title: Design and analysis of an improved Halbach tubular linear motor with non-ferromagnetic mover tube for direct-driven EHA

abstract: Direct-driven Electrical Hydrostatic Actuator (D-EHA) is a novel actuator for large aircraft Power-By-Wire (PBW) system. Achieving high dynamic response is an important task for D-EHA. In this paper, a tubular oscillating motor with improved Halbach array and non-ferromagnetic mover tube is proposed for high-dynamic system. Its structure and operating principle is first clarified. Then, focusing on the novel magnet array, analytical modelling is put forward and validated by FEM simulation. Comparisons with common used quasi-Halbach design over flux distribution, flux density and thrust are conducted. The results indicate that this novel magnet design has superior thrust and dynamic performance over traditional ones, especially for oscillating motor with relatively small stroke.


User-Generated Content in Social Media


title: Confusion Network Based Recurrent Neural Network Language Modeling for Chinese OCR Error Detection

abstract: This paper presents a new framework for OCR error detection, which uses a conditional random field model to combine rich features from multiple sources, including confusion networks (c-nets), lexical local context and recurrent neural network language model (RNNLM)1. We propose a novel, efficient method for computing character-level c-net based RNNLM scores by using dynamic programming and c-net partial unfolding. Our experiments show that our error detection model has consistent observable improvements over a high baseline employed by our current OCR demo system, as measured by average precision and detection error trade-off curve on two test sets of Chinese image documents. Both linguistic and recognition features contribute to the high performance, with the former especially informative. In addition, we show that the new feature we proposed, the c-net RNNLM feature, plays a remarkable beneficial role in improving error detection rate. These results suggest that applications on top of image text recognition can benefit substantially from a hybrid strategy that combines techniques from optical character recognition and natural language processing.


title: EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes

abstract: Two experiments were designed to examine the effects of attentional demands on the electroencephalogram during cognitive and emotional tasks. We found an interaction of task with hemisphere as well as more overall parietal alpha for tasks not requiring attention to the environment, such as mental arithmetic, than for those requiring such attention. Differential hemispheric activation for beta was found most strongly in the temporal areas for emotionally positive or negative tasks and in the parietal areas for cognitive tasks.


title: Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed Sensing.

abstract: The ability to automatically detect certain types of cells or cellular subunits in microscopy images is of significant interest to a wide range of biomedical research and clinical practices. Cell detection methods have evolved from employing hand-crafted features to deep learning-based techniques. The essential idea of these methods is that their cell classifiers or detectors are trained in the pixel space, where the locations of target cells are labeled. In this paper, we seek a different route and propose a convolutional neural network (CNN)-based cell detection method that uses encoding of the output pixel space. For the cell detection problem, the output space is the sparsely labeled pixel locations indicating cell centers. We employ random projections to encode the output space to a compressed vector of fixed dimension. Then, CNN regresses this compressed vector from the input pixels. Furthermore, it is possible to stably recover sparse cell locations on the output pixel space from the predicted compressed vector using $L_1$-norm optimization. In the past, output space encoding using compressed sensing (CS) has been used in conjunction with linear and non-linear predictors. To the best of our knowledge, this is the first successful use of CNN with CS-based output space encoding. We made substantial experiments on several benchmark datasets, where the proposed CNN + CS framework (referred to as CNNCS) achieved the highest or at least top-3 performance in terms of F1-score, compared with other state-of-the-art methods.


title: Event-Based Programming Without Inversion of Control

abstract: Concurrent programming is indispensable. On the one hand, distributed and mobile environments naturally involve concurrency. On the other hand, there is a general trend towards multi-core processors that are capable of running multiple threads in parallel.


title: A Dirty Model for Multi-task Learning

abstract: We consider multi-task learning in the setting of multiple linear regression, and where some relevant features could be shared across the tasks. Recent research has studied the use of l1/lq norm block-regularizations with q > 1 for such block-sparse structured problems, establishing strong guarantees on recovery even under high-dimensional scaling where the number of features scale with the number of observations. However, these papers also caution that the performance of such block-regularized methods are very dependent on the extent to which the features are shared across tasks. Indeed they show [8] that if the extent of overlap is less than a threshold, or even if parameter values in the shared features are highly uneven, then block l1/lq regularization could actually perform worse than simple separate elementwise l1 regularization. Since these caveats depend on the unknown true parameters, we might not know when and which method to apply. Even otherwise, we are far away from a realistic multi-task setting: not only do the set of relevant features have to be exactly the same across tasks, but their values have to as well. ::: ::: Here, we ask the question: can we leverage parameter overlap when it exists, but not pay a penalty when it does not? Indeed, this falls under a more general question of whether we can model such dirty data which may not fall into a single neat structural bracket (all block-sparse, or all low-rank and so on). With the explosion of such dirty high-dimensional data in modern settings, it is vital to develop tools - dirty models - to perform biased statistical estimation tailored to such data. Here, we take a first step, focusing on developing a dirty model for the multiple regression problem. Our method uses a very simple idea: we estimate a superposition of two sets of parameters and regularize them differently. We show both theoretically and empirically, our method strictly and noticeably outperforms both l1 or l1/lq methods, under high-dimensional scaling and over the entire range of possible overlaps (except at boundary cases, where we match the best method).


title: Crosstalk analysis for dual stripline with parallel and angled routing

abstract: Dual stripline routing is more and more widely used in the modern high speed PCB design due to its cost advantage of reduced overall layer count. However, the major challenge of a successful dual stripline design is to handle the additional interferences introduced by the signals on adjacent layers. This paper studies the crosstalk effect of the dual stripline with both parallel and angled routing, and proposes design solutions to tackle the challenge. Analytical and empirical algorithms are proposed to estimate the crosstalk waveforms from multiple aggressors, which provide quick design risk assessment, and the waveform is well correlated to the 3D full wave EM simulation results.


title: An Interleaved Boost Converter With Zero-Voltage Transition

abstract: This paper proposes a novel soft-switching interleaved boost converter composed of two shunted elementary boost conversion units and an auxiliary inductor. This converter is able to turn on both the active power switches at zero voltage to reduce their switching losses and evidently raise the conversion efficiency. Since the two parallel-operated elementary boost units are identical, operation analysis and design for the converter module becomes quite simple. A laboratory test circuit is built, and the circuit operation shows satisfactory agreement with the theoretical analysis. The experimental results show that this converter module performs very well with the output efficiency as high as 95%.


title: Modeling of Information Diffusion on Social Networks with Applications to WeChat

abstract: Traces of user activities recorded in online social networks such as the creation, viewing and forwarding/sharing of information over time open new possibilities to quantitatively and systematically understand the information diffusion process on social networks. From an online social network like WeChat, we could collect a large number of information cascade trees, each of which tells the spreading trajectory of a message/information such as which user creates the information and which users view or forward the information shared by which neighbors. In this work, we propose two heterogeneous non-linear models. Both models are validated by the WeChat data in reproducing and explaining key features of cascade trees. Specifically, we firstly apply the Random Recursive Tree (RRT) to model the cascade tree topologies, capturing key features, i.e. the average path length and degree variance of a cascade tree in relation to the number of nodes (size) of the tree. The RRT model with a single parameter $\theta$ describes the growth mechanism of a tree, where a node in the existing tree has a probability $d_i^{\theta}$ of being connected to a newly added node that depends on the degree $d_i$ of the existing node. The identified parameter $\theta$ quantifies the relative depth or broadness of the cascade trees, indicating that information propagates via a star-like broadcasting or viral-like hop by hop spreading. The RRT model explains the appearance of hubs, thus a possibly smaller average path length as the cascade size increases, as observed in WeChat. We further propose the stochastic Susceptible View Forward Removed (SVFR) model to depict the dynamic user behaviors including creating, viewing, forwarding and ignoring a message on a given social network.


title: Advice weaving in AspectJ

abstract: This paper describes the implementation of advice weaving in AspectJ. The AspectJ language picks out dynamic join points in a program's execution with pointcuts and uses advice to change the behavior at those join points. The core task of AspectJ's advice weaver is to statically transform a program so that at runtime it will behave according to the AspeetJ language semantics. This paper describes the 1.1 implementation which is based on transforming bytecode. We describe how AspectJ's join points are mapped to regions of bytecode, how these regions are efficiently matched by AspectJ's pointcuts, and how pieces of advice are efficiently implemented at these regions. We also discuss both run-time and compile-time performance of this implementation.


Centralization vs. Decentralization Issues in Internet-based Knowledge Management Systems: Experiences from Expert Recommender Systems


title: The illusion of transparency: Biased assessments of others' ability to read one's emotional states

abstract: Three sets of studies provide evidence for an illusion of transparency, or a tendency for people to overestimate the extent to which others can discern their internal states. People often mistakenly believe that their internal states "leak out" more than they really do. The authors attribute this bias to a tendency for people to adjust insufficiently from the "anchor" of their own phenomenological experience when attempting to take another's perspective. Evidence for this illusion is provided by showing that liars overestimate the detectability of their lies (Studies la, lb, and lc) and that people believe their feelings of disgust are more apparent than they actually are (Studies 2a and 2b). A final pair of experiments ( Studies 3a and 3b) explores the implications of the illusion of transparency for people's reluctance to intervene in emergencies. All 3 sets of studies also provide evidence consistent with the proposed anchoring and adjustment interpretation. Fans of Edgar Allan Poe will recall that the key passage in The Tell-Tale Heart is one in which the protagonist does his best to play it cool during a conversation with three police officers. It is a performance made more difficult by the fact that the officers happen to be standing directly above the hidden body of the protagonist's murder victim. As he becomes increasingly anxious that the officers suspect his guilt, he begins to hear what he takes to be his victim's heart beating underneath the floorboards. He becomes convinced that the sound, which in reality is the beating of his own heart, can be heard by the officers as well. Eventually, his emotions get the best of him and he gives himself away:


title: ATOMS: Air Traffic Operations and Management Simulator

abstract: In this paper, we introduce the air traffic operations and management simulator (ATOMS), which is an air traffic and airspace modeling and simulation system for the analysis of free-flight concepts. This paper describes the design, architecture, functionality, and applications of the ATOMS. It is an intent-based simulator that discretizes the airspace in equal-sized hyper-rectangular cells to maintain intent reference points. It can simulate end-to-end airspace operations and air navigation procedures for conventional air traffic, as well as for free flight. Atmospheric and wind data that are modeled in the ATOMS result in accurate trajectory predictions. The ATOMS uses a multiagent-based modeling paradigm for modular design and easy integration of various air traffic subsystems. A variety of advanced air traffic management (ATM) concepts that are envisioned in free flight are prototyped in the ATOMS, including airborne separation assurance (ASA), cockpit display of traffic information (CDTI), weather avoidance, and decision support systems (DSSs). Experimental results indicate that advanced ATM concepts make a sound case for free flight; however, there is a need to investigate and understand their complex interaction under nonnominal scenarios.


title: Semi-Supervised Text Classification With Universum Learning

abstract: Universum, a collection of nonexamples that do not belong to any class of interest, has become a new research topic in machine learning. This paper devises a semi-supervised learning with Universum algorithm based on boosting technique, and focuses on situations where only a few labeled examples are available. We also show that the training error of AdaBoost with Universum is bounded by the product of normalization factor, and the training error drops exponentially fast when each weak classifier is slightly better than random guessing. Finally, the experiments use four data sets with several combinations. Experimental results indicate that the proposed algorithm can benefit from Universum examples and outperform several alternative methods, particularly when insufficient labeled examples are available. When the number of labeled examples is insufficient to estimate the parameters of classification functions, the Universum can be used to approximate the prior distribution of the classification functions. The experimental results can be explained using the concept of Universum introduced by Vapnik, that is, Universum examples implicitly specify a prior distribution on the set of classification functions.


title: Skip-graph: Learning graph embeddings with an encoder-decoder model

abstract: In this work, we study the problem of feature representation learning for graph-structured data. Many of the existing work in the area are task-specific and based on supervised techniques. We study a method for obtaining a generic feature representation for a graph using an unsupervised approach. The neural encoder-decoder model is a method that has been used in the natural language processing domain to learn feature representations of sentences. In our proposed approach, we train the encoder-decoder model to predict the random walk sequence of neighboring regions in a graph given a random walk along a particular region. The goal is to map subgraphs — as represented by their random walks — that are structurally and functionally similar to nearby locations in feature space. We evaluate the learned graph vectors using several real-world datasets on the graph classification task. The proposed model is able to achieve good results against state-of- the-art techniques.


Corpus-independent Generic Keyphrase Extraction Using Word Embedding Vectors


title: Comparison of seven business model innovation tools for IoT ecosystems

abstract: This paper presents seven different business model innovation tools and how they compare when applied to business model innovation using the internet of thing ecosystem. The work leading up to this publication encompasses research that reviewed existing literature on business innovation tools and establishing internet of things ecosystem characteristic that need to be exposed when working with business model innovation. The conclusion is that the more mature and easy-to-use tools fall short on taking into consideration important internet of things characteristics, while the less mature specialized tools are more inline by exposing these characteristics but falling short on maturity and usability. The paper clearly identifies this gap.


title: Unsupervised Feature Selection for Multi-View Clustering on Text-Image Web News Data

abstract: Unlabeled high-dimensional text-image web news data are produced every day, presenting new challenges to unsupervised feature selection on multi-view data. State-of-the-art multi-view unsupervised feature selection methods learn pseudo class labels by spectral analysis, which is sensitive to the choice of similarity metric for each view. For text-image data, the raw text itself contains more discriminative information than similarity graph which loses information during construction, and thus the text feature can be directly used for label learning, avoiding information loss as in spectral analysis. We propose a new multi-view unsupervised feature selection method in which image local learning regularized orthogonal nonnegative matrix factorization is used to learn pseudo labels and simultaneously robust joint $l_{2,1}$-norm minimization is performed to select discriminative features. Cross-view consensus on pseudo labels can be obtained as much as possible. We systematically evaluate the proposed method in multi-view text-image web news datasets. Our extensive experiments on web news datasets crawled from two major US media channels: CNN and FOXNews demonstrate the efficacy of the new method over state-of-the-art multi-view and single-view unsupervised feature selection methods.


title: Machine learning for high-speed corner detection

abstract: Where feature points are used in real-time frame-rate applications, a high-speed feature detector is necessary. Feature detectors such as SIFT (DoG), Harris and SUSAN are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Here we show that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of tlie available processing time. By comparison neither the Harris detector (120%) nor the detection stage of SIFT (300%) can operate at full frame rate. Clearly a high-speed detector is of limited use if the features produced are unsuitable for downstream processing. In particular, the same scene viewed from two different positions should yield features which correspond to the same real-world 3D locations[1]. Hence the second contribution of this paper is a comparison corner detectors based on this criterion applied to 3D scenes. This comparison supports a number of claims made elsewhere concerning existing corner detectors. Further, contrary to our initial expectations, we show that despite being principally constructed for speed, our detector significantly outperforms existing feature detectors according to this criterion.


title: A probabilistic rating inference framework for mining user preferences from reviews

abstract: We propose a novel Probabilistic Rating infErence Framework, known as Pref, for mining user preferences from reviews and then mapping such preferences onto numerical rating scales. Pref applies existing linguistic processing techniques to extract opinion words and product features from reviews. It then estimates the sentimental orientations (SO) and strength of the opinion words using our proposed relative-frequency-based method. This method allows semantically similar words to have different SO, thereby addresses a major limitation of existing methods. Pref takes the intuitive relationships between class labels, which are scalar ratings, into consideration when assigning ratings to reviews. Empirical results validated the effectiveness of Pref against several related algorithms, and suggest that Pref can produce reasonably good results using a small training corpus. We also describe a useful application of Pref as a rating inference framework. Rating inference transforms user preferences described as natural language texts into numerical rating scales. This allows Collaborative Filtering (CF) algorithms, which operate mostly on databases of scalar ratings, to utilize textual reviews as an additional source of user preferences. We integrated Pref with a classical CF algorithm, and empirically demonstrated the advantages of using rating inference to augment ratings for CF.


Holistic Query Evaluation over Information Extraction Pipelines


title: Pouch Motors: Printable Soft Actuators Integrated with Computational Design

abstract: Abstract We propose pouch motors, a new family of printable soft actuators integrated with computational design. The pouch motor consists of one or more inflatable gas-tight bladders made of sheet materials. This printable actuator is designed and fabricated in a planar fashion. It allows both easy prototyping and mass fabrication of affordable robotic systems. We provide theoretical models of the actuators compared with the experimental data. The measured maximum stroke and tension of the linear pouch motor are up to 28% and 100 N, respectively. The measured maximum range of motion and torque of the angular pouch motor are up to 80° and 0.2 N, respectively. We also develop an algorithm that automatically generates the patterns of the pouches and their fluidic channels. A custom-built fabrication machine streamlines the automated process from design to fabrication. We demonstrate a computer-generated life-sized hand that can hold a foam ball and perform gestures with 12 pouch motors, which can be fabricat...


A STUDY OF THE RELATIONSHIP BETWEEN THE VALUE PERCEPTION AND LOYALTY INTENTION TOWARD AN E-RETAILER WEBSITE


title: A simple tractor-trailer backing control law for path following with side-slope compensation

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


title: Logarithmic edge detection with applications

abstract: In real world machine vision problems, issues such as noise and variable scene illumination make edge and object detection difficult. There exists no universal edge detection method which works under all conditions. In this paper, we propose a logarithmic edge detection method. This achieves a higher level of scene illumination and noise independence. We present experimental results for this method, and compare results of the algorithm against several leading edge detection methods, such as Sobel and Canny. For an objective basis of comparison, we use Pratt's Figure of Merit. We further demonstrate the application of the algorithm in conjunction with Edge Detection based Image Enhancement (EDIE), showing that the use of this edge detection algorithm results in better image enhancement, as quantified by the Logarithmic AME measure.


title: A notion of task relatedness yielding provable multiple-task learning guarantees

abstract: The approach of learning multiple "related" tasks simultaneously has proven quite successful in practice; however, theoretical justification for this success has remained elusive. The starting point for previous work on multiple task learning has been that the tasks to be learned jointly are somehow "algorithmically related", in the sense that the results of applying a specific learning algorithm to these tasks are assumed to be similar. We offer an alternative approach, defining relatedness of tasks on the basis of similarity between the example generating distributions that underlie these tasks. ::: ::: We provide a formal framework for this notion of task relatedness, which captures a sub-domain of the wide scope of issues in which one may apply a multiple task learning approach. Our notion of task similarity is relevant to a variety of real life multitask learning scenarios and allows the formal derivation of generalization bounds that are strictly stronger than the previously known bounds for both the learning-to-learn and the multitask learning scenarios. We give precise conditions under which our bounds guarantee generalization on the basis of smaller sample sizes than the standard single-task approach.


title: Intensive care unit environment may affect the course of delirium

abstract: PURPOSE ::: Delirium is a common disorder in intensive care unit (ICU) patients. It is unclear whether ICU environment affects delirium. We investigated the influence of ICU environment on the number of days with delirium during ICU admission. ::: ::: ::: METHODS ::: In this prospective before-after study, ICU delirium was compared between a conventional ICU with wards and a single-room ICU with, among others, improved daylight exposure. We included patients admitted for more than 24 h between March and June 2009 (ICU with wards) or between June and September 2010 (single-room ICU). Patients who remained unresponsive throughout ICU admission were excluded. The presence of delirium in the preceding 24 h was assessed daily with the confusion assessment method for the ICU (CAM-ICU) by research physicians combined with evaluation of medical and nursing charts. The number of days with delirium was investigated with Poisson regression analysis. ::: ::: ::: RESULTS ::: We included 55 patients (449 observation days) in the ICU with wards and 75 patients (468 observation days) in the single-room ICU. After adjusting for confounding, the number of days with delirium decreased by 0.4 days (95 % confidence interval 0.1-0.7) in the single-room ICU (p = 0.005). The incidence of delirium during ICU stay was similar in the ICU with wards (51 %) and in the single-room ICU (45 %, p = 0.53). ::: ::: ::: CONCLUSIONS ::: This study is the first to show that ICU environment may influence the course of delirium in ICU patients.


title: Feasibility and limits of wi-fi imaging

abstract: We explore the feasibility of achieving computational imaging using Wi-Fi signals. To achieve this, we leverage multi-path propagation that results in wireless signals bouncing off of objects before arriving at the receiver. These reflections effectively light up the objects, which we use to perform imaging. Our algorithms separate the multi-path reflections from different objects into an image. They can also extract depth information where objects in the same direction, but at different distances to the receiver, can be identified. We implement a prototype wireless receiver using USRP-N210s at 2.4 GHz and demonstrate that it can image objects such as leather couches and metallic shapes in line-of-sight and non-line-of-sight scenarios. We also demonstrate proof-of-concept applications including localization of static humans and objects, without the need for tagging them with RF devices. Our results show that we can localize static human subjects and metallic objects with a median accuracy of 26 and 15 cm respectively. Finally, we discuss the limits of our Wi-Fi based approach to imaging.


title: ns-3 Project Goals

abstract: This paper reports on the project plan to develop a new major version of the popular ns-2 networking simulator. The authors have organized an NSF-funded, four-year community infrastructure project to develop the next version of ns. The project will also be oriented towards community development and open source software practices to encourage participation from the broader research and educational community. The purpose of this paper is to expand on the goals and initial design concepts for this new software development effort.


title: Overlapping representations of the neck and whiskers in the rat motor cortex revealed by mapping at different anaesthetic depths

abstract: The primary motor cortex of mammals has an orderly representation of different body parts. Within the representation of each body part the organization is more complex, with groups of neurons representing movements of a muscle or a group of muscles. In rats, uncertainties continue to exist regarding organization of the primary motor cortex in the whisker and the neck region. Using intracortical microstimulation (ICMS) we show that movements evoked in the whisker and the neck region of the rat motor cortex are highly sensitive to the depth of anaesthesia. At light anaesthetic depth, whisker movements are readily evoked from a large medial region of the motor cortex. Lateral to this is a small region where movements of the neck are evoked. However, in animals under deep anaesthesia whisker movements cannot be evoked. Instead, neck movements are evoked from this region. The neck movement region thus becomes greatly expanded. An analysis of the threshold currents required to evoke movements at different anaesthetic depths reveals that the caudal portion of the whisker region has dual representation, of both the whisker and the neck movements. The results also underline the importance of carefully controlling the depth of anaesthesia during ICMS experiments.


title: Word embeddings for idiolect identification

abstract: The term idiolect refers to the unique and distinctive use of language of an individual and it is the theoretical foundation of Authorship Attribution. In this paper we are focusing on learning distributed representations (embeddings) of social media users that reflect their writing style. These representations can be considered as stylistic fingerprints of the authors. We are exploring the performance of the two main flavours of distributed representations, namely embeddings produced by Neural Probabilistic Language models (such as word2vec) and matrix factorization (such as GloVe).


title: A 60-GHz SiGe BiCMOS Monostatic Transceiver for FMCW Radar Applications

abstract: In this paper, a high-range 60-GHz monostatic transceiver system suitable for frequency-modulated continuous-wave (FMCW) applications is presented. The RF integrated circuit is fabricated using a 0.13- $\mu \text{m}$ SiGe BiCMOS technology with $f_{T}$ / $f_{\max }$ of 250/340 GHz and occupies a very compact area of $1.42 \times 0.72$ mm2. All of the internal blocks are designed fully differential with an in-phase/quadrature receiver (RX) conversion gain of 14.8 dB and −18.2 dBm of input-referred 1-dB compression point and a transmitter (TX) with 6.4 dBm of output power. The 60-GHz voltage-controlled oscillator is of a push-push type Colpitts oscillator integrated into a frequency divider with an output frequency between 910 MHz and 1 GHz with the help of 3-bit frequency tuning mechanism for external phase-locked loop operations. Between the TX and RX channels, a tunable coupler is placed to guarantee a high isolation between channels which could withstand any fabrication failures and provide a single differential antenna output. On the TX side, two power detectors are placed in order to monitor the transmitted and reflected powers on the TX channel by passing through a branch-line coupler for built-in-self-test purposes. The total current consumption of this transceiver is 156 mA at 3.3 V of single supply. Considering the successful real-time radar measurements, which the radar is able to detect the objects in more than 90-m range, it proves the suitability of this monostatic chip in high-range FMCW radar systems.


title: Earthquake detection through computationally efficient similarity search

abstract: Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection—identification of seismic events in continuous data—is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection methods. Our method, called Fingerprint And Similarity Thresholding (FAST), can analyze a week of continuous seismic waveform data in less than 2 hours, or 140 times faster than autocorrelation. FAST adapts a data mining algorithm, originally designed to identify similar audio clips within large databases; it first creates compact “fingerprints” of waveforms by extracting key discriminative features, then groups similar fingerprints together within a database to facilitate fast, scalable search for similar fingerprint pairs, and finally generates a list of earthquake detections. FAST detected most (21 of 24) cataloged earthquakes and 68 uncataloged earthquakes in 1 week of continuous data from a station located near the Calaveras Fault in central California, achieving detection performance comparable to that of autocorrelation, with some additional false detections. FAST is expected to realize its full potential when applied to extremely long duration data sets over a distributed network of seismic stations. The widespread application of FAST has the potential to aid in the discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes.


title: A Partition-Based Framework for Building and Validating Regression Models

abstract: Regression models play a key role in many application domains for analyzing or predicting a quantitative dependent variable based on one or more independent variables. Automated approaches for building regression models are typically limited with respect to incorporating domain knowledge in the process of selecting input variables (also known as feature subset selection). Other limitations include the identification of local structures, transformations, and interactions between variables. The contribution of this paper is a framework for building regression models addressing these limitations. The framework combines a qualitative analysis of relationship structures by visualization and a quantification of relevance for ranking any number of features and pairs of features which may be categorical or continuous. A central aspect is the local approximation of the conditional target distribution by partitioning 1D and 2D feature domains into disjoint regions. This enables a visual investigation of local patterns and largely avoids structural assumptions for the quantitative ranking. We describe how the framework supports different tasks in model building (e.g., validation and comparison), and we present an interactive workflow for feature subset selection. A real-world case study illustrates the step-wise identification of a five-dimensional model for natural gas consumption. We also report feedback from domain experts after two months of deployment in the energy sector, indicating a significant effort reduction for building and improving regression models.


title: Persuading to Reflect: Role of Reflection and Insight in Persuasive Systems Design for Physical Health

abstract: Mobile applications and self-tracking devices help us to maintain health and support our goals in changing our behaviors. Most of these tools aim to increase our self-knowledge by providing information upon which to reflect. However, merely spending time reflecting does not always lead to insightful outcomes. In this study surveying users of mobile health and fitness applications (N = 147), we have used structural equation modeling to explore the need for and roles of reflection and insight in the persuasive systems design model. The findings suggest that both dialogue support and primary task support features influence the need for reflection, paving the way to personal insight. The perceived credibility of the system played different roles for women and men and seemed to originate from different sources. In addition, we found significant roles for the attitude and unobtrusiveness of the system. This article contributes to positioning the reflective capabilities of a system in the design framework of per...


Software Tools and Techniques for Big Data Computing in Healthcare Clouds


title: HeapDefender: A Mechanism of Defending Embedded Systems against Heap Overflow via Hardware

abstract: Buffer overflow attacks have been causing serious security problems for decades. While numerous approaches have been proposed to prevent stack overflows, heap overflows remain a security threat and a frequent source of bugs. Embedded systems can be easily attacked by the heap overflow attacks. In this paper, based on analyzing the security of an embedded processor at instruction level, we propose a hardware defense mechanism, Heap Defender, which aims to detect heap buffer overflow attacks. Heap Defender, a module of hardware located the inside of the embedded processor, neither modifies the program nor destroys the pipeline integrity. The instructions parsed in parallel within the Heap Defender are synchronized with the CPU pipeline which makes the Heap Defender have little performance overhead. As demonstrated in an FPGA (Field Programmable Gate Array) prototyping, the experimental results show that Heap Defender can effectively detect heap buffer overflow attacks with around 15% hardware cost overhead and only 0.1% performance penalty.


title: NOVEL VOLTAGE-CONTROLLED AMPLIFIERS FOR MULTISTANDARD INTEGRATED RADIO RECEIVERS

abstract: This paper proposes a new architecture for transconductance (Gm)-based variable-gain amplifiers (VGA) that is better suited for multi-standard radio receivers than the classical two-Gm structure: its bandwidth and linearity are largely independent of the gain setting and both the gain range and the bandwidth can be programmed through digital controls. Unlike the classical structure - that uses two Gm cells and modifies the gain by changing the transconductance of at least one of them, usually by varying the biasing conditions - the new VGA employs three Gm cells with constant biasing; the gain variation is achieved through a differential current steering scheme. Two transistor-level implementations of the proposed VGA structure are presented, one using simple differential pairs and the other based on a more complex, highly linear Gm cell. New circuits for controlling the output common-mode voltage are proposed for both cases; these circuits do no require the placement of additional circuitry in the differential signal path. The VGAs were designed in a standard 0.18um CMOS technology to same specifications: to cover the gain range of 0dB-18dB while handling signals with amplitudes up to 800mVpkpkdiff without significant distortions; maximum bandwidth above 30MHz while driving 1pF; power consumption bellow 6mW. Simulation results show that both VGAs meet the requirements, thus validating the circuit implementations and proving the potential of the proposed architecture.


THE HEALTH CENTER DOCTOR IN INDIA


title: Software evolution storylines

abstract: This paper presents a technique for visualizing the interactions between developers in software project evolution. The goal is to produce a visualization that shows more detail than animated software histories, like code_swarm [15], but keeps the same focus on aesthetics and presentation. Our software evolution storylines technique draws inspiration from XKCD's "Movie Narrative Charts" and the aesthetic design of metro maps. We provide the algorithm, design choices, and examine the results of using the storylines technique. Our conclusion is that the it is able to show more details when compared to animated software project history videos. However, it does not scale to the largest projects, such as Eclipse and Mozilla.


ELLIPTIC CURVE CRYPTOGRAPHY: PRE AND POST QUANTUM


title: Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling

abstract: Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers from overfitting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs. It yields a principled Bayesian learning algorithm, adding gradient noise during training (enhancing exploration of the model-parameter space) and model averaging when testing. Extensive experiments on various RNN models and across a broad range of applications demonstrate the superiority of the proposed approach over stochastic optimization.


title: A Bicycle Can Be Self-Stable Without Gyroscopic or Caster Effects

abstract: A riderless bicycle can automatically steer itself so as to recover from falls. The common view is that this self-steering is caused by gyroscopic precession of the front wheel, or by the wheel contact trailing like a caster behind the steer axis. We show that neither effect is necessary for self-stability. Using linearized stability calculations as a guide, we built a bicycle with extra counter-rotating wheels (canceling the wheel spin angular momentum) and with its front-wheel ground-contact forward of the steer axis (making the trailing distance negative). When laterally disturbed from rolling straight, this bicycle automatically recovers to upright travel. Our results show that various design variables, like the front mass location and the steer axis tilt, contribute to stability in complex interacting ways.


title: HumanEva: Synchronized video and Motion Capture Dataset for Evaluation of Articulated Human Motion

abstract: While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. Current algorithms make many different choices about how to model the human body, how to exploit image evidence and how to approach the inference problem. We argue that there is a need for common datasets that allow fair comparison between different methods and their design choices. Until recently gathering ground-truth data for evaluation of results (especially in 3D) was challenging. In this report we present a novel dataset obtained using a unique setup for capturing synchronized video and ground-truth 3D motion. Data was captured simultaneously using a calibrated marker-based motion capture system and multiple high-speed video capture systems. The video and motion capture streams were synchronized in software using a direct optimization method. The resulting HumanEvaI dataset contains multiple subjects performing a set of predefined actions with a number of repetitions. On the order of 50,000 frames of synchronized motion capture and video was collected at 60 Hz with an additional 37,000 frames of pure motion capture data. The data is partitioned into training, validation, and testing sub-sets. A standard set of error metrics is defined that can be used for evaluation of both 2D and 3D pose estimation and tracking algorithms. Support software and an on-line evaluation system for quantifying results using the test data is being made available to the community. This report provides an overview of the dataset and evaluation metrics and provides pointers into the dataset for additional details. It is our hope that HumanEva-I will become a standard dataset for the evaluation of articulated human motion and pose estimation.


title: SDDs are Exponentially More Succinct than OBDDs

abstract: Introduced by Darwiche (2011), sentential decision diagrams (SDDs) are essentially as tractable as ordered binary decision diagrams (OBDDs), but tend to be more succinct in practice. This makes SDDs a prominent representation language, with many applications in artificial intelligence and knowledge compilation. ::: ::: We prove that SDDs are more succinct than OBDDs also in theory, by constructing a family of boolean functions where each member has polynomial SDD size but exponential OBDD size. This exponential separation improves a quasipolynomial separation recently established by Razgon (2014a), and settles an open problem in knowledge compilation (Darwiche 2011).


title: Low-power high-level synthesis for FPGA architectures

abstract: This paper addresses two aspects of low-power design for FPGA circuits. First, we present an RT-level power estimator for FPGAs with consideration of wire length. The power estimator closely reflects both dynamic and static power contributed by various FPGA components in 0.1 /spl mu/m technology. The power estimation error is 16.2% on average. Second, we present a low power high level synthesis system, named LOPASS, for FPGA designs. It includes two algorithms for power consumption reduction: (i) a simulated annealing engine that carries out resource selection function unit binding, scheduling, register binding, and data pat. generation simultaneously to effectively reduce power; (ii) an enhanced weighted bipartite matching algorithm that is able to reduce the total amount of MUX ports by 22.7%. Experimental results show that LOPASS is able to reduce-power consumption by 35.8% compared to the results-of -Synopsys' Behavioral Compiler.


title: CEDAR: a large scale multiprocessor

abstract: This paper presents an overview of Cedar, a large scale multiprocessor being designed at the University of Illinois. This machine is designed to accommodate several thousand high performance processors which are capable of working together on a single job, or they can be partitioned into groups of processors where each group of one or more processors can work on separate jobs. Various aspects of the machine are described including the control methodology, communication network, optimizing compiler and plans for construction. 13 references.


title: Novel Broadband Circularly Polarized Cavity-Backed Aperture Antenna With Traveling Wave Excitation

abstract: A novel broadband circularly polarized aperture antenna is presented that uses traveling-wave excitation as the design concept. The antenna configuration consists of a circular radiating aperture, a backed cavity, and an equiangular tapered strip outer-fed by a microstrip transmission line. Operating with a traveling wave excitation, the proposed antenna contains inherent broadband characteristics in terms of the impedance, axial ratio, and gain performances. The presented antenna is comprehensively investigated, including the working principles, design consideration, and parametric studies. In addition, the research interests are extended to a 2 × 2 antenna array. Promising results from the experimental 2 × 2 array are achieved, including the 10-dB return loss bandwidth of 70%, the 3-dB axial ratio bandwidth of 50%, and the half-power (3-dB) gain bandwidth of 40% with a maximum gain about 11 dBi. The measured and simulated results are well complied with each other.


title: A Deep Learning Approach for Motor Imagery EEG Signal Classification

abstract: Over the last few decades, the use of electroencephalography (EEG) signals for motor imagery based brain-computer interface (MI-BCI) has gained widespread attention. Deep learning have also gained widespread attention and used in various application such as natural language processing, computer vision and speech processing. However, deep learning has been rarely used for MI EEG signal classification. In this paper, we present a deep learning approach for classification of MI-BCI that uses adaptive method to determine the threshold. The widely used common spatial pattern (CSP) method is used to extract the variance based CSP features, which is then fed to the deep neural network for classification. Use of deep neural network (DNN) has been extensively explored for MI-BCI classification and the best framework obtained is presented. The effectiveness of the proposed framework has been evaluated using dataset IVa of the BCI Competition III. It is found that the proposed framework outperforms all other competing methods in terms of reducing the maximum error. The framework can be used for developing BCI systems using wearable devices as it is computationally less expensive and more reliable compared to the best competing methods.


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

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


title: Study of bi-directional buck-boost converter with different control methods

abstract: In this paper, four types control methods have been proposed for buck-boost cascade topology. This converter used in hybrid electric vehicle can step the battery voltage level either up or down. The target is to find the most appropriate control method for the converter with certain specifications. Critical conduction mode control, fixed frequency control, the buck/boost control and the buck + boost control have been mentioned. The inductor design is presented according to the given parameters. The relationship among the parameters for buck/boost mode, buck mode and boost mode in critical conduction mode are proposed. The efficiency comparison for four control methods is given. The loss breakdown of different control methods is also presented by mathematical calculation.


title: A high-voltage integrated bipolar pulser for medical ultrasound scanner applications

abstract: In this paper, a high-voltage integrated bipolar pulser for medical ultrasound scanner applications is proposed. The pulser employs high-speed level shifters and a high-voltage push-pull output stage that can produce 100 Vpp (±50V) output pulses with rising and falling times of 10.6 ns and 10.5 ns, respectively, under a 1-KΩ resistance parallel with a 100-pF capacitance load. The transmitted signal shows about −40 dBc second harmonic signal distortion (HD2). The measured results indicate the designed pulser can be used for medical imaging with low second harmonic sideband signals.


title: A 56Gb/s PAM4 wireline transceiver using a 32-way time-interleaved SAR ADC in 16nm FinFET

abstract: A 56Gb/s PAM4 wireline transceiver testchip is implemented in 16nm FinFET. The CML transmitter incorporates an auxiliary current injection at the output nodes to maintain PAM4 amplitude linearity. The receiver consists of continuous-time linear equalizers with constant DC-gain and a 28GSa/s 32-way time-interleaved SAR ADC. The transceiver achieves 1e-8 BER over a backplane channel with 25dB loss at 14GHz while consuming 550mW power, excluding DSP.


title: Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting

abstract: Large-scale component-based enterprise applications that leverage Cloud resources expect Quality of Service(QoS) guarantees in accordance with service level agreements between the customer and service providers. In the context of Cloud computing, auto scaling mechanisms hold the promise of assuring QoS properties to the applications while simultaneously making efficient use of resources and keeping operational costs low for the service providers. Despite the perceived advantages of auto scaling, realizing the full potential of auto scaling is hard due to multiple challenges stemming from the need to precisely estimate resource usage in the face of significant variability in client workload patterns. This paper makes three contributions to overcome the general lack of effective techniques for workload forecasting and optimal resource allocation. First, it discusses the challenges involved in auto scaling in the cloud. Second, it develops a model-predictive algorithm for workload forecasting that is used for resource auto scaling. Finally, empirical results are provided that demonstrate that resources can be allocated and deal located by our algorithm in a way that satisfies both the application QoS while keeping operational costs low.


title: Joint Discovery of Object States and Manipulation Actions

abstract: Many human activities involve object manipulations aiming to modify the object state. Examples of common state changes include full/empty bottle, open/closed door, and attached/detached car wheel. In this work, we seek to automatically discover the states of objects and the associated manipulation actions. Given a set of videos for a particular task, we propose a joint model that learns to identify object states and to localize state-modifying actions. Our model is formulated as a discriminative clustering cost with constraints. We assume a consistent temporal order for the changes in object states and manipulation actions, and introduce new optimization techniques to learn model parameters without additional supervision. We demonstrate successful discovery of seven manipulation actions and corresponding object states on a new dataset of videos depicting real-life object manipulations. We show that our joint formulation results in an improvement of object state discovery by action recognition and vice versa.


title: Cross View Link Prediction by Learning Noise-resilient Representation Consensus

abstract: Link Prediction has been an important task for social and information networks. Existing approaches usually assume the completeness of network structure. However, in many real-world networks, the links and node attributes can usually be partially observable. In this paper, we study the problem of Cross View Link Prediction (CVLP) on partially observable networks, where the focus is to recommend nodes with only links to nodes with only attributes (or vice versa). We aim to bridge the information gap by learning a robust consensus for link-based and attribute-based representations so that nodes become comparable in the latent space. Also, the link-based and attribute-based representations can lend strength to each other via this consensus learning. Moreover, attribute selection is performed jointly with the representation learning to alleviate the effect of noisy high-dimensional attributes. We present two instantiations of this framework with different loss functions and develop an alternating optimization framework to solve the problem. Experimental results on four real-world datasets show the proposed algorithm outperforms the baseline methods significantly for cross-view link prediction.


title: Optimal Surface Smoothing as Filter Design

abstract: Smooth surfaces are approximated by polyhedral surfaces for a number of computational purposes. An inherent problem of these approximation algorithms is that the resulting polyhedral surfaces appear faceted. Within a recently introduced signal processing approach to solving this problem [7, 8], surface smoothing corresponds to low-pass filtering. In this paper we look at the filter design problem in more detail. We analyze the stability properties of the low-pass filter described in [7, 8], and show how to minimize its running time. We show that most classical techniques used to design finite impulse response (FIR) digital filters can also be used to design significantly faster surface smoothing filters. Finally, we describe an algorithm to estimate the power spectrum of a signal, and use it to evaluate the performance of the different filter design techniques described in the paper.


title: A Fast Algorithm for Hierarchical Text Classification

abstract: Text classification is becoming more important with the proliferation of the Internet and the huge amount of data it transfers. We present an efficient algorithm for text classification using hierarchical classifiers based on a concept hierarchy. The simple TFIDF classifier is chosen to train sample data and to classify other new data. Despite its simplicity, results of experiments on Web pages and TV closed captions demonstrate high classification accuracy. Application of feature subset selection techniques improves the performance. Our algorithm is computationally efficient being bounded by O(n log n) for n samples.


title: Human face detection algorithm via Haar cascade classifier combined with three additional classifiers

abstract: Human face detection has been a challenging issue in the areas of image processing and patter recognition. A new human face detection algorithm by primitive Haar cascade algorithm combined with three additional weak classifiers is proposed in this paper. The three weak classifiers are based on skin hue histogram matching, eyes detection and mouth detection. First, images of people are processed by a primitive Haar cascade classifier, nearly without wrong human face rejection (very low rate of false negative) but with some wrong acceptance (false positive). Secondly, to get rid of these wrongly accepted non-human faces, a weak classifier based on face skin hue histogram matching is applied and a majority of non-human faces are removed. Next, another weak classifier based on eyes detection is appended and some residual non-human faces are determined and rejected. Finally, a mouth detection operation is utilized to the remaining non-human faces and the false positive rate is further decreased. With the help of OpenCV, test results on images of people under different occlusions and illuminations and some degree of orientations and rotations, in both training set and test set show that the proposed algorithm is effective and achieves state-of-the-art performance. Furthermore, it is efficient because of its easiness and simplicity of implementation.


title: Semi-supervised auto-encoder based on manifold learning

abstract: Auto-encoder is a popular representation learning technique which can capture the generative model of data via a encoding and decoding procedure typically driven by reconstruction errors in an unsupervised way. In this paper, we propose a semi-supervised manifold learning based auto-encoder (named semAE). semAE is based on a regularized auto-encoder framework which leverages semi-supervised manifold learning to impose regularization based on the encoded representation. Our proposed approach suits more practical scenarios in which a small number of labeled data are available in addition to a large number of unlabeled data. Experiments are conducted on several well-known benchmarking datasets to validate the efficacy of semAE from the aspects of both representation and classification. The comparisons to state-of-the-art representation learning methods on classification performance in semi-supervised settings demonstrate the superiority of our approach.


title: Mining anchor text for query refinement

abstract: When searching large hypertext document collections, it is often possible that there are too many results available for ambiguous queries. Query refinement is an interactive process of query modification that can be used to narrow down the scope of search results. We propose a new method for automatically generating refinements or related terms to queries by mining anchor text for a large hypertext document collection. We show that the usage of anchor text as a basis for query refinement produces high quality refinement suggestions that are significantly better in terms of perceived usefulness compared to refinements that are derived using the document content. Furthermore, our study suggests that anchor text refinements can also be used to augment traditional query refinement algorithms based on query logs, since they typically differ in coverage and produce different refinements. Our results are based on experiments on an anchor text collection of a large corporate intranet.


title: Hugo: Entity-based News Search and Summarisation

abstract: We describe Hugo -- a service initially available on iOS that solicits a structured, semantic query and returns entity-specific news articles. Retrieval is powered by a semantic annotation pipeline that includes named entity linking and automatic summarisation. Search and entity linking use an in-house knowledge base initialised with Wikipedia data and continually curated to include new entities. Hugo delivers timely knowledge about a user's professional network, in particular new people they want to know more about.


title: High linearity SPDT switch for dual band wireless LAN applications

abstract: This paper presents a high linear and power-handling single-pole double-throw (SPDT) switch for WLAN 802.11 a/b/g applications. The switch circuit has asymmetric and stacked topology to have high power-handling and linearity for the Tx path. This SPDT switch has been implemented with 0.25 /spl mu/m GaAs pHEMT process by ETRI. The designed SPDT switch has the measured insertion loss of better than 1dB and isolation of better than 40dB for the Tx path and 25dB for the Rx path and the high power handling capability with P/sub 1dB/ of about 23dBm with control voltage of -3/0 V. The fabricated SPDT switch chip size is 1.8 mm /spl times/ 1.8 mm.


Mutual Information for Registration of Monomodal Brain Images using Modified Adaptive Polar Transform


title: Estimating communication skills using dialogue acts and nonverbal features in multiple discussion datasets

abstract: This paper focuses on the computational analysis of the individual communication skills of participants in a group. The computational analysis was conducted using three novel aspects to tackle the problem. First, we extracted features from dialogue (dialog) act labels capturing how each participant communicates with the others. Second, the communication skills of each participant were assessed by 21 external raters with experience in human resource management to obtain reliable skill scores for each of the participants. Third, we used the MATRICS corpus, which includes three types of group discussion datasets to analyze the influence of situational variability regarding to the discussion types. We developed a regression model to infer the score for communication skill using multimodal features including linguistic and nonverbal features: prosodic, speaking turn, and head activity. The experimental results show that the multimodal fusing model with feature selection achieved the best accuracy, 0.74 in R2 of the communication skill. A feature analysis of the models revealed the task-dependent and task-independent features to contribute to the prediction performance.


title: Influence of network delay and jitter on cooperation in multiplayer games

abstract: Like most applications deployed on the Internet, modern multiplayer games are subject to the impact of transmission delays and the variability thereof. These delays can be introduced either by the physical limitations of signal transmission speed or overload and queuing problems in intermediate nodes. The influence of this delay is far-reaching and impacts most interactive applications. More specifically, quantitative and qualitative studies have been conducted on competitive game genres, such as first person shooter and racing games. In contrast, this work investigates how network delay affects player experience in cooperative games, where players have to interact with shared objects and obstacles. In this game genre, one might expect an increased sensitivity to detrimental network factors due to the reliance on the (near-)perfect synchronization of actions between participants. In this paper, a series of consecutive user tests were carried out with one of the most recent games, Little Big Planet 2; which focuses primarily on the cooperative aspect. Analysis has shown that delays over 100 ms significantly decrease player performance and the way in which network quality is perceived. At the same time jitter negatively affects user performance, though players do not perceive this impairment as disturbing.


title: Measuring sexual risk for HIV: a Rasch scaling approach.

abstract: In this study, we developed an HIV transmission risk scale and examined its psychometric properties using data on sexual behavior obtained from a probability sample of adult men who have sex with men living in Chicago. We used Messick’s (Am Psychol 50:741–749, 1995) conceptualization of unified validity theory to organize the psychometric properties of data. Evidence related to scale content was investigated via Rasch item fit statistics, point-measure correlations, and expert evaluation. The substantive aspect of validity was addressed by interpreting the meaningfulness of the item difficulty hierarchy (continuum of risky behaviors) and assessment of person fit. The structural aspect of validity was assessed using Rasch item fit statistics, principal component analysis of standardized residuals, and other residual analyses. The generalizability aspect of validity was investigated via internal consistency reliability estimates for both items and persons, and aspects of external validity were addressed by examining between-group differences with respect to levels of risky behavior. Applications and suggested future studies are discussed.


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

abstract: The active component of marijuana, Δ9-tetrahydrocannabinol, activates the CB1 and CB2 cannabinoid receptors, thus mimicking the action of endogenous cannabinoids. CB1 is predominantly neuronal and mediates the cannabinoid psychotropic effects. CB2 is predominantly expressed in peripheral tissues, mainly in pathological conditions. So far the main endocannabinoids, anandamide and 2-arachidonoylglycerol, have been found in bone at ‘brain’ levels. The CB1 receptor is present mainly in skeletal sympathetic nerve terminals, thus regulating the adrenergic tonic restrain of bone formation. CB2 is expressed in osteoblasts and osteoclasts, stimulates bone formation, and inhibits bone resorption. Because low bone mass is the only spontaneous phenotype so far reported in CB2 mutant mice, it appears that the main physiologic involvement of CB2 is associated with maintaining bone remodeling at balance, thus protecting the skeleton against age-related bone loss. Indeed, in humans, polymorphisms in CNR2, the gene encodi...


title: Effectiveness of Olanzapine, Quetiapine, Risperidone, and Ziprasidone in Patients With Chronic Schizophrenia Following Discontinuation of a Previous Atypical Antipsychotic

abstract: Background: In the treatment of schizophrenia, changing antipsychotics is common when one treatment is suboptimally effective, but the relative effectiveness of drugs used in this strategy is unknown. This randomized, double-blind study compared olanzapine, quetiapine, risperidone, and ziprasidone in patients who had just discontinued a different atypical antipsychotic. Method: Subjects with schizophrenia (N = 444) who had discontinued the atypical antipsychotic randomly assigned during phase 1 of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) investigation were randomly reassigned to double-blind treatment with a different antipsychotic (olanzapine, 7.5–30 mg/day [N = 66]; quetiapine, 200–800 mg/day [N = 63]; risperidone, 1.5–6.0 mg/day [N = 69]; or ziprasidone, 40–160 mg/day [N = 135]). The primary aim was to determine if there were differences between these four treatments in effectiveness measured by time until discontinuation for any reason. Results: The time to treatment dis...


title: Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission

abstract: In machine learning often a tradeoff must be made between accuracy and intelligibility. More accurate models such as boosted trees, random forests, and neural nets usually are not intelligible, but more intelligible models such as logistic regression, naive-Bayes, and single decision trees often have significantly worse accuracy. This tradeoff sometimes limits the accuracy of models that can be applied in mission-critical applications such as healthcare where being able to understand, validate, edit, and trust a learned model is important. We present two case studies where high-performance generalized additive models with pairwise interactions (GA2Ms) are applied to real healthcare problems yielding intelligible models with state-of-the-art accuracy. In the pneumonia risk prediction case study, the intelligible model uncovers surprising patterns in the data that previously had prevented complex learned models from being fielded in this domain, but because it is intelligible and modular allows these patterns to be recognized and removed. In the 30-day hospital readmission case study, we show that the same methods scale to large datasets containing hundreds of thousands of patients and thousands of attributes while remaining intelligible and providing accuracy comparable to the best (unintelligible) machine learning methods.


title: Relative Study of Zig-Bee/IEEE 802.15.4 WPAN Performance under Different Topologies

abstract: Today in the era of wireless technology, everything seems to be possible. One of the wireless techniques that made the things easier is Zig-Bee technology. Zig-Bee is a wireless personal area network (WPAN) based on IEEE 802.15.4 standard to provide communication in short distance with low data rate in low cost. Zig-Bee 802.15.4 WPANs are made up of end devices, routers and a co-ordinator by using one of the three topologies which are star, tree and mesh topology. These networks have a wide range of areas where they perform spectacularly. These are the network when once deployed, need not any maintenance. This research work aims to have comparative analysis of network performance in star topology and mesh topology of a WPAN.


title: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

abstract: This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using dierent interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classier for invasive tumor classication using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classication mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.


title: Information-theoretic measures for anomaly detection

abstract: Anomaly detection is an essential component of protection mechanisms against novel attacks. We propose to use several information-theoretic measures, namely, entropy, conditional entropy, relative conditional entropy, information gain, and information cost for anomaly detection. These measures can be used to describe the characteristics of an audit data set, suggest the appropriate anomaly detection model(s) to be built, and explain the performance of the model(s). We use case studies on Unix system call data, BSM data, and network tcpdump data to illustrate the utilities of these measures.


title: Ranking factors of team success

abstract: As an increasing number of human activities are moving to the Web, more and more teams are predominantly virtual. Therefore, formation and success of virtual teams is an important issue in a wide range of fields. In this paper we model social behavior patterns of team work using data from virtual communities. In particular, we use data about the Web community of the multiplayer online game Dota 2 to study cooperation within teams. By applying statistical analysis we investigate how and to which extent different factors of the team in the game, such as role distribution, experience, number of friends and national diversity, have an influence on the team's success. In order to complete the picture we also rank the factors according to their influence. The results of our study imply that cooperation within the team is better than competition.


title: Sparse Tensor Discriminant Color Space for Face Verification

abstract: As one of the fundamental features, color provides useful information and plays an important role for face recognition. Generally, the choice of a color space is different for different visual tasks. How can a color space be sought for the specific face recognition problem? To address this problem, we propose a sparse tensor discriminant color space (STDCS) model that represents a color image as a third-order tensor in this paper. The model cannot only keep the underlying spatial structure of color images but also enhance robustness and give intuitionistic or semantic interpretation. STDCS transforms the eigenvalue problem to a series of regression problems. Then one spare color space transformation matrix and two sparse discriminant projection matrices are obtained by applying lasso or elastic net on the regression problems. The experiments on three color face databases, AR, Georgia Tech, and Labeled Faces in the Wild face databases, show that both the performance and the robustness of the proposed method outperform those of the state-of-the-art TDCS model.


title: FastTrack: efficient and precise dynamic race detection

abstract: \begin{abstract} Multithreaded programs are notoriously prone to race conditions. Prior work on dynamic race detectors includes fast but imprecise race detectors that report false alarms, as well as slow but precise race detectors that never report false alarms. The latter typically use expensive vector clock operations that require time linear in the number of program threads. This paper exploits the insight that the full generality of vector clocks is unnecessary in most cases. That is, we can replace heavyweight vector clocks with an adaptive lightweight representation that, for almost all operations of the target program, requires only constant space and supports constant-time operations. This representation change significantly improves time and space performance, with no loss in precision. Experimental results on Java benchmarks including the Eclipse development environment show that our FastTrack race detector is an order of magnitude faster than a traditional vector-clock race detector, and roughly twice as fast as the high-performance DJIT+ algorithm. FastTrack is even comparable in speed to Eraser on our Java benchmarks, while never reporting false alarms.


Regulation of functional activities and essential oil production in Vetiveria zizanioides L. Nash after γ-irradiated sodium alginate elicitation


title: Reflections on the REST architectural style and "principled design of the modern web architecture" (impact paper award)

abstract: Seventeen years after its initial publication at ICSE 2000, the Representational State Transfer (REST) architectural style continues to hold significance as both a guide for understanding how the World Wide Web is designed to work and an example of how principled design, through the application of architectural styles, can impact the development and understanding of large-scale software architecture. However, REST has also become an industry buzzword: frequently abused to suit a particular argument, confused with the general notion of using HTTP, and denigrated for not being more like a programming methodology or implementation framework. In this paper, we chart the history, evolution, and shortcomings of REST, as well as several related architectural styles that it inspired, from the perspective of a chain of doctoral dissertations produced by the University of California's Institute for Software Research at UC Irvine. These successive theses share a common theme: extending the insights of REST to new domains and, in their own way, exploring the boundary of software engineering as it applies to decentralized software architectures and architectural design. We conclude with discussion of the circumstances, environment, and organizational characteristics that gave rise to this body of work.


title: Hardware Implementation and Performance Analysis of a Current-Sensor-Free Single Cell MPPT for High Performance Vehicle Solar Arrays

abstract: A maximum power point tracker has been previously developed for the single high performance triple junction solar cell for hybrid and electric vehicle applications. The maximum power point tracking (MPPT) control method is based on the incremental conductance (IncCond) but removes the need for current sensors. This paper presents the hardware implementation of the maximum power point tracker. Significant efforts have been made to reduce the size to 18 mm times 21 mm (0.71 in times 0.83 in) and the cost to close to $5 US. This allows the MPPT hardware to be integrable with a single solar cell. Precision calorimetry measurements are employed to establish the converter power loss and confirm that an efficiency of 96.2% has been achieved for the 650-mW converter with 20-kHz switching frequency. Finally, both the static and the dynamic tests are conducted to evaluate the tracking performances of the MPPT hardware. The experimental results verify a tracking efficiency higher than 95% under three different insolation levels and a power loss less than 5% of the available cell power under instantaneous step changes between three insolation levels.


title: Break-even distance analysis of residential solar photovoltaic system and line extension for off-grid electrification methodology in the Philippines

abstract: Electricity plays a vital role in the economic development of an area, especially in a developing country like the Philippines. Government agencies have various electrification programs to provide electricity, and consequently economic development, to unelectrified communities in the Philippines. This paper presents a methodology that compares two electrification alternatives (residential solar photovoltaic or solar PV systems and power line extension) by means of break-even distance analysis. The proposed method aims to provide government agencies a unified and systematic framework in planning and implementation of electrification programs in the Philippines. The study reveals that solar PV system is the more viable option when electrifying a remote community. However, extending power lines would be a more cost-effective option if multiple nearby communities were to be electrified. Results also show proper load forecasting is important in the comparison of electrification alternatives.


title: Evaluation of field resistance of Daucus carota cultivars to Cercospora carotae (carrot leaf spot)

abstract: A world collection of 142 cultivars of carrot was tested for resistance to Cercospora carotae in the field. C. carotae infection was observed in all the cultivars. Field resistance was observed in only about 30% of the cultivars, the remainder being highly susceptible.


title: The networked cultural diffusion of Korean wave

abstract: Purpose ::: ::: ::: ::: – The purpose of this paper is to examine the diffusion of a popular Korean music video on the video-sharing web site YouTube. It applies a webometric approach in the diffusion of innovations framework to study three elements of diffusion in a Web 2.0 environment: users, user-to-user relationship and user-generated comment. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: – The webometric approach combines profile analyses, social network analyses, semantic and sentiment analyses. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: – The results show that male users in the US played a dominant role in the early-stage diffusion. The dominant users represented the innovators and early adopters in the evaluation stage of the diffusion, and they engaged in continuous discussions about the cultural origin of the video and expressed criticisms. Overall, the discussion between users varied according to their gender, age, and cultural background. Specifically, male users were more interactive than female users, and users in countries culturally similar to Korea were more likely to express favourable attitudes toward the video. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: – The study provides a webometric approach to examine the Web 2.0-based social system in the early-stage global diffusion of cultural offerings. This approach connects the diffusion of innovations framework to the new context of Web 2.0-based diffusion.


title: SymDrive: Testing Drivers without Devices

abstract: Device-driver development and testing is a complex and error-prone undertaking. For example, testing error-handling code requires simulating faulty inputs from the device. A single driver may support dozens of devices, and a developer may not have access to any of them. Consequently, many Linux driver patches include the comment "compile tested only." ::: ::: SymDrive is a system for testing Linux and FreeBSD drivers without their devices present. The system uses symbolic execution to remove the need for hardware, and extends past tools with three new features. First, SymDrive uses static-analysis and source-to-source transformation to greatly reduce the effort of testing a new driver. Second, SymDrive checkers are ordinary C code and execute in the kernel, where they have full access to kernel and driver state. Finally, SymDrive provides an execution-tracing tool to identify how a patch changes I/O to the device and to compare device-driver implementations. In applying SymDrive to 21 Linux drivers and 5 FreeBSD drivers, we found 39 bugs.


title: A Wearable Microfluidic Sensing Patch for Dynamic Sweat Secretion Analysis.

abstract: Wearable sweat sensing is a rapidly rising research area driven by its promising potential in health, fitness, and diagnostic applications. Despite the growth in the field, major challenges in relation to sweat metrics remain to be addressed. These challenges include sweat rate monitoring for its complex relation with sweat compositions and sweat sampling for sweat dynamics studies. In this work, we present a flexible microfluidic sweat sensing patch that enhances real-time electrochemical sensing and sweat rate analysis via sweat sampling. The device contains a spiral-patterned microfluidic component that is embedded with ion-selective sensors and an electrical impedance-based sweat rate sensor on a flexible plastic substrate. The patch is enabled to autonomously perform sweat analysis by interfacing the sensing component with a printed circuit board that is capable of on-site signal conditioning, analysis, and transmission. Progressive sweat flow in the microfluidic device, governed by the pressure induced by the secreted sweat, enhances sweat sampling and electrochemical detection via a defined sweat collection chamber and a directed sweat route. The characteristic of the sweat rate sensor is validated through a theoretical simulation, and the precision and accuracy of the flow rate is verified with a commercial syringe pump and a Macroduct sweat collector. On-body simultaneous monitoring of ion (H+, Na+, K+, Cl-) concentration and sweat rate is also demonstrated for sensor functionality. This sweat sensing patch provides an integrated platform for a comprehensive sweat secretion analysis and facilitates physiological and clinical investigations by closely monitoring interrelated sweat parameters.


title: A 140-mV Variation-Tolerant Deep Sub-Threshold SRAM in 65-nm CMOS

abstract: This paper presents a sub-threshold SRAM, which eliminates bitline (BL) leakage-induced read failures. The proposed architecture clamps the current ratio between differential BLs to a fixed value, thus permitting reliable ultra-low-voltage read-out. A de-multiplexed wordline interleaving scheme is presented to compensate for bitcell area overhead. The interleaving technique achieves 9% reduction in decoder area and 50% reduction in clock load within the decoder. A sense amplifier circuit with reduced sensitivity to process variations is proposed to further enhance the reliability of the differential read-out. Measurement results from a 1-kb SRAM, fabricated in an industrial 65-nm low-power CMOS process, show 13.1-kHz operation at 140 mV, with active read and leakage power figures of 30.5 and 28.1 nW, respectively.


title: Analysis and Design Techniques for Supply-Noise Mitigation in Phase-Locked Loops

abstract: Supply noise affects the jitter performance of ring oscillator-based phase-locked loops (PLLs) significantly. While the focus of much of the prior art is on supply noise in oscillators, this paper illustrates that supply noise in other building blocks also contribute significantly to PLL output jitter. Analytical expressions for supply-noise sensitivities are derived for each of the circuit blocks used in the PLL and insight into the mechanism through which supply noise appears at the PLL output is provided. Efficient supply-regulation schemes that combine a split-tuned PLL architecture with an optimized low-dropout regulator to achieve better than -22 dB of worst case supply-noise sensitivity for the whole PLL are presented. Fabricated in a 0.18 μm digital CMOS process, the prototype PLL occupies an area of 0.18 μm and operates from a 1.8 V supply. At 1.5 GHz, the total power consumption is 3.3 mW, of which 0.54 mW is consumed in the regulators. The measured output peak-to-peak jitter is 33 ps and 41 ps with no supply noise and with a 100-mV amplitude supply noise tone injected at the worst case noise frequency, respectively.


title: [Studies on the serum types and identification efficiency on Diarrheagenic Escherichia coli isolated from diarrhea patients, in Zhejiang province].

abstract: Objective: To investigate the serotypes of Diarrheagenic Escherichia coli (DEC) isolated from diarrheal patients in Zhejiang province and to explore the identification efficiency of serological screening methods. Methods: Serological agglutination tests were carried out in 696 strains of DEC (through the identification of virulence genes) which were selected from the Infectious Diarrhea Pathogen Monitoring Network Strain Bank of Zhejiang province, from July 2009 to June 2013. Results of virulence genes, serological identification and classification were compared. Results: Among the 696 isolates of DEC, O antigen type was identified in 288 (41.4%) isolates which belonging to 35 different 'O' serum types. H antigen was seen in 171 (24.6%) isolates and determined as having 21 types. The agglutination rates of EAEC, ETEC, EPEC and EHEC isolates were 31.9% (130/408), 70.6% (127/180), 31.5% (29/92) and 14.3% (2/14), respectively and belonged to 30, 18, 15 kinds of 'O' sero-groups, respectively. One EHEC isolate was identified as O157∶H7. Serum groups were diverse for EAEC and EPEC, while relatively concentrated on ETEC. Different types of DEC might belong to the same sero-group or type. Among the 74 strains of DEC available for classification serologically, 41 isolates were in consistent with virulence gene identification and another 33 strains were not. Conclusions: The sero-group/type of DEC strains in Zhejiang were varied. Based on the serological screening method alone, DEC classification might end in getting the wrong answer, thus we would recommend the use of virulence gene for the purpose of identification.


title: Bipedal walking control based on Capture Point dynamics

abstract: This paper builds up on the Capture Point concept and exploits the simple form of the dynamical equations of the Linear Inverted Pendulum model when formulated in terms of the center of mass and the Capture Point. The presented methods include (i) the derivation of a Capture Point (CP) control principle based on the natural dynamics of the linear inverted pendulum (LIP), which stabilizes the walking robot and motivates (ii) the design of a CP tracking and a CP end-of-step controller. The exponential stability of the CP control law is proven. Tilting is avoided by proper projection of the commanded zero moment point. The robustness of the derived control algorithms is analyzed analytically and verified in simulation and experiments.


title: A resistorless current reference source for 65 nm CMOS technology with low sensitivity to process, supply voltage and temperature variations

abstract: A reistorless current reference source, e.g. for fast communication interfaces, has been described. Addition of currents with opposite temperature coefficient (PTC and NTC) and body effect have been used to temperature compensation. Cascode structures have been used to improve the power supply rejection ratio. The reference current source has been designed in a GLOBALFOUNDRIES 65 nm technology. The presented circuit achieves 55 ppm/°C temperature coefficient over range of −40 °C to 125 °C. Reference current susceptibility to process parameters variation is ±3 %. The power supply rejection ratio without any filtering capacitor at 100 Hz and 10 MHz is lower than −127 dB and −103 dB, respectively.


title: Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields

abstract: Automated Facial Expression Recognition (FER) has been a challenging task for decades. Many of the existing works use hand-crafted features such as LBP, HOG, LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as Support Vector Machines for expression recognition. These methods often require rigorous hyperparameter tuning to achieve good results. Recently Deep Neural Networks (DNN) have shown to outperform traditional methods in visual object recognition. In this paper, we propose a two-part network consisting of a DNN-based architecture followed by a Conditional Random Field (CRF) module for facial expression recognition in videos. The first part captures the spatial relation within facial images using convolutional layers followed by three Inception- ResNet modules and two fully-connected layers. To capture the temporal relation between the image frames, we use linear chain CRF in the second part of our network. We evaluate our proposed network on three publicly available databases, viz. CK+, MMI, and FERA. Experiments are performed in subjectindependent and cross-database manners. Our experimental results show that cascading the deep network architecture with the CRF module considerably increases the recognition of facial expressions in videos and in particular it outperforms the stateof- the-art methods in the cross-database experiments and yields comparable results in the subject-independent experiments.


Reduction of teeter angle excursions for a two-bladed downwind rotor using cyclic pitch control “ EWEC 2007 Conference


title: Robot rights? Towards a social-relational justification of moral consideration

abstract: Should we grant rights to artificially intelligent robots? Most current and near-future robots do not meet the hard criteria set by deontological and utilitarian theory. Virtue ethics can avoid this problem with its indirect approach. However, both direct and indirect arguments for moral consideration rest on ontological features of entities, an approach which incurs several problems. In response to these difficulties, this paper taps into a different conceptual resource in order to be able to grant some degree of moral consideration to some intelligent social robots: it sketches a novel argument for moral consideration based on social relations. It is shown that to further develop this argument we need to revise our existing ontological and social-political frameworks. It is suggested that we need a social ecology, which may be developed by engaging with Western ecology and Eastern worldviews. Although this relational turn raises many difficult issues and requires more work, this paper provides a rough outline of an alternative approach to moral consideration that can assist us in shaping our relations to intelligent robots and, by extension, to all artificial and biological entities that appear to us as more than instruments for our human purposes.


title: Ontologies for the Integration of Air Quality Models and 3D City Models

abstract: The holistic approach to sustainable urban planning implies using different models in an integrated way that is capable of simulating the urban system. As the interconnection of such models is not a trivial task, one of the key elements that may be applied is the description of the urban geometric properties in an "interoperable" way. Focusing on air quality as one of the most pronounced urban problems, the geometric aspects of a city may be described by objects such as those defined in CityGML, so that an appropriate air quality model can be applied for estimating the quality of the urban air on the basis of atmospheric flow and chemistry equations. ::: In this paper we first present theoretical background and motivations for the interconnection of 3D city models and other models related to sustainable development and urban planning. Then we present a practical experiment based on the interconnection of CityGML with an air quality model. Our approach is based on the creation of an ontology of air quality models and on the extension of an ontology of urban planning process (OUPP) that acts as an ontology mediator.


title: Body motion design and analysis for fighting game interface

abstract: This paper presents a full-body motion-control game interface based on a Kinect device. A set of postures and motions for controlling game characters is presented, and the posture-detection algorithm for each posture is implemented by using a rule-based technique with rule-and-threshold optimization. Our experiments show that the proposed optimization can improve the accuracy of posture detection by 13.70%. The proposed techniques are applied to the fighting game called FightingICE, a game platform in recent CIG competitions.