MODELING, SIMULATION AND CONTROL OF HYBRID ELECTRIC VEHICLE DRIVE WHILE MINIMIZING ENERGY INPUT REQUIREMENTS USING OPTIMIZED GEAR RATIOS


title: An Algorithmic Framework for Shape Formation Problems in Self-Organizing Particle Systems

abstract: This material is based on work in progress. Imagine that we had a piece of matter that can change its physical properties like shape, density, conductivity, or color in a programmable fashion based on either user input or autonomous sensing. This is the vision behind what is commonly known as programmable matter. Programmable matter is the subject of many recent novel distributed computing proposals --- ranging from DNA tiles, shape-changing molecules, and synthetic cells, to reconfigurable modular robotics --- each pursuing solutions for specific application scenarios with their own, special capabilities and constraints.


title: An efficient hybrid incompressible SPH solver with interface handling for boundary conditions

abstract: We propose a hybrid smoothed particle hydrodynamics solver for efficientlysimulating incompressible fluids using an interface handling method for boundary conditions in the pressure Poisson equation. We blend particle density computed with one smooth and one spiky kernel to improve the robustness against both fluid–fluid and fluid–solid collisions. To further improve the robustness and efficiency, we present a new interface handling method consisting of two components: free surface handling for Dirichlet boundary conditions and solid boundary handling for Neumann boundary conditions. Our free surface handling appropriately determines particles for Dirichlet boundary conditions using Jacobi-based pressure prediction while our solid boundary handling introduces a new term to ensure the solvability of the linear system. We demonstrate that our method outperforms the state-of-the-art particle-based fluid solvers.


title: Listing All Minimal Separators of a Graph

abstract: An efficient algorithm listing all minimal vertex separators of an undirected graph is given. The algorithm needs polynomial time per separator that is found.


title: SafeFS: a modular architecture for secure user-space file systems: one FUSE to rule them all

abstract: The exponential growth of data produced, the ever faster and ubiquitous connectivity, and the collaborative processing tools lead to a clear shift of data stores from local servers to the cloud. This migration occurring across different application domains and types of users---individual or corporate---raises two immediate challenges. First, out-sourcing data introduces security risks, hence protection mechanisms must be put in place to provide guarantees such as privacy, confidentiality and integrity. Second, there is no "one-size-fits-all" solution that would provide the right level of safety or performance for all applications and users, and it is therefore necessary to provide mechanisms that can be tailored to the various deployment scenarios. In this paper, we address both challenges by introducing S afe FS, a modular architecture based on software-defined storage principles featuring stackable building blocks that can be combined to construct a secure distributed file system. S afe FS allows users to specialize their data store to their specific needs by choosing the combination of blocks that provide the best safety and performance tradeoffs. The file system is implemented in user space using FUSE and can access remote data stores. The provided building blocks notably include mechanisms based on encryption, replication, and coding. We implemented S afe FS and performed in-depth evaluation across a range of workloads. Results reveal that while each layer has a cost, one can build safe yet efficient storage architectures. Furthermore, the different combinations of blocks sometimes yield surprising tradeoffs.


title: A methodology for controlling the size of a test suite

abstract: This paper presents a technique to select a representative set of test cases from a test suite that provides the same coverage as the entire test suite. This selection is performed by identifying, and then eliminating, the redundant and obsolete test cases in the test suite. The representative set replaces the original test suite and thus, potentially produces a smaller test suite. The representative set can also be used to identify those test cases that should be rerun to test the program after it has been changed. Our technique is independent of the testing methodology and only requires an association between a testing requirement and the test cases that satisfy the requirement. We illustrate the technique using the data flow testing methodology. The reduction that is possible with our technique is illustrated by experimental results.


title: The cost structure of sensemaking

abstract: Making sense of a body of data is a common activity in any kind of analysis. Sensemaking is the process of searching for a representation and encoding data in that representation to answer task-specific questions. Different operations during sensemaking require different cognitive and external resources. Representations are chosen and changed to reduce the cost of operations in an information processing task. The power of these representational shifts is generally under-appreciated as is the relation between sensemaking and information retrieval. We analyze sensemaking tasks and develop a model of the cost structure of sensemaking. We discuss implications for the integrated design of user interfaces, representational tools, and information retrieval systems.


title: Reducing the risk of query expansion via robust constrained optimization

abstract: We introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion framework in which model estimation is cast as a robust constrained optimization problem. This framework provides a powerful method for modeling and solving complex expansion problems, by allowing multiple sources of domain knowledge or evidence to be encoded as simultaneous optimization constraints. Our robust optimization approach provides a clean theoretical way to model not only expansion benefit, but also expansion risk, by optimizing over uncertainty sets for the data. In addition, we introduce risk-reward curves to visualize expansion algorithm performance and analyze parameter sensitivity. We show that a robust approach significantly reduces the number and magnitude of expansion failures for a strong baseline algorithm, with no loss in average gain. Our approach is implemented as a highly efficient post-processing step that assumes little about the baseline expansion method used as input, making it easy to apply to existing expansion methods. We provide analysis showing that this approach is a natural and effective way to do selective expansion, automatically reducing or avoiding expansion in risky scenarios, and successfully attenuating noise in poor baseline methods.


A Context-Driven IoT Middleware Architecture


title: A Framework for Detecting MAC and IP Spoofing Attacks with Network Characteristics

abstract: This paper presents a spoofing attack detection framework based on physical network characteristics (e.g., received signal strength indicator round trip time and link quality indicator) that cannot easily be mimicked by artificial means. Unlike most previous studies that are sensitive to changes in network conditions, we propose a spoofing attack detection method, which is highly robust to the changes of network conditions over time. The proposed framework can monitor devices' physical network characteristics in real time and check if any significant changes have been made in the monitored measurements to effectively detect device spoofing attacks. To demonstrate the feasibility of the proposed framework, we analyzed how the RSSI values of packets were changed with varying physical distances in a real ZigBee (IEEE 802.15.4) network environment.


Characterization of Embedded Poly-Heater PMOSFETs and its Application on In-Line Wafer Level NBTI Monitor


title: Recommending Learning Materials according to Ontology-based Learning Styles

abstract: Personalized online systems have been developed to make learning more effective. One of the ways to achieve this personalization is to recommend the use of learning materials according to learning styles. However, information on learning materials and learning styles must be formalized to make automatic processing by the computer possible. The objective of this work is to propose an ontology that allows the recommendation of learning materials according to learning styles. The experiments presented here have proved that the use of ontology to meet the established objective is viable.


title: Autotuning Performance on Multicore Computers

abstract: For the last decade, the exponential potential of Moore's Law has been squandered in the effort to increase single thread performance, which is now limited by the memory, instruction, and power walls. In response, the computing industry has boldly placed its hopes on the multicore gambit. That is, abandon instruction-level parallelism and frequency-scaling in favor of the exponential scaling of the number of compute cores per microprocessor. The massive thread-level parallelism results in tremendous potential performance, but demands efficient parallel programming—a task existing software tools are ill-equipped for. We desire performance portability—the ability to write a program once and not only have it deliver good performance on the development computer, but on all multicore computers today and tomorrow. ::: This thesis accepts for fact that multicore is the basis for all future computers. Furthermore, we regiment our study by organizing it around the computational patterns and motifs as set forth in the Berkeley View. Although domain experts may be extremely knowledgeable on the mathematics and algorithms of their fields, they often lack the detailed computer architecture knowledge required to achieve high performance. Forthcoming heterogeneous architectures will exacerbate the problem for everyone. Thus, we extend the auto-tuning approach to program optimization and performance portability to the menagerie of multicore computers. In an automated fashion, an auto-tuner will explore the optimization space for a particular computational kernel of a motif on a particular computer. In doing so, it will determine the best combination of algorithm, implementation, and data structure for the combination of architecture and input data. ::: We implement and evaluate auto-tuners for two important kernels: Lattice Boltzmann Magnetohydrodynamics (LBMHD) and sparse matrix-vector multiplication (SpMV). They are representative of two of the computational motifs: structured grids and sparse linear algebra. To demonstrate the performance portability that our auto-tuners deliver, we selected an extremely wide range of architectures as an experimental test bed. These include conventional dual- and quad-core superscalar x86 processors both with and without integrated memory controllers. We also include the rather unconventional chip multithreaded (CMT) Sun Niagara2 (Victoria Falls) and the heterogeneous, local store-based IBM Cell Broadband Engine. In some experiments we sacrifice the performance portability of a common C representation, by creating ISA-specific auto-tuned versions of these kernels to gain architectural insight. To quantify our success, we created the Roofline model to perform a bound and bottleneck analysis for each kernel-architecture combination. ::: Despite the common wisdom that LBMHD and SpMV are memory bandwidth-bound, and thus nothing can be done to improve performance, we show that auto-tuning consistently delivers speedups in excess of 3× across all multicore computers except the memory-bound Intel Clovertown, where the benefit was as little as 1.5×. The Cell processor, with its explicitly managed memory hierarchy, showed far more dramatic speedups of between 20× and 130×. The auto-tuners includes both architecture-independent optimizations based solely on source code transformations and high-level kernel knowledge, as well as architecture-specific optimizations like the explicit use of single instruction, multiple data (SIMD) extensions or the use Cell's DMA-based memory operations. We observe that the these ISA-specific optimizations are becoming increasingly important as architectures evolve.


title: A Passive UHF-RFID System for the Localization of an Indoor Autonomous Vehicle

abstract: A global localization system combining odometry data with radio frequency identification (RFID) readings is proposed. RFID tags are placed at the ceiling of the environment and can be detected by a mobile robot unit traveling below them. The detection of the tags is the only information used in the proposed approach (no distance or bearing to the tag is considered available), but differently from similar localization setups reported in the literature, only a small number (about one each square meter or less) of tags are used. This is possible using a suitable tag's antenna in ultrahigh frequency band, expressly designed to obtain regular and stable RFID detection regions, which allows us to consider an efficient Kalman filtering approach to fuse RFID readings with the vehicle odometry data. A satisfactory performance is achieved, with an average position error of about 0.1 m. The hardware/software localization setup described in this paper is cheap and easy to use and may provide a satisfactory approach in several industrial and domestic scenarios.


Annual Review of Psychology Infant Statistical Learning


title: Causal Discovery from Temporally Aggregated Time Series

abstract: Discovering causal structure of a dynamical system from observed time series is a traditional and important problem. In many practical applications, observed data are obtained by applying subsampling or temporally aggregation to the original causal processes, making it difficult to discover the underlying causal relations. Subsampling refers to the procedure that for every k consecutive observations, one is kept, the rest being skipped, and recently some advances have been made in causal discovery from such data. With temporal aggregation, the local averages or sums of k consecutive, non-overlapping observations in the causal process are computed as new observations, and causal discovery from such data is even harder. In this paper, we investigate how to recover causal relations at the original causal frequency from temporally aggregated data when k is known. Assuming the time series at the causal frequency follows a vector autoregressive (VAR) model, we show that the causal structure at the causal frequency is identifiable from aggregated time series if the noise terms are independent and non-Gaussian and some other technical conditions hold. We then present an estimation method based on non-Gaussian state-space modeling and evaluate its performance on both synthetic and real data.


title: Born Again Neural Networks

abstract: Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher}) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student's compactness. %we desire a compact model with performance close to the teacher's. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these {Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating a role of the teacher outputs on both predicted and non-predicted classes. We present experiments with students of various capacities, focusing on the under-explored case where students overpower teachers. Our experiments show significant advantages from transferring knowledge between DenseNets and ResNets in either direction.


title: Fast, Accurate, and Stable Feature Selection Using Neural Networks

abstract: Multi-voxel pattern analysis often necessitates feature selection due to the high dimensional nature of neuroimaging data. In this context, feature selection techniques serve the dual purpose of potentially increasing classification accuracy and revealing sets of features that best discriminate between classes. However, feature selection techniques in current, widespread use in the literature suffer from a number of deficits, including the need for extended computational time, lack of consistency in selecting features relevant to classification, and only marginal increases in classifier accuracy. In this paper we present a novel method for feature selection based on a single-layer neural network which incorporates cross-validation during feature selection and stability selection through iterative subsampling. Comparing our approach to popular alternative feature selection methods, we find increased classifier accuracy, reduced computational cost and greater consistency with which relevant features are selected. Furthermore, we demonstrate that importance mapping, a technique used to identify voxels relevant to classification, can lead to the selection of irrelevant voxels due to shared activation patterns across categories. Our method, owing to its relatively simple architecture, flexibility and speed, can provide a viable alternative for researchers to identify sets of features that best discriminate classes.


title: Dual N- and C-Terminal Helices Are Required for Endoplasmic Reticulum and Lipid Droplet Association of Alcohol Acetyltransferases in Saccharomyces cerevisiae

abstract: In the yeast Saccharomyces cerevisiae two alcohol acetyltransferases (AATases), Atf1 and Atf2, condense short chain alcohols with acetyl-CoA to produce volatile acetate esters. Such esters are, in large part, responsible for the distinctive flavors and aromas of fermented beverages including beer, wine, and sake. Atf1 and Atf2 localize to the endoplasmic reticulum (ER) and Atf1 is known to localize to lipid droplets (LDs). The mechanism and function of these localizations are unknown. Here, we investigate potential mechanisms of Atf1 and Atf2 membrane association. Segments of the N- and C-terminal domains of Atf1 (residues 24–41 and 508–525, respectively) are predicted to be amphipathic helices. Truncations of these helices revealed that the terminal domains are essential for ER and LD association. Moreover, mutations of the basic or hydrophobic residues in the N-terminal helix and hydrophobic residues in the C-terminal helix disrupted ER association and subsequent sorting from the ER to LDs. Similar amphipathic helices are found at both ends of Atf2, enabling ER and LD association. As was the case with Atf1, mutations to the N- and C-terminal helices of Atf2 prevented membrane association. Sequence comparison of the AATases from Saccharomyces, non-Saccharomyces yeast (K. lactis and P. anomala) and fruits species (C. melo and S. lycopersicum) showed that only AATases from Saccharomyces evolved terminal amphipathic helices. Heterologous expression of these orthologs in S. cerevisiae revealed that the absence of terminal amphipathic helices eliminates LD association. Combined, the results of this study suggest a common mechanism of membrane association for AATases via dual N- and C-terminal amphipathic helices.


title: Accurate Passive Location Estimation Using TOA Measurements

abstract: Localization of objects is fast becoming a major aspect of wireless technologies, with applications in logistics, surveillance, and emergency response. Time-of-arrival (TOA) localization is ideally suited for high-precision localization of objects in particular in indoor environments, where GPS is not available. This paper considers the case where one transmitter and multiple, distributed, receivers are used to estimate the location of a passive (reflecting) object. It furthermore focuses on the situation when the transmitter and receivers can be synchronized, so that TOA (as opposed to time-difference-of-arrival (TDOA)) information can be used. We propose a novel, Two-Step estimation (TSE) algorithm for the localization of the object. We then derive the Cramer-Rao Lower Bound (CRLB) for TOA and show that it is an order of magnitude lower than the CRLB of TDOA in typical setups. The TSE algorithm achieves the CRLB when the TOA measurements are subject to small Gaussian-distributed errors, which is verified by analytical and simulation results. Moreover, practical measurement results show that the estimation error variance of TSE can be 33 dB lower than that of TDOA based algorithms.


title: The restoration of IVIg efficacy by plasma exchange in CIDP

abstract: Intravenous immunoglobulin (IVIg) is an effective and safe treatment for some patients with chronic inflammatory demyelinating polyneuropathy (CIDP). [1-4] Some patients who initially responded to IVIg infusion may subsequently deteriorate clinically, become refractory to increasing IVIg dosages, and require treatment with more toxic immunosuppressive agents. The reasons why IVIg becomes ineffective are unclear, but may involve an inability to ``keep up'' with autoantibody production. We describe a patient with CIDP who, despite an impressive initial response to IVIg, became refractory to progressively higher dosages. A short course of plasma exchange (PE) restored longterm IVIg efficacy. ::: ::: A 57-year-old man presented with 3 months of progressive numbness of the legs and arms, facial numbness, left arm and leg weakness, and prominent fatigue. He had previously been diagnosed with CIDP, based on clinical and electrophysiologic results, and had been on prednisone (60 mg/d) and azathioprine (150 mg/d) for the preceding 3 months without improvement. ::: ::: Neurologic examination demonstrated marked hypesthesia in the mental nerve distribution bilaterally. Strength was 4/5 in proximal arm muscles and 4-/5 in forearm and intrinsic hand muscles. Right leg strength was 5-/5 throughout, and left leg strength was 4/5 proximally and 4-/5 distally. The patient was unable to walk on …


title: Extensions of black-litterman portfolio optimization model with downside risk measure

abstract: This paper considers extensions of the Black-Litterman(BL) portfolio optimization model. Recognizing only the expected returns of the stocks are updated by investor's private information in traditional BL model, we extend such approach by integrating the information of the variance(volatility) of the stock from the correspondent options. By using the inverse optimization technique, it is possible to find the updated estimation of the expected return and covariance matrix by solving one Semidefinite Programming problem(SDP). Furthermore, we combine these improved estimations of the return statistics with the mean-CVaR based portfolio optimization model. Without assuming the prior distribution of the random return of the assets, we use the moment matching technique to generate the discrete scenarios, which is a key step in computing mean-CVaR portfolio. Our real market data based computational example demonstrates the prominent feature of our method.


title: 1995–2005: A Decade of Innovation in Low Temperature Plasma and Its Applications

abstract: Scientific breakthroughs tend to come in spurts when unique societal, economical, and political circumstances conspire (knowingly or unknowingly) and create an environment ripe for creativity. The field of low temperature plasma (LTP) recently experienced such an upheaval, which this paper attempts to relate in some details. There have been “roadmap” papers published before, which look towards the future of the field, but all roads start somewhere and even “new” roads are often paved over older roads that were discovered and traveled by early pioneers. With the sharp decrease in funding for fusion research in the USA in the early 1990s the plasma science community was faced with a dire situation that threatened to choke off plasma physics advances. However, in the background and far from the visibility accorded to fusion research, a few laboratories were quietly engaged in innovative research that in due time revolutionized the LTP field and breathed new life into plasma science. Groundbreaking applications of LTP were investigated that until today constitute most of the LTP research activities. These innovations spanned a wide spectrum that included the invention of novel devices, improvement of existing ones, and the deployment of these devices to areas ranging from industrial to biomedical applications. These efforts turned out to have impactful scientific and societal implications. In this paper plasma sources and applications developed during this uniquely innovative decade are briefly discussed.


title: A new compact microstrip two-layer bandpass filter using aperture-coupled SIR-hairpin resonators with transmission zeros

abstract: A new design of a compact two-layer bandpass filter using aperture-coupled microstrip stepped-impedance hairpin resonators is proposed and investigated in this paper. The proposed filter is composed of four stepped-impedance hairpin resonators located on two stacked microstrip layers, and the couplings between the resonators on the upper layer and those on the lower one are obtained by using three coupling apertures etched on a common ground plane placed between the two layers. In addition, the use of tapped input/output couplings allow this bandpass filter to generate two independent transmission zeros. A full-wave simulator is used to design the proposed structure and calculate the aperture coupling coefficients. With this two-layer configuration, the proposed filter becomes very compact and occupies a small space. To demonstrate the proposed design, a four-order bandpass-filter prototype was designed, fabricated, and tested at 2.14 GHz. Furthermore, a single-layer filter prototype was also designed and fabricated at the same frequency band and then compared to the first one. As a result, a significant reduction of approximately 50% was achieved. These features make the proposed structure suitable for compact and high-performance circuit component designs in microwave circuits.


title: Grid-Based Method for GPS Route Analysis for Retrieval

abstract: Grids are commonly used as histograms to process spatial data in order to detect frequent patterns, predict destinations, or to infer popular places. However, they have not been previously used for GPS trajectory similarity searches or retrieval in general. Instead, slower and more complicated algorithms based on individual point-pair comparison have been used. We demonstrate how a grid representation can be used to compute four different route measures: novelty, noteworthiness, similarity, and inclusion. The measures may be used in several applications such as identifying taxi fraud, automatically updating GPS navigation software, optimizing traffic, and identifying commuting patterns. We compare our proposed route similarity measure, C-SIM, to eight popular alternatives including Edit Distance on Real sequence (EDR) and Frechet distance. The proposed measure is simple to implement and we give a fast, linear time algorithm for the task. It works well under noise, changes in sampling rate, and point shifting. We demonstrate that by using the grid, a route similarity ranking can be computed in real-time on the Mopsi20141 route dataset, which consists of over 6,000 routes. This ranking is an extension of the most similar route search and contains an ordered list of all similar routes from the database. The real-time search is due to indexing the cell database and comes at the cost of spending 80% more memory space for the index. The methods are implemented inside the Mopsi2 route module.


title: The Impact of Current-Transformer Saturation on Transformer Differential Protection

abstract: Current-transformer (CT) saturation has a major impact on the operation of differential protection schemes. Transformer differential protection must maintain security during CT saturation for external faults while preserving high sensitivity and speed of operation for lower magnitude internal faults. Relay manufacturers provide very little information or direction for protection engineers to set relays adequately to achieve these goals. If an engineer lacks a deep understanding of relay behavior during CT saturation, he or she could make setting implementation mistakes that may lead to spurious trips or failures to trip with catastrophic consequences. This work investigated the impact of CT saturation on transformer differential relays in order to provide guidance on scheme behavior. It was discovered that harmonic blocking is triggered by CT saturation—a significant effect which is not described in the literature or relay manuals. This effect was modeled and the model's accuracy was verified with CT simulations, relay secondary injections, and high current primary injections. It is demonstrated that harmonic blocking provides effective security against nuisance trips caused by CT saturation. It is also shown that the traditional use of a high slope 2 is mostly redundant wherever harmonic blocking is enabled.


title: Catch Me If You Can: Evaluating Android Anti-Malware Against Transformation Attacks

abstract: Mobile malware threats (e.g., on Android) have recently become a real concern. In this paper, we evaluate the state-of-the-art commercial mobile anti-malware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware). Such an evaluation is important for not only measuring the available defense against mobile malware threats, but also proposing effective, next-generation solutions. We developed DroidChameleon, a systematic framework with various transformation techniques, and used it for our study. Our results on 10 popular commercial anti-malware applications for Android are worrisome: none of these tools is resistant against common malware transformation techniques. In addition, a majority of them can be trivially defeated by applying slight transformation over known malware with little effort for malware authors. Finally, in light of our results, we propose possible remedies for improving the current state of malware detection on mobile devices.


title: A Distributed Position-Based Protocol for Emergency Messages Broadcasting in Vehicular Ad Hoc Networks

abstract: Vehicular ad hoc networks (VANETs) can help reduce traffic accidents through broadcasting emergency messages among vehicles in advance. However, it is a great challenge to timely deliver the emergency messages to the right vehicles which are interested in them. Some protocols require to collect nearby real-time information before broadcasting a message, which may result in an increased delivery latency. In this paper, we proposed an improved position-based protocol to disseminate emergency messages among a large scale vehicle networks. Specifically, defined by the proposed protocol, messages are only broadcasted along their regions of interest, and a rebroadcast of a message depends on the information including in the message it has received. The simulation results demonstrate that the proposed protocol can reduce unnecessary rebroadcasts considerably, and the collisions of broadcast can be effectively mitigated.


title: Peer to Peer Hate: Hate Speech Instigators and Their Targets

abstract: While social media has become an empowering agent to individual voices and freedom of expression, it also facilitates anti-social behaviors including online harassment, cyberbullying, and hate speech. In this paper, we present the first comparative study of hate speech instigators and target users on Twitter. Through a multi-step classification process, we curate a comprehensive hate speech dataset capturing various types of hate. We study the distinctive characteristics of hate instigators and targets in terms of their profile self-presentation, activities, and online visibility. We find that hate instigators target more popular and high profile Twitter users, and that participating in hate speech can result in greater online visibility. We conduct a personality analysis of hate instigators and targets and show that both groups have eccentric personality facets that differ from the general Twitter population. Our results advance the state of the art of understanding online hate speech engagement.


title: Hair analysis for the biomonitoring of pesticide exposure: comparison with blood and urine in a rat model

abstract: Urine and plasma have been used to date for the biomonitoring of exposure to pollutants and are still the preferred fluids for this purpose; however, these fluids mainly provide information on the short term and may present a high level of variability regarding pesticide concentrations, especially for nonpersistent compounds. Hair analysis may provide information about chronic exposure that is averaged over several months; therefore, this method has been proposed as an alternative to solely relying on these fluids. Although the possibility of detecting pesticides in hair has been demonstrated over the past few years, the unknown linkage between exposure and pesticides concentration in hair has limited the recognition of this matrix as a relevant tool for assessing human exposure. Based on a rat model in which there was controlled exposure to a mixture of pesticides composed of lindane, β-hexachlorocyclohexane, β-endosulfan, p,p′-DDT, p,p′-DDE, dieldrin, pentachlorophenol, diazinon, chlorpyrifos, cyhalothrin, permethrin, cypermethrin, propiconazole, fipronil, oxadiazon, diflufenican, trifluralin, carbofuran, and propoxur, the current work demonstrates the association between exposure intensity and resulting pesticide concentration in hair. We also compared the results obtained from a hair analysis to urine and plasma collected from the same rats. Hair, blood, and urine were collected from rats submitted to 90-day exposure by gavage to the aforementioned mixture of common pesticides at different levels. We observed a linear relationship between exposure intensity and the concentration of pesticides in the rats’ hair (RPearson 0.453–0.978, p < 0.01). A comparison with results from urine and plasma samples demonstrated the relevance of hair analysis and, for many chemicals, its superiority over using fluids for differentiating animals from different groups and for re-attributing animals to their correct groups of exposure based on pesticide concentrations in the matrix. Therefore, this study strongly supports hair analysis as a reliable tool to be used during epidemiological studies to investigate exposure-associated adverse health effects.


Psychological resilience State of knowledge and future research agendas


title: Influence of saturation effects in a transverse flux machine

abstract: In this paper the torque density of transverse flux machines is investigated against the flux density inside the active parts with analytical calculations and FEM simulations. Especially saturation effects, which reduce the flux linkage with the coil, and core losses, are considered for both, the flux concentrating setup and the surface mounted magnets topology. The calculations are verified by measurements on an existing prototype. Finally new design guidelines are developed for the construction of TFMs with regard to the magnetic circuit.


title: High-Frequency High-Efficiency $CLL$ Resonant Converters With Synchronous Rectifiers

abstract: This paper proposes a CLL resonant dc-dc converter as an option for offline applications. This topology can achieve zero-voltage switching from zero load to a full load and zero-current switching for output rectifiers and makes the implementation of a secondary rectifier easy. This paper also presents a novel methodology for designing CLL resonant converters based on efficiency and holdup time requirements. An optimal transformer structure is proposed, which uses a current-type synchronous rectifier (SR) drive scheme. An 800-kHz 250-W CLL resonant converter prototype is built to verify the proposed circuit, design method, transformer structure, and SR drive scheme.


title: Electron microscopy of non-neuronal cellular changes accompanying neural degeneration in thalamic nuclei of the rabbit. II. Reactive elements within the neuropil.

abstract: Surgical destruction of the cortical projections of thalamic neurons results in rapid and profound atrophy of these neurons accompanied by a variety of non-neuronal reactive changes. During the first post-operative week, non-neuronal elements, distinct from astrocytes and normal oligodendrocytes and tentatively designated as “M” cells, become evident in the degenerating thalamic nuclei. Some “M” cells are characterized by heterochromatin nuclei and numerous free ribosomes, and resemble the agranular leukocytes accumulating simultaneously within the perivascular space of small vessels coursing through these regions (Matthews and Kruger, '73). Examples of such cells were sometimes seen bridging the external basal lamina. Other “M” cells display many profiles of granular endoplasmic reticulum and fewer free ribosomes but cannot be precisely classified as to nature and possible source. From the second through fourteenth week, “M” cells increase in prominence and accumulate electronlucent lipid vacuoles and phosphotungstic acid (PTA) positive dense bodies, presumably lysosomes, until these enlarged lipid-laden cells constitute the predominant non-neuronal entities in the zone of degeneration, in addition to hypertrophied astrocytes. Thirty-four weeks post-operatively “M” cells have decreased in size and apparent numbers, and dense bodies display a lessened affinity for PTA in addition to morphologic patterns suggestive of terminal lysosomes. Normal light and dark oligodendrocytes are encountered within the zone of degeneration throughout the entire survival period. ::: ::: ::: ::: It is suggested that a significant proportion of “M” cells are of mesodermal (hematogenous; perivascular) origin although “M” cells display sufficient diversity of morphology to warrant consideration of additional sources for the reactive vacuolated forms. The experimental observations are discussed in relation to past and present concepts concerning the nature, origin and pathologic transformation of elements classified as microglia.


Design Methodology for Educational Games based on Interactive Screenplays


title: PyMT: a post-WIMP multi-touch user interface toolkit

abstract: Multi-touch and tabletop input paradigms open novel doors for post-WIMP (Windows, Icons, Menus, Pointer) user interfaces. Developing these novel interfaces and applications poses unique challenges for designers and programmers alike. We present PyMT (Python Multi-Touch), a toolkit aimed at addressing these challenges. We discuss PyMT's architecture and sample applications to demonstrate how it enables rapid development of prototypes and interaction techniques while being accessible to novice programmers and providing great flexibility and creative freedom to advanced users. We share experiences gathered in the open source development of PyMT to explore design and programming challenges posed by multi-touch tabletop and post-WIMP interfaces. Specifically, we discuss changes to the event model and the implementation of development and debugging tools that we found useful along the way.


title: Variation in venom yield and protein concentration of the centipedes Scolopendra polymorpha and Scolopendra subspinipes.

abstract: Venom generally comprises a complex mixture of compounds representing a non-trivial metabolic expense. Accordingly, natural selection should fine-tune the amount of venom carried within an animal's venom gland(s). The venom supply of scolopendromorph centipedes likely influences their venom use and has implications for the severity of human envenomations, yet we understand very little about their venom yields and the factors influencing them. We investigated how size, specifically body length, influenced volume yield and protein concentration of electrically extracted venom in Scolopendra polymorpha and Scolopendra subspinipes. We also examined additional potential influences on yield in S. polymorpha, including relative forcipule size, relative mass, geographic origin (Arizona vs. California), sex, time in captivity, and milking history. Volume yield was linearly related to body length, and S. subspinipes yielded a larger length-specific volume than S. polymorpha. Body length and protein concentration were uncorrelated. When considering multiple influences on volume yield in S. polymorpha, the most important factor was body length, but yield was also positively associated with relative forcipule length and relative body mass. S. polymorpha from California yielded a greater volume of venom with a higher protein concentration than conspecifics from Arizona, all else being equal. Previously milked animals yielded less venom with a lower protein concentration. For both species, approximately two-thirds of extractable venom was expressed in the first two pulses, with remaining pulses yielding declining amounts, but venom protein concentration did not vary across pulses. Further study is necessary to ascertain the ecological significance of the factors influencing venom yield and how availability may influence venom use.


Field Test Validation of Single-Element Antenna with Anti-Jam and Spoof Detection


title: Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions

abstract: In this paper, we propose to study the problem of COURT VIEW GENeration from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequenceto-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.


title: MalPat: Mining Patterns of Malicious and Benign Android Apps via Permission-Related APIs

abstract: The dramatic rise of Android application (app) marketplaces has significantly gained the success of convenience for mobile users. Consequently, with the advantage of numerous Android apps, Android malware seizes the opportunity to steal privacy-sensitive data by pretending to provide functionalities as benign apps do. To distinguish malware from millions of Android apps, researchers have proposed sophisticated static and dynamic analysis tools to automatically detect and classify malicious apps. Most of these tools, however, rely on manual configuration of lists of features based on permissions, sensitive resources, intents, etc., which are difficult to come by. To address this problem, we study real-world Android apps to mine hidden patterns of malware and are able to extract highly sensitive APIs that are widely used in Android malware. We also implement an automated malware detection system, MalPat, to fight against malware and assist Android app marketplaces to address unknown malicious apps. Comprehensive experiments are conducted on our dataset consisting of 31 185 benign apps and 15 336 malware samples. Experimental results show that MalPat is capable of detecting malware with a high $F_1$ score (98.24%) comparing with the state-of-the-art approaches.


title: Towards Max-Min Fair Resource Allocation for Stream Big Data Analytics in Shared Clouds

abstract: Distributed stream big data analytics platforms have emerged to tackle the continuously generated data streams. In stream big data analytics, the data processing workflow is abstracted as a directed graph referred to as a topology. Data are read from the storage and processed tuple by tuple, and these processing results are updated dynamically. The performance of a topology is evaluated by its throughput. This paper proposes an efficient resource allocation scheme for a heterogeneous stream big data analytics cluster shared by multiple topologies, in order to achieve max-min fairness in the utilities of the throughput for all the topologies. We first formulate a novel resource allocation problem, which is a mixed 0-1 integer program. The NP-hardness of the problem is rigorously proven. To tackle this problem, we transform the non-convex constraint to several linear constraints using linearization and reformulation techniques. Based on the analysis of the problem-specific structure and characteristics, we propose an approach that iteratively solves the continuous problem with a fixed set of discrete variables optimally, and updates the discrete variables heuristically. Simulations show that our proposed resource allocation scheme remarkably improves the max-min fairness in utilities of the topology throughput, and is low in computational complexity.


title: A novel cross-shape DGS applied to design ultra-wide stopband low-pass filters

abstract: This letter presents a novel low-pass filter with an ultra-wide stopband. The proposed filter is comprised of a new cross-shape defected ground structure (CSDGS). By using this structure, the filter not only supports conventional DGS performances with a sharp rejection, but also exhibits an ultra-wide stopband. For the deigned low-pass filter, an insertion loss of less than 2dB from dc to 3.5GHz and the rejection is better than 20dB from 4.3 to 15.8GHz. Predicted performances show widened and deepened stopband beyond the low passband. Furthermore, it is confirmed by measurement.


title: Lossy volume compression using Tucker truncation and thresholding

abstract: Tensor decompositions, in particular the Tucker model, are a powerful family of techniques for dimensionality reduction and are being increasingly used for compactly encoding large multidimensional arrays, images and other visual data sets. In interactive applications, volume data often needs to be decompressed and manipulated dynamically; when designing data reduction and reconstruction methods, several parameters must be taken into account, such as the achievable compression ratio, approximation error and reconstruction speed. Weighing these variables in an effective way is challenging, and here we present two main contributions to solve this issue for Tucker tensor decompositions. First, we provide algorithms to efficiently compute, store and retrieve good choices of tensor rank selection and decompression parameters in order to optimize memory usage, approximation quality and computational costs. Second, we propose a Tucker compression alternative based on coefficient thresholding and zigzag traversal, followed by logarithmic quantization on both the transformed tensor core and its factor matrices. In terms of approximation accuracy, this approach is theoretically and empirically better than the commonly used tensor rank truncation method.


title: Anti-influenza Viral Effects of Honey In Vitro: Potent High Activity of Manuka Honey

abstract: Background and Aims Influenza viruses are a serious threat to human health and cause thousands of deaths annually. Thus, there is an urgent requirement for the development of novel anti-influenza virus drugs. Therefore, the aim of this study was to evaluate the anti-influenza viral activity of honey from various sources. Methods Antiviral activities of honey samples were evaluated using MDCK cells. To elucidate the possible mechanism of action of honey, plaque inhibition assays were used. Synergistic effects of honey with known anti-influenza virus drugs such as zanamivir or oseltamivir were tested. Results Manuka honey efficiently inhibited influenza virus replication (IC 50 = 3.6 ± 1.2 mg/mL; CC 50 = 82.3 ± 2.2 mg/mL; selective index = 22.9), which is related to its virucidal effects. In the presence of 3.13 mg/mL manuka honey, the IC 50 of zanamivir or oseltamivir was reduced to nearly 1/1000th of their single use. Conclusions Our results showed that honey, in general, and particularly manuka honey, has potent inhibitory activity against the influenza virus, demonstrating a potential medicinal value.


title: LoRaWAN — A low power WAN protocol for Internet of Things: A review and opportunities

abstract: The Internet of Things (IoT) vision requires increasingly more sensor nodes interconnected and a network solution that may accommodate these requirements accordingly. In wireless sensor networks, there are energy-limited devices; therefore techniques to save energy have become a significant research trend. Other issues such as latency, range coverage, and bandwidth are important aspects in IoT. It is considering the massive number of expected nodes connected to the Internet. The LoRaWAN (Low Power WAN Protocol for Internet of Things), a data-link layer with long range, low power, and low bit rate, appeared as a promising solution for IoT in which, end-devices use LoRa to communicate with gateways through a single hop. While proprietary LPWAN (Low Power Wide Area Network) technologies are already hitting a large market, this paper addresses the LoRa architecture and the LoRaWAN protocol that is expected to solve the connectivity problem of tens of billions of devices in the next decade. Use cases are considered to illustrate its application alongside with a discussion about open issues and research opportunities.


title: Ecological Thresholds: The Key to Successful Environmental Management or an Important Concept with No Practical Application?

abstract: An ecological threshold is the point at which there is an abrupt change in an ecosystem quality, property or phenomenon, or where small changes in an environmental driver produce large responses in the ecosystem. Analysis of thresholds is complicated by nonlinear dynamics and by multiple factor controls that operate at diverse spatial and temporal scales. These complexities have challenged the use and utility of threshold concepts in environmental management despite great concern about preventing dramatic state changes in valued ecosystems, the need for determining critical pollutant loads and the ubiquity of other threshold-based environmental problems. In this paper we define the scope of the thresholds concept in ecological science and discuss methods for identifying and investigating thresholds using a variety of examples from terrestrial and aquatic environments, at ecosystem, landscape and regional scales. We end with a discussion of key research needs in this area.


title: Smoothed Dual Embedding Control

abstract: We revisit the Bellman optimality equation with Nesterov's smoothing technique and provide a unique saddle-point optimization perspective of the policy optimization problem in reinforcement learning based on Fenchel duality. A new reinforcement learning algorithm, called Smoothed Dual Embedding Control or SDEC, is derived to solve the saddle-point reformulation with arbitrary learnable function approximator. The algorithm bypasses the policy evaluation step in the policy optimization from a principled scheme and is extensible to integrate with multi-step bootstrapping and eligibility traces. We provide a PAC-learning bound on the number of samples needed from one single off-policy sample path, and also characterize the convergence of the algorithm. Finally, we show the algorithm compares favorably to the state-of-the-art baselines on several benchmark control problems.


title: A Systems Engineering Approach to Unmanned Aerial Vehicle Design

abstract: Complex UAV systems, due to the high cost and the risks associated with their development become a prime candidate for the adoption of systems engineering methodologies. A successful UAV designer needs not only a good understanding of design, but also systems engineering approach. The design of a UAV begins with the requirements definition and extends through functional analysis and allocation, design synthesis and evaluation, and finally validation. An optimized UAV, with a minimum of undesirable side effects, requires the application of an integrated life-cycle oriented “system” approach. In this paper, conceptual design, preliminary design and detail design of an UAV based on systems engineering approach are introduced. In each stage, application of this approach is described by presenting the design flow chart and practical steps of design. A fair amount of the paper is devoted to the detail design phase.


Imaging case of the month: Abnormalities of the cochlear nerves and internal auditory canals in pontine tegmental cap dysplasia.


title: M-learning in Nigerian higher education: an experimental study with Edmodo

abstract: Social media technologies have recently gained remarkable popularity in the education sector. Recent research indicated that students rely on social media for educational purposes like social networking, chatting, and knowledge sharing. With social media aiding learning experiences, m-learning is anticipated to improve the application of social media. In the paper, we investigated the preference of social media tools and mobile devices for learning, their benefits and effectiveness, and how they can possibly improve learning process in Nigeria. Furthermore, we evaluated learning experiences of students using Edmodo social media-based learning environment in a Nigerian university. We used a mixed method research approach, where the data was triangulated from two questionnaires and students' interviews. Data gathered after the course shows that students learning response was improved. Students were eager to use mobile devices for accessing social media sites, thus providing experimental evidence of the place of social media in m-learning.


title: Sexual differentiation of the human brain: Relation to gender identity, sexual orientation and neuropsychiatric disorders

abstract: Abstract During the intrauterine period a testosterone surge masculinizes the fetal brain, whereas the absence of such a surge results in a feminine brain. As sexual differentiation of the brain takes place at a much later stage in development than sexual differentiation of the genitals, these two processes can be influenced independently of each other. Sex differences in cognition, gender identity (an individual’s perception of their own sexual identity), sexual orientation (heterosexuality, homosexuality or bisexuality), and the risks of developing neuropsychiatric disorders are programmed into our brain during early development. There is no evidence that one’s postnatal social environment plays a crucial role in gender identity or sexual orientation. We discuss the relationships between structural and functional sex differences of various brain areas and the way they change along with any changes in the supply of sex hormones on the one hand and sex differences in behavior in health and disease on the other.


title: A Large-Scale Study of Flash Memory Failures in the Field

abstract: Servers use flash memory based solid state drives (SSDs) as a high-performance alternative to hard disk drives to store persistent data. Unfortunately, recent increases in flash density have also brought about decreases in chip-level reliability. In a data center environment, flash-based SSD failures can lead to downtime and, in the worst case, data loss. As a result, it is important to understand flash memory reliability characteristics over flash lifetime in a realistic production data center environment running modern applications and system software. This paper presents the first large-scale study of flash-based SSD reliability in the field. We analyze data collected across a majority of flash-based solid state drives at Facebook data centers over nearly four years and many millions of operational hours in order to understand failure properties and trends of flash-based SSDs. Our study considers a variety of SSD characteristics, including: the amount of data written to and read from flash chips; how data is mapped within the SSD address space; the amount of data copied, erased, and discarded by the flash controller; and flash board temperature and bus power. Based on our field analysis of how flash memory errors manifest when running modern workloads on modern SSDs, this paper is the first to make several major observations: (1) SSD failure rates do not increase monotonically with flash chip wear; instead they go through several distinct periods corresponding to how failures emerge and are subsequently detected, (2) the effects of read disturbance errors are not prevalent in the field, (3) sparse logical data layout across an SSD's physical address space (e.g., non-contiguous data), as measured by the amount of metadata required to track logical address translations stored in an SSD-internal DRAM buffer, can greatly affect SSD failure rate, (4) higher temperatures lead to higher failure rates, but techniques that throttle SSD operation appear to greatly reduce the negative reliability impact of higher temperatures, and (5) data written by the operating system to flash-based SSDs does not always accurately indicate the amount of wear induced on flash cells due to optimizations in the SSD controller and buffering employed in the system software. We hope that the findings of this first large-scale flash memory reliability study can inspire others to develop other publicly-available analyses and novel flash reliability solutions.


Amplification, Attenuation, and Integration


title: Modeling Character Canvases from Cartoon Drawings

abstract: We introduce a novel technique for the construction of a 3D character proxy, or canvas, directly from a 2D cartoon drawing and a user-provided correspondingly posed 3D skeleton. Our choice of input is motivated by the observation that traditional cartoon characters are well approximated by a union of generalized surface of revolution body parts, anchored by a skeletal structure. While typical 2D character contour drawings allow ambiguities in 3D interpretation, our use of a 3D skeleton eliminates such ambiguities and enables the construction of believable character canvases from complex drawings. Our canvases conform to the 2D contours of the input drawings, and are consistent with the perceptual principles of Gestalt continuity, simplicity, and contour persistence. We first segment the input 2D contours into individual body-part outlines corresponding to 3D skeletal bones using the Gestalt continuation principle to correctly resolve inter-part occlusions in the drawings. We then use this segmentation to compute the canvas geometry, generating 3D generalized surfaces of revolution around the skeletal bones that conform to the original outlines and balance simplicity against contour persistence. The combined method generates believable canvases for characters drawn in complex poses with numerous inter-part occlusions, variable contour depth, and significant foreshortening. Our canvases serve as 3D geometric proxies for cartoon characters, enabling unconstrained 3D viewing, articulation, and non-photorealistic rendering. We validate our algorithm via a range of user studies and comparisons to ground-truth 3D models and artist-drawn results. We further demonstrate a compelling gallery of 3D character canvases created from a diverse set of cartoon drawings with matching 3D skeletons.


title: Morphological Priors for Probabilistic Neural Word Embeddings

abstract: Word embeddings allow natural language processing systems to share statistical information across related words. These embeddings are typically based on distributional statistics, making it difficult for them to generalize to rare or unseen words. We propose to improve word embeddings by incorporating morphological information, capturing shared sub-word features. Unlike previous work that constructs word embeddings directly from morphemes, we combine morphological and distributional information in a unified probabilistic framework, in which the word embedding is a latent variable. The morphological information provides a prior distribution on the latent word embeddings, which in turn condition a likelihood function over an observed corpus. This approach yields improvements on intrinsic word similarity evaluations, and also in the downstream task of part-of-speech tagging.


Bayesian Data Analysis for Data Mining


title: A Survey on Human Motion Detection and Surveillance

abstract: Over years detecting human beings in a video scene of a surveillance system is one of the most active research topics in computer vision. This interest is driven by wide applications in many areas such as virtual reality, smart surveillance and perceptual interface, human gait characterization person counting in a dense crowd, person identification, gender classification, and fall detection for elderly people. Video surveillance system mainly deals with tracking and classification of moving objects. The general processing steps of human motion detection for video surveillance includes modeling of environments, detection of motion, object detection and classification human detection, activity recognition and behavior understanding. The aim of this paper is to review recent developments and analyze future open directions in visual surveillance systems.


title: Fruit and Vegetable Consumption of College Students: What is the Role of Food Culture?

abstract: Our primary objective is to assess the role of culture by linking college students’ current consumption of fruits and vegetables with their produce consumption levels while with family. Using a Tobit model, we analyzed data from an online survey with college students. Family consumption of fruits was highly predictive of the individual’s consumption of fruits. For each unit increase in the reported consumption of fruits (vegetables) with the family, the respondents’ fruit (vegetable) consumption at school increased by 0.65 (0.30) units compared to the base consumption level of 1.95 (1.82) times of fruit intake per day.


title: Cold atmospheric plasma discharged in water and its potential use in cancer therapy

abstract: Cold atmospheric plasma (CAP) has emerged as a novel technology for cancer treatment. CAP can directly treat cells and tissue but such direct application is limited to skin or can be invoked as a supplement during open surgery. In this study we report indirect plasma treatment using CAP discharged in deionized (DI) water using three gases as carriers (argon (Ar), helium (He), and nitrogen (N2)). Plasma stimulated water was applied to the human breast cancer cell line (MDA-MB-231). MTT (3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay tests showed that using Ar plasma had the strongest effect on inducing apoptosis in cultured human breast cancer cells. This result is attributed to the elevated production of reactive oxygen species and reactive nitrogen species in water.


title: Unity Power Factor Isolated Three-Phase Rectifier With Two Single-Phase Buck Rectifiers Based on the Scott Transformer

abstract: This paper proposes an isolated three-phase rectifier power-factor correction using two single-phase buck preregulators in continuous conduction mode. The use of the Scott transformer renders a simple and robust rectifier to operate with unity power factor. With only two active switches, the rectifier is able to generate symmetrical currents in the line and a regulated voltage output without any necessary synchronous switches. The proposed control technique with sinusoidal pulse width modulation uses a feedforward of the output inductor current and only one voltage control regulates the output. Complete simulation results under closed-loop operation are given and a 12-kW prototype has been implemented in the laboratory, which demonstrated to operate successfully with excellent performance, and thus can feasibly be implemented in higher power applications.


title: Robot Programming by Demonstration with situated spatial language understanding

abstract: Robot Programming by Demonstration (PbD) allows users to program a robot by demonstrating the desired behavior. Providing these demonstrations typically involves moving the robot through a sequence of states, often by physically manipulating it. This requires users to be co-located with the robot and have the physical ability to manipulate it. In this paper, we present a natural language based interface for PbD that removes these requirements and enables hands-free programming. We focus on programming object manipulation actions—our key insight is that such actions can be decomposed into known types of manipulator movements that are naturally described using spatial language; e.g., object reference expressions and prepositions. Our method takes a natural language command and the current world state to infer the intended movement command and its parametrization. We implement this method on a two-armed mobile manipulator and demonstrate the different types of manipulation actions that can be programmed with it. We compare it to a kinesthetic PbD interface and we demonstrate our method's ability to deal with incomplete language.


title: Alzheimer's Disease and the Amyloid Cascade Hypothesis: A Critical Review

abstract: Since 1992, the amyloid cascade hypothesis has played the prominent role in explaining the etiology and pathogenesis of Alzheimer's disease (AD). It proposes that the deposition of β-amyloid (Aβ) is the initial pathological event in AD leading to the formation of senile plaques (SPs) and then to neurofibrillary tangles (NFTs), neuronal cell death, and ultimately dementia. While there is substantial evidence supporting the hypothesis, there are also limitations: (1) SP and NFT may develop independently, and (2) SPs and NFTs may be the products rather than the causes of neurodegeneration in AD. In addition, randomized clinical trials that tested drugs or antibodies targeting components of the amyloid pathway have been inconclusive. This paper provides a critical overview of the evidence for and against the amyloid cascade hypothesis in AD and provides suggestions for future directions.


title: Back to the app: the costs of mobile application interruptions

abstract: Smartphone users might be interrupted while interacting with an application, either by intended or unintended circumstances. In this paper, we report on a large-scale observational study that investigated mobile application interruptions in two scenarios: (1) intended back and forth switching between applications and (2) unintended interruptions caused by incoming phone calls. Our findings reveal that these interruptions rarely happen (at most 10% of the daily application usage), but when they do, they may introduce a significant overhead (can delay completion of a task by up to 4 times). We conclude with a discussion of the results, their limitations, and a series of implications for the design of mobile phones.


title: A 0.8-V 0.25-mW Current-Mirror OTA With 160-MHz GBW in 0.18-$\mu{\hbox {m}}$ CMOS

abstract: A low-voltage low-power CMOS operational transconductance amplifier (OTA) with near rail-to-rail output swing is presented in this brief. The proposed circuit is based on the current-mirror OTA topology. In addition, several circuit techniques are adopted to enhance the voltage gain. Simulated from a 0.8-V supply voltage, the proposed OTA achieves a 62-dB dc gain and a gain-bandwidth product of 160 MHz while driving a 2-pF load. The OTA is designed in a 0.18-mum CMOS process. The power consumption is 0.25 mW including the common-mode feedback circuit


title: Miniaturized 1” dual-band GPS antenna element

abstract: In recent years, Global Position System integration into portable devices has called for smaller and more robust GPS antenna elements. Previous works in [1–3] have reduced GPS antenna sizes from 4” down to 1.2”. A slot-loaded dual stacked patch GPS antenna element was presented in [4] occupying only 1” aperture. However, this antenna suffered from poor matching and radiation at the feed junctions causing undue coupling to adjacent GPS elements in an array. To address these issues, in this paper we introduce a new fork-type feed that provides good matching at both GPS bands: 1575MHz (L1 band) and 1225MHz (L2 band). As a result, a reflection coefficient of less than −10dB is achieved at both bands providing gains of 5dBi and 4dBi at 1575MHz and 1225MHz, respectively. Below, we describe the fork-feed design and compare measurement with simulations.


title: The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment

abstract: Objectives: To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. ::: ::: Design: Validation study. ::: ::: Setting: A community clinic and an academic center. ::: ::: Participants: Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score≥17), and 90 healthy elderly controls (NC). ::: ::: Measurements: The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. ::: ::: Results: Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). ::: ::: Conclusion: MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.


Strategies for Competitive Advantage in Electronic Commerce


title: Abstract interpretation frameworks

abstract: Interpretation Frameworks Patrick Cousot LIENS, Ecole Normale Superieure 45, rue d’Ulm 75230 Paris cedex 05 (France) cousot@dmi.ens.fr Radhia Cousot LIX, Ecole Polytechnique 91128 Palaiseau cedex (France) radhia@polytechnique.fr


Towards a definition of the Internet of Things ( IoT )


title: The impact of a mass media campaign on personal risk perception, perceived self-efficacy and on other behavioural predictors

abstract: To determine whether an AIDS prevention mass media campaign influenced risk perception, self-efficacy and other behavioural predictors. We used household survey data collected from 2,213 sexually experienced male and female Kenyans aged 15–39. Respondents were administered a questionnaire asking them about their exposure to branded and generic mass media messages concerning HIV/AIDS and condom use. They were asked questions concerning their personal risk perception, self-efficacy, condom effectiveness, condom availability, and their embarrassment in obtaining condoms. Logistic regression analysis was used to determine the impact of exposure to mass media messages on these predictors of behaviour change. Those exposed to branded advertising messages were significantly more likely to consider themselves at higher risk of acquiring HIV and to believe in the severity of AIDS. Exposure to branded messages was also associated with a higher level of personal self-efficacy, a greater belief in the efficacy of condoms, a lower level of perceived difficulty in obtaining condoms and reduced embarrassment in purchasing condoms. Moreover, there was a dose–response relationship: a higher intensity of exposure to advertising was associated with more positive outcomes. Exposure to generic advertising messages was less frequently associated with positive health beliefs and these relationships were also weaker. Branded mass media campaigns that promote condom use as an attractive lifestyle choice are likely to contribute to the development of perceptions that are conducive to the adoption of condom use.


FEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS


title: Older Adolescents' Motivations for Social Network Site Use: The Influence of Gender, Group Identity, and Collective Self-Esteem

abstract: Abstract This study assessed motives for social network site (SNS) use, group belonging, collective self-esteem, and gender effects among older adolescents. Communication with peer group members was the most important motivation for SNS use. Participants high in positive collective self-esteem were strongly motivated to communicate with peer group via SNS. Females were more likely to report high positive collective self-esteem, greater overall use, and SNS use to communicate with peers. Females also posted higher means for group-in-self, passing time, and entertainment. Negative collective self-esteem correlated with social compensation, suggesting that those who felt negatively about their social group used SNS as an alternative to communicating with other group members. Males were more likely than females to report negative collective self-esteem and SNS use for social compensation and social identity gratifications.


title: Self Tuning Texture Optimization

abstract: The goal of example-based texture synthesis methods is to generate arbitrarily large textures from limited exemplars in order to fit the exact dimensions and resolution required for a specific modeling task. The challenge is to faithfully capture all of the visual characteristics of the exemplar texture, without introducing obvious repetitions or unnatural looking visual elements. While existing non-parametric synthesis methods have made remarkable progress towards this goal, most such methods have been demonstrated only on relatively low-resolution exemplars. Real-world high resolution textures often contain texture details at multiple scales, which these methods have difficulty reproducing faithfully. In this work, we present a new general-purpose and fully automatic self-tuning non-parametric texture synthesis method that extends Texture Optimization by introducing several key improvements that result in superior synthesis ability. Our method is able to self-tune its various parameters and weights and focuses on addressing three challenging aspects of texture synthesis: i irregular large scale structures are faithfully reproduced through the use of automatically generated and weighted guidance channels; ii repetition and smoothing of texture patches is avoided by new spatial uniformity constraints; iii a smart initialization strategy is used in order to improve the synthesis of regular and near-regular textures, without affecting textures that do not exhibit regularities. We demonstrate the versatility and robustness of our completely automatic approach on a variety of challenging high-resolution texture exemplars.


Quantitative Evaluation of Visual Aesthetics of Human-Machine Interaction Interface Layout


title: IGNOR: Image-guided Neural Object Rendering

abstract: We propose a learned image-guided rendering technique that combines the benefits of image-based rendering and GAN-based image synthesis. The goal of our method is to generate photo-realistic re-renderings of reconstructed objects for virtual and augmented reality applications (e.g., virtual showrooms, virtual tours \&sightseeing, the digital inspection of historical artifacts). A core component of our work is the handling of view-dependent effects. Specifically, we directly train an object-specific deep neural network to synthesize the view-dependent appearance of an object. As input data we are using an RGB video of the object. This video is used to reconstruct a proxy geometry of the object via multi-view stereo. Based on this 3D proxy, the appearance of a captured view can be warped into a new target view as in classical image-based rendering. This warping assumes diffuse surfaces, in case of view-dependent effects, such as specular highlights, it leads to artifacts. To this end, we propose EffectsNet, a deep neural network that predicts view-dependent effects. Based on these estimations, we are able to convert observed images to diffuse images. These diffuse images can be projected into other views. In the target view, our pipeline reinserts the new view-dependent effects. To composite multiple reprojected images to a final output, we learn a composition network that outputs photo-realistic results. Using this image-guided approach, the network does not have to allocate capacity on ``remembering'' object appearance, instead it learns how to combine the appearance of captured images. We demonstrate the effectiveness of our approach both qualitatively and quantitatively on synthetic as well as on real data.


title: The Space of Possible Mind Designs

abstract: The paper attempts to describe the space of possible mind designs by first equating all minds to software. Next it proves some properties of the mind design space such as infinitude of minds, size and representation complexity of minds. A survey of mind design taxonomies is followed by a proposal for a new field of investigation devoted to study of minds, intellectology.


title: Global erp implementations and harmonization of practices in multinational corporations: A conceptual framework

abstract: Multinational corporations have been at the front of the ERP movement since its origins. The globalization of markets had a profound effect on multinational corporations’ IT strategies, including ERP systems, to facilitate supporting the global outlook. Despite the increasing growth of global ERP systems, the focus of research has been mostly on the intraorganizational aspects. Implementation of ERP systems in multinational corporations is considerably more difficult due to the presence of different business actors and their heterogeneous interests influenced by the local strategies and context. Inspired by the social and economic theories explaining standardization, convergence and transfer of practices in a network context, this study is intended to provide a descriptive and explanatory framework of factors decisive for harmonization of practices during global ERP implementation in multinational corporations. The framework is tentatively verified based on the secondary data as well as a case study conducted in a multinational corporation.


title: Optical character recognition of the Orthodox Hellenic Byzantine Music notation

abstract: In this paper we present for the first time, the development of a new system for the off-line optical recognition of the characters used in the orthodox Hellenic Byzantine Music notation, that has been established since 1814. We describe the structure of the new system and propose algorithms for the recognition of the 71 distinct character classes, based on Wavelets, 4-projections and other structural and statistical features. Using a nearest neighbor classifier, combined with a post classification schema and a tree-structured classification philosophy, an accuracy of 99.4% was achieved, in a database of about 18,000 Byzantine character patterns that have been developed for the needs of the system.


title: D.: Seisa: set expansion by iterative similarity aggregation

abstract: In this paper, we study the problem of expanding a set of given seed entities into a more complete set by discovering other entities that also belong to the same concept set. A typical example is to use "Canon" and "Nikon" as seed entities, and derive other entities (e.g., "Olympus") in the same concept set of camera brands. In order to discover such relevant entities, we exploit several web data sources, including lists extracted from web pages and user queries from a web search engine. While these web data are highly diverse with rich information that usually cover a wide range of the domains of interest, they tend to be very noisy. We observe that previously proposed random walk based approaches do not perform very well on these noisy data sources. Accordingly, we propose a new general framework based on iterative similarity aggregation, and present detailed experimental results to show that, when using general-purpose web data for set expansion, our approach outperforms previous techniques in terms of both precision and recall.


title: A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering

abstract: In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.


title: Dynamic Consent: A Possible Solution to Improve Patient Confidence and Trust in How Electronic Patient Records Are Used in Medical Research

abstract: With one million people treated every 36 hours, routinely collected UK National Health Service (NHS) health data has huge potential for medical research. Advances in data acquisition from electronic patient records (EPRs) means such data are increasingly digital and can be anonymised for research purposes. NHS England’s care.data initiative recently sought to increase the amount and availability of such data. However, controversy and uncertainty following the care.data public awareness campaign led to a delay in rollout, indicating that the success of EPR data for medical research may be threatened by a loss of patient and public trust. The sharing of sensitive health care data can only be done through maintaining such trust in a constantly evolving ethicolegal and political landscape. We propose that a dynamic consent model, whereby patients can electronically control consent through time and receive information about the uses of their data, provides a transparent, flexible, and user-friendly means to maintain public trust. This could leverage the huge potential of the EPR for medical research and, ultimately, patient and societal benefit.


title: Understanding dynamic capabilities

abstract: Defining ordinary or ‘zero-level’ capabilities as those that permit a firm to ‘make a living’ in the short term, one can define dynamic capabilities as those that operate to extend, modify or create ordinary capabilities. Logically, one can then proceed to elaborate a hierarchy of higher-order capabilities. However, it is argued here that the strategic substance of capabilities involves patterning of activity, and that costly investments are typically required to create and sustain such patterning—for example, in product development. Firms can accomplish change without reliance on dynamic capability, by means here termed ‘ad hoc problem solving.’ Whether higher-order capabilities are created or not depends on the costs and benefits of the investments relative to ad hoc problem solving, and so does the ‘level of the game’ at which strategic competition effectively occurs. Copyright © 2003 John Wiley & Sons, Ltd.


title: Disease concepts and the logic of classes*

abstract: Four different disease concepts are examined. In pre-scientific times, diseases were regarded as independent entities outside and inside the body of patients. This view is still alive in such expressions as ‘disease carrier’ and similar idioms. Sydenham' disease entity was akin to a Platonic ‘idea’ namely as ‘species’, ‘substantial form’ or ‘essence’ which had a singular independent existence in some metaphysical realm. Therefore, the talk at the time was that a patient suffered from, say, ‘the’ pox. Virchow's disease entities were semi-independent parasitic parts in the bodies of different patients. Therefore, patients were said to suffer from, say, ‘a’ pneumonia. In modern times diseases are no longer regarded as independent or semi-independent entities. They are merely attribute complexes in patients. As such, they require no grammatical article. Patients are said to suffer from, say, pneumonia. ::: ::: ::: ::: The different views of disease concepts are finally examined within a framework provided by the modern logic of classes. The pre-scientific and Virchowian disease entities are members in a class ‘extension’. Sydenham's disease entity corresponds to a class entity abstracted from class members. Modern diseases are attributes listed in the class ‘intension’. The different disease concepts thus have different logical implications.


A Leucyl-tRNA Synthetase Without the Editing Domain and its Directed Evolution for Unnatural Protein Engineering


title: Test Results Of A Digital Beamforming GPS Receiver For Mobile Applications

abstract: NAVSYS High Gain Advanced GPS Receiver (HAGR) uses a digital beam-steering antenna array to provide additional gain in the direction of the GPS satellite signals. This increases the received signal/noise ratio on the satellites tracked and also improves the accuracy of the pseudo-range and carrier-phase observations. The directivity of the digital beams created from the antenna array also reduces the effect of jamming and interference. This paper describes the operation of the HAGR digital beam steering array in a jamming environment and includes test data collected from the HAGR at the Electronic Proving Grounds, Fort Huachuca, that demonstrate the performance of a digital, beam-steering receiver against interference sources.


title: BlueTone: a framework for interacting with public displays using dual-tone multi-frequency through bluetooth

abstract: Large information displays are common in public and semi-public spaces but still require rapid and lightweight ways for users to interact with them. We present BlueTone, a framework for developing large display applications which will interpret and react to dual-tone multi-frequency sounds transmitted from mobile phones paired with the display using the Bluetooth headset profile. BlueTone enables text entry, cursor manipulation and menu selection without requiring the installation of any special software on a user's mobile phone.


title: Tensor-based learning for predicting stock movements

abstract: Stock movements are essentially driven by new information. Market data, financial news, and social sentiment are believed to have impacts on stock markets. To study the correlation between information and stock movements, previous works typically concatenate the features of different information sources into one super feature vector. However, such concatenated vector approaches treat each information source separately and ignore their interactions. In this article, we model the multi-faceted investors' information and their intrinsic links with tensors. To identify the nonlinear patterns between stock movements and new information, we propose a supervised tensor regression learning approach to investigate the joint impact of different information sources on stock markets. Experiments on CSI 100 stocks in the year 2011 show that our approach outperforms the state-of-the-art trading strategies.


title: Constant transconductance bias circuit with an on-chip resistor

abstract: A method to generate stable transconductance (g/sub m/) without using precise external components is presented. The off-chip resistor in a conventional constant-g/sub m/ bias circuit is replaced with a variable on-chip resistor. A MOSFET biased in triode region is used as a variable resistor. The resistance of the MOSFET is tuned by a background tuning scheme to achieve the stable transconductance that is immune to process, voltage and temperature variation. The transconductance generated by the constant-g/sub m/ bias circuit designed in 0.18/spl mu/m CMOS process with 1.5F supply displays less than 1% variation for a 20% change in power supply voltage and less than /spl plusmn/1.5% variation for a 60/spl deg/C change in temperature. The whole circuit draws approximately 850/spl mu/A from a supply.


title: Virtual Humans and Persuasion: The Effects of Agency and Behavioral Realism

abstract: Two studies examined whether participant attitudes would change toward positions advocated by an ingroup member even if the latter was known to be an embodied agent; that is, a human-like representation of a computer algorithm. While immersed in a virtual environment, participants listened to a persuasive communication from a digital representation of another student. The latter was actually an embodied agent (a computer-controlled digital representation of a human). Study 1 examined the extent to which gender of the virtual human, participant gender, and the agent's behavior affected attitude change. Results revealed gender-based ingroup favoritism in the form of greater attitude change for same gender virtual humans. Study 2 examined behavioral realism and agency beliefs; that is, whether participants believed the other to be an agent or an avatar (an online representation of an actual person). Results supported Blascovich and colleague's model of social influence within immersive virtual environments. ...


title: PowerScope: a tool for profiling the energy usage of mobile applications

abstract: We describe the design and implementation of PowerScope, a tool for profiling energy usage by applications. PowerScope maps energy consumption to program structure, in much the same way that CPU profilers map processor cycles to specific processes and procedures. Our approach combines hardware instrumentation to measure current level with kernel software support to perform statistical sampling of system activity. Postprocessing software maps the sample data to program structure and produces a profile of energy usage by process and procedure. Using PowerScope, we have been able to reduce the energy consumption of an adaptive video playing application by 46%.


title: A CMOS spiking neuron for dense memristor-synapse connectivity for brain-inspired computing

abstract: Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy-inefficient. Here we describe a novel CMOS spiking leaky integrate-and-fire neuron circuit. Building on a reconfigurable architecture with a single opamp, the described neuron accommodates a large number of memristor synapses, and enables online spike timing dependent plasticity (STDP) learning with optimized power consumption. Simulation results of an 180nm CMOS design showed 97% power efficiency metric when realizing STDP learning in 10,000 memristor synapses with a nominal 1MΩ memristance, and only 13µA current consumption when integrating input spikes. Therefore, the described CMOS neuron contributes a generalized building block for large-scale brain-inspired neuromorphic systems.


title: Movie genre classification with Convolutional Neural Networks

abstract: Automatic pattern recognition from videos is a high-complexity task, and well-established Machine Learning algorithms have difficulties in handling it in an efficient and effective fashion. Convolutional Neural Networks are the state-of-the-art method for supervised image classification, borrowing concepts from image processing in order to ensure some degree of scale and position invariance. They are capable of detecting primary features, which are then combined by subsequent layers of the CNN architecture, resulting in the detection of higher-order complex and relevant novel features. Considering that a video is a set of ordered images in time, we propose in this paper to explore CNNs in the context of movie trailers genre classification. Our contributions are twofold. First, we have developed a novel movie trailers dataset with more than 3500 trailers whose genres are known, and we make it publicly available for the interested reader. Second, we detail a novel classification method that encapsulates a CNN architecture to perform movie trailer genre classification, namely CNN-MoTion, and we compare it with state-of-the-art feature extraction techniques for movie classification such as Gist, CENTRIST, w-CENTRIST, and low-level feature extraction. Results show that our novel method significantly outperforms the current state-of-the-art approaches.


title: A generic framework for ontology-based information retrieval and image retrieval in web data

abstract: In the internet era, search engines play a vital role in information retrieval from web pages. Search engines arrange the retrieved results using various ranking algorithms. Additionally, retrieval is based on statistical searching techniques or content-based information extraction methods. It is still difficult for the user to understand the abstract details of every web page unless the user opens it separately to view the web content. This key point provided the motivation to propose and display an ontology-based object-attribute-value (O-A-V) information extraction system as a web model that acts as a user dictionary to refine the search keywords in the query for subsequent attempts. This first model is evaluated using various natural language processing (NLP) queries given as English sentences. Additionally, image search engines, such as Google Images, use content-based image information extraction and retrieval of web pages against the user query. To minimize the semantic gap between the image retrieval results and the expected user results, the domain ontology is built using image descriptions. The second proposed model initially examines natural language user queries using an NLP parser algorithm that will identify the subject-predicate-object (S-P-O) for the query. S-P-O extraction is an extended idea from the ontology-based O-A-V web model. Using this S-P-O extraction and considering the complex nature of writing SPARQL protocol and RDF query language (SPARQL) from the user point of view, the SPARQL auto query generation module is proposed, and it will auto generate the SPARQL query. Then, the query is deployed on the ontology, and images are retrieved based on the auto-generated SPARQL query. With the proposed methodology above, this paper seeks answers to following two questions. First, how to combine the use of domain ontology and semantics to improve information retrieval and user experience? Second, does this new unified framework improve the standard information retrieval systems? To answer these questions, a document retrieval system and an image retrieval system were built to test our proposed framework. The web document retrieval was tested against three key-words/bag-of-words models and a semantic ontology model. Image retrieval was tested on IAPR TC-12 benchmark dataset. The precision, recall and accuracy results were then compared against standard information retrieval systems using TREC_EVAL. The results indicated improvements over the standard systems. A controlled experiment was performed by test subjects querying the retrieval system in the absence and presence of our proposed framework. The queries were measured using two metrics, time and click-count. Comparisons were made on the retrieval performed with and without our proposed framework. The results were encouraging.