title: Hunger in Children in the United States: Potential Behavioral and Emotional Correlates

abstract: Objective . Results from a recent series of surveys from 9 states and the District of Columbia by the Community Childhood Hunger Identification Project (CCHIP) provide an estimate that 4 million American children experience prolonged periodic food insufficiency and hunger each year, 8% of the children under the age of 12 in this country. The same studies show that an additional 10 million children are at risk for hunger. The current study examined the relationship between hunger as defined by the CCHIP measure (food insufficiency attributable to constrained resources) and variables reflecting the psychosocial functioning of low-income, school-aged children. Methods . The study group included 328 parents and children from a CCHIP study of families with at least 1 child under the age of 12 years living in the city of Pittsburgh and the surrounding Allegheny County. A two-stage area probability sampling design with standard cluster techniques was used. All parents whose child was between the ages of 6 and 12 years at the time of interview were asked to complete a Pediatric Symptom Checklist, a brief parent-report questionnaire that assesses children9s emotional and behavioral symptoms. Hunger status was defined by parent responses to the standard 8 food-insufficiency questions from the CCHIP survey that are used to classify households and children as “hungry,” “at-risk for hunger,” or “not hungry.” Results . In an area probability sample of low-income families, those defined as hungry on the CCHIP measure were significantly more likely to have clinical levels of psychosocial dysfunction on the Pediatric Symptom Checklist than children defined as at-risk for hunger or not hungry. Analysis of individual items and factor scores on the Pediatric Symptom Checklist showed that virtually all behavioral, emotional, and academic problems were more prevalent in hungry children, but that aggression and anxiety had the strongest degree of association with experiences of hunger. Conclusion . Children from families that report multiple experiences of food insufficiency and hunger are more likely to show behavioral, emotional, and academic problems on a standardized measure of psychosocial dysfunction than children from the same low-income communities whose families do not report experiences of hunger. Although causality cannot be determined from a cross-sectional design, the strength of these findings suggests the importance of greater awareness on the part of health care providers and public health officials of the role of food insufficiency and hunger in the lives of poor children.


Building Scalable and High-Performance Java Web Applications Using J2EE Technology


title: Interference theory of multiple optical vortex states in spiral phase plate etalon: thick-plate and thin-plate approximation

abstract: The spiral phase plate etalon transmission function is calculated from the low-reflectivity to high-reflectivity regime. Two approximations are considered: thick-plate approximation and thin-plate approximation. The thick-plate approximation explicitly takes into account the angle between the azimuthally increasing surface and the flat surface, while the thin-plate approximation does not. The two results are in agreement in the low-reflectivity regime, but not in the high-reflectivity regime. The thick-plate approximation is expected to provide a more accurate and general description of the device in all regimes. Origins of the device output intensity dependence on angle due to multiple vortex states present in the device are discussed, and a constraint on the number of internal reflections due to device geometry is also discussed.


IEA International Computer and Information Literacy Study 2018 Assessment Framework


title: Wideband RCS Reduction of a Slot Array Antenna Using Polarization Conversion Metasurfaces

abstract: A new approach to reducing the monostatic radar cross section (RCS) and preserving the radiation characteristics of a slot array antenna by employing polarization conversion metasurfaces (PCMs) is presented in this communication. The PCM is arranged in a chessboard configuration consisting of fishbone-shaped element. It is placed on the surface of the slot array antenna. The characteristics and mechanism of the RCS reduction are analyzed. Simulated and experimental results show that the monostatic RCS reduction band of the antenna with PCM ranges between 6.0 and 18.0 GHz for normally impinging both $x$ - and $y$ -polarized waves. The radiation characteristics of the antenna are well preserved simultaneously in terms of the impedance bandwidth, radiation patterns, and realized boresight gains.


title: Parallel and distributed simulation

abstract: Research and development efforts in the parallel and distributed simulation field over the last 15 years has progressed, largely independently, in two separate camps: the largely academic high performance parallel and distributed (discrete event) simulation (PADS) community, and the DoD-centered distributed interactive simulation (DIS) community. This tutorial gives an overview and comparison of work in these two areas, emphasizing issues related to distributed execution where these fields have the most overlap. Differences in the fundamental assumptions routinely used within each community are contrasted, followed by overviews of work in each community.


title: Natural Evolution Strategies

abstract: This paper presents natural evolution strategies (NES), a novel algorithm for performing real-valued dasiablack boxpsila function optimization: optimizing an unknown objective function where algorithm-selected function measurements constitute the only information accessible to the method. Natural evolution strategies search the fitness landscape using a multivariate normal distribution with a self-adapting mutation matrix to generate correlated mutations in promising regions. NES shares this property with covariance matrix adaption (CMA), an evolution strategy (ES) which has been shown to perform well on a variety of high-precision optimization tasks. The natural evolution strategies algorithm, however, is simpler, less ad-hoc and more principled. Self-adaptation of the mutation matrix is derived using a Monte Carlo estimate of the natural gradient towards better expected fitness. By following the natural gradient instead of the dasiavanillapsila gradient, we can ensure efficient update steps while preventing early convergence due to overly greedy updates, resulting in reduced sensitivity to local suboptima. We show NES has competitive performance with CMA on unimodal tasks, while outperforming it on several multimodal tasks that are rich in deceptive local optima.


title: Dual Circularly Polarized Equilateral Triangular Patch Array

abstract: This paper presents a novel sequentially rotated patch array antenna with dual circular polarizations (CP). The present design has the advantages of compact size, simple feed networks, low cross polarization, and high isolation. The radiating elements are dual linearly polarized (LP) equilateral triangular patches with hybrid feeds. The vertical polarization (VP) of the patch antenna is obtained by using an aperture-coupled feed and the horizontal polarization (HP) is obtained by using a proximity feed. These two feeds are orthogonally positioned and are printed on different PCB layers, which leads to the high isolation between these two ports and facilitates the design of the feed networks. The array antenna consists of six such dual-LP triangular patches sequentially rotated by $60 {^\circ}$ and fed by separated feed networks. Since LP antenna elements are used, the design of the feed network is much simplified. Through sequentially varying the feeding phase by $\pm 60{^\circ}$ , dual circular polarizations are obtained. The operation principle of the array antenna is also analytically explained in this paper. To verify the design concept, one dual LP equilateral triangular patch and one dual CP equilateral triangular patch array resonating at 10.5 GHz are designed, fabricated, and tested. There is a good agreement between the simulation and measurement results, both of which show that the array antenna exhibits high port isolation and good circular polarizations with low cross polarization at different planes. The proposed design technique can be applied to the design of dual CP array antennas operating at other frequency bands.


title: Animating paper using shape memory alloys

abstract: Our aim is to make shape memory alloys (SMAs) accessible and visible as creative crafting materials by combining them with paper. In this paper, we begin by presenting mechanisms for actuating paper with SMAs along with a set of design guidelines for achieving dramatic movement. We then describe how we tested the usability and educational potential of one of these mechanisms in a workshop where participants, age 9 to 15, made actuated electronic origami cranes. We found that participants were able to successfully build constructions integrating SMAs and paper, that they enjoyed doing so, and were able to learn skills like circuitry design and soldering over the course of the workshop.


title: A Survey of Euler’s Constant

abstract: The mathematical constant y ? lim^oo (YH=i \ ? ln(rc)) ? 0.5772156..., known as Euler's constant, is not as well known as its cousins 7T, e, i, but is still important enough to warrant serious consideration in the circles of applied mathematics, calcu lus, and number theory. Some authors will occasionally refer to y as the Euler-Mascheroni constant, so named after the Italian geometer Lorenzo Mascheroni (1750-1800), who actually in troduced the symbol y for the constant (although there is controversy about this claim) and also computed, though with error, the first 32 digits [16, 34]. Sometimes one will find in older texts the symbols C (this was Euler's constant of integration) and A (also from Mascheroni) to represent the constant, but these notations seem to have disap peared in the modern era [27]. Our aim in this article is to present a survey of y that is both manageable by, and enlightening to, those who favor mathematics at the undergraduate level. To try and follow in the footsteps of the big boys it and e is quite a chore, but this brief historical description of y and colorful portfolio of applications and surprising appearances in a multitude of settings is both impressive and mathematically educational.


title: MEDARM: a rehabilitation robot with 5DOF at the shoulder complex

abstract: A key approach for reducing motor impairment and regaining independence after stroke is frequent and repetitive functional training. A number of robotic devices have been developed to assist therapists with the labourious task of providing treatment. Although robotic technology is showing significant potential, its effectiveness for upper limb rehabilitation is limited in part by the inability to make functional reaching movements. A major contributor to this problem is that current robots do not replicate motion of the shoulder girdle despite the fact that the shoulder girdle plays a critical role in stabilizing and orienting the upper limb during activities of daily living. To address this issue, a new adjustable robotic exoskeleton called MEDARM is proposed for motor rehabilitation of the shoulder complex. MEDARM provides independent control of six degrees of freedom (DOF) of the upper limb: two at the sternoclavicular joint, three at the glenohumeral joint and one at the elbow. Its joint axes are optimally arranged to mimic the natural upper- limb workspace while avoiding singular configurations and while maximizing manipulability. This mechanism also permits reduction to planar shoulder/elbow motion in any plane by locking all but the last two joints. Electric motors actuate the joint using a combination of cable and belt transmissions designed to maximize the power-to-weight ratio of the robot while maintaining backdriveability and minimizing inertia. Thus, the robot can provide any level of movement assistance and gravity compensation. This paper describes the proposed technical design for MEDARM.


title: A Survey on Device-to-Device Communication in Cellular Networks

abstract: Device-to-device (D2D) communications was initially proposed in cellular networks as a new paradigm for enhancing network performance. The emergence of new applications such as content distribution and location-aware advertisement introduced new user cases for D2D communications in cellular networks. The initial studies showed that D2D communications has advantages such as increased spectral efficiency and reduced communication delay. However, this communication mode introduces complications in terms of interference control overhead and protocols that are still open research problems. The feasibility of D2D communications in Long-Term Evolution Advanced is being studied by academia, industry, and standardization bodies. To date, there are more than 100 papers available on D2D communications in cellular networks, but there is no survey on this field. In this paper, we provide a taxonomy based on the D2D communicating spectrum and review the available literature extensively under the proposed taxonomy. Moreover, we provide new insights into the over-explored and under-explored areas that lead us to identify open research problems of D2D communications in cellular networks.


title: Smart Batching: A Load-Sensitive Self-Tuning Packet I/O Using Dynamic Batch Sizing

abstract: Batch processing is an essential technique to improve packet rates under high-speed network, which has been widely used in high-performance packet IO frameworks. Conventionally, we need to make a tradeoff between high latency at low input loads and high throughput at high loads. In this work, we intend to leverage the smart batching mechanism to dynamically balance the input traffic rate and the available processing ability, so as to achieve both low latency and high throughput.%We conduct experimental investigations on the impact of different batching sizes on throughput, latency and CPU costs on our testbed which utilizesThe impact of different batching sizes on throughput, latency and CPU costs is investigated on our testbed which utilizes MoonGen and DPDK platform. We build a model for the batch processing procedure to explore various factors influencing latency and throughput. We find that batching size is a controllable variable linking the input rate and the available system processing rate. In light of this understanding, we design the smart batching mechanism to dynamically tune batching size according to the input traffic rate and the processing capability available. Smart batching mechanism is implemented and evaluated on DPDK platform under different traffic patterns and workload. The results show that it is sensitive and adaptive to the instantaneous states of network and server. Besides, it is simple enough and portable to most packet IO frameworks.


title: Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

abstract: Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.


title: Chinese Part-Of-Speech Tagging: One-At-A-Time Or All-At-Once? Word-Based Or Character-Based?

abstract: Chinese part-of-speech (POS) tagging assigns one POS tag to each word in a Chinese sentence. However, since words are not demarcated in a Chinese sentence, Chinese POS tagging requires word segmentation as a prerequisite. We could perform Chinese POS tagging strictly after word segmentation (one-at-a-time approach), or perform both word segmentation and POS tagging in a combined, single step simultaneously (all-atonce approach). Also, we could choose to assign POS tags on a word-by-word basis, making use of word features in the surrounding context (word-based), or on a character-by-character basis with character features (character-based). This paper presents an in-depth study on such issues of processing architecture and feature representation for Chinese POS tagging, within a maximum entropy framework. We found that while the all-at-once, characterbased approach is the best, the one-at-a-time, character-based approach is a worthwhile compromise, performing only slightly worse in terms of accuracy, but taking shorter time to train and run. As part of our investigation, we also built a state-of-the-art Chinese word segmenter, which outperforms the best SIGHAN 2003 word segmenters in the closed track on 3 out of 4 test corpora.


title: CIMS: a framework for infrastructure interdependency modeling and analysis

abstract: Today's society relies greatly upon an array of complex national and international infrastructure networks, such as transportation, utilities, telecommunication, and even financial networks. While modeling and simulation tools have provided insight into the behavior of individual infrastructure networks, a far less understood area is that of the interrelationships among multiple infrastructure networks including the potential cascading effects that may result due to these interdependencies. This paper first describes infrastructure interdependencies, as well as presenting a formalization of interdependency types. Next the paper describes a modeling and simulation framework called CIMS© and the work that is being conducted at the Idaho National Laboratory (INL) to model and simulate infrastructure interdependencies and the complex behaviors that can result.


title: SIFT Keypoint Descriptors for Range Image Analysis

abstract: This paper presents work in progress to extend the two-dimensional (2D) Scale Invariant Feature Transform (SIFT) into the 2.5 dimensional (2.5D) domain. Feature descriptors are extracted from range images of human faces and the form of these descriptors is analogous to the structure of Lowe’s 2D SIFT. Lowe’s descriptors are derived from the histogram of the image gradient orientations, computed over a Gaussian weighted local support region centred on each sampling (keypoint) location. We adapt this concept into the 2.5D domain by extracting the relative frequencies of the [-1,1] bounded range surface shape index and the relative frequencies of the range surface in-plane orientations simultaneously at each sampled keypoint location. Nine Gaussian weighted sub-regions, overlapped by one standard deviation, are used to sample each keypoint location and thereby construct a keypoint descriptor. Since these overlapped Gaussian sub-regions are spatially correlated, this sampling configuration reduces both the spatial aliasing and the sensitivity to small keypoint location errors in the extracted descriptors. Each histogram pair, extracted from each Gaussian weighted sub-region, is normalised and concatenated to form a feature descriptor that is tolerant to a degree of viewpoint rotational change. We have validated the current 2.5D SIFT formulation using synthetically rotated human face data over the range 30 out-of-plane rotation, and demonstrate that our 2.5D SIFT maintains a similar matching performance to 2D SIFT applied to the (comparatively richer) intensity images of the same face.


title: Hysteroscopy: Textbook and Atlas

abstract: Hysteroscopy is a method of endoscopic viewing of the uterus. This book provides basic information on the technical fundamentals, the various indications for hysterscopy, hysteroscopic findings of physiological and pathological changes in the endometrium, hysteroscopic differential diagnosis, and on correlations to histology. Indications and contraindications are also stated. More and more, hysteroscopy is being performed in the doctor's office. The work gives a detailed account of the morphological confirmation of the endoscopic findings (streak curretage, endometrium cytology), and includes operative hystersocopy.


title: Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning

abstract: We present an algorithm for rapidly learning controllers for robotics systems. The algorithm follows the model-based reinforcement learning paradigm, and improves upon existing algorithms; namely Probabilistic learning in Control (PILCO) and a sample-based version of PILCO with neural network dynamics (Deep-PILCO). We propose training a neural network dynamics model using variational dropout with truncated Log-Normal noise. This allows us to obtain a dynamics model with calibrated uncertainty, which can be used to simulate controller executions via rollouts. We also describe set of techniques, inspired by viewing PILCO as a recurrent neural network model, that are crucial to improve the convergence of the method. We test our method on a variety of benchmark tasks, demonstrating data-efficiency that is competitive with PILCO, while being able to optimize complex neural network controllers. Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle. This demonstrates the potential of the algorithm for scaling up the dimensionality and dataset sizes, in more complex control tasks.


title: A fully integrated, 1-µs start-up time, 32-MHz relaxation oscillator for low-power intermittent systems

abstract: This paper proposes a fully integrated 32-MHz relaxation oscillator (ROSC) capable of fast start-up time operation for low-power intermittent VLSI systems. The proposed ROSC employs current mode architecture that is different from conventional voltage mode architecture. This enables compact and fast switching speed to be achieved. By designing transistor sizes equally between one in a bias circuit and another in a voltage to current converter, the effect of process variation can be minimized. A prototype chip in a 0.18-µm CMOS demonstrated that the ROSC generates a stable clock frequency of 32.6 MHz within 1-µs start-up time. Measured line regulation and temperature coefficient were ±0.69% and ±0.38%, respectively.


title: Text Classification using Data Mining

abstract: Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using data mining that requires fewer documents for training. Instead of using words, word relation i.e. association rules from these words is used to derive feature set from pre-classified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.


title: Caffeine and cardiovascular diseases: critical review of current research

abstract: Caffeine is a most widely consumed physiological stimulant worldwide, which is consumed via natural sources, such as coffee and tea, and now marketed sources such as energy drinks and other dietary supplements. This wide use has led to concerns regarding the safety of caffeine and its proposed beneficial role in alertness, performance and energy expenditure and side effects in the cardiovascular system. The question remains "Which dose is safe?", as the population does not appear to adhere to the strict guidelines listed on caffeine consumption. Studies in humans and animal models yield controversial results, which can be explained by population, type and dose of caffeine and low statistical power. This review will focus on comprehensive and critical review of the current literature and provide an avenue for further study.


title: SwATrack: A Swarm Intelligence-based Abrupt Motion Tracker

abstract: Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or constrained motion model; where the motion is often governed by a fixed Gaussian distribution. Abrupt motion however, is not subjected to motion continuity and smoothness. To assuage this, we propose a novel abrupt motion tracker that is based on swarm intelligence - the SwATrack. Unlike existing swarm-based filtering methods, we firstly introduce an optimised swarm-based sampling strategy to enrich the trade-off between the exploration and exploitation of the search space in search for the optimal proposal distribution. Secondly, we propose adaptive acceleration parameters to allow on the fly tuning of the best mean and variance of the distribution for sampling. The adaptive strategy requires no training stage thus allowing flexibility in the motion model, while relaxing the number of particles deployed. Experimental results in both the quantitative and qualitative measures demonstrate the effectiveness of the proposed method in tracking abrupt motions.


title: Model-Based Testing of SDN Firewalls: A Case Study

abstract: In Software defined networking (SDN), security mechanisms such as firewalls need to deal with dynamic network environments. This raises challenges for quality assurance of these security mechanisms. This paper presents a case study on model-based testing of SDN firewall programs. The subject program is the firewall module of Floodlight, one of the most popular SDN platforms in Java. We model the expected firewall behavior with function nets, a modeling formalism in the MISTA tool. The test code is generated automatically by MISTA. The result of our case study shows that the generated model-based tests have achieved much higher mutation coverage than the existing Junit tests in the Floodlight firewall program. This indicates that model-based testing can be a viable option for quality assurance of SDN-based firewall programs.


title: Vehicle Routing Problem with Vector Profits (VRPVP) with Max-Min Criterion

abstract: ABSTRACTThis article introduces a new routing problem referred to as the vehicle routing problem with vector profits. Given a network composed of nodes (depot/sites) and arcs connecting the nodes, the problem determines routes that depart from the depot, visit sites to collect profits, and return to the depot. There are multiple stakeholders interested in the mission and each site is associated with a vector whose kth element represents the profit value for the kth stakeholder. The objective of the problem is to maximize the profit sum for the least satisfied stakeholder, i.e. the stakeholder with the smallest total profit value. An approach based on linear programming relaxation and column-generation to solve this max–min type routing problem was developed. Two case studies—the planetary surface exploration and the Rome tour cases—were presented to demonstrate the effectiveness of the proposed problem formulation and solution methodology.


title: Heuristic evaluation checklist for mobile ERP user interfaces

abstract: The emergence of full-screen mobile phones and tablet PCs has motivated ERP vendors to adopt a mobile strategy in order to extend their products' features. Several researchers have pointed out that ERP systems' UIs suffer from poor usability, due to their complex, rigid and bloated UIs, since these systems have to process and integrate a large amount of data. Therefore, accessing ERP systems via mobile devices like fullscreen phones or tablet PCs suffers from UIs usability challenges. Currently, mobile ERP is a young topic in research and practice, there are only few academic papers available about mobile ERP. Particularly, there is a knowledge gap in addressing the usability issues when designing the mobile ERP's UIs and determining a suitable usability evaluation method and criteria which are specific to mobile ERP. This paper aims at narrowing that gap by proposing a set of usability heuristics and a UI design checklist dedicated to evaluate mobile ERP's UIs which can be used by mobile ERP developers.


title: Virtual machine showdown: Stack versus registers

abstract: Virtual machines (VMs) enable the distribution of programs in an architecture-neutral format, which can easily be interpreted or compiled. A long-running question in the design of VMs is whether a stack architecture or register architecture can be implemented more efficiently with an interpreter. We extend existing work on comparing virtual stack and virtual register architectures in three ways. First, our translation from stack to register code and optimization are much more sophisticated. The result is that we eliminate an average of more than 46p of executed VM instructions, with the bytecode size of the register machine being only 26p larger than that of the corresponding stack one. Second, we present a fully functional virtual-register implementation of the Java virtual machine (JVM), which supports Intel, AMD64, PowerPC and Alpha processors. This register VM supports inline-threaded, direct-threaded, token-threaded, and switch dispatch. Third, we present experimental results on a range of additional optimizations such as register allocation and elimination of redundant heap loads. On the AMD64 architecture the register machine using switch dispatch achieves an average speedup of 1.48 over the corresponding stack machine. Even using the more efficient inline-threaded dispatch, the register VM achieves a speedup of 1.15 over the equivalent stack-based VM.


title: Vision-based motion capture system for tracking crawling motions of infants

abstract: This work is part of an ongoing effort to explore a new mode of intervention for children at risk of cerebral palsy. The intervention uses an autonomous mobile robot that recognizes crawling like movements to respond to the infant. We propose a new system to capture crawling motions of infants. Our methodology involves placing planar marker patterns on the body and tracking them using cameras. A single camera can be used to track the markers in 3D. Such markers are generally used in augmented reality (AR) applications but are relatively unheard of for motion capture. In this paper, we present the system design and some preliminary test results. The test results indicate that it is possible to detect displacement typical of crawling with markers as small as 27 mm on a side.


title: Forward kinematics analysis of the spindle-coupled road simulator with modified newton-raphson algorithm

abstract: The forward kinematics analysis of the six-degree-of-freedom(DOF) spindle-coupled road simulator (SCRS) with a complex hybrid serial-parallel mechanism(HSPM) has been performed. The forward kinematics with an efficient method is significant for control, singularity and workspace analysis of SCRS. Applying the vector matrix theory, the geometric restriction function was expressed in concise and compact form. A modified Newton-Raphson algorithm which could guarantee the convergence and improve the rapidity was proposed to solve the problem of the forward kinematics. The validity of forward kinematic formulations was verified through Matlab/Simulink and ADAMS. The numerical results show that the established model is accurate and the algorithm is effective which lays a good foundation for both mechanical system optimization and control scheme design of SCRS.


title: Encoder Based Lifelong Learning

abstract: This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catastrophic forgetting: as they tend to adapt to the most recently seen task, they lose performance on the tasks that were learned previously. Our method aims at preserving the knowledge of the previous tasks while learning a new one by using autoencoders. For each task, an under-complete autoencoder is learned, capturing the features that are crucial for its achievement. When a new task is presented to the system, we prevent the reconstructions of the features with these autoencoders from changing, which has the effect of preserving the information on which the previous tasks are mainly relying. At the same time, the features are given space to adjust to the most recent environment as only their projection into a low dimension submanifold is controlled. The proposed system is evaluated on image classification tasks and shows a reduction of forgetting over the state-ofthe-art.


title: Keyword search in databases: the power of RDBMS

abstract: Keyword search in relational databases (RDBs) has been extensively studied recently. A keyword search (or a keyword query) in RDBs is specified by a set of keywords to explore the interconnected tuple structures in an RDB that cannot be easily identified using SQL on RDBMS. In brief, it finds how the tuples containing the given keywords are connected via sequences of connections (foreign key references) among tuples in an RDB. Such interconnected tuple structures can be found as connected trees up to a certain size, sets of tuples that are reachable from a root tuple within a radius, or even multi-center subgraphs within a radius. In the literature, there are two main approaches. One is to generate a set of relational algebra expressions and evaluate every such expression using SQL on an RDBMS directly or in a middleware on top of an RDBMS indirectly. Due to a large number of relational algebra expressions needed to process, most of the existing works take a middleware approach without fully utilizing RDBMSs. The other is to materialize an RDB as a graph and find the interconnected tuple structures using graph-based algorithms in memory. In this paper we focus on using SQL to compute all the interconnected tuple structures for a given keyword query. We use three types of interconnected tuple structures to achieve that and we control the size of the structures. We show that the current commercial RDBMSs are powerful enough to support such keyword queries in RDBs efficiently without any additional new indexing to be built and maintained. The main idea behind our approach is tuple reduction. In our approach, in the first reduction step, we prune tuples that do not participate in any results using SQL, and in the second join step, we process the relational algebra expressions using SQL over the reduced relations. We conducted extensive experimental studies using two commercial RDBMSs and two large real datasets, and we report the efficiency of our approaches in this paper.


title: An Interactive Spreadsheet For Teaching The Forward-Backward Algorithm

abstract: This paper offers a detailed lesson plan on the forward-backward algorithm. The lesson is taught from a live, commented spreadsheet that implements the algorithm and graphs its behavior on a whimsical toy example. By experimenting with different inputs, one can help students develop intuitions about HMMs in particular and Expectation Maximization in general. The spreadsheet and a coordinated follow-up assignment are available.


title: LOW-COST 3D LASER SCANNING IN AIR ORWATER USING SELF-CALIBRATINGSTRUCTURED LIGHT

abstract: Abstract. In-situ calibration of structured light scanners in underwater environments is time-consuming and complicated. This paper presents a self-calibrating line laser scanning system, which enables the creation of dense 3D models with a single fixed camera and a freely moving hand-held cross line laser projector. The proposed approach exploits geometric constraints, such as coplanarities, to recover the depth information and is applicable without any prior knowledge of the position and orientation of the laser projector. By employing an off-the-shelf underwater camera and a waterproof housing with high power line lasers an affordable 3D scanning solution can be built. In experiments the performance of the proposed technique is studied and compared with 3D reconstruction using explicit calibration. We demonstrate that the scanning system can be applied to above-the-water as well as underwater scenes.


title: Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

abstract: Magnetic anomaly detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.


title: Subacute transverse myelitis with Lyme profile dissociation

abstract: Introduction: Transverse myelitis is a very rare neurologic syndrome with an incidence per year of 1-5 per million population. We are presenting an interesting case of subacute transverse myelitis with its MRI (magnetic resonance imaging) and CSF (cerebrospinal fluid) findings. ::: ::: Case: A 46-year-old African-American woman presented with decreased sensation in the lower extremities which started three weeks ago when she had a 36-hour episode of sore throat. She reported numbness up to the level just below the breasts. Lyme disease antibodies total IgG (immunoglobulin G) and IgM (immunoglobulin M) in the blood was positive. Antinuclear antibody profile was within normal limits. MRI of the cervical spine showed swelling in the lower cervical cord with contrast enhancement. Cerebrospinal fluid was clear with negative Borrelia Burgdorferi IgG and IgM. Herpes simplex, mycoplasma, coxiella, anaplasma, cryptococcus and hepatitis B were all negative. No oligoclonal bands were detected. Quick improvement ensued after she was given IV Ceftriaxone for 7 days. The patient was discharged on the 8th day in stable condition. She continued on doxycycline for 21 days. ::: ::: Conclusions: Transverse myelitis should be included in the differential diagnosis of any patient presenting with acute or subacute myelopathy in association with localized contrast enhancement in the spinal cord especially if flu-like prodromal symptoms were reported. Lyme disease serology is indicated in patients with neurological symptoms keeping in mind that dissociation in Lyme antibody titers between the blood and the CSF is possible.


title: Freezing of Gait detection in Parkinson's disease using accelerometer based smart clothes

abstract: Parkinson's disease (PD) is a degenerative neurological disorder. Patients suffer from different movement disturbances. Amongst others, freezing of gait (FOG) is a common and disabling feature. A new method for FOG detection using a garment with integrated accelerometers, the MiMed-Pants, is presented and evaluated. MiMed-Pants are loose fitting washable pants for measuring movements and were previously developed. The accelerations of shanks and belt of 6 patients were recorded while walking on two standardized courses. The occurrence of FOG were detected and documented both by the new method and by a physician. Those recordings were then compared afterwards to determine the new method's sensitivity and specificity. With this wearable measurement device FOG could be detected without the distracting effects of an extensive measurement installation.


title: Ecological foliar fertilization effects on essential oil composition of sweet basil (Ocimum basilicum L.) cultivated in a field system

abstract: Abstract Ocimum basilicum L. (basil) is a valuable aromatic plant, widely used in traditional and modern medicine, perfumery, the food industry, cosmetics and landscaping. In order to study the effect of organic foliar fertilization on essential oil (EO) content and composition, a field experiment was conducted. Treatments including four different organic foliar fertilizers: Fylo® (F1), Geolino Plants&Flowers® (F2), Cropmax® (F3), Fitokondi® (F4) and control were set up in a factorial experiment based on split plot design with three replications. Recovery of the EO ranged from 0.2% to 0.42% for basil leaves and 0.52–0.69% for basil flowers. GC–MS analysis of the essential oils revealed the presence of linalool, α-muurolol, methyl chavicol, eugenol, γ-cadinene, α-bergamotene, eucalyptol, germacrene D, cubenol, β-elemene and α-bulnesene as main compounds in all the samples but their percentages in each part of the plant (leaves and flowers) were greatly different. Moreover, certain variations in the content of particular compounds in EO were found, depending on the fertilization applied. Accordingly, it can be concluded that organic foliar fertilization can considerably improve the essential oil content, without drastic effects on the chemical composition.


title: What makes a great software engineer?

abstract: Good software engineers are essential to the creation of good software. However, most of what we know about software-engineering expertise are vague stereotypes, such as 'excellent communicators' and 'great teammates'. The lack of specificity in our understanding hinders researchers from reasoning about them, employers from identifying them, and young engineers from becoming them. Our understanding also lacks breadth: what are all the distinguishing attributes of great engineers (technical expertise and beyond)? We took a first step in addressing these gaps by interviewing 59 experienced engineers across 13 divisions at Microsoft, uncovering 53 attributes of great engineers. We explain the attributes and examine how the most salient of these impact projects and teams. We discuss implications of this knowledge on research and the hiring and training of engineers.


title: Molecular and biotechnological aspects of xylanases.

abstract: Hemicellulolytic microorganisms play a significant role in nature by recycling hemicellulose, one of the main components of plant polysaccharides. Xylanases (EC 3.2.1.8) catalyze the hydrolysis of xylan, the major constituent of hemicellulose. The use of these enzymes could greatly improve the overall economics of processing lignocellulosic materials for the generation of liquid fuels and chemicals. Recently cellulase-free xylanases have received great attention in the development of environmentally friendly technologies in the paper and pulp industry. In microorganisms that produce xylanases low molecular mass fragments of xylan and their positional isomers play a key role in regulating its biosynthesis. Xylanase and cellulase production appear to be regulated separately, although the pleiotropy of mutations, which causes the elimination of both genes, suggests some linkage in the synthesis of the two enzymes. Xylanases are found in a cornucopia of organisms and the genes encoding them have been cloned in homologous and heterologous hosts with the objectives of overproducing the enzyme and altering its properties to suit commercial applications. Sequence analyses of xylanases have revealed distinct catalytic and cellulose binding domains, with a separate non-catalytic domain that has been reported to confer enhanced thermostability in some xylanases. Analyses of three-dimensional structures and the properties of mutants have revealed the involvement of specific tyrosine and tryptophan residues in the substrate binding site and of glutamate and aspartate residues in the catalytic mechanism. Many lines of evidence suggest that xylanases operate via a double displacement mechanism in which the anomeric configuration is retained, although some of the enzymes catalyze single displacement reactions with inversion of configuration. Based on a dendrogram obtained from amino acid sequence similarities the evolutionary relationship between xylanases is assessed. In addition the properties of xylanases from extremophilic organisms have been evaluated in terms of biotechnological applications.


CFR-D: Solving Imperfect Information Games Using Decomposition


title: Classification of streaming time series under more realistic assumptions

abstract: Much of the vast literature on time series classification makes several assumptions about data and the algorithm's eventual deployment that are almost certainly unwarranted. For example, many research efforts assume that the beginning and ending points of the pattern of interest can be correctly identified, during both the training phase and later deployment. Another example is the common assumption that queries will be made at a constant rate that is known ahead of time, thus computational resources can be exactly budgeted. In this work, we argue that these assumptions are unjustified, and this has in many cases led to unwarranted optimism about the performance of the proposed algorithms. As we shall show, the task of correctly extracting individual gait cycles, heartbeats, gestures, behaviors, etc., is generally much more difficult than the task of actually classifying those patterns. Likewise, gesture classification systems deployed on a device such as Google Glass may issue queries at frequencies that range over an order of magnitude, making it difficult to plan computational resources. We propose to mitigate these problems by introducing an alignment-free time series classification framework. The framework requires only very weakly annotated data, such as "in this ten minutes of data, we see mostly normal heartbeats$$\ldots $$?," and by generalizing the classic machine learning idea of data editing to streaming/continuous data, allows us to build robust, fast and accurate anytime classifiers. We demonstrate on several diverse real-world problems that beyond removing unwarranted assumptions and requiring essentially no human intervention, our framework is both extremely fast and significantly more accurate than current state-of-the-art approaches.


title: A framework for Collaborative Planning, Forecasting and Replenishment (CPFR)

abstract: Purpose – Although many papers purport the significant value attributable to supply chain performance from the use of Collaborative Planning, Forecasting and Replenishment (CPFR), the question of “what are the main constructs and efficient framework for successful implementation of CPFR?” remains largely unanswered. This question will be addressed by identifying and analysing the main constructs for successful implementation of CPFR. The purpose of this paper is to attempt first to seek answers to this question. Second, to review the scope and value of CPFR using a devised state-of-the-art taxonomy for the classification of selected bibliographical references and third, to develop a conceptual framework by identifying areas which need more research. Design/methodology/approach – The method underlying this paper followed the steps of a systematic literature review process outlined by Soni and Kodali (2011). The review is based on a total of 93 papers published from 1998 to 2013 on CPFR. Findings – Four mai...


title: Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches

abstract: Web content mining is intended to help people discover valuable information from large amount of unstructured data on the web. Movie review mining classifies movie reviews into two polarities: positive and negative. As a type of sentiment-based classification, movie review mining is different from other topic-based classifications. Few empirical studies have been conducted in this domain. This paper investigates movie review mining using two approaches: machine learning and semantic orientation. The approaches are adapted to movie review domain for comparison. The results show that our results are comparable to or even better than previous findings. We also find that movie review mining is a more challenging application than many other types of review mining. The challenges of movie review mining lie in that factual information is always mixed with real-life review data and ironic words are used in writing movie reviews. Future work for improving existing approaches is also suggested.


title: SECURE: Self-Protection Approach in Cloud Resource Management

abstract: In the current scenario of cloud computing, heterogeneous resources are located in various geographical locations requiring security-aware resource management to handle security threats. However, existing techniques are unable to protect systems from security attacks. To provide a secure cloud service, a security-based resource management technique is required that manages cloud resources automatically and delivers secure cloud services. In this paper, we propose a self-protection approach in cloud resource management called SECURE, which offers self-protection against security attacks and ensures continued availability of services to authorized users. The performance of SECURE has been evaluated using SNORT. The experimental results demonstrate that SECURE performs effectively in terms of both the intrusion detection rate and false positive rate. Further, the impact of security on quality of service (QoS) has been analyzed.


title: A Conformal Circularly Polarized Antenna for Wireless Capsule Endoscope Systems

abstract: A conformal circularly polarized (CP) capsule antenna, designed for ingestible wireless capsule endoscope (WCE) systems at industrial, scientific, and medical (ISM) band (2.4–2.48 GHz), is presented. The antenna consists of a rectangular loop, an asymmetric U-shaped strip, and a protruding L-shaped stub printed on the top of the dielectric substrate. The CP wave is generated by the corner-fed U-shaped strip protruding into the rectangular slot. Further, by adjusting the size of the U-shaped strip and the L-shaped stub, the 3 dB axial-ratio (AR) band can be fully covered by the 10 dB impedance band. The simulated 10 dB impedance bandwidth (BW) and 3 dB ARBW are 31.58% and 13.11%, respectively. The simulated results show that the overlapped impedance BW and ARBW are from 2.28 to 2.6 GHz, which completely covers the 2.4 GHz ISM band. The measured 10 dB impedance BW is 39.21%. Finally, the radiation performance, safety consideration, and link budget of the antenna are examined and characterized. Owing to the broad overlapped impedance BW and ARBW, the minimized in-capsule foot print, and ease of fabrication, the designed antenna is a capable candidate for a WCE system.


title: Scaffolding information problem solving in web-based collaborative inquiry learning

abstract: This study investigated the impact of different modes of scaffolding on students who are learning science through a web-based collaborative inquiry project in authentic classroom settings and explored the interaction effects with students' characteristics. The intervention study aimed to improve domain-specific knowledge and metacognitive awareness during online information problem solving (IPS) as part of an online inquiry project. Three experimental conditions (teacher-enhanced scaffolding, technology-enhanced scaffolding, and both forms of scaffolding) were compared with a control condition in a two-by-two factorial quasi-experimental design. Moreover, gender and prior knowledge were examined as two factors which may have a significant impact on Web-based learning. In a four-week field study in secondary science education, pretest-posttest differences were measured. In total 347 students from 18 secondary school classes were involved and the classes were randomly distributed over the 4 conditions. Our findings support the notion of multiple scaffolding as an approach to enhance both knowledge acquisition and metacognitive awareness with respect to IPS-processes and to meet a mix of students with different needs within the context of a web-based inquiry learning project.


Applying Winnow to Context-Sensitive Spelling Correction


title: Barriers to early presentation and diagnosis of breast cancer among African women living in sub-Saharan Africa

abstract: Background ::: Breast cancer (BC) has been described as the leading cause of cancer deaths among women especially in the developing world including sub Saharan Africa (SSA). Delayed presentation and late diagnosis at health facilities are parts of the contributing factors of high BC mortality in Africa. This review aimed to appraise the contributing factors to delayed breast cancer presentation and diagnosis among SSA women. ::: ::: ::: Methods ::: Five databases encompassing medical and social sciences were systematically searched using predefined search terms linked with breast cancer presentation and diagnosis and sub Saharan Africa. Reference lists of relevant papers were also hand searched. Quality of quantitative and qualitative articles were assessed using the National Institute of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies and the Critical Appraisal Skills Programme (CASP) quality appraisal checklist. Thematic analysis was used to synthesize the qualitative studies to integrate findings. ::: ::: ::: Results ::: Fourteen (14) quantitative studies, two (2) qualitative studies and one (1) mixed method study merited inclusion for analysis. This review identified low knowledge of breast cancer among SSA women. This review also found lack of awareness of early detection treatment, poor perception of BC, socio-cultural factors such as belief, traditions and fear as factors impacting African women’s health seeking behavior in relation to breast cancer. ::: ::: ::: Conclusion ::: Improving African women’s knowledge and understanding will improve behaviors related to breast cancer and facilitate early presentation and detection and enhance proper management and treatment of breast cancer.


title: ZHT: A Light-Weight Reliable Persistent Dynamic Scalable Zero-Hop Distributed Hash Table

abstract: This paper presents ZHT, a zero-hop distributed hash table, which has been tuned for the requirements of high-end computing systems. ZHT aims to be a building block for future distributed systems, such as parallel and distributed file systems, distributed job management systems, and parallel programming systems. The goals of ZHT are delivering high availability, good fault tolerance, high throughput, and low latencies, at extreme scales of millions of nodes. ZHT has some important properties, such as being light-weight, dynamically allowing nodes join and leave, fault tolerant through replication, persistent, scalable, and supporting unconventional operations such as append (providing lock-free concurrent key/value modifications) in addition to insert/lookup/remove. We have evaluated ZHT's performance under a variety of systems, ranging from a Linux cluster with 512-cores, to an IBM Blue Gene/P supercomputer with 160K-cores. Using micro-benchmarks, we scaled ZHT up to 32K-cores with latencies of only 1.1ms and 18M operations/sec throughput. This work provides three real systems that have integrated with ZHT, and evaluate them at modest scales. 1) ZHT was used in the FusionFS distributed file system to deliver distributed meta-data management at over 60K operations (e.g. file create) per second at 2K-core scales. 2) ZHT was used in the IStore, an information dispersal algorithm enabled distributed object storage system, to manage chunk locations, delivering more than 500 chunks/sec at 32-nodes scales. 3) ZHT was also used as a building block to MATRIX, a distributed job scheduling system, delivering 5000 jobs/sec throughputs at 2K-core scales. We compared ZHT against other distributed hash tables and key/value stores and found it offers superior performance for the features and portability it supports.


title: Finding Concurrency Bugs in Java

abstract: Because threads are a core feature of the Java language, the widespread adoption of Java has exposed a much wider audience to concurrency than previous languages have. Concurrent programs are notoriously difficult to write correctly, and many subtle bugs can result from incorrect use of threads and synchronization. Therefore, finding techniques to find concurrency bugs is an important problem. Through development and use of an automatic static analysis tool, we have found a significant number of concurrency bugs in widely used Java applications and libraries. Interestingly, we have found that race conditions abound in concurrent Java programs; underuse of synchronization is the rule rather than the exception. We have also found many examples of other kinds of concurrency errors, suggesting that many Java programmers have fundamental misconceptions about how to write correct multithreaded programs. This paper makes two main contributions. First, it describes simple analysis techniques that are effective at finding concurrency errors in real programs. Second, it provides evidence that threads and concurrency are widely misused in Java, even in programs written by experienced programmers.


title: Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption

abstract: Many modern parallel computing systems are heterogeneous at their node level. Such nodes may comprise general purpose CPUs and accelerators (such as, GPU, or Intel Xeon Phi) that provide high performance with suitable energy-consumption characteristics. However, exploiting the available performance of heterogeneous architectures may be challenging. There are various parallel programming frameworks (such as, OpenMP, OpenCL, OpenACC, CUDA) and selecting the one that is suitable for a target context is not straightforward. In this paper, we study empirically the characteristics of OpenMP, OpenACC, OpenCL, and CUDA with respect to programming productivity, performance, and energy. To evaluate the programming productivity we use our homegrown tool CodeStat, which enables us to determine the percentage of code lines required to parallelize the code using a specific framework. We use our tools MeterPU and x-MeterPU to evaluate the energy consumption and the performance. Experiments are conducted using the industry-standard SPEC benchmark suite and the Rodinia benchmark suite for accelerated computing on heterogeneous systems that combine Intel Xeon E5 Processors with a GPU accelerator or an Intel Xeon Phi co-processor.


title: Do tangible user interfaces impact spatial cognition in collaborative design

abstract: Developments in digital design workbenches that combine Augmented Reality (AR) systems and tangible user interfaces (TUIs) on a horizontal display surface provide a new kind of physical and digital environment for collaborative design. The combination of tangible interaction with AR display techniques change the dynamics of the collaboration and have an impact on the designers’ perception of 3D models. We are studying the effects of TUIs on designers’ spatial cognition and design communication in order to identify how such tangible systems can be used to provide better support for collaborative design. Specifically, we compared tangible user interfaces (TUIs) with graphical user interfaces (GUIs) in a collaborative design task with a focus on characterising the impact these user interfaces have on spatial cognition.


title: ATAC: a 1000-core cache-coherent processor with on-chip optical network

abstract: Based on current trends, multicore processors will have 1000 cores or more within the next decade. However, their promise of increased performance will only be realized if their inherent scaling and programming challenges are overcome. Fortunately, recent advances in nanophotonic device manufacturing are making CMOS-integrated optics a reality-interconnect technology which can provide significantly more bandwidth at lower power than conventional electrical signaling. Optical interconnect has the potential to enable massive scaling and preserve familiar programming models in future multicore chips. This paper presents ATAC, a new multicore architecture with integrated optics, and ACKwise, a novel cache coherence protocol designed to leverage ATAC's strengths. ATAC uses nanophotonic technology to implement a fast, efficient global broadcast network which helps address a number of the challenges that future multicores will face. ACKwise is a new directory-based cache coherence protocol that uses this broadcast mechanism to provide high performance and scalability. Based on 64-core and 1024-core simulations with Splash2, Parsec, and synthetic benchmarks, we show that ATAC with ACKwise out-performs a chip with conventional interconnect and cache coherence protocols. On 1024-core evaluations, ACKwise protocol on ATAC outperforms the best conventional cache coherence protocol on an electrical mesh network by 2.5x with Splash2 benchmarks and by 61% with synthetic benchmarks.


Erp Systems Critical Success Factors ICT Perspective


title: Beanstalk: A unity game addressing balance principles, socio-emotional learning and scientific inquiry

abstract: Beanstalk is an educational game for children ages 6-10 teaching balance-fulcrum principles while folding in scientific inquiry and socio-emotional learning. This paper explores the incorporation of these additional dimensions using intrinsic motivation and a framing narrative. Four versions of the game are detailed, along with preliminary player data in a 2×2 pilot test with 64 children shaping the modifications of Beanstalk for much broader testing.


title: Comparative Study of Filter-Bank Mean-Energy Distance for Automated Segmentation of Speech Signals

abstract: This paper describes a method of automated segmentation of speech assuming the signal is continuously time varying rather than the traditional short time stationary model. It has been shown that this representation gives comparable if not marginally better results than the other techniques for automated segmentation. A formulation of the 'Bach' (music semitonal) frequency scale filter-bank is proposed. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks considering this model. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. 'Bach' filters are seen to marginally outperform the other filters


title: Towards an Economic Framework for Network Neutrality Regulation

abstract: The paper develops an economic framework for network neutrality regulation. Network neutrality rules forbid network operators to discriminate against third-party applications, content or portals or to exclude them from their network. The analysis shows that calls for network neutrality regulation are justified: In the absence of network neutrality regulation, there is a real threat that network providers will discriminate against independent producers of applications, content or portals or exclude them from their network. This threat reduces the amount of innovation in the markets for applications, content and portals at significant costs to society. While network neutrality rules remove this threat, they are not without costs. Due to the potentially enormous benefits of application-level innovation for economic growth, however, increasing the amount of application-level innovation through network neutrality regulation is more important than the costs associated with it. Apart from advancing the debate over network neutrality, the paper highlights important limitations of the 'one monopoly rent' argument: It shows that there are more exceptions to the 'one monopoly rent' argument than have previously been identified and that these new exceptions may be quite common in the Internet context. It shows that exclusion may be a profitable strategy, even if the excluding actor does not manage to drive its competitors from the complementary market. It also shows that competition in the primary market may not always be sufficient to remove the ability and incentive to engage in exclusionary conduct.


title: Rapid assessment of well‐being: The Short Depression‐Happiness Scale (SDHS)

abstract: The purpose of this paper was: first, to develop the short six-item form of the Depression-Happiness Scale; and second, to examine evidence of reliability and validity for the short form. Three studies are presented. In the first study, principal components analysis is reported and used to select six items to compose the short form of the scale. In the second study, re-analyses of data from three previous studies are presented which confirm that the short scale has good psychometric properties of internal consistency reliability, test — retest reliability, and convergent and discriminant validity. In the third study, the short form is found to have a single component structure and convergent validity with measures of depression, happiness and personality.


title: A spaceborne telemetry loaded bifilar helical antenna for LEO satellites

abstract: In this paper we describe the design, manufacturing and measurement of an antenna with an isoflux type radiation pattern used in the space segment TT&C subsystem. In particular, the antenna corresponds to the flight model of mission SAC-D/AQUARIUS, which is a LEO earth observation satellite. The chosen solution is a loaded bifilar helical antenna, which presents a higher gain than a typical hemispherical antenna but which doesn't require the use of a beam forming network like the classical quadrifilar Kilgus type antenna. This reduces the number of required parts, and due to its loading, smaller dimension and higher structural stiffness are achieved.


title: Personalized next-song recommendation in online karaokes

abstract: In this paper, we propose Personalized Markov Embedding (PME), a next-song recommendation strategy for online karaoke users. By modeling the sequential singing behavior, we first embed songs and users into a Euclidean space in which distances between songs and users reflect the strength of their relationships. Then, given each user's last song, we can generate personalized recommendations by ranking the candidate songs according to the embedding. Moreover, PME can be trained without any requirement of content information. Finally, we perform an experimental evaluation on a real world data set provided by ihou.com which is an online karaoke website launched by iFLYTEK, and the results clearly demonstrate the effectiveness of PME.


title: Nutraceuticals as potential therapeutic agents for colon cancer: a review

abstract: Colon cancer is a world-wide health problem and the second-most dangerous type of cancer, affecting both men and women. The modern diet and lifestyles, with high meat consumption and excessive alcohol use, along with limited physical activity has led to an increasing mortality rate for colon cancer worldwide. As a result, there is a need to develop novel and environmentally benign drug therapies for colon cancer. Currently, nutraceuticals play an increasingly important role in the treatment of various chronic diseases such as colon cancer, diabetes and Alzheimer׳s disease. Nutraceuticals are derived from various natural sources such as medicinal plants, marine organisms, vegetables and fruits. Nutraceuticals have shown the potential to reduce the risk of colon cancer and slow its progression. These dietary substances target different molecular aspects of colon cancer development. Accordingly, this review briefly discusses the medicinal importance of nutraceuticals and their ability to reduce the risk of colorectal carcinogenesis.


title: High-level semantic feature for 3D shape based on deep belief networks

abstract: Deep learning has emerged as a powerful technique to extract high-level features from low-level information, which shows that hierarchical representation can be easily achieved. However, applying deep learning into 3D shape is still a challenge. In this paper, we propose a novel high-level feature learning method for 3D shape retrieval based on deep learning. In this framework, the low-level 3D shape descriptors are first encoded into visual bag-of-words, and then highlevel shape features are generated via deep belief network, which facilitates a good semantic preserving ability for the tasks of shape classification and retrieval. Experiments on 3D shape recognition and retrieval demonstrate the superior performance of the proposed method in comparison to the state-of-the-art methods.


title: Compact LTE frame antenna with a narrow metal clearance and a radiating feed network for the metal-casing smartphone

abstract: A compact LTE frame antenna with a 2-mm narrow metal clearance for the metal-casing smartphone is presented. The antenna requires only a radiating metal frame section of about a half of the short edge of the smartphone. The metal frame section is fed by a radiating feed network, which includes a feed network and two radiating branches. The feed network includes an inductive feed path (L-path), a capacitive feed path (C-path) and a matching circuit. By feeding the metal frame section through the L-path and C-path, a fundamental mode at low band and a dual-resonance mode at high band are generated. Radiating branches offer additional resonant modes to combine with the fundamental mode and the dual-resonance mode contributed by the radiating metal frame section to cover the LTE low band (824–960 MHz) and the LTE high band (1710–2690 MHz). The antenna structure, operation principle, and the experimental results of the proposed antenna are presented.


title: Matrix completion by Truncated Nuclear Norm Regularization

abstract: Estimating missing values in visual data is a challenging problem in computer vision, which can be considered as a low rank matrix approximation problem. Most of the recent studies use the nuclear norm as a convex relaxation of the rank operator. However, by minimizing the nuclear norm, all the singular values are simultaneously minimized, and thus the rank can not be well approximated in practice. In this paper, we propose a novel matrix completion algorithm based on the Truncated Nuclear Norm Regularization (TNNR) by only minimizing the smallest N-r singular values, where N is the number of singular values and r is the rank of the matrix. In this way, the rank of the matrix can be better approximated than the nuclear norm. We further develop an efficient iterative procedure to solve the optimization problem by using the alternating direction method of multipliers and the accelerated proximal gradient line search method. Experimental results in a wide range of applications demonstrate the effectiveness of our proposed approach.


title: SafeMem: exploiting ECC-memory for detecting memory leaks and memory corruption during production runs

abstract: Memory leaks and memory corruption are two major forms of software bugs that severely threaten system availability and security. According to the US-CERT vulnerability notes database, 68% of all reported vulnerabilities in 2003 were caused by memory leaks or memory corruption. Dynamic monitoring tools, such as the state-of-the-art Purify, are commonly used to detect memory leaks and memory corruption. However, most of these tools suffer from high overhead, with up to a 20 times slowdown, making them infeasible to be used for production-runs. This paper proposes a tool called SafeMem to detect memory leaks and memory corruption on-the-fly during production-runs. This tool does not rely on any new hardware support. Instead, it makes a novel use of existing ECC memory technology and exploits intelligent dynamic memory usage behavior analysis to detect memory leaks and corruption. We have evaluated SafeMem with seven real-world applications that contain memory leak or memory corruption bugs. SafeMem detects all tested bugs with low overhead (only 1.6%-14.4%), 2-3 orders of magnitudes smaller than Purify. Our results also show that ECC-protection is effective in pruning false positives for memory leak detection, and in reducing the amount of memory waste (by a factor of 64-74) used for memory monitoring in memory corruption detection compared to page-protection.


title: A DUAL-BAND RF ENERGY HARVESTING USING FREQUENCY LIMITED DUAL-BAND IMPEDANCE MATCHING

abstract: In this paper, a novel dual-band RF-harvesting RF-DC converter with a frequency limited impedance matching network (M/N) is proposed. The proposed RF-DC converter consists of a dual-band impedance matching network, a rectifler circuit with villard structure, a wideband harmonic suppression low-pass fllter (LPF), and a termination load. The proposed dual-band M/N can match two receiving band signals and suppress the out-of-band signals efiectively, so the back-scattered nonlinear frequency components from the nonlinear rectifying diodes to the antenna can be blocked. The fabricated circuit provides the maximum RF-DC conversion e-ciency of 73.76% and output voltage 7.09V at 881MHz and 69.05% with 6.86V at 2.4GHz with an individual input signal power of 22dBm. Moreover, the conversion e-ciency of 77.13% and output voltage of 7.25V are obtained when two RF waves with input dual-band signal power of 22dBm are fed simultaneously.


title: Application of neural networks to biological data mining: a case study in protein sequence classification

abstract: Biological data mining aims to extract signi cant information from DNA, RNA and proteins. The signi cant information may refer to motifs, functional sites, clustering and classi cation rules. This paper presents an example of biological data mining: the classi cation of protein sequences using neural netw orks. We proposenew tec hniques to extract features from protein data and use them in combination with the Ba yesianneural network to classify protein sequences obtained from the PIR protein database maintained at the National Biomedical Research Foundation. T o evaluate the performance of the proposed approach, we compare it with other protein classi ers built based on sequence alignment and machine learning methods. Experimental results sho w the high precision of the proposed classi er and the complementarity of the tools studied in the paper.


title: Conversational Markers of Constructive Discussions

abstract: Group discussions are essential for organizing every aspect of modern life, from faculty meetings to senate debates, from grant review panels to papal conclaves. While costly in terms of time and organization effort, group discussions are commonly seen as a way of reaching better decisions compared to solutions that do not require coordination between the individuals (e.g. voting)---through discussion, the sum becomes greater than the parts. However, this assumption is not irrefutable: anecdotal evidence of wasteful discussions abounds, and in our own experiments we find that over 30% of discussions are unproductive. ::: We propose a framework for analyzing conversational dynamics in order to determine whether a given task-oriented discussion is worth having or not. We exploit conversational patterns reflecting the flow of ideas and the balance between the participants, as well as their linguistic choices. We apply this framework to conversations naturally occurring in an online collaborative world exploration game developed and deployed to support this research. Using this setting, we show that linguistic cues and conversational patterns extracted from the first 20 seconds of a team discussion are predictive of whether it will be a wasteful or a productive one.


title: Establishing the Factors Affecting the Growth of Small and Medium-sized Enterprises in Algeria

abstract: The study presented in this paper analyzes the factors influencing the growth rate of small and medium-sized enterprises (SMEs) in Algeria and explores the extent to which their success or failure depends on the wider business climate. This study also examines different internal factors that may be responsible for the unstable and limited growth ofSMEs. Our research reveals that the growth of SMEs in Algeria is hampered by several interrelated factors, which include business environmental factors that are beyond the SMEs’ control and internal factors of the SMEs. The external factors include the legal and regulatory framework, access to external financing, and human resources capacities. The internal factors comprise entrepreneurial characteristics, management capacities, marketing skills, and technological capacities.


title: Evolving Multimodal Behavior Through Subtask and Switch Neural Networks

abstract: While neuroevolution has been used successfully to discover effective control policies for intelligent agents, it has been difficult to evolve behavior that is multimodal, i.e. consists of distinctly different behaviors in different situations. This article proposes a new method, Modular NeuroEvolution of Augmenting Topologies (ModNEAT), to meet this challenge. ModNEAT decomposes complex tasks into tractable subtasks and utilizes neuroevolution to learn each subtask. Switch networks are evolved with the subtask networks to arbitrate among them and thus combine separate subtask networks into a complete hierarchical policy. Further, the need for new subtask modules is detected automatically by monitoring fitness of the agent population. Experimental results in the machine learning game of OpenNERO showed that ModNEAT outperforms the non-modular rtNEAT in both agent fitness and training efficiency.


title: Design and development of sensor-based mini projects for embedded system laboratory using ARM Cortex-M3(LPC1768)

abstract: In majority of Indian Universities, advanced courses on embedded system design and its related laboratory are not available until the post-graduate level. Early exposure to embedded system design with advanced microcontroller is necessary for computer science and engineering students to face the design challenges in the today's world. This paper presents a mini project-based laboratory for learning embedded system design with different sensors. The aim of this laboratory is to motivate the students to learn the building blocks of embedded systems and control algorithm using the basic hardware and software programming skills provided in this paper, making use of ARM Cortex-M3 processor which is widely used in modern microcontroller products, System on Chip (SoC) and Application Specific Standard Products (ASSP). To enhance the learning process, students are allowed to take this laboratory in three sessions and sensor interfacing projects are provided in the third session. The sensor based projects developed with schematic circuits and software algorithm makes the students to perform all the projects easily and individually. The components including programmer/debugger are inexpensive and can be implemented as take-home projects. It also provides an opportunity to make use as hands-on experience which is then integrated by students to complete their mini project. The feedback from the students shows that most of them were motivated to learn actively all the skills included in the laboratory for embedded system design.


title: Detecting misinformation in online social networks using cognitive psychology

abstract: The paper explores the use of concepts in cognitive psychology to evaluate the spread of misinformation, disinformation and propaganda in online social networks. Analysing online social networks to identify metrics to infer cues of deception will enable us to measure diffusion of misinformation. The cognitive process involved in the decision to spread information involves answering four main questions viz consistency of message, coherency of message, credibility of source and general acceptability of message. We have used the cues of deception to analyse these questions to obtain solutions for preventing the spread of misinformation. We have proposed an algorithm to effectively detect deliberate spread of false information which would enable users to make informed decisions while spreading information in social networks. The computationally efficient algorithm uses the collaborative filtering property of social networks to measure the credibility of sources of information as well as quality of news items. The validation of the proposed methodology has been done on the online social network `Twitter’.


title: A Data Structure for Dynamic Trees

abstract: Abstract A data structure is proposed to maintain a collection of vertex-disjoint trees under a sequence of two kinds of operations: a link operation that combines two trees into one by adding an edge, and a cut operation that divides one tree into two by deleting an edge. Each operation requires O (log n ) time. Using this data structure, new fast algorithms are obtained for the following problems: 1. (1) Computing nearest common ancestors. 2. (2) Solving various network flow problems including finding maximum flows, blocking flows, and acyclic flows. 3. (3) Computing certain kinds of constrained minimum spanning trees. 4. (4) Implementing the network simplex algorithm for minimum-cost flows. The most significant application is (2); an O ( mn log n )-time algorithm is obtained to find a maximum flow in a network of n vertices and m edges, beating by a factor of log n the fastest algorithm previously known for sparse graphs.


title: Chronic insomnia and its negative consequences for health and functioning of adolescents: a 12-month prospective study.

abstract: PURPOSE ::: To estimate prevalence and chronicity of insomnia and the impact of chronic insomnia on health and functioning of adolescents. ::: ::: ::: METHODS ::: Data were collected from 4175 youths 11-17 at baseline and 3134 a year later sampled from managed care groups in a large metropolitan area. Insomnia was assessed by youth-reported DSM-IV symptom criteria. Outcomes are three measures of somatic health, three measures of mental health, two measures of substance use, three measures of interpersonal problems, and three of daily activities. ::: ::: ::: RESULTS ::: Over one-fourth reported one or more symptoms of insomnia at baseline and about 5% met diagnostic criteria for insomnia. Almost 46% of those who reported one or more symptoms of insomnia in Wave 1 continued to be cases at Wave 2 and 24% met DSM-IV symptom criteria for chronic insomnia (cases in Wave 1 were also cases in Wave 2). Multivariate analyses found chronic insomnia increased subsequent risk for somatic health problems, interpersonal problems, psychological problems, and daily activities. Significant odds (p < .05) ranged from 1.6 to 5.6 for poor outcomes. These results are the first reported on chronic insomnia among youths, and corroborate, using prospective data, previous findings on correlates of disturbed sleep based on cross-sectional studies. ::: ::: ::: CONCLUSIONS ::: Insomnia is both common and chronic among adolescents. The data indicate that the burden of insomnia is comparable to that of other psychiatric disorders such as mood, anxiety, disruptive, and substance use disorders. Chronic insomnia severely impacts future health and functioning of youths. Those with chronic insomnia are more likely to seek medical care. These data suggest primary care settings might provide a venue for screening and early intervention for adolescent insomnia.


title: β-Glucosidase excretion by Trichoderma pseudokoningii: Correlation with cell wall bound β-1.3-glucanase activities

abstract: The formation and excretion of β-glucosidase from Trichoderma pseudokoningii was studied during growth on different carbon sources. The enzyme was present under all conditions examined, but increased activity was found during growth on carbon sources favouring slow growth.


title: Line-of-sight guidance for formation control of quadrotor

abstract: In the broad class of unmanned aerial vehicles (UAV), Quadrotor is an important member. Cooperative control of UAVs are currently being researched for a wide range of applications. An active and important research area of cooperative control of multiple UAVs is the formation control. Formation flying is the disciplined flight of two or more aircraft under the command of a flight leader. The main goal is to coordinate a group of UAVs to achieve a desired formation pattern and avoid inter-vehicle collisions at the same time. Formation flight of a team of UAVs has broad applications such as aerial mapping, traffic monitoring, terrain and utilities inspection, search and rescue operations, perimeter planning, disaster monitoring, reconnaissance mission, surveillance etc. Here we propose a Line Of Sight (LOS) guidance based flight formation control strategy for multiple quad rotor UAVs in leader-follower framework. The guidance laws for 2D formation are implemented with the help of LOS angles, between adjacent quadrotors to keep the current quadrotor's position in formation. The full dynamic model of quadrotor incorporating LOS guidance law is considered for designing PID controller and the simulation analysis was done using MATLAB/ Simulink.


title: The sequence memoizer

abstract: Probabilistic models of sequences play a central role in most machine translation, automated speech recognition, lossless compression, spell-checking, and gene identification applications to name but a few. Unfortunately, real-world sequence data often exhibit long range dependencies which can only be captured by computationally challenging, complex models. Sequence data arising from natural processes also often exhibits power-law properties, yet common sequence models do not capture such properties. The sequence memoizer is a new hierarchical Bayesian model for discrete sequence data that captures long range dependencies and power-law characteristics, while remaining computationally attractive. Its utility as a language model and general purpose lossless compressor is demonstrated.


title: High impedance holographic metasurfaces for conformal and high gain antenna applications

abstract: We present simulated and experimental results from a conformal meta-antenna that was designed with a holographic-derived surface impedance for placement on a cylindrical surface. This class of antenna relies on the interaction between a surface wave generated by a simple radiator, e.g., a vertical monopole, and a spatially modulated surface impedance to generate high gain and predictable far-field radiation profiles. The surface impedance profile was formed using a distribution of sub-wavelength isotropic metal patches printed on a thin grounded dielectric substrate. An analysis of the meta-antenna radiative performance on both planar and cylindrical substrate geometries illustrates the importance of appropriately tailoring the surface impedance profile to account for the influence of curvature.


title: The suitability of caffeinated drinks for children: a systematic review of randomised controlled trials, observational studies and expert panel guidelines

abstract: BACKGROUND: The increased availability of caffeinated drinks raises questions about the level of caffeine that is appropriate for children, as well as the benefits and risks associated with their consumption. METHODS: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this systematic review evaluates evidence from randomised controlled trials investigating the effects of caffeine on cognition, behaviour, mood and exercise performance in children. Observational studies and expert panel guidelines are also discussed. RESULTS: One hundred and nine studies were found, with 11 randomised controlled trials and 13 observational studies meeting the criteria. High caffeine intakes (e.g. >5 mg kg(-1) body weight day(-1) ) were associated with an increased risk of anxiety and withdrawal symptoms. However, smaller amounts were not linked with such effects and may benefit cognitive function and sports performance based on adult studies. The evidence suggests that children and adolescents should limit daily caffeine consumption to 2.5 mg kg(-1) body weight day(-1) , equating to one or two cups of tea or one small cup of coffee. Lower contributors of caffeine, such as tea, may be more appropriate for children because they contribute to daily fluid intakes and provide flavonoids. By contrast, caffeinated soft drinks may be less suitable options for children as a result of their acidity, higher caffeine content, presence of added sugar (in some cases) and absence of bioactive compounds. CONCLUSIONS: More studies are needed to determine the intakes that represent a risk and whether there may be benefits for alertness and sports performance with moderate intakes of caffeine. Language: en


title: Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

abstract: Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.


title: Learning Inputs in Greybox Fuzzing

abstract: Greybox fuzzing is a lightweight testing approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by complex checks. In this paper, we present a technique that extends greybox fuzzing with a method for learning new inputs based on already explored program executions. These inputs can be learned such that they guide exploration toward specific executions, for instance, ones that increase path coverage or reveal vulnerabilities. We have evaluated our technique and compared it to traditional greybox fuzzing on 26 real-world benchmarks. In comparison, our technique significantly increases path coverage (by up to 3X) and detects more bugs (up to 38% more), often orders-of-magnitude faster.


title: Deep learning at the shallow end: Malware classification for non-domain experts

abstract: Abstract Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.


title: Implementation and Evaluation of Different Parallel Designs of AES Using CUDA

abstract: The Advanced Encryption Standard(AES) is used in security areas widely now. However, there is still a large room for further improvement of its execution efficiency. Since the graphics processing unit(GPU) with potent ability of parallel computing has been applied in general purpose of computation, people have tried to use it to faster execution time in various cryptographic algorithms. This paper discusses how the performance of CBCAES decryption based on GPU is influenced by 4 key parameters that include the size of input data, the number of threads per block, memory allocation style and parallel granularity. Further more, we compare the performance of AES on GPU to that of standard AES, AES-NI and find that when the size of input data is different, the implementations with different parameters setting achieve the best performance. So we provide several advices about how to implement CBC-AES on GPU aiming at different size of input data. In particular, our best performance of experiments on GPU(NVIDIA Tesla K40m) is about 112 times faster than the implementation of AES on CPU (Intel Xeon E5-2650) by using our optimization method.


title: The Screening and Diagnosis of Autistic Spectrum Disorders 1

abstract: The Child Neurology Society and American Academy of Neurology recently proposed to formulate Practice Parameters for the Diagnosis and Evaluation of Autism for their memberships. This endeavor was expanded to include representatives from nine professional organizations and four parent organizations, with liaisons from the National Institutes of Health. This document was written by this multidisciplinary Consensus Panel after systematic analysis of over 2,500 relevant scientific articles in the literature. The Panel concluded that appropriate diagnosis of autism requires a dual-level approach: (a) routine developmental surveillance, and (b) diagnosis and evaluation of autism. Specific detailed recommendations for each level have been established in this document, which are intended to improve the rate of early suspicion and diagnosis of, and therefore early intervention for, autism.


title: Toward Monitoring Parkinson's Through Analysis of Static Handwriting Samples: A Quantitative Analytical Framework

abstract: Detection of changes in micrographia as a manifestation of symptomatic progression or therapeutic response in Parkinson's disease (PD) is challenging as such changes can be subtle. A computerized toolkit based on quantitative analysis of handwriting samples would be valuable as it could supplement and support clinical assessments, help monitor micrographia, and link it to PD. Such a toolkit would be especially useful if it could detect subtle yet relevant changes in handwriting morphology, thus enhancing resolution of the detection procedure. This would be made possible by developing a set of metrics sensitive enough to detect and discern micrographia with specificity. Several metrics that are sensitive to the characteristics of micrographia were developed, with minimal sensitivity to confounding handwriting artifacts. These metrics capture character size-reduction, ink utilization, and pixel density within a writing sample from left to right. They are used here to “score” handwritten signatures of 12 different individuals corresponding to healthy and symptomatic PD conditions, and sample control signatures that had been artificially reduced in size for comparison purposes. Moreover, metric analyses of samples from ten of the 12 individuals for which clinical diagnosis time is known show considerable informative variations when applied to static signature samples obtained before and after diagnosis. In particular, a measure called pixel density variation showed statistically significant differences ( $p ) between two comparison groups of remote signature recordings: earlier versus recent, based on independent and paired t -test analyses on a total of 40 signature samples. The quantitative framework developed here has the potential to be used in future controlled experiments to study micrographia and links to PD from various aspects, including monitoring and assessment of applied interventions and treatments. The inherent value in this methodology is further enhanced by its reliance solely on static signatures, not requiring dynamic sampling with specialized equipment.


title: A novel bubble tolerant thermometer-to-binary encoder for flash A/D converter

abstract: A thermometer-to-binary encoder, which is suitable for high-speed and low-resolution flash-ADC, is proposed. The encoding scheme can effectively reduce the bubble induced error, by the use of separated one-out-of-N circuit for each bit of binary codes. In comparison with the most commonly adopted Gray-ROM encoder, the proposed method can outperform it by a factor of 2 in the bubble error reduction. The long latency gray-to-binary encoder which limits the total conversion speed can also be removed from the proposed circuit. Two types of implementation circuit including a ROM-based encoder and a fully digital encoder are developed.