title: tDCS Modulates Visual Gamma Oscillations and Basal Alpha Activity in Occipital Cortices: Evidence from MEG

abstract: Transcranial direct-current stimulation (tDCS) is now a widely used method for modulating the human brain, but the resulting physiological effects are not understood. Recent studies have combined magnetoencephalography (MEG) with simultaneous tDCS to evaluate online changes in occipital alpha and gamma oscillations, but no study to date has quantified the offline (i.e., after tDCS) alterations in these responses. Thirty-five healthy adults received active or sham anodal tDCS to the occipital cortices, and then completed a visual stimulation paradigm during MEG that is known to elicit robust gamma and alpha oscillations. The resulting MEG data were imaged and peak voxel time series were extracted to evaluate tDCS effects. We found that tDCS to the occipital increased the amplitude of local gamma oscillations, and basal alpha levels during the baseline. tDCS was also associated with network-level effects, including increased gamma oscillations in the prefrontal cortex, parietal, and other visual attention regions. Finally, although tDCS did not modulate peak gamma frequency, this variable was inversely correlated with gamma amplitude, which is consistent with a GABA-gamma link. In conclusion, tDCS alters gamma oscillations and basal alpha levels. The net offline effects on gamma activity are consistent with the view that anodal tDCS decreases local GABA.


title: Sensing as a Service Model for Smart Cities Supported by Internet of Things

abstract: The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in ICT to find sustainable solutions to the growing issues. The Internet of Things (IoT) has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today infrastructure, platforms, and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the Internet of Things. Our objective is to investigate the concept of sensing as a service model in technological, economical, and social perspectives and identify the major open challenges and issues.


title: Open Source Digital Forensics Tools: The Legal Argument

abstract: This paper addresses digital forensic analysis tools and their use in a legal setting. To enter scientific evidence into a United States court, a tool must be reliable and relevant. The reliability of evidence is tested by applying “Daubert” guidelines. To date, there have been few legal challenges to digital evidence, but as the field matures this will likely change. This paper examines the Daubert guidelines and shows that open source tools may more clearly and comprehensively meet the guidelines than closed source tools.


A Contextual Bandits Framework for Personalized Learning Action Selection


title: New Horizons in the Assessment of Computer Science at School and Beyond: Leveraging on the ViVA Platform

abstract: A revolution is taking place across Europe and worldwide in how we teach our children about computing, in primary and secondary school. Out goes ICT and how to use Microsoft Office; in comes coding and computer science. Assessment has a crucial role to play in this revolution. If teachers use low-quality assessment instruments we will end-up teaching the wrong subject; and viceversa. This paper reviews the state of the field, and makes concrete, achievable proposals for developing shared, high quality assessments for computer science. Central to this proposal is the collaborative platform VIVA (the Vilnius collaboratively coded and Validated computer science questions/tasks for Assess- ment). Two requirements are key to VIVA: 1) support for multiple competency frameworks, so that the contributors can meta-tag resources with respect to the framework they are most familiar with; and 2) support for crowdsourcing the validation of each question/task and its mapping to competencies. The use of a taxonomy of questions/tasks type that has been mapped to computational thinking concepts and to a competency framework is proposed. Some seed questions are already available in the online platform prototype, and various supporters have granted permission to use large questions banks. The design requirements of a full implementation of the VIVA platform for a modern and effective approach to assessment including support for digital badges, are outlined; and some preliminary results from a survey administered to the initial contributors to VIVA are presented.


title: Microstructure of a spatial map in the entorhinal cortex

abstract: The ability to find one's way depends on neural algorithms that integrate information about place, distance and direction, but the implementation of these operations in cortical microcircuits is poorly understood. Here we show that the dorsocaudal medial entorhinal cortex (dMEC) contains a directionally oriented, topographically organized neural map of the spatial environment. Its key unit is the 'grid cell', which is activated whenever the animal's position coincides with any vertex of a regular grid of equilateral triangles spanning the surface of the environment. Grids of neighbouring cells share a common orientation and spacing, but their vertex locations (their phases) differ. The spacing and size of individual fields increase from dorsal to ventral dMEC. The map is anchored to external landmarks, but persists in their absence, suggesting that grid cells may be part of a generalized, path-integration-based map of the spatial environment.


title: Three critical challenges for modeling and simulation in healthcare

abstract: By most measures, the adoption of modeling and simulation techniques in healthcare service development falls well short of the uptake of such techniques evident in other sectors, such as business and commerce or aerospace and the military. The question is, why? To answer this, we consider three questions and then turn to the nature of answer which might lead towards greater adoption. The first is the vexed question of how good is good enough? The second concerns how best modeling should link through to decision-making; and the third concerns the culture needed to make the most of modeling and simulation (and whether it is worth the effort to make the transformation). From these, we draw an agenda for further enquiry in terms of stakeholders, their culture, data, and expectations, and the case in terms of value.


title: Drift Instability in Collisionless Alkali Metal Plasmas

abstract: Measurements of low‐frequency (ω≪Ωi) oscillations in collisionless, low‐β, alkali metal Q‐device plasmas are reported. These oscillations are conclusively identified as density‐gradient‐driven drift waves by the agreement between experiment and the predictions of the linear theory with regard to frequency, wave‐number, and stabilization by ion Landau damping. The linear theory of collisionless drift waves is modified to take into account effects of cylindrical geometry. Also considered are radial gradients in the zero‐order diamagnetic drift and plasma rotation frequencies. The enhancement of resonant particle effects by the finite length of the plasma column is computed. The occurrence of discrete radial eigenfunctions is found to depend on the radial variation of the equilibrium plasma potential. The observed dependence of amplitude on r agrees with that predicted by the theory. The experimental results demonstrate the importance of centrifugal and Coriolis force effects in determining the oscillation f...


title: Application of deep-learning algorithms to MSTAR data

abstract: In this paper, a new All-Convolutional Networks (A-ConvNets) is proposed and applied to Moving and Stationary Target Acquisition and Recognition (MSTAR) data. Conventional deep learning algorithms, especially the deep convolutional networks (ConvNets) have achieved many success state-of-art results. However, directly applying ConvNets to SAR data will yield severe overfitting because of limited data availability. The proposed A-ConvNets can significantly reduce the number of free parameters and the degree of overfitting. Average accuracy of 99.1% on classification of 10-class targets was obtained by applying A-ConvNets to MSTAR datasets.


title: Insight Into Gate-Induced Drain Leakage in Silicon Nanowire Transistors

abstract: In this paper, detailed physical mechanisms of gate-induced drain leakage (GIDL) in gate-all-around silicon nanowire transistors (SNWTs) are investigated and verified by experiments and TCAD studies. The results show that the SNWTs will suffer from a more severe GIDL issue in small diameter ( $D_{\rm nw}$ ) devices under low $|V_{\rm gs}|$ . It is believed that this unexpected GIDL problem in SNWTs origins from the longitudinal band-to-band tunneling (L-BTBT) at the body/drain junction enhanced by the strong gate coupling to the depletion region, which usually can be neglected in planar devices. On the other hand, the traditional transverse BTBT (T-BTBT) only dominates at high $|V_{\rm gs}|$ with relatively large $D_{\rm nw}$ . Systematic study of GIDL dependence on process parameters, including $D_{\rm nw}$ cross-sectional shape, doping, and overlap length ( $L_{\rm ov}$ ), shows that both T-BTBT and L-BTBT can be alleviated by reducing the doping and rounding the corner, but L-BTBT is worsened by reducing $D_{\rm nw}$ and $L_{\rm ov}$ despite of the alleviated T-BTBT. As the extension process engineering strongly impacts the short-channel effect and driving current of SNWTs, a GIDL optimization strategy considering the leakage power and device performance is given for low-power SNWT design.


title: Syntactic/Semantic Structures for Textual Entailment Recognition

abstract: In this paper, we describe an approach based on off-the-shelf parsers and semantic resources for the Recognizing Textual Entailment (RTE) challenge that can be generally applied to any domain. Syntax is exploited by means of tree kernels whereas lexical semantics is derived from heterogeneous resources, e.g. WordNet or distributional semantics through Wikipedia. The joint syntactic/semantic model is realized by means of tree kernels, which can exploit lexical related-ness to match syntactically similar structures, i.e. whose lexical compounds are related. The comparative experiments across different RTE challenges and traditional systems show that our approach consistently and meaningfully achieves high accuracy, without requiring any adaptation or tuning.


A product perspective on total data quality management


title: Long Short-Term Memory for Japanese Word Segmentation

abstract: This study presents a Long Short-Term Memory (LSTM) neural network approach to Japanese word segmentation (JWS). Previous studies on Chinese word segmentation (CWS) succeeded in using recurrent neural networks such as LSTM and gated recurrent units (GRU). However, in contrast to Chinese, Japanese includes several character types, such as hiragana, katakana, and kanji, that produce orthographic variations and increase the difficulty of word segmentation. Additionally, it is important for JWS tasks to consider a global context, and yet traditional JWS approaches rely on local features. In order to address this problem, this study proposes employing an LSTM-based approach to JWS. The experimental results indicate that the proposed model achieves state-of-the-art accuracy with respect to various Japanese corpora.


title: Bastadin 6, a spongean brominated tyrosine derivative, inhibits tumor angiogenesis by inducing selective apoptosis to endothelial cells.

abstract: Bastadin 6, a macrocyclic tetramer of a brominated tyrosine derivative, was isolated from a marine sponge and its anti-angiogenic activity was evaluated. Bastadin 6 was found to inhibit vascular endothelial growth factor (VEGF)- or basic fibroblast growth factor (bFGF)-dependent proliferation (IC50=0.052 micromol/l) of human umbilical vein endothelial cells (HUVECs) 20- to 100-fold selectively in comparison with normal fibroblast (3Y1) or several tumor cells (KB3-1, K562 and Neuro2A). Bastadin 6 also inhibited VEGF- or bFGF-induced tubular formation (0.1 micromol/l, 6 h treatment) and VEGF-induced migration (1 micromol/l, 4 h treatment) of HUVECs. Moreover, bastadin 6 almost completely blocked VEGF- or bFGF-induced in vivo neovascularization in the mice corneal assay and suppressed growth of s.c. inoculated A431 solid tumor in nude mice (100 mg/kg, i.p.). Bastadin 6 induced cell death of HUVECs with an apoptotic phenotype, whereas it showed no effect on the VEGF-induced auto-phosphorylation of VEGF receptors Flt-1 and KDR/Flk-1. These results suggest that the anti-angiogenic effect of bastadin 6 is closely related to selective induction activity of apoptosis against endothelial cells.


title: Neural Adaptive controller for Magnetic levitation system

abstract: In this study a Neural Adaptive method is used for position control and identification of a Magnetic levitation system. This controller consists of three parts: PID controller, radial basis function (RBF) network controller and radial basis function (RBF) network identifier. The combination of controllers produces a stable system which adapts to optimize performance. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of a nonlinear mathematical model of the system.


title: A shape-based approach to the segmentation of medical imagery using level sets

abstract: We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras, we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this representation are then manipulated to minimize an objective function for segmentation. The resulting algorithm is able to handle multidimensional data, can deal with topological changes of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.


title: A framework for integrated design of algorithmic architectural forms

abstract: Abstract This paper presents a methodology and software tools for parametric design of complex architectural objects, called digital or algorithmic forms. In order to provide a flexible tool, the proposed design philosophy involves two open source utilities DONKEY and MIDAS written in Grasshopper algorithm editor and C++, respectively, that are to be linked with a scripting-based architectural modellers Rhinoceros, IntelliCAD and the open source Finite Element solver OOFEM. The emphasis is put on the structural response in order to provide architects with a consistent learning framework and an insight into structural behaviour of designed objects. As demonstrated on three case studies, the proposed modular solution is capable of handling objects of considerable structural complexity, thereby accelerating the process of finding procedural design parameters from orders of weeks to days or hours.


title: Rigorous dynamics of expectation-propagation-based signal recovery from unitarily invariant measurements

abstract: Signal recovery from unitarily invariant measurements is investigated in this paper. A message-passing algorithm is formulated on the basis of expectation propagation (EP). A rigorous analysis is presented for the dynamics of the algorithm in the large system limit, where both input and output dimensions tend to infinity while the compression rate is kept constant. The main result is the justification of state evolution (SE) equations conjectured by Ma and Ping in 2016. This result implies that the EP-based algorithm achieves the Bayes-optimal performance derived by Takeda et al. in 2006 via a non-rigorous tool in statistical physics, when the compression rate is larger than a threshold. The proof is based on an extension of a conventional conditioning technique for the standard Gaussian matrix to the case of the Haar matrix.


title: Face Recognition via Local Directional Pattern

abstract: In this paper, we propose an illumination-robust face recognition system via local directional pattern images. Usually, local pattern descriptors including local binary pattern and local directional pattern have been used in the field of the face recognition and facial expression recognition, since local pattern descriptors have important properties to be robust against the illumination changes and computational simplicity. Thus, this paper represents the face recognition approach that employs the local directional pattern descriptor and twodimensional principal analysis algorithms to achieve enhanced recognition accuracy. In particular, we propose a novel methodology that utilizes the transformed image obtained from local directional pattern descriptor as the direct input image of two-dimensional principal analysis algorithms, unlike that most of previous works employed the local pattern descriptors to acquire the histogram features. The performance evaluation of proposed system was performed using well-known approaches such as principal component analysis and Gabor-wavelets based on local binary pattern, and publicly available databases including the Yale B database and the CMU-PIE database were employed. Through experimental results, the proposed system showed the best recognition accuracy compared to different approaches, and we confirmed the effectiveness of the proposed method under varying lighting conditions.


title: The cuticle *

abstract: The nematode cuticle is an extremely flexible and resilient exoskeleton that permits locomotion via attachment to muscle, confers environmental protection and allows growth by molting. It is synthesised five times, once in the embryo and subsequently at the end of each larval stage prior to molting. It is a highly structured extra-cellular matrix (ECM), composed predominantly of cross-linked collagens, additional insoluble proteins termed cuticlins, associated glycoproteins and lipids. The cuticle collagens are encoded by a large gene family that are subject to strict patterns of temporal regulation. Cuticle collagen biosynthesis involves numerous co- and post-translational modification, processing, secretion and cross-linking steps that in turn are catalysed by specific enzymes and chaperones. Mutations in individual collagen genes and their biosynthetic pathway components can result in a range of defects from abnormal morphology (dumpy and blister) to embryonic and larval death, confirming an essential role for this structure and highlighting its potential as an ECM experimental model system.


title: Problem solving and collaboration using mobile serious games

abstract: This paper presents the results obtained with the implementation of a series of learning activities based on Mobile Serious Games (MSGs) for the development of problem solving and collaborative skills in Chilean 8th grade students. Three MSGs were developed and played by teams of four students in order to solve problems collaboratively. A quasi-experimental design was used. The data shows that the experimental group achieved a higher perception of their own collaboration skills and a higher score in the plan execution dimension of the problem solving cycle than did the non-equivalent control group, revealing that MSG-based learning activities may contribute to such learning improvements. This challenges future research to identify under which conditions learning activities based on mobile serious games can promote the development of higher order skills.


title: Learning to Detect Anomalies in Surveillance Video

abstract: Detecting anomalies in surveillance videos, that is, finding events or objects with low probability of occurrence, is a practical and challenging research topic in computer vision community. In this paper, we put forward a novel unsupervised learning framework for anomaly detection. At feature level, we propose a Sparse Semi-nonnegative Matrix Factorization (SSMF) to learn local patterns at each pixel, and a Histogram of Nonnegative Coefficients (HNC) can be constructed as local feature which is more expressive than previously used features like Histogram of Oriented Gradients (HOG). At model level, we learn a probability model which takes the spatial and temporal contextual information into consideration. Our framework is totally unsupervised requiring no human-labeled training data. With more expressive features and more complicated model, our framework can accurately detect and localize anomalies in surveillance video. We carried out extensive experiments on several benchmark video datasets for anomaly detection, and the results demonstrate the superiority of our framework to state-of-the-art approaches, validating the effectiveness of our framework.


A Comparative Study of Machine Learning Frameworks for Demand Forecasting


title: How consistent is your cloud application?

abstract: Current cloud datastores usually trade consistency for performance and availability. However, it is often not clear how an application is affected when it runs under a low level of consistency. In fact, current application designers have basically no tools that would help them to get a feeling of which and how many inconsistencies actually occur for their particular application. In this paper, we propose a generalized approach for detecting consistency anomalies for arbitrary cloud applications accessing various types of cloud datastores in transactional or non-transactional contexts. We do not require any knowledge on the business logic of the studied application nor on its selected consistency guarantees. We experimentally verify the effectiveness of our approach by using the Google App Engine and Cassandra datastores.


title: A novel algorithm for activity state recognition using smartwatch data

abstract: This work presents a novel algorithm for recognizing activity states which are of interest for assessing the general well-being of cancer, frail and elderly patients. Using the novel idea of two-level classification, misclassification due to unwanted hand motion noise, which is a common source of error in wrist-worn sensing systems, is mitigated. The algorithm is verified using data from 20 subjects performing a sequence of related activities. It is shown that the proposed algorithm improves the accuracy value for the “activity state” which includes “sit”, “stand” and “move” by up to 8%.


title: A Survey on Smart Grid Cyber-Physical System Testbeds

abstract: An increasing interest is emerging on the development of smart grid cyber-physical system testbeds. As new communication and information technologies emerge, innovative cyber-physical system testbeds need to leverage realistic and scalable platforms. Indeed, the interdisciplinary structure of the smart grid concept compels heterogeneous testbeds with different capabilities. There is a significant need to evaluate new concepts and vulnerabilities as opposed to counting on solely simulation studies especially using hardware-in-the-loop test platforms. In this paper, we present a comprehensive survey on cyber-physical smart grid testbeds aiming to provide a taxonomy and insightful guidelines for the development as well as to identify the key features and design decisions while developing future smart grid testbeds. First, this survey provides a four step taxonomy based on smart grid domains, research goals, test platforms, and communication infrastructure. Then, we introduce an overview with a detailed discussion and an evaluation on existing testbeds from the literature. Finally, we conclude this paper with a look on future trends and developments in cyber-physical smart grid testbed research.


title: Learning Aligned Cross-Modal Representations from Weakly Aligned Data

abstract: People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize cross-modal scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for crossmodal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality. Our experiments suggest that our scene representation can help transfer representations across modalities for retrieval. Moreover, our visualizations suggest that units emerge in the shared representation that tend to activate on consistent concepts independently of the modality.


title: Non-Count Symmetries in Boolean&Multi-Valued Prob. Graphical Models

abstract: Lifted inference algorithms commonly exploit symmetries in a probabilistic graphical model (PGM) for efficient inference. However, existing algorithms for Boolean-valued domains can identify only those pairs of states as symmetric, in which the number of ones and zeros match exactly (count symmetries). Moreover, algorithms for lifted inference in multi-valued domains also compute a multi-valued extension of count symmetries only. These algorithms miss many symmetries in a domain. In this paper, we present first algorithms to compute non-count symmetries in both Boolean-valued and multi-valued domains. Our methods can also find symmetries between multi-valued variables that have different domain cardinalities. The key insight in the algorithms is that they change the unit of symmetry computation from a variable to a variable-value (VV) pair. Our experiments find that exploiting these symmetries in MCMC can obtain substantial computational gains over existing algorithms.


title: bold: The Barcode of Life Data System (http://www.barcodinglife.org)

abstract: The Barcode of Life Data System ( BOLD ) is an informatics workbench aiding the acquisition, storage, analysis and publication of DNA barcode records. By assembling molecular, morphological and distributional data, it bridges a traditional bioinformatics chasm. BOLD is freely available to any researcher with interests in DNA barcoding. By providing specialized services, it aids the assembly of records that meet the standards needed to gain BARCODE designation in the global sequence databases. Because of its web-based delivery and flexible data security model, it is also well positioned to support projects that involve broad research alliances. This paper provides a brief introduction to the key elements of BOLD , discusses their functional capabilities, and concludes by examining computational resources and future prospects.


title: Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network

abstract: Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposed to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification. Experiments on the CADDementia MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the ADNI dataset.


title: Chinese Clinical Entity Recognition via Attention-Based CNN-LSTM-CRF

abstract: Chinese clinical entity recognition is a fundamental task of Chinese clinical natural language processing, which has attracted plenty of attention. In this paper, we propose a novel neural network, called attention-based CNN-LSTM-CRF, for this task. The neural network employs a CNN (convolutional neural network) layer to capture local context information of words of interest, a LSTM (long-short term memory) layer to obtain global information of each sentence, an attention layer to select relevant words, and a CRF layer to predict a label sequence for an input sentence. In order to evaluate the performance of the proposed method, we compare it with other two state-of-the-art methods, CRF (conditional random field) and LSTM-CRF, on two benchmark datasets. Experimental results show that the proposed neural network outperforms CRF and LSTM-CRF.


title: Using prediction markets and Twitter to predict a swine flu pandemic

abstract: We explore the hypothesis that social media such as Twitter encodes the belief of a large number of people about some concrete statement about the world. Here, these beliefs are aggregated using a Prediction Market specifically concerning the possibility of a Swine Flu Pandemic in 2009. Using a regression framework, we are able to show that simple features extracted from Tweets can reduce the error associated with modelling these beliefs. Our approach is also shown to outperform some baseline methods based purely on time-series information from the Market.


DBN-ivector framework for acoustic emotion recognition


title: Investigation of chained analog signal processors in Programmable Analog Computer

abstract: This elaboration presents a new device based on dynamically programmable Analog Signal Processors (dpASP) developed for hard real-time control applications. The developed Programmable Analog Computer/Controller (PAC) is an open hardware for a wide range of signal processing tasks. In this report the analog signal routing and filter based signal processing realized by the dpASP is presented. This research was focused on the signals form as they are processed and transferred between analog processors. The investigation is exampled by the low-pass filter(s) embedded into every analog processor. For complex signal processing tasks a few dpASP must be routed together. The dpASP chain is created for signal diagnosis and due to the limited resources of a single device. Final conclusions related to chaining analog processors are given.


title: An adaptive power system equivalent for real-time estimation of stability margin using phase-plane trajectories

abstract: This paper studies real-time estimation of stability margin of a power system under disturbances by continuous measurement data. The paper presents a Ball-On-Concave-Surface (BOCS) mechanics system as a power system's adaptive equivalent representing its real-time status and the stability region about a monitored variable. The parameters of the equivalent are adaptive to the operating condition and can online be identified from the phase-plane trajectories of the monitored variable. Accordingly, the stability margin and risk of instability can be estimated. Case studies on a two-generator system and a 179-bus system show that the BOCS system can be applied either locally or for wide-area stability monitoring in real-time calculation of stability margin. This paper proposes a new idea for using real-time measurements to develop and identify a power system's adaptive equivalent as a basis for online prediction of instability.


title: An Association Thesaurus for Information Retrieval

abstract: Although commonly used in both commercial and experimental information retrieval systems, thesauri have not demonstrated consistent benefits for retrieval performance, and it is difficult to construct a thesaurus automatically for large text databases. In this paper, an approach, called PhraseFinder, is proposed to construct collection-dependent association thesauri automatically using large full-text document collections. The association thesaurus can be accessed through natural language queries in INQUERY, an information retrieval system based on the probabilistic inference network. Experiments are conducted in INQUERY to evaluate different types of association thesauri, and thesauri constructed for a variety of collections.


title: The Living Conditions of Children with Shared Residence – the Swedish Example

abstract: Among children with separated parents, shared residence – i.e., joint physical custody where the child is sharing his or her time equally between two custodial parents’ homes – is increasing in many Western countries and is particularly common in Sweden. The overall level of living among children in Sweden is high; however, the potential structural differences between children in various post-separation family arrangements have not been sufficiently studied. Potential risks for children with shared residence relate to the daily hassles and stress when having two homes. This study aims at investigating the living conditions of children with shared residence compared with children living with two custodial parents in the same household and those living with one custodial parent, respectively. Swedish national survey data collected from children aged 10–18 years (n ≈ 5000) and their parents were used. The outcomes were grouped into: Economic and material conditions, Social relations with parents and peers, Health and health behaviors, Working conditions and safety in school and in the neighborhood, and Culture and leisure time activities. Results from a series of linear probability models showed that most outcomes were similar for children with shared residence and those living with two custodial parents in the same household, while several outcomes were worse for children living with one parent. However, few differences due to living arrangements were found regarding school conditions. This study highlights the inequalities in the living conditions of Swedish children, with those living with one parent having fewer resources compared with other children.


Video Summarization via Semantic Attended Networks


title: Flow-free Video Object Segmentation

abstract: Segmenting foreground object from a video is a challenging task because of the large deformations of the objects, occlusions, and background clutter. In this paper, we propose a frame-by-frame but computationally efficient approach for video object segmentation by clustering visually similar generic object segments throughout the video. Our algorithm segments various object instances appearing in the video and then perform clustering in order to group visually similar segments into one cluster. Since the object that needs to be segmented appears in most part of the video, we can retrieve the foreground segments from the cluster having maximum number of segments, thus filtering out noisy segments that do not represent any object. We then apply a track and fill approach in order to localize the objects in the frames where the object segmentation framework fails to segment any object. Our algorithm performs comparably to the recent automatic methods for video object segmentation when benchmarked on DAVIS dataset while being computationally much faster.


title: Compositional Language Understanding with Text-based Relational Reasoning

abstract: Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference. However, it is also crucial to understand the extent to which neural networks can perform relational reasoning and combinatorial generalization from natural language---abilities that are often obscured by annotation artifacts and the dominance of language modeling in standard QA benchmarks. In this work, we present a novel benchmark dataset for language understanding that isolates performance on relational reasoning. We also present a neural message-passing baseline and show that this model, which incorporates a relational inductive bias, is superior at combinatorial generalization compared to a traditional recurrent neural network approach.


title: ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion

abstract: This paper proposes a voice conversion method based on fully convolutional sequence-to-sequence (seq2seq) learning. The present method, which we call "ConvS2S-VC", learns the mapping between source and target speech feature sequences using a fully convolutional seq2seq model with an attention mechanism. Owing to the nature of seq2seq learning, our method is particularly noteworthy in that it allows the flexible conversion of not only the voice characteristics but also the pitch contour and duration of the input speech. The current model consists of six networks, namely source and target encoders, a target decoder, source and target reconstructors and a postnet, which are designed using dilated causal convolution networks with gated linear units. Subjective evaluation experiments revealed that the proposed method obtained higher sound quality and speaker similarity than a baseline method.


title: INTEGRATED GHz VOLTAGE CONTROLLED OSCILLATORS

abstract: The voltage controlled oscillator (VCO) is a critical sub-block in communications transceivers. The role of the VCO in a transceiver and the VCO requirements are first reviewed. The necessity of GHz VCOs and the driving factors towards the monolithic integration of the VCO are examined. VCO design techniques are outlined and design trade-offs are explored. The performance of VCOs in different implementation styles is compared to evaluate when and if VCO integration is desirable.


title: Teacher Attendance Effects on Student Achievement: Research Study of Ohio Schools.

abstract: Review of Literature Schools throughout the United States attempt to pinpoint student learning problems on a yearly basis. Those charged with analyzing data look for variables affecting student achievement, directly and indirectly. Student attendance, socioeconomic status, parent involvement, curriculum, and testing climate are some of the factors noted as affecting learning (Gottfried, 2009, King, 2000, Ramai et al, 2007). The focus of this study will be teacher attendance, and its impact on student achievement. Woods (1990) completed a study to determine the impact of teacher absences on student achievement. Two schools participated in the study, one in Wyoming and one in Indiana. Data analysis from the study revealed that teacher attendance had a significant impact on student learning. Teachers with lower absent rates had students scoring better on achievement tests, when compared to teachers with significantly absence. The research also indicated principals do not consider absent rate when evaluating teachers. In a study by Kremer et al (2005), teacher absenteeism in India was not associated with lower pay. However, it was noted that teacher absence correlated with daily incentives to attend work, such as school inspection and better infrastructure. Glewwe et al (2004) found that teacher attendance did not increase with an incentive program, but the affects impacted student scores significantly on some tests. Test scores were less likely to increase when incentives were not provided. Boyer (1994) studied the affects of paying teachers for unused sick leave to reduce the absenteeism, which was an obstacle to academic achievement. The Georgia school study revealed there was no significant relationship between sick-leave provisions and teacher attendance rates. The extrinsic motivator, money, made little or no difference in teachers' attendance. Other educators, including Miller et al (2008, 2006), Bruno (2002), Ehrenberg (1991), Jacobson (1990, 1989), Duflo & Hanna (2005), Scott (1998), Pitkoff (1993), and Woods & Montagno (1997) have completed narratives involving teacher attendance/absenteeism. Complimenting the studies above, these position papers and editorials present problems, concerns and affects of teacher absenteeism. Study Focus and Research Questions The objective of this study is for educators to gain insight pertaining to teacher attendance affecting student achievement. As can be seen from the review of studies and articles regarding student achievement, teacher absenteeism appears to be a variable worth further investigation. Teacher attendance will be reviewed based on the following research questions: 1. Is there a statistically significant difference in average teacher attendance rates when comparing schools with the lowest and highest attendance rates of teachers? 2. Is there a statistically significant difference in student attendance rates when comparing the lowest and highest rates, from the sample schools used in research number question one? 3. Is there a statistically significant difference in student achievement, comparing school performance index data derived from the sample schools in research question number one? 4. Is there a statistically significant difference in percent of academic standards achieved when comparing schools from the sample used in research question number one? 5. Is there a statistically significant difference in the number of schools meeting adequate yearly progress goals, when comparing schools used for analyzing research question number one? Methodology For comparison of average attendance rates, mean, standard deviation, and t test were employed. Adequate year progress data was computed in percentages, and for comparison purposes, t was calculated. School ratings were not calculated, due to being reported in non-numerical levels. Those results were noted using percentage comparisons. …


NON-CONTACT FRICTION FORCE MICROSCOPY EXPLOITING LATERAL RESONANCE ENHANCEMENT


title: A customer classification prediction model based on machine learning techniques

abstract: Most of the service providers and product based companies while launching brand new products, services or releasing new versions of existent products need to campaign to reach at the potential customers. While doing so they target their already existing customers who are the ambassadors of their company. To address the existing customers, they maintain the detailed customer data at all levels as customer maser data [9]. In this paper, we have built a prediction model to identify the customers who would most likely respond to the prospective offerings of the company basing on their past purchasing trends. Experiments have been conducted using the well known classifiers, viz., Naive Bayes, KNN and SVM to classify a bank customer data. Subsequently, we have compared the effectiveness of these techniques and found out which one produces the maximum accuracy for the existing data set.


title: Haptic interfaces and devices

abstract: Haptic interfaces enable person‐machine communication through touch, and most commonly, in response to user movements. We comment on a distinct property of haptic interfaces, that of providing for simultaneous information exchange between a user and a machine. We also comment on the fact that, like other kinds of displays, they can take advantage of both the strengths and the limitations of human perception. The paper then proceeds with a description of the components and the modus operandi of haptic interfaces, followed by a list of current and prospective applications and a discussion of a cross‐section of current device designs.


title: A delay model and speculative architecture for pipelined routers

abstract: This paper introduces a router delay model that accurately models key aspects of modern routers. The model accounts for the pipelined nature of contemporary routers, the specific flow control method employed the delay of the flow control credit path, and the sharing of crossbar ports across virtual channels. Motivated by this model, we introduce a microarchitecture for a speculative virtual-channel router that significantly reduces its router latency to that of a brown hole router. Simulations using our pipelined model give results that differ considerably from the commonly assumed 'unit-latency' model which is unreasonably optimistic. Using realistic pipeline models, we compare wormhole and virtual-channel flow control. Our results show that a speculative virtual-channel router has the same per-hop router latency as a wormhole router while improving throughput by up to 40%.


title: Joint Task Assignment and Resource Allocation in the Heterogeneous Multi-Layer Mobile Edge Computing Networks

abstract: Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing~(MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer some disadvantages of limited computing capacity, preventing computation-intensive tasks to be processed on time. To relief this issue, we propose a heterogeneous multi-layer MEC (HetMEC) network where data that cannot be timely processed at the edge are allowed to be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We design the latency minimization algorithm by jointly coordinating the task assignment, computing and transmission resources among the EDs, multi-layer MEC servers, and the CC. Simulation results indicate that our proposed algorithm can achieve a lower latency and stronger robustness than the conventional MEC schemes.


title: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics

abstract: A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error covariance equation are avoided because storage and evolution of the error covariance matrix itself are not needed. The results are also better than what is provided by the extended Kalman filter since there is no closure problem and the quality of the forecast error statistics therefore improves. The method should be feasible also for more sophisticated primitive equation models. The computational load for reasonable accuracy is only a fraction of what is required for the extended Kalman filter and is given by the storage of, say, 100 model states for an ensemble size of 100 and thus CPU requirements of the order of the cost of 100 model integrations. The proposed method can therefore be used with realistic nonlinear ocean models on large domains on existing computers, and it is also well suited for parallel computers and clusters of workstations where each processor integrates a few members of the ensemble.


title: Accuracy of a Wrist-Worn Wearable Device for Monitoring Heart Rates in Hospital Inpatients: A Prospective Observational Study

abstract: BACKGROUND ::: As the sensing capabilities of wearable devices improve, there is increasing interest in their application in medical settings. Capabilities such as heart rate monitoring may be useful in hospitalized patients as a means of enhancing routine monitoring or as part of an early warning system to detect clinical deterioration. ::: ::: ::: OBJECTIVE ::: To evaluate the accuracy of heart rate monitoring by a personal fitness tracker (PFT) among hospital inpatients. ::: ::: ::: METHODS ::: We conducted a prospective observational study of 50 stable patients in the intensive care unit who each completed 24 hours of heart rate monitoring using a wrist-worn PFT. Accuracy of heart rate recordings was compared with gold standard measurements derived from continuous electrocardiographic (cECG) monitoring. The accuracy of heart rates measured by pulse oximetry (Spo2.R) was also measured as a positive control. ::: ::: ::: RESULTS ::: On a per-patient basis, PFT-derived heart rate values were slightly lower than those derived from cECG monitoring (average bias of -1.14 beats per minute [bpm], with limits of agreement of 24 bpm). By comparison, Spo2.R recordings produced more accurate values (average bias of +0.15 bpm, limits of agreement of 13 bpm, P<.001 as compared with PFT). Personal fitness tracker device performance was significantly better in patients in sinus rhythm than in those who were not (average bias -0.99 bpm vs -5.02 bpm, P=.02). ::: ::: ::: CONCLUSIONS ::: Personal fitness tracker-derived heart rates were slightly lower than those derived from cECG monitoring in real-world testing and not as accurate as Spo2.R-derived heart rates. Performance was worse among patients who were not in sinus rhythm. Further clinical evaluation is indicated to see if PFTs can augment early warning systems in hospitals. ::: ::: ::: TRIAL REGISTRATION ::: ClinicalTrials.gov NCT02527408; https://clinicaltrials.gov/ct2/show/NCT02527408 (Archived by WebCite at http://www.webcitation.org/6kOFez3on).


title: Robust hovering control of a quad tilt-wing UAV

abstract: This paper presents design of a robust hovering controller for a quad tilt-wing UAV to hover at a desired position under external wind and aerodynamic disturbances. Wind and the aerodynamic disturbances are modeled using the Dryden model. In order to increase the robustness of the system, a disturbance observer is utilized to estimate the unknown disturbances acting on the system. Nonlinear terms which appear in the dynamics of the vehicle are also treated as disturbances and included in the total disturbance. Proper compensation of disturbances implies a linear model with nominal parameters. Thus, for robust hovering control, only PID type simple controllers have been employed and their performances have been found very satisfactory. Proposed hovering controller has been verified with several simulations and experiments.


title: A literature survey of low-rank tensor approximation techniques

abstract: During the last years, low-rank tensor approximation has been established as a new tool in scientific computing to address large-scale linear and multilinear algebra problems, which would be intractable by classical techniques. This survey attempts to give a literature overview of current developments in this area, with an emphasis on function-related tensors.


title: The efficacy ofSilybum marianum (L.) Gaertn. (silymarin) in the treatment of type II diabetes: a randomized, double-blind, placebo-controlled, clinical trial

abstract: Oxidative stresses are increasingly implicated in the pathogenesis of diabetic complications which may either cause direct pancreatic β -cell damage or lead to metabolic abnormalities that can induce or aggravate diabetes. The valuable effect of antioxidant nutrients on the glycemic control of diabetic patients has been reported in experimental and clinical studies. The present study was designed to investigate the effects of the herbal medicine, Silybum marianum seed extract (silymarin), which is known to have antioxidant properties on the glycemic profile in diabetic patients. A 4-month randomized double-blind clinical trial was conducted in 51 type II diabetic patients in two well-matched groups. The first group (n = 25) received a silymarin (200 mg) tablet 3 times a day plus conventional therapy. The second group (n = = = = 26) received the same therapy but a placebo tablet instead of silymarin. The patients were visited monthly and glycosylated hemoglobin (HbA1c), fasting blood glucose (FBS), insulin, total cholesterol, LDL and HDL, triglyceride, SGOT and SGPT levels were determined at the beginning and the end of the study. The results showed a significant decrease in HbA1c, FBS, total cholesterol, LDL, triglyceride SGOT and SGPT levels in silymarin treated patients compared with placebo as well as with values at the beginning of the study in each group. In conclusion, silymarin treatment in type II diabetic patients for 4 months has a beneficial effect on improving the glycemic profile. Copyright © 2006 John Wiley & Sons, Ltd.


title: MARCOnI—ConvNet-Based MARker-Less Motion Capture in Outdoor and Indoor Scenes

abstract: Marker-less motion capture has seen great progress, but most state-of-the-art approaches fail to reliably track articulated human body motion with a very low number of cameras, let alone when applied in outdoor scenes with general background. In this paper, we propose a method for accurate marker-less capture of articulated skeleton motion of several subjects in general scenes, indoors and outdoors, even from input filmed with as few as two cameras. The new algorithm combines the strengths of a discriminative image-based joint detection method with a model-based generative motion tracking algorithm through an unified pose optimization energy. The discriminative part-based pose detection method is implemented using Convolutional Networks (ConvNet) and estimates unary potentials for each joint of a kinematic skeleton model. These unary potentials serve as the basis of a probabilistic extraction of pose constraints for tracking by using weighted sampling from a pose posterior that is guided by the model. In the final energy, we combine these constraints with an appearance-based model-to-image similarity term. Poses can be computed very efficiently using iterative local optimization, since joint detection with a trained ConvNet is fast, and since our formulation yields a combined pose estimation energy with analytic derivatives. In combination, this enables to track full articulated joint angles at state-of-the-art accuracy and temporal stability with a very low number of cameras. Our method is efficient and lends itself to implementation on parallel computing hardware, such as GPUs. We test our method extensively and show its advantages over related work on many indoor and outdoor data sets captured by ourselves, as well as data sets made available to the community by other research labs. The availability of good evaluation data sets is paramount for scientific progress, and many existing test data sets focus on controlled indoor settings, do not feature much variety in the scenes, and often lack a large corpus of data with ground truth annotation. We therefore further contribute with a new extensive test data set called MPI-MARCOnI for indoor and outdoor marker-less motion capture that features $12$ scenes of varying complexity and varying camera count, and that features ground truth reference data from different modalities, ranging from manual joint annotations to marker-based motion capture results. Our new method is tested on these data, and the data set will be made available to the community.


title: Mean shift for accurate license plate localization

abstract: This paper presents a region-based algorithm for accurate license plate localization, where mean shift is utilized to filter and segment color vehicle images into candidate regions. Three features are extracted in order to decide whether a candidate region represents a real license plate, namely, rectangularity, aspect ratio, and edge density. Then, the Mahalanobis classifier is used with respect to above three features to classify license plate regions and non-license plate regions. Experimental results show that the proposed algorithm produces high robustness and accuracy.


title: Online State-of-Health Estimation of VRLA Batteries Using State of Charge

abstract: This paper presents an online method for the estimation of the state of health (SOH) of valve-regulated lead acid (VRLA) batteries. The proposed method is based on the state of charge (SOC) of the battery. The SOC is estimated using the extended Kalman filter and a neural-network model of the battery. Then, the SOH is estimated online based on the relationship between the SOC and the battery open-circuit voltage using fuzzy logic and the recursive least squares method. To obtain the open-circuit voltage while the battery is operating, the reflective charging process is employed. Experimental results show good estimation of the SOH of VRLA batteries.


title: A deep convolutional neural network for video sequence background subtraction

abstract: We propose a novel approach based on deep learning for background subtraction from video sequences.A new algorithm to generate background model has been proposed.Input image patches and their corresponding background images are fed into CNN to do background subtraction.We utilized median filter to enhance the segmentation results.Experiments of Change detection results confirm the performance of the proposed approach. In this work, we present a novel background subtraction from video sequences algorithm that uses a deep Convolutional Neural Network (CNN) to perform the segmentation. With this approach, feature engineering and parameter tuning become unnecessary since the network parameters can be learned from data by training a single CNN that can handle various video scenes. Additionally, we propose a new approach to estimate background model from video sequences. For the training of the CNN, we employed randomly 5% video frames and their ground truth segmentations taken from the Change Detection challenge 2014 (CDnet 2014). We also utilized spatial-median filtering as the post-processing of the network outputs. Our method is evaluated with different data-sets, and it (so-called DeepBS) outperforms the existing algorithms with respect to the average ranking over different evaluation metrics announced in CDnet 2014. Furthermore, due to the network architecture, our CNN is capable of real time processing.


title: Convolutional Network for Attribute-driven and Identity-preserving Human Face Generation

abstract: This paper focuses on the problem of generating human face pictures from specific attributes. The existing CNN-based face generation models, however, either ignore the identity of the generated face or fail to preserve the identity of the reference face image. Here we address this problem from the view of optimization, and suggest an optimization model to generate human face with the given attributes while keeping the identity of the reference image. The attributes can be obtained from the attribute-guided image or by tuning the attribute features of the reference image. With the deep convolutional network "VGG-Face", the loss is defined on the convolutional feature maps. We then apply the gradient decent algorithm to solve this optimization problem. The results validate the effectiveness of our method for attribute driven and identity-preserving face generation.


title: Outer rotor IPM generator with wide constant power region for automotive applications

abstract: In this paper an outer rotor Interior Permanent Magnet generator for automotive applications is presented. The rotor geometry has been optimized by means of a Multi-Objective algorithm. The aim is to maximize the output power of the generator and to minimize the permanent magnet volume. In any case, the machine has to yield the largest constant power region after the base speed, so as the generator can operate at the nominal power for a wide speed region. The result of the optimization is the optimal Pareto front that gives a set of optimal solutions. After considering different rotor topologies, the criteria to select a proper machine are given.


title: Developing an automated Bangla parts of speech tagged dictionary

abstract: This paper develops an algorithm for making an automated Bangla Parts Of Speech (POS) tagged dictionary. Natural Language Processing is one of the most vigorous research areas of computer science. It enables to communicate and retrieve information form computer based system more effectively and efficiently. Researches on Bangla language processing have started long back. However, this research area still suffers from resource scarcity. A POS tagged corpus is a cardinal element for language processing. POS tagging is the process of categorizing a particular word to a particular part of speech or syntactic category. In Bangla, we do not have any large POS tagged dictionary. In this paper we develop an automated way to make a POS tagged dictionary of Noun, Verb and Adjective. Initially, a suffix (or postfix) list is created manually for Bangla language. Based on this suffix list the POS tagged dictionary is developed. The proposed algorithm is evaluated using a paragraph consisting of manually tagged 10,000 words with 11 tags. We found that POS tagging is obtained more accurately for Verb than Noun and Adjective.


title: Generalized vector spaces model in information retrieval

abstract: In information retrieval, it is common to model index terms and documents as vectors in a suitably defined vector space. The main difficulty with this approach is that the explicit representation of term vectors is not known a priori. For this reason, the vector space model adopted by Salton for the SMART system treats the terms as a set of orthogonal vectors. In such a model it is often necessary to adopt a separate, corrective procedure to take into account the correlations between terms. In this paper, we propose a systematic method (the generalized vector space model) to compute term correlations directly from automatic indexing scheme. We also demonstrate how such correlations can be included with minimal modification in the existing vector based information retrieval systems. The preliminary experimental results obtained from the new model are very encouraging.


title: CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering

abstract: In just 3 years CRISPR genome editing has transformed biology, and its popularity and potency continue to grow. New CRISPR effectors and rules for locating optimum targets continue to be reported, highlighting the need for computational CRISPR targeting tools to compile these rules and facilitate target selection and design. CHOPCHOP is one of the most widely used web tools for CRISPR- and TALEN-based genome editing. Its overarching principle is to provide an intuitive and powerful tool that can serve both novice and experienced users. In this major update we introduce tools for the next generation of CRISPR advances, including Cpf1 and Cas9 nickases. We support a number of new features that improve the targeting power, usability and efficiency of CHOPCHOP. To increase targeting range and specificity we provide support for custom length sgRNAs, and we evaluate the sequence composition of the whole sgRNA and its surrounding region using models compiled from multiple large-scale studies. These and other new features, coupled with an updated interface for increased usability and support for a continually growing list of organisms, maintain CHOPCHOP as one of the leading tools for CRISPR genome editing. CHOPCHOP v2 can be found at http://chopchop.cbu.uib.no


The shortest isoform of dystrophin (Dp40) interacts with a group of presynaptic proteins to form a presumptive novel complex in the mouse brain


title: Cortical Surface-Based Analysis -- I. Segmentation and Surface Reconstruction

abstract: Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. r 1999 Academic Press


title: Autotagger: a model for predicting social tags from acoustic features on large music databases

abstract: Abstract Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web 2.0” recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of 360 classifiers trained using the online ensemble learning algorithm FilterBoost, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the “cold-start problem” common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommend...


title: A Cryptographic Approach to Securely Share and Query Genomic Sequences

abstract: To support large-scale biomedical research projects, organizations need to share person-specific genomic sequences without violating the privacy of their data subjects. In the past, organizations protected subjects' identities by removing identifiers, such as name and social security number; however, recent investigations illustrate that deidentified genomic data can be ldquoreidentifiedrdquo to named individuals using simple automated methods. In this paper, we present a novel cryptographic framework that enables organizations to support genomic data mining without disclosing the raw genomic sequences. Organizations contribute encrypted genomic sequence records into a centralized repository, where the administrator can perform queries, such as frequency counts, without decrypting the data. We evaluate the efficiency of our framework with existing databases of single nucleotide polymorphism (SNP) sequences and demonstrate that the time needed to complete count queries is feasible for real world applications. For example, our experiments indicate that a count query over 40 SNPs in a database of 5000 records can be completed in approximately 30 min with off-the-shelf technology. We further show that approximation strategies can be applied to significantly speed up query execution times with minimal loss in accuracy. The framework can be implemented on top of existing information and network technologies in biomedical environments.


title: THEORY OF LOW MASS STARS, BROWN DWARFS AND EXTRA-SOLAR GIANT PLANETS

abstract: We present a review of the most recent improvements in the theory of low-mass star-like objects, in the stellar and sub-stellar domains. Comparison is made with various recent observations.


title: Tree quantization for large-scale similarity search and classification

abstract: We propose a new vector encoding scheme (tree quantization) that obtains lossy compact codes for high-dimensional vectors via tree-based dynamic programming. Similarly to several previous schemes such as product quantization, these codes correspond to codeword numbers within multiple codebooks. We propose an integer programming-based optimization that jointly recovers the coding tree structure and the codebooks by minimizing the compression error on a training dataset. In the experiments with diverse visual descriptors (SIFT, neural codes, Fisher vectors), tree quantization is shown to combine fast encoding and state-of-the-art accuracy in terms of the compression error, the retrieval performance, and the image classification error.


Live coding youtube: organizing streaming media for an audiovisual performance


title: Refinement of technique in injection lipolysis based on scientific studies and clinical evaluation.

abstract: The unusual evolution of the practice of injection lipolysis has generated doubt regarding its safety and efficacy among many physicians. During the early years of this decade, mesotherapy was practiced by a few physicians, but the practice was not widespread. Paramedical practitioners and business developers saw the market potential for nonsurgical fat reduction, and the practice of injection lipolysis was packaged and sold before the mechanism of action was understood. Because of the early lack of scientific research and understanding of the limitations of injection lipolysis, many unsuitable patients were treated with this modality. To better understand the way injection lipolysis works, the inclusion and exclusion criteria for patients desiring treatment, and an accurate clinical evaluation format for potential treatment regions, a series of scientific studies was performed in 2007 and early 2008. These studies included a serial histopathology evaluation of treated patients over time, a stem cell study performed with the McGowan Research Institute in Pittsburgh, an animal study performed in conjunction with the Colorado State University veterinary school, and a prospective multicenter clinical trial using injection lipolysis in the back roll region. The purpose of these studies was to determine the way injection lipolysis works, how modifications of the formula and technique change the outcome, the role of each constituent component of various formulas, and the degree of fat reduction and skin retraction that is attainable with these treatments. The influence of depth of injection, distance between injection points, volume of injection, and ratios of constituent components was studied. The degree of topographic contour correction and the amount of volume reduction were evaluated. Following a review of these recent studies, an updated recommendation for the clinical practice of injection lipolysis was formulated.


title: A study of supervised term weighting scheme for sentiment analysis

abstract: We propose a supervised term weighting scheme for sentiment analysis.This scheme use ITD and ITS to measure the importance of a term in sentiment analysis.Experiment results on three real datasets show that our scheme outperforms state-of-the-art unsupervised approaches. Term weighting is a strategy that assigns weights to terms to improve the performance of sentiment analysis and other text mining tasks. In this paper, we propose a supervised term weighting scheme based on two basic factors: Importance of a term in a document (ITD) and importance of a term for expressing sentiment (ITS), to improve the performance of analysis. For ITD, we explore three definitions based on term frequency. Then, seven statistical functions are employed to learn the ITS of each term from training documents with category labels. Compared with the previous unsupervised term weighting schemes originated from information retrieval, our scheme can make full use of the available labeling information to assign appropriate weights to terms. We have experimentally evaluated the proposed method against the state-of-the-art method. The experimental results show that our method outperforms the method and produce the best accuracy on two of three data sets.


title: Decoupled Vector Control Scheme for Dual Three-Phase Permanent Magnet Synchronous Machines

abstract: Dual three-phase electric machines have many advantages compared with conventional three-phase machines. However, the properties of such machines present challenges for current control that make it difficult to produce sufficient performance for the electric drive. This paper introduces an improved vector control scheme for dual three-phase permanent magnet synchronous machines. The study includes detailed solutions for key parts of the control such as reference frame transformations, decoupling of the current control loops, and modulation. The performance of the suggested control scheme is evaluated using finite-element analyses and experimental results. The results show that the scheme can produce desired dynamics for the current control and guarantees balanced current sharing between the winding sets. In addition, the proposed solution is capable of reducing current harmonics produced by the internal structure of the machine. This problem is, however, only partly solved since complete elimination of harmonic components is not achieved. Nevertheless, the suggested control scheme overcomes many of the disadvantages found with other control solutions. Improved control performance allows the full benefits of dual three-phase drives to be utilized even in demanding applications.


A method for Automatic Text Summarization using Consensus of Multiple Similarity Measures and Ranking Techniques


title: Extended Bayesian Information Criteria for Gaussian Graphical Models

abstract: Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest in many modern applications. For the problem of recovering the graphical structure, information criteria provide useful optimization objectives for algorithms searching through sets of graphs or for selection of tuning parameters of other methods such as the graphical lasso, which is a likelihood penalization technique. In this paper we establish the consistency of an extended Bayesian information criterion for Gaussian graphical models in a scenario where both the number of variables p and the sample size n grow. Compared to earlier work on the regression case, our treatment allows for growth in the number of non-zero parameters in the true model, which is necessary in order to cover connected graphs. We demonstrate the performance of this criterion on simulated data when used in conjunction with the graphical lasso, and verify that the criterion indeed performs better than either cross-validation or the ordinary Bayesian information criterion when p and the number of non-zero parameters q both scale with n.


title: Discovering links among social networks

abstract: Distinct social networks are interconnected via bridge users, who play thus a key role when crossing information is investigated in the context of Social Internetworking analysis. Unfortunately, not always users make their role of bridge explicit by specifying the so-called me edge (i.e., the edge connecting the accounts of the same user in two distinct social networks), missing thus a potentially very useful information. As a consequence, discovering missing me edges is an important problem to face in this context yet not so far investigated. In this paper, we propose a common-neighbors approach to detecting missing me edges, which returns good results in real life settings. Indeed, an experimental campaign shows both that the state-of-the-art common-neighbors approaches cannot be effectively applied to our problem and, conversely, that our approach returns precise and complete results.


title: Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources

abstract: Recent work has shown success in learning word embeddings with neural network language models (NNLM). However, the majority of previous NNLMs represent each word with a single embedding, which fails to capture polysemy. In this paper, we address this problem by representing words with multiple and sense-specific embeddings, which are learned from bilingual parallel data. We evaluate our embeddings using the word similarity measurement and show that our approach is significantly better in capturing the sense-level word similarities. We further feed our embeddings as features in Chinese named entity recognition and obtain noticeable improvements against single embeddings.


title: Credit Card Fraud Detection: A case study

abstract: In this research, a technique for ‘Credit Card Fraud Detection’ is developed. As fraudsters are increasing day by day. And fallacious transactions are done by the credit card and there are various types of fraud. So to solve this problem combination of technique is used like Genetic Algorithm, Behavior Based Technique and Hidden Markov Model. By this transaction is tested individually and whatever suits the best is further proceeded. And the foremost goal is to detect fraud by filtering the above techniques to get better result.


title: Image-based phenotyping of plant disease symptoms

abstract: Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant-pathogen systems, we lack knowledge of the physiological mechanisms that link pathogen infection and the production of disease symptoms in the host. A variety of quantitative high-throughput image-based methods for phenotyping plant growth and development are currently being developed. These methods range from detailed analysis of a single plant over time to broad assessment of the crop canopy for thousands of plants in a field and employ a wide variety of imaging technologies. Application of these methods to the study of plant disease offers the ability to study quantitatively how host physiology is altered by pathogen infection. These approaches have the potential to provide insight into the physiological mechanisms underlying disease symptom development. Furthermore, imaging techniques that detect the electromagnetic spectrum outside of visible light allow us to quantify disease symptoms that are not visible by eye, increasing the range of symptoms we can observe and potentially allowing for earlier and more thorough symptom detection. In this review, we summarize current progress in plant disease phenotyping and suggest future directions that will accelerate the development of resistant crop varieties.


title: A Control Map for a Bidirectional PWM Plus Phase-Shift-Modulated Push–Pull DC–DC Converter

abstract: In this paper, a bidirectional push–pull converter is investigated with new add-on feature of three-level PWM plus phase-shift (PPS) control scheme, and a control map is proposed to assist converter operation and design in the optimized region. The PPS control strategy is adopted to reduce peak current of the transistors, to increase the input-voltage-variation range and to expand the soft-switching region. A comprehensive analysis of operation principle, transmission capability, and soft-switching operation in 12 different operation modes is presented. Based on the complete analysis, a control map is proposed to assist converter operation that can effectively avoid high-circulating-current regions, low-power-transmission-capability regions, and hard-switched regions. In the control map, a circuit parameter k is signified and its range is found, beyond which the PPS converter no longer operates normally. The proposed control map and the circuit parameter k are general to all PPS converters, providing a meaningful analyzing method to design and operate all PPS converters. A 30∼48 V/380 V prototype rated at 100–1000 W was built under the guideline. The analysis is verified by both simulation and experimental results.


title: Beyond Manual Tuning of Hyperparameters

abstract: The success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal: (1) automated optimization of hyperparameters (including mechanisms for feature selection, preprocessing, model selection, etc) and (2) the development of algorithms with reduced sets of hyperparameters. Since many research directions (e.g., deep learning), show a tendency towards increasingly complex algorithms with more and more hyperparamters, the demand for both of these strategies continuously increases. We review recent hyperparameter optimization methods and discuss data-driven approaches to avoid the introduction of hyperparameters using unsupervised learning. We end in discussing how these complementary strategies can work hand-in-hand, representing a very promising approach towards autonomous machine learning.


title: Malware Characterization Using Windows API Call Sequences

abstract: In this research we have used Windows API (Win-API) call sequences to capture the behaviour of malicious applications. Detours library by Microsoft has been used to hook the Win-APIs call sequences. To have a higher level of abstraction, related Win-APIs have been mapped to a single category. A total set of 534 important Win-APIs have been hooked and mapped to 26 categories (A…Z). Behaviour of any malicious application is captured through sequence of these 26 categories of APIs. In our study, five classes of malware have been analyzed: Worm, Trojan-Downloader, Trojan-Spy, Trojan-Dropper and Backdoor. 400 samples for each of these classes have been taken for experimentation. So a total of 2000 samples were taken as training set and their API call sequences were analyzed. For testing, 120 samples were taken for each class. Fuzzy hashing algorithm ssdeep was applied to generate fuzzy hash based signature. These signatures were matched to quantify the API call sequence homologies between test samples and training samples. Encouraging results have been obtained in classification of these samples to the above mentioned 5 categories. Further, N-gram analysis has also been done to extract different API call sequence patterns specific to each of the 5 categories of malware.


title: Substrate-Integrated Waveguide Ku-Band Cavity-Backed 2 $\,\times\,$ 2 Microstrip Patch Array Antenna

abstract: A Ku-band cavity-backed microstrip patch 2 x 2 antenna array has been implemented using the substrate-integrated waveguide (SIW) technology-a low-cost multilayer printed circuit board (PCB) process. Cavities are emulated using vias, and the patches are fed using microstrip lines that are centrally fed by a shielded coaxial probe feed line. Simple design guidelines for the cavity, patch, and substrate selection are presented. The array was fabricated, and its measured results agreed very well with theoretical predictions and indicated a relatively high efficiency and wide bandwidth of greater than 70% and 9%, respectively.


title: SUGAMAN: Describing Floor Plans for Visually Impaired by Annotation Learning and Proximity based Grammar

abstract: In this study, the authors propose a framework SUGAMAN (Supervised and Unified framework using Grammar and Annotation Model for Access and Navigation). SUGAMAN is a Hindi word meaning ‘easy passage from one place to another’. SUGAMAN synthesises textual description from a given floor plan image, usable by visually impaired to navigate by understanding the arrangement of rooms and furniture. It is the first framework for describing a floor plan and giving direction for obstacle-free movement within a building. The model learns five classes of room categories from 1355 room image samples under a supervised learning paradigm. These learned annotations are fed into a description synthesis framework to yield a holistic description of a floor plan image. Authors demonstrate the performance of various supervised classifiers on room learning and provided a comparative analysis of system generated and human-written descriptions. The contribution of this study includes a novel framework for description generation from document images with graphics while proposing a new feature representing the floor plans, text annotations for a publicly available data set, and an algorithm for door to door obstacle avoidance navigation. This work can be applied to areas like understanding floor plans and design of historical monuments, and retrieval.


title: Automatic cinematography and multilingual NLG for generating video documentaries

abstract: Automatically constructing a complete documentary or educational film from scattered pieces of images and knowledge is a significant challenge. Even when this information is provided in an annotated format, the problems of ordering, structuring and animating sequences of images, and producing natural language descriptions that correspond to those images within multiple constraints, are each individually difficult tasks. This paper describes an approach for tackling these problems through a combination of rhetorical structures with narrative and film theory to produce movie-like visual animations from still images along with natural language generation techniques needed to produce text descriptions of what is being seen in the animations. The use of rhetorical structures from NLG is used to integrate separate components for video creation and script generation. We further describe an implementation, named Glamour, that produces actual, short video documentaries, focusing on a cultural heritage domain, and that have been evaluated by professional filmmakers.


title: Brand Knowledge, Trust and Loyalty – A Conceptual Model of Destination Branding

abstract: The present study conceptualizes a destination branding model that reflects the process of tourist decision making. The model integrates key sequential concepts such as brand knowledge, brand trust, and brand loyalty. Destination brand knowledge is further defined by its constituents of brand awareness, cognitive brand image, and affective brand image. The concept of brand trust is defined in the context of risk and expectations. Brand loyalty is operationalized as behavioral intention to visit/revisit or make a referral. The conceptual model results from the systheses of literature in the fields of general branding and marketing, psychology, sociology, and consmer behavior, as well as tourism. Both theorectical and empirical works were consulted in structuring the model and specification of its various concepts. Theoretical contributions of the model are discussed.


title: Design and implementation of a 120A resonant inverter for induction furnace

abstract: This paper presents the design and implementation of a series resonant inverter operating at high frequency for induction heating applications. The main advantage of this work is focused in applying power electronics, particularly a series resonant inverter based on an IGBT's full bridge and a series RLC Load matched with a high frequency coupling transformer. The series resonant inverter is designed to deliver a current load up to 120A with a one phase AC source. Due to the inherent high speed operation and high efficiency of switching converters, other advantages of the induction furnace presented in this paper are that it require less time to start melting, and require less physical space than other traditional furnaces.


title: Audio-based emotion estimation for interactive robotic therapy for children with autism spectrum disorder

abstract: Recently, efforts in the development of speech recognition systems and robots have come to fruition with an overflow of applications in our daily lives. However, we are still far from achieving natural interaction between humans and robots, given that robots do not take into account the emotional state of speakers. The objective of this research is to create an automatic emotion classifier integrated with a robot, such that the robot can understand the emotional state of a human user by analyzing the speech signals from the user. This becomes particularly relevant in the realm of using assistive robotics to tailor therapeutic techniques towards assisting children with autism spectrum disorder (ASD). Over the past two decades, the number of children being diagnosed with ASD has been rapidly increasing, yet the clinical and societal support have not been enough to cope with the needs. Therefore, finding alternative, affordable, and accessible means of therapy and assistance has become more of a concern. Improving audio-based emotion prediction for children with ASD will allow for the robotic system to properly assess the engagement level of the child and modify its responses to maximize the quality of interaction between the robot and the child and sustain an interactive learning environment.


title: Potentiation of the antitumor effect of 11-keto-β-boswellic acid by its 3-α-hexanoyloxy derivative

abstract: Abstract We recently discovered that a propionyloxy derivative of 11-keto- β -boswellic acid (PKBA) showed better anticancer potential than other boswellic acids including AKBA, encompassing the importance of acyl group at the 3-α-hydroxy position of KBA. In continuation of our previous work, other higher derivatives (with increasing alkoxy chain length at 3-α-hydroxy position) including butyryloxy (BKBA) and hexanoyloxy (HKBA) derivatives of KBA were synthesized. The respective IC 50 values of BKBA and HKBA in HL-60 cells were found to be 7.7 and 4.5 μg/ml. IC 50 value of HKBA was comparatively lower than that of BKBA, and further lower than that of the previously reported derivative (PKBA, IC 50 8.7 μg/ml). In order to compare the anticancer potential of HKBA with PKBA, detailed in vitro pro-apoptotic and in vivo anticancer studies were carried out. The induction of apoptosis by HKBA was measured using various parameters including fluorescence and scanning electron microscopy, DNA fragmentation and Annexin V-FITC binding. The extent of DNA damage was measured using neutral comet assay. HKBA was further evaluated for its effect on DNA cell cycle and mitochondria where it was found to arrest cells in G 2 /M phase and also induced loss of mitochondrial membrane potential. These events were associated with increased expression of cytosolic cytochrome c and cleavage of PARP. Target based studies showed that HKBA inhibited the enzymatic activity of topoisomerases I and II at low doses than that of PKBA. In vivo studies also revealed a low dose inhibitory effect of HKBA on ascitic and solid murine tumor models.


title: Unbiased look at dataset bias

abstract: Datasets are an integral part of contemporary object recognition research. They have been the chief reason for the considerable progress in the field, not just as source of large amounts of training data, but also as means of measuring and comparing performance of competing algorithms. At the same time, datasets have often been blamed for narrowing the focus of object recognition research, reducing it to a single benchmark performance number. Indeed, some datasets, that started out as data capture efforts aimed at representing the visual world, have become closed worlds unto themselves (e.g. the Corel world, the Caltech-101 world, the PASCAL VOC world). With the focus on beating the latest benchmark numbers on the latest dataset, have we perhaps lost sight of the original purpose? The goal of this paper is to take stock of the current state of recognition datasets. We present a comparison study using a set of popular datasets, evaluated based on a number of criteria including: relative data bias, cross-dataset generalization, effects of closed-world assumption, and sample value. The experimental results, some rather surprising, suggest directions that can improve dataset collection as well as algorithm evaluation protocols. But more broadly, the hope is to stimulate discussion in the community regarding this very important, but largely neglected issue.


title: Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities

abstract: Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations. To address this challenge, we propose Piggyback CrowdSensing (PCS), a system for collecting mobile sensor data from smartphones that lowers the energy overhead of user participation. Our approach is to collect sensor data by exploiting Smartphone App Opportunities -- that is, those times when smartphone users place phone calls or use applications. In these situations, the energy needed to sense is lowered because the phone need no longer be woken from an idle sleep state just to collect data. Similar savings are also possible when the phone either performs local sensor computation or uploads the data to the cloud. To efficiently use these sporadic opportunities, PCS builds a lightweight, user-specific prediction model of smartphone app usage. PCS uses this model to drive a decision engine that lets the smartphone locally decide which app opportunities to exploit based on expected energy/quality trade-offs. We evaluate PCS by analyzing a large-scale dataset (containing 1,320 smartphone users) and building an end-to-end crowdsourcing application that constructs an indoor WiFi localization database. Our findings show that PCS can effectively collect large-scale mobile sensor datasets (e.g., accelerometer, GPS, audio, image) from users while using less energy (up to 90% depending on the scenario) compared to a representative collection of existing approaches.


title: Enhanced ARP: preventing ARP poisoning-based man-in-the-middle attacks

abstract: In this letter, an enhanced version of Address Resolution Protocol (ARP) is proposed to prevent ARP poisoning-based Man-in-the-Middle (MITM) attacks. The proposed mechanism is based on the following concept. When a node knows the correct Media Access Control (MAC) address for a given IP address, if it retains the IP/MAC address mapping while that machine is alive, then MITM attack is impossible for that IP address. In order to prevent MITM attacks even for a new IP address, a voting-based resolution mechanism is proposed. The proposed scheme is backward compatible with existing ARP and incrementally deployable.


title: A two-stage intelligent search algorithm for the two-dimensional strip packing problem

abstract: This paper presents a two-stage intelligent search algorithm for a two-dimensional strip packing problem without guillotine constraint. In the first stage, a heuristic algorithm is proposed, which is based on a simple scoring rule that selects one rectangle from all rectangles to be packed, for a given space. In the second stage, a local search and a simulated annealing algorithm are combined to improve solutions of the problem. In particular, a multi-start strategy is designed to enhance the search capability of the simulated annealing algorithm. Extensive computational experiments on a wide range of benchmark problems from zero-waste to non-zero-waste instances are implemented. Computational results obtained in less than 60 seconds of computation time show that the proposed algorithm outperforms the supposedly excellent algorithms reported recently, on average. It performs particularly better for large instances.


title: On the Biomimetic Design of the Berkeley Lower Extremity Exoskeleton (BLEEX)

abstract: Many places in the world are too rugged or enclosed for vehicles to access. Even today, material transport to such areas is limited to manual labor and beasts of burden. Modern advancements in wearable robotics may make those methods obsolete. Lower extremity exoskeletons seek to supplement the intelligence and sensory systems of a human with the significant strength and endurance of a pair of wearable robotic legs that support a payload. This paper outlines the use of Clinical Gait Analysis data as the framework for the design of such a system at UC Berkeley.