title: Planar Helical Antenna of Circular Polarization

abstract: A planar helical antenna is presented for achieving wideband end-fire radiation of circular polarization while maintaining a very low profile. The helix is formed using printed strips with straight-edge connections implemented by plated via-holes. The currents flowing on the strips and along via-holes of the helix contribute to the horizontal and vertical polarizations, respectively. Besides, the current on the ground plane is utilized to weaken the strong amplitude of the horizontal electric field generated by the one on the strips. Thus, a good circular polarization can be achieved. Furthermore, a tapered helix and conducting side-walls are employed to broaden the axial ratio (AR) bandwidth as well as to improve the end-fire radiation pattern. The designed antenna operates at the center frequency of 10 GHz. Simulated results show that the planar helical antenna achieves wide-impedance bandwidth ( $\vert S_{11} \vert ) from 7.4 to 12.8 GHz (54%) and 3-dB AR bandwidth from 8.2 to 11.6 GHz (34%), while retaining a thickness of only $0.11{{{\lambda }}_0}$ at the center frequency. A prototype of the proposed antenna is fabricated and tested. Measured results are in good agreement with simulated ones.


title: Zero-Resource Multilingual Model Transfer: Learning What to Share

abstract: Modern natural language processing and understanding applications have enjoyed a great boost utilizing neural networks models. However, this is not the case for most languages especially low-resource ones with insufficient annotated training data. Cross-lingual transfer learning methods improve the performance on a low-resource target language by leveraging labeled data from other (source) languages, typically with the help of cross-lingual resources such as parallel corpora. In this work, we propose a zero-resource multilingual transfer learning model that can utilize training data in multiple source languages, while not requiring target language training data nor cross-lingual supervision. Unlike existing methods that only rely on language-invariant features for cross-lingual transfer, our approach utilizes both language-invariant and language-specific features in a coherent way. Our model leverages adversarial networks to learn language-invariant features and mixture-of-experts models to dynamically exploit the relation between the target language and each individual source language. This enables our model to learn effectively what to share between various languages in the multilingual setup. It results in significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging tasks including a large-scale real-world industry dataset.


title: Opportunities and advances in ultra-wideband electronically scanned arrays

abstract: This paper discusses bandwidth and polarization challenges in modern dual-polarized ultra-wideband (UWB) electronically scanned array (ESA) aperture design and two recent solution advances aiming to remedy such issues: a 6:1 Planar Ultrawideband Modular Antenna (PUMA) array and a 10:1 Sliced Notch Antenna (SNA) array. The proposed arrays enable wide bandwidth, low cross-polarization, and high efficiency from both low-profile and high-profile array research directions to appeal to a wider variety of applications and design embodiments. The proposed UWB-ESAs are analyzed alongside a conventional 10:1 all-metal Vivaldi array to shed light upon modern design trends and implications.


title: Trajectory Generation and Tracking Control for Double-Steering Tractor–Trailer Mobile Robots With On-Axle Hitching

abstract: A multisteering tractor–trailer mobile robot (MSTTMR) is a kind of complex multibody system, which consists of a tractor and a chain of steerable trailers. If both the tractor and the trailers can track an identical geometric path, the overall width of the system is only equal to that of the tractor or the trailer. Thus, the robot can perform transport tasks in a narrow space. The MSTTMR with one trailer is referred to as the double-steering tractor–trailer mobile robot (DSTTMR), since the system has two steering inputs. In this paper, trajectory generation and tracking control for the DSTTMR with on-axle hitching are addressed. We first propose an approach to generate the desired full-state trajectory from a desired geometric path given in the Cartesian plane, in order that the desired state trajectories of the tractor and trailer can form the same desired path. Then, a trajectory tracking controller is designed based on the backstepping technique to drive the system's states to converge to their desired trajectories. As a result, the tractor and the trailer can track the desired geometric path simultaneously. Simulation and experimental results are presented to show the effectiveness of the proposed approach.


Automated segmentation of cell nuclei in PAP smear images


A Report of Three Cases of Omohyoid Muscle Syndrome


title: Enhanced Recovery After Surgery Programs Versus Traditional Care for Colorectal Surgery

abstract: BACKGROUND ::: Enhanced recovery after surgery programs in colorectal surgery aim to attenuate the surgical stress response, reduce complications and shorten hospital stay. ::: ::: ::: OBJECTIVE ::: This study aimed to assess the safety and efficacy of enhanced recovery after surgery programs in colorectal surgery in comparison with traditional care. ::: ::: ::: DATA SOURCES ::: PubMed, Embase, and Cochrane databases were electronically searched (date range, January 1966 to July 2012). ::: ::: ::: STUDY SELECTION ::: Randomized controlled trials were selected that compared enhanced recovery after surgery programs with traditional care in elective colorectal surgery. ::: ::: ::: INTERVENTION ::: Articles were reviewed independently by 2 reviewers, who extracted the data and assessed the quality of the included studies. The outcome measures were analyzed, and the quality of evidence for each outcome was assessed by using the Grading of Recommendations Assessment, Development, and Evaluation system. ::: ::: ::: MAIN OUTCOME MEASURES ::: The primary outcome measures were primary and total postoperative hospital stay, readmission rates, total postoperative complications (including general and surgical complications), and mortality. ::: ::: ::: RESULTS ::: Thirteen studies (total, 1910 patients) were included in the meta-analysis. In comparison with traditional care, enhanced recovery after surgery programs were associated with significantly decreased primary hospital stay (weighted mean difference, -2.44 days; 95% CI, -3.06 to -1.83 days; p < 0.00001), total hospital stay (weighted mean difference, -2.39 days; 95% CI, -3.70 to -1.09 days; p = 0.0003), total complications (relative risk, 0.71; 95% CI, 0.58-0.86; p = 0.0006), and general complications (relative risk, 0.68; 95% CI, 0.56-0.82; p < 0.0001). No significant differences were found for readmission rates, surgical complications, and mortality. ::: ::: ::: LIMITATIONS ::: This study was limited by the risk of bias in most included studies. ::: ::: ::: CONCLUSIONS ::: Enhanced recovery after surgery programs are safe and effective, and increased implementation is justified for perioperative care in colorectal surgery. Future studies may examine the benefits of enhanced recovery after surgery programs in elderly patients and in other GI surgery.


title: A Gaussian Mixture Model layer jointly optimized with discriminative features within a Deep Neural Network architecture

abstract: This article proposes and evaluates a Gaussian Mixture Model (GMM) represented as the last layer of a Deep Neural Network (DNN) architecture and jointly optimized with all previous layers using Asynchronous Stochastic Gradient Descent (ASGD). The resulting “Deep GMM” architecture was investigated with special attention to the following issues: (1) The extent to which joint optimization improves over separate optimization of the DNN-based feature extraction layers and the GMM layer; (2) The extent to which depth (measured in number of layers, for a matched total number of parameters) helps a deep generative model based on the GMM layer, compared to a vanilla DNN model; (3) Head-to-head performance of Deep GMM architectures vs. equivalent DNN architectures of comparable depth, using the same optimization criterion (frame-level Cross Entropy (CE)) and optimization method (ASGD); (4) Expanded possibilities for modeling offered by the Deep GMM generative model. The proposed Deep GMMs were found to yield Word Error Rates (WERs) competitive with state-of-the-art DNN systems, at the cost of pre-training using standard DNNs to initialize the Deep GMM feature extraction layers. An extension to Deep Subspace GMMs is described, resulting in additional gains.


title: PAMAS—power aware multi-access protocol with signalling for ad hoc networks

abstract: In this paper we develop a new multiaccess protocol for ad hoc radio networks. The protocol is based on the original MACA protocol with the adition of a separate signalling channel. The unique feature of our protocol is that it conserves battery power at nodes by intelligently powering off nodes that are not actively transmitting or receiving packets. The manner in which nodes power themselves off does not influence the delay or throughput characteristics of our protocol. We illustrate the power conserving behavior of PAMAS via extensive simulations performed over ad hoc networks containing 10-20 nodes. Our results indicate that power savings of between 10% and 70% are attainable in most systems. Finally, we discuss how the idea of power awareness can be built into other multiaccess protocols as well.


title: Adaptive On/Off Delay-Compensated Active Rectifiers for Wireless Power Transfer Systems

abstract: An adaptive on/off delay-compensation technique is proposed to improve the performance of CMOS active rectifiers for wireless power transfer (WPT) systems. The effects of the on/off delays on the performance of the active rectifiers with either a parallel-resonant or a series-resonant circuit at the secondary coil are studied, which include power conversion efficiency (PCE), voltage conversion ratio (VCR) and output voltage ripple. By adding two feedback loops to the active diodes to generate the switched-offset currents for the comparators adaptively, both on- and off-delays are compensated for accurately against PVT variations and mismatches. As a design example, a fully integrated active rectifier for biomedical applications with a parallel-resonant secondary was fabricated in a standard 0.35 μm CMOS process. With an AC input that ranges from 1.8 to 3.6 V, the measured VCR is higher than 90% and the measured PCE is higher than 89.1% for a load resistor of $500\ \Omega$ . In particular, the PCE is increased by 9% compared to the active rectifier without using the proposed technique.


title: Topic Detection from Large Scale of Microblog Stream with High Utility Pattern Clustering

abstract: With the popularity of social media, detecting topics from microblog streams have become an increasingly important task. However, it's a challenge due to microblog streams have the characteristics of high-dimension, short and noisy content, fast changing, huge volume and so on. In this paper, we propose a high utility pattern clustering (HUPC) framework over microblog streams. This framework first extracts a group of representative patterns from the microblog stream, and then groups these patterns into topic clusters. This approach works well on large scale of microblog streams because it clusters the patterns that perform better in describing topics, rather than clustering noises and microblogs directly. Furthermore, the proposed framework can detect coherent topics and new emerging topics simultaneously. Extensive experimental results on Twitter streams and Sina Weibo streams show that the developed method achieves better performance than other existing topic detection methods, leading to a desirable solution of detecting event from microblog streams.


title: Vehicle Logo Recognition and attributes prediction by multi-task learning with CNN

abstract: Vehicle Logo Recognition(VLR) has been an important study field in intelligent Transportation system (ITS). This paper proposes to recognize vehicle logo and predict logo attributes by combining Convolutional Neural Network (CNN) with Multi-Task Learning(MTL). In order to accelerate convergence of multi-task model, an adaptive weight training strategy is employed. To verify the algorithm, the Xiamen University Vehicle logo recognition dataset is extended into a larger vehicle logo dataset including 15 brands, 6 visual attributes and 3 no-visual attributes. The experiment results indicate that the proposed multi-task CNN model perform well for both of logo classification and attribution prediction with overall accuracy 98.14%.


title: An RFID based system for monitoring free weight exercises

abstract: In this paper we present preliminary results and future directions of work for a project in which we are building an RFID based system to sense and monitor free weight exercises.


title: ROAR: An architecture for Real-Time Opportunistic Spectrum Access in Cloud-assisted Cognitive Radio Networks

abstract: Need for Radio Frequency (RF) spectrum is increasing along with increasing wireless subscriptions and devices that is causing scarcity in total available RF spectrum. Opportunistic spectrum access in cognitive radio networks is emerging for maximizing the RF spectrum efficiency where unlicensed Secondary Users (SUs) access idle spectrum bands without causing any harmful interference to licensed Primary Users (PUs). All SUs are required to scan/sense RF spectrum to find idle bands or search for idle bands in a spectrum database not to interfere with PUs while using those idle bands. In this paper, we propose a Real-time Opportunistic Spectrum Access in Cloud-assisted Cognitive Radio Networks (ROAR) architecture where SUs (i.e., USRP wireless devices) are equipped with wide-band (50 MHz – 6 GHz) antenna and GPS units. ROAR uses cloud computing platform for real-time processing of wide-band data since SUs' performance is considerably constrained by their limited power, memory and computational capacity. In ROAR architecture, there are two parts: spectrum sensing to create a database of idle channels and dynamic spectrum access for opportunistic SU communications using idle channels. For the spectrum database, RF sensors scan/sense RF bands to find idle bands and report the geo-location of idle band, channel frequency and time stamp to the database installed in the distributed cloud platform. For opportunistic spectrum access, each SU interested for opportunistic communication queries a spectrum database to find idle channels. Distributed cloud computing is used to find idle channels for the SU where geolocation and other demands (e.g., data rate) are checked to find whether the SU is admissible or not for a given geo-location and time. If the SU is admissible based on the admissibility criteria, spectrum server sends the list of channels available for a given location and time to the SU. Then SU chooses the best suited channel to communicate opportunistically. We evaluate the ROAR architecture using numerical results obtained from extensive experiments.


title: Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression.

abstract: The first brain-wide voxel-level resting state functional connectivity neuroimaging analysis of depression is reported, with 421 patients with major depressive disorder and 488 control subjects. Resting state functional connectivity between different voxels reflects correlations of activity between those voxels and is a fundamental tool in helping to understand the brain regions with altered connectivity and function in depression. One major circuit with altered functional connectivity involved the medial orbitofrontal cortex Brodmann area 13, which is implicated in reward, and which had reduced functional connectivity in depression with memory systems in the parahippocampal gyrus and medial temporal lobe, especially involving the perirhinal cortex Brodmann area 36 and entorhinal cortex Brodmann area 28. The Hamilton Depression Rating Scale scores were correlated with weakened functional connectivity of the medial orbitofrontal cortex Brodmann area 13. Thus in depression there is decreased reward-related and memory system functional connectivity, and this is related to the depressed symptoms. The lateral orbitofrontal cortex Brodmann area 47/12, involved in non-reward and punishing events, did not have this reduced functional connectivity with memory systems. Second, the lateral orbitofrontal cortex Brodmann area 47/12 had increased functional connectivity with the precuneus, the angular gyrus, and the temporal visual cortex Brodmann area 21. This enhanced functional connectivity of the non-reward/punishment system (Brodmann area 47/12) with the precuneus (involved in the sense of self and agency), and the angular gyrus (involved in language) is thus related to the explicit affectively negative sense of the self, and of self-esteem, in depression. A comparison of the functional connectivity in 185 depressed patients not receiving medication and 182 patients receiving medication showed that the functional connectivity of the lateral orbitofrontal cortex Brodmann area 47/12 with these three brain areas was lower in the medicated than the unmedicated patients. This is consistent with the hypothesis that the increased functional connectivity of the lateral orbitofrontal cortex Brodmann area 47/12 is related to depression. Relating the changes in cortical connectivity to our understanding of the functions of different parts of the orbitofrontal cortex in emotion helps to provide new insight into the brain changes related to depression. ::: ::: * Abbreviations ::: : BWAS ::: : brain-wide association study ::: HAMD ::: : Hamilton Depression Rating Scale ::: OFC ::: : orbitofrontal cortex ::: MDD ::: : major depressive disorder


title: Nengo: a Python tool for building large-scale functional brain models

abstract: Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world’s largest functional brain model (Eliasmith et al., 2012). Simulating Spaun highlighted limitations in Nengo 1.4’s ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.


title: Efficient Inverse Kinematics for General 6R Manipulators

abstract: In this paper, we present an algorithm and implementation for efficient inverse kinematics for a general six-revolute (6R) manipulator. When stated mathematically, the problem reduces to solving a system of multivariate equations. We make use of the algebraic properties of the system and the symbolic formulation used for reducing the problem to solving a univariate polynomial. However, the polynomial is expressed as a matrix determinant and its roots are computed by reducing to an eigenvalue problem. The other roots of the multivariate system are obtained by computing eigenvectors and substitution. The algorithm involves symbolic preprocessing, matrix computations and a variety of other numerical techniques. The average running time of the algorithm, for most cases, is 11 milliseconds on an IBM RS/6000 workstation. This approach is applicable to inverse kinematics of all serial manipulators. >


title: Introduction to Arabic Speech Recognition Using CMUSphinx System

abstract: In this paper Arabic was investigated from the speech recognition problem point of view. We propose a novel approach to build an Arabic Automated Speech Recognition System (ASR). This system is based on the open source CMU Sphinx-4, from the Carnegie Mellon University. CMU Sphinx is a large-vocabulary; speaker-independent, continuous speech recognition system based on discrete Hidden Markov Models (HMMs). We build a model using utilities from the OpenSource CMU Sphinx. We will demonstrate the possible adaptability of this system to Arabic voice recognition.


title: Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles

abstract: Precise trajectory tracking is a crucial property for \acp{MAV} to operate in cluttered environment or under disturbances. In this paper we present a detailed comparison between two state-of-the-art model-based control techniques for \ac{MAV} trajectory tracking. A classical \ac{LMPC} is presented and compared against a more advanced \ac{NMPC} that considers the full system model. In a careful analysis we show the advantages and disadvantages of the two implementations in terms of speed and tracking performance. This is achieved by evaluating hovering performance, step response, and aggressive trajectory tracking under nominal conditions and under external wind disturbances.


title: Text detection and recognition in images and video frames

abstract: Text embedded in images and videos represents a rich source of information for content-based indexing and retrieval applications. In this paper, we present a new method for localizing and recognizing text in complex images and videos. Text localization is performed in a two step approach that combines the speed of a focusing step with the strength of a machine learning based text verification step. The experiments conducted show that the support vector machine is more appropriate for the verification task than the more commonly used neural networks. To perform text recognition on the localized regions, we propose a new multi-hypotheses method. Assuming different models of the text image, several segmentation hypotheses are produced. They are processed by an optical character recognition (OCR) system, and the result is selected from the generated strings according to a confidence value computed using language modeling and OCR statistics. Experiments show that this approach leads to much better results than the conventional method that tries to improve the individual segmentation algorithm. The whole system has been tested on several hours of videos and showed good performance when integrated in a sports video annotation system and a video indexing system within the framework of two European projects.


title: A game based assistive tool for rehabilitation of dysphonic patients

abstract: This is the preprint version of our paper on 3rd International Workshop on Virtual and Augmented Assistive Technology (VAAT) at IEEE Virtual Reality 2015 (VR2015). An assistive training tool for rehabilitation of dysphonic patients is designed and developed according to the practical clinical needs. The assistive tool employs a space flight game as the attractive logic part, and microphone arrays as input device, which is getting rid of ambient noise by setting a specific orientation. The therapist can guide the patient to play the game as well as the voice training simultaneously side by side, while not interfere the patient voice. The voice information can be recorded and extracted for evaluating the long-time rehabilitation progress. This paper outlines a design science approach for the development of an initial useful software prototype of such a tool, considering 'Intuitive', 'Entertainment', 'Incentive' as main design factors.


title: Markov Model Based Jamming and Anti-Jamming Performance Analysis for Cognitive Radio Networks

abstract: In this ::: paper, we conduct a cross-layer analysis of both the jamming capability of the cognitiveradio-based ::: jammers and the anti-jamming capability of the cognitive radio networks (CRN). ::: We use a Markov chain to model the CRN operations in spectrum sensing, channel ::: access and channel switching under jamming. With various jamming models, the ::: jamming probabilities and the throughputs of the CRN are obtained in ::: closed-form expressions. Furthermore, the models and expressions are simplified ::: to determine the minimum and the maximum CRN throughput expressions under ::: jamming, and to optimize important anti-jamming parameters. The results are ::: helpful for the optimal anti-jamming CRN design. The model and the analysis ::: results are verified by simulations.


title: Analysing road accident data using association rule mining

abstract: Road accident is one of the crucial areas of research in India. A variety of research has been done on data collected through police records covering a limited portion of highways. The analysis of such data can only reveal information regarding that portion only; but accidents are scattered not only on highways but also on local roads. A different source of road accident data in India is Emergency Management research Institute (EMRI) which serves and keeps track of every accident record on every type of road and cover information of entire State's road accidents. In this paper, we have used data mining techniques to analyze the data provided by EMRI in which we first cluster the accident data and further association rule mining technique is applied to identify circumstances in which an accident may occur for each cluster. The results can be utilized to put some accident prevention efforts in the areas identified for different categories of accidents to overcome the number of accidents.


title: Analysis and design of compact two-way Wilkinson power dividers using coupled lines

abstract: This paper analyzes the two-way Wilkinson power dividers using coupled lines as λ/4 impedance transformer to have more compact layout. The analysis shows that the input port matching is only influenced by the even mode impedance of the coupled lines. Once the even mode impedance is fixed, all other specifications of the power divider are determined by the odd mode impedance of the coupled lines. Design tradeoffs among the output matching, isolation and the odd mode impedance are shown. By using smaller odd mode impedance, layout constraint can be relaxed. To validate the analysis, Wilkinson power dividers using coupled lines with areas of 1.5 cm2 and 1.25 cm2 are designed together with a conventional divider with an area of 3.8 cm2. The experimental results of the three designs are comparable. When coupled lines are used, the layout is more compact with a reduction in size of more than 50% compared to the conventional design.


title: Vehicle model recognition from frontal view image measurements

abstract: This paper deals with a novel vehicle manufacturer and model recognition scheme, which is enhanced by color recognition for more robust results. A probabilistic neural network is assessed as a classifier and it is demonstrated that relatively simple image processing measurements can be used to obtain high performance vehicle authentication. The proposed system is assisted by a previously developed license plate recognition, a symmetry axis detector and an image phase congruency calculation modules. The reported results indicate a high recognition rate and a fast processing time, making the system suitable for real-time applications.


title: Hydrothermally Treated Chitosan Hydrogel Loaded with Copper and Zinc Particles as a Potential Micronutrient-Based Antimicrobial Feed Additive

abstract: Large-scale use of antibiotics in food animal farms as growth promoters is considered as one of the driving factors behind increasing incidence of microbial resistance. Several alternatives are under investigation to reduce the amount of total antibiotics used in order to avoid any potential transmission of drug resistant microbes to humans through food chain. Copper sulfate and zinc oxide salts are used as feed supplement as they exhibit antimicrobial properties in addition to being micronutrients. However, higher dosage of copper and zinc (often needed for growth promoting effect) to animals is not advisable because of potential environmental toxicity arising from excreta. Innovative strategies are needed to utilize the complete potential of trace minerals as growth promoting feed supplements. To this end, we describe here the development and preliminary characterization of hydrothermally treated chitosan as a delivery vehicle for copper and zinc nanoparticles that could act as a micronutrient based antimicrobial feed supplement. Material characterization studies showed that hydrothermal treatment makes a chitosan hydrogel that re-arranged to capture the copper and zinc metal particles. Systemic antimicrobial assays showed that this chitosan biopolymer matrix embedded with copper (57.6 μg/ml) and zinc (800 μg/ml) reduced the load of model gut-bacteria (target organisms of growth promoting antibiotics) such as Escherichia coli, Enterococcus faecalis, Staphylococcus aureus and Lactobacillus fermentum under in vitro conditions. Particularly, the chitosan/copper/zinc hydrogel exhibited significantly higher antimicrobial effect against L. fermentum, one of the primary targets of antibiotic growth promoters. Additionally, the chitosan matrix ameliorated the cytotoxicity levels of metal supplements when screened against a murine macrophage cell line RAW 264.7 and in TE-71, a murine thymic epithelial cell line. In this proof of concept study, we show that by using chitosan as a delivery platform, micronutrient-based metal feed additives could be used to minimize the undesirable levels of microbial population without causing significant cytotoxic effect under in vitro conditions. These findings provide the platform for further studies in target animal models to quantify the required physiological concentrations of copper and zinc when delivered via a chitosan hydrogel platform to elicit a growth promoting effect without causing any toxicity.


title: Scalable Behaviors for Crowd Simulation

abstract: Crowd simulation for virtual environments offers many challenges centered on the trade-offs between rich behavior, control and computational cost. In this paper we present a new approach to controlling the behavior of agents in a crowd. Our method is scalable in the sense that increasingly complex crowd behaviors can be created without a corresponding increase in the complexity of the agents. Our approach is also more authorable; users can dynamically specify which crowd behaviors happen in various parts of an environment. Finally, the character motion produced by our system is visually convincing. We achieve our aims with a situation-based control structure. Basic agents have very limited behaviors. As they enter new situations, additional, situation-specic behaviors are composed on the y to enable agents to respond appropriately. The composition is done using a probabilistic mechanism. We demonstrate our system with three environments including a city street and a theater.


title: Emerging Positive Organizational Behavior

abstract: Although the value of positivity has been assumed over the years, only recently has it become a major focus area for theory building, research, and application in psychology and now organizational behavior. This review article examines, in turn, selected representative positive traits (Big Five personality, core self-evaluations, and character strengths and virtues), positive state-like psychological resource capacities (efficacy, hope, optimism, re siliency, and psychological capital), positive organizations (drawn from positive organization scholarship), and positive behaviors (organizational citizenship and courageous principled action). This review concludes with recommendations for future research and effective application.


title: A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image

abstract: When we look at a picture, our prior knowledge about the world allows us to resolve some of the ambiguities that are inherent to monocular vision, and thereby infer 3d information about the scene. We also recognize different objects, decide on their orientations, and identify how they are connected to their environment. Focusing on the problem of autonomous 3d reconstruction of indoor scenes, in this paper we present a dynamic Bayesian network model capable of resolving some of these ambiguities and recovering 3d information for many images. Our model assumes a "floorwall" geometry on the scene and is trained to recognize the floor-wall boundary in each column of the image. When the image is produced under perspective geometry, we show that this model can be used for 3d reconstruction from a single image. To our knowledge, this was the first monocular approach to automatically recover 3d reconstructions from single indoor images.


title: A Survey of Colormaps in Visualization

abstract: Colormaps are a vital method for users to gain insights into data in a visualization. With a good choice of colormaps, users are able to acquire information in the data more effectively and efficiently. In this survey, we attempt to provide readers with a comprehensive review of colormap generation techniques and provide readers a taxonomy which is helpful for finding appropriate techniques to use for their data and applications. Specifically, we first briefly introduce the basics of color spaces including color appearance models. In the core of our paper, we survey colormap generation techniques, including the latest advances in the field by grouping these techniques into four classes: procedural methods, user-study based methods, rule-based methods, and data-driven methods; we also include a section on methods that are beyond pure data comprehension purposes. We then classify colormapping techniques into a taxonomy for readers to quickly identify the appropriate techniques they might use. Furthermore, a representative set of visualization techniques that explicitly discuss the use of colormaps is reviewed and classified based on the nature of the data in these applications. Our paper is also intended to be a reference of colormap choices for readers when they are faced with similar data and/or tasks.


title: In-Depth Enforcement of Dynamic Integrity Taint Analysis

abstract: Dynamic taint analysis can be used as a defense against low-integrity data in applications with untrusted user interfaces. An important example is defense against XSS and injection attacks in programs with web interfaces. Data sanitization is commonly used in this context, and can be treated as a precondition for endorsement in a dynamic integrity taint analysis. However, sanitization is often incomplete in practice. We develop a model of dynamic integrity taint analysis for Java that addresses imperfect sanitization with an in-depth approach. To avoid false positives, results of sanitization are endorsed for access control (aka prospective security), but are tracked and logged for auditing and accountability (aka retrospective security). We show how this heterogeneous prospective/retrospective mechanism can be specified as a uniform policy, separate from code. We then use this policy to establish correctness conditions for a program rewriting algorithm that instruments code for the analysis. The rewriting itself is a model of existing, efficient Java taint analysis tools.


title: A D-VIKOR Method for Medicine Provider Selection

abstract: Medical service has become more and more significant recently. Lots of researchers have focused on this field. This paper aims to select medicine providers for the certain emergency medical service, such as natural disaster, virus infection and so on. In order to solve this problem, the D-VIKOR method is proposed, which is successful to deal with the uncertainty and incomplete information existed in the decision making process. In the proposed method, D numbers are used to calculate the weights of criteria and deal with the uncertain and incomplete evaluation information provided by the medical experts. Then, the ranking of the feasible alternatives is obtained by the VIKOR method. Finally, a numerical example is illustrated to analyze and demonstrate the effectiveness and practicality of the proposed method.


title: Analysis and Extension of Arc-Cosine Kernels for Large Margin Classification

abstract: We investigate a recently proposed family of positive-denite kernels that mimic the computation in large neural networks. We examine the properties of these kernels using tools from dierential geometry; specically, we analyze the geometry of surfaces in Hilbert space that are induced by these kernels. When this geometry is described by a Riemannian manifold, we derive results for the metric, curvature, and volume element. Interestingly, though, we nd that the simplest kernel in this family does not admit such an interpretation. We explore two variations of these kernels that mimic computation in neural networks with dierent activation functions. We experiment with these new


title: Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values

abstract: Estimating the mean and the covariance matrix of an incomplete dataset and filling in missing values with imputed values is generally a nonlinear problem, which must be solved iteratively. The expectation maximization (EM) algorithm for Gaussian data, an iterative method both for the estimation of mean values and covariance matrices from incomplete datasets and for the imputation of missing values, is taken as the point of departure for the development of a regularized EM algorithm. In contrast to the conventional EM algorithm, the regularized EM algorithm is applicable to sets of climate data, in which the number of variables typically exceeds the sample size. The regularized EM algorithm is based on iterated analyses of linear regressions of variables with missing values on variables with available values, with regression coefficients estimated by ridge regression, a regularized regression method in which a continuous regularization parameter controls the filtering of the noise in the data. The regularization parameter is determined by generalized cross-validation, such as to minimize, approximately, the expected mean-squared error of the imputed values. The regularized EM algorithm can estimate, and exploit for the imputation of missing values, both synchronic and diachronic covariance matrices, which may contain information on spatial covariability, stationary temporal covariability, or cyclostationary temporal covariability. A test of the regularized EM algorithm with simulated surface temperature data demonstrates that the algorithm is applicable to typical sets of climate data and that it leads to more accurate estimates of the missing values than a conventional noniterative imputation technique.


title: Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation

abstract: It has been shown recently that deep convolutional generative adversarial networks (GANs) can learn to generate music in the form of piano-rolls, which represent music by binary-valued time-pitch matrices. However, existing models can only generate real-valued piano-rolls and require further post-processing, such as hard thresholding (HT) or Bernoulli sampling (BS), to obtain the final binary-valued results. In this paper, we study whether we can have a convolutional GAN model that directly creates binary-valued piano-rolls by using binary neurons. Specifically, we propose to append to the generator an additional refiner network, which uses binary neurons at the output layer. The whole network is trained in two stages. Firstly, the generator and the discriminator are pretrained. Then, the refiner network is trained along with the discriminator to learn to binarize the real-valued piano-rolls the pretrained generator creates. Experimental results show that using binary neurons instead of HT or BS indeed leads to better results in a number of objective measures. Moreover, deterministic binary neurons perform better than stochastic ones in both objective measures and a subjective test. The source code, training data and audio examples of the generated results can be found at this https URL .


title: Seq2seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models

abstract: Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and “what if”-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.


title: Fast View Synthesis with Deep Stereo Vision

abstract: Novel view synthesis is an important problem in computer vision and graphics. Over the years a large number of solutions have been put forward to solve the problem. However, the large-baseline novel view synthesis problem is far from being"solved". Recent works have attempted to use Convolutional Neural Networks (CNNs) to solve view synthesis tasks. Due to the difficulty of learning scene geometry and interpreting camera motion, CNNs are often unable to generate realistic novel views. In this paper, we present a novel view synthesis approach based on stereo-vision and CNNs that decomposes the problem into two sub-tasks: view dependent geometry estimation and texture inpainting. Both tasks are structured prediction problems that could be effectively learned with CNNs. Experiments on the KITTI Odometry dataset show that our approach is more accurate and significantly faster than the current state-of-the-art. The code and supplementary material will be publicly available. Results could be found here https://youtu.be/5pzS9jc-5t0


title: A computational model for TensorFlow: an introduction

abstract: TensorFlow is a powerful, programmable system for machine learning. This paper aims to provide the basics of a conceptual framework for understanding the behavior of TensorFlow models during training and inference: it describes an operational semantics, of the kind common in the literature on programming languages. More broadly, the paper suggests that a programming-language perspective is fruitful in designing and in explaining systems such as TensorFlow.


title: Tracking the construction of episodic future thoughts

abstract: The ability to mentally simulate possible futures (episodic future thinking) is of fundamental importance for various aspects of human cognition and behavior, but precisely how humans construct mental representations of future events is still essentially unknown. We suggest that episodic future thoughts consist of transitory patterns of activation over knowledge structures at different levels of specificity, with general knowledge about the personal future (i.e., personal semantic information and anticipated general events) providing a context or frame for retrieving, integrating, and interpreting episodic details. In line with this hypothesis, Study 1 showed that the construction of episodic future thoughts is frequently a protracted generative process in which general personal knowledge is accessed before episodic details. We then explored in more detail the nature of this general personal knowledge and tested the hypothesis that it is mainly organized in terms of personal goals. Study 2 showed that cuing participants with knowledge about personal goals increased the ease of future event production during a fluency task. Study 3 further demonstrated that cuing participants with their personal goals facilitated access to episodic details during the imagination of future events. Taken together, these findings indicate that general personal knowledge and, in particular, knowledge about personal goals plays an important role in the construction of episodic future thoughts.


title: Business Models: A Discovery Driven Approach

abstract: The business model concept offers strategists a fresh way to consider their options in uncertain, fast-moving and unpredictable environments. In contrast to conventional assumptions, recognizing that more new business models are both feasible and actionable than ever before is creating unprecedented opportunities for today's organizations. However, unlike conventional strategies that emphasize analysis, strategies that aim to discover and exploit new models must engage in significant experimentation and learning – a ‘discovery driven,’ rather than analytical approach.


title: The Use of Process Mining in Business Process Simulation Model Construction

abstract: Abstract The paper focuses on the use of process mining (PM) to support the construction of business process simulation (BPS) models. Given the useful BPS insights that are available in event logs, further research on this topic is required. To provide a solid basis for future work, this paper presents a structured overview of BPS modeling tasks and how PM can support them. As directly related research efforts are scarce, a multitude of research challenges are identified. In an effort to provide suggestions on how these challenges can be tackled, an analysis of PM literature shows that few PM algorithms are directly applicable in a BPS context. Consequently, the results presented in this paper can encourage and guide future research to fundamentally bridge the gap between PM and BPS.


title: Intrinsic and adaptive myelination—A sequential mechanism for smart wiring in the brain

abstract: The concept of adaptive myelination-myelin plasticity regulated by activity-is an important advance for the field. What signals set up the adaptable pattern in the first place? Here we review work that demonstrates an intrinsic pathway within oligodendrocytes requiring only an axon-shaped substrate to generate multilayered and compacted myelin sheaths of a physiological length. Based on this, we discuss a model we proposed in 2015 which argues that myelination has two phases-intrinsic and then adaptive-which together generate "smart wiring," in which active axons become more myelinated. This model explains why prior studies have failed to identify a signal necessary for central nervous system myelination and argues that myelination, like synapses, might contribute to learning by the activity-dependent modification of an initially hard-wired pattern. © 2017 The Authors. Developmental Neurobiology Published by Wiley Periodicals, Inc. Develop Neurobiol 78: 68-79, 2018.


title: Expectations versus reality: a snapshot of consumer experiences with Internet retailing

abstract: Recent surveys have predicted that Internet shopping will show considerable growth over the next few years although there is still much uncertainty about the factors that will drive or inhibit this growth. This paper presents a study of consumers' experiences of Internet shopping. By examining more than 100 purchases of a wide range of products from Internet sites in five countries, the transactions and the experiences of consumers were compared with expected outcomes based on current academic and industry research. The study found that consumers' expectations of convenience, customised service and access to a wider variety of products were realised in 70% of cases. However, consumer concerns about security, ease of use, levels of service and overall cost pose significant constraints on growth. On the basis of these findings a more integrated approach to research in Internet shopping is proposed.


title: Anterior hippocampus: the anatomy of perception, imagination and episodic memory

abstract: The anterior hippocampus is thought to be involved in a wide range of cognitive functions, including memory, navigation and perception. In this Opinion article, Zeidman and Maguire show how improved functional imaging techniques are shedding light on the precise anatomy of this region and the contribution of different parts of the anterior hippocampus to specific cognitive functions.


title: Improving Storage Availability in Cloud-of-Clouds with Hybrid Redundant Data Distribution

abstract: With the increasing utilization and popularity of the cloud infrastructure, more and more data are moved to the cloud storage systems. This makes the availability of cloud storage services critically important, particularly given the fact that outages of cloud storage services have indeed happened from time to time. Thus, solely depending on a single cloud storage provider for storage services can risk violating the service-level agreement (SLA) due to the weakening of service availability. This has led to the notion of Cloud-of-Clouds, where data redundancy is introduced to distribute data among multiple independent cloud storage providers, to address the problem. The key in the effectiveness of the Cloud-of-Clouds approaches lies in how the data redundancy is incorporated and distributed among the clouds. However, the existing Cloud-of-Clouds approaches utilize either replication or erasure codes to redundantly distribute data across multiple clouds, thus incurring either high space or high performance overheads. In this paper, we propose a hybrid redundant data distribution approach, called HyRD, to improve the cloud storage availability in Cloud-of-Clouds by exploiting the workload characteristics and the diversity of cloud providers. In HyRD, large files are distributed in multiple cost-efficient cloud storage providers with erasure-coded data redundancy while small files and file system metadata are replicated on multiple high-performance cloud storage providers. The experiments conducted on our lightweight prototype implementation of HyRD show that HyRD improves the cost efficiency by 33.4% and 20.4%, and reduces the access latency by 58.7% and 34.8% than the DuraCloud and RACS schemes, respectively.


title: Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems

abstract: Domain Adaptation arises when we aim at learning from source domain a model that can per- form acceptably well on a different target domain. It is especially crucial for Natural Language Generation (NLG) in Spoken Dialogue Systems when there are sufficient annotated data in the source domain, but there is a limited labeled data in the target domain. How to effectively utilize as much of existing abilities from source domains is a crucial issue in domain adaptation. In this paper, we propose an adversarial training procedure to train a Variational encoder-decoder based language generator via multiple adaptation steps. In this procedure, a model is first trained on a source domain data and then fine-tuned on a small set of target domain utterances under the guidance of two proposed critics. Experimental results show that the proposed method can effec- tively leverage the existing knowledge in the source domain to adapt to another related domain by using only a small amount of in-domain data.


Overview of the 6th Author Profiling Task at PAN 2018: Multimodal Gender Identification in Twitter


title: Three-dimensional focusing with multipass SAR data

abstract: Deals with the use of multipass synthetic aperture radar (SAR) data in order to achieve three-dimensional tomography reconstruction in presence of volumetric scattering. Starting from azimuth- and range-focused SAR data relative to the same area, neglecting any mutual interaction between the targets, and assuming the propagation in homogeneous media, we investigate the possibility to focus the data also in the elevation direction. The problem is formulated in the framework of linear inverse problem and the solution makes use of the singular value decomposition of the relevant operator. This allows us to properly take into account nonuniform orbit separation and to exploit a priori knowledge regarding the size of the volume interested by the scattering mechanism, thus leading to superresolution in the elevation direction. Results obtained on simulated data demonstrate the feasibility of the proposed processing technique.


title: Learning Graph-based POI Embedding for Location-based Recommendation

abstract: With the rapid prevalence of smart mobile devices and the dramatic proliferation of location-based social networks (LBSNs), location-based recommendation has become an important means to help people discover attractive and interesting points of interest (POIs). However, the extreme sparsity of user-POI matrix and cold-start issue create severe challenges, causing CF-based methods to degrade significantly in their recommendation performance. Moreover, location-based recommendation requires spatiotemporal context awareness and dynamic tracking of the user's latest preferences in a real-time manner. To address these challenges, we stand on recent advances in embedding learning techniques and propose a generic graph-based embedding model, called GE, in this paper. GE jointly captures the sequential effect, geographical influence, temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs (POI-POI, POI-Region, POI-Time and POI-Word)into a shared low dimensional space. Then, to support the real-time recommendation, we develop a novel time-decay method to dynamically compute the user's latest preferences based on the embedding of his/her checked-in POIs learnt in the latent space. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets, and the experimental results show its superiority over other competitors, especially in recommending cold-start POIs. Besides, we study the contribution of each factor to improve location-based recommendation and find that both sequential effect and temporal cyclic effect play more important roles than geographical influence and semantic effect.


title: Adversarial Collaborative Auto-encoder for Top-N Recommendation

abstract: During the past decade, model-based recommendation methods have evolved from latent factor models to neural network-based models. Most of these techniques mainly focus on improving the overall performance, such as the root mean square error for rating predictions and hit ratio for top-N recommendation, where the users' feedback is considered as the ground-truth. However, in real-world applications, the users' feedback is possibly contaminated by imperfect user behaviours, namely, careless preference selection. Such data contamination poses challenges on the design of robust recommendation methods. In this work, to address the above issue, we propose a general adversial training framework for neural network-based recommendation models, which improves both the model robustness and the overall performance. We point out the tradeoffs between performance and robustness enhancement with detailed instructions on how to strike a balance. Specifically, we implement our approach on the collaborative auto-encoder, followed by experiments on three public available datasets: MovieLens-1M, Ciao, and FilmTrust. We show that our approach outperforms highly competitive state-of-the-art recommendation methods. In addition, we carry out a thorough analysis on the noise impacts, as well as the complex interactions between model nonlinearity and noise levels. Through simple modifications, our adversarial training framework can be applied to a host of neural network-based models whose robustness and performance are expected to be both enhanced.


title: Splitting Compounds by Semantic Analogy

abstract: Compounding is a highly productive word-formation process in some languages that is often problematic for natural language processing applications. In this paper, we investigate whether distributional semantics in the form of word embeddings can enable a deeper, i.e., more knowledge-rich, processing of compounds than the standard string-based methods. We present an unsupervised approach that exploits regularities in the semantic vector space (based on analogies such as "bookshop is to shop as bookshelf is to shelf") to produce compound analyses of high quality. A subsequent compound splitting algorithm based on these analyses is highly effective, particularly for ambiguous compounds. German to English machine translation experiments show that this semantic analogy-based compound splitter leads to better translations than a commonly used frequency-based method.


title: A MultiWinding Transformer Cell-to-Cell Active Equalization Method for Lithium-Ion Batteries With Reduced Number of Driving Circuits

abstract: The bidirectional cell-to-cell equalization methods perform well to prevent the series-connected batteries from overcharging and undercharging. However, it remains a challenge to achieve fast equalization and high equalization efficiency with low complexity. To address the issue, we propose a new bidirectional cell-to-cell active equalization method using a multiwinding transformer. The new method allows the energy to transfer directly from the highest voltage cell to the lowest one by fly-back operation or forward operation which gives a short balancing path and guarantees a fast equalization speed. We adopt the bidirectional switches of low conduction loss for high equalization efficiency. Since the bidirectional switches are turned on or off simultaneously, a common driving circuit for the bidirectional switch has been designed to reduce the number of the driving circuits by half. The experiments with a six 50-Ah Lithium-ion battery strings are conducted and the results have demonstrated that our new approach has achieved a good overall performance of equalization in terms of speed, efficiency, and complexity of the circuit.


title: Reinforcement Learning in Continuous Action Spaces

abstract: Quite some research has been done on reinforcement learning in continuous environments, but the research on problems where the actions can also be chosen from a continuous space is much more limited. We present a new class of algorithms named continuous actor critic learning automaton (CACLA) that can handle continuous states and actions. The resulting algorithm is straightforward to implement. An experimental comparison is made between this algorithm and other algorithms that can handle continuous action spaces. These experiments show that CACLA performs much better than the other algorithms, especially when it is combined with a Gaussian exploration method


title: Attack Surfaces: A Taxonomy for Attacks on Cloud Services

abstract: The new paradigm of cloud computing poses severe security risks to its adopters. In order to cope with these risks, appropriate taxonomies and classification criteria for attacks on cloud computing are required. In this work-in-progress paper we present one such taxonomy based on the notion of attack surfaces of the cloud computing scenario participants.


title: Data provenance hybridization supporting extreme-scale scientific workflow applications

abstract: As high performance computing (HPC) infrastructures continue to grow in capability and complexity, so do the applications that they serve. HPC and distributed-area computing (DAC) (e.g. grid and cloud) users are looking increasingly toward workflow solutions to orchestrate their complex application coupling, pre- and post-processing needs. To that end, the US Department of Energy Integrated end-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) project is currently investigating an integrated approach to prediction and diagnosis of these extreme-scale scientific workflows. To gain insight and a more quantitative understanding of a workflow's performance our method includes not only the capture of traditional provenance information, but also the capture and integration of system environment metrics helping to give context and explanation for a workflow's execution. In this paper, we describe IPPD's provenance management solution (ProvEn) and its hybrid data store combining both of these data provenance perspectives. We discuss design and implementation details that include provenance disclosure, scalability, data integration, and a discussion on query and analysis capabilities. We also present use case examples for climate modeling and thermal modeling application domains.


title: Interacting with Virtual Agents in Shared Space: Single and Joint Effects of Gaze and Proxemics

abstract: The Equilibrium Theory put forward by Argyle and Dean, posits that in human-human interactions, gaze and proxemic behaviors work together in establishing and maintaining a particular level of intimacy. This theory has been evaluated and used in Virtual Reality settings where people interact with Virtual Humans. In this study we disentangle the single and joint effects of proxemic and gaze behavior in this setting further, and examine how these behaviors affect the perceived personality of the agents. We simulate a social encounter with Virtual Humans in immersive Virtual Reality. Gaze and proxemic behaviors of the agents are manipulated dynamically while the participants’ gaze and proxemic responses are being measured. As could be expected, participants showed strongest gaze and proxemic responses when agents manipulated both at the same time. However, agents that only manipulated gaze elicited weaker responses compared to agents that only manipulated proxemics. Agents that exhibited more directed gaze and reduced interpersonal distance were attributed higher scores on intimacy related items than agents that exhibited averted gaze and increased interpersonal distance.


title: JavaML: A Markup Language for Java Source Code

abstract: Abstract The classical plain-text representation of source code is convenient for programmers but requires parsing to uncover the deep structure of the program. While sophisticated software tools parse source code to gain access to the program's structure, many lightweight programming aids such as grep rely instead on only the lexical structure of source code. I describe a new XML application that provides an alternative representation of Java source code. This XML-based representation, called JavaML, is more natural for tools and permits easy specification of numerous software-engineering analyses by leveraging the abundance of XML tools and techniques. A robust converter built with the Jikes Java compiler framework translates from the classical Java source code representation to JavaML, and an XSLT stylesheet converts from JavaML back into the classical textual form.


title: Understanding requirement prioritization techniques

abstract: Software is becoming progressively more integral part of day-to-day life. Developing the software that meet stakeholders' need is the ultimate goal in today's environment. As the complexity of software increases so does the requirements. There are many requirements which should be fulfilled in the given time duration on the other hand some requirements should be considered first to reduce the risks. Hence, proper gathering and prioritizing requirements may leads to the successive development of the software. In literature there are number of techniques which focus on requirement prioritization problem. This paper presents the comparative study of various requirement prioritization techniques.


title: Acquisition and transfer of declarative and procedural knowledge by memory-impaired patients: A computer data-entry task

abstract: Abstract Previous research demonstrated that a single amnesic patient could acquire complex knowledge and processes required for the performance of a computer data-entry task. The present study extends the earlier work to a larger group of brain-damaged patients with memory disorders of varying severity and of various etiologies and with other accompanying cognitive deficits. All patients were able to learn both the data-entry procedures and the factual information associated with the task. Declarative knowledge was acquired by patients at a much slower rate than normal whereas procedural learning proceeded at approximately the same rate in patients and control subjects. Patients also showed evidence of transfer of declarative knowledge to the procedural task, as well as transfer of the data-entry procedures across changes in materials.


title: Ichthyosis congenita, harlequin fetus type: a case report

abstract: Ichthyosis is a very heterogeneous family of skin disorders with harlequin ichthyosis being the most severe genetic form. It is a rare autosomal recessive condition, characterized by dry, severely thickened skin with large plates of hyperkeratotic scale, separated by deep fissures. Infants are very susceptible to metabolic abnormalities and infections. They usually do not survive for very long, but several long term survivals have been noted. The vast majority of affected individuals are homozygous for mutations in the ABCA12 gene, which cause a deficiency of the epidermal lipid transporter, resulting in hyperkeratosis and abnormal barrier function. We report a case of a newborn with harlequin ichthyosis, born to unrelated parents, who had a favorable evolution with topical treatment and intensive care.


title: Input validation analysis and testing

abstract: This research addresses the problem of statically analyzing input command syntax as defined in interface and requirements specifications and then generating test cases for dynamic input validation testing. The IVAT (Input Validation Analysis and Testing) technique has been developed, a proof-of-concept tool (MICASA) has been implemented, and a case study validation has been performed. Empirical validation on large-scale industrial software (from the Tomahawk Cruise Missile) shows that as compared with senior, experienced analysts and testers, MICASA found more syntactic requirement specification defects, generated test cases with higher syntactic coverage, and found additional defects. The experienced analysts found more semantic defects than MICASA, and the experienced testers' cases found 7.4 defects per test case as opposed to an average of 4.6 defects found by MICASA test cases. Additionally, the MICASA tool performed at less cost.


title: HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval

abstract: As the rapid growth of multi-modal data, hashing methods for cross-modal retrieval have received considerable attention. Deep-networks-based cross-modal hashing methods are appealing as they can integrate feature learning and hash coding into end-to-end trainable frameworks. However, it is still challenging to find content similarities between different modalities of data due to the heterogeneity gap. To further address this problem, we propose an adversarial hashing network with attention mechanism to enhance the measurement of content similarities by selectively focusing on informative parts of multi-modal data. The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities. In our framework, the generative module and the discriminative module are trained in an adversarial way: the generator is learned to make the discriminator cannot preserve the similarities of multi-modal data w.r.t. the background feature representations, while the discriminator aims to preserve the similarities of multi-modal data w.r.t. both the foreground and the background feature representations. Extensive evaluations on several benchmark datasets demonstrate that the proposed HashGAN brings substantial improvements over other state-of-the-art cross-modal hashing methods.


title: Rocker-Pillar : Design of the rough terrain mobile robot platform with caterpillar tracks and rocker bogie mechanism

abstract: The ability to overcome rough terrain is a main issue of mobile robots. However, as the speed of a robot increases, stability becomes another issue because it is directly related to the mobility of the robot. Without stability, the robot is exposed to risks of overturn. Therefore, a mobile robot needs to be not only maneuverable but also stable. We present a new mobile platform named “Rocker-Pillar” which enables a robot to overcome rough terrains with stability. The robot is composed of two sets of caterpillar tracks, four wheels, and a pair of two linkages. The robot can travel a rugged terrain at a speed 50m/min while maintaining stability, and can overcome complex terrains such as holes, steps without frontal sides, and stairs. The robot can overcome a hole 1.5 times larger than the wheel diameter of the robot and a step without the frontal side that is 1.25 times higher than wheel diameter.


title: How Rude are You?: Evaluating Politeness and Affect in Interaction

abstract: Recent research on conversational agents emphasises the need to build affective conversational systems with social intelligence. Politeness is an integral part of socially appropriate and affective conversational behaviour, e.g. consider the difference in the pragmatic effect of realizing the same communicative goal with either "Get me a glass of water mate!" or "I wonder if I could possibly have some water please?" This paper presents POLLy (Politeness for Language Learning), a system which combines a spoken language generator with an artificial intelligence planner to model Brown and Levinson's theory of politeness in collaborative task-oriented dialogue, with the ultimate goal of providing a fun and stimulating environment for learning English as a second language. An evaluation of politeness perceptions of POLLy's output shows that: (1) perceptions are generally consistent with Brown and Levinson's predictions for choice of form and for discourse situation, i.e. utterances to strangers need to be much more polite than those to friends; (2) our indirect strategies which should be the politest forms, are seen as the rudest; and (3) English and Indian native speakers of English have different perceptions of politeness.


title: Congenital aneurysm of the left atrium.

abstract: Left atrial aneurysm is a rare condition. Only 29 cases have been reported, to our knowledge. We report 1 such case in a 24-year-old man who complained of dyspnea and arrhythmias. Diagnosis was suspected on review of chest roentgenogram and confirmed by echocardiography and cardiac catheterization. Surgical repair was achieved without complications, and preoperative symptoms disappeared completely. According to the literature, these patients are almost always asymptomatic. When present, the most common symptoms are arrhythmias, heart failure, emboli, and chest pain. This lesion is seen mainly in young adults (mean age, 23.5 years). The diagnosis should be confirmed by echocardiography, nuclear imaging, and cardiac catheterization. A review of the literature indicates that surgical repair can be accomplished with low mortality and that arrhythmias usually disappear postoperatively.


The Supposed Dimorphism of Lithospermum longiflorum


title: Improving Performance Portability in OpenCL Programs

abstract: We study the performance portability of OpenCL across diverse architectures including NVIDIA GPU, Intel Ivy Bridge CPU, and AMD Fusion APU. We present detailed performance analysis at assembly level on three exemplar OpenCL benchmarks: SGEMM, SpMV, and FFT. We also identify a number of tuning knobs that are critical to performance portability, including threads-data mapping, data layout, tiling size, data caching, and operation-specific factors. We further demonstrate that proper tuning could improve the OpenCL portable performance from the current 15% to a potential 67% of the state-of-the-art performance on the Ivy Bridge CPU. Finally, we evaluate the current OpenCL programming model, and propose a list of extensions that improve performance portability.


title: Fast Katsevich Algorithm Based on GPU for Helical Cone-Beam Computed Tomography

abstract: Katsevich reconstruction algorithm represents a breakthrough for helical cone-beam computed tomography (CT) reconstruction, because it is the first exact cone-beam reconstruction algorithm of filtered backprojection (FBP) type with 1-D shift-invariant filtering. Although FBP-type reconstruction algorithm is effective, 3-D CT reconstruction is time-consuming, and the accelerations of Katsevich algorithm on CPU or cluster have been widely studied. In this paper, Katsevich algorithm is accelerated by using graphics processing unit, including flat-detector and curved-detector geometry in the case of helical orbit. An overscan formula is derived, which helps to avoid unnecessary overscan in practical CT scanning. Based on the overscan formula, a volume-blocking method in device memory is proposed. One advantage of the blocking method is that it can reconstruct large volume with high speed.


Watermarking of Digital Images


title: ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data

abstract: A cell’s epigenome arises from interactions among regulatory factors—transcription factors and histone modifications—co-localized at particular genomic regions. We developed a novel statistical method, ChromNet, to infer a network of these interactions, the chromatin network, by inferring conditional-dependence relationships among a large number of ChIP-seq data sets. We applied ChromNet to all available 1451 ChIP-seq data sets from the ENCODE Project, and showed that ChromNet revealed previously known physical interactions better than alternative approaches. We experimentally validated one of the previously unreported interactions, MYC–HCFC1. An interactive visualization tool is available at http://chromnet.cs.washington.edu.


title: The Predictive Validity of Ideal Partner Preferences: A Review and Meta-Analysis

abstract: A central element of interdependence theory is that people have standards against which they compare their current outcomes, and one ubiquitous standard in the mating domain is the preference for particular attributes in a partner (ideal partner preferences). This article reviews research on the predictive validity of ideal partner preferences and presents a new integrative model that highlights when and why ideals succeed or fail to predict relational outcomes. Section 1 examines predictive validity by reviewing research on sex differences in the preference for physical attractiveness and earning prospects. Men and women reliably differ in the extent to which these qualities affect their romantic evaluations of hypothetical targets. Yet a new meta-analysis spanning the attraction and relationships literatures (k = 97) revealed that physical attractiveness predicted romantic evaluations with a moderate-to-strong effect size (r = ~.40) for both sexes, and earning prospects predicted romantic evaluations with a small effect size (r = ~.10) for both sexes. Sex differences in the correlations were small (rdifference = .03) and uniformly nonsignificant. Section 2 reviews research on individual differences in ideal partner preferences, drawing from several theoretical traditions to explain why ideals predict relational evaluations at different relationship stages. Furthermore, this literature also identifies alternative measures of ideal partner preferences that have stronger predictive validity in certain theoretically sensible contexts. Finally, a discussion highlights a new framework for conceptualizing the appeal of traits, the difference between live and hypothetical interactions, and the productive interplay between mating research and broader psychological theories. (PsycINFO Database Record (c) 2014 APA, all rights reserved)


title: Ego, drives, and the dynamics of internal objects

abstract: This paper addresses the relationship between the ego, id, and internal objects. While ego psychology views the ego as autonomous of the drives, a less well-known alternative position views the ego as constituted by the drives. Based on Freud’s ego-instinct account, this position has developed into a school of thought which postulates that the drives act as knowers. Given that there are multiple drives, this position proposes that personality is constituted by multiple knowers. Following on from Freud, the ego is viewed as a composite sub-set of the instinctual drives (ego-drives), whereas those drives cut off from expression form the id. The nature of the ‘self’ is developed in terms of identification and the possibility of multiple personalities is also established. This account is then extended to object-relations and the explanatory value of the ego-drive account is discussed in terms of the addressing the nature of ego-structures and the dynamic nature of internal objects. Finally, the impact of psychological conflict and the significance of repression for understanding the nature of splits within the psyche are also discussed.


title: CHAPTER 19 Meditation and the Neuroscience of Consciousness: An Introduction

abstract: The overall goal of this chapter is to explore the initial findings of neuroscientific research on meditation; in doing so, the chapter also suggests potential avenues of further inquiry. It has three sections that, although integral to the chapter as a whole, may also be read independently. The first section, “Defining Meditation,” notes the need for a more precise understanding of meditation as a scientific explanandum. Arguing for the importance of distinguishing the particularities of various traditions, the section presents the theory of meditation from the paradigmatic perspective of Buddhism, and it discusses the difficulties encountered when working with such theories. The section includes an overview of three practices that have been the subject of research, and it ends with a strategy for developing a questionnaire to define more precisely a practice under examination. The second section, “The Intersection of Neuroscience and Meditation,” explores some scientific motivations for the neuroscientific examination of meditation in terms of its potential impact


title: Like trainer, like bot? Inheritance of bias in algorithmic content moderation

abstract: The internet has become a central medium through which ‘networked publics’ express their opinions and engage in debate. Offensive comments and personal attacks can inhibit participation in these spaces. Automated content moderation aims to overcome this problem using machine learning classifiers trained on large corpora of texts manually annotated for offence. While such systems could help encourage more civil debate, they must navigate inherently normatively contestable boundaries, and are subject to the idiosyncratic norms of the human raters who provide the training data. An important objective for platforms implementing such measures might be to ensure that they are not unduly biased towards or against particular norms of offence. This paper provides some exploratory methods by which the normative biases of algorithmic content moderation systems can be measured, by way of a case study using an existing dataset of comments labelled for offence. We train classifiers on comments labelled by different demographic subsets (men and women) to understand how differences in conceptions of offence between these groups might affect the performance of the resulting models on various test sets. We conclude by discussing some of the ethical choices facing the implementers of algorithmic moderation systems, given various desired levels of diversity of viewpoints amongst discussion participants.


Learning a POS tagger for AAVE-like language


title: Doubly Stochastic Variational Inference for Deep Gaussian Processes

abstract: Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm, which does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression.


title: A preliminary proposal of a conceptual educational data mining framework for science education: Scientific competences development and self-regulated learning

abstract: The present paper is part of a wider study, focussed on the development of a digital educational resource for Science Education in primary school, integrating an Educational Data Mining framework. The proposed conceptual framework aims to infer the impact of the adopted learning approach for the development of scientific competences and students' self-regulated learning. Thus, students' exploration of learning sequences and students' behaviour towards available help, formative feedback and recommendations will be analysed. The framework derives from the proposed learning approach, as well as from the literature review. Before introducing it, the authors present an overview of the digital educational resource learning approach and the adopted Educational Data Mining methods. Finally, we present the proposed conceptual Educational Data Mining framework for Science Education, focussing its relevance on the development of students' scientific competences and self-regulated learning.


title: Multicasting in UMTS

abstract: Future third generation wireless network operators, such as those holding UMTS (Universal Mobile Telecommunications System) licenses will be able to deliver higher bandwidth content rich services over new infrastructure to end-users of innovative new terminal equipment. In order to reduce the risk of potential network overload, it is envisaged that in future, multimedia of mass interest will be multicast simultaneously to multiple users in the network. Applications such as those becoming more widely deployed on the Internet, (video/audio streaming) will provide the revenue pull to compel UMTS operators to consider re-using them in their networks. The Internet Engineering Task Force, (IETF) has accounted for these traffic requirements by introducing optional protocols that would avoid the replication of packets so that applications could be transported in a more cost effective way, (IP Multicast). In order to allow similar one-to-many services to be used in UMTS, the Third Generation Partnership Project, (3GPP) has begun optimising the future global standards for UMTS. In future UMTS networks, high bit rate services could be transported over bearers in a more resource efficient manner, (broadcast/multicast applications) addressing the issues that are unique to wide area wireless environment. This paper provides an overview of the potential architectural design challenges that would allow more efficient distribution of unidirectional point-to-multipoint multimedia to multiple subscribers, whilst conserving network resources both in the core-network and the radio access network.


title: Ownership and technical efficiency of hospitals: evidence from Ghana using data envelopment analysis

abstract: BACKGROUND ::: In order to measure and analyse the technical efficiency of district hospitals in Ghana, the specific objectives of this study were to (a) estimate the relative technical and scale efficiency of government, mission, private and quasi-government district hospitals in Ghana in 2005; (b) estimate the magnitudes of output increases and/or input reductions that would have been required to make relatively inefficient hospitals more efficient; and (c) use Tobit regression analysis to estimate the impact of ownership on hospital efficiency. ::: ::: ::: METHODS ::: In the first stage, we used data envelopment analysis (DEA) to estimate the efficiency of 128 hospitals comprising of 73 government hospitals, 42 mission hospitals, 7 quasi-government hospitals and 6 private hospitals. In the second stage, the estimated DEA efficiency scores are regressed against hospital ownership variable using a Tobit model. This was a retrospective study. ::: ::: ::: RESULTS ::: In our DEA analysis, using the variable returns to scale model, out of 128 district hospitals, 31 (24.0%) were 100% efficient, 25 (19.5%) were very close to being efficient with efficiency scores ranging from 70% to 99.9% and 71 (56.2%) had efficiency scores below 50%. The lowest-performing hospitals had efficiency scores ranging from 21% to 30%.Quasi-government hospitals had the highest mean efficiency score (83.9%) followed by public hospitals (70.4%), mission hospitals (68.6%) and private hospitals (55.8%). However, public hospitals also got the lowest mean technical efficiency scores (27.4%), implying they have some of the most inefficient hospitals.Regarding regional performance, Northern region hospitals had the highest mean efficiency score (83.0%) and Volta Region hospitals had the lowest mean score (43.0%).From our Tobit regression, we found out that while quasi-government ownership is positively associated with hospital technical efficiency, private ownership negatively affects hospital efficiency. ::: ::: ::: CONCLUSIONS ::: It would be prudent for policy-makers to examine the least efficient hospitals to correct widespread inefficiency. This would include reconsidering the number of hospitals and their distribution, improving efficiency and reducing duplication by closing or scaling down hospitals with efficiency scores below a certain threshold. For private hospitals with inefficiency related to large size, there is a need to break down such hospitals into manageable sizes.


Optimal Margin Distribution Clustering


abstract: Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.


Minimal intervention dentistry: part 5. Atraumatic restorative treatment (ART) – a minimum intervention and minimally invasive approach for the management of dental caries


title: Mathematical Knowledge Representation: Semantic Models and Formalisms

abstract: The paper provides a survey of semantic methods for solution of fundamental tasks in mathematical knowledge management. Ontological models and formalisms are discussed. We propose an ontology of mathematical knowledge, covering wide range of fields of mathematics. We demonstrate applications of this representation in mathematical formula search, and learning.


title: An X-band SiGe LNA with 1.36 dB mean noise figure for monolithic phased array transmit/receive radar modules

abstract: This paper presents an X-band silicon-germanium (SiGe) low-noise amplifier (LNA) for a monolithically integrated phased array transmit/receive (T/R) radar module. Implemented in a 200 GHz SiGe BiCMOS technology, the LNA occupies 730 /spl times/ 720 /spl mu/m/sup 2/ (including bondpads), and dissipates 15 mW from a 2.5 V power supply. The circuit exhibits a gain greater than 19 dB from 8.5 to 10.5 GHz, and a mean noise figure (NF) of 1.36 dB across X-band. At 10 GHz, the input 1-dB compression point (IP/sub 1-dB/) and the input third-order intercept point (IIP/sub 3/) are -10.0 dBm and 0.8 dBm, respectively. To our knowledge, this LNA achieves the lowest noise figure of any LNA in Si-based technology at X-band.


title: LCI: a social channel analysis platform for live customer intelligence

abstract: The rise of Web 2.0 with its increasingly popular social sites like Twitter, Facebook, blogs and review sites has motivated people to express their opinions publicly and more frequently than ever before. This has fueled the emerging field known as sentiment analysis whose goal is to translate the vagaries of human emotion into hard data. LCI is a social channel analysis platform that taps into what is being said to understand the sentiment with the particular ability of doing so in near real-time. LCI integrates novel algorithms for sentiment analysis and a configurable dashboard with different kinds of charts including dynamic ones that change as new data is ingested. LCI has been researched and prototyped at HP Labs in close interaction with the Business Intelligence Solutions (BIS) Division and a few customers. This paper presents an overview of the architecture and some of its key components and algorithms, focusing in particular on how LCI deals with Twitter and illustrating its capabilities with selected use cases.


title: The Effect of External Interference on Avalanche Transceiver Functionality

abstract: For over 40 years, avalanche transceivers have been helping searchers locate and rescue victims buried in avalanches. They rely on internal electronics receiving and interpreting radio waves being transmitted by the victim's avalanche transceiver, and their success and usefulness is measured by how quickly and effectively they can follow that signal to the buried victim. Up until now, little has been done to understand the effect that external interference has on this signal, and therefore the effectiveness of the beacon itself. In this study, personal electronic devices were held near a searching beacon to see if they had any influence on the beacon's ability to follow a signal. This was done by finding both the normal and affected ranges and comparing the two values. In the end, there was clear evidence that electronic devices, when held close enough to a searching beacon, had a negative effect on that beacons functionality.


Studies in American Indian Literature


Genetic Algorithms + Data Structures = Evolution Programs


title: The link between metabolic abnormalities and endothelial dysfunction in type 2 diabetes: an update

abstract: Despite abundant clinical evidence linking metabolic abnormalities to diabetic vasculopathy, the molecular basis of individual susceptibility to diabetic vascular complications is still largely undetermined. Endothelial dysfunction in diabetes-associated vascular complications is considered an early stage of vasculopathy and has attracted considerable research interests. Type 2 diabetes is characterized by metabolic abnormalities, such as hyperglycemia, excess liberation of free fatty acids (FFA), insulin resistance and hyperinsulinemia. These abnormalities exert pathological impact on endothelial function by attenuating endothelium-mediated vasomotor function, enhancing endothelial apoptosis, stimulating endothelium activation/endothelium-monocyte adhesion, promoting an atherogenic response and suppressing barrier function. There are multiple signaling pathways contrib- uting to the adverse effects of glucotoxicity on endothelial function. Insulin maintains the normal balance for release of several factors with vasoactive properties. Abnormal insulin signaling in the endothelium does not affect the whole-body glucose metabolism, but impairs endothelial response to insulin and accelerates atherosclerosis. Excessive level of FFA is implicated in the pathogenesis of insulin resistance. FFA induces endothelial oxidative stress, apoptosis and inflammatory response, and inhibits insulin signaling. Although hyperglycemia, insulin resis- tance, hyperinsulinemia and dyslipidemia independently contribute to endothelial dysfunction via various distinct mechanisms, the mutual interactions may synergistically accelerate their adverse effects. Oxidative stress and inflammation are predicted to be among the first alterations which may trigger other downstream mediators in diabetes associated with endothelial dysfunction. These mecha- nisms may provide insights into potential therapeutic tar- gets that can delay or reverse diabetic vasculopathy.


title: Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling

abstract: This paper presents an alternative approach to pseudo measurement modeling in the context of distribution system state estimation (DSSE). In the proposed approach, pseudo measurements are generated from a few real measurements using artificial neural networks (ANNs) in conjunction with typical load profiles. The error associated with the generated pseudo measurements is made suitable for use in the weighted least squares (WLS) state estimation by decomposition into several components through the Gaussian mixture model (GMM). The effect of ANN-based pseudo measurement modeling on the quality of state estimation is demonstrated on a 95-bus section of the U.K. generic distribution system (UKGDS) model.


title: Multipath Wide-Bandwidth CMOS Magnetic Sensors

abstract: This paper proposes a multipath multisensor architecture for CMOS magnetic sensors, which effectively extends their bandwidth without compromising either their offset or resolution. Two designs utilizing the proposed architecture were fabricated in a 0.18- $\mu \text{m}$ standard CMOS process. In the first, the combination of spinning-current Hall sensors and nonspun Hall sensors achieves an offset of 40 $\mu \text{T}$ and a resolution of 272 $\mu \text{T}_{\mathrm {rms}}$ in a bandwidth of 400 kHz, which is 40 times more than previous low-offset CMOS Hall sensors. In the second, the combination of spinning-current Hall sensors and pickup coils achieves the same offset, with a resolution of 210 $\mu \text{T}_{\mathrm {rms}}$ in a further extended bandwidth of 3 MHz, which is the widest bandwidth ever reported for a CMOS magnetic sensor.


title: Auxiliary Beam Pair Enabled AoD and AoA Estimation in Closed-Loop Large-Scale mmWave MIMO Systems

abstract: Channel estimation is of critical importance in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. Due to the use of large antenna arrays, low-complexity mmWave specific channel estimation algorithms are required. In this paper, an auxiliary beam pair design is proposed to provide high-resolution estimates of the channel's angle-of-departure (AoD) and angle-of-arrival (AoA) for mmWave MIMO systems. By performing an amplitude comparison with respect to each auxiliary beam pair, a set of ratio measures that characterize the channel's AoD and AoA are obtained by the receiver. Either the best ratio measure or the estimated AoD is quantized and fed back to the transmitter via a feedback channel. The proposed technique can be incorporated into control channel design to minimize initial access delay. Though the design principles are derived assuming a high-power regime, evaluation under more realistic assumption shows that by employing the proposed method, good angle estimation performance is achieved under various signal-to-noise ratio levels and channel conditions.