title: Mining User Mobility Features for Next Place Prediction in Location-Based Services

text: Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.


title: Two/Too Simple Adaptations of Word2Vec for Syntax Problems

text: We present two simple modifications to the models in the popular Word2Vec tool, in order to generate embeddings more suited to tasks involving syntax. The main issue with the original models is the fact that they are insensitive to word order. While order independence is useful for inducing semantic representations, this leads to suboptimal results when they are used to solve syntax-based problems. We show improvements in part-ofspeech tagging and dependency parsing using our proposed models.


title: Digital Fabrication Techniques for Cultural Heritage: A Survey

text: Digital fabrication devices exploit basic technologies in order to create tangible reproductions of 3D digital models. Although current 3D printing pipelines still suffer from several restrictions, accuracy in reproduction has reached an excellent level. The manufacturing industry has been the main domain of 3D printing applications over the last decade. Digital fabrication techniques have also been demonstrated to be effective in many other contexts, including the consumer domain. The Cultural Heritage is one of the new application contexts and is an ideal domain to test the flexibility and quality of this new technology. This survey overviews the various fabrication technologies, discussing their strengths, limitations, and costs. Various successful uses of 3D printing in the Cultural Heritage are analysed, which should also be useful for other application contexts. We review works that have attempted to extend fabrication technologies in order to deal with the specific issues in the use of digital fabrication in the Cultural Heritage. Finally, we also propose areas for future research.


title: A Multi-Cloud Framework for Measuring and Describing Performance Aspects of Cloud Services Across Different Application Types

text: Cloud services have emerged as an innovative IT provisioning model in the recent years. However, after their usage severe considerations have emerged with regard to their varying performance due to multitenancy and resource sharing issues. These issues make it very difficult to provide any kind of performance estimation during application design or deployment time. The aim of this paper is to present a mechanism and process for measuring the performance of various Cloud services and describing this information in machine understandable format. The framework is responsible for organizing the execution and can support multiple Cloud providers. Furthermore we present approaches for measuring service performance with the usage of specialized metrics for ranking the services according to a weighted combination of cost, performance and workload.


title: Design of novel capacitive type torque sensor for robotic applications

text: This paper introduces a novel capacitive type torque sensor. The developed torque sensor measures single axis torque value by using two capacitance transducing cells. To make a linear relation between the capacitance and the deformation, the shape of flexure hinge is considered to be deformed constantly. Also, the nonlinear curve fitting method is used to fit the capacitance linearly. Finally, the performance of the sensor is verified in reference to a commercial torque sensor.


title: Overview of High-Step-Up Coupled-Inductor Boost Converters

text: High-step-up, high-efficiency, and cost-effective dc-dc converters, serving as an interfacing cell to boost the low-voltage output of renewable sources to the utility voltage level, are an important part in renewable energy systems. Over the past few years, there has been a substantial amount of studies devoted to high-step-up dc-dc converters. Among them, the category of coupled-inductor boost converters is widely researched and considered to be a promising solution for high-step-up applications. In this paper, these converters are categorized into five groups according to the major topological features. The derivation process, advantages, and disadvantages of these converters are systematically discussed, compared, and scrutinized. This paper aims to provide an introduction, review, and framework for the category of high-step-up coupled-inductor boost converters. General structures for the topologies are proposed to clarify the topological derivation process and to show potential gaps. Furthermore, challenges or directions are presented in this paper for deriving new topologies in this field.


title: Design and Analysis of a Two Stage Operational Amplifier for High Gain and High Bandwidth

text: In this paper a design and comparison between a fully differential RC Miller compensated CMOS op-amp and conventional op-amp is presented. High gain enables the circuit to operate efficiently in a closed loop feedback system, whereas high bandwidth makes it suitable for high speed applications. A novel RC Miller compensation technique is used to optimize the parameters of gain and bandwidth for high speed applications are illustrated in this research work. The design is also able to address any fluctuation in supply or dc input voltages and stabilizes the operation by nullifying. The design is implemented on TSMC 0.18 m CMOS process at 3.3 V as supply voltage under room temperature 27 C. The simulated result shows that a unity gain bandwidth of 136.8 MHz with a high gain of 92.27 dB is achieved for the proposed op-amp circuit. The total areas of the layouts are 0.000158 mm and 0.000532 mm for conventional and proposed respectively. Key wrods: Two-Stage Op-amp, fully differential, high gain, high bandwidth


title: Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data

text: Recent years have witnessed the wide proliferation of geo-sensory applications wherein a bundle of sensors are deployed at different locations to cooperatively monitor the target condition. Given massive geo-sensory data, we study the problem of mining spatial co-evolving patterns (SCPs), i.e., groups of sensors that are spatially correlated and co-evolve frequently in their readings. SCP mining is of great importance to various real-world applications, yet it is challenging because (1) the truly interesting evolutions are often flooded by numerous trivial fluctuations in the geo-sensory time series; and (2) the pattern search space is extremely large due to the spatiotemporal combinatorial nature of SCP. In this paper, we propose a two-stage method called Assember. In the first stage, Assember filters trivial fluctuations using wavelet transform and detects frequent evolutions for individual sensors via a segment-and-group approach. In the second stage, Assember generates SCPs by assembling the frequent evolutions of individual sensors. Leveraging the spatial constraint, it conceptually organizes all the SCPs into a novel structure called the SCP search tree, which facilitates the effective pruning of the search space to generate SCPs efficiently. Our experiments on both real and synthetic data sets show that Assember is effective, efficient, and scalable.


title: A Markov Model for Headway/Spacing Distribution of Road Traffic

text: In this paper, we link two research directions of road traffic-the mesoscopic headway distribution model and the microscopic vehicle interaction model-together to account for the empirical headway/spacing distributions. A unified car-following model is proposed to simulate different driving scenarios, including traffic on highways and at intersections. Unlike our previous approaches, the parameters of this model are directly estimated from the Next Generation Simulation (NGSIM) Trajectory Data. In this model, empirical headway/spacing distributions are viewed as the outcomes of stochastic car-following behaviors and the reflections of the unconscious and inaccurate perceptions of space and/or time intervals that people may have. This explanation can be viewed as a natural extension of the well-known psychological car-following model (the action point model). Furthermore, the fast simulation speed of this model will benefit transportation planning and surrogate testing of traffic signals.


title: Design of a Marx Generator for HEMP filter evaluation taking account of parasitic effect of components

text: This paper introduces the design of a Marx Generator that can generate high attitude pulses up to 25 kV and 2.5 kA. The pulses have a wave shape representative of that used to test High-altitude ElectroMagnetic (HEMP) filters against the pulse current injection (PCI) requirements. The rise time is 20 ns and the pulse width is 230 ns on a 10 Ω resistor. In the design, it was found that parasitic effect of circuit components could significantly affect the performance of the generator. This is mainly because the pulse width is very narrow and its frequency spectrum is very wideband. The high-frequency circuit models of the components used in the generator were obtained by calculation and optimization, and then further verified by experiment. The verified circuit models were then used to design the three-stage generator. The measured performance agrees very well with the simulated one using the proposed circuit models.


title: Brain activation during human male ejaculation.

text: Brain mechanisms that control human sexual behavior in general, and ejaculation in particular, are poorly understood. We used positron emission tomography to measure increases in regional cerebral blood flow (rCBF) during ejaculation compared with sexual stimulation in heterosexual male volunteers. Manual penile stimulation was performed by the volunteer's female partner. Primary activation was found in the mesodiencephalic transition zone, including the ventral tegmental area, which is involved in a wide variety of rewarding behaviors. Parallels are drawn between ejaculation and heroin rush. Other activated mesodiencephalic structures are the midbrain lateral central tegmental field, zona incerta, subparafascicular nucleus, and the ventroposterior, midline, and intralaminar thalamic nuclei. Increased activation was also present in the lateral putamen and adjoining parts of the claustrum. Neocortical activity was only found in Brodmann areas 7/40, 18, 21, 23, and 47, exclusively on the right side. On the basis of studies in rodents, the medial preoptic area, bed nucleus of the stria terminalis, and amygdala are thought to be involved in ejaculation, but increased rCBF was not found in any of these regions. Conversely, in the amygdala and adjacent entorhinal cortex, a decrease in activation was observed. Remarkably strong rCBF increases were observed in the cerebellum. These findings corroborate the recent notion that the cerebellum plays an important role in emotional processing. The present study for the first time provides insight into which regions in the human brain play a primary role in ejaculation, and the results might have important implications for our understanding of how human ejaculation is brought about, and for our ability to improve sexual function and satisfaction in men.


title: Data Center Energy Consumption Modeling: A Survey

text: Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.


title: Differential quasi self-complimentary (QSC) ultra-wideband (UWB) MIMO antenna

text: In this paper, a differentially excited ultra-wideband (UWB) MIMO antenna is designed for pattern diversity applications, utilizing quasi self-complimentary (QSC) antenna elements. Four QSC elements are realized using half- octagon shaped monopoles, and their complementary cuts from the ground plane. Due to the QSC property, wide impedance bandwidth covering the UWB frequency range of 3–11 GHz is achieved within very less antenna footprint. The QSC elements are placed symmetrically about the four edges of a square substrate. Two oppositely positioned elements are fed with 180o phase difference, in order to form a single differential pair. Two such differential pairs are placed perpendicular to each other for realizing pattern diversity performance with high value of differential isolation.


title: Airway management and smoke inhalation injury in the burn patient.

text: Smoke inhalation injury, a unique form of acute lung injury, greatly increases the occurrence of postburn morbidity and mortality. In addition to early intubation for upper-airway protection, subsequent critical care of patients who have this injury should be directed at maintaining distal airway patency. High-frequency ventilation, inhaled heparin, and aggressive pulmonary toilet are among the therapies available. Even so, immunosuppression, intubation, and airway damage predispose these patients to pneumonia and other complications.


title: Tibiofemoral movement 1: the shapes and relative movements of the femur and tibia in the unloaded cadaver knee.

text: In six unloaded cadaver knees we used MRI to determine the shapes of the articular surfaces and their relative movements. These were confirmed by dissection. Medially, the femoral condyle in sagittal section is composed of the arcs of two circles and that of the tibia of two angled flats. The anterior facets articulate in extension. At about 20 degrees the femur 'rocks' to articulate through the posterior facets. The medial femoral condyle does not move anteroposteriorly with flexion to 110 degrees. Laterally, the femoral condyle is composed entirely, or almost entirely, of a single circular facet similar in radius and arc to the posterior medial facet. The tibia is roughly flat. The femur tends to roll backwards with flexion. The combination during flexion of no anteroposterior movement medially (i.e., sliding) and backward rolling (combined with sliding) laterally equates to internal rotation of the tibia around a medial axis with flexion. About 5 degrees of this rotation may be obligatory from 0 degrees to 10 degrees flexion; thereafter little rotation occurs to at least 45 degrees. Total rotation at 110 degrees is about 20 degrees, most if not all of which can be suppressed by applying external rotation to the tibia at 90 degrees.


title: A Layered Security Approach for Cloud Computing Infrastructure

text: This paper introduces a practical security model based on key security considerations by looking at a number of infrastructure aspects of Cloud Computing such as SaaS, Utility, Web, Platform and Managed Services, Service commerce platforms and Internet Integration which was introduced with a concise literature review. The purpose of this paper is to offer a macro level solution for identified common infrastructure security requirements. This model with a number of emerged patterns can be applied to infrastructure aspect of Cloud Computing as a proposed shared security approach in system development life cycle focusing on the plan-built-run scope.


title: Development of a three-dimensional ball rotation sensing system using optical mouse sensors

text: Robots using ball(s) as spherical wheels have the advantage of omnidirectional motion. Some of these robots use only a single ball as their wheel and dynamically balance on it. However, driving a ball wheel is not straightforward. These robots usually use one or more motor-driven rollers or wheels in frictional contact with the ball wheel. Some slippage can occur with these schemes, so it is important to measure the actual rotation of the ball wheel for good control. This paper proposes one method for measuring the three dimensional rotation of a sphere, which is applicable to the case of a ball wheel. The system measures surface speed by using two or more optical mouse sensors and transforms them into the angular velocity vector of the ball, followed by integration to give the rotational angle. Experiments showed the correctness of this method, yielding an error of approximately 1%, with a 10 ms response.


title: IMPLEMNTATION OF SIMULINK BASED MODEL USING SOBEL EDGE DETECTOR FOR DENTAL PROBLEMS Deepika Nagpal

text: Image Segmentation is the process of partitioning a digital image into multiple regions or sets of pixels.Edge Detection is one of the main Technique used in Segmentation.In this paper we used Sobel edge detector for segmenting the dental X-ray image.Using MATLAB,Image is segmented.Sysytem Test tool is used for the verification of the Simulink Model. The Simulink Model based Image Segmentation is a new function in image processing and offers a model based design for processing. Dental Caries is the main problem occurred in the teeths.Segmentation help to identify the places where the problems of dental caries are present.


title: Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages

text: Early diagnosis of Alzheimer disease (AD), while still at the stage known as mild cognitive impairment (MCI), is important for the development of new treatments. However, brain degeneration in MCI evolves with time and differs from patient to patient, making early diagnosis a very challenging task. Despite these difficulties, many machine learning techniques have already been used for the diagnosis of MCI and for predicting MCI to AD conversion, but the MCI group used in previous works is usually very heterogeneous containing subjects at different stages. The goal of this paper is to investigate how the disease stage impacts on the ability of machine learning methodologies to predict conversion. After identifying the converters and estimating the time of conversion (TC) (using neuropsychological test scores), we devised 5 subgroups of MCI converters (MCI-C) based on their temporal distance to the conversion instant (0, 6, 12, 18 and 24 months before conversion). Next, we used the FDG-PET images of these subgroups and trained classifiers to distinguish between the MCI-C at different stages and stable non-converters (MCI-NC). Our results show that MCI to AD conversion can be predicted as early as 24 months prior to conversion and that the discriminative power of the machine learning methods decreases with the increasing temporal distance to the TC, as expected. These findings were consistent for all the tested classifiers. Our results also show that this decrease arises from a reduction in the information contained in the regions used for classification and by a decrease in the stability of the automatic selection procedure.


title: Graph Partitioning using Quantum Annealing on the D-Wave System

text: Graph partitioning (GP) applications are ubiquitous throughout mathematics, computer science, chemistry, physics, bio-science, machine learning, and complex systems. Post Moore's era supercomputing has provided us an opportunity to explore new approaches for traditional graph algorithms on quantum computing architectures. In this work, we explore graph partitioning using quantum annealing on the D-Wave 2X machine. Motivated by a recently proposed graph-based electronic structure theory applied to quantum molecular dynamics (QMD) simulations, graph partitioning is used for reducing the calculation of the density matrix into smaller subsystems rendering the calculation more computationally efficient. Unconstrained graph partitioning as community clustering based on the modularity metric can be naturally mapped into the Hamiltonian of the quantum annealer. On the other hand, when constraints are imposed for partitioning into equal parts and minimizing the number of cut edges between parts, a quadratic unconstrained binary optimization (QUBO) reformulation is required. This reformulation may employ the graph complement to fit the problem in the Chimera graph of the quantum annealer. Partitioning into 2 parts and k parts concurrently for arbitrary k are demonstrated with benchmark graphs, random graphs, and small material system density matrix based graphs. Results for graph partitioning using quantum and hybrid classical-quantum approaches are shown to be comparable to current "state of the art" methods and sometimes better.


title: Typology of Distributed Ledger based Business Models

text: The potential of distributed ledger technology and its application in various industries is a controversially debated topic. Advocates of the technology emphasize the economic benefits of decentralization and transparency, leading to cost reductions as well as the alleviation of several of today`s economic and technological problems. In contrast, critics assert that the potential of distributed ledgers might be overhyped, possibly leading to the next tech bubble. This paper contributes to the discussion by developing a typology of business models that are based on distributed ledger technology. In particular, this paper is a first step towards a more differentiated discussion on the potential of distributed ledges, by taking the underlying business models into consideration. Despite a characterization of the types, a discussion about special features of distributed ledger based business models is provided in the context of contemporary business model literature and the associated role of IT. It is proposed that future research must evaluate each business model isolated to achieve a comprehensive assessment of the potential of distributed ledgers. This paper can be interpreted as starting point for more fruitful discussions and the repeal of the partially diametrical opposed opinions towards the potentials of the technology.


title: Learning structural SVMs with latent variables

text: We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application problems, with an optimization problem that can be solved efficiently using Concave-Convex Programming. The generality and performance of the approach is demonstrated through three applications including motiffinding, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.


title: Transferring a semantic representation for person re-identification and search

text: Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation. However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches. This is due to their nonscalable need for extensive domain (camera) specific annotation. In this paper we present a new semantic attribute learning approach for person re-identification and search. Our model is trained on existing fashion photography datasets - either weakly or strongly labelled. It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision. The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively. Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.


title: Supervised Descent Method and Its Applications to Face Alignment

text: Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface.


title: An Optimal Algorithm for Generalized Causal Message Ordering (Abstract)

text: Asynchronous execution of processes and unpredictable communication delays create nondeterminism in distributed systems that complicates the design, verification, and analysis of distributed programs. To simplify the design and development of distributed applications, the idea of “causal message ordering” (CO) was introduced by Birman and Joseph [1]. If for any two messages M and M’ sent to the same destination d, Send(M) + Send(M’), then CO ensures that Delivered + Deliver-yd(lf’) at d. Existing algorithms that provide CO have high message overhead and use more storage than is required by an optimal algorithm, or make simplifying assumptions by assuming certain topolog y/communicat ion pat t ems and inbuilt synchronize ation. We present an optimal CO algorithm [2] under the following framework:


title: Private Release of Graph Statistics using Ladder Functions

text: Protecting the privacy of individuals in graph structured data while making accurate versions of the data available is one of the most challenging problems in data privacy. Most efforts to date to perform this data release end up mired in complexity, overwhelm the signal with noise, and are not effective for use in practice. In this paper, we introduce a new method which guarantees differential privacy. It specifies a probability distribution over possible outputs that is carefully defined to maximize the utility for the given input, while still providing the required privacy level. The distribution is designed to form a 'ladder', so that each output achieves the highest 'rung' (maximum probability) compared to less preferable outputs. We show how our ladder framework can be applied to problems of counting the number of occurrences of subgraphs, a vital objective in graph analysis, and give algorithms whose cost is comparable to that of computing the count exactly. Our experimental study confirms that our method outperforms existing methods for counting triangles and stars in terms of accuracy, and provides solutions for some problems for which no effective method was previously known. The results of our algorithms can be used to estimate the parameters of suitable graph models, allowing synthetic graphs to be sampled.


title: Cloud Computing : Types , Architecture , Applications , Concerns , Virtualization and Role of IT Governance in Cloud

text: Technology innovation and its adoption are two critical successful factors for any business/organization. Cloud computing is a recent technology paradigm that enables organizations or individuals to share various services in a seamless and cost-effective manner. This paper describes cloud computing, a computing platform for the next generation of the Internet. The paper defines clouds, types of cloud Provides, Comparison of Cloud Computing with Grid Computing, applications and concerns of Cloud Computing , Concept of Virtualization in Cloud Computing. Readers will also discover the working, Architecture and Role of I.T Governance in Cloud Computing.


title: BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks

text: In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Coarse guidance using deformation fields obtained by an existing registration method; and (2) Fine guidance using image similarity. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods.


title: Thin Images Reflected in the Water: Narcissism and Girls' Vulnerability to the Thin-Ideal.

text: The purpose of this research is to test how adolescent girls' narcissistic traits-characterized by a need to impress others and avoid ego-threat-influence acute adverse effects of thin-ideal exposure. Participants (11-15 years; total N = 366; all female) reported their narcissistic traits. Next, in two experiments, they viewed images of either very thin or average-sized models, reported their wishful identification with the models (Experiment 2), and tasted high-calorie foods in an alleged taste test (both experiments). Narcissism kept girls from wishfully identifying with thin models, which is consistent with the view that narcissistic girls are prone to disengage from thin-ideal exposure. Moreover, narcissism protected vulnerable girls (those who experience low weight-esteem) from inhibiting their food intake, and led other girls (those who consider their appearance relatively unimportant) to increase their food intake. These effects did not generalize to conceptually related traits of self-esteem and perfectionism, and were not found for a low-calorie foods outcome, attesting to the specificity of findings. These experiments demonstrate the importance of narcissism at reducing girls' thin-ideal vulnerability. Girls high in narcissism disengage self-protectively from threats to their self-image, a strategy that renders at least subsets of them less vulnerable to the thin-ideal.


title: Channel Estimation Algorithms for OFDM Systems

text: In this work we have compared different type of channel estimation algorithm for Orthogonal Frequency Division Multiplexing (OFDM) systems. The result of the Adaptive Boosting (AdaBoost) algorithm was compared with other algorithms such Least Square (LS), Best Linear Unbiased Estimator (BLUE) and Minimum Mean Square Error (MMSE).


title: EchoFlex: Hand Gesture Recognition using Ultrasound Imaging

text: Recent improvements in ultrasound imaging enable new opportunities for hand pose detection using wearable devices. Ultrasound imaging has remained under-explored in the HCI community despite being non-invasive, harmless and capable of imaging internal body parts, with applications including smart-watch interaction, prosthesis control and instrument tuition. In this paper, we compare the performance of different forearm mounting positions for a wearable ultrasonographic device. Location plays a fundamental role in ergonomics and performance since the anatomical features differ among positions. We also investigate the performance decrease due to cross-session position shifts and develop a technique to compensate for this misalignment. Our gesture recognition algorithm combines image processing and neural networks to classify the flexion and extension of 10 discrete hand gestures with an accuracy above 98%. Furthermore, this approach can continuously track individual digit flexion with less than 5% NRMSE, and also differentiate between digit flexion at different joints.


title: Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus

text: The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.


title: Indoor Navigation Validation Framework for Visually Impaired Users

text: In this paper, we introduce the first validation framework of an indoor navigation system for blind and visually impaired (BVI) users, which is a significant step toward the development of cost effective indoor way-finding solutions for BVI users. The BVI users require detailed landmark-based navigation instructions that will help them arrive at the chosen destination independently. These users will interact with the navigation instructions on a smartphone using an accessible user interface. The validation framework includes the following three main components: 1) virtual reality-based simulation that simulates a BVI user traversing and interacting with the physical environment, developed using Unity game engine; 2) generation of action codes that emulate the avatar movement in the virtual environment, developed using a natural language processing parser of the navigation instructions; and 3) accessible user interface, which enables the user to access the navigation instructions, developed using Sikuli script library. We introduce a case study that illustrates the use of the validation tool using PERCEPT system. It is our strong belief that the validation framework we provide in this paper will encourage other developers to invent indoor navigation systems for BVI users. In addition, we would like to mention that this tool is the first step of validating an indoor navigation system and should be followed by trials with human subjects.


title: SENTIMENT ANALYSIS IN E-COMMERCE USING RECOMMENDATION SYSTEM

text: In the field of text mining the sentiment analysis is one of the current research topics. Sentiment analysis is the best solution for opinions and sentiment mining from natural language. Sentiment analysis gives significant information for decision making in various domains. The sentiments include ratings, reviews and emoticons. Several sentiment detection approaches are available which affect the quality of result. This paper finding the sentiments of people related to the services of E-shopping websites. The central goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best product. The proposed system use stochastic learning algorithm which analyze various feedbacks related to the services and then the sentiments are classified as negative, positive and neutral. The pre-processing of the data is greatly affecting the quality of detected sentiments so that the fake reviews in the website are analyzed. Finally the classification technique can be used to analysis the products which are posted in E-shopping website. The proposed system find out fake reviews made by posting fake comments about a product by identifying the MAC address along with review posting patterns. In order to find out the review is fake or genuine the proposed system find out the MAC address of the user if the system observes fake review send by the same MAC address many a times it will inform the admin to remove that review from the system. The proposed system helps the user to find out correct review of the product.


title: Fetal facial profile markers of Down syndrome in the second and third trimesters of pregnancy.

text:
OBJECTIVES
To investigate the use of the maxilla-nasion-mandible (MNM) angle and fetal profile (FP) line to assess the degree of midfacial hypoplasia in Down-syndrome fetuses in the second and third trimesters of pregnancy.

METHODS
The MNM angle and FP line were measured retrospectively in stored two-dimensional images or three-dimensional volumes of fetuses with Down syndrome. Data collected from January 2006 to July 2013 were retrieved from the digital databases of participating units. The MNM angle was expressed as a continuous variable (degrees) and the FP line as positive, negative or zero. Measurements were obtained from stored images in the midsagittal plane by two experienced examiners and compared with our previously reported normal ranges for euploid fetuses. A MNM angle below the 5(th) centile of the reference range and a positive or negative FP line were considered as abnormal.

RESULTS
A total of 133 fetuses with Down syndrome were available for analysis, eight of which were subsequently excluded because of inadequate images. The MNM angle was not influenced by gestational age (P = 0.48) and was significantly smaller in Down-syndrome fetuses than in euploid fetuses (mean, 12.90° vs 13.53°, respectively; P = 0.015). The MNM angle was below the 5th centile for euploid fetuses in 16.8% of fetuses with Down syndrome (P < 0.01). In the cohort of Down-syndrome fetuses, a positive FP line was present in 41.6% of cases (with a false-positive rate (FPR) of 6.3%) and was positively correlated with Down syndrome and gestational age (P < 0.01). There was no case with a negative FP line. In cases of Down syndrome, a positive FP line was correlated with a small MNM angle (P < 0.01).

CONCLUSIONS
A small MNM angle and a positive FP line can be regarded as novel markers for Down syndrome. The FP line is an easy marker to measure, has a low FPR, does not require knowledge of normal reference values and has the potential to differentiate between Down syndrome and trisomy 18, as, in the latter, the FP line is often negative.


title: Abstraction Learning

text: ion Learning Fei Deng fei.deng@rutgers.edu Jinsheng Ren rjs17@mails.tsinghua.edu.cn Feng Chen chenfeng@mail.tsinghua.edu.cn


title: A Low-Noise Wideband Digital Phase-Locked Loop Based on a Coarse–Fine Time-to-Digital Converter With Subpicosecond Resolution

text: This paper presents the design of a digital PLL which uses a high-resolution time-to-digital converter (TDC) for wide loop bandwidth. The TDC uses a time amplification technique to reduce the quantization noise down to less than 1 ps root mean square (RMS). Additionally TDC input commutation reduces low-frequency spurs due to inaccurate TDC scaling factor in a counter-assisted digital PLL. The loop bandwidth is set to 400 kHz with a 25 MHz reference. The in-band phase noise contribution from the TDC is -116 dBc/Hz, the phase noise is -117 dBc/Hz at high band (1.8 GHz band) 400 kHz offset, and the RMS phase error is 0.3deg.


title: Continuous Glucose Monitoring Systems: A Review.

text: There have been continuous advances in the field of glucose monitoring during the last four decades, which have led to the development of highly evolved blood glucose meters, non-invasive glucose monitoring (NGM) devices and continuous glucose monitoring systems (CGMS). Glucose monitoring is an integral part of diabetes management, and the maintenance of physiological blood glucose concentration is the only way for a diabetic to avoid life-threatening diabetic complications. CGMS have led to tremendous improvements in diabetic management, as shown by the significant lowering of glycated hemoglobin (HbA1c) in adults with type I diabetes. Most of the CGMS have been minimally-invasive, although the more recent ones are based on NGM techniques. This manuscript reviews the advances in CGMS for diabetes management along with the future prospects and the challenges involved.


title: OWL rules: A proposal and prototype implementation

text: Although the OWL Web Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. We present SWRL (the Semantic Web Rules Language), a Horn clause rules extension to OWL that overcomes many of these limitations. SWRL extends OWL in a syntactically and semantically coherent manner: the basic syntax for SWRL rules is an extension of the abstract syntax for OWL DL and OWL Lite; SWRL rules are given formal meaning via an extension of the OWL DL model-theoretic semantics; SWRL rules are given an XML syntax based on the OWL XML presentation syntax; and a mapping from SWRL rules to RDF graphs is given based on the OWL RDF/XML exchange syntax. We discuss the expressive power of SWRL, showing that the ontology consistency problem is undecidable, provide several examples of SWRL usage, and discuss a prototype implementation of reasoning support for SWRL.


title: The neural basis of economic decision-making in the Ultimatum Game.

text: The nascent field of neuroeconomics seeks to ground economic decision making in the biological substrate of the brain. We used functional magnetic resonance imaging of Ultimatum Game players to investigate neural substrates of cognitive and emotional processes involved in economic decision-making. In this game, two players split a sum of money;one player proposes a division and the other can accept or reject this. We scanned players as they responded to fair and unfair proposals. Unfair offers elicited activity in brain areas related to both emotion (anterior insula) and cognition (dorsolateral prefrontal cortex). Further, significantly heightened activity in anterior insula for rejected unfair offers suggests an important role for emotions in decision-making.


title: Security/privacy of wearable fitness tracking IoT devices

text: As wearable fitness trackers gain widespread acceptance among the general population, there is a concomitant need to ensure that associated privacy and security vulnerabilities are kept to a minimum. We discuss potential vulnerabilities of these trackers, in general, and specific vulnerabilities in one such tracker - Fitbit - identified by Rahman et al. (2013) who then proposed means to address identified vulnerabilities. However, the `fix' has its own vulnerabilities. We discuss possible means to alleviate related issues.


title: Using BPMN to Model a BPEL Process

text: The Business Process Modeling Notation (BPMN) has been developed to enable business user to develop readily understandable graphical representations of business processes. BPMN is also supported with appropriate graphical object properties that will enable the generation of executable BPEL. Thus, BPMN creates a standardized bridge for the gap between the business process design and process implementation. This paper presents a simple, yet instructive example of how a BPMN diagram can be used to generate a BPEL process.


title: Cloud computing: state-of-the-art and research challenges

text: Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.


title: Generalized Pythagoras Trees for visualizing hierarchies

text: Pythagoras Trees are fractals that can be used to depict binary hierarchies. But this binary encoding is an obstacle for visualizing hierarchical data such as file systems or phylogenetic trees, which branch into n sub-hierarchies. Although any hierarchy can be modeled as a binary one by subsequently dividing n-ary branches into a sequence of n − 1 binary branches, we follow a different strategy. In our approach extending Pythagoras Trees to arbitrarily branching trees, we only need a single visual element for an n-ary branch instead of spreading the binary branches along a strand. Each vertex in the hierarchy is visualized as a rectangle sized according to a metric. We analyze several visual parameters such as length, width, order, and color of the nodes against the use of different metrics. The usefulness of our technique is illustrated by two case studies visualizing directory structures and a large phylogenetic tree. We compare our approach with existing tree diagrams and discuss questions of geometry, perception, readability, and aesthetics.


title: Effective missing data prediction for collaborative filtering

text: Memory-based collaborative filtering algorithms have been widely adopted in many popular recommender systems, although these approaches all suffer from data sparsity and poor prediction quality problems. Usually, the user-item matrix is quite sparse, which directly leads to inaccurate recommendations. This paper focuses the memory-based collaborative filtering problems on two crucial factors: (1) similarity computation between users or items and (2) missing data prediction algorithms. First, we use the enhanced Pearson Correlation Coefficient (PCC) algorithm by adding one parameter which overcomes the potential decrease of accuracy when computing the similarity of users or items. Second, we propose an effective missing data prediction algorithm, in which information of both users and items is taken into account. In this algorithm, we set the similarity threshold for users and items respectively, and the prediction algorithm will determine whether predicting the missing data or not. We also address how to predict the missing data by employing a combination of user and item information. Finally, empirical studies on dataset MovieLens have shown that our newly proposed method outperforms other state-of-the-art collaborative filtering algorithms and it is more robust against data sparsity.


title: ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines

text: Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present ”ItemRank”, a random–walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top–rank items to potentially interested users. We tested our algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.


title: Using Linear Algebra for Intelligent Information Retrieval

text: Currently most approaches to retrieving textual materials from scienti c databases depend on a lexical match between words in users requests and those in or assigned to documents in a database Because of the tremendous diversity in the words people use to describe the same document lexical methods are necessarily incomplete and imprecise Using the singular value decomposition SVD one can take advantage of the implicit higher order structure in the association of terms with documents by determining the SVD of large sparse term by document matrices Terms and documents represented by of the largest singular vectors are then matched against user queries We call this retrieval method Latent Semantic Indexing LSI because the subspace represents important associative relationships between terms and documents that are not evident in individual documents LSI is a completely automatic yet intelligent indexing method widely applicable and a promising way to improve users access to many kinds of textual materials or to documents and services for which textual descriptions are available A survey of the computational requirements for managing LSI encoded databases as well as current and future applications of LSI is presented


title: On the Duration, Addressability, and Capacity of Memory-Augmented Recurrent Neural Networks

text: Memory-augmented recurrent neural networks (M-RNNs) have demonstrated empirically that they are very attractive for many applications, but a good theoretical understanding of their behaviors is unclear yet. In this paper, three analytical indicators named duration, addressability, and capacity of general forms of the additional memory in M-RNNs are formalized. The analysis results of the interactions among these indicators reveal that it is hard for an M-RNN to simultaneously provide good performance on more than two out of three of indicators. Meanwhile, the duration, addressability, and capacity are applied to analyze and compare two M-RNNs: long short term memory and neural turing machine for different cases. The comparison results show that none of the models has better performance on one indicator than the other model all the time. Moreover, it is found that separating memory system into sub-memories can bring the increasing duration and addressability and the decreasing capacity for the whole memory system.


Top-Down Induction of First-Order Logical Decision Trees


title: Effective Implementation of DGEMM on Modern Multicore CPU

text: In this paper we will present a detailed study on tuning double-precision matrix-matrix multiplication (DGEMM) on the Intel Xeon E5-2680 CPU. We selected an optimal algorithm from the instruction set perspective as well software tools optimized for Intel Advance Vector Extensions (AVX). Our optimizations included the use of vector memory operations, and AVX instructions. Our proposed algorithm achieves a performance improvement of 33% compared to the latest results achieved using the Intel Math Kernel Library DGEMM subroutine.


title: New trends in gender and mathematics performance: a meta-analysis.

text: In this article, we use meta-analysis to analyze gender differences in recent studies of mathematics performance. First, we meta-analyzed data from 242 studies published between 1990 and 2007, representing the testing of 1,286,350 people. Overall, d = 0.05, indicating no gender difference, and variance ratio = 1.08, indicating nearly equal male and female variances. Second, we analyzed data from large data sets based on probability sampling of U.S. adolescents over the past 20 years: the National Longitudinal Surveys of Youth, the National Education Longitudinal Study of 1988, the Longitudinal Study of American Youth, and the National Assessment of Educational Progress. Effect sizes for the gender difference ranged between -0.15 and +0.22. Variance ratios ranged from 0.88 to 1.34. Taken together, these findings support the view that males and females perform similarly in mathematics.


title: Fuzzy logic control of energy storage system in microgrid operation

text: Recent development in Renewable Energy Sources (RES) have led to a higher penetration in existing power systems. As the majority of RES are intermittent by nature, it presents major challenges to the grid operators. An Energy Storage System (ESS) can be connected to mitigate this intermittent sources. When multiple renewable energy sources, flexible loads and ESS are connected to the grid, complexity of the system is significantly increased. Such problems have multiple constraints and objective hence it is challenging to design an effective rule-based control strategy. A control strategy based on fuzzy logic which is similar to human reasoning tolerates uncertainties and imprecision. The proposed fuzzy inference system (FIS) aims to reduce the grid fluctuation and increase the energy storage life-cycle by deciding when and how much to charge/discharge the ESS. A real data was used to test and validate the proposed FIS. In this paper, MATLAB/Simulink is used to create and implement the microgrid test bench and FIS. The proposed methodology is tested against a rule-based control strategy. Simulation studies were carried out on the developed model and results have shown that the proposed FIS can effectively reduce the fluctuation and prolong the life cycle of the ESS.


title: Multiple Text Document Summarization System using hybrid Summarization technique

text: Text Summarization plays an important role in the area of text mining and natural language processing. As the information resources are increasing tremendously, readers are overloaded with loads of information. Finding out the relevant data and manually summarizing it in short time is much more difficult, challenging and tedious task for a human being. Text Summarization aims to compress the source text into a shorter and concise form with preserving its information content and overall meaning. Summarization can be classified into two main categories i.e. extractive summarization and abstractive summarization. This paper presents a novel approach to generate abstractive summary from extractive summary using WordNet ontology. An experimental result shows the generated summary in well-compressed, grammatically correct and human readable format.


title: The facial width-to-height ratio determines interpersonal distance preferences in the observer.

text: Facial width-to-height ratio (fWHR) is correlated with a number of aspects of aggressive behavior in men. Observers appear to be able to assess aggressiveness from male fWHR, but implications for interpersonal distance preferences have not yet been determined. This study utilized a novel computerized stop-distance task to examine interpersonal space preferences of female participants who envisioned being approached by a man; men's faces photographed posed in neutral facial expressions were shown in increasing size to mimic approach. We explored the effect of the men's fWHR, their behavioral aggression (measured previously in a computer game), and women's ratings of the men's aggressiveness, attractiveness, and masculinity on the preferred interpersonal distance of 52 German women. Hierarchical linear modelling confirmed the relationship between the fWHR and trait judgements (ratings of aggressiveness, attractiveness, and masculinity). There were effects of fWHR and actual aggression on the preferred interpersonal distance, even when controlling statistically for men's and the participants' age. Ratings of attractiveness, however, was the most influential variable predicting preferred interpersonal distance. Our results extend earlier findings on fWHR as a cue of aggressiveness in men by demonstrating implications for social interaction. In conclusion, women are able to accurately detect aggressiveness in emotionally neutral facial expressions, and adapt their social distance preferences accordingly.


title: Denoising autoencoder with modulated lateral connections learns invariant representations of natural images

text: Suitable lateral connections between encoder and decoder are shown to allow higher layers of a denoising autoencoder (dAE) to focus on invariant representations. In regular autoencoders, detailed information needs to be carried through the highest layers but lateral connections from encoder to decoder relieve this pressure. It is shown that abstract invariant features can be translated to detailed reconstructions when invariant features are allowed to modulate the strength of the lateral connection. Three dAE structures with modulated and additive lateral connections, and without lateral connections were compared in experiments using real-world images. The experiments verify that adding modulated lateral connections to the model 1) improves the accuracy of the probability model for inputs, as measured by denoising performance; 2) results in representations whose degree of invariance grows faster towards the higher layers; and 3) supports the formation of diverse invariant poolings.


title: Short text classification based on Wikipedia and Word2vec

text: Different from long texts, the features of Chinese short texts is much sparse, which is the primary cause of the low accuracy in the classification of short texts by using traditional classification methods. In this paper, a novel method was proposed to tackle the problem by expanding the features of short text based on Wikipedia and Word2vec. Firstly, build the semantic relevant concept sets of Wikipedia. We get the articles that have high relevancy with Wikipedia concepts and use the word2vec tools to measure the semantic relatedness between target concepts and related concepts. And then we use the relevant concept sets to extend the short texts. Compared to traditional similarity measurement between concepts using statistical method, this method can get more accurate semantic relatedness. The experimental results show that by expanding the features of short texts, the classification accuracy can be improved. Specifically, our method appeared to be more effective.


title: The effect of shame and shame memories on paranoid ideation and social anxiety.

text:
BACKGROUND
Social wariness and anxiety can take different forms. Paranoid anxiety focuses on the malevolence of others, whereas social anxiety focuses on the inadequacies in the self in competing for social position and social acceptance. This study investigates whether shame and shame memories are differently associated with paranoid and social anxieties.

METHOD
Shame, traumatic impact of shame memory, centrality of shame memory, paranoia and social anxiety were assessed using self-report questionnaires in 328 participants recruited from the general population.

RESULTS
Results from path analyses show that external shame is specifically associated with paranoid anxiety. In contrast, internal shame is specifically associated with social anxiety. In addition, shame memories, which function like traumatic memories, or that are a central reference point to the individual's self-identity and life story, are significantly associated with paranoid anxiety, even when current external and internal shame are considered at the same time. Thus, traumatic impact of shame memory and centrality of shame memory predict paranoia (but not social anxiety) even when considering for current feelings of shame.

CONCLUSION
Our study supports the evolutionary model suggesting there are two different types of 'conspecific' anxiety, with different evolutionary histories, functions and psychological processes. Paranoia, but less so social anxiety, is associated with traumatic impact and the centrality of shame memories. Researchers and clinicians should distinguish between types of shame memory, particularly those where the self might have felt vulnerable and subordinate and perceived others as threatening and hostile, holding malevolent intentions towards the self.


title: Measuring Computer Performance: A Practitioner's Guide

text: A practitioner's guide Measuring computer performance sets out the fundamental techniques used in analyzing and understanding the performance of computer systems. Throughout the book, the emphasis is on practical methods of measurement, simulation and analytical modeling. The author discusses performance metrics and provides detailed coverage of the strategies used in benchmark programs. He gives intuitive explanations of the key statistical tools needed to interpret measured performance data. He also describes the generaìdesign of experiments' technique, and shows how the maximum amount of information can be obtained for the minimum effort. The book closes with a chapter on the technique of queueing analysis. Appendices listing common probability distributions and statistical tables are included, along with a glossary of important technical terms. This practically oriented book will be of great interest to anyone who wants a detailed, yet intuitive, understanding of computer systems performance analysis.


title: Two hearts in three-quarter time : How to waltz the social media / viral marketing dance

text: The concept of viral marketing has been discussed in the literature for over 15 years, since Jeffrey Rayport first introduced the term in 1996. However, the more widespread use of social media has recently pushed this idea to a whole new level. We provide insight into the relationship between social media and viral marketing, and illustrate the six steps executives should take in order to dance the social media/viral marketing waltz. We define viral marketing as electronic word-of-mouth whereby some form of marketing message related to a company, brand, or product is transmitted in an exponentially growing wayâ€”often through the use of social media applications. We consider the three conditions that need to be fulfilled to create a viral marketing epidemic (i.e., giving the right message to the right messengers in the right environment) and present four different groups of social media viral marketing campaigns (nightmares, strokes-of-luck, homemade issues, and triumphs). We conclude Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.


title: A 5MHz, 24V-to-1.2V, AO2T current mode buck converter with one-cycle transient response and sensorless current detection for medical meters

text: With a stringent input-to-output conversion ratio (CR) of 20 (24V input and 1.2V output) for a DC-DC converter, two stage cascaded architectures are easy to implement, but suffer from poor efficiency and doubled number of power components. This paper presents a single-stage solution with a proposed Adaptive ON-OFF Time (AO2T) control. In steady state, the control works as an adaptive ON-time valley current mode control to accommodate the large CR. During load transient periods, both ON- and OFF-time are adaptively adjustable to instantaneous load change, in order to accomplish fast load transient response within one switching cycle. To facilitate the high speed current mode control, a sensorless current detection circuit is also proposed. Operating at 5MHz, the converter achieves a maximum efficiency of 89.8% at 700mA and an efficiency of 85% at 2A full load. During a load current slew rate of 1.8A/200ns, the undershoot/overshoot voltages at VO are 23mV and 37mV respectively.


title: Metamaterial-Substrate Antenna Array for MIMO Communication System

text: We demonstrate how a magnetic permeability enhanced metamaterial can enhance the antenna array of a multiple-input multiple-output(MIMO) communication system. The performance of a rectangular patch antenna array on a metamaterial substrate was studied relative to a similar array constructed on a conventional FR4 substrate. Differently spaced arrays were analytically compared using array correlation coefficients and mean effective gain as performance metrics. Achievable channel capacity were obtained through channel measurements made on a MIMO testbed. While results show that arrays on conventional FR4 substrates have higher capacity due to gain and efficiency factors, arrays can be made smaller, and have less mutual coupling and correlation coefficients, when using a metamaterial substrate, but the antenna built on the metamaterial substrate can be made more efficient through the use of better host materials. This was reflected in the analysis of both antenna arrays normalized to remove efficiency and gain differences where they showed similar performances. Hence, metamaterial substrates are a cost-effective solution when antenna miniaturization is a key design criteria compared to conventional substrates that achieve the same miniaturization factor without significantly sacrificing performance.


title: The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms

text: Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle taxis to passengers in need. To precisely balance the supply and the demand of taxis, online taxicab platforms need to predict the Unit Original Taxi Demand (UOTD), which refers to the number of taxi-calling requirements submitted per unit time (e.g., every hour) and per unit region (e.g., each POI). Predicting UOTD is non-trivial for large-scale industrial online taxicab platforms because both accuracy and flexibility are essential. Complex non-linear models such as GBRT and deep learning are generally accurate, yet require labor-intensive model redesign after scenario changes (e.g., extra constraints due to new regulations). To accurately predict UOTD while remaining flexible to scenario changes, we propose LinUOTD, a unified linear regression model with more than 200 million dimensions of features. The simple model structure eliminates the need of repeated model redesign, while the high-dimensional features contribute to accurate UOTD prediction. We further design a series of optimization techniques for efficient model training and updating. Evaluations on two large-scale datasets from an industrial online taxicab platform verify that LinUOTD outperforms popular non-linear models in accuracy. We envision our experiences to adopt simple linear models with high-dimensional features in UOTD prediction as a pilot study and can shed insights upon other industrial large-scale spatio-temporal prediction problems.


title: KeLP: a Kernel-based Learning Platform for Natural Language Processing

text: Kernel-based learning algorithms have been shown to achieve state-of-the-art results in many Natural Language Processing (NLP) tasks. We present KELP, a Java framework that supports the implementation of both kernel-based learning algorithms and kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms: once a new kernel function has been implemented it can be adopted in all the available kernelmachine algorithms. The platform includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. This paper will show the main aspects of the framework by applying it to different NLP tasks.


title: Linear Machine Decision Trees

text: This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks.


title: Biological network analysis and comparison: mining new biological knowledge

text: The mechanisms underlying life machinery are still not completely understood. Something is known, something is “probably” known, other things are still unknown. Scientists all over the world are working very hard to clarify the processes regulating the cell life cycle and bioinformaticians try to support them by developing specialized automated tools. Within the plethora of applications devoted to the study of life mechanisms, tools for the analysis and comparison of biological networks are catching the attention of many researchers. It is interesting to investigate why.


title: The Function of Fiction is the Abstraction and Simulation of Social Experience.

text: Fiction literature has largely been ignored by psychology researchers because its only function seems to be entertainment, with no connection to empirical validity. We argue that literary narratives have a more important purpose. They offer models or simulations of the social world via abstraction, simplification, and compression. Narrative fiction also creates a deep and immersive simulative experience of social interactions for readers. This simulation facilitates the communication and understanding of social information and makes it more compelling, achieving a form of learning through experience. Engaging in the simulative experiences of fiction literature can facilitate the understanding of others who are different from ourselves and can augment our capacity for empathy and social inference.


title: Machine Learning to Balance the Load in Parallel Branch-and-Bound

text: We describe in this paper a new approach to parallelize branchand-bound on a certain number of processors. We propose to split the optimization of the original problem into the optimization of several subproblems that can be optimized separately with the goal that the amount of work that each processor carries out is balanced between the processors, while achieving interesting speedups. The main innovation of our approach consists in the use of machine learning to create a function able to estimate the difficulty (number of nodes) of a subproblem of the original problem. We also present a set of features that we developed in order to characterize the encountered subproblems. These features are used as input of the function learned with machine learning in order to estimate the difficulty of a subproblem. The estimates of the numbers of nodes are then used to decide how to partition the original optimization tree into a given number of subproblems, and to decide how to distribute them among the available processors. The experiments that we carry out show that our approach succeeds in balancing the amount of work between the processors, and that interesting speedups can be achieved with little effort.


title: Cervical radiculopathy.

text: Cervical radiculopathy is the result of irritation and/or compression of nerve root as it exits the cervical spine. Pain is a common presenting symptom and may be accompanied by motor or sensory deficits in areas innervated by the affected nerve root. Diagnosis is suggested by history and corresponding physical examination findings. Confirmation is achieved with MRI. A multimodal approach to treatment helps patients improve. Medications may be used to alleviate symptoms and manage pain. Physical therapy and manipulation may improve neck discomfort. Guided corticosteroid injections and selected nerve blocks may help control nerve root pain. Most patients improve with a conservative, nonoperative treatment course.


title: Flower species recognition system using convolution neural networks and transfer learning

text: Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While modern search engines provide methods to visually search for a query image that contains a flower, it lacks in robustness because of the intra-class variation among millions of flower species around the world. Hence in this proposed research work, a Deep learning approach using Convolutional Neural Networks (CNN) is used to recognize flower species with high accuracy. Images of the plant species are acquired using the built-in camera module of a mobile phone. Feature extraction of flower images is performed using a Transfer Learning approach (i.e. extraction of complex features from a pre-trained network). A machine learning classifier such as Logistic Regression or Random Forest is used on top of it to yield a higher accuracy rate. This approach helps in minimizing the hardware requirement needed to perform the computationally intensive task of training a CNN. It is observed that, CNN combined with Transfer Learning approach as feature extractor outperforms all the handcrafted feature extraction methods such as Local Binary Pattern (LBP), Color Channel Statistics, Color Histograms, Haralick Texture, Hu Moments and Zernike Moments. CNN combined with Transfer Learning approach yields impressive Rank-1 accuracies of 73.05%, 93.41% and 90.60% using OverFeat, Inception-v3 and Xception architectures, respectively as Feature Extractors on FLOWERS102 dataset.


title: Deep learning of binary hash codes for fast image retrieval

text: Approximate nearest neighbor search is an efficient strategy for large-scale image retrieval. Encouraged by the recent advances in convolutional neural networks (CNNs), we propose an effective deep learning framework to generate binary hash codes for fast image retrieval. Our idea is that when the data labels are available, binary codes can be learned by employing a hidden layer for representing the latent concepts that dominate the class labels. The utilization of the CNN also allows for learning image representations. Unlike other supervised methods that require pair-wised inputs for binary code learning, our method learns hash codes and image representations in a point-wised manner, making it suitable for large-scale datasets. Experimental results show that our method outperforms several state-of-the-art hashing algorithms on the CIFAR-10 and MNIST datasets. We further demonstrate its scalability and efficacy on a large-scale dataset of 1 million clothing images.


title: Describing objects by their attributes

text: We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.


title: Style Finder : Fine-Grained Clothing Style Recognition and Retrieval

text: With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text. However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem. First, an attribute vocabulary is constructed using human annotations obtained on a novel finegrained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles. We report benchmark recognition and retrieval results on Women’s Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.


title: Efficient Object Category Recognition Using Classemes

text: We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories are selected from an ontology of visual concepts, but the intention is not to encode an explicit decomposition of the scene. Rather, we accept that existing object category classifiers often encode not the category per se but ancillary image characteristics; and that these ancillary characteristics can combine to represent visual classes unrelated to the constituent categories’ semantic meanings. The advantage of this descriptor is that it allows object-category queries to be made against image databases using efficient classifiers (efficient at test time) such as linear support vector machines, and allows these queries to be for novel categories. Even when the representation is reduced to 200 bytes per image, classification accuracy on object category recognition is comparable with the state of the art (36% versus 42%), but at orders of magnitude lower computational cost.


title: Low-Profile Dual-Wideband Inverted-T Open Slot Antenna for the LTE/WWAN Tablet Computer With a Metallic Frame

text: An inverted-T open slot (ITOS) antenna with a low profile suitable for the LTE/WWAN tablet computer with a metallic frame is presented. With a low profile of 7 mm above the top edge of the device ground plane of the tablet computer, the ITOS antenna can provide two wide operating bands of 698-960 and 1710-2690 MHz to cover the LTE/WWAN operation. The ITOS antenna shows a simple structure and can fit in the narrow region between the metallic frame and the display panel of the tablet computer. The lower and higher operating bands of the antenna can be, respectively, controlled by the low-band and high-band feeds thereof. The low-band feed excites the antenna's longer slot arm to obtain a wide lower band covering the 698-960 MHz band. The high-band feed excites the antenna's shorter slot arm to achieve a wide higher band covering the 1710-2690 MHz band. Working principle of the low-profile ITOS antenna for generating the dual-wideband operation is described. Simulated and experimental results of the antenna are presented and discussed.


title: Locational marginal pricing basics for restructured wholesale power markets

text: Although Locational Marginal Pricing (LMP) plays an important role in many restructured wholesale power markets, the detailed derivation of LMPs as actually used in industry practice is not readily available. This lack of transparency greatly hinders the efforts of researchers to evaluate the performance of these markets. In this paper, different AC and DC optimal power flow (OPF) models are presented to help understand the derivation of LMPs. As a byproduct of this analysis, the paper provides a rigorous explanation of the basic LMP and LMP-decomposition formulas (neglecting real power losses) presented without derivation in the business practice manuals of the U.S. Midwest Independent System Operator (MISO).


title: On the O(1=k) convergence of asynchronous distributed alternating Direction Method of Multipliers

text: We consider a network of agents that are cooperatively solving a global optimization problem, where the objective function is the sum of privately known local objective functions of the agents and the decision variables are coupled via linear constraints. Recent literature focused on special cases of this formulation and studied their distributed solution through either subgradient based methods with O(1/√k) rate of convergence (where k is the iteration number) or Alternating Direction Method of Multipliers (ADMM) based methods, which require a synchronous implementation and a globally known order on the agents. In this paper, we present a novel asynchronous ADMM based distributed method for the general formulation and show that it converges at the rate O (1=k).


title: Joint Optimization of Resource Provisioning in Cloud Computing

text: Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.


title: Psychophysiology of False Memories in a Deese-Roediger-McDermott Paradigm with Visual Scenes

text: Remembering something that has not in fact been experienced is commonly referred to as false memory. The Deese-Roediger-McDermott (DRM) paradigm is a well-elaborated approach to this phenomenon. This study attempts to investigate the peripheral physiology of false memories induced in a visual DRM paradigm. The main research question is whether false recognition is different from true recognition in terms of accompanying physiological responses.Sixty subjects participated in the experiment, which included a study phase with visual scenes each showing a group of interrelated items in social contexts. Subjects were divided into an experimental group undergoing a classical DRM design and a control group without DRM manipulation. The control group was implemented in order to statistically control for possible biases produced by memorability differences between stimulus types. After a short retention interval, a pictorial recognition phase was conducted in the manner of a Concealed Information Test. Simultaneous recordings of electrodermal activity, respiration line length, phasic heart rate, and finger pulse waveform length were used. Results yielded a significant Group by Item Type interaction, showing that true recognition is accompanied by greater electrodermal activity than false recognition.Results are discussed in the light of Sokolov's Orienting Reflex, the Preliminary Process Theory and the Concealed Information Test. Implications and restrictions of the introduced design features are critically discussed. This study demonstrates the applicability of measures of peripheral physiology to the field of false memory research.


title: Making business sense of the Internet.

text: For managers in large, well-established businesses, the Internet is a tough nut to crack. It is very simple to set up a Web presence and very difficult to create a Web-based business model. Established businesses that over decades have carefully built brands and physical distribution relationships risk damaging all they have created when they pursue commerce through the Net. Still, managers can't avoid the impact of electronic commerce on their businesses. They need to understand the opportunities available to them and recognize how their companies may be vulnerable if rivals seize those opportunities first. Broadly speaking, the Internet presents four distinct types of opportunities. First, it links companies directly to customers, suppliers, and other interested parties. Second, it lets companies bypass other players in an industry's value chain. Third, it is a tool for developing and delivering new products and services to new customers. Fourth, it will enable certain companies to dominate the electronic channel of an entire industry or segment, control access to customers, and set business rules. As he elaborates on these four points, the author gives established companies a systematic way to sort through the risks and rewards of doing business in cyberspace.


Events can be defined as "something that occurs at specific place and time associated with some specific actions". In general, events extracted from news articles and social networks are used to map the information from web to the various phenomena that occur in our physical world. One of the main steps to perform this relationship is the use of machine learning algorithms for event classification, which has received great attention in the web document engineering field in recent years. Traditional machine learning algorithms are based on vector space model representations and supervised classification. However, events are composed of multiple representations such as textual data, temporal information, geographic location and other types of metadata. All these representations are poorly represented together in a vector space model. Moreover, supervised classification requires the labeling of a significant sample of events to construct a training set for learning process, thereby hampering the practical application of event classification. In this paper, we propose a method called TECHN (Transductive Event Classification through Heterogeneous Networks), which considers event metadata as different objects in an heterogeneous network. Besides, the TECHN method has the ability to automatically learn which types of network objects (event metadata) are most efficient in the classification task. In addition, our TECHN method is based on a transductive classification that considers both labeled events and a vast amount of unlabeled events. The experimental results show that TECHN method obtains promising results, especially when we consider different weights of importance for each type of event metadata and a small set of labeled events.


title: Executive Functions Predict the Success of Top-Soccer Players

text: While the importance of physical abilities and motor coordination is non-contested in sport, more focus has recently been turned toward cognitive processes important for different sports. However, this line of studies has often investigated sport-specific cognitive traits, while few studies have focused on general cognitive traits. We explored if measures of general executive functions can predict the success of a soccer player. The present study used standardized neuropsychological assessment tools assessing players' general executive functions including on-line multi-processing such as creativity, response inhibition, and cognitive flexibility. In a first cross-sectional part of the study we compared the results between High Division players (HD), Lower Division players (LD) and a standardized norm group. The result shows that both HD and LD players had significantly better measures of executive functions in comparison to the norm group for both men and women. Moreover, the HD players outperformed the LD players in these tests. In the second prospective part of the study, a partial correlation test showed a significant correlation between the result from the executive test and the numbers of goals and assists the players had scored two seasons later. The results from this study strongly suggest that results in cognitive function tests predict the success of ball sport players.


title: Dataset Augmentation in Feature Space

text: Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. While effective in tasks such as visual recognition, the set of transformations must be carefully designed, implemented, and tested for every new domain, limiting its re-use and generality. In this paper, we adopt a simpler, domain-agnostic approach to dataset augmentation. We start with existing data points and apply simple transformations such as adding noise, interpolating, or extrapolating between them. Our main insight is to perform the transformation not in input space, but in a learned feature space. A re-kindling of interest in unsupervised representation learning makes this technique timely and more effective. It is a simple proposal, but to-date one that has not been tested empirically. Working in the space of context vectors generated by sequence-to-sequence models, we demonstrate a technique that is effective for both static and sequential data.