title: A Name Resolution Model for Distributed Systems

abstract: Naming is an important issue in any programming system, especially when the system spans multiple computers. We present a model that describes names and Dame resolution in distributed systems, in which we view names to be purely syntactic entities, and name resolution to be a syntaxdriven operation. We use the model to formally define familiar concepts and terminology involved in naming. We also describe the role of name servers in name resolution. Examples from computer mail, a distributed application that uses many forms of names, illustrate our model. Tbis project is supported in part by grants from Lbe National Science Foundalion (MC5-821917B) and SUN Microsystems Incorporated.


11. Constructing C1 Surfaces of Arbitrary Topology Using Biquadratic and Bicubic Splines


title: Benchmarking 6DOF Urban Visual Localization in Changing Conditions

abstract: Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. We will make our benchmark publicly available.


title: The Wnt code: cnidarians signal the way

abstract: Cnidarians are the simplest metazoans with a nervous system. They are well known for their regeneration capacity, which is based on the restoration of a signalling centre (organizer). Recent work has identified the canonical Wnt pathway in the freshwater polyp Hydra, where it acts in organizer formation and regeneration. Wnt signalling is also essential for cnidarian embryogenesis. In the sea anemone Nematostella vectensis 11 of the 12 known wnt gene subfamilies were identified. Different wnt genes exhibit serial and overlapping expression domains along the oral–aboral axis of the embryo (the ‘wnt code’). This is reminiscent of the hox code (cluster) in bilaterian embryogenesis that is, however, absent in cnidarians. It is proposed that the common ancestor of cnidarians and bilaterians invented a set of wnt genes that patterned the ancient main body axis. Major antagonists of Wnt ligands (e.g. Dkk 1/2/4) that were previously known only from chordates, are also present in cnidarians and exhibit a similar conserved function. The unexpectedly high level of genetic complexity of wnt genes evolved in early multi-cellular animals about 650 Myr ago and suggests a radical expansion of the genetic repertoire, concurrent with the evolution of multi-cellularity and the diversification of eumetazoan body plans.


title: Semi-supervised clustering with metric learning: An adaptive kernel method

abstract: Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel methods for semi-supervised clustering not only face the problem of manually tuning the kernel parameters due to the fact that no sufficient supervision is provided, but also lack a measure that achieves better effectiveness of clustering. In this paper, we propose an adaptive Semi-supervised Clustering Kernel Method based on Metric learning (SCKMM) to mitigate the above problems. Specifically, we first construct an objective function from pairwise constraints to automatically estimate the parameter of the Gaussian kernel. Then, we use pairwise constraint-based K-means approach to solve the violation issue of constraints and to cluster the data. Furthermore, we introduce metric learning into nonlinear semi-supervised clustering to improve separability of the data for clustering. Finally, we perform clustering and metric learning simultaneously. Experimental results on a number of real-world data sets validate the effectiveness of the proposed method.


title: ADHD and Dysgraphia: Underlying Mechanisms

abstract: Abstract Multiple complaints in the domain of writing are common among children with Attention Deficit Hyperactivity Disorder (ADHD). In this work we sought to characterize the writing disorder by studying dysgraphia in twenty 6 th grade boys with ADHD and normal reading skills matched to 20 healthy boys who served as a comparison group. Dysgraphia, defined as deficits in spelling and handwriting, was assessed according to neuropsychological explanatory processes within 3 primary domains: linguistic processing, motor programming and motor kinematics. Children with ADHD made significantly more spelling errors, but showed a unique pattern introducing letter insertions, substitutions, transpositions and omissions. This error type, also known as graphemic buffer errors, can be explained by impaired attention aspects needed for motor planning. Kinematic manifestations of writing deficits were fast, inaccurate and an inefficient written product accompanied by higher levels of axial pen pressure. These results suggest that the spelling errors and writing deficits seen in children with ADHD and normal reading skills stem primarily from non-linguistic deficits, while linguistic factors play a secondary role. Recommendations for remediation include educational interventions, use of word processing and judicious use of ::: psychostimulants.


title: Modeling and simulation of supersonic gas separations

abstract: Abstract Supersonic separation is an operation potentially applicable to gas processing in large scale chemical plants. Supersonic flows are achieved in properly sized converging–diverging nozzles, whose performance is simulated in this paper. The overall simulation procedure consists of different types of thermodynamic calculations, including the determination of phase equilibrium conditions and of thermodynamic sound speed in non-ideal single-phase and multiple-phase systems. The thermodynamic properties of the fluid are evaluated using the Peng–Robinson equation of state, but the procedure is general and compatible with other thermodynamic models. Despite being less detailed than computational fluid dynamics (CFD) simulations previously reported in the literature, the results are in very good agreement with CFD. The proposed procedure constitutes a tool for the preliminary design of supersonic separators that is conceptually simple and accurate, with rigorous evaluations of the thermodynamic properties.


title: SEPARATION OF N-CONTAINING SPECIES BY MEANS OF A NEW VERSATILE ION INTERACTION RP-HPLC METHODOLOGY

abstract: Abstract A new reverse phase HPLC methodology is presented for the separation of nitrites, nitrates, aliphatic and aromatic amines contained in the same mixture. The same basic methodology can be made very versatile by properly varying the experimental chromatographic conditions. Fundamentally, the proposed technique makes use of reverse phase RP-18 columns and of two interaction reagents, namely octylaminium salicylate and octylaminium ortho-phosphate. Conductometric and spectrophotometric (both direct and indirect) detections are employed. Through the appropriate choice of mobile phase, kind of detection and detection wavelength, the methodology makes possible the separation and the definite identification of N-containing species, both inorganic and organic. The method can be very usefully employed in analysis both of drinking water and of waste water.


title: Workload Characterization: A Survey Revisited

abstract: Workload characterization is a well-established discipline that plays a key role in many performance engineering studies. The large-scale social behavior inherent in the applications and services being deployed nowadays leads to rapid changes in workload intensity and characteristics and opens new challenging management and performance issues. A deep understanding of user behavior and workload properties and patterns is therefore compelling. This article presents a comprehensive survey of the state of the art of workload characterization by addressing its exploitation in some popular application domains. In particular, we focus on conventional web workloads as well as on the workloads associated with online social networks, video services, mobile apps, and cloud computing infrastructures. We discuss the peculiarities of these workloads and present the methodological approaches and modeling techniques applied for their characterization. The role of workload models in various scenarios (e.g., performance evaluation, capacity planning, content distribution, resource provisioning) is also analyzed.


title: Reinforcement Learning in Robot Path Optimization

abstract: Along with the development of robot technology, a robot not only need to complete a specific task, but aslo need to do path planning in the process of performing the task. So, path planning is widly studied. This paper introduce a method of robot path planning based on reinforcement learning,which aimed at Markovian decision process. In this paper, we introduce the basic concept, principle and the method of reinforcement learning and some other algorithms.Then,we do research from single robot’s path planning in the static invironment based on Q-learning, and describe the application of this algorithm on the path planning by setting off state space and action space reasonablly and designing reinforcement function.By edditting Matlab program,we do some simulation experiments,which incarnate the algorithm visually and get the optimal path.


title: Plasma rich in growth factors (PRGF) for the treatment of androgenetic alopecia

abstract: Background ::: Androgenetic alopecia (AGA) is a common hair disorder that affects both men and women all over the world. Although it is far from being a life-threatening disease, the psychosocial impact of AGA can provoke stress and a significant decrease in life quality. The objective of this retrospective study is to investigate the safety and efficacy of autologous plasma rich in growth factors (PRGF) for the treatment of AGA.


Sentiment Analysis of Yelp ‘ s Ratings Based on Text


title: The SGGX microflake distribution

abstract: We introduce the Symmetric GGX (SGGX) distribution to represent spatially-varying properties of anisotropic microflake participating media. Our key theoretical insight is to represent a microflake distribution by the projected area of the microflakes. We use the projected area to parameterize the shape of an ellipsoid, from which we recover a distribution of normals. The representation based on the projected area allows for robust linear interpolation and prefiltering, and thanks to its geometric interpretation, we derive closed form expressions for all operations used in the microflake framework. We also incorporate microflakes with diffuse reflectance in our theoretical framework. This allows us to model the appearance of rough diffuse materials in addition to rough specular materials. Finally, we use the idea of sampling the distribution of visible normals to design a perfect importance sampling technique for our SGGX microflake phase functions. It is analytic, deterministic, simple to implement, and one order of magnitude faster than previous work.


title: Improved route planning and scheduling of waste collection and transport

abstract: The collection of waste is a highly visible and important municipal service that involves large expenditures. Waste collection problems are, however, one of the most difficult operational problems to solve. This paper describes the optimization of vehicle routes and schedules for collecting municipal solid waste in Eastern Finland. The solutions are generated by a recently developed guided variable neighborhood thresholding metaheuristic that is adapted to solve real-life waste collection problems. Several implementation approaches to speed up the method and cut down the memory usage are discussed. A case study on the waste collection in two regions of Eastern Finland demonstrates that significant cost reductions can be obtained compared with the current practice.


title: Flying sensors: Merging Nano-UAV with radiofrequency identification

abstract: The concurrent evolutions of micro/nano Unmanned Aerial Vehicles (UAVs) and sensor-oriented Radio frequency identification (RFID) technology, prompts the idea of winged-tags for an ubiquitous and dynamic monitoring of harsh environments and of large indoors spaces. It is here described and experimentally characterized the concept of Tag-Drone comprising a low-cost nano-quadcopter and a miniaturized RFID tag having temperature sensing capability. The weight of the RFID sensor is compatible with the modest payload of the quadcopter and it can be interrogated up to three meters. A simple experimental procedure, involving a reproducible guided flight of the Tag-Drone, is hence described to estimate the visibility time windows with respect to a base station for on-the-flight data exchange in case of different operational modes.


title: Promising Secure Element Alternatives for NFC Technology

abstract: In 2004, The Near Field Communication (NFC) forum was formed to promote and supervise the use of the NFC technology. Nowadays, 150 members are taking part init and a growing number of stakeholders are fully involved in the development of this emerging technology. Consequently, many pilots have been launched worldwide in order to test and improve it. Issues related to the level of security that should be provided by the NFC handset to store personal data and application in a safe place have then arisen. Indeed, multiple secure element alternatives, depending on the position of secure element in the handset can be considered and NFC stakeholders encounter difficulties to define which of them should be favored. After giving a brief overview over the NFC technology, the role,as well as the possible position of the secure element in the NFC enabled handset will be highlighted. Then, a discussion will be engaged in order to define what could be the most promising secure element alternatives. Afterwards, by focusing on NFC pilots in which our laboratory is taking part into, this paper will describe technical architectures currently in use. Finally, a conclusion on the topic will be provided in the last section.


title: An Observer of Tire–Road Forces and Friction for Active Security Vehicle Systems

abstract: The estimation of vehicle dynamic variables is essential for the enhancement of safety, in particular for braking and trajectory-control systems. This paper proposes a new estimation process to calculate lateral tire forces, vehicle sideslip angle, and road friction. The estimation process (an adaptive observer) is constructed by combining a vehicle model and a tire force model. More specifically, this study proposes an adaptive tire force model that takes variations in road friction into account. The adaptive observer is evaluated in comparison with two nonadaptive observers that use tire force models with fixed parameters. This study also proposes a road friction identification method operating in an online context. The observers and the road friction identification method are first evaluated using vehicle simulator software. Subsequently, observers are compared to real data acquired using an experimental vehicle. This paper also includes a description of the experimental platform. Results show the accuracy and potential of the estimation process.


title: TECHNOLOGY ADOPTION IN THE BIM IMPLEMENTATION FOR LEAN ARCHITECTURAL PRACTICE

abstract: Abstract Justification for research The construction companies are facing barriers and challenges in BIM adoption as there is no clear guidance or best practice studies from which they can learn and build up their capacity for BIM use in order to increase productivity, efficiency, quality, and to attain competitive advantages in the global market and to achieve the targets in environmental sustainability. Purpose This paper aims to explain a comprehensive and systemic evaluation and assessment of the relevant BIM technologies as part of the BIM adoption and implementation to demonstrate how efficiency gains have been achieved towards a lean architectural practice. Design/methodology/approach The research is undertaken through a KTP (Knowledge Transfer Partnership) project between the University of Salford and the John McCall Architects based in Liverpool, which is an SME (Small Medium Enterprise). The overall aim of KTP is to develop a lean design practice through the BIM adoption and implementation. The overall BIM implementation approach uses a socio-technical view in which it does not only consider the implementation of technology but also considers the socio-cultural environment that provides the context for its implementation. The technology adoption methodology within the BIM implementation approach is the action research oriented qualitative and quantitative research for discovery, comparison, and experimentation as the KTP project with JMA provides an environment for “learning by doing”. Findings Research has proved that BIM technology adoption should be undertaken with a bottom-up approach rather than top-down approach for successful change management and dealing with the resistance to change. As a result of the BIM technology adoption, efficiency gains are achieved through the piloting projects and the design process is improved through the elimination of wastes and value generation. Originality/value Successful BIM adoption needs an implementation strategy. However, at operational level, it is imperative that professional guidelines are required as part of the implementation strategy. This paper introduces a systematic approach for BIM technology adoption based on a case study implementation and it demonstrates a guideline at the operational level for other architectural companies of architectural practices.


title: Credibilist occupancy grids for vehicle perception in dynamic environments

abstract: In urban environments, moving obstacles detection and free space determination are key issues for driving assistance systems and autonomous vehicles. When using lidar sensors scanning in front of the vehicle, uncertainty arises from ignorance and errors. Ignorance is due to the perception of new areas and errors come from imprecise pose estimation and noisy measurements. Complexity is also increased when the lidar provides multi-echo and multi-layer information. This paper presents an occupancy grid framework that has been designed to manage these different sources of uncertainty. A way to address this problem is to use grids projected onto the road surface in global and local frames. The global one generates the mapping and the local one is used to deal with moving objects. A credibilist approach is used to model the sensor information and to do a global fusion with the world-fixed map. Outdoor experimental results carried out with a precise positioning system show that such a perception strategy increases significantly the performance compared to a standard approach.


title: HEVC tile based streaming to head mounted displays

abstract: 360° video streaming to clients using Virtual Reality head mounted displays is a challenge for traditional video delivery. As transmission of the complete content in a desirable quality sacrifices a large fraction of available client and network resources, adaptivity to the user viewport promises substantial benefits. An efficient way to achieve viewport adaptive streaming without per-user or per-orientation encoding, i.e. essentially transcoding, is to make use of motion-constrained HEVC tiles. DASH can be used for tiled streaming, where tiled content resides on the server at multiple resolutions. The DASH client selects the resolutions of each tile according to the current viewport. This demonstration paper presents an agile and responsive streaming prototype system for 360° video content. In order to achieve acceptable responsiveness, the DASH client relies on small buffer sizes and shifted random access points across tiles when suitable.


Merging brain-computer interfaces and virtual reality


title: Summary in context: Searching versus browsing

abstract: The use of text summaries in information-seeking research has focused on query-based summaries. Extracting content that resembles the query alone, however, ignores the greater context of the document. Such context may be central to the purpose and meaning of the document. We developed a generic, a query-based, and a hybrid summarizer, each with differing amounts of document context. The generic summarizer used a blend of discourse information and information obtained through traditional surface-level analysis. The query-based summarizer used only query-term information, and the hybrid summarizer used some discourse information along with query-term information. The validity of the generic summarizer was shown through an intrinsic evaluation using a well-established corpus of human-generated summaries. All three summarizers were then compared in an information-seeking experiment involving 297 subjects. Results from the information-seeking experiment showed that the generic summaries outperformed all others in the browse tasks, while the query-based and hybrid summaries outperformed the generic summary in the search tasks. Thus, the document context of generic summaries helped users browse, while such context was not helpful in search tasks. Such results are interesting given that generic summaries have not been studied in search tasks and the that majority of Internet search engines rely solely on query-based summaries.


title: Adapting skyline computation to the MapReduce framework: algorithms and experiments

abstract: This paper addresses the problem of skyline computation under the MapReduce framework. As a parallel programming model for data-intensive computing applications, MapReduce runs on a cluster of commercial PCs with the main idea of task decomposition and result reduction. Based on different data partitioning strategies, three MapReduce style skyline computation algorithms are developed: MapReduce based BNL (MR-BNL), MapReduce based SFS (MR-SFS) and MapReduce based Bitmap (MR-Bitmap). Extensive experiments are conducted to evaluate and compare the three algorithms under different settings of data distribution, dimensionality, buffer size and cluster size.


title: Detection and visualization of vortices

abstract: A vortex is characterized by the swirling motion of fluid around a central region. This characterization stems from the visual perception of swirling phenomena that are pervasive throughout the natural world. However, translating this intuitive description of a vortex into a formal definition has been quite a challenge. Despite the lack of a formal definition, various detection algorithms have been implemented that can adequately identify vortices in most computational datasets. This chapter presents an overview of existing detection methods; in particular, the focus is on nine methods that are representative of the state of the art. The chapter begins by presenting three taxonomies for classifying these nine detection methods. It then describes each algorithm, along with pseudo-code where appropriate. Next, the chapter describes a recently developed verification algorithm for swirling flows. The chapter also discusses the different visualization techniques for vortices.


title: Big Log Data Stream Processing: Adapting an Anomaly Detection Technique

abstract: With the continuous increase in data velocity and volume nowadays, preserving system and data security is particularly affected. In order to handle the huge amount of data and to discover security incidents in real-time, analyses of log data streams are required. However, most of the log anomaly detection techniques fall short in considering continuous data processing. Thus, this paper aligns an anomaly detection technique for data stream processing. It thereby provides a conceptual basis for future adaption of other techniques and further delivers proof of concept by prototype implementation.


title: Linearity and Efficiency Improvements in Phased-Array Transmitters with Large Number of Elements and Complex Modulation

abstract: Phased array transmit systems use several antennas to combine RF power-amplifiers in free space. If all the RF channels are identical, then the transmit spectrum in the far-field, including both linear and nonlinear components, would be a scaled version of the output spectrum of each channel. However, random variations in the nonlinear components (AM-AM and AM-PM conversion) between the channels improves the nonlinearity of the overall array as the number of elements increases. In this paper, measured results are used to show that the adjacent channel power ratio, which is one metric of linearity, improves with the number of elements at a fixed backoff from the 1 dB compression point. Also, for a 100 Mbaud 64QAM signal and a fixed ACPR of −32 dBc, a 256-element phased-array can be operated at $P_{1dB}-2\mathbf{dB}$ , while an 8-element phased-array would need to be operated at $P_{1dB}-4\mathbf{dB}$ for the same ACPR level. This work has great implications on the overall efficiency of 5G phased arrays since it implies that large phased-arrays can be operated at less back-off than small phased-arrays (or single antennas with high power amplifiers). Thus, in reality and taking the ACPR as the figure of merit, phased-arrays are 2 dB more efficient than what is predicted by standard system simulations which assume the same non-linear response for all the phased-array channels.


title: UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks

abstract: We present a novel method for neural network quantization that emulates a non-uniform $k$-quantile quantizer, which adapts to the distribution of the quantized parameters. Our approach provides a novel alternative to the existing uniform quantization techniques for neural networks. We suggest to compare the results as a function of the bit-operations (BOPS) performed, assuming a look-up table availability for the non-uniform case. In this setup, we show the advantages of our strategy in the low computational budget regime. While the proposed solution is harder to implement in hardware, we believe it sets a basis for new alternatives to neural networks quantization.


title: A 1.5 nW, 32.768 kHz XTAL Oscillator Operational From a 0.3 V Supply

abstract: This paper presents an ultra-low power crystal (XTAL) oscillator circuit for generating a 32.768 kHz clock source for real-time clock generation. An inverting amplifier operational from 0.3 V $V_{DD}$ oscillates the XTAL resonator and achieves a power consumption of 2.1 nW. A duty-cycling technique powers down the XTAL amplifier without losing the oscillation and reduces the power consumption to 1.5 nW. The proposed circuit is implemented in 130 nm CMOS with an area of $0.0625\ \text{mm}^{2}$ and achieves a temperature stability of 1.85 ppm/°C.


title: A multi-frame graph matching algorithm for low-bandwidth RGB-D SLAM

abstract: This paper presents a novel multi-frame graph matching algorithm for reliable partial alignments among point clouds. We use this algorithm to stitch frames for 3D environment reconstruction. The idea is to utilize both descriptor similarity and mutual spatial coherency of features existed in multiple frames to match these frames. The proposed multi-frame matching algorithm can extract coarse correspondence among multiple point clouds more reliably than pairwise matching algorithms, especially when the data are noisy and the overlap is relatively small. When there are insufficient consistent features that appeared in all these frames, our algorithm reduces the number of frames to match to deal with it adaptively. Hence, it is particularly suitable for cost-efficient robotic Simultaneous Localization and Mapping (SLAM). We design a prototype system integrating our matching and reconstruction algorithm on a remotely controlled navigation iRobot, equipped with a Kinect and a Raspberry Pi. Our reconstruction experiments demonstrate the effectiveness of our algorithm and design. A novel multi-frame graph matching algorithm for reliable partial matching.An online SLAM algorithm for noisy RGBD data sequence with sparse sampling rate.A prototype system using iRobot, Raspberry Pi, and Kinect for 3D indoor mapping.


title: Affect-aware tutors: Recognizing and responding to student affect

abstract: Theories and technologies are needed to understand and integrate the knowledge of student affect (e.g., frustration, motivation and self-confidence) into learning models. Our goals are to redress the cognitive versus affective imbalance in teaching systems, develop tools that model student affect and build tutors that elicit, measure and respond to student affect. This article describes our broad approach towards this goal and our three main objectives: develop tools for affect recognition, interventions in response to student affect, and emotionally animated agents.


title: Blockchain consensus mechanisms : the case of natural disasters

abstract: Blockchain is described as a trustworthy distributed service for parties that do not fully trust each other. It enables business transactions to be handled without a third party or central governan ...


title: Low cost high gain triple band mmWave Sierpinski antenna loaded with uniplanar EBG for 5G applications

abstract: A triple band mmWave microstrip antenna on low-cost FR4 substrate is proposed in this paper. The triple band is achieved using Sierpinski fractal structure with an optimized ground plane. The resonances observed are at 29.05 GHz, 35.75 GHz and 41 GHz with 10 dB impedance bandwidths of 4 GHz, 2.06 GHz and 2 GHz respectively. These bands fall into the candidate bands for upcoming 5G applications. The proposed unloaded triple band antenna has low gain throughout these bands, hence a uniplanar optimized EBG structure is proposed for gain enhancement across the intended bands of operation. The reflection-phase of various structures of EBG unit cells were characterized to obtain the optimum design. The optimum unit cell was loaded onto the triple band antenna to obtain Peak Gain of 2.37 dBi and 17.37 dBi in the 28 and 38 GHz bands, respectively. The proposed EBG loaded antenna is fabricated and characterized.


Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing


title: Substrate selectivity in starch polysaccharide monooxygenases

abstract: Degradation of polysaccharides is central to numerous biological and industrial processes. Starch-active polysaccharide monooxygenases (AA13 PMOs) oxidatively degrade starch and can potentially be used with industrial amylases to convert starch into a fermentable carbohydrate. The oxidative activities of the starch-active PMOs from the fungi Neurospora crassa and Myceliophthora thermophila, NcAA13 and MtAA13, respectively, on three different starch substrates are reported here. Using high-performance anion-exchange chromatography coupled with pulsed amperometry detection (HPAEC-PAD), we observed that both enzymes have significantly higher oxidative activity on amylose than on amylopectin and cornstarch. Analysis of the product distribution revealed that NcAA13 and MtAA13 more frequently oxidize glycosidic linkages separated by multiples of a helical turn consisting of six glucose units on the same amylose helix. Docking studies identified important residues that are involved in amylose binding and suggest that the shallow groove that spans the active-site surface of AA13 PMOs favors the binding of helical amylose substrates over non-helical substrates. Truncations of NcAA13 that removed its native carbohydrate-binding module resulted in diminished binding to amylose, but truncated NcAA13 still favored amylose oxidation over other starch substrates. These findings establish that AA13 PMOs preferentially bind and oxidize the helical starch substrate amylose. Moreover, the product distributions of these two enzymes suggest a unique interaction with starch substrates.


title: Design and Optimization of a 3-Coil Inductive Link for Efficient Wireless Power Transmission

abstract: Inductive power transmission is widely used to energize implantable microelectronic devices (IMDs), recharge batteries, and energy harvesters. Power transfer efficiency (PTE) and power delivered to the load (PDL) are two key parameters in wireless links, which affect the energy source specifications, heat dissipation, power transmission range, and interference with other devices. To improve the PTE, a 4-coil inductive link has been recently proposed. Through a comprehensive circuit-based analysis that can guide a design and optimization scheme, we have shown that despite achieving high PTE at larger coil separations, the 4-coil inductive links fail to achieve a high PDL. Instead, we have proposed a 3-coil inductive power transfer link with comparable PTE over its 4-coil counterpart at large coupling distances, which can also achieve high PDL. We have also devised an iterative design methodology that provides the optimal coil geometries in a 3-coil inductive power transfer link. Design examples of 2-, 3-, and 4-coil inductive links have been presented, and optimized for a 13.56-MHz carrier frequency and 12-cm coupling distance, showing PTEs of 15%, 37%, and 35%, respectively. At this distance, the PDL of the proposed 3-coil inductive link is 1.5 and 59 times higher than its equivalent 2- and 4-coil links, respectively. For short coupling distances, however, 2-coil links remain the optimal choice when a high PDL is required, while 4-coil links are preferred when the driver has large output resistance or small power is needed. These results have been verified through simulations and measurements.


Bioaugmentation for Electricity Generation from Corn Stover Biomass Using Microbial Fuel Cells


Sentiment Analysis Using Common-Sense and Context Information


BBN/UMD at DUC-2004: Topiary


title: QoS computation and policing in dynamic web service selection

abstract: The emerging Service-Oriented Computing (SOC) paradigm promises to enable businesses and organizations to collaborate in an unprecedented way by means of standard web services. To support rapid and dynamic composition of services in this paradigm, web services that meet requesters' functional requirements must be able to be located and bounded dynamically from a large and constantly changing number of service providers based on their Quality of Service (QoS). In order to enable quality-driven web service selection, we need an open, fair, dynamic and secure framework to evaluate the QoS of a vast number of web services. The fair computation and enforcing of QoS of web services should have minimal overhead but yet able to achieve sufficient trust by both service requesters and providers. In this paper, we presented our open, fair and dynamic QoS computation model for web services selection through implementation of and experimentation with a QoS registry in a hypothetical phone service provisioning market place application.


title: Linear-time algorithms for linear programming in R3 and related problems

abstract: Linear-time algorithms for linear programming in $R^2 $ and $R^3 $ are presented. The methods used are applicable for other graphic and geometric problems as well as quadratic programming. For exam...


title: Theorizing the IT Governance Role in IT Sourcing Research

abstract: Previous research has identified significant risks involved in IT sourcing, which can undermine business performance. IT governance research has proposed mechanisms to mitigate some of these risks. While this relationship seems evident, it remains unexplored in most IT sourcing research. Our review of IT sourcing articles published in the top three IS journals shows that, only 48% addressed governance issues, and fewer (36%) modelled its effect on IT sourcing. As such, opportunities to address IT sourcing issues from the perspective of IT governance are underutilized. We identify this gap, and provide a roadmap for researchers to explicitly tie IT sourcing to IT governance. We contribute by developing a framework to explore a large body of work and systematically analyzed and categorized it to reveal theoretical underpinnings and major findings. Furthermore, we identify gaps in the literature relating to specific governance mechanisms, and propose research questions to motivate future research.


title: LLC series resonant converter based LED lamp driver with ZVS

abstract: This paper proposes an LLC resonant converter based LED (Light Emitting Diode) lamp driver with high power factor. The proposed circuit uses a boost converter for PFC (Power Factor Correction) which operates in continuous conduction mode (CCM) and a quasi half bridge resonant converter to drive the LED lamp load. The LLC converter is designed such that solid state switches of quasi half bridge are working under zero voltage switching (ZVS) to reduce switching losses. The analysis, design, modeling and simulation of 50 W LED driver are carried out using MATLAB/Simulink tool for universal AC mains. The power quality indices are calculated such as total harmonic distortion of AC mains current (THD i ), power factor (PF) and crest factor (CF) to evaluate the performance of proposed LED lamp driver.


Virtual, Augmented, and Mixed Realities in Education


title: Broadband 3 x 3 butler matrix

abstract: In this paper, a novel design of a broadband 3 x 3 Butler matrix is proposed. It is composed of three single-section coupled-line directional couplers and two Schiffman phase shifters and operates in the frequency range 2.5 GHz – 3.5 GHz. The fabricated 3 x 3 Butler matrix features very good frequency response in terms of transmission imbalance, isolation and impedance match.


title: SemEval-2018 Task 3: Irony Detection in English Tweets

abstract: This paper presents the first shared task on irony detection: given a tweet, automatic natural language processing systems should determine whether the tweet is ironic (Task A) and which type of irony (if any) is expressed (Task B). The ironic tweets were collected using irony-related hashtags (i.e. #irony, #sarcasm, #not) and were subsequently manually annotated to minimise the amount of noise in the corpus. Prior to distributing the data, hashtags that were used to collect the tweets were removed from the corpus. For both tasks, a training corpus of 3,834 tweets was provided, as well as a test set containing 784 tweets. Our shared tasks received submissions from 43 teams for the binary classification Task A and from 31 teams for the multiclass Task B. The highest classification scores obtained for both subtasks are respectively F1= 0.71 and F1= 0.51 and demonstrate that fine-grained irony classification is much more challenging than binary irony detection.


title: BackDoor: Making Microphones Hear Inaudible Sounds

abstract: Consider sounds, say at 40kHz, that are completely outside the human's audible range (20kHz), as well as a microphone's recordable range (24kHz). We show that these high frequency sounds can be designed to become recordable by unmodified microphones, while remaining inaudible to humans. The core idea lies in exploiting non-linearities in microphone hardware. Briefly, we design the sound and play it on a speaker such that, after passing through the microphone's non-linear diaphragm and power-amplifier, the signal creates a "shadow" in the audible frequency range. The shadow can be regulated to carry data bits, thereby enabling an acoustic (but inaudible) communication channel to today's microphones. Other applications include jamming spy microphones in the environment, live watermarking of music in a concert, and even acoustic denial-of-service (DoS) attacks. This paper presents BackDoor, a system that develops the technical building blocks for harnessing this opportunity. Reported results achieve upwards of 4kbps for proximate data communication, as well as room-level privacy protection against electronic eavesdropping.


title: Crowd Wisdom: The Impact of Opinion Diversity and Participant Independence on Crowd Performance

abstract: Recent advances in information technologies such as Web 2.0 have dramatically expanded the use of crowd wisdom in dealing with a wide range of problems. Prior research confirms the importance of crowd wisdom in the context of stock markets, but fails to investigate the impact of crowd characteristics on crowd performance. We study the influence of opinion diversity and participant independence on the crowd performance in the context of online investment communities. Instead of using a survey research methodology, which is usually time-consuming and limited to a small sample, we develop text mining based measures for the variables in our research model. The empirical results show that opinion distance is positively related to crowd performance. Opinion content similarity and participant dependence are negatively associated with crowd performance. Crowd size significantly moderates the relationships between crowd characteristics and crowd performance. Related theoretical and practical implications are also discussed.


title: End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware

abstract: Malware allegedly developed by nation-states, also known as advanced persistent threats (APT), are becoming more common. The task of attributing an APT to a specific nation-state or classifying it to the correct APT family is challenging for several reasons. First, each nation-state has more than a single cyber unit that develops such malware, rendering traditional authorship attribution algorithms useless. Furthermore, the dataset of such available APTs is still extremely small. Finally, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. In this paper, we use a deep neural network (DNN) as a classifier for nation-state APT attribution. We record the dynamic behavior of the APT when run in a sandbox and use it as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. We also use the same raw features for APT family classification. Finally, we use the feature abstractions learned by the APT family classifier to solve the attribution problem. Using a test set of 1000 Chinese and Russian developed APTs, we achieved an accuracy rate of 98.6%.


title: A molecular molybdenum-oxo catalyst for generating hydrogen from water

abstract: A major pursuit in the chemical community involves the search for efficient and inexpensive catalysts that can produce large quantities of hydrogen gas from water. Here, a molybdenum-oxo complex has been identified that can catalytically generate hydrogen gas either from pure water at neutral pH, or from sea water. The work has implications for the design of 'green' chemistry cycles.


title: Historical trends in contamination of estuarine and coastal sediments: An introduction to the dedicated issue

abstract: This dedicated issue of ESTUARIES focuses primarily on the use of dated sediment cores for environmental research. The composition of surface waters (river, lake, and coastal) has changed over time. Well known at present are the changes due to the Industrial Revolution, dating from the middle of the last century. These changes are expressed by increased levels of natural components like heavy metals , po lya roma t i c h y d r o c a r b o n s (PAHs), nutrients, and additionally, since the start of this century, xenobiotic compounds like polychlorinated biphenyls (PCBs) and pesticides. Since the early 1960s, regulatory measures have been taken to decrease the amount of pollutants entering our waterways. Because of a lack of data, the extent of past pollution and the effect of recent limitations is often difficult to assess. Befbre the 1960s, metal and nutr ient research was mainly an academic exercise and surface waters were not analyzed on a regular basis. In addition, the analytical methods necessary to analyze the various chemicals enter ing our waterways, in particular, organic compounds, did not exist. The analysis of sediment cores presents a way out of this dilemma. Sediments are continually deposited in lakes, rivers, and estuaries. Most pollutants have an affinity for and adsorb easily onto sediments and fine particles. There tbre , by analyzing cores of undisturlwcl scciinlt'llt, il is I.~s,~ihle to assess the historic P I l u t i ~ m ~1 :~ givcn sysleln and determine the elfi'cl ivt'm.ss <~1' regulalcwy measures, the majority of which were institmt,dsim'r the 1970s. Sedimeu! t:~wes ch~ m~t rt,lh,cl (lilly lilt' history of pollutanl ('ollcr lint, ill a(hlition, the changes in ec~d.gy q~l '.t water b.d),. Igw example, changes il.I t'utrcqflficalic~ll art' relh'cled m organic matter, nil r()gt ' l l , i~h~sphcwc~s, and carbonate content, whih, ]akc m'i~lilicatitu! II'al|sl:tlt's into a change in the ttialcml :lsscmblagcs. I"-r hmger time-related modili,'aliq,ls, c.rt,s can als . Iw used to detect global climale ch:ll|gcs. The use of cored setlimeuls Ic) ret ' ( ) l lS l l ' t l ( ' l t i l t ' chronology of coastal o r es t t i q r i i l ( , ('()lll:tlllin~ltir is not, however, (tev.i(I o1 iw~flfl(,ms aut l cattlioll has to be exercised. Sedimt,.l mixing by biol.gical or physical processes c;m <)hs(' l lr t , lilt' rt'sllits oi)tained by such studies. In a~hliliqm, diagcm.sis and contaminants mobility have Ilit' very carel'ully evaluated in order to exlr;.l('l reliable resHIIs. Studies on dated setlimctll c~wes ]lave many applications and these procec(liugs <fl'tlle Special Session of the Estuarine Rese:lvt'h l"e(h'r:|lion Meelillg held in San Francisco ill N(wemht,r 1991, highlighl some of their current al)lflical iotas ill t,nvironmelltal sciences. One of the m..s t i lnl)()rt ; l l l l restlllS t<) date is the observed declim, in re(ell( yt'ars ()[coll-


title: OMSV enables accurate and comprehensive identification of large structural variations from nanochannel-based single-molecule optical maps

abstract: Human genomes contain structural variations (SVs) that are associated with various phenotypic variations and diseases. SV detection by sequencing is incomplete due to limited read length. Nanochannel-based optical mapping (OM) allows direct observation of SVs up to hundreds of kilo-bases in size on individual DNA molecules, making it a promising alternative technology for identifying large SVs. SV detection from optical maps is non-trivial due to complex types of error present in OM data, and no existing methods can simultaneously handle all these complex errors and the wide spectrum of SV types. Here we present a novel method, OMSV, for accurate and comprehensive identification of SVs from optical maps. OMSV detects both homozygous and heterozygous SVs, SVs of various types and sizes, and SVs with and without creating/destroying restriction sites. In an extensive series of tests based on real and simulated data, OMSV achieved both high sensitivity and specificity, with clear performance gains over the latest existing method. Applying OMSV to a human cell line, we identified hundreds of SVs >2kbp, with 65% of them missed by sequencing-based callers. Independent experimental validations confirmed the high accuracy of these SVs. We also demonstrate how OMSV can incorporate sequencing data to determine precise SV break points and novel sequences in the SVs not contained in the reference. We provide OMSV as open-source software to facilitate systematic studies of large SVs.


title: Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec Approach

abstract: Traditional disease surveillance can be augmented with a wide variety of real-time sources such as, news and social media. However, these sources are in general unstructured and, construction of surveillance tools such as taxonomical correlations and trace mapping involves considerable human supervision. In this paper, we motivate a disease vocabulary driven word2vec model (Dis2Vec) to model diseases and constituent attributes as word embeddings from the HealthMap news corpus. We use these word embeddings to automatically create disease taxonomies and evaluate our model against corresponding human annotated taxonomies. We compare our model accuracies against several state-of-the art word2vec methods. Our results demonstrate that Dis2Vec outperforms traditional distributed vector representations in its ability to faithfully capture taxonomical attributes across different class of diseases such as endemic, emerging and rare.


title: Uniform Luminance Light Field Near Eye Display Using Pinhole Arrays and Gradual Virtual Aperture

abstract: In conventional light field near eye display (LF-NED) using pinhole aperture arrays, as the pupil size changes under different conditions, the overlapping area of adjacent elemental images is not certain. This is the primary cause of an uncertain net-shaped bright-dark pattern on the image plane. In this paper, a proof-of-concept light field near eye display prototype for virtual reality applications based on pinhole aperture arrays is built, with a binocular field of view (FOV) of 84°×77°. Then, we design gradual virtual apertures on the original light field image, to relieve the net-shaped bright-dark pattern. By blocking the useless part of the image and modulating the luminance of the overlapping areas gradually according to their positions, the bright-dark pattern is effectively relieved and an approximately uniform luminance distribution on the image plane is generated.


title: Visualizing evolving networks: minimum spanning trees versus pathfinder networks

abstract: Network evolution is an ubiquitous phenomenon in a wide variety of complex systems. There is an increasing interest in statistically modeling the evolution of complex networks such as small-world networks and scale-free networks. In this article, we address a practical issue concerning the visualizations of co-citation networks of scientific publications derived by two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and pathfinder networks (PFNETs). Our primary goal is to identify the strengths and weaknesses of the two methods in fulfilling the need for visualizing evolving networks. Two criteria are derived for assessing visualizations of evolving networks in terms of topological properties and dynamical properties. We examine the animated visualization models of the evolution of botulinum toxin research in terms of its co-citation structure across a 58-year span (1945-2002). The results suggest that although high-degree nodes dominate the structure of MST models, such structures can be inadequate in depicting the essence of how the network evolves because MST removes potentially significant links from high-order shortest paths. In contrast, PFNET models clearly demonstrate their superiority in maintaining the cohesiveness of some of the most pivotal paths, which in turn make the growth animation more predictable and interpretable. We suggest that the design of visualization and modeling tools for network evolution should take the cohesiveness of critical paths into account.


Analgesia in children. Why is it underused in emergency departments?


title: Demystifying Incentives in the Consensus Computer

abstract: Cryptocurrencies like Bitcoin and the more recent Ethereum system allow users to specify scripts in transactions and contracts to support applications beyond simple cash transactions. In this work, we analyze the extent to which these systems can enforce the correct semantics of scripts. We show that when a script execution requires nontrivial computation effort, practical attacks exist which either waste miners' computational resources or lead miners to accept incorrect script results. These attacks drive miners to an ill-fated choice, which we call the verifier's dilemma, whereby rational miners are well-incentivized to accept unvalidated blockchains. We call the framework of computation through a scriptable cryptocurrency a consensus computer and develop a model that captures incentives for verifying computation in it. We propose a resolution to the verifier's dilemma which incentivizes correct execution of certain applications, including outsourced computation, where scripts require minimal time to verify. Finally we discuss two distinct, practical implementations of our consensus computer in real cryptocurrency networks like Ethereum.


title: Finding Largest Rectangles in Convex Polygons

abstract: We consider the following geometric optimization problem: find a maximum-area rectangle and a maximum-perimeter rectangle contained in a given convex polygon with n vertices. We give exact algorithms that solve these problems in time O ( n 3 ) . We also give ( 1 - e ) -approximation algorithms that take time O ( e - 1 / 2 log ? n + e - 3 / 2 ) .


Automatic recognition of unified parkinson's disease rating from speech with acoustic, i-vector and phonotactic features.


title: Minimizing Efforts in Validating Crowd Answers

abstract: In recent years, crowdsourcing has become essential in a wide range of Web applications. One of the biggest challenges of crowdsourcing is the quality of crowd answers as workers have wide-ranging levels of expertise and the worker community may contain faulty workers. Although various techniques for quality control have been proposed, a post-processing phase in which crowd answers are validated is still required. Validation is typically conducted by experts, whose availability is limited and who incur high costs. Therefore, we develop a probabilistic model that helps to identify the most beneficial validation questions in terms of both, improvement of result correctness and detection of faulty workers. Our approach allows us to guide the expert's work by collecting input on the most problematic cases, thereby achieving a set of high quality answers even if the expert does not validate the complete answer set. Our comprehensive evaluation using both real-world and synthetic datasets demonstrates that our techniques save up to 50% of expert efforts compared to baseline methods when striving for perfect result correctness. In absolute terms, for most cases, we achieve close to perfect correctness after expert input has been sought for only 20\% of the questions.


title: Resting-state functional connectivity of the vermal and hemispheric subregions of the cerebellum with both the cerebral cortical networks and subcortical structures

abstract: The human cerebellum is a heterogeneous structure, and the pattern of resting-state functional connectivity (rsFC) of each subregion has not yet been fully characterized. We aimed to systematically investigate rsFC pattern of each cerebellar subregion in 228 healthy young adults. Voxel-based analysis revealed that several subregions showed similar rsFC patterns, reflecting functional integration; however, different subregions displayed distinct rsFC patterns, representing functional segregation. The same vermal and hemispheric subregions showed either different patterns or different strengths of rsFCs with the cerebrum, and different subregions of lobules VII and VIII displayed different rsFC patterns. Region of interest (ROI)-based analyses also confirmed these findings. Specifically, strong rsFCs were found: between lobules I-VI and vermal VIIb-IX and the visual network; between hemispheric VI, VIIb, VIIIa and the auditory network; between lobules I-VI, VIII and the sensorimotor network; between lobule IX, vermal VIIIb and the default-mode network; between lobule Crus I, hemispheric Crus II and the fronto-parietal network; between hemispheric VIIb, VIII and the task-positive network; between hemispheric VI, VIIb, VIII and the salience network; between most cerebellar subregions and the thalamus; between lobules V, VIIb and the midbrain red nucleus; between hemispheric Crus I, Crus II, vermal VIIIb, IX and the caudate nucleus; between lobules V, VI, VIIb, VIIIa and the pallidum and putamen; and between lobules I-V, hemispheric VIII, IX and the hippocampus and amygdala. These results confirm the existence of both functional integration and segregation among cerebellar subregions and largely improve our understanding of the functional organization of the human cerebellum. (C) 2012 Elsevier Inc. All rights reserved.


title: The Occurrence of Tax Amnesties: Theory and Evidence

abstract: This paper presents a theoretical model and empirical evidence to explain the occurrence of tax amnesties. We treat amnesties as endogenous, resulting from a strategic game between many taxpayers discounting future payments from punishment and a government that balances costs and benefits of amnesty programs. From the model we derive hypotheses about the factors that should influence the occurrence of tax amnesties. To test these predictions empirically, we rely on amnesty information from US States between 1981 and 2011. In line with the theoretical model, our empirical findings suggest that the likelihood of amnesties is mainly driven by a government's fiscal requirements and the taxpayers' expectations on future amnesties.


title: Wound healing and diabetes mellitus.

abstract: Diabetes mellitus is one of the major contributors to chronic wound healing problems. When diabetic patients develop an ulcer, they become at high risk for major complications, including infection and amputation. The pathophysiologic relationship between diabetes and impaired healing is complex. Vascular, neuropathic, immune function, and biochemical abnormalities each contribute to the altered tissue repair. Despite treatment of these chronic wounds, which involves tight glucose control and meticulous wound care, the prognosis for their healing is quite poor. Newer modalities that deliver natural or engineered growth factors show a great deal of promise. All of the studies clearly show the continued need for well-controlled clinical trials.


title: GPU ray casting of virtual globes

abstract: Accurately rendering an ellipsoid is a fundamental problem for virtual globes in GIS and aerospace applications where the Earth's standard reference surface is non-spherical. The traditional approach of tessellating an ellipsoid into triangles and rendering via rasterization has several drawbacks [Miller and Gaskins 2009]. Geodetic grid tessellations oversample at the poles (2a), which leads to shading artifacts and ineffective culling. Tessellations based on subdividing an inscribed platonic solid lead to problematic triangles crossing the International Date Line and poles (2b).


title: MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks

abstract: Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data. In recent years, autoencoders have been shown to be effective anomaly detectors that train only on "normal" data. Generative adversarial networks (GANs) have been used to generate additional training samples for classifiers, thus making them more accurate and robust. However, in anomaly detection GANs are only used to reconstruct existing samples rather than to generate additional ones. This stems both from the small amount and lack of diversity of anomalous data in most domains. In this study we propose MDGAN, a novel GAN architecture for improving anomaly detection through the generation of additional samples. Our approach uses two discriminators: a dense network for determining whether the generated samples are of sufficient quality (i.e., valid) and an autoencoder that serves as an anomaly detector. MDGAN enables us to reconcile two conflicting goals: 1) generate high-quality samples that can fool the first discriminator, and 2) generate samples that can eventually be effectively reconstructed by the second discriminator, thus improving its performance. Empirical evaluation on a diverse set of datasets demonstrates the merits of our approach.


title: Constrained Expectation-Maximization Methods for Effective Reinforcement Learning

abstract: Recent advancement on reinforcement learning (RL) algorithms shows that effective learning of parametric action- selection policies can often be achieved through direct opti- mization of a performance lower bound subject to pre-defined policy behavioral constraints. Driven by this understanding, this paper seeks to develop new policy search techniques where RL is achieved through maximizing a performance lower bound ob- tained originally based on an Expectation-Maximization method. For reliable RL, our new learning techniques must also simul- taneously guarantee constrained policy behavioral changes mea- sured through KL divergence. Two separate approaches will be pursued to tackle our constrained policy optimization problems, resulting in two new RL algorithms. The first algorithm utilizes a conjugate gradient technique and a Bayesian learning method for approximate optimization. The second algorithm focuses on minimizing a loss function derived from solving the Lagrangian for constrained policy search. Both algorithms have been experi- mentally examined on several benchmark problems provided by OpenAI GYM. The experiment results clearly demonstrate that our algorithms can be highly effective in comparison to several well-known RL algorithms.


title: Visual Navigation with Actor-Critic Deep Reinforcement Learning

abstract: Visual navigation in complex environments is crucial for intelligent agents. In this paper, we propose an efficient deep reinforcement learning (DRL) method to tackle visual navigation tasks. We present the synchronous advantage actor-critic (A2C) with generalized advantage estimator (GAE) algorithm. The A2C enables agents to learn from multiple processes, which significantly reduces the training time. The GAE used to estimate the advantage function improves the policy gradient estimates. We focus on visual navigation tasks in ViZDoom, and train agents in two health gathering scenarios. The experimental results show this method successfully teaches our agents to navigate in these scenarios. The A2C with GAE agent reaches the highest score in the first task, and a competitive score in the second task. In addition, this agent has better average scores and lower variances in both tasks.


title: MITRE: Seven Systems for Semantic Similarity in Tweets

abstract: This paper describes MITRE’s participation in the Paraphrase and Semantic Similarity in Twitter task (SemEval-2015 Task 1). This effort placed first in Semantic Similarity and second in Paraphrase Identification with scores of Pearson’s r of 61.9%, F1 of 66.7%, and maxF1 of 72.4%. We detail the approaches we explored including mixtures of string matching metrics, alignments using tweet-specific distributed word representations, recurrent neural networks for modeling similarity with those alignments, and distance measurements on pooled latent semantic features. Logistic regression is used to tie the systems together into the ensembles submitted for evaluation.


title: Critical factors of WAP services adoption: an empirical study

abstract: Abstract Mobile commerce is becoming increasingly important in business. Wireless application protocol (WAP) is one of the most widespread technical standards for mobile commerce. Following continuous technical evolution, WAP has now included various new features. However, WAP services continue to struggle for market share. Hence, understanding the adoption of WAP services is increasingly important for enterprises interested in developing mobile commerce. This study aims to: (i) identify the critical factors of WAP services adoption; (ii) explore the relative importance of each factor between WAP adopters and non-adopters; and (iii) examine the causal relationships among variables on WAP services adoption behavior. This study reports empirical test of the adoption of WAP services in Taiwan, based on the theory of planned behavior and innovation diffusion theory. The results indicate that the critical factors influencing the adoption of WAP services include connection speed, service costs, user satisfaction, personal innovativeness, ease of use, peer influence, and facilitating conditions. Some suggestions for subsequent researchers and practitioners seeking to understand WAP services adoption behavior are also provided.


title: A Survey and Comparative Study of Tweet Sentiment Analysis via Semi-Supervised Learning

abstract: Twitter is a microblogging platform in which users can post status messages, called “tweets,” to their friends. It has provided an enormous dataset of the so-called sentiments, whose classification can take place through supervised learning. To build supervised learning models, classification algorithms require a set of representative labeled data. However, labeled data are usually difficult and expensive to obtain, which motivates the interest in semi-supervised learning. This type of learning uses unlabeled data to complement the information provided by the labeled data in the training process; therefore, it is particularly useful in applications including tweet sentiment analysis, where a huge quantity of unlabeled data is accessible. Semi-supervised learning for tweet sentiment analysis, although appealing, is relatively new. We provide a comprehensive survey of semi-supervised approaches applied to tweet classification. Such approaches consist of graph-based, wrapper-based, and topic-based methods. A comparative study of algorithms based on self-training, co-training, topic modeling, and distant supervision highlights their biases and sheds light on aspects that the practitioner should consider in real-world applications.


title: Anatomy of the anterolateral ligament of the knee.

abstract: In 1879, the French surgeon Segond described the existence of a 'pearly, resistant, fibrous band' at the anterolateral aspect of the human knee, attached to the eponymous Segond fracture. To date, the enigma surrounding this anatomical structure is reflected in confusing names such as '(mid-third) lateral capsular ligament', 'capsulo-osseous layer of the iliotibial band' or 'anterolateral ligament', and no clear anatomical description has yet been provided. In this study, the presence and characteristics of Segond's 'pearly band', hereafter termed anterolateral ligament (ALL), was investigated in 41 unpaired, human cadaveric knees. The femoral and tibial attachment of the ALL, its course and its relationship with nearby anatomical structures were studied both qualitatively and quantitatively. In all but one of 41 cadaveric knees (97%), the ALL was found as a well-defined ligamentous structure, clearly distinguishable from the anterolateral joint capsule. The origin of the ALL was situated at the prominence of the lateral femoral epicondyle, slightly anterior to the origin of the lateral collateral ligament, although connecting fibers between the two structures were observed. The ALL showed an oblique course to the anterolateral aspect of the proximal tibia, with firm attachments to the lateral meniscus, thus enveloping the inferior lateral geniculate artery and vein. Its insertion on the anterolateral tibia was grossly located midway between Gerdy's tubercle and the tip of the fibular head, definitely separate from the iliotibial band (ITB). The ALL was found to be a distinct ligamentous structure at the anterolateral aspect of the human knee with consistent origin and insertion site features. By providing a detailed anatomical characterization of the ALL, this study clarifies the long-standing enigma surrounding the existence of a ligamentous structure connecting the femur with the anterolateral tibia. Given its structure and anatomic location, the ALL is hypothesized to control internal tibial rotation and thus to affect the pivot shift phenomenon, although further studies are needed to investigate its biomechanical function.


title: Exploring the Meaning behind Twitter Hashtags through Clustering

abstract: Social networks are generators of large amount of data produced by users, who are not limited with respect to the content of the information they exchange. The data generated can be a good indicator of trends and topic preferences among users. In our paper we focus on analyzing and representing hashtags by the corpus in which they appear. We cluster a large set of hashtags using K-means on map reduce in order to process data in a distributed manner. Our intention is to retrieve connections that might exist between different hashtags and their textual representation, and grasp their semantics through the main topics they occur with.


Anthrometric measurements of external ear : An in vivo study


title: Image Preprocessing for Improving OCR Accuracy

abstract: Digital cameras are convenient image acquisition devices: they are fast, versatile, mobile, do not touch the object, and are relatively cheap. In OCR applications, however, digital cameras suffer from a number of limitations, like geometrical distortions. In this paper, we deal with the preprocessing step before text recognition, specifically with images from a digital camera. Experiments, performed with the FineReader 7.0 software as the back-end recognition tool, confirm importance of image preprocessing in OCR applications.


title: Dynamic Shared Context Processing in an E-Collaborative Learning Environment

abstract: In this paper, we propose a dynamic shared context processing method based on DSC (Dynamic Shared Context) model, applied in an e-collaborative learning environment. Firstly, we present the model. This is a way to measure the relevance between events and roles in collaborative environments. With this method, we can share the most appropriate event information for each role instead of sharing all information to all roles in a collaborative work environment. Then, we apply and verify this method in our project with Google App supported e-learning collaborative environment. During this experiment, we compared DSC method measured relevance of events and roles to manual measured relevance. And we describe the favorable points from this comparison and our finding. Finally, we discuss our future research of a hybrid DSC method to make dynamical information shared more effective in a collaborative work environment.


Training for Diversity in Image Paragraph Captioning


title: Cloud computing adoption assessment model (CAAM)

abstract: Cloud Computing (Clouds) is a new trend in IT, not yet widely adopted by large-scale organisations extensive IT infrastructure and complex IT processes as they find it difficult to adopt Clouds. There are technical and socio-technical challenges that need to be addressed before adoption. There is a need for an assessment framework that would assist adoption by evaluating the organisation, its key capabilities and processes affected by Clouds. The objective of this research study is to develop a model that helps organisations in assessing and improving their readiness for Cloud Computing. Primary data in this research are user concerns and issues that are barrier in the adoption of Clouds. The primary data will be collected using Systematic Literature Review (SLR) methodology. Interviews with senior executives and IT Managers are planned to validate the findings of the SLR. The anticipated outcome of this project will be Cloud Computing Adoption Assessment Model (CAAM) that will help organisations to adopt and integrate Clouds with Enterprise IT, define new capabilities, identify their organisational readiness and provide strategies for successful adoption. The case study method will be used for evaluating CAAM in conjunction with organisations willing to adopt cloud computing. Three case studies are planned to evaluate the effectiveness of CAAM in assessing organisation's readiness for the adoption of Cloud Computing.


title: Framework for managing business development in the networked organisation

abstract: Successful business practice is traditionally discussed in terms of the operational characteristics of business organisations. How successful organisations operate and behave is of interest to all analysts. Increasingly, however, business success is viewed through the process of managing development, where management decisions are informed by appropriate and timely information. Managing development information such as customer requirements, business strategies, development projects and idea generation is central to achieving business growth. This paper presents research aimed at developing an information architecture and associated toolset for understanding and managing the process of business development. The research has lead to the creation of a groupware application that marries a number of distinct areas of research including strategic planning, performance measurement, enterprise modelling, project portfolio management and team management. The groupware helps communicate all the core development information necessary for informed decision-making and brings all the people responsible for development in the organisation closer together.


title: Beyond engagement analytics: which online mixed-data factors predict student learning outcomes?

abstract: This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large sample from an online undergraduate business course at an accredited American university (n = 228). Recent studies have discounted the ability to predict student learning outcomes from big data analytics but a few significant indicators have been found by some researchers. Current studies tend to use quantitative factors in learning analytics to forecast outcomes. This study extends that work by testing the common quantitative predictors of learning outcome, but qualitative data is also examined to triangulate the evidence. Pre and post testing of information technology understanding is done at the beginning of the course. First quantitative data is collected, and depending on the hypothesis test results, qualitative data is collected and analyzed with text analytics to uncover patterns. Moodle engagement analytics indicators are tested as predictors in the model. Data is also taken from the Moodle system logs. Qualitative data is collected from student reflection essays. The result was a significant General Linear Model with four online interaction predictors that captured 77.5 % of grade variance in an undergraduate business course.


title: Privacy-Preserving Access Control in Cloud Federations

abstract: A Cloud federation is a collaboration of organizations sharing data hosted on their private cloud infrastructures in order to exploit a common business opportunity. However, the adoption of cloud federations is hindered by member organizations' concerns on sharing their data with potentially competing organizations. For cloud federations to be viable, federated organizations' privacy concerns should be alleviated by providing mechanisms that allow organizations to control which users from other federated organizations can access which data. We propose the architecture of a novel identity and access management system part of FaaS, a cloud federation service developed by the H2020 SUNFISH project. Our system allows federated organizations to enforce attribute-based access control policies on their data in a privacy-preserving fashion. Users are granted access to federated data when their identity attributes match the policies, but without revealing their attributes in clear. The architecture relies on two novel technologies, blockchain and Intel SGX hardware platform to guarantee integrity of the policy evaluation process.


title: A sequential algorithm for training text classifiers

abstract: The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.


title: Multi-step Linear Dyna-style Planning

abstract: In this paper we introduce a multi-step linear Dyna-style planning algorithm. The key element of the multi-step linear Dyna is a multi-step linear model that enables multi-step projection of a sampled feature and multi-step planning based on the simulated multi-step transition experience. We propose two multi-step linear models. The first iterates the one-step linear model, but is generally computationally complex. The second interpolates between the one-step model and the infinite-step model (which turns out to be the LSTD solution), and can be learned efficiently online. Policy evaluation on Boyan Chain shows that multi-step linear Dyna learns a policy faster than single-step linear Dyna, and generally learns faster as the number of projection steps increases. Results on Mountain-car show that multi-step linear Dyna leads to much better online performance than single-step linear Dyna and model-free algorithms; however, the performance of multi-step linear Dyna does not always improve as the number of projection steps increases. Our results also suggest that previous attempts on extending LSTD for online control were unsuccessful because LSTD looks infinite steps into the future, and suffers from the model errors in non-stationary (control) environments.


title: GutenTag: an NLP-driven Tool for Digital Humanities Research in the Project Gutenberg Corpus

abstract: This paper introduces a software tool, GutenTag, which is aimed at giving literary researchers direct access to NLP techniques for the analysis of texts in the Project Gutenberg corpus. We discuss several facets of the tool, including the handling of formatting and structure, the use and expansion of metadata which is used to identify relevant subcorpora of interest, and a general tagging framework which is intended to cover a wide variety of future NLP modules. Our hope that the shared ground created by this tool will help create new kinds of interaction between the computational linguistics and digital humanities communities, to the benefit of both.


title: Unsupervised extrinsic calibration of depth sensors in dynamic scenes

abstract: While inexpensive depth sensors are becoming increasingly ubiquitous, field of view and self-occlusion constraints limit the information a single sensor can provide. For many applications one may instead require a network of depth sensors, registered to a common world frame and synchronized in time. Historically such a setup has required a tedious manual calibration procedure, making it infeasible to deploy these networks in the wild, where spatial and temporal drift are common. In this work, we propose an entirely unsupervised procedure for calibrating the relative pose and time offsets of a pair of depth sensors. So doing, we make no use of an explicit calibration target, or any intentional activity on the part of a user. Rather, we use the unstructured motion of objects in the scene to find potential correspondences between the sensor pair. This yields a rough transform which is then refined with an occlusion-aware energy minimization. We compare our results against the standard checkerboard technique, and provide qualitative examples for scenes in which such a technique would be impossible.


title: THE RELATIONSHIP BETWEEN THEORY AND POLICY IN INTERNATIONAL RELATIONS

abstract: ▪ Abstract Policy makers pay relatively little attention to the vast theoretical literature in IR, and many scholars seem uninterested in doing policy-relevant work. These tendencies are unfortunate because theory is an essential tool of statecraft. Many policy debates ultimately rest on competing theoretical visions, and relying on a false or flawed theory can lead to major foreign policy disasters. Theory remains essential for diagnosing events, explaining their causes, prescribing responses, and evaluating the impact of different policies. Unfortunately, the norms and incentives that currently dominate academia discourage many scholars from doing useful theoretical work in IR. The gap between theory and policy can be narrowed only if the academic community begins to place greater value on policy-relevant theoretical work.


title: MATLAB/Simulink model of stand-alone Solar PV system with MPPT enabled optimized Power conditioning unit

abstract: This paper deals with modelling of a stand-alone solar PV system with MPPT (Maximum Power Point Tracking) algorithm to enhance its efficiency for a dedicated load pattern. The Perturb & Observe (P&O) algorithm has been used to track the maximum power operating point of 80W P stand-alone Solar PV system. Here the DC-DC Boost converter has been designed as a Power conditioning unit for controlling the operating point of the PV source to achieve the MPP that enhances overall system efficiency.


Lightweight Cryptography: Cryptographic Engineering for a Pervasive World


title: Integrating customer relationship management in hotel operations: managerial and operational implications

abstract: Abstract As travelers are becoming more price sensitive, less brand loyal and more sophisticated, Customer Relationship Management (CRM) becomes a strategic necessity for attracting and increasing guests’ patronage. Although CRM in hospitality has overstated the importance of ICT, it is now widely recognised that successful CRM implementation should effectively combine and align ICT functionality with business operations. Given the lack of a widely accepted framework for CRM implementation, this paper proposed a model for managing and integrating ICT capabilities into CRM strategies and business processes. The model argues that successful CRM implementation requires the management and alignment of three managerial processes: ICT, relationship (internal and external) and knowledge management. The model is tested by gathering data from Greek hotels, while findings provide useful practical implications and suggestions for future research.


title: Learning to Schedule Control Fragments for Physics-Based Characters Using Deep Q-Learning

abstract: Given a robust control system, physical simulation offers the potential for interactive human characters that move in realistic and responsive ways. In this article, we describe how to learn a scheduling scheme that reorders short control fragments as necessary at runtime to create a control system that can respond to disturbances and allows steering and other user interactions. These schedulers provide robust control of a wide range of highly dynamic behaviors, including walking on a ball, balancing on a bongo board, skateboarding, running, push-recovery, and breakdancing. We show that moderate-sized Q-networks can model the schedulers for these control tasks effectively and that those schedulers can be efficiently learned by the deep Q-learning algorithm.


title: Morphological, structural, and functional alterations of the prefrontal cortex and the basolateral amygdala after early lesion of the rat mediodorsal thalamus

abstract: Early postnatal damage to the mediodorsal thalamus (MD) produces deficits in cognition and behavior believed to be associated with early prefrontal cortical maldevelopment. We assessed the role of MD afferents during development on the morphological and functional maturation of the prefrontal cortex (PFC) and the basolateral amygdala (BLA). Sprague—Dawley rat pups (n = 56) received a bilateral electrolytic lesion of the MD or a MD Sham lesion on postnatal day 4. 7 weeks later, all rats were tested in anxiety-related and cognitive paradigms using the elevated plus maze and novel object recognition tests. Following behavioral testing (P70), rats were killed and the baseline expression of C-Fos protein and the number of GABAergic neurons were evaluated in the PFC and the BLA. The dendritic morphology and spine density in the PFC using Golgi-Cox staining was also evaluated. Adult rats with early postnatal bilateral MD damage exhibited disrupted recognition memory and increased anxiety-like behaviors. The lesion also caused a significant diminution of C-Fos immunolabeling and an increase of the number of GABAergic neurons in the PFC. In the BLA, the number of GABAergic neurons was significantly reduced, associated with an increase in C-Fos immunolabeling. Furthermore, in the PFC the lesion induced a significant reduction in dendritic branching and spine density. Our data are consistent with the hypothesis that the MD plays a role in the development of the PFC and, therefore, may be a good animal model to investigate cognitive symptoms associated with schizophrenia.


title: PUPPIES: Transformation-Supported Personalized Privacy Preserving Partial Image Sharing

abstract: Sharing photos through Online Social Networks isan increasingly popular fashion. However, it poses a seriousthreat to end users as private information in the photos maybeinappropriately shared with others without their consent. Thispaper proposes a design and implementation of a system using a dynamic privacy preserving partial image sharing technique (namely PUPPIES), which allows data owners to stipulate specific private regions (e.g., face, SSN number) in an image and correspondingly set different privacy policies for each user. As a generic technique and system, PUPPIES targets at threats about over-privileged and unauthorized sharing of photos at photo service provider (e.g., Flicker, Facebook, etc) side. To this end, PUPPIES leverages the image perturbation technique to "encrypt" the sensitive areas in the original images, and therefore it can naturally support popular image transformations (such as cropping, rotation) and is well compatible with most image processing libraries. The extensive experiments on 19,000 images demonstrate that PUPPIES is very effective for privacy protection and incurs only a small computational overhead. In addition, PUPPIES offers high flexibility for different privacy settings, and is very robust to different types of privacy attacks.


title: Hard Mixtures of Experts for Large Scale Weakly Supervised Vision

abstract: Training convolutional networks (CNN's) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large CNN's that do not fit in the memory of a few GPU cards, or for parallelizing CNN training. In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks. Mixture of experts models are not new (Jacobs et. al. 1991, Collobert et. al. 2003), but in the past, researchers have had to devise sophisticated methods to deal with data fragmentation. We show empirically that modern weakly supervised data sets are large enough to support naive partitioning schemes where each data point is assigned to a single expert. Because the experts are independent, training them in parallel is easy, and evaluation is cheap for the size of the model. Furthermore, we show that we can use a single decoding layer for all the experts, allowing a unified feature embedding space. We demonstrate that it is feasible (and in fact relatively painless) to train far larger models than could be practically trained with standard CNN architectures, and that the extra capacity can be well used on current datasets.


title: Do Threatening Stimuli Draw or Hold Visual Attention in Subclinical Anxiety

abstract: Biases in information processing undoubtedly play an important role in the maintenance of emotion and emotional disorders. In an attentional cueing paradigm, threat words and angry faces had no advantage over positive or neutral words (or faces) in attracting attention to their own location, even for people who were highly state-anxious. In contrast, the presence of threatening cues (words and faces) had a strong impact on the disengagement of attention. When a threat cue was presented and a target subsequently presented in another location, high state-anxious individuals took longer to detect the target relative to when either a positive or a neutral cue was presented. It is concluded that threat-related stimuli affect attentional dwell time and the disengage component of attention, leaving the question of whether threat stimuli affect the shift component of attention open to debate.


title: Common-Mode Suppression in Microstrip Differential Lines by Means of Complementary Split Ring Resonators: Theory and Applications

abstract: This paper is focused on the application of complementary split-ring resonators (CSRRs) to the suppression of the common (even) mode in microstrip differential transmission lines. By periodically and symmetrically etching CSRRs in the ground plane of microstrip differential lines, the common mode can be efficiently suppressed over a wide band whereas the differential signals are not affected. Throughout the paper, we present and discuss the principle for the selective common-mode suppression, the circuit model of the structure (including the models under even- and odd-mode excitation), the strategies for bandwidth enhancement of the rejected common mode, and a methodology for common-mode filter design. On the basis of the dispersion relation for the common mode, it is shown that the maximum achievable rejection bandwidth can be estimated. Finally, theory is validated by designing and measuring a differential line and a balanced bandpass filter with common-mode suppression, where double-slit CSRRs (DS-CSRRs) are used in order to enhance the common-mode rejection bandwidth. Due to the presence of DS-CSRRs, the balanced filter exhibits more than 40 dB of common-mode rejection within a 34% bandwidth around the filter pass band.


title: Movie Review Classification Based on a Multiple Classifier

abstract: In this paper, we propose a method to classify movie review documents into positive or negative opinions. There are several approaches to classify documents. The previous studies, however, used only a single classifier for the classification task. We describe a multiple classifier for the review document classification task. The method consists of three classifiers based on SVMs, ME and score calculation. We apply two voting methods and SVMs to the integration process of single classifiers. The integrated methods improved the accuracy as compared with the three single classifiers. The experimental results show the effectiveness of our method.


title: Evolving automotive radar — From the very beginnings into the future

abstract: Automatic cruise control (ACC) systems based on mm-wave radar have been under development for several decades [1-5]. In the early 1970s the first test cars with 35 GHz sensors were road-tested. More than 20 years later in 1998 the Mercedes-Benz DISTRONIC system at - then - 77 GHz became operational. 8 years later this was followed by the DISTRONIC PLUS system going in series production for premium cars, combining a 77 GHz long range radar sensor (LRR) with two 24 GHz short range radar sensors (SRR) and making the system fit for urban traffic. Today this technology has moved to commercial stage with all major automotive manufacturers worldwide, lately introducing ACC systems also in the small car segment. With the introduction of the new Mercedes-Benz B-Class and A-Class in 2011 and 2012, respectively, “DISTRONIC PLUS” became available as a SA (Sonderausstattung: special equipment) product in smaller cars. The “PRE-SAFE BRAKE” system of Mercedes-Benz or the “Intelligent Brake Assist” from NISSAN take such ACC-systems as a prerequisite for advanced braking-systems, to reduce the number and the severity of road accidents. Besides 77 GHz for LRR the 24/ 26 GHz frequency range was adopted for short range automotive radar sensors (SRR), being employed for Blind Spot Detection (BSD) or as a Lane Change Warner (LCW). Narrow-Band (NB) Systems, operating in the ISM-Band (24.05 to 24.25 GHz) and Ultra-WideBand (UWB) systems between 21.65 to 26.65 GHz with different advantages and disadvantages are on the market today. Very recently Digital Beam Forming (DBF) antenna technology combined with electronic scanning techniques has been introduced. Now existing radar sensors can be easily adjusted for new applications. For example a BSD sensor can easily be adopted to function as a “rear cross traffic alert” (RTCA) sensor. Entirely new market opportunities are opening up. Medium range radar (MRR) systems in the 24/ 26 GHz range are on the market today. In spring 2014, with the introduction of the new Mercedes-Benz C-Class “COLLISION PREVENTION ASSIST” (CPA) 3.0 at 24 GHz (built by Autoliv) will become available as a standard series product. Thus, PRE-SAFE braking will be a standard series product for the first time . being no longer a special equipment only ....


title: A survey of resources for introducing coding into schools

abstract: Within TACCLE 3 -- Coding European Union Erasmus+ KA2 Programme project, a review and evaluation of a set of resources that can contribute to teaching programming to younger children has made. This paper presents a survey of this review including the most outstanding products in order to help teachers to introduce programming in pre-university studies.


title: Nutrition and education: a randomized trial of the effects of breakfast in rural primary school children

abstract: Hunger during school may prevent children in developing countries from benefiting from education. Although many countries have implemented school feeding programs, few programs have been rigorously evaluated. We conducted a randomized, controlled trial of giving breakfast to undernourished and adequately nourished children. The undernourished group comprised 407 children in grades 2-5 in 16 rural Jamaican schools (weights-for-age ≤ -1 SD of the National Center for Health Statistics references) and the adequately nourished group comprised 407 children matched for school and class (weights-for-age >-1 SD). Both groups were stratified by class and school, then randomly assigned to breakfast or control groups. After the initial measurements, breakfast was provided every school day for 1 school year. Children in the control group were given one-quarter of an orange and the same amount of attention as children in the breakfast group. All children had their heights and weights measured and were given the Wide Range Achievement Test before and after the intervention. School attendance was taken from the schools' registers. Compared with the control group, height, weight, and attendance improved significantly in the breakfast group. Both groups made poor progress in Wide Range Achievement Test scores. Younger children in the breakfast group improved in arithmetic. There was no effect of nutritional group on the response to breakfast. In conclusion, the provision of a school breakfast produced small benefits in children's nutritional status, school attendance, and achievement. Greater improvements may occur in more undernourished populations; however, the massive problem of poor achievement levels requires integrated programs including health and educational inputs as well as school meals.


title: Warpage Simulation and Optimization of Fan-Out Wafer level Package(FO-WLP) with TMV under Different Processes

abstract: Fan-Out wafer-level package (FO-WLP) has become a mainstream advanced packaging technology and from the first generation fan-out package, the second generation of new fan-out packaging technologies, such as FO MCP, FO POP, and POP SiP is gradually derived. In FO-WLP, wafer warpage caused by mismatch of coefficients of thermal expansion (CTE) between materials has always been an important matter. This paper aims at a FO POP package, which uses through mold via (TMV) to achieve the upper and lower interconnections between packages. The packaging method is chip-first and face-up. The wafer warpage in each thermal process of the package is simulated and analyzed. At the same time, the chip spacing, molding compound thickness, TMV diameter, carrier thickness and material effect on warpage in the plastic molding process and copper layer thickness, copper area ration, dielectric layer thickness and material effect on warpage in the re-distribution layer (RDL) process is studied. And an effective method for reducing wafer warpage in each thermal process is proposed.


title: Stein Neural Sampler

abstract: We propose two novel samplers to produce high-quality samples from a given (un-normalized) probability density. The sampling is achieved by transforming a reference distribution to the target distribution with neural networks, which are trained separately by minimizing two kinds of Stein Discrepancies, and hence our method is named as Stein neural sampler. Theoretical and empirical results suggest that, compared with traditional sampling schemes, our samplers share the following three advantages: 1. Being asymptotically correct; 2. Experiencing less convergence issue in practice; 3. Generating samples instantaneously.