title: Low-Profile Two-Degree-of-Freedom Wrist Exoskeleton Device Using Multiple Spring Blades

text: Robotically assisted rehabilitation therapy, which is therapy performed using a robotic device that guides or assists motion and in some cases triggered by a biological signal (such as a myoelectric signal or brain signal) is effective in recovering motor function following impairment. In the field of rehabilitation robots and human support robot, exoskeleton robots have been widely studied both for upper and lower limbs. Applying the exoskeleton to a wrist joint remains challenging because the wrist has two degrees of freedom in a structure smaller than the structures of other upper and lower limb joints and the center of rotation moves according to motion owing to the complex anatomical structure. To tackle this problem, we developed a wrist exoskeleton mechanism that consists of two elastic elements that largely deform during motion in transmitting forces from two linear actuators implemented on the user's forearm and in transforming the forces to wrist flexion/extension and adduction/abduction. The advantages of the presented device are the device's compactness, low weight, and inherent flexibility due to the elastic structure. The present letter presents an overview of the newly developed wrist exoskeleton mechanism, prototype implementation, and preliminary mechanical evaluations. The technical verification of the exoskeleton revealed the feasibility of generating forces that can generate wrist flexion/extension and adduction/abduction movements.


title: An efficient message access quality model in vehicular communication networks

text: In vehicular ad hoc network (VANET), vehicles equipped with computing, sensing, and communication capabilities can exchange information within a geographical area to distribute emergency messages and achieve safety system. Then how to enforce fine grained control of these messages and ensure the receiving messages coming from the claimed source in such a highly dynamic environments remains a key challenge that affects the quality of service. In this paper, we propose a hierarchical access control with authentication scheme for transmitted messages with security assurance over VANET. By extending ciphertext-policy attribute-based encryption (CP-ABE) with a hierarchical structure of multiple authorities, the scheme not only achieves scalability due to its hierarchical structure, but also inherits fine-grained access control on the transmitted messages. Also by exploiting attribute-based signature (ABS), the scheme can authorize the vehicles that can most appropriately deal with the message efficiently. The results of efficiency analysis and comparison with the related works show that the proposed scheme is efficient and scalable in dealing with access control and message authentication for data dissemination in VANET. & 2014 Elsevier B.V. All rights reserved.


title: Modelling of Parasitic Capacitances for Single-Gate, Double-Gate and Independent Double-Gate MOSFET

text: This paper discusses the type of capacitances for Single Gate MOSFET and Double Gate MOSFET including their quantity. The effect of parasitic capacitance makes double gate MOSFET more suitable component for the designing of digital logic switches than single gate MOSFET. Here, we introducing Independent double gate MOSFET operation based on VeSFET concept. Then introducing with the total capacitance model of Independent double gate MOSFET and compared its performance parameters with double gate MOSFET and the single gate MOSFET. Double gate transistor circuit is the first choice for reduction of short channel effect in application of MOSFET. The basic advantage of double gate MOSFET is its area required. But design CMOS double gate transistor for AND functionality suffering from high leakage current; while using Independent double gate MOSFET based on VeSFET concept reduction of that leakage current is possible. So, we can easily implement logic circuits while using CMOS design based on Independent double gate MOSFET which gives high performance.


title: Deep Relative Attributes

text: Visual attributes are great means of describing images or scenes, in a way both humans and computers understand. In order to establish a correspondence between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand-crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the applicability of those methods for more realistic cases. We introduce a deep neural network architecture for the task of relative attribute prediction. A convolutional neural network (ConvNet) is adopted to learn the features by including an additional layer (ranking layer) that learns to rank the images based on these features. We adopt an appropriate ranking loss to train the whole network in an end-to-end fashion. Our proposed method outperforms the baseline and state-of-the-art methods in relative attribute prediction on various coarse and fine-grained datasets. Our qualitative results along with the visualization of the saliency maps show that the network is able to learn effective features for each specific attribute. Source code of the proposed method is available at https://github.com/yassersouri/ghiaseddin.


title: A 330nA energy-harvesting charger with battery management for solar and thermoelectric energy harvesting

text: Harvesting energy from ambient energy sources such as solar and thermal gradients is one solution to address the dramatic increase in energy consumption of personal electronics. In this paper, an ultra low quiescent current charger and battery management IC that can efficiently extract energy from solar panels and thermoelectric generators to charge batteries and super capacitors is presented. While previous works on energy harvesting [1-5] report efficient DC-DC converters to extract the energy from the harvester, not all of them provide complete battery management functionality for various chemistries. Also, maximum power point tracking (MPPT) which is critical in energy harvesting applications is not provided in some of the published work. In this paper, a charger and battery management IC with 330nA quiescent current is presented. The IC can cold start from 330mV and 5μW of input power. The charger achieves efficiency greater than 80% at single cell solar voltages of 0.5V. A low quiescent current battery management architecture involving sampled circuits, sub regulated rails and clock gating is demonstrated.


title: On the Design and Scalability of Distributed Shared-Data Databases

text: Database scale-out is commonly implemented by partitioning data across several database instances. This approach, however, has several restrictions. In particular, partitioned databases are inflexible in large-scale deployments and assume a partition-friendly workload in order to scale. In this paper, we analyze an alternative architecture design for distributed relational databases that overcomes the limitations of partitioned databases. The architecture is based on two fundamental principles: We decouple query processing and transaction management from data storage, and we share data across query processing nodes. The combination of these design choices provides scalability, elasticity, and operational flexibility without making any assumptions on the workload. As a drawback, sharing data among multiple database nodes causes synchronization overhead. To address this limitation, we introduce techniques for scalable transaction processing in shared-data environments. Specifically, we describe mechanisms for efficient data access, concurrency control, and data buffering. In combination with new hardware trends, the techniques enable performance characteristics that top state-of-the-art partitioned databases.


title: High-gain 60-GHz patch antenna array using GaN MMIC technology

text: In this paper, a high-gain 2×2 antenna array at 60 GHz using GaN MMIC process technology is presented. For gain enhancement, a modified patch antenna element with two parasitic strips on each non-radiation edge and a parasitic patch on one of the radiation edges is proposed. This antenna element achieves a gain of 3.42 dBi at 60 GHz, about 2 dBi higher than a conventional patch antenna element using the same process technology. The 2×2 antenna array consisting of four modified patch antenna element exhibits a 10-dB impedance bandwidth of 10.1% and a peak gain of 5.7 dBi at 60 GHz.


title: Increasing psychological well-being and resilience by psychotherapeutic methods.

text: A specific psychotherapeutic strategy for increasing psychological well-being and resilience, well-being therapy, has been developed and validated in a number of randomized controlled trials. The findings indicate that flourishing and resilience can be promoted by specific interventions leading to a positive evaluation of one's self, a sense of continued growth and development, the belief that life is purposeful and meaningful, the possession of quality relations with others, the capacity to manage effectively one's life, and a sense of self-determination. A decreased vulnerability to depression and anxiety has been demonstrated after well-being therapy in high-risk populations. There are important implications for the state/trait dichotomy in psychological well-being and for the concept of recovery in mood and anxiety disorders.


title: MIMO for ATSC 3.0

text: This paper provides an overview of the optional multiple-input multiple-output (MIMO) antenna scheme adopted in ATSC 3.0 to improve robustness or increase capacity via additional spatial diversity and multiplexing by sending two data streams in a single radio frequency channel. Although it is not directly specified, it is expected in practice to use cross-polarized 2×2 MIMO (i.e., horizontal and vertical polarization) to retain multiplexing capabilities in line-of-sight conditions. MIMO allows overcoming the channel capacity limit of single antenna wireless communications in a given channel bandwidth without any increase in the total transmission power. But in the U.S. MIMO can actually provide a larger comparative gain because it would be allowed to increase the total transmit power, by transmitting the nominal transmit power in each polarization. Hence, in addition to the MIMO gains (array, diversity, and spatial multiplexing), MIMO could exploit an additional 3 dB power gain. The MIMO scheme adopted in ATSC 3.0 re-uses the single-input single-output antenna baseline constellations, and hence it introduces the use of MIMO with non-uniform constellations.


title: Adversarial support vector machine learning

text: Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it is important to model the adversary's attack strategy and develop robust learning models to mitigate the attack. These are the two objectives of this paper. We consider two attack models: a free-range attack model that permits arbitrary data corruption and a restrained attack model that anticipates more realistic attacks that a reasonable adversary would devise under penalties. We then develop optimal SVM learning strategies against the two attack models. The learning algorithms minimize the hinge loss while assuming the adversary is modifying data to maximize the loss. Experiments are performed on both artificial and real data sets. We demonstrate that optimal solutions may be overly pessimistic when the actual attacks are much weaker than expected. More important, we demonstrate that it is possible to develop a much more resilient SVM learning model while making loose assumptions on the data corruption models. When derived under the restrained attack model, our optimal SVM learning strategy provides more robust overall performance under a wide range of attack parameters.


title: New Algorithms for SIMD Alignment

text: Optimizing programs for modern multiprocessor or vector platforms is a major important challenge for compilers today. In this work, we focus on one challenging aspect: the SIMD ALIGNMENT problem. Previously, only heuristics were used to solve this problem, without guarantees on the number of shifts in the obtained solution. We study two interesting and realistic special cases of the SIMD ALIGNMENT problem and present two novel and efficient algorithms that provide optimal solutions for these two cases. The new algorithms employ dynamic programming and a MIN-CUT/MAX-FLOW algorithm as subroutines. We also discuss the relation between the SIMD ALIGNMENT problem and the MULTIWAY CUT and NODE MULTIWAY CUT problems; and we show how to derive an approximated solution to the SIMD ALIGNMENT problem based on approximation algorithms to these two known problems.


title: Toward normative expert systems: Part I. The Pathfinder project.

text: Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymph-node diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this article, we describe Pathfinder and our research in uncertain-reasoning paradigms that was stimulated by the development of the program. We discuss limitations with early decision-theoretic methods for reasoning under uncertainty and our initial attempts to use non-decision-theoretic methods. Then, we describe experimental and theoretical results that directed us to return to reasoning methods based in probability and decision theory.


title: Field and Torque Calculation and Transient Analysis in Variable Reluctance Machines

text: The variable reluctance machine (VRM), also known as the switched reluctance machine, is a class of electrical machines that exhibits many interesting characteristics. However, the satisfactory analytical formulation of its working equations (torque and voltage equations) in a saturated regime is not yet completely available for covering some of its operational aspects, such as the analysis of transient state. This paper provides such equations for the general case in which a VRM operates with a pronounced level of magnetic saturation. These equations together with help of lookup tables allow online access of the instant torque on the shaft as well as the flux linkage of a phase, since the computation times are reduced to a few microseconds, whereas in a numerical approach such as the finite-element method, would take a few minutes of CPU time, which is totally inappropriate for such investigations.


title: Interactions between auditory and dorsal premotor cortex during synchronization to musical rhythms

text: When listening to music, we often spontaneously synchronize our body movements to a rhythm's beat (e.g. tapping our feet). The goals of this study were to determine how features of a rhythm such as metric structure, can facilitate motor responses, and to elucidate the neural correlates of these auditory-motor interactions using fMRI. Five variants of an isochronous rhythm were created by increasing the contrast in sound amplitude between accented and unaccented tones, progressively highlighting the rhythm's metric structure. Subjects tapped in synchrony to these rhythms, and as metric saliency increased across the five levels, louder tones evoked longer tap durations with concomitant increases in the BOLD response at auditory and dorsal premotor cortices. The functional connectivity between these regions was also modulated by the stimulus manipulation. These results show that metric organization, as manipulated via intensity accentuation, modulates motor behavior and neural responses in auditory and dorsal premotor cortex. Auditory-motor interactions may take place at these regions with the dorsal premotor cortex interfacing sensory cues with temporally organized movement.


title: Vision Assisted SCARA Manipulator Design and Control Using Arduino and LabVIEW

text: Using vision systems with SCARA robotic manipulators is beneficial in many industrial applications specially in automated object picking and placing tasks. In this paper, the equations of forward and inverse kinematics of a SCARA manipulator are derived using D-H parameters convention. The mechanical structure, as well as the electrical, electronic and pneumatic components of the manipulator were designed and developed. The manipulator's control software, animation and simulation were developed using LabVIEW and Arduino. In addition to that, a vision system was developed and integrated with the manipulator system where a mobile phone camera was used to acquire images, then transmit them wirelessly to LabVIEW's Vision Assistant codes for processing and calibration. The developed vision system allowed detection and calculation of object positions within the manipulator's workspace. Finally, the manipulator's positioning errors were determined and discussed based on a number of repeatability experiments.


title: Students' Misconceptions and Other Difficulties in Introductory Programming: A Literature Review

text:
Efforts to improve computer science education are underway, and teachers of computer science are challenged in introductory programming courses to help learners develop their understanding of programming and computer science. Identifying and addressing students’ misconceptions is a key part of a computer science teacher's competence. However, relevant research on this topic is not as fully developed in the computer science education field as it is in mathematics and science education. In this article, we first review relevant literature on general definitions of misconceptions and studies about students’ misconceptions and other difficulties in introductory programming. Next, we investigate the factors that contribute to the difficulties. Finally, strategies and tools to address difficulties including misconceptions are discussed.
 Based on the review of literature, we found that students exhibit various misconceptions and other difficulties in syntactic knowledge, conceptual knowledge, and strategic knowledge. These difficulties experienced by students are related to many factors including unfamiliarity of syntax, natural language, math knowledge, inaccurate mental models, lack of strategies, programming environments, and teachers’ knowledge and instruction. However, many sources of students’ difficulties have connections with students’ prior knowledge. To better understand and address students’ misconceptions and other difficulties, various instructional approaches and tools have been developed. Nevertheless, the dissemination of these approaches and tools has been limited. Thus, first, we suggest enhancing the dissemination of existing tools and approaches and investigating their long-term effects. Second, we recommend that computing education research move beyond documenting misconceptions to address the development of students’ (mis)conceptions by integrating conceptual change theories. Third, we believe that developing and enhancing instructors’ pedagogical content knowledge (PCK), including their knowledge of students’ misconceptions and ability to apply effective instructional approaches and tools to address students’ difficulties, is vital to the success of teaching introductory programming.


title: A 0.2–3.6-GHz 10-dBm B1dB 29-dBm IIP3 Tunable Filter for Transmit Leakage Suppression in SAW-Less 3G/4G FDD Receivers

text: A tunable N-path filter with more than one decade tuning range is demonstrated to address transmit (TX) leakage in a surface-acoustic-wave-less diversity path receiver for frequency-division duplexing cellular systems. The proposed filter simultaneously creates a reject band and passband anywhere from 0.2 to 3.6 GHz to suppress TX leakage while minimizing insertion loss of the receive signal and is implemented in 45-nm CMOS silicon-on-insulator. Measurements show the 3-dB bandwidth of the passband is greater than 80 MHz with an independently tunable reject band, providing the ultimate rejection is from 33 to 41 dB while the passband insertion loss is between 2.6 dB and 4.3 dB over the tuning range. The proposed filter offers 29-dBm out-of-band (OOB) third-order input-intercept power (IIP3) and 22-dBm in-band (IB) IIP3 with a 10-dBm blocker 1-dB compression point (B1dB). To the authors' knowledge, this is the highest B1dB, IB IIP3, and OOB IIP3 for a CMOS tunable filter.


title: A Li-Ion Battery Charger With Smooth Control Circuit and Built-In Resistance Compensator for Achieving Stable and Fast Charging

text: A built-in resistance compensator (BRC) technique is presented to speed up the charging time of a lithium-ion battery. A smooth control circuit (SCC) is proposed to ensure the stable transition from the constant-current (CC) to the constant-voltage (CV) stage. Due to the external parasitic resistance of the Li-ion battery-pack system, the charger circuit switches from the CC to the CV stage without fully charging the cell. The BRC technique dynamically estimates the external resistance to extend the CC stage. The experimental results show that the period of the CC stage can be extended to 40% of that of the original design. The charging time is effectively reduced.


title: Fuzzy-controlled Li-ion battery charge system with active state-of-charge controller

text: A fuzzy-controlled active state-of-charge controller (FC-ASCC) for improving the charging behavior of a lithium–ion (Li–ion) battery is proposed. The proposed FC-ASCC is designed to replace the general constant-voltage charging mode by two kinds of modes: sense and charge. A fuzzy-controlled algorithm is built with the predicted charger performance to program the charging trajectory faster and to remain the charge operation in a proposed safe-charge area (SCA). A modeling work is conducted for analyzing and describing the Li–ion battery in charging process. A three-dimensional Y-mesh diagram for describing the charging trajectories of the proposed FC charger is simulated. A prototype of a Li–ion battery charger with FC-ASCC is simulated and realized to assess the predicted charging performance. Experiment shows that the charging speed of the proposed FC charger compared with the general one increases about 23% and the charger can safely work in the SCA.


title: Advanced lithium ion battery charger

text: The requirements for state-of-charge and voltage control for lithium ion batteries are reviewed. Strategies for controlling the state-of-charge of the individual Li-ion cells that comprise a battery are described. The design and test results for several of these charge control strategies are presented.


title: Accurate, Compact, and Power-Efficient Li-Ion Battery Charger Circuit

text: A novel, accurate, compact, and power-efficient lithium-ion (Li-Ion) battery charger designed to yield maximum capacity, cycle life, and therefore runtime is presented and experimentally verified. The proposed charger uses a diode to smoothly (i.e., continuously) transition between two high-gain linear feedback loops and control a single power MOS device, automatically charging the battery with constant current and then constant voltage. An adaptive power-efficient charging scheme in the form of a cascaded switching regulator supply ensures the voltage across the charging power-intensive pMOS remains low, thereby reducing its power losses and yielding up to 27% better overall power efficiency. An 83% power-efficient printed circuit board prototype was built and used to charge several Li-Ion batteries to within plusmn0.43% of their optimum full-charge voltage and therefore within a negligibly small fraction of their full capacity


title: Secure data transfer in IoT environment: Adopting both cryptography and steganography techniques

text: Owing to the unprecedented growth in computing power, electronics miniaturization and mobile and wireless network interconnections the internet has metamorphosed into Internet of Things (IoT) which refers to next stage of the information revolution whose context involves billions of individuals and devices interconnected to facilitate exchange of huge volume of data and information from diverse locations, demanding the consequent necessity for smart data aggregation followed by an increased obligation to index, hoard and process data with higher efficiency and effectiveness. But along with its myriad offered benefits and applications, emerges a novel complexity aspect in terms of many inherent hassles primarily security concerns during data transfer phases in IoT covering mostly data confidentiality and integrity features. Thus to enhance safe data transfer in smart IoT environment, a security scheme is proposed in this paper which addresses both the aforesaid issues, employing an integrated approach of lightweight cryptography and steganography (Simple LSB Substitution) technique during data transfer between IoT device and Home Server and adoption of combined approach of cryptography and steganography (Proposed MSB-LSB Substitution) technique during data transfer phases between Home Server and Cloud Servers.


title: Providing device integration with OPC UA

text: OPC UA allows the company wide access and integration of information. One important part of this information system is the automation system and its automation devices. The important issue for presenting devices in such an enterprise system is standardization of device representation. This document provides a proposal on how to standardize the presentation of devices based on a comparison of existing device integration technologies.


title: Seizure classification in EEG signals utilizing Hilbert-Huang transform

text:
BACKGROUND
Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals.

METHOD
Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering.

RESULTS
The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use.

CONCLUSION
An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.


title: Transfer Learning in Multi-Agent Reinforcement Learning Domains

text: Transfer learning refers to the process of reusing knowledge from past tasks in order to speed up the learning procedure in new tasks. In reinforcement learning, where agents often require a considerable amount of training, transfer learning comprises a suitable solution for speeding up learning. Transfer learning methods have primarily been applied in single-agent reinforcement learning algorithms, while no prior work has addressed this issue in the case of multi-agent learning. This work proposes a novel method for transfer learning in multi-agent reinforcement learning domains. We test the proposed approach in a multiagent domain under various setups. The results demonstrate that the method helps to reduce the learning time and increase the asymptotic performance.


title: Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons

text: This paper introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon, and compares it with five existing lexicons: Hu & Liu Opinion Lexicon, MPQA Subjectivity Lexicon, General Inquirer, NRC Word-Sentiment Association Lexicon and SO-CAL lexicon. The effectiveness of the sentiment lexicons for sentiment categorization at the document-level and sentence-level was evaluated using an Amazon product review dataset and a news headlines dataset. WKWSCI, MPQA, Hu & Liu and SO-CAL lexicons are equally good for product review sentiment categorization, obtaining accuracy rates of 75% to 77% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu & Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words, for both document-level and sentencelevel sentiment categorization. The WKWSCI lexicon obtained the best accuracy of 69% on the news headlines sentiment categorization task, and the sentiment strength values obtained a Pearson correlation of 0.57 with human assigned sentiment values. It is recommended that the Hu & Liu lexicon be used for product review texts, and the WKWSCI lexicon for non-review texts.


title: Bluetooth positioning using RSSI and triangulation methods

text: Location based services are the hottest applications on mobile devices nowadays and the growth is continuing. Indoor wireless positioning is the key technology to enable location based services to work well indoors, where GPS normally could not work. Bluetooth has been widely used in mobile devices like phone, PAD etc. therefore Bluetooth based indoor positioning has great market potential. Radio Signal Strength (RSS) is a key parameter for wireless positioning. New Bluetooth standard (since version 2.1) enables RSS to be discovered without time consuming pre-connection. In this research, general wireless positioning technologies are firstly analysed. Then RSS based Bluetooth positioning using the new feature is studied. The mathematical model is established to analyse the relation between RSS and the distance between two Bluetooth devices. Three distance-based algorithms are used for Bluetooth positioning: Least Square Estimation, Three-border and Centroid Method. Comparison results are analysed and the ways to improve the positioning accuracy are discussed.


title: Parent and family impact of autism spectrum disorders: a review and proposed model for intervention evaluation.

text: Raising a child with an autism spectrum disorder (ASD) can be an overwhelming experience for parents and families. The pervasive and severe deficits often present in children with ASD are associated with a plethora of difficulties in caregivers, including decreased parenting efficacy, increased parenting stress, and an increase in mental and physical health problems compared with parents of both typically developing children and children with other developmental disorders. In addition to significant financial strain and time pressures, high rates of divorce and lower overall family well-being highlight the burden that having a child with an ASD can place on families. These parent and family effects reciprocally and negatively impact the diagnosed child and can even serve to diminish the positive effects of intervention. However, most interventions for ASD are evaluated only in terms of child outcomes, ignoring parent and family factors that may have an influence on both the immediate and long-term effects of therapy. It cannot be assumed that even significant improvements in the diagnosed child will ameliorate the parent and family distress already present, especially as the time and expense of intervention can add further family disruption. Thus, a new model of intervention evaluation is proposed, which incorporates these factors and better captures the transactional nature of these relationships.


title: Marijuana Use in the Elderly: Implications and Considerations.

text:
OBJECTIVE
This article reviews the literature on the use of marijuana in the elderly. Pharmacists play an important role in the management of medications including drug use of potentially illegal drugs, including marijuana. The use of both recreational and medical marijuana has grown exponentially in the general population, including in older adults. As of 2017, marijuana for medical use is legal in 26 states and the District of Columbia.

DATA SOURCES
PubMed and Internet search using the following terms: marijuana, cannabis, delta-9-tetrhydrocannabinol (THC), cannabidiol, cannabinoid, elderly, geriatric, and pharmacology. Findings are based on data collected from older adults (65 years of age and older) through August 2016.

STUDY SELECTION
Because of the lack of research and funding, reputable literature on the impact of marijuana on older adults is scarce. The available evidence suggests that elderly individuals should be cautious when consuming marijuana, especially those who have certain comorbid conditions.

DATA EXTRACTION
The geriatric population has a higher likelihood of having multiple comorbidities and is subject to polypharmacy. Marijuana use, medicinal or recreational, complicates the picture with additive central nervous system side effects.

DATA SYNTHESIS
This article reviews the growing information on marijuana use and discusses issues to consider and cautions in usage that can apply to day-to-day clinical practice and geriatric care. The role of the pharmacist in educating patients, caregivers, and health care providers is expanding with the growing number of states that have legalized medical marijuana (26 states and the District of Columbia, as of 2017). Important education points including drug-drug interactions, drug-disease interactions, and signs and symptoms of acute overdose should be considered.

CONCLUSION
With this review, pharmacists will be informed on recommendations on the use of marijuana in the older adult. Monitoring of therapy, as well as adverse effects, will be reviewed, including some legal issues and challenges.


title: ‘Internet Addiction’: A Critical Review

text: It has been alleged by some academics that excessive Internet use can be pathological and addictive. This paper reviews what is known from the empirical literature on ‘Internet addiction’ and its derivatives (e.g., Internet Addiction Disorder, Pathological Internet Use, etc.) and assesses to what extent it exists. Empirical research into ‘Internet addiction’ can roughly be divided into five areas: (1) survey studies that compare excessive Internet users with non-excessive users, (2) survey studies that have examined vulnerable groups of excessive Internet use, most notably students, (3) studies that examine the psychometric properties of excessive Internet use, (4) case studies of excessive Internet users and treatment case studies, and (5) correlational studies examining the relationship of excessive Internet use with other behaviours (e.g., psychiatric problems, depression, self-esteem, etc.). Each of these areas is reviewed. It is concluded that if ‘Internet addiction’ does indeed exist, it affects a relatively small percentage of the online population. However, exactly what it is on the Internet that they are addicted to still remains unclear.


title: Title Design and realization of a smart battery management system

text: Battery management system (BMS) emerges a decisive system component in battery-powered applications, such as (hybrid) electric vehicles and portable devices. However, due to the inaccurate parameter estimation of aged battery cells and multi-cell batteries, current BMSs cannot control batteries optimally, and therefore affect the usability of products. In this paper, we proposed a smart management system for multi-cell batteries, and discussed the development of our research study in three directions: i) improving the effectiveness of battery monitoring and current sensing, ii) modeling the battery aging process, and iii) designing a self-healing circuit system to compensate performance variations due to aging and other variations.


title: ModSpec: An open, flexible specification framework for multi-domain device modelling

text: We describe ModSpec, a MATLAB/Octave based specification format suitable for modelling devices across a wide variety of application domains, including circuits, optics, fluidics and biology. The ModSpec format and associated API are centered around describing the nonlinear differential equations at the core of any device model. The format is open, general and easy to use, and is supported by toolchains that translate and automatically differentiate models, set up equations for systems of interacting devices, and provide simulation facilities. We illustrate the use of ModSpec for modelling semiconductor, photovoltaic, fluidic and neuronal devices and systems.


title: An efficient IP address lookup algorithm based on a small balanced tree using entry reduction

text: Due to a tremendous increase in internet traffic, backbone routers must have the capability to forward massive incoming packets at several gigabits per second. IP address lookup is one of the most challenging tasks for high-speed packet forwarding. Some high-end routers have been implemented with hardware parallelism using ternary content addressable memory (TCAM). However, TCAM is much more expensive in terms of circuit complexity as well as power consumption. Therefore, efficient algorithmic solutions are essentially required to be implemented using network processors as low cost solutions. Among the state-of-the-art algorithms for IP address lookup, a binary search based on a balanced tree is effective in providing a low-cost solution. In order to construct a balanced search tree, the prefixes with the nesting relationship should be converted into completely disjointed prefixes. A leaf-pushing technique is very useful to eliminate the nesting relationship among prefixes [V. Srinivasan, G. Varghese, Fast address lookups using controlled prefix expansion, ACM Transactions on Computer Systems 17 (1) (1999) 1–40]. However, it creates duplicate prefixes, thus expanding the search tree. This paper proposes an efficient IP address lookup algorithm based on a small balanced tree using entry reduction. The leaf-pushing technique is used for creating the completely disjointed entries. In the leaf-pushed prefixes, there are numerous pairs of adjacent prefixes with similarities in prefix strings and output ports. The number of entries can be significantly reduced by the use of a new entry reduction method which merges pairs with these similar prefixes. After sorting the reduced disjointed entries, a small balanced tree is constructed with a very small node size. Based on this small balanced tree, a native binary search can be effectively used in address lookup issue. In addition, we propose a new multi-way search algorithm to improve a binary search for IPv4 address lookup. As a result, the proposed algorithms offer excellent lookup performance along with reduced memory requirements. Besides, these provide good scalability for large amounts of routing data and for the address migration toward IPv6. Using both various IPv4 and IPv6 routing data, the performance evaluation results demonstrate that the proposed algorithms have better performance in terms of lookup speed, memory requirement and scalability for the growth of entries and IPv6, as compared with other algorithms based on a binary search. 2011 Elsevier B.V. All rights reserved. . All rights reserved. x: +82 2 313 8053.


title: Happiness Runs in a Circular Motion : Evidence for a Positive Feedback Loop between Prosocial Spending and Happiness

text: We examine whether a positive feedback loop exists between spending money on others (i.e. prosocial spending) and happiness. Participants recalled a previous purchase made for either themselves or someone else and then reported their happiness. Afterward, participants chose whether to spend a monetary windfall on themselves or someone else. Participants assigned to recall a purchase made for someone else reported feeling significantly happier immediately after this recollection; most importantly, the happier participants felt, the more likely they were to choose to spend a windfall on someone else in the near future. Thus, by providing initial evidence for a positive feedback loop between prosocial spending and well-being, these data offer one potential path to sustainable happiness: prosocial spending increases happiness which in turn encourages prosocial spending.


title: Accessory Axillary Breast Tissues

text: Dr. Emsen recently published a case report describing a patient with accessory axillary breast tissue treated with ultrasound-guided liposuction [3]. We recently treated a similar case of bilateral axillary breast tissue, and we offer our contribution to the debate (Figs. 1, 2). Accessory axillary breast tissues are common embriogenic alterations found in 1% to 6% of women that often manifest bilaterally [1,2]. Ectopic breast tissue is at risk for benign and malignant breast disease with reported diagnoses of fibrocystic disease, mastitis, fibroadenoma, atypical hyperplasia, and carcinoma [4 6]. In a review of 82 ectopic breast cancer cases, Marshall et al. [5] found an increased incidence of cancer in aberrant breast tissue. Ectopic cancers located in the axilla seemed to present with more extensive disease at an earlier age, suggesting that aberrant breast tissue may be at increased risk for malignant change. According to this view, as for any ectopic mass, tissue diagnosis is required and mandatory [4]. We recently treated a 43-year-old woman who was asymptomatic until her second pregnancy (2004), when two bilateral masses appeared in axillae and began to increase in size. Six months after parturition, a clear fluid was continuously secreted throughout few small pores on the skin. Ultrasounddiagnosed breast tissue and excision with histologic examination confirmed the radiologic findings. In this case, we preoperatively contemplated, as Dr. Emsen did, less invasive and aesthetic methods such as liposuction. However, a collegial discussion with our pathologist and an analysis of literature data suggested en bloc removal for a more precise analysis not only of cellular, but also of histologic characteristics of suspected mammary ducts. We believe that liposuction, more traumatic on tissues than simple excision, does not allow for this. Furthermore, although liposuction leaves minimal incision scars, the Fig. 1. A 43-year-old woman. Right axilla with accessory breast tissue.


title: 2D ear classification based on unsupervised clustering

text: Ear classification refers to the process by which an input ear image is assigned to one of several pre-defined classes based on a set of features extracted from the image. In the context of large-scale ear identification, where the input probe image has to be compared against a large set of gallery images in order to locate a matching identity, classification can be used to restrict the matching process to only those images in the gallery that belong to the same class as the probe. In this work, we utilize an unsupervised clustering scheme to partition ear images into multiple classes (i.e., clusters), with each class being denoted by a prototype or a centroid. A given ear image is assigned class labels (i.e., cluster indices) that correspond to the clusters whose centroids are closest to it. We compare the classification performance of three different texture descriptors, viz. Histograms of Oriented Gradients, uniform Local Binary Patterns and Local Phase Quantization. Extensive experiments using three different ear datasets suggest that the Local Phase Quantization texture descriptor scheme along with PCA for dimensionality reduction results in a 96.89% hit rate (i.e., 3.11% pre-selection error rate) with a penetration rate of 32.08%. Further, we demonstrate that the hit rate improves to 99.01% with a penetration rate of 47.10% when a multi-cluster search strategy is employed.


title: Beyond the PDP-11: Architectural Support for a Memory-Safe C Abstract Machine

text:
We propose a new memory-safe interpretation of the C abstract machine that provides stronger protection to benefit security and debugging. Despite ambiguities in the specification intended to provide implementation flexibility, contemporary implementations of C have converged on a memory model similar to the PDP-11, the original target for C. This model lacks support for memory safety despite well-documented impacts on security and reliability.
 Attempts to change this model are often hampered by assumptions embedded in a large body of existing C code, dating back to the memory model exposed by the original C compiler for the PDP-11. Our experience with attempting to implement a memory-safe variant of C on the CHERI experimental microprocessor led us to identify a number of problematic idioms. We describe these as well as their interaction with existing memory safety schemes and the assumptions that they make beyond the requirements of the C specification. Finally, we refine the CHERI ISA and abstract model for C, by combining elements of the CHERI capability model and fat pointers, and present a softcore CPU that implements a C abstract machine that can run legacy C code with strong memory protection guarantees.


title: Design principles for sensemaking support systems in environmental sustainability transformations

text: Correspondence: Stefan Seidel, University of Liechtenstein, Fuerst-Franz-Josef-Strasse 23, 9490 Vaduz, Liechtenstein. Tel: +423 265 1303; E-mail: stefan.seidel@uni.li Abstract This paper reports on the results of a design science research (DSR) study that develops design principles for information systems (IS) that support organisational sensemaking in environmental sustainability transformations. We identify initial design principles based on salient affordances required in organisational sensemaking and revise them through three rounds of developing, demonstrating and evaluating a prototypical implementation. Through our analysis, we learn how IS can support essential sensemaking practices in environmental sustainability transformations, including experiencing disruptive ambiguity through the provision of environmental data, noticing and bracketing, engaging in an open and inclusive communication and presuming potential alternative environmentally responsible actions. We make two key contributions: First, we provide a set of theory-inspired design principles for IS that support sensemaking in sustainability transformations, and revise them empirically using a DSR method. Second, we show how the concept of affordances can be used in DSR to investigate how IS can support organisational practices. While our findings are based on the investigation of the substantive context of environmental sustainability transformation, we suggest that they might be applicable in a broader set of contexts of organisational sensemaking and thus for a broader class of sensemaking support systems. European Journal of Information Systems (2017). doi:10.1057/s41303-017-0039-0


title: Transforming Web Tables to a Relational Database

text: HTML tables represent a significant fraction of web data. The often complex headers of such tables are determined accurately using their indexing property. Isolated headers are factored to extract category hierarchies. Web tables are then transformed into a canonical form and imported into a relational database. The proposed processing allows for the formulation of arbitrary SQL queries over the collection of induced relational tables. Keywords—table segmentation; Wang categories; header paths; relational table SQL queries


title: MULTIPLE-ATTRIBUTE TEXT REWRITING

text: The dominant approach to unsupervised “style transfer” in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its “style”. In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.


title: Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks

text: In this paper, we study the problem of activity recognition and abnormal behaviour detection for elderly people with dementia. Very few studies have attempted to address this problem presumably because of the lack of experimental data in the context of dementia care. In particular, the paper investigates three variants of Recurrent Neural Networks (RNNs): Vanilla RNNs (VRNN), Long Short Term RNNs (LSTM) and Gated Recurrent Unit RNNs (GRU). Here activity recognition is considered as a sequence labelling problem, while abnormal behaviour is flagged based on the deviation from normal patterns. To provide an adequate discussion of the performance of RNNs in this context, we compare them against the state-of-art methods such as Support Vector Machines (SVMs), Naı̈ve Bayes (NB), Hidden Markov Models (HMMs), Hidden Semi-Markov Models (HSMM) and Conditional Random Fields (CRFs). The results obtained indicate that RNNs are competitive with those state-of-art methods. Moreover, the paper presents a methodology for generating synthetic data reflecting on some behaviours of people with dementia given the difficulty of obtaining real-world data. c 6 t r . ublis e ls i r . . i ilit f t f re ce r ra hairs.


title: Community Evaluation and Exchange of Word Vectors at wordvectors.org

text: Vector space word representations are useful for many natural language processing applications. The diversity of techniques for computing vector representations and the large number of evaluation benchmarks makes reliable comparison a tedious task both for researchers developing new vector space models and for those wishing to use them. We present a website and suite of offline tools that that facilitate evaluation of word vectors on standard lexical semantics benchmarks and permit exchange and archival by users who wish to find good vectors for their applications. The system is accessible at: www.wordvectors.org.


title: Central pattern generators for locomotion control in animals and robots: A review

text: The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, low-dimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed.


title: Inverse Kinematics for a Point-Foot Quadruped Robot with Dynamic Redundancy Resolution

text: In this work we examine the control of center of mass and swing leg trajectories in LittleDog, a point-foot quadruped robot. It is not clear how to formulate a function to compute forward kinematics of the center of mass of the robot as a function of actuated joint angles because point-foot walkers have no direct actuation between the feet and the ground. Nevertheless, we show that a whole-body Jacobian exists and is well defined when at least three of the feet are on the ground. Also, the typical approach of work-space centering for redundancy resolution causes destabilizing motions when executing fast motions. An alternative redundancy resolution optimization is proposed which projects single-leg inverse kinematic solutions into the nullspace. This hybrid approach seems to minimize 1) unnecessary rotation of the body, 2) twisting of the stance legs, and 3) whole-body involvement in achieving a step leg trajectory. In simulation, this control allows the robot to perform significantly more dynamic behaviors while maintaining stability.


title: Inverse dynamics control of floating base systems using orthogonal decomposition

text: Model-based control methods can be used to enable fast, dexterous, and compliant motion of robots without sacrificing control accuracy. However, implementing such techniques on floating base robots, e.g., humanoids and legged systems, is non-trivial due to under-actuation, dynamically changing constraints from the environment, and potentially closed loop kinematics. In this paper, we show how to compute the analytically correct inverse dynamics torques for model-based control of sufficiently constrained floating base rigid-body systems, such as humanoid robots with one or two feet in contact with the environment. While our previous inverse dynamics approach relied on an estimation of contact forces to compute an approximate inverse dynamics solution, here we present an analytically correct solution by using an orthogonal decomposition to project the robot dynamics onto a reduced dimensional space, independent of contact forces. We demonstrate the feasibility and robustness of our approach on a simulated floating base bipedal humanoid robot and an actual robot dog locomoting over rough terrain.


title: The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation

text: F or D walkingcontr ol of a biped robot we analyze the dynamics of a three dimensional inverted pendu lum in which motion is constr aine d to move along an arbitr arily de ned plane This analysis leads us a sim ple line ar dynamics the Thr ee Dimensional Linear In verte d Pendulum Mode D LIPM Geometric nature of trajectories under the D LIPM and a method for walking pattern gener ationare discusse d A simula tion r esult of a walking contr ol using a d o f biped robot model is also shown


title: Capture Point: A Step toward Humanoid Push Recovery

text: It is known that for a large magnitude push a human or a humanoid robot must take a step to avoid a fall. Despite some scattered results, a principled approach towards "when and where to take a step" has not yet emerged. Towards this goal, we present methods for computing capture points and the capture region, the region on the ground where a humanoid must step to in order to come to a complete stop. The intersection between the capture region and the base of support determines which strategy the robot should adopt to successfully stop in a given situation. Computing the capture region for a humanoid, in general, is very difficult. However, with simple models of walking, computation of the capture region is simplified. We extend the well-known linear inverted pendulum model to include a flywheel body and show how to compute exact solutions of the capture region for this model. Adding rotational inertia enables the humanoid to control its centroidal angular momentum, much like the way human beings do, significantly enlarging the capture region. We present simulations of a simple planar biped that can recover balance after a push by stepping to the capture region and using internal angular momentum. Ongoing work involves applying the solution from the simple model as an approximate solution to more complex simulations of bipedal walking, including a 3D biped with distributed mass.


title: Zero is Not Enough : On The Lower Limit of Agent Intelligence For Continuous Double Auction Markets †

text: agent continuous double auction, trade Gode and Sunder's (1993) results from using "zero-intelligence" (ZI) traders, that act randomly within a structured market, appear to imply that convergence to the theoretical equilibrium price in continuous double-auction markets is determined more by market structure than by the intelligence of the traders in that market. However, it is demonstrated here that the average transaction prices of ZI traders can vary significantly from the theoretical equilibrium value when the market supply and demand are asymmetric, and that the degree of difference from equilibrium is predictable from a priori probabilistic analysis. In this sense, it is shown here that Gode and Sunder's results are artefacts of their experimental regime. Following this, 'zero-intelligence-plus' (ZIP) traders are introduced: like ZI traders, these simple agents make stochastic bids. Unlike ZI traders, they employ an elementary form of machine learning. Groups of ZIP traders interacting in experimental markets similar to those used by Smith (1962) and Gode and Sunder (1993) are demonstrated, and we show that the performance of ZIP traders is significantly closer to the human data than is the performance of Gode and Sunder's ZI traders. Because ZI traders are too simple, we offer ZIP traders as a slight upward revision of the intelligence required to achieve equilibrium in continuous double auction markets.


title: Durable write cache in flash memory SSD for relational and NoSQL databases

text: In order to meet the stringent requirements of low latency as well as high throughput, web service providers with large data centers have been replacing magnetic disk drives with flash memory solid-state drives (SSDs). They commonly use relational and NoSQL database engines to manage OLTP workloads in the warehouse-scale computing environments. These modern database engines rely heavily on redundant writes and frequent cache flushes to guarantee the atomicity and durability of transactional updates. This has become a serious bottleneck of performance in both relational and NoSQL database engines. This paper presents a new SSD prototype called DuraSSD equipped with tantalum capacitors. The tantalum capacitors make the device cache inside DuraSSD durable, and additional firmware features of DuraSSD take advantage of the durable cache to support the atomicity and durability of page writes. It is the first time that a flash memory SSD with durable cache has been used to achieve an order of magnitude improvement in transaction throughput without compromising the atomicity and durability. Considering that the simple capacitors increase the total cost of an SSD no more than one percent, DuraSSD clearly provides a cost-effective means for transactional support. DuraSSD is also expected to alleviate the problem of high tail latency by minimizing write stalls.


title: Multi-Task Learning For Parsing The Alexa Meaning Representation Language

text: The Alexa Meaning Representation Language (AMRL) is a compositional graph-based semantic representation that includes fine-grained types, properties, actions, and roles and can represent a wide variety of spoken language. AMRL increases the ability of virtual assistants to represent more complex requests, including logical and conditional statements as well as ones with nested clauses. Due to this representational capacity, the acquisition of large scale data resources is challenging, which limits the accuracy of resulting models. This paper has two primary contributions. The first contribution is a linearization of the AMRL parses that aligns it to a related task of spoken language understanding (SLU) and a deep neural network architecture that uses multi-task learning to predict AMRL fine-grained types, properties and intents. The second contribution is a deep neural network architecture that leverages embeddings from the large-scale data resources that are available for SLU. When combined, these contributions enable the training of accurate models of AMRL parsing, even in the presence of data sparsity. The proposed models, which use the linearized AMRL parse, multi-task learning, residual connections and embeddings from SLU, decrease the error rates in the prediction of the full AMRL parse by 3.56%


title: BIER — Boosting Independent Embeddings Robustly

text: Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings. In this work, we show how to improve the robustness of embeddings by exploiting independence in ensembles. We divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each learner receives a reweighted training sample from the previous learners. This leverages large embedding sizes more effectively by significantly reducing correlation of the embedding and consequently increases retrieval accuracy of the embedding. Our method does not introduce any additional parameters and works with any differentiable loss function. We evaluate our metric learning method on image retrieval tasks and show that it improves over state-ofthe- art methods on the CUB-200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID datasets by a significant margin.


title: MAGIX: Model Agnostic Globally Interpretable Explanations

text: Explaining the behavior of a black box machine learning model at the instance level is useful for building trust. However, it is also important to understand how the model behaves globally. Such an understanding provides insight into both the data on which the model was trained and the patterns that it learned. We present here an approach that learns if-then rules to globally explain the behavior of black box machine learning models that have been used to solve classification problems. The approach works by first extracting conditions that were important at the instance level and then evolving rules through a genetic algorithm with an appropriate fitness function. Collectively, these rules represent the patterns followed by the model for decisioning and are useful for understanding its behavior. We demonstrate the validity and usefulness of the approach by interpreting black box models created using publicly available data sets as well as a private digital marketing data set.


title: Integrating occlusion culling with parallel LOD for rendering complex 3D environments on GPU

text: In many research domains, such as mechanical engineering, game development and virtual reality, a typical output model is usually produced in a multi-object manner for efficient data management. To have a complete description of the whole model, tens of thousands of, or even millions of, such objects are created, which makes the entire dataset exceptionally complex. Consequently, visualizing the model becomes a computationally intensive process that impedes a real-time rendering and interaction.


title: New Frontiers in Formal Methods: Learning, Cyber-Physical Systems, Education, and Beyond

text: We survey promising directions for research in the area of formal methods and its applications, including fundamental questions about the combination of formal methods and machine learning, and applications to cyberphysical systems and education.


title: Electrick: Low-Cost Touch Sensing Using Electric Field Tomography

text: Current touch input technologies are best suited for small and flat applications, such as smartphones, tablets and kiosks. In general, they are too expensive to scale to large surfaces, such as walls and furniture, and cannot provide input on objects having irregular and complex geometries, such as tools and toys. We introduce Electrick, a low-cost and versatile sensing technique that enables touch input on a wide variety of objects and surfaces, whether small or large, flat or irregular. This is achieved by using electric field tomography in concert with an electrically conductive material, which can be easily and cheaply added to objects and surfaces. We show that our technique is compatible with commonplace manufacturing methods, such as spray/brush coating, vacuum forming, and casting/molding enabling a wide range of possible uses and outputs. Our technique can also bring touch interactivity to rapidly fabricated objects, including those that are laser cut or 3D printed. Through a series of studies and illustrative example uses, we show that Electrick can enable new interactive opportunities on a diverse set of objects and surfaces that were previously static.


title: Treacher collins syndrome.

text: Treacher Collins syndrome is a genetic disorder resulting in congenital craniofacial malformation. Patients typically present with downslanting palpebral fissures, lower eyelid colobomas, microtia, and malar and mandibular hypoplasia. This autosomal dominant disorder has a variable degree of phenotypic expression, and patients have no associated developmental delay or neurologic disease. Care for these patients requires a multidisciplinary team from birth through adulthood. Proper planning, counseling and surgical techniques are essential for optimizing patient outcomes. Here the authors review the features, genetics, and treatment of Treacher Collins syndrome.


title: Generating entangled photon pairs in a parallel crystal geometry

text: We present recent findings towards developing brighter entangled photon sources in critically phase matched (CPM) nonlinear crystals. Specifically, we use type-I collinear phase matching at non-degenerate wavelengths in parallel β-Barium Borate (BBO) crystals to generate pairs of polarization entangled photons for free-space quantum key distribution (QKD). We first review the entangled source configuration and then discuss ways to further improve the source brightness by means of tailoring the pump and collection modes. We present preliminary results that may lead to brighter entangled photon sources that are intrinsically robust to changes in their environment.


title: A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments

text: Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments.


title: Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions

text: An unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically, is proposed in this work. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of the three energies to separate a primary object from its background. However, the hybrid energy is nonconvex. Therefore, we develop the alternate convex optimization (ACO) scheme, which decomposes the nonconvex optimization into two quadratic programs. Moreover, we propose the forward-backward strategy, which performs the segmentation sequentially from the first to the last frames and then vice versa, to exploit temporal correlations. Experimental results on extensive datasets demonstrate that the proposed ACO algorithm outperforms the state-of-the-art techniques significantly.


title: A qualitative evaluation of evolution of a learning analytics tool

text: New systems are designed with improved quality or features. However, not every new system lives to its pre-development expectations when tested with end-users. The evaluation of the first version of our LOCO-Analyst tool for providing teachers with feedback on students learning activities and performance led to the enhancing the visualization features of the tool. The second evaluation of the improved tool allowed us to see how the improvements affect the user perceptions of the tool. Here, we present the qualitative results of our two evaluations. The results show that educators find the kinds of feedback implemented in the tool informative and they value the mix of textual and graphical representations of different kinds of feedback provided by the tool.


title: Automatic Readability Classification of Crowd-Sourced Data based on Linguistic and Information-Theoretic Features

text: This paper presents a classifier of text readability based on information-theoretic features. The classifier was developed based on a linguistic approach to readability that explores lexical, syntactic and semantic features. For this evaluation we extracted a corpus of 645 articles from Wikipedia together with their quality judgments. We show that information-theoretic features perform as well as their linguistic counterparts even if we explore several linguistic levels at once.


title: Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review

text: Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach to speed up the DNA sequencing process. In this review, we specifically discuss such a trend. In particular, starting from an analysis of the interest of the Internet community in both NGS and deep learning, we present a taxonomic analysis highlighting the major software solutions based on deep learning algorithms available for each specific NGS application field. We discuss future challenges in the perspective of cloud computing services aimed at deep learning based solutions for NGS.


title: Rigor mortis in an unusual position: Forensic considerations

text: We report a case in which the dead body was found with rigor mortis in an unusual position. The dead body was lying on its back with limbs raised, defying gravity. Direction of the salivary stains on the face was also defying the gravity. We opined that the scene of occurrence of crime is unlikely to be the final place where the dead body was found. The clues were revealing a homicidal offence and an attempt to destroy the evidence. The forensic use of 'rigor mortis in an unusual position' is in furthering the investigations, and the scientific confirmation of two facts - the scene of death (occurrence) is different from the scene of disposal of dead body, and time gap between the two places.


title: A conceptual model of client-driven agile requirements prioritization: results of a case study

text: Requirements (re)prioritization is an essential mechanism of agile development approaches to maximize the value for the clients and to accommodate changing requirements. Yet, in the agile Requirements Engineering (RE) literature, very little is known about how agile (re)prioritization happens in practice. Conceptual models about this process are missing, which, in turn, makes it difficult for both practitioners and researchers to reason about requirements decision-making at inter-iteration time. We did a multiple case study on agile requirements prioritization methods to yield a conceptual model for understanding the inter-iteration prioritization process. The model is derived by using interview data from practitioners in 8 development organizations. Such a model makes explicit the concepts that are used tacitly in the agile requirements prioritization practice and can be used for structuring future empirical investigations about this topic, and for analyzing, supporting, and improving the process in real-life projects.


title: A Penalty-Function Approach for Pruning Feedforward Neural Networks

text: This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.


title: The Cascade-Correlation Learning Architecture

text: Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology, Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network. This research was sponsored in part by the National Science Foundation under Contract Number EET-8716324 and by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976 under Contract F33615-87-C-1499 and monitored by: Avionics Laboratory, Air Force Wright Aeronautical Laboratories, Aeronautical Systems Division (AFSC), Wright-Patterson AFB, OH 45433-6543. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government. 1. Why is Back-Propagation Learning So Slow? The Cascade-Correlation learning algorithm was developed in an attempt to overcome certain problems and limitations of the popular back-propagation (or “backprop”) learning algorithm [Rumelhart, 1986]. The most important of these limitations is the slow pace at which backprop learns from examples. Even on simple benchmark problems, a back-propagation network may require many thousands of epochs to learn the desired behavior from examples. (An epoch is defined as one pass through the entire set of training examples.) We have attempted to analyze the reasons why backprop learning is so slow, and we have identified two major problems that contribute to the slowness. We call these the step-size problem and the moving target problem. There may, of course, be other contributing factors that we have not yet identified. 1.1. The Step-Size Problem The step-size problem occurs because the standard back-propagation method computes only E w, the partial first derivative of the overall error function with respect to each weight in the network. Given these derivatives, we can perform a gradient descent in weight space, reducing the error with each step. It is straightforward to show that if we take infinitesimal steps down the gradient vector, running a new training epoch to recompute the gradient after each step, we will eventually reach a local minimum of the error function. Experience has shown that in most situations this local minimum will be a global minimum as well, or at least a “good enough” solution to the problem at hand. In a practical learning system, however, we do not want to take infinitesimal steps; for fast learning, we want to take the largest steps that we can. Unfortunately, if we choose a step size that is too large, the network will not reliably converge to a good solution. In order to choose a reasonable step size, we need to know not just the slope of the error function, but something about its higher-order derivatives—its curvature—in the vicinity of the current point in weight space. This information is not available in the standard back-propagation algorithm. A number of schemes have been proposed for dealing with the step-size problem. Some form of “momentum” [Rumelhart, 1986] is often used as a crude way of summarizing the slope of the error surface at earlier points in the computation. Conjugate gradient methods have been explored in the context of artificial neural networks by a number of researchers [Watrous, 1988, Lapedes, 1987, Kramer, 1989] with generally good results. Several schemes, for example [Franzini, 1987] and jacobs:dynamic, have been proposed that adjust the step-size dynamically, based on the change in gradient from one step to another. Becker and LeCun [Becker, 1988] explicitly compute an approximation to the second derivative of the error function at each step and use that information to guide the speed of descent. Fahlman’s quickprop algorithm [Fahlman, 1988] is one of the more successful algorithms for handling the step-size problem in back-propagation systems. Quickprop computes the E w values just as in standard backprop, but instead of simple gradient descent, Quickprop uses a second-order method, related to Newton’s method, to update the weights. On the learning benchmarks we have collected, quickprop consistently out-performs other backprop-like algorithms, sometimes by a large factor. Quickprop’s weight-update procedure depends on two approximations: first, that small changes in one weight have relatively little effect on the error gradient observed at other weights; second, that the error function with respect to each weight is locally quadratic. For each weight, quickprop keeps a copy of


title: Dynamic node creation in backpropagation networks

text: Summary form only given. A novel method called dynamic node creation (DNC) that attacks issues of training large networks and of testing networks with different numbers of hidden layer units is presented. DNC sequentially adds nodes one at a time to the hidden layer(s) of the network until the desired approximation accuracy is achieved. Simulation results for parity, symmetry, binary addition, and the encoder problem are presented. The procedure was capable of finding known minimal topologies in many cases, and was always within three nodes of the minimum. Computational expense for finding the solutions was comparable to training normal backpropagation (BP) networks with the same final topologies. Starting out with fewer nodes than needed to solve the problem actually seems to help find a solution. The method yielded a solution for every problem tried. BP applied to the same large networks with randomized initial weights was unable, after repeated attempts, to replicate some minimum solutions found by DNC.<<ETX>>


title: Emotion-based music recommendation by affinity discovery from film music

text: 0957-4174/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.eswa.2008.09.042 q Part of the content of this paper has been publi International Conference on Multimedia, 2005. * Corresponding author. Tel.: +886 2 29393091x67 E-mail address: mkshan@cs.nccu.edu.tw (M.-K. Sh With the growth of digital music, the development of music recommendation is helpful for users to pick desirable music pieces from a huge repository of music. The existing music recommendation approaches are based on a user’s preference on music. However, sometimes, it might better meet users’ requirement to recommend music pieces according to emotions. In this paper, we propose a novel framework for emotion-based music recommendation. The core of the recommendation framework is the construction of the music emotion model by affinity discovery from film music, which plays an important role in conveying emotions in film. We investigate the music feature extraction and propose the Music Affinity Graph and Music Affinity Graph-Plus algorithms for the construction of music emotion model. Experimental result shows the proposed emotion-based music recommendation achieves 85% accuracy in average. 2008 Elsevier Ltd. All rights reserved.


title: Autonomous cleaning robot: Roboking system integration and overview

text: This paper would present the system integration and overview of the autonomous cleaning robot Roboking. Roboking is self-propelled autonomously navigating vacuum cleaning robot. It uses several sensors in order to protect indoor environments and itself while cleaning. In this paper, we would describe the principle of operation along with system structure, sensors, functions and integrated subsystems.


title: Design of auxetic structures via mathematical optimization.

text: The design of auxetic structures is traditionally based on engineering experience, see, for instance, refs. [1,2]. The goal of this article is to demonstrate how an existing auxetic structure can be improved by structural optimization techniques and manufactured by a selective electron beam melting (SEBM) system. Auxetic materials possess negative Poisson’s ratios ν which is in contrast to almost all engineering materials (e. g. metals ν ≈ 0.3). The negative Poisson’s ratio translates into the unusual geometric effect that these materials expand when stretched and contract when compressed. This behavior is of interest for example for fastener systems and fi lters. [ 2 , 3 ] It also results in interesting mechanical behavior like increased penetration resistance for large negative Poisson’s ratios [ 4 ] and in the case of isotropy for higher shear strength relative to the Young’s modulus [ 2 ] of the structure. The auxetic behavior can always be traced back to complex mesoor microscopic geometric mechanisms, e. g. unfolding of inverted elements or rotation of star shaped rigid units relative to each other. [ 5 – 7 ] These geometries can be realized on vastly different scales from specifi cally designed polymers to reactor cores. [ 2 , 8 ]


title: CMT-bone: A Mini-App for Compressible Multiphase Turbulence Simulation Software

text: Designed with the goal of mimicking key features of real HPC workloads, mini-apps have become an important tool for co-design. An investigation of mini-app behavior can provide system designers with insight into the impact of architectures, programming models, and tools on application performance. Mini-apps can also serve as a platform for fast algorithm design space exploration, allowing the application developers to evaluate their design choices before significantly redesigning the application codes. Consequently, it is prudent to develop a mini-app alongside the full blown application it is intended to represent. In this paper, we present CMT-bone a mini-app for the compressible multiphase turbulence (CMT) application, CMT-nek, being developed to extend the physics of the CESAR Nek5000 application code. CMT-bone consists of the most computationally intensive kernels of CMT-nek and the communication operations involved in nearest-neighbor updates and vector reductions. The mini-app represents CMT-nek in its most mature state and going forward it will be developed in parallel with the CMT-nek application to keep pace with key new performance impacting changes. We describe these kernels and discuss the role that CMT-bone has played in enabling interdisciplinary collaboration by allowing application developers to work with computer scientists on performance optimization on current architectures and performance analysis on notional future systems.


title: A typology for visualizing uncertainty

text: Information analysts, especially those working in the field of intelligence analysis, must assess not only the information presented to them, but the confidence they have in that information. Visual representations of information are challenged to incorporate a notion of confidence or certainty because the factors that influence the certainty or uncertainty of information vary with the type of information and the type of decisions being made. Visualization researchers have no model or framework for describing uncertainty as it relates to intelligence analysis, thus no consistent basis for constructing visualizations of uncertainty. This paper presents a typology describing the aspects of uncertainty related to intelligence analysis, drawing on frameworks for uncertainty representation in scientific computing.


title: Broadcast news story segmentation using latent topics on data manifold

text: This paper proposes to use Laplacian Probabilistic Latent Semantic Analysis (LapPLSA) for broadcast news story segmentation. The latent topic distributions estimated by LapPLSA are used to replace term frequency vector as the representation of sentences and measure the cohesive strength between the sentences. Subword n-gram is used as the basic term unit in the computation. Dynamic Programming is used for story boundary detection. LapPLSA projects the data into a low-dimensional semantic topic representation while preserving the intrinsic local geometric structure of the data. The locality preserving property attempts to make the estimated latent topic distributions more robust to the noise from automatic speech recognition errors. Experiments are conducted on the ASR transcripts of TDT2 Mandarin broadcast news corpus. Our proposed approach is compared with other approaches which use dimensionality reduction technique with the locality preserving property, and two different topic modeling techniques. Experiment results show that our proposed approach provides the highest F1-measure of 0.8228, which significantly outperforms the best previous approaches.


title: A 10-year retrospective of research in health mass media campaigns: where do we go from here?

text: Mass media campaigns have long been a tool for promoting public health. How effective are such campaigns in changing health-related attitudes and behaviors, however, and how has the literature in this area progressed over the past decade? The purpose of the current article is threefold. First, I discuss the importance of health mass media campaigns and raise the question of whether they are capable of effectively impacting public health. Second, I review the literature and discuss what we have learned about the effectiveness of campaigns over the past 10 years. Finally, I conclude with a discussion of possible avenues for the health campaign literature over the next 10 years. The overriding conclusion is the following: The literature is beginning to amass evidence that targeted, well-executed health mass media campaigns can have small-to-moderate effects not only on health knowledge, beliefs, and attitudes, but on behaviors as well, which can translate into major public health impact given the wide reach of mass media. Such impact can only be achieved, however, if principles of effective campaign design are carefully followed.


title: Conditional Generative Adversarial Networks for Commonsense Machine Comprehension

text: Recently proposed Story Cloze Test [Mostafazadeh et al., 2016] is a commonsense machine comprehension application to deal with natural language understanding problem. This dataset contains a lot of story tests which require commonsense inference ability. Unfortunately, the training data is almost unsupervised where each context document followed with only one positive sentence that can be inferred from the context. However, in the testing period, we must make inference from two candidate sentences. To tackle this problem, we employ the generative adversarial networks (GANs) to generate fake sentence. We proposed a Conditional GANs (CGANs) in which the generator is conditioned by the context. Our experiments show the advantage of the CGANs in discriminating sentence and achieve state-of-the-art results in commonsense story reading comprehension task compared with previous feature engineering and deep learning methods.


title: Outliers , Level Shifts , and Variance Changes in Time Series

text: Outliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.


title: A simple tractor-trailer backing control law for path following

text: Backing of tractor-trailer systems is a problem addressed in many literatures. It is usually solved using various nonlinear-based control methods, which are often not easy to implement or tune. Similar to other work focused on backing a single axle trailer with a car like vehicle, we propose a two-tier controller that is simple and intuitive. However, ours is based upon curvature as opposed to hitch angle, which allows the control input to be more directly related to path specification and to handle path curvature discontinuity better. Experimental results are provided to illustrate the capability of this new algorithm applied to a full scale autonomous vehicle and trailer system in a real field environment using minimal sensing capability. Results demonstrate good performance on sloped grounds with various grades.


title: Weighted graph comparison techniques for brain connectivity analysis

text: The analysis of brain connectivity is a vast field in neuroscience with a frequent use of visual representations and an increasing need for visual analysis tools. Based on an in-depth literature review and interviews with neuroscientists, we explore high-level brain connectivity analysis tasks that need to be supported by dedicated visual analysis tools. A significant example of such a task is the comparison of different connectivity data in the form of weighted graphs. Several approaches have been suggested for graph comparison within information visualization, but the comparison of weighted graphs has not been addressed. We explored the design space of applicable visual representations and present augmented adjacency matrix and node-link visualizations. To assess which representation best support weighted graph comparison tasks, we performed a controlled experiment. Our findings suggest that matrices support these tasks well, outperforming node-link diagrams. These results have significant implications for the design of brain connectivity analysis tools that require weighted graph comparisons. They can also inform the design of visual analysis tools in other domains, e.g. comparison of weighted social networks or biological pathways.


title: Identifying Customer Preferences about Tourism Products Using an Aspect-based Opinion Mining Approach

text: In this study we extend Bing Liu’s aspect-based opinion mining technique to apply it to the tourism domain. Using this extension, we also offer an approach for considering a new alternative to discover consumer preferences about tourism products, particularly hotels and restaurants, using opinions available on the Web as reviews. An experiment is also conducted, using hotel and restaurant reviews obtained from TripAdvisor, to evaluate our proposals. Results showed that tourism product reviews available on web sites contain valuable information about customer preferences that can be extracted using an aspect-based opinion mining approach. The proposed approach proved to be very effective in determining the sentiment orientation of opinions, achieving a precision and recall of 90%. However, on average, the algorithms were only capable of extracting 35% of the explicit aspect expressions. c © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of KES International.


title: Cannabinoids and the skeleton: from marijuana to reversal of bone loss.

text: The active component of marijuana, Delta(9)-tetrahydrocannabinol, activates the CB1 and CB2 cannabinoid receptors, thus mimicking the action of endogenous cannabinoids. CB1 is predominantly neuronal and mediates the cannabinoid psychotropic effects. CB2 is predominantly expressed in peripheral tissues, mainly in pathological conditions. So far the main endocannabinoids, anandamide and 2-arachidonoylglycerol, have been found in bone at 'brain' levels. The CB1 receptor is present mainly in skeletal sympathetic nerve terminals, thus regulating the adrenergic tonic restrain of bone formation. CB2 is expressed in osteoblasts and osteoclasts, stimulates bone formation, and inhibits bone resorption. Because low bone mass is the only spontaneous phenotype so far reported in CB2 mutant mice, it appears that the main physiologic involvement of CB2 is associated with maintaining bone remodeling at balance, thus protecting the skeleton against age-related bone loss. Indeed, in humans, polymorphisms in CNR2, the gene encoding CB2, are strongly associated with postmenopausal osteoporosis. Preclinical studies have shown that a synthetic CB2-specific agonist rescues ovariectomy-induced bone loss. Taken together, the reports on cannabinoid receptors in mice and humans pave the way for the development of 1) diagnostic measures to identify osteoporosis-susceptible polymorphisms in CNR2, and 2) cannabinoid drugs to combat osteoporosis.


title: A Bayesian Approach to Predict Performance of a Student (BAPPS): A Case with Ethiopian Students

text: The importance of accurate estimation of student’s future performance is essential in order to provide the student with adequate assistance in the learning process. To this end, this research aimed at investigating the use of Bayesian networks for predicting performance of a student, based on values of some identified attributes. We presented empirical experiments on the prediction of performance with a data set of high school students containing 8 attributes. The paper demonstrates an application of the Bayesian approach in the field of education and shows that the Bayesian network classifier has a potential to be used as a tool for prediction of student performance.


title: A Prolog Framework for Integrating Business Rules into Java Applications

text: Business specifications – that formerly only supported IT development – increasingly become business configurations in the form of rules that can be loaded directly into IT solutions. PROLOG is well–known for its qualities in the development of sophisticated rule systems. It is desirable to combine the advantages of PROLOG with JAVA, since JAVA has become one of the most used programming languages in industry. However, experts of both programming languages are rare. To overcome the resulting interoperability problems, we have developed a framework which generates a JAVA archive that provides methods to query a given set of PROLOG rules; it ensures that valid knowledge bases are transmitted between JAVA and PROLOG. We use XML Schema for describing the format for exchanging a knowledge base between PROLOG and JAVA. From the XML Schema desciption, we scaffold JAVA classes; the JAVA programmer can use them and fill in the open slots by statements accessing other JAVA data structures. The data structure on the JAVA side reflects the complex structured knowledge base, with which the PROLOG rules work, in an object–oriented way. We can to some extend verify the correctness of the data set / knowledge base sent from JAVA to PROLOG using standard methods for XML Schema. Moreover, we can add constraints that go beyond XML. For instance, we can specify standard integrity constraints known from relational databases, such as primary key, foreign key, and not–null constraints. Since we are dealing with complex structured XML data, however, there can be far more general integrity constraints. These can be expressed by standard PROLOG rules, which can be evaluated on the PROLOG side; they could also be compiled to JAVA by available PROLOG to JAVA converters such as Prolog Cafe – since they will usually be written in a supported subset of PROLOG. We have used our framework for integrating PROLOG business rules into a commercial E–Commerce system written in JAVA.