadband. This concept is demonstrated in substrate integrated waveguide technology at Ka-band, and the lens is integrated in the same technology. Full-wave simulations and experimental results are presented to demonstrate the potential of our proposal. Our manufactured prototype has more than 20% frequency bandwidth for the 3-dB pattern at $\varphi =31^\circ $ , and the main radiating direction steers only ±0.5° from 35 to 40 GHz with a half-power beamwidth of 8°.


title: Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

abstract: Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.


title: Exploring storage class memory with key value stores

abstract: In the near future, new storage-class memory (SCM) technologies -- such as phase-change memory and memristors -- will radically change the nature of long-term storage. These devices will be cheap, non-volatile, byte addressable, and near DRAM density and speed. While SCM offers enormous opportunities, profiting from them will require new storage systems specifically designed for SCM's properties. This paper presents Echo, a persistent key-value storage system designed to leverage the advantages and address the challenges of SCM. The goals of Echo include high performance for both small and large data objects, recoverability after failure, and scalability on multicore systems. Echo achieves its goals through the use of a two-level memory design targeted for memory systems containing both DRAM and SCM, exploitation of SCM's byte addressability for fine-grained transactions in non-volatile memory, and the use of snapshot isolation for concurrency, consistency, and versioning. Our evaluation demonstrates that Echo's SCM-centric design achieves the durability guarantees of the best disk-based stores with the performance characteristics approaching the best in-memory key-value stores.


title: Flexible Combinatory Categorial Grammar Parsing Using the CYK Algorithm and Answer Set Programming

abstract: Combinatory Categorial Grammar CCG is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses a small set of combinatory rules to combine these categories in order to parse sentences. In this work we describe and implement a new approach to CCG parsing that relies on Answer Set Programming ASP -- a declarative programming paradigm.Different from previous work, we present an encoding that is inspired by the algorithm due to Cocke, Younger, and Kasami CYK. We also show encoding extensions for parse tree normalization and best-effort parsing and outline possible future extensions which are possible due to the usage of ASP as computational mechanism. We analyze performance of our approach on a part of the Brown corpus and discuss lessons learned during experiments with the ASP tools dlv, gringo, and clasp. The new approach is available in the open source CCG parsing toolkit AspCcgTk which uses the C&C supertagger as a preprocessor to achieve wide-coverage natural language parsing.


title: Graphite: Iterative Generative Modeling of Graphs

abstract: Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite an algorithmic framework for unsupervised learning of representations over nodes in a graph using deep latent variable generative models. Our model is based on variational autoencoders (VAE), and differs from existing VAE frameworks for data modalities such as images, speech, and text in the use of graph neural networks for parameterizing both the generative model (i.e., decoder) and inference model (i.e., encoder). The use of graph neural networks directly incorporates inductive biases due to the spatial, local structure of graphs directly in the generative model. Moreover, we draw novel connections between graph neural networks and approximate inference via kernel embeddings of distributions. We demonstrate empirically that Graphite outperforms state-of-the-art approaches for the tasks of density estimation, link prediction, and node classification on synthetic and benchmark datasets.


title: A dual-band multimode monopulse tracking antenna for land-mobile satellite communications in Ka-band

abstract: The growing interest in bi-directional mobile satellite communications in Ka-band necessitates the development of dedicated antenna systems and feeds. In this paper we describe a mobile satellite communications terminal with a cassegrain reflector antenna fed from a multimode monopulse tracking system for circular polarisation at the satellite downlink frequency of 20 GHz. The structure is operational at 30 GHz as well, to cover the uplink frequency range. The antenna system is mounted on a high-performance mechanical positioner to facilitate a fast compensation of the movements of the carrier vehicle. This work contributes to the development of a satellite terminal for mobile communications in disaster scenarios [1].


title: Aspect and Entity Extraction for Opinion Mining

abstract: Opinion mining or sentiment analysis is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities such as products, services, organizations, individuals, events, and their different aspects. It has been an active research area in natural language processing and Web mining in recent years. Researchers have studied opinion mining at the document, sentence and aspect levels. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides the detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. In this chapter, we provide a broad overview of the tasks and the current state-of-the-art extraction techniques.


title: Neural differences in the mapping of verb and noun concepts onto novel words

abstract: Abstract A dissociation between noun and verb processing has been found in brain damaged patients leading to the proposal that different word classes are supported by different neural representations. This notion is supported by the facts that children acquire nouns faster and adults usually perform better for nouns than verbs in a range of tasks. In the present study, we simulated word learning in a variant of the human simulation paradigm that provided only linguistic context information and required young healthy adults to map noun or verb meanings to novel words. The mapping of a meaning associated with a new-noun and a new-verb recruited different brain regions as revealed by functional magnetic resonance imaging. While new-nouns showed greater activation in the left fusiform gyrus, larger activation was observed for new-verbs in the left posterior middle temporal gyrus and left inferior frontal gyrus (opercular part). Furthermore, the activation in several regions of the brain (for example the bilateral hippocampus and bilateral putamen) was positively correlated with the efficiency of new-noun but not new-verb learning. The present results suggest that the same brain regions that have previously been associated with the representation of meaning of nouns and verbs are also associated with the mapping of such meanings to novel words, a process needed in second language learning.


title: Laptop multitasking hinders classroom learning for both users and nearby peers

abstract: Laptops are commonplace in university classrooms. In light of cognitive psychology theory on costs associated with multitasking, we examined the effects of in-class laptop use on student learning in a simulated classroom. We found that participants who multitasked on a laptop during a lecture scored lower on a test compared to those who did not multitask, and participants who were in direct view of a multitasking peer scored lower on a test compared to those who were not. The results demonstrate that multitasking on a laptop poses a significant distraction to both users and fellow students and can be detrimental to comprehension of lecture content.


title: Reliable Orientation Estimation of Vehicles in High-Resolution Radar Images

abstract: With new generations of high-resolution imaging radars, the orientation of vehicles can be estimated without temporal filtering. This enables time-critical systems to respond even faster. Based on a large data set, this paper compares three generic algorithms for the orientation estimation of a vehicle. An experimental MIMO imaging radar is used to highlight the requirements of a robust algorithm. The well-known orientated bounding box and the so-called L-fit are adapted for radar measurements and compared with a brute-force approach. A quality function selects the best fitted model and is a key factor to minimize alignment errors. Moreover, the reliability of the estimation is evaluated with respect to the aspect angle, the distance to the target, and the number of sensors. An approach to estimate the reliability of the current orientation estimation is introduced. It is shown that the root mean square error of the orientation estimation is 9.77° and 38% smaller compared with the common algorithm. In 50% of the evaluated measurements the orientation estimation error is smaller than 3.73°.


title: IoT based low-cost distant patient ECG monitoring system

abstract: Internet of Things is the new revolution that is going to impact every facet of our lives. Internet of Things (IoT) is the new technology which includes the collection of Sensors, Actuators, processors and other development boards (eg. Raspberry pi, Arduino etc) to collect the data from different sources depending upon domain of application, process the acquired data before sending to distant cloud or mobile application depending upon the requirement[7]. The most promising application of IoT is in the field of health care sector. In some countries, people still do not have access to quality health facilities owing to different barriers. The physical distance between patient and clinic is one of the foremost reasons. This paper studies the application of IoT in health care domain and a system is proposed to monitor the ECG of the distant patient. In the proposed system the Bio signals are collected from the Body of the patient using ECG sensor and after the required processing using development boards, sent to distant cloud named as Bluemix[14] which is owned by IBM, for further analysis by a physician or other authorized person. The Bluemix cloud uses MQTT (Message Queuing Telemetry Transport) protocol which is used to connect different types of applications and devices together across the globe. MQTT is a lightweight protocol which is developed for a machine to machine (M2M) communication.


title: Estimating the Maximum Expected Value: An Analysis of (Nested) Cross Validation and the Maximum Sample Average

abstract: We investigate the accuracy of the two most common estimators for the maximum expected value of a general set of random variables: a generalization of the maximum sample average, and cross validation. No unbiased estimator exists and we show that it is non-trivial to select a good estimator without knowledge about the distributions of the random variables. We investigate and bound the bias and variance of the aforementioned estimators and prove consistency. The variance of cross validation can be significantly reduced, but not without risking a large bias. The bias and variance of different variants of cross validation are shown to be very problem-dependent, and a wrong choice can lead to very inaccurate estimates.


title: The Effects of News Sentiment and Coverage on Credit Rating Analysis

abstract: Credit ratings convey credit risk information to participants in financial markets, including investors, issuers, intermediaries and regulators. This paper proposes an automatic text analysis system for financial news and analyzes the effects of news coverage and sentiment factors on credit ratings. Our experiment results show that firms with higher news coverage received worse ratings in the next quarter. The effect is especially stronger for speculative grade firms. We have also found that news polarity is linked to credit rating in the following quarter for investment grade firms. These results suggest that news data are useful in credit rating modelling and should not be omitted in this type of research.


title: The global prevalence of intimate partner homicide: a systematic review

abstract: Findings Data were obtained for 66 countries. Overall 13·5% (IQR 9·2–18·2) of homicides were committed by an intimate partner, and this proportion was six times higher for female homicides than for male homicides (38·6%, 30·8–45·3, vs 6·3%, 3·1–6·3). M edian percentages for all (male and female) and female intimate partner homicide were highest in high-income countries (all, 14·9%, 9·2–18·2; female homicide, 41·2%, 30·8–44·5) and in southeast Asia (18·8%, 11·3–18·8; 58·8%, 58·8–58·8). Adjustments to account for unknown victim–off ender relationships generally increased the prevalence, suggesting that results presented are conservative. Interpretation At least one in seven homicides globally and more than a third of female homicides are perpetrated by an intimate partner. Such violence commonly represents the culmination of a long history of abuse. Strategies to reduce homicide risk include increased investment in intimate partner violence prevention, risk assessments at diff erent points of care, support for women experiencing intimate partner violence, and control of gun ownership for people with a history of violence. Improvements in country-level data collection and monitoring systems are also essential, because data availability and quality varied strongly across regions.


title: Learning Crosslingual Word Embeddings without Bilingual Corpora

abstract: Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools. However, previous attempts had expensive resource requirements, difficulty incorporating monolingual data or were unable to handle polysemy. We address these drawbacks in our method which takes advantage of a high coverage dictionary in an EM style training algorithm over monolingual corpora in two languages. Our model achieves state-of-the-art performance on bilingual lexicon induction task exceeding models using large bilingual corpora, and competitive results on the monolingual word similarity and cross-lingual document classification task.


title: Self-aligning exoskeleton hip joint: Kinematic design with five revolute, three prismatic and one ball joint

abstract: Kinematic compatibility is of paramount importance in wearable robotic and exoskeleton design. Misalignments between exoskeletons and anatomical joints of the human body result in interaction forces which make wearing the exoskeleton uncomfortable and even dangerous for the human. In this paper we present a kinematically compatible design of an exoskeleton hip to reduce kinematic incompatibilities, so called macro- and micro-misalignments, between the human's and exoskeleton's joint axes, which are caused by inter-subject variability and articulation. The resulting design consists of five revolute, three prismatic and one ball joint. Design parameters such as range of motion and joint velocities are calculated based on the analysis of human motion data acquired by motion capture systems. We show that the resulting design is capable of self-aligning to the human hip joint in all three anatomical planes during operation and can be adapted along the dorsoventral and mediolateral axis prior to operation. Calculation of the forward kinematics and FEM-simulation considering kinematic and musculoskeletal constraints proved sufficient mobility and stiffness of the system regarding the range of motion, angular velocity and torque admissibility needed to provide 50 % assistance for an 80 kg person.


title: Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments

abstract: The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper. Unlike traditional deep learning methods, ADL features a flexible structure where its network structure can be constructed from scratch with the absence of an initial network structure via the self-constructing network structure. ADL specifically addresses catastrophic forgetting by having a different-depth structure which is capable of achieving a trade-off between plasticity and stability. Network significance (NS) formula is proposed to drive the hidden nodes growing and pruning mechanism. Drift detection scenario (DDS) is put forward to signal distributional changes in data streams which induce the creation of a new hidden layer. The maximum information compression index (MICI) method plays an important role as a complexity reduction module eliminating redundant layers. The efficacy of ADL is numerically validated under the prequential test-then-train procedure in lifelong environments using nine popular data stream problems. The numerical results demonstrate that ADL consistently outperforms recent continual learning methods while characterizing the automatic construction of network structures.


title: Restricted Boltzmann Machines for the Prediction of Trends in Financial Time Series

abstract: Nowadays, it is possible to note many machine learning techniques being applied to predict financial time series. However, recent studies indicate that the performance of such techniques can be strongly affected by data representation. In this manuscript we propose a combination of two machine learning algorithms to detect trends in stock market prices. In this approach, Boltzmann Restricted Machines are used as the latent feature extractor and Support Vector Machines work as the classifier. We performed tests with real data of five assets from the Brazilian Stock Market, BM&FBOVESPA. The results obtained with the proposed combination were better when compared to those ones reached by Support Vector Machines only. This suggests that the proposed approach can be suitable for the considered application.


title: Prevalence of dyslipidemia and mean blood lipid values in Taiwan: results from the Nutrition and Health Survey in Taiwan (NAHSIT, 1993-1996).

abstract: This paper reports the blood lipid status of people aged 4 years and older in Taiwan. The data is based on the Nutrition and Health Survey in Taiwan (NAHSIT: 1993-1996), which adopted a multi-stage, stratified clustering sampling scheme. Altogether, 5097 subjects (2451 males and 2646 females) had data on triglyceride and 5643 subjects (2736 males and 2907 females) had data on cholesterol. We found that (a) cholesterol levels of males were lower than females in mid-to old age group (≥45 years old); (b) triglyceride values of females were lower than males in young adulthood (19~44 years), but higher than males after the age of 45 years, and (c) adult females had higher HDL-C value and lower ratio of total cholesterol to HDL-C than males. The prevalence of hypercholesterolemia was 10.2% in adult males and 12.6% in mid-to-old aged men, and that in females was 11.2% and 24.4%, respectively. The prevalence of hypertriglyceridemia was 13.4% and 6.1% in adult males and females (≥19 years as a whole), respectively. It was 12.3% in mid-to-old aged men (≥45 years), and 11.9% in women. ::: The mean cholesterol values were similar to values of several previous surveys in different areas of Taiwan. But it was higher than those in some areas of Mainland China, and lower than those of western countries. People in metropolitan cities had a higher level of blood cholesterol than other areas. The average triglyceride values of males and females were higher than those of previous studies in Taiwan and of people in Mainland China. Mountainous stratum with predominantly aboriginal residents had higher level of triglycerides and body mass index (BMI) than other strata. ::: The associations between dietary intakes of men and women and blood lipids were examined controlling for age and BMI. Result showed that Keys score, which was derived from saturated fat, polyunsaturated fat and dietary cholesterol of a 24-hour recall, was positively related to blood cholesterol and LDL-C in men, but not in women. Average alcohol intakes per day were related to HDL-C positively, but LDL-C negatively in men and women. The regional differences in blood lipid profiles in Taiwan are consistent with the dietary and life-style variations island-wide.


title: Anatomical study of phrenic nerve using ultrasound

abstract: PURPOSE ::: To demonstrate whether ultrasonography may allow a precise assessment of the course and relations of the phrenic nerve in its cervical portion. ::: ::: ::: METHODS ::: This study, initially undertaken in five cadavers, was followed by high-resolution ultrasonographic study in 20 volunteers (40 nerves) by two radiologists in consensus. The location, course and relations of the phrenic nerve to the adjacent anatomical structures were analysed. ::: ::: ::: RESULTS ::: The phrenic nerve could be nicely identified through the majority of its course by high-resolution ultrasonography. Useful anatomic landmarks for the detection of the nerve could be defined; these include the transverse cervical and ascending cervical arteries. Some anatomical variations were observed. ::: ::: ::: CONCLUSION ::: Knowledge of the nerve's precise location, which may demonstrate individual variations, may have useful clinical applications.


title: MapReduce for Parallel Reinforcement Learning

abstract: We investigate the parallelization of reinforcement learning algorithms using MapReduce, a popular parallel computing framework. We present parallel versions of several dynamic programming algorithms, including policy evaluation, policy iteration, and off-policy updates. Furthermore, we design parallel reinforcement learning algorithms to deal with large scale problems using linear function approximation, including model-based projection, least squares policy iteration, temporal difference learning and recent gradient temporal difference learning algorithms. We give time and space complexity analysis of the proposed algorithms. This study demonstrates how parallelization opens new avenues for solving large scale reinforcement learning problems.


title: The robustness of pollination networks to the loss of species and interactions : a quantitative approach incorporating pollinator behaviour

abstract: Species extinctions pose serious threats to the functioning of ecological communities worldwide. We used two qualitative and quantitative pollination networks to simulate extinction patterns following three removal scenarios: random removal and systematic removal of the strongest and weakest interactors. We accounted for pollinator behaviour by including potential links into temporal snapshots (12 consecutive 2-week networks) to reflect mutualists' ability to 'switch' interaction partners (re-wiring). Qualitative data suggested a linear or slower than linear secondary extinction while quantitative data showed sigmoidal decline of plant interaction strength upon removal of the strongest interactor. Temporal snapshots indicated greater stability of re-wired networks over static systems. Tolerance of generalized networks to species extinctions was high in the random removal scenario, with an increase in network stability if species formed new interactions. Anthropogenic disturbance, however, that promote the extinction of the strongest interactors might induce a sudden collapse of pollination networks.


title: Gallbladder Carcinoma Update: Multimodality Imaging Evaluation, Staging, and Treatment Options

abstract: OBJECTIVE. The purpose of this article is to review the epidemiology, multimodality imaging findings, differential diagnosis, pathologic staging, and current treatment options of gallbladder carcinoma.CONCLUSION. Understanding the characteristic appearances of primary gallbladder carcinoma at multiple imaging modalities can facilitate diagnosis and enable more accurate staging for triage to extended resection or an alternate therapy.


title: Device-to-Device Communications; Functional Prospects for LTE-Advanced Networks

abstract: In this paper the possibility of device-to-device (D2D) communications as an underlay of an LTE-A network is introduced. The D2D communication enables new service opportunities and reduces the eNB load for short range data intensive peer-to-peer communication. The cellular network may establish a new type of radio bearer dedicated for D2D communications and stay in control of the session setup and the radio resources without routing the user plane traffic. The paper addresses critical issues and functional blocks to enable D2D communication as an add-on functionality to the LTE SAE architecture. Unlike 3G spread spectrum cellular and OFDM WLAN techniques, LTE-A resource management is fast and operates in high time-frequency resolution. This could allow the use of non-allocated time-frequency resources, or even partial reuse of the allocated resources for D2D with eNB controlled power constraints. The feasibility and the range of D2D communication, and its impact to the power margins of cellular communications are studied by simulations in two example scenarios. The results demonstrate that by tolerating a modest increase in interference, D2D communication with practical range becomes feasible. By tolerating higher interference power the D2D range will increase.


title: Early regulation of CD8 T cell alloreactivity by CD4+CD25-T cells in recipients of anti-CD154 antibody and allogeneic BMT is followed by rapid peripheral deletion of donor-reactive CD8+ T cells, precluding a role for sustained regulation

abstract: While acquisition of regulatory function by CD4+CD25- T cells has been reported following antigenic stimulation, "naturally occurring" regulatory CD4+ T cells (Treg) are believed to express CD25. We examined the mechanisms involved in peripheral CD8 T cell tolerance by induction of mixed chimerism using non-myeloablative conditioning with low-dose (3 Gy) total body irradiation and anti-CD154 antibody. Recipient CD4+ T cells were initially required for the induction of CD8 cell tolerance, but were not needed beyond 2 weeks. Depletion of CD25+ Treg prior to bone marrow transplantation and blockade of IL-2 with neutralizing antibody did not impede tolerance induction. Tolerance was dependent on CTLA4, but not on IFN-gamma. In C57BL/6 mice containing a fraction of 2C TCR transgenic CD8+ T cells, which recognize the MHC class I alloantigen Ld, induction of chimerism with L(d+), but not Ld-, bone marrow cells led to deletion of peripheral 2C+ CD8+ cells within 1 week in peripheral blood and spleen. Complete deletion required the presence of recipient CD4+ T cells. Thus, a novel, rapid form of regulation by CD4+CD25- T cells permits initial CD8 T cell tolerance in this model. Rapid peripheral deletion of donor-specific CD8 T cells precludes an ongoing requirement for CD4 T cell-mediated regulation.


title: GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning

abstract: Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibitively large number of training samples for learning complex tasks. Many recent works on speeding up Deep RL have focused on distributed training and simulation. While distributed training is often done on the GPU, simulation is not. In this work, we propose using GPU-accelerated RL simulations as an alternative to CPU ones. Using NVIDIA Flex, a GPU-based physics engine, we show promising speed-ups of learning various continuous-control, locomotion tasks. With one GPU and CPU core, we are able to train the Humanoid running task in less than 20 minutes, using 10-1000x fewer CPU cores than previous works. We also demonstrate the scalability of our simulator to multi-GPU settings to train more challenging locomotion tasks.


title: The Principles of Software QRS Detection Reviewing and Comparing Algorithms for Detecting this Important ECG Waveform

abstract: The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.


title: REX-J: Japanese referring expression corpus of situated dialogs

abstract: Identifying objects in conversation is a fundamental human capability necessary to achieve efficient collaboration on any real world task. Hence the deepening of our understanding of human referential behaviour is indispensable for the creation of systems that collaborate with humans in a meaningful way. We present the construction of REX-J, a multi-modal Japanese corpus of referring expressions in situated dialogs, based on the collaborative task of solving the Tangram puzzle. This corpus contains 24 dialogs with over 4 h of recordings and over 1,400 referring expressions. We outline the characteristics of the collected data and point out the important differences from previous corpora. The corpus records extra-linguistic information during the interaction (e.g. the position of pieces, the actions on the pieces) in synchronization with the participants' utterances. This in turn allows us to discuss the importance of creating a unified model of linguistic and extra-linguistic information from a new perspective. Demonstrating the potential uses of this corpus, we present the analysis of a specific type of referring expression ("action-mentioning expression") as well as the results of research into the generation of demonstrative pronouns. Furthermore, we discuss some perspectives on potential uses of this corpus as well as our planned future work, underlining how it is a valuable addition to the existing databases in the community for the study and modeling of referring expressions in situated dialog.


title: Contrast-Enhanced Black and White Images

abstract: This paper investigates contrast enhancement as an approach to tone reduction, aiming to convert a photograph to black and white. Using a filter-based approach to strengthen contrast, we avoid making a hard decision about how to assign tones to segmented regions. Our method is inspired by sticks filtering, used to enhance medical images but not previously used in non-photorealistic rendering. We amplify contrast of pixels along the direction of greatest local difference from the mean, strengthening even weak features if they are most prominent. A final thresholding step converts the contrast-enhanced image to black and white. Local smoothing and contrast enhancement balances abstraction and structure preservation; the main advantage of our method is its faithful depiction of image detail. Our method can create a set of effects: line drawing, hatching, and black and white, all having superior details to previous black and white methods.