title: Convolutional Neural Networks Without Any Checkerboard Artifacts

abstract: It is well-known that a number of convolutional neural networks (CNNs) generate checkerboard artifacts in both of two processes: forward-propagation of upsampling layers and backpropagation of convolutional layers. A condition to avoid the checkerboard artifacts is proposed in this paper. So far, checkerboard artifacts have been mainly studied for linear multirate systems, but the condition to avoid checkerboard artifacts can not be applied to CNNs due to the non-linearity of CNNs. We extend the avoiding condition for CNNs, and apply the proposed structure to some typical CNNs to confirm the effectiveness of the new scheme. Experiment results demonstrate that the proposed structure can perfectly avoid to generate checkerboard artifacts, while keeping excellent properties that the CNNs have.


title: A MOX gas sensors resistance-to-digital CMOS interface with 8-bits resolution and 128dB dynamic range for low-power consumer applications

abstract: In this paper an interface circuit for MOX gas sensor is presented. It is based on a resistance-to-frequency converter and improves existing solutions in term of performance (offset) and power efficiency. The resistive range covered is 100Ω-1MΩ, with an equivalent 8-bit precision in a total measurement time of 1 second. This corresponds to a dynamic range of about 128dB. Power consumption and design strategy are optimized for mass production targeting consumer applications. The interface is implemented in a standard CMOS 130nm technology with an area of 125000 μm2 and 450μA of current consumption.


title: Breast Cancer Diagnosis by using k-Nearest Neighbor with Different Distances and Classification Rules

abstract: Cancer diagnosis is one of the most studied problems in the medical domain. Several researchers have focused in order to improve performance and achieve to obtain satisfactory results. Breast cancer is one of cancer killer in the world. The diagnosis of this cancer is a big problem in cancer diagnosis researches. In artificial intelligent, machine learning is a discipline which allows to the machine to evolve through a process. Machine learning is widely used in bio informatics and particularly in breast cancer diagnosis. One of the most popular methods is K-nearest neighbors (K-NN) which is a supervised learning method. Using the K-NN in medical diagnosis is very interesting. The quality of the results depends largely on the distance and the value of the parameter “k” which represent the number of the nearest neighbors. In this paper, we study and evaluate the performance of different distances that can be used in the K-NN algorithm. Also, we analyze this distance by using different values of the parameter “k” and by using several rules of classification (the rule used to decide how to classify a sample). Our work will be performed on the WBCD database (Wisconsin Breast Cancer Database) obtained by the university of Wisconsin Hospital.


title: Diversity of denitrifying microflora and ability to reduce N2O in two soils

abstract: The ozone-depleting gas N2O is an intermediate in denitrification, the biological reduction of NO3– to the gaseous products N2O and N2 gas. The molar ratio of N2O produced (N2O/N2O+N2) varies temporally and spatially, and in some soils N2O may be the dominant end product of denitrification. The fraction of NO3–-N emitted as N2O may be due at least in part to the abundance and activity of denitrifying bacteria which possess N2O reductase. In this study, we enumerated NO3–-reducing and denitrifying bacteria, and compared and contrasted collections of denitrifying bacteria isolated from two agricultural soils, one (Auxonne, soil A) with N2O as the dominant product of denitrification, the other (Châlons, soil C) with N2 gas as the dominant product. Isolates were tested for the ability to reduce N2O, and the presence of the N2O reductase (nosZ)-like gene was evaluated by polymerase chain reaction (PCR) using specific primers coupled with DNA hybridization using a specific probe. The diversity and phylogenetic relationships of members of the collections were established by PCR/restriction fragment length polymorphism of 16s rDNA. The two soils had similar numbers of bacteria which used NO3– as a terminal electron acceptor anaerobically. However, the soil A had many more denitrifiers which reduced NO3– to gaseous products (N2O or N2) than did soil C. Collections of 258 and 281 bacteria able to grow anaerobically in the presence of NO3– were isolated from soil A and soil C, respectively. These two collections contained 66 and 12 denitrifying isolates, respectively, the others reducing NO3– only as far as NO2–. The presence of nosZ sequences was generally a poor predictor of N2O reducing ability: there was agreement between the occurrence of nosZ sequences and the N2O reducing ability for only 42% of the isolates; 35% of the isolates (found exclusively in soil A) without detectable nosZ sequences reduced N2O whereas 21% of the isolates carrying nosZ sequences did not reduce this gas under our assay conditions. Twenty-eight different 16S rDNA restriction patterns (using two restriction endonucleases) were distinguished among the 78 denitrifying isolates. Two types of patterns appeared to be common to both soils. Twenty-three and three types of patterns were found exclusively among bacteria isolated from soils A and C, respectively. The specific composition of denitrifying communities appeared to be different between the two soils studied. This may partly explain the differences in the behaviour of the soils concerning N2O reduction during denitrification.


title: An O(log 2 k)-competitive Algorithm for Metric Bipartite Matching

abstract: We consider the online metric matching problem. In this problem, we are given a graph with edge weights satisfying the triangle inequality, and k vertices that are designated as the right side of the matching. Over time up to k requests arrive at an arbitrary subset of vertices in the graph and each vertex must be matched to a right side vertex immediately upon arrival. A vertex cannot be rematched to another vertex once it is matched. The goal is to minimize the total weight of the matching. ::: ::: We give a O(log2 k) competitive randomized algorithm for the problem. This improves upon the best known guarantee of O(log3 k) due to Meyerson, Nanavati and Poplawski [19]. It is well known that no deterministic algorithm can have a competitive less than 2k - 1, and that no randomized algorithm can have a competitive ratio of less than ln k.


title: Is unrecognized bipolar disorder a frequent contributor to apparent treatment resistant depression

abstract: Abstract Background There is widespread clinical belief that unrecognized bipolar disorder (BD) is a frequent contributor to apparent treatment resistant depression (TRD). This review attempts to assess the degree to which prevailing empirical data supports that view. Methods All English-language articles published between January 1998 and January 2008 that focused on adults with major depressive disorder (MDD) and BD bearing on the question “Is unrecognized BD a frequent contributor to apparent TRD in patients initially diagnosed with MDD?” were reviewed. Results 196 articles were reviewed; the preponderance of the data suggested: 1) TRD populations demonstrate high rates of hidden bipolar disorder, 2) there is not sufficient evidence to unequivocally support or reject the hypothesis that patients who relapse despite continued antidepressant treatment are likely to have bipolar spectrum disorder, 3) patients initially diagnosed with MDD do not demonstrate high rates of switching to mania or hypomania when treated with antidepressants and 4) in patients diagnosed with BD, antidepressants are not robustly effective and are poorly tolerated. Limitations The main limitation of this review is that none of the individual studies were designed to test our primary hypothesis. Conclusions This review provides at least moderate support to the hypothesis that BD is a contributor to apparent TRD. Thus, clinicians treating MDD are urged to search for “soft” signs of bipolarity and to be prepared to alter diagnosis and treatment strategies accordingly.


title: Computer-Based Accounting Systems in Small and Medium Enterprises: Empirical Evidence from a Randomized Trial in Nigeria

abstract: The advent of IT has offered significant improvement to the way financial transactions in business are processed by professional accountants. Hence, in most parts of developed countries, it is almost impossible to function as an accountant without requisite IT skills. However, the pattern seem not attain similar forte in SMEs especially in developing economies. Consequently, this study would provide answer to the degree of computer-based accounting systems adoption by SMEs in Nigeria. We have conducted an empirical investigation through a structured survey focused on finance and accounts executives of the enterprises that holds primarily responsibility for business transactions processing. This information was sought in order to expound on the extent of CBAS adoption by Nigerian SMEs. Respondents were obtained from manufacturing, agriculture, construction and mining, hotel and hospitality, IT services, medical services, wholesale and retail trade, and general services industries. Through a combination of cross tabulation and logistics regression analysis, our findings showed that the use of CBAS by Nigerian SMEs is highly significant as all companies operating in all industries surveyed uses one type of accounting software or another. This outcome suggests that professional accountants must endeavour to continually horn their skills for continued relevance in the profession.


title: Aesthetics in the Adoption of Information and Communication Technology

abstract: Borrowing from Davis’ (1989) Technology Acceptance Model (TAM), Venkatesh and Morris (2000) TAM2 and Kant’s (1790) Theory of Aesthetics; we aim to expand on the contributions and frameworks provided by the literature by testing the nomological relationships between aesthetic judgment, user’s personality and the adoption of innovation in Information and Communication Technology (ICT). This study contributes an exploratory scale for the measurement of aesthetics in ICT. Survey data is utilized to explain perceived aesthetics, moderated by aesthetic centrality of a user, in addition to perceived usefulness, as dimensions of an ICT product that influence adoption intent. Preliminary results also show a weakening influence of social norms, non-significant ease of use indicators. We propose a shift in the paradigm of adoption of ICT innovation in which design, brand affinity and usefulness define the competitiveness of an ICT device in today’s market.


title: Allocentric and egocentric spatial representations: Definitions, distinctions, and interconnections

abstract: Although the literatures on human spatial cognition and animal navigation often make distinctions between egocentric and allocentric (also called exocentric or geocentric) representations, the terms have not generally been well defined. This chapter begins by making formal distinctions between three kinds of representations: allocentric locational, egocentric locational, and allocentric heading representations. These distinctions are made in the context of whole-body navigation (as contrasted, e.g., with manipulation). They are made on the basis of primitive parameters specified by each representation, and the representational distinctions are further supported by work on brain mechanisms used for animal navigation. From the assumptions about primitives, further inferences are made as to the kind of information each representation potentially makes available. Empirical studies of how well people compute primitive and derived spatial parameters are briefly reviewed. Finally, the chapter addresses what representations humans may use for processing spatial information during physical and imagined movement, and work on imagined updating of spatial position is used to constrain the connectivity among representations.


title: Deriving marketing intelligence from online discussion

abstract: Weblogs and message boards provide online forums for discussion that record the voice of the public. Woven into this mass of discussion is a wide range of opinion and commentary about consumer products. This presents an opportunity for companies to understand and respond to the consumer by analyzing this unsolicited feedback. Given the volume, format and content of the data, the appropriate approach to understand this data is to use large-scale web and text data mining technologies.This paper argues that applications for mining large volumes of textual data for marketing intelligence should provide two key elements: a suite of powerful mining and visualization technologies and an interactive analysis environment which allows for rapid generation and testing of hypotheses. This paper presents such a system that gathers and annotates online discussion relating to consumer products using a wide variety of state-of-the-art techniques, including crawling, wrapping, search, text classification and computational linguistics. Marketing intelligence is derived through an interactive analysis framework uniquely configured to leverage the connectivity and content of annotated online discussion.


title: A Cost-Effective Method of Electric Brake With Energy Regeneration for Electric Vehicles

abstract: This paper proposes a simple but effective method of electric brake with energy regeneration for a brushless DC motor of an electric vehicle (EV). During the braking period, the proposed method only changes the switching sequence of the inverter to control the inverse torque so that the braking energy will return to the battery. Compared with the presented methods, the proposed solution simultaneously achieves dual goals of the electric brake and the energy regeneration without using additional converter, ultracapacitor, or complex winding-changeover technique. Since the braking kinetic energy is converted into the electrical energy and then returns to the battery, the energy regeneration could increase the driving range of an EV. In addition to the braking period, the duration of release throttle is also included in the energy-regenerative mechanism such that the EV is similar to engine vehicles having the engine brake. Therefore, the electric brake can improve rider's comfort and enhance the EV's safety. Finally, the feasibility of the proposed method is demonstrated by experimental results. It shows that the driving range of the EV could be increased to about 16.2%.


title: Reconstruction of the geometry of volcanic vents by trajectory tracking of fast ejecta - the case of the Eyjafjallajökull 2010 eruption (Iceland)

abstract: Two methods are introduced to estimate the depth of origin of ejecta trajectories (depth to magma level in conduit) and the diameter of a conduit in an erupting crater, using analysis of videos from the Eyjafjallajokull 2010 eruption to evaluate their applicability. Both methods rely on the identification of straight, initial trajectories of fast ejecta, observed near the crater rims before they are appreciably bent by air drag and gravity. In the first method, through tracking these straight trajectories and identifying a cut-off angle, the inner diameter and the depth level of the vent can be constrained. In the second method, the intersection point of straight trajectories from individual pulses is used to determine the maximum possible depth from which the tracked ejecta originated and the width of the region from which the pulses emanated. The two methods give nearly identical results on the depth to magma level in the crater of Eyjafjallajokull on 8 to 10 May of 51 ± 7 m. The inner vent diameter, at the level of origin of the pulses and ejecta, is found to have been 8 to 15 m. These methods open up the possibility to feed (near) real-time monitoring systems with otherwise inaccessible information about vent geometry during an ongoing eruption and help defining important eruption source parameters.


title: Machine Learning Paradigms for Speech Recognition: An Overview

abstract: Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems arising from the inherently sequential and dynamic nature of speech. On the other hand, even though ASR is available commercially for some applications, it is largely an unsolved problem - for almost all applications, the performance of ASR is not on par with human performance. New insight from modern ML methodology shows great promise to advance the state-of-the-art in ASR technology. This overview article provides readers with an overview of modern ML techniques as utilized in the current and as relevant to future ASR research and systems. The intent is to foster further cross-pollination between the ML and ASR communities than has occurred in the past. The article is organized according to the major ML paradigms that are either popular already or have potential for making significant contributions to ASR technology. The paradigms presented and elaborated in this overview include: generative and discriminative learning; supervised, unsupervised, semi-supervised, and active learning; adaptive and multi-task learning; and Bayesian learning. These learning paradigms are motivated and discussed in the context of ASR technology and applications. We finally present and analyze recent developments of deep learning and learning with sparse representations, focusing on their direct relevance to advancing ASR technology.


title: Bidirectional OLED buck driver

abstract: This paper presents a bidirectional OLED buck driver with the proposed variable-frequency control mechanism. The bulky parasitic capacitor of the OLED causes the significant delay on the dimming response of the OLED. Therefore, the bidirectional OLED buck driver with the variable-frequency control mechanism is proposed to increase the dimming response. Finally, the prototype circuit of a 6.4W bidirectional OLED buck driver with the variable-frequency control mechanism is built to verify the response performance.


title: OpenStreetMap: User-Generated Street Maps

abstract: The OpenStreetMap project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.


title: Low power integrated scan-retention mechanism

abstract: This paper presents a methodology for unifying the scan mechanism and data retention in latches which leads to scannable latches with the data retention capability achieved at a very low power overhead during the active mode. A detailed analysis of power and area overhead is presented, with layout examples for various common latch styles. Implications of using different power gating techniques for reducing leakage during sleep mode on the design of retention latches are considered, including well biasing for leakage control and sharing wells between gated logic and retention latch devices.


title: ON THE BINARY DIGITS OF ALGEBRAIC NUMBERS

abstract: Borel conjectured that all algebraic irrational numbers are normal in base 2. However, very little is known about this problem. We improve the lower bounds for the number of digit changes in the binary expansions of algebraic irrational numbers.


title: An improved branch and bound algorithm for feature selection

abstract: Feature selection plays an important role in pattern classification. In this paper, we present an improved branch and bound algorithm for Optimal feature subset selection. This algorithm searches for an optimal solution in a large solution tree in an efficient manner by cutting unnecessary paths which are guaranteed not to contain the optimal solution. Our experimental results demonstrate the effectiveness of the new algorithm.


Forensic mycology: current perspectives


title: Effects of mechanical and chemical processes on the degradation of plastic beach debris on the island of Kauai, Hawaii.

abstract: Abstract Plastic debris is accumulating on the beaches of Kauai at an alarming rate, averaging 484 pieces/day in one locality. Particles sampled were analyzed to determine the effects of mechanical and chemical processes on the breakdown of polymers in a subtropical setting. Scanning electron microscopy (SEM) indicates that plastic surfaces contain fractures, horizontal notches, flakes, pits, grooves, and vermiculate textures. The mechanically produced textures provide ideal loci for chemical weathering to occur which further weakens the polymer surface leading to embrittlement. Fourier transform infrared spectroscopy (FTIR) results show that some particles have highly oxidized surfaces as indicated by intense peaks in the lower wavenumber region of the spectra. Our textural analyses suggest that polyethylene has the potential to degrade more readily than polypropylene. Further evaluation of plastic degradation in the natural environment may lead to a shift away from the production and use of plastic materials with longer residence times.


title: Preventing reading difficulties in young children

abstract: 1 Front Matter 2 Executive Summary 3 Part I: Introduction to Reading 4 1. Introduction 5 2. The Process of Learning to Read 6 Part II: Who Are We Talking About? 7 3. Who Has Reading Difficulties? 8 4. Predictors of Success and Failure in Reading 9 Part III: Prevention and Intervention 10 5. Preventing Reading Difficulties Before Kindergarten 11 6. Instructional Strategies for Kindergarten and the Primary Grades 12 7. Organizational Strategies for Kindergarten and the Primary Grades 13 8. Helping Children with Reading Difficulties in Grades 1 to 3 14 Part IV: Knowledge into Action 15 9. The Agents of Change 16 10. Recommendations for Practice and Research 17 References 18 Biographical Sketches 19 Index


title: Investigating the Influence of Avatar Facial Characteristics on the Social Behaviors of Children with Autism

abstract: Autism spectrum disorder (ASD) is characterized by unusual social communication and interaction. These traits are often targets for intervention, particularly computer-based interventions (CBIs). We examined whether interactive behaviors in children with autism could be influenced by modifying the facial characteristics of computer avatars and how behavior toward avatars compared to that toward video. Participants spoke with a therapist over a modified videoconferencing system that permitted manipulation of her appearance (i.e., using cartoon or more realistic avatars versus video) and motion (i.e., exaggerating or damping facial movements). We measured the participants' speech, gaze, and gestures. In the first study, we found that the appearance complexity of the avatar did not significantly affect any social interaction behaviors. However, the results of the second study suggest that exaggerated facial motion can improve nonverbal social behaviors, such as gaze and gesture. These findings have implications for character design in CBIs for ASD.


title: Dual-Band Antenna With Compact Radiator for 2.4/5.2/5.8 GHz WLAN Applications

abstract: This paper presents a dual-band planar antenna with a compact radiator for 2.4/5.2/5.8-GHz wireless local area network (WLAN) applications. The antenna consists of an L-shaped and -shaped radiating elements to generate two resonant modes for dual-band operation. The -element fed directly by a 50-Ω microstrip line is designed to generate a frequency band at around 5.5 GHz to cover the two higher bands of the WLAN system (using the IEEE 802.11a standard). The L-element is coupled-fed through the L-element and designed to generate a frequency band at 2.44 GHz to cover the lower band of the WLAN system (using the 802.11 b/g standards). As a result, the L- and E-elements together are very compact with a total area of only 8 × 11.3 mm2. Parametric study on the key dimensions is investigated using computer simulation. For verification of simulation results, the antenna is fabricated on a 40 × 30 × 0.8 mm3 substrate and measured. The effects of the feeding cable used in the measurement system and the housing and liquid crystal display of wireless devices on the return loss, radiation pattern, gain and efficiency are also investigated by computer simulation and measurement.


title: Positive and negative regulatory mechanisms in high-affinity IgE receptor-mediated mast cell activation

abstract: Mast cells are important effector cells in allergic inflammatory reactions. The aggregation of the high-affinity IgE receptor (FcepsilonRI) on the surface of mast cells initiates a complex cascade of signaling events that ultimately leads to the release of various mediators involved in allergic inflammation and anaphylactic reactions. The release of these mediators is tightly controlled by signaling pathways that are propagated through the cell by specific phosphorylation and dephosphorylation events. These events are controlled by protein kinases and protein phosphatases which either positively or negatively regulate the propagation of the signal through the cell. This review summarizes the role of both positive and negative regulators of FcepsilonRI-induced mast cell activation.


title: Innovative Financial Approach for Agricultural Sustainability: A Case Study of Alibaba

abstract: Sustainability and agricultural finance are two important issues attracting attention from industry and academia. This research adopts an in-depth case study methodology to investigate the agricultural finance initiatives of Alibaba Group, and explores how the agricultural finance practices of an e-commerce platform facilitate its sustainability goal. A reference framework is proposed to prove the adoption of agricultural finance. The influence of three moderating variables, namely, IT support, financial attractiveness, and cooperation with other entities, is analyzed. We find that advanced IT support and financial attractiveness are two indispensable enablers for agricultural finance initiatives, and collaboration with other entities is necessary in adopting agricultural supply chain finance.


title: Machine learning methods for turbulence modeling in subsonic flows over airfoils

abstract: In recent years, the data-driven turbulence model has attracted widespread concern in fluid mechanics. The existing approaches modify or supplement the original turbulence model by machine learning based on the experimental/numerical data, in order to augment the capability of the present turbulence models. Different from the previous researches, this paper directly reconstructs a mapping function between the turbulent eddy viscosity and the mean flow variables by neural networks and completely replaces the original partial differential equation model. On the other hand, compared with the machine learning models for the low Reynolds (Re) number flows based on direct numerical simulation data, high Reynolds number flows around airfoils present the apparent scaling effects and strong anisotropy, which induce large challenges in accuracy and generalization capability for the machine learning algorithm. We mainly concentrate on the high Reynolds number turbulent flows around the airfoils and take the results calculated by the computational fluid dynamics solver with the Spallart-Allmaras (SA) model as training data to construct a high-dimensional data-driven network model based on machine learning. The radial basis function neural network and the auxiliary optimization methods are adopted, and the individual models are built separately for the flow fields of the near-wall region, wake region, and far-field region. The training data in this paper is extracted from only three subsonic flow fields of NACA0012 airfoil. The data-driven turbulence model can be applied to various airfoils and flow states, and the predicted eddy viscosity, lift/drag coefficients, and skin friction distributions are all in good agreement with the results of the original SA model. This research demonstrates the promising prospect of machine learning methods in future studies about turbulence modeling.In recent years, the data-driven turbulence model has attracted widespread concern in fluid mechanics. The existing approaches modify or supplement the original turbulence model by machine learning based on the experimental/numerical data, in order to augment the capability of the present turbulence models. Different from the previous researches, this paper directly reconstructs a mapping function between the turbulent eddy viscosity and the mean flow variables by neural networks and completely replaces the original partial differential equation model. On the other hand, compared with the machine learning models for the low Reynolds (Re) number flows based on direct numerical simulation data, high Reynolds number flows around airfoils present the apparent scaling effects and strong anisotropy, which induce large challenges in accuracy and generalization capability for the machine learning algorithm. We mainly concentrate on the high Reynolds number turbulent flows around the airfoils and take the results ...


Framing the Pacific in the 21st century : coexistence and friction


title: Describing Modeling Deltas by Model Transformation

abstract: Since large scaled software models typically exist in many revisions, extraction and representation of differences between versions is a crucial issue of model version systems. While handling model differences is playing an important role in evolution of models, there is a need for appropriate techniques to represent model differences. This paper shows a meta-model-generic and transformation based approach to the representation of model differences. Domain specific languages are generated to represent model differences. Differences are mapped to a set of model transformation rules. To demonstrate the approach, it is applied to activity diagrams.


title: The Embedded Systems Design Challenge ?

abstract: We summarize some current trends in embedded systems design and point out some of their characteristics, such as the chasm between analytical and computational models, and the gap between safety-critical and best-effort engineering practices. We call for a coherent scientific foundation for embedded systems design, and we discuss a few key demands on such a foundation: the need for encompassing several manifestations of heterogeneity, and the need for constructivity in design. We believe that the development of a satisfactory Embedded Systems Design Science provides a timely challenge and opportunity for reinvigorating computer science.


title: Comparison of SOAP and REST Based Web Services Using Software Evaluation Metrics

abstract: The usage of Web services has recently increased. Therefore, it is important to select right type of Web services at the project design stage. The most common implementations are based on SOAP (Simple Object Access Protocol) and REST (Representational State Transfer Protocol) styles. Maintainability of REST and SOAP Web services has become an important issue as popularity of Web services is increasing. Choice of the right approach is not an easy decision since it is influenced by development requirements and maintenance considerations. In the present research, we present the comparison of SOAP and REST based Web services using software evaluation metrics. To achieve this aim, a systematic literature review will be made to compare REST and SOAP Web services in terms of the software evaluation metrics.


title: Grid-Connected Integrated Battery Chargers in Vehicle Applications: Review and New Solution

abstract: For vehicles using grid power to charge the battery, traction circuit components are not engaged during the charging time, so there is a possibility to use them in the charger circuit to have an onboard integrated charger. The battery charger can be galvanically isolated or nonisolated from the grid. Different examples of isolated or nonisolated integrated chargers are reviewed and explained. Moreover, a novel isolated-high-power three-phase battery charger based on a split-phase permanent-magnet motor and its winding configuration is presented in this paper. The proposed charger is a bidirectional high-power charger with a unity power factor operation capability that has high efficiency.


title: Adaptive image contrast enhancement using generalizations of histogram equalization

abstract: This paper proposes a scheme for adaptive image-contrast enhancement based on a generalization of histogram equalization (HE). HE is a useful technique for improving image contrast, but its effect is too severe for many purposes. However, dramatically different results can be obtained with relatively minor modifications. A concise description of adaptive HE is set out, and this framework is used in a discussion of past suggestions for variations on HE. A key feature of this formalism is a "cumulation function," which is used to generate a grey level mapping from the local histogram. By choosing alternative forms of cumulation function one can achieve a wide variety of effects. A specific form is proposed. Through the variation of one or two parameters, the resulting process can produce a range of degrees of contrast enhancement, at one extreme leaving the image unchanged, at another yielding full adaptive equalization.


title: Sensory Processing Abilities of Children Who Have Sustained Traumatic Brain Injuries

abstract: OBJECTIVE. This study describes the sensory processing abilities of children ages 3�10 who sustained a moderate or severe traumatic brain injury (TBI). ::: ::: METHOD. We used a prospective, descriptive study design with convenience sampling of 20 children who were admitted to a pediatric neurosurgical unit. Caregivers provided information regarding their child�s sensory processing abilities using the Sensory Profile (Dunn, 1999). We also collected demographic information related to the mechanism and severity of injury. Data were analyzed descriptively, and summary statistics were used to describe the Sensory Profiles of the children in comparison to normative data. ::: ::: RESULTS. Proportionally more children with TBI than children in the normative sample demonstrated behaviors outside of the typical range in all sections of the Sensory Profile except for oral sensory processing. ::: ::: CONCLUSION. These findings strongly support the need to include evaluation of sensory processing in any clinical assessment of children who have sustained TBI


title: Modified reduced switch symmetrical multilevel inverter

abstract: Multilevel inverters (MLI) are widely used at the range of high voltage and high power for industrial applications. It is because of low harmonic distortion, less electromagnetic interference and upper range of dc link voltages. But the following type of inverter has few disadvantages like difficulty in voltage balancing, higher number of components and complexity in pulse width modulation control method. Objective of this paper is to compare the existing topologies by designing a 7-level modified reduced switch symmetrical MLI (MRSMLI) with five switches and same voltage sources in each level. Simulation and comparison of seven-level seven-switch, seven-level six-switch and seven-level five-switch MLI will be done using MATLAB/SIMULINK environment. The results obtained from a five-switch MLI will be verified with FFT Analysis design of seven-switch MLI. Output obtained will be then utilized for control of an induction motor. Simulation results will be verified by designing hardware setup of the proposed MRSMLI.


Moon IME: Neural-based Chinese Pinyin Aided Input Method with Customizable Association


Syntactic and Lexical Approaches to Reading Comprehension


Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning


title: Single-user MIMO versus multi-user MIMO in distributed antenna systems with limited feedback

abstract: This article investigates the performance of cellular networks employing distributed antennas in addition to the central antennas of the base station. Distributed antennas are likely to be implemented using remote radio units, which is enabled by a low latency and high bandwidth dedicated link to the base station. This facilitates coherent transmission from potentially all available antennas at the same time. Such distributed antenna system (DAS) is an effective way to deal with path loss and large-scale fading in cellular systems. DAS can apply precoding across multiple transmission points to implement single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO) transmission. The throughput performance of various SU-MIMO and MU-MIMO transmission strategies is investigated in this article, employing a Long-Term evolution (LTE) standard compliant simulation framework. The previously theoretically established cell-capacity improvement of MU-MIMO in comparison to SU-MIMO in DASs is confirmed under the practical constraints imposed by the LTE standard, even under the assumption of imperfect channel state information (CSI) at the base station. Because practical systems will use quantized feedback, the performance of different CSI feedback algorithms for DASs is investigated. It is shown that significant gains in the CSI quantization accuracy and in the throughput of especially MU-MIMO systems can be achieved with relatively simple quantization codebook constructions that exploit the available temporal correlation and channel gain differences.


title: Generating Notifications for Missing Actions: Don't Forget to Turn the Lights Off!

abstract: We all have experienced forgetting habitual actions among our daily activities. For example, we probably have forgotten to turn the lights off before leaving a room or turn the stove off after cooking. In this paper, we propose a solution to the problem of issuing notifications on actions that may be missed. This involves learning about interdependencies between actions and being able to predict an ongoing action while segmenting the input video stream. In order to show a proof of concept, we collected a new egocentric dataset, in which people wear a camera while making lattes. We show promising results on the extremely challenging task of issuing correct and timely reminders. We also show that our model reliably segments the actions, while predicting the ongoing one when only a few frames from the beginning of the action are observed. The overall prediction accuracy is 46.2% when only 10 frames of an action are seen (2/3 of a sec). Moreover, the overall recognition and segmentation accuracy is shown to be 72.7% when the whole activity sequence is observed. Finally, the online prediction and segmentation accuracy is 68.3% when the prediction is made at every time step.


title: PaperID: A Technique for Drawing Functional Battery-Free Wireless Interfaces on Paper

abstract: We describe techniques that allow inexpensive, ultra-thin, battery-free Radio Frequency Identification (RFID) tags to be turned into simple paper input devices. We use sensing and signal processing techniques that determine how a tag is being manipulated by the user via an RFID reader and show how tags may be enhanced with a simple set of conductive traces that can be printed on paper, stencil-traced, or even hand-drawn. These traces modify the behavior of contiguous tags to serve as input devices. Our techniques provide the capability to use off-the-shelf RFID tags to sense touch, cover, overlap of tags by conductive or dielectric (insulating) materials, and tag movement trajectories. Paper prototypes can be made functional in seconds. Due to the rapid deployability and low cost of the tags used, we can create a new class of interactive paper devices that are drawn on demand for simple tasks. These capabilities allow new interactive possibilities for pop-up books and other papercraft objects.


title: Interpersonal Relations: A Theoretical Framework for Application in Nursing Practice

abstract: Peplau discusses major features of the theory of interpersonal relations. She describes her theory as among the most useful to apply during nursing practice in order to understand nurse-patient interactive phenomena. Peplau addresses how she derived constructs from clinical data and identified their congruence with nursing practice. She further addresses the specific concepts of her theory and their relations, and specific uses of the theory in practice.


title: muMQ: A lightweight and scalable MQTT broker

abstract: A message broker is an imperative component in IoT systems, and it works as a gateway between IoT devices and application platforms. With the growth of IoT devices today, these systems can easily overwhelm message brokers unless the software can fully utilize hardware resources such as multi-core facility. This paper presents muMQ, a high-performance MQTT broker running on Commercial-Off-The-Shelf hardware. It tackles the challenge to improve the performance of message brokering on a single machine by efficiently utilizing multi-core CPUs. First, muMQ exploits an event-driven I/O mechanism for multi-core scalability. Each CPU core equally handles dispatched TCP connections and locally processes MQTT logic. Second, muMQ adopts a user-level TCP/IP stack, mTCP with DPDK, to avoid the overhead of the in-kernel TCP/IP stack, including system call overhead and resource contention. We evaluate the effectiveness of our approach through experiments. The results show that muMQ can handle 512K or greater long-lived subscribers with no message loss; muMQ achieves a publish messaging rate at 930K messages per second, which is 5.38 times faster than an existing MQTT broker. We also confirm mTCP accelerates the performance by 1.8 times compared with muMQ using the in-kernel TCP/IP stack.


title: Flexible carbon nanotube sensors for nerve agent simulants

abstract: Chemiresistor-based vapour sensors made from network films of single-walled carbon nanotube (SWNT) bundles on flexible plastic substrates (polyethylene terephthalate, PET) can be used to detect chemical warfare agent simulants for the nerve agents Sarin (diisopropyl methylphosphonate, DIMP) and Soman (dimethyl methylphosphonate, DMMP). Large, reproducible resistance changes (75–150%), are observed upon exposure to DIMP or DMMP vapours, and concentrations as low as 25 ppm can be detected. Robust sensor response to simulant vapours is observed even in the presence of large equilibrium concentrations of interferent vapours commonly found in battle-space environments, such as hexane, xylene and water (10 000 ppm each), suggesting that both DIMP and DMMP vapours are capable of selectively displacing other vapours from the walls of the SWNTs. Response to these interferent vapours can be effectively filtered out by using a2 µ mt hickbarrier film of the chemoselective polymer polyisobutylene (PIB )o nt he SWNT surface. These network films are composed of a 1–2 µm thick non-woven mesh of SWNT bundles (15–30 nm diameter), whose sensor response is qualitatively and quantitatively different from previous studies on individual SWNTs, or a network of individual SWNTs, suggesting that vapour sorption at interbundle sites could be playing an important role. This study also shows that the line patterning method used in device fabrication to obtain any desired pattern of films of SWNTs on flexible substrates can be used to rapidly screen simulants at high concentrations before developing more complicated sensor systems. (Some figures in this article are in colour only in the electronic version)


title: An Online Algorithm for Task Offloading in Heterogeneous Mobile Clouds

abstract: Mobile cloud computing is emerging as a promising approach to enrich user experiences at the mobile device end. Computation offloading in a heterogeneous mobile cloud environment has recently drawn increasing attention in research. The computation offloading decision making and tasks scheduling among heterogeneous shared resources in mobile clouds are becoming challenging problems in terms of providing global optimal task response time and energy efficiency. In this article, we address these two problems together in a heterogeneous mobile cloud environment as an optimization problem. Different from conventional distributed computing system scheduling problems, our joint offloading and scheduling optimization problem considers unique contexts of mobile clouds such as wireless network connections and mobile device mobility, which makes the problem more complex. We propose a context-aware mixed integer programming model to provide off-line optimal solutions for making the offloading decisions and scheduling the offloaded tasks among the shared computing resources in heterogeneous mobile clouds. The objective is to minimize the global task completion time (i.e., makespan). To solve the problem in real time, we further propose a deterministic online algorithm—the Online Code Offloading and Scheduling (OCOS) algorithm—based on the rent/buy problem and prove the algorithm is 2-competitive. Performance evaluation results show that the OCOS algorithm can generate schedules that have around two times shorter makespan than conventional independent task scheduling algorithms. Also, it can save around 30% more on makespan of task execution schedules than conventional offloading strategies, and scales well as the number of users grows.


title: Adaptive Dynamic Walking of a Quadruped Robot on Irregular Terrain Based on Biological Concepts

abstract: We have been trying to induce a quadruped robot to walk with medium walking speed on irregular terrain based on biological concepts. We propose the necessary conditions for stable dynamic walking on irregular terrain in general, and we design the mechanical system and the neural system by comparing biological concepts with those necessary conditions described in physical terms. A PD controller at the joints can construct the virtual spring-damper system as the visco-elasticity model of a muscle. The neural system model consists of a central pattern generator (CPG) and reflexes. A CPG receives sensory input and changes the period of its own active phase. The desired angle and P-gain of each joint in the virtual spring-damper system is switched based on the phase signal of the CPG. CPGs, the motion of the virtual spring-damper system of each leg and the rolling motion of the body are mutually entrained through the rolling motion feedback to CPGs, and can generate adaptive walking. We report on our experimen...


title: Memristor based computation-in-memory architecture for data-intensive applications

abstract: One of the most critical challenges for today's and future data-intensive and big-data problems is data storage and analysis. This paper first highlights some challenges of the new born Big Data paradigm and shows that the increase of the data size has already surpassed the capabilities of today's computation architectures suffering from the limited bandwidth, programmability overhead, energy inefficiency, and limited scalability. Thereafter, the paper introduces a new memristor-based architecture for data-intensive applications. The potential of such an architecture in solving data-intensive problems is illustrated by showing its capability to increase the computation efficiency, solving the communication bottleneck, reducing the leakage currents, etc. Finally, the paper discusses why memristor technology is very suitable for the realization of such an architecture; using memristors to implement dual functions (storage and logic) is illustrated.


title: Stock market sentiment lexicon acquisition using microblogging data and statistical measures

abstract: Lexicon acquisition is a key issue for sentiment analysis. This paper presents a novel and fast approach for creating stock market lexicons. The approach is based on statistical measures applied over a vast set of labeled messages from StockTwits, which is a specialized stock market microblog. We compare three adaptations of statistical measures, such as Pointwise Mutual Information (PMI), two new complementary statistics and the use of sentiment scores for affirmative and negated contexts. Using StockTwits, we show that the new lexicons are competitive for measuring investor sentiment when compared with six popular lexicons. We also applied a lexicon to easily produce Twitter investor sentiment indicators and analyzed their correlation with survey sentiment indexes. The new microblogging indicators have a moderate correlation with popular Investors Intelligence (II) and American Association of Individual Investors (AAII) indicators. Thus, the new microblogging approach can be used alternatively to traditional survey indicators with advantages (e.g., cheaper creation, higher frequencies). Proposal of an automatic procedure for the creation of stock market lexicons.The procedure uses diverse statistical measures on StockTwits labeled messages.The new lexicons obtain better investor sentiment indicators than general lexicons.The new Twitter sentiment indicators correlate with survey sentiment indicators.


title: Shape design of products based on a decision support system

abstract: From a historical perspective, two fundamental issues are observed for industrial designers: (1) what is the shape design process within the context of a modern product design process, and (2) how shape design theories, methods, tools and computer aided software can be effectively utilized for creating product shapes. A framework is proposed to resolve the issues by describing the relationships of the product design problems, product design processes, shape design processes, shape design methods and tools with consideration of the functional, ergonomic, emotional and manufacturing requirements. The framework implemented here is a new type of decision support system (DSS) — an object-oriented decision support system to assist the designers in designing product shapes.


SVMs — a practical consequence of learning theory


title: Voice recognition and touch screen control based wheel chair for paraplegic persons

abstract: The wide spread prevalence of lost limbs and sensing system is of major concern in present day due to accident, age and health problems. To assist people with such defects, the proposed intelligent wheelchair system uses dual control for navigation in familiar environments. The two modes of input control to the wheelchair are voice recognition and touch screen. When one want to change the direction, the touch screen sensor is modelled by pressing finger against the various quadrants on the touch screen, which has different values programmed for different direction. This can also be controlled through simple voice commands using voice controller. By storing a single letter in voice recognition kit for each direction control, the recognition time is reduced drastically and thus quick reach to destination is obtained. The wheelchair consists of DC brushless motors at the rear end and it is controlled by using PWM technique. A brake control mechanism is included to control the wheelchair. From previous literature surveys observed, the accuracy of the touch screen was found to be 50%. In this proposed system achievement of wheelchair movement in all direction is obtained with an accuracy of 94.6%. Voice recognition has accuracy of 80.8% which is 30% higher than the study done by Prathyusha et al. This device helps the disabled to have automatic advancement to their destination through predefined paths in the indoor system.


title: SoCIN: a parametric and scalable network-on-chip

abstract: Networks-on-chip (NoCs) interconnection architectures, to be used in future billion-transistor systems-on-chip (SoCs), meet the major communication requirements of these systems, offering, at the same time, reusability, scalability and parallelism in communication. Furthermore, they cope with other issues like power constraints and clock distribution. Currently, there are a number of research works which explore different features of NoCs. In this paper, we present SoCIN, a scalable network based on a parametric router architecture to be used in the synthesis of customized low cost NoCs. The architecture of SoCIN and its router are described, and some synthesis results are presented.


title: Reconstruction-Based Disentanglement for Pose-Invariant Face Recognition

abstract: Deep neural networks (DNNs) trained on large-scale datasets have recently achieved impressive improvements in face recognition. But a persistent challenge remains to develop methods capable of handling large pose variations that are relatively underrepresented in training data. This paper presents a method for learning a feature representation that is invariant to pose, without requiring extensive pose coverage in training data. We first propose to generate non-frontal views from a single frontal face, in order to increase the diversity of training data while preserving accurate facial details that are critical for identity discrimination. Our next contribution is to seek a rich embedding that encodes identity features, as well as non-identity ones such as pose and landmark locations. Finally, we propose a new feature reconstruction metric learning to explicitly disentangle identity and pose, by demanding alignment between the feature reconstructions through various combinations of identity and pose features, which is obtained from two images of the same subject. Experiments on both controlled and in-the-wild face datasets, such as MultiPIE, 300WLP and the profile view database CFP, show that our method consistently outperforms the state-of-the-art, especially on images with large head pose variations. Detail results and resource are referred to this https URL


title: Canonical Correlation Analysis: An Overview with Application to Learning Methods

abstract: We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.


Style Finder: Fine-Grained Clothing Style Recognition and Retrieval


title: Modeling and Characterization of Extensible Wearable Textile-Based Electrogoniometers

abstract: Measuring and monitoring through wearable technology parameters related to human movement, posture, and gesture are gaining momentum because of their wide range of potential applications in daily-life conditions. In previous studies, carbon elastomers (CEs) have been used as strain sensors. Recent developments of CE sensors mathematical modeling demonstrated that the CEs can be used as electrogoniometers. It was proved that for small local curvatures of CE layers, the resistance of a strip constituting a layer depends only on the total curvature of the same layer and not on the particular shape that the sensor keeps in adherence with a surface. Further, it was proved, theoretically and experimentally, that a double-layer configuration provides better accuracy with respect to a single-layer configuration. These results have been obtained under the hypothesis that the device was bent, but not extended. In this paper, we substituted the inextensible insulating layer in the sensors with an elastic one, allowing the system to extend its length. This improvement required further study to make it fit for biomechanical applications following epithelial deformations produced by joint movements and minimizes skin motion artifacts.


title: Confidential Business Process Execution on Blockchain

abstract: One of the main issues in service collaborations among business partners is the possible lack of trust among them. A promising approach to cope with this issue is leveraging on blockchain technology by encoding with smart contracts the business process workflow. This brings the benefits of trust decentralization, transparency, and accountability of the service composition process. However, data in the blockchain are public, implying thus serious consequences on confidentiality and privacy. Moreover, smart contracts can access data outside the blockchain only through Oracles, which might pose new confidentiality risks if no assumptions are made on their trustworthiness. For these reasons, in this paper, we are interested in investigating how to ensure data confidentiality during business process execution on blockchain even in the presence of an untrusted Oracle.


title: VISNU: A Novel Visualization Methodology of Security Events Optimized for a Centralized SOC

abstract: Intrusion detection system(IDS) is one of the most powerful security devices in order to monitor cyber threats happening on the network. Since IDS raises an extremely large number of alerts (hereafter refered to as 'security events'), security experts are unable to analyze all of them in real time. To make matters worse, most of IDS provide only text-based information for the security events. In order to cope with this limitation, many approaches have been proposed on visualizing the security events. Since the existing visualization approaches focus on only a single organization, in many cases, they are not suitable for a centralized security operation center (CSOC) which is in charging of monitoring many organizations. In this paper, we propose a novel visualization VISualization system for finding out Network based Underneath attacks (VISNU) which can help security experts of the CSOC to analyze the security events more effectively. To this end, the VISNU classifies the security events according to each organization and displays them based on both real time and accumulated information such as the appreance patterns and their history, etc. The experimental results demonstrated that it is very useful for finding out an abnormal activites from the security events and provides better understandings and insights for analyzing them.


title: High-Frame-Rate Synthetic Aperture Ultrasound Imaging Using Mismatched Coded Excitation Waveform Engineering: A Feasibility Study

abstract: Mismatched coded excitation (CE) can be employed to increase the frame rate of synthetic aperture ultrasound imaging. The high autocorrelation and low cross correlation (CC) of transmitted signals enables the identification and separation of signal sources at the receiver. Thus, the method provides B-mode imaging with simultaneous transmission from several elements and capability of spatial decoding of the transmitted signals, which makes the imaging process equivalent to consecutive transmissions. Each transmission generates its own image and the combination of all the images results in an image with a high lateral resolution. In this paper, we introduce two different methods for generating multiple mismatched CEs with an identical frequency bandwidth and code length. Therefore, the proposed families of mismatched CEs are able to generate similar resolutions and signal-to-noise ratios. The application of these methods is demonstrated experimentally. Furthermore, several techniques are suggested that can be used to reduce the CC between the mismatched codes.


title: Bilingualism, Theory of Mind and Perspective-Taking: The Effect of Early Bilingual Exposure

abstract: The present study investigates the link between bilingualism, Theory of Mind and empathy among 240 mono- and bilinguals. We hypothesize that bilinguals have better theory of mind capabilities and empathic skills. Possible explanations for this evidence of a bilingual advantage are the early exposure to a second language and/or greater inhibitory control. 240 individuals, divided into two groups, participated in this study: Hungarian-Serbian bilinguals and Hungarian monolinguals. They filled out two questionnaires, one for the language profiles and Davis’s Interpersonal Reactivity Index; and they participated in the Adult Theory of Mind test. Our hypotheses have been proven to be true. Bilinguals really have better empathic skills and the stories in the Theory of Mind test showed the difference in favor of the bilinguals, so we can assume a correlation between bilingualism, theory of mind and empathy, it is a trend and it is important for further upcoming bilingual researches.


title: Analyzing performance of students by using data mining techniques a literature survey

abstract: With the birth of new technologies which can harness data associated with education, the field of Educational Data Mining (EDM) has bloomed. EDM is a research area which uses data mining techniques, machine learning algorithms and statistical techniques to understand how students learn, predict students' academic performance and how a student's learning can be improved. This paper conducts extensive review of the literature on the use of EDM for analyzing performance of student.


title: Style-based inverse kinematics

abstract: This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in real-time. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles.Our style-based IK can replace conventional IK, wherever it is used in computer animation and computer vision. We demonstrate our system in the context of a number of applications: interactive character posing, trajectory keyframing, real-time motion capture with missing markers, and posing from a 2D image.


title: Usability of CAPTCHAs or usability issues in CAPTCHA design

abstract: CAPTCHA is now almost a standard security technology, and has found widespread application in commercial websites. Usability and robustness are two fundamental issues with CAPTCHA, and they often interconnect with each other. This paper discusses usability issues that should be considered and addressed in the design of CAPTCHAs. Some of these issues are intuitive, but some others have subtle implications for robustness (or security). A simple but novel framework for examining CAPTCHA usability is also proposed.


title: Bargaining-based energy trading market for interconnected microgrids

abstract: This paper studies the energy trading among multiple connected microgrids, and analyzes the impacts of such trading on the microgrids' costs. In our model, microgrids with excessive power generations can trade with other microgrids in deficit of power supplies for mutual benefits. We design a bargaining-based energy trading market, where all the interconnected microgrids cooperatively decide the amount of energy trade and the associated payments. We propose a decentralized algorithm to solve the bargaining problem, with minimum information exchange overhead. Numerical studies based on realistic data demonstrate the effectiveness of the bargaining-based energy trading market design, and show that the reduction of total cost of the interconnected-microgrids system can be up to 22% comparing with the case of no trading.


title: Towards Air Quality Estimation Using Collected Multimodal Environmental Data

abstract: This paper presents an open platform, which collects multimodal environmental data related to air quality from several sources including official open sources, social media and citizens. Collecting and fusing different sources of air quality data into a unified air quality indicator is a highly challenging problem, leveraging recent advances in image analysis, open hardware, machine learning and data fusion. The collection of data from multiple sources aims at having complementary information, which is expected to result in increased geographical coverage and temporal granularity of air quality data. This diversity of sources constitutes also the main novelty of the platform presented compared with the existing applications.


title: Arm movement experiments with joint space force fields using an exoskeleton robot

abstract: A new experimental platform permits us to study a novel variety of issues of human motor control, particularly full 3-D movements involving the major seven degrees-of-freedom (DOF) of the human arm. We incorporate a seven DOF robot exoskeleton, and can minimize weight and inertia through gravity, Coriolis, and inertia compensation, such that subjects' arm movements are largely unaffected by the manipulandum. Torque perturbations can be individually applied to any or all seven joints of the human arm, thus creating novel dynamic environments, or force fields, for subjects to respond and adapt to. Our first study investigates a joint space force field where the shoulder velocity drives a disturbing force in the elbow joint. Results demonstrate that subjects learn to compensate for the force field within about 100 trials, and from the strong presence of aftereffects when removing the field in some randomized catch trials, that an inverse dynamics, or internal model, of the force field is formed by the nervous system. Interestingly, while post-learning hand trajectories return to baseline, joint space trajectories remained changed in response to the field, indicating that besides learning a model of the force field, the nervous system also chose to exploit the null space to minimize the effects of the force field on the realization of the endpoint trajectory plan. Further applications for our apparatus include studies in motor system redundancy resolution and inverse kinematics, as well as rehabilitation.


title: Cloud Services vs. On-Premise Solutions Cost Comparison Calculator

abstract: There are different methods that can be used by product managers in order to set the subscription price of cloud services. No matter the methodology used, the price of a cloud service has to be able to stand against the total-cost-of-ownership of the cloud service on-premise equivalent. Calculating the cost difference between a cloud service and its on-premise equivalent can be a valuable tool for both customers and product managers. This paper will try to develop a Cloud vs. On-Premise Cost Comparison Calculator.


title: LipNet: End-to-End Sentence-level Lipreading

abstract: Lipreading is the task of decoding text from the movement of a speaker's mouth. Traditional approaches separated the problem into two stages: designing or learning visual features, and prediction. More recent deep lipreading approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman, 2016a). However, existing work on models trained end-to-end perform only word classification, rather than sentence-level sequence prediction. Studies have shown that human lipreading performance increases for longer words (Easton & Basala, 1982), indicating the importance of features capturing temporal context in an ambiguous communication channel. Motivated by this observation, we present LipNet, a model that maps a variable-length sequence of video frames to text, making use of spatiotemporal convolutions, a recurrent network, and the connectionist temporal classification loss, trained entirely end-to-end. To the best of our knowledge, LipNet is the first end-to-end sentence-level lipreading model that simultaneously learns spatiotemporal visual features and a sequence model. On the GRID corpus, LipNet achieves 95.2% accuracy in sentence-level, overlapped speaker split task, outperforming experienced human lipreaders and the previous 86.4% word-level state-of-the-art accuracy (Gergen et al., 2016).


title: Middle-Status Conformity: Theoretical Restatement and Empirical Demonstration in Two Markets

abstract: This article aims to reestablish the long‐standing conjecture that conformity is high at the middle and low at either end of a status order. On a theoretical level, the article clarifies the basis for expecting such an inverted \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage[OT2,OT1]{fontenc} \newcommand\cyr{ \renewcommand\rmdefault{wncyr} \renewcommand\sfdefault{wncyss} \renewcommand\encodingdefault{OT2} \normalfont \selectfont} \DeclareTextFontCommand{\textcyr}{\cyr} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \landscape $\textsf{U}$\end{document} ‐shaped curve, taking care to specify key scope conditions on the social‐psychological orientations of the actors, the characteristics of the status structure, and the nature of the relevant actions. It also validates the conjecture...


title: A Virtual Garment Design and Simulation System

abstract: In this paper, a 3D graphics environment for virtual garment design and simulation is presented. The proposed system enables the three dimensional construction of a garment from its cloth panels, for which the underlying structure is a mass-spring model. The garment construction process is performed through automatic pattern generation, posterior correction, and seaming. Afterwards, it is possible to do fitting on virtual mannequins as if in a real life tailor's workshop. The system provides the users with the flexibility to design their own garment patterns and make changes on the garment even after the dressing of the model. Furthermore, rendering alternatives for the visualization of knitted and woven fabric are presented.


title: Beyond the Gamepad: HCI and Game Controller Design and Evaluation

abstract: In recent years there has been an increasing amount of computer game focused HCI research, but the impact of controller-related issues on user experience remains relatively unexplored. In this chapter we highlight the limitations of current practices with respect to designing support for both standard and innovative controllers in games. We proceed to explore the use of McNamara and Kirakowski’s (Interactions 13(6):26–28, 2006) theoretical framework of interaction in order to better design and evaluate controller usage in games. Finally, we will present the findings of a case study applying this model to the evaluation and comparison of three different game control techniques: gamepad, keyboard and force feedback steering wheel. This study highlights not only the need for greater understanding of user experience with game controllers, but also the need for parallel research of both functionality and usability in order to understand the interaction as a whole.


title: Raw waveform-based speech enhancement by fully convolutional networks

abstract: This study proposes a fully convolutional network (FCN) model for raw waveform-based speech enhancement. The proposed system performs speech enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which dif-fers from most existing denoising methods that process the magnitude spectrum (e.g., log power spectrum (LPS)) only. Because the fully connected layers, which are involved in deep neural networks (DNN) and convolutional neural networks (CNN), may not accurately characterize the local information of speech signals, particularly with high frequency components, we employed fully convolutional layers to model the waveform. More specifically, FCN consists of only convolutional layers and thus the local temporal structures of speech signals can be efficiently and effectively preserved with relatively few weights. Experimental results show that DNN- and CNN-based models have limited capability to restore high frequency components of waveforms, thus leading to decreased intelligibility of enhanced speech. By contrast, the proposed FCN model can not only effectively recover the waveforms but also outperform the LPS-based DNN baseline in terms of short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ). In addition, the number of model parameters in FCN is approximately only 0.2% compared with that in both DNN and CNN.


title: Implementation of adaptive optics in fluorescent microscopy using wavefront sensing and correction

abstract: Adaptive optics (AO) improves the quality of astronomical imaging systems by using real time measurement of the ::: turbulent medium in the optical path using a guide star (natural or artificial) as a point source reference beacon [1]. AO ::: has also been applied to vision science to improve the view of the human eye. This paper will address our current ::: research focused on the improvement of fluorescent microscopy for biological imaging utilizing current AO technology. ::: A Shack-Hartmann wavefront sensor (SHWS) is used to measure the aberration introduced by a Drosophila ::: Melanogaster embryo with an implanted 1 micron fluorescent bead that serves as a point source reference beacon. ::: Previous measurements of the wavefront aberrations have found an average peak-to-valley and root-mean-square (RMS) ::: wavefront error of 0.77 micrometers and 0.15 micrometers, respectively. Measurements of the Zernike coefficients ::: indicated that the correction of the first 14 Zernike coefficients is sufficient to correct the aberrations we measured. Here ::: we show that a MEMS deformable mirror with 3.5 microns of stroke and 140 actuators is sufficient to correct these ::: aberrations. The design, assembly and initial results for the use of a MEMS deformable mirror, SHWS and implanted ::: fluorescent reference beacon for wavefront correction are discussed.


title: Multi-Agent Reinforcement Learning for Intrusion Detection

abstract: A terminal or the like for insertion into a plated through hole in a printed circuit board. The terminal has a pair of adjacent torsion members which are turned when they are press fit into the printed circuit board hole.


title: Marginalizing stacked linear denoising autoencoders

abstract: Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. They have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized Stacked Linear Denoising Autoencoder (mSLDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSLDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters -- in fact, the linear formulation gives rise to a closed-form solution. Consequently, mSLDA, which can be implemented in only 20 lines of MATLAB™, is about two orders of magnitude faster than a corresponding SDA. Furthermore, the representations learnt by mSLDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks.


title: The IoT: Exciting Possibilities for Bettering Lives: Special application scenarios

abstract: The Internet of Things (IoT) is a Network of Recognizable Physical objects (or things). It includes anyone, anything, any service, and any network connected to the Internet used to retrieve information at any time and from anywhere. The IoT is smart enough to offer us solutions for a wide range of applications, including intelligent transportation systems, personal communications, consumer electronics, health care, organizational services, securities and monitoring, and industrial controls.


title: Handwriting recognition using Cohort of LSTM and lexicon verification with extremely large lexicon

abstract: State-of-the-art methods for handwriting recognition are based on Long Short Term Memory (LSTM) recurrent neural networks (RNN), which now provides very impressive character recognition performance. The character recognition is generally coupled with a lexicon driven decoding process which integrates dictionaries. Unfortunately these dictionaries are limited to hundred of thousands words for the best systems, which prevent from having a good language coverage, and therefore limit the global recognition performance. In this article, we propose an alternative to the lexicon driven decoding process based on a lexicon verification process, coupled with an original cascade architecture. The cascade is made of a large number of complementary networks extracted from a single training (called cohort), making the learning process very light. The proposed method achieves new state-of-the art word recognition performance on the Rimes and IAM databases. Dealing with gigantic lexicon of 3 millions words, the methods also demonstrates interesting performance with a fast decision stage.


Kinematic Analysis and Simulation of a 6-DOF Industrial Manipulator


title: The simulation hybrid fuzzy control of SCARA robot

abstract: This paper presents the simulation of a Hybrid Fuzzy Controller suitable for industrial applications. The developed Hybrid Fuzzy Controller consists of a weighted combination of the Direct Fuzzy Controller and Indirect Fuzzy Controller and a gradually activated supervisory controller. The unique feature of the Hybrid Fuzzy Controller is that no mathematical models of the plant are required and the proposed controller is able to adequately estimate the bound functions on-line, which are required for the determination of the supervisory controller. The supervisor controller in the sense that all signals are bounded guarantees global stability of the closed-loop system. The approach of rapid prototyping is utilized to implement the Hybrid Fuzzy Controller so as to control a Selectively Compliance Assembly Robot Arm. Simulink, an interactive graphical software for simulating dynamic systems, is used to model, simulate and analyse the dynamic system.


title: Nonlinear Dynamics of Memristor Oscillators

abstract: A thorough investigation of the nonlinear dynamics of networks of memristor oscillators is a key step towards the design of systems based upon them, such as neuromorphic circuits and dense nonvolatile memories. A wide gamut of complex dynamic behaviors, including chaos, is observed even in a simple network of memristor oscillators, proposed here as a good candidate for the realization of oscillatory associative and dynamic memories. A detailed study of number and stability of all periodic and nonperiodic oscillations appearing in the network may not leave aside a preliminary deep understanding of the local and global behavior of the basic oscillator. Depending on two bifurcation parameters, controlling memristor nonlinearity, the oscillator exhibits different dynamic behaviors, analyzed here through application of state-of-the-art techniques from the theory of nonlinear dynamics to the oscillator model.


title: Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news

abstract: In this study we evaluate the effectiveness of augmenting numerical market data with textual-news data, using data mining methods, for forecasting stock returns in intraday trading. Integrating these two sources of data not only enriches the information available for the forecasting model, but it can potentially capture joint patterns that may not otherwise be identified when each data source is employed separately. We start with market data and then gradually add various textual data representations, going from simple representations, such as word counts, to more advanced representations involving sentiment analysis. To find the incremental value of each data representation, we build an end-to-end recommendation process including data preprocessing, modeling, validation, trade recommendations and economic evaluation. Each component of the modeling process is optimized to remove human bias and to allow us to impartially compare the results of the various models. Additionally, we experiment with several forecasting algorithms to find the one that yields the ''best'' results according to a variety of performance criteria. We employ data representation procedures and modeling improvements beyond those used in previous related studies. The economic evaluation of the results is conducted using a simulation procedure that inherently accounts for transaction costs and eliminates biases that have potentially affected previous related data-mining studies. This research is one of the largest-scale data-mining studies for evaluating the effectiveness of integrating market data with textual news data for the purpose of stock investment recommendations. The results of our study are promising in that they show that augmenting market data with advanced textual data representation significantly improves stock purchase decisions. Best results are achieved when the approach is implemented with a nonlinear neural network forecasting algorithm.


title: Modern electrical machine analysis and design techniques applied to hybrid vehicle drive machines

abstract: In this paper a review is made of the different analysis and design techniques that can be used in modern electrical machine design, and references them to the developing area of hybrid vehicle drives. These machines are characterized by high transient torque requirement, compactness and forced cooling. While rare-earth magnet machines are commonly used in these applications, there is an increasing interest in motors without magnets. An example is made of the published Toyota Prius permanent-magnet machine specification. An alternative induction motor design is addressed.


title: Structural similarity for document image classification and retrieval

abstract: Abstract This paper presents a novel approach to defining document image structural similarity for the applications of classification and retrieval. We first build a codebook of SURF descriptors extracted from a set of representative training images. We then encode each document and model the spatial relationships between them by recursively partitioning the image and computing histograms of codewords in each partition. A random forest classifier is trained with the resulting features, and used for classification and retrieval. We demonstrate the effectiveness of our approach on table and tax form retrieval, and show that the proposed method outperforms previous approaches even when the training data is limited.


title: Gender Role Portrayal and the Disney Princesses

abstract: The popular Disney Princess line includes nine films (e.g., Snow White, Beauty and the Beast) and over 25,000 marketable products. Gender role depictions of the prince and princess characters were examined with a focus on their behavioral characteristics and climactic outcomes in the films. Results suggest that the prince and princess characters differ in their portrayal of traditionally masculine and feminine characteristics, these gender role portrayals are complex, and trends towards egalitarian gender roles are not linear over time. Content coding analyses demonstrate that all of the movies portray some stereotypical representations of gender, including the most recent film, The Princess and the Frog. Although both the male and female roles have changed over time in the Disney Princess line, the male characters exhibit more androgyny throughout and less change in their gender role portrayals.


title: An automated bacterial colony counting and classification system

abstract: Bacterial colony enumeration is an essential tool for many widely used biomedical assays. However, bacterial colony enumerating is a low throughput, time consuming and labor intensive process since there may exist hundreds or thousands of colonies on a Petri dish, and the counting process is usually manually performed by well-trained technicians. In this paper, we introduce a fully automatic yet cost-effective bacterial colony counter which can not only count but also classify colonies. Our proposed method can recognize chromatic and achromatic images and thus can deal with both color and clear medium. In addition, the proposed method is software-centered and can accept general digital camera images as its input. The counting process includes detecting dish/plate regions, identifying colonies, separating aggregated colonies, and reporting colony counts. In order to differentiate colonies of different species, the proposed counter adopts one-class Support Vector Machine (SVM) with Radial Basis Function (RBF) as the classifier. Our proposed counter demonstrates a promising performance in terms of both precision and recall, and is robust and efficient in terms of labor-and time-savings.


RDMA Reads: To Use or Not to Use?


title: Fast, automatic character animation pipelines

abstract: Humanoid three-dimensional 3D models can be easily acquired through various sources, including through online marketplaces. The use of such models within a game or simulation environment requires human input and intervention in order to associate such a model with a relevant set of motions and control mechanisms. In this paper, we demonstrate a pipeline where humanoid 3D models can be incorporated within seconds into an animation system and infused with a wide range of capabilities, such as locomotion, object manipulation, gazing, speech synthesis and lip syncing. We offer a set of heuristics that can associate arbitrary joint names with canonical ones and describe a fast retargeting algorithm that enables us to instill a set of behaviors onto an arbitrary humanoid skeleton on-the-fly. We believe that such a system will vastly increase the use of 3D interactive characters due to the ease that new models can be animated.Copyright © 2013 John Wiley & Sons, Ltd.


title: Topic-based dissimilarity and sensitivity models for translation rule selection

abstract: Translation rule selection is a task of selecting appropriate translation rules for an ambiguous source-language segment. As translation ambiguities are pervasive in statistical machine translation, we introduce two topic-based models for translation rule selection which incorporates global topic information into translation disambiguation. We associate each synchronous translation rule with source- and target-side topic distributions. With these topic distributions, we propose a topic dissimilarity model to select desirable (less dissimilar) rules by imposing penalties for rules with a large value of dissimilarity of their topic distributions to those of given documents. In order to encourage the use of non-topic specific translation rules, we also present a topic sensitivity model to balance translation rule selection between generic rules and topic-specific rules. Furthermore, we project target-side topic distributions onto the source-side topic model space so that we can benefit from topic information of both the source and target language. We integrate the proposed topic dissimilarity and sensitivity model into hierarchical phrase-based machine translation for synchronous translation rule selection. Experiments show that our topic-based translation rule selection model can substantially improve translation quality.


title: Responses in glabrous skin mechanoreceptors during precision grip in humans

abstract: Impulses in single tactile units innervating the human glabrous skin were recorded percutaneously from the median nerve using tungsten electrodes. The units were classified as belonging to one of the four categories: fast adapting with small receptive fields (FA I), fast adapting with large receptive fields (FA II), slowly adapting with small fields (SA I), and slowly adapting with large fields (SA II). A small test object was lifted, positioned in space and replaced using the precision grip between fingers and thumb. The grip force, the load force (vertical lifting force), the vertical movements of the object and vibrations (accelerations) in the object were recorded. After being virtually silent between lifts, the FA I units whose fields contacted the object became highly active during the initial period of grip force increase (initial response). This was also true for most SA I units. Accordingly, most of the skin deformation changes took place at low grip forces (below ca. 1 N). Later, while the load and grip forces increased in parallel during isometric conditions, the FA I and SA I units continued firing but generally at declining impulse rates. As long as the object was held in the air, the SA I units generally maintained firing with a tendency to adaptation. A minority of the FA I unit also discharged, especially during periods of pronounced physiological muscle tremor. The SA I units usually became silent when the grip and load forces in parallel declined to zero during isometric conditions after the object had contacted the table. However, during the very release of the grip the FA I units and some SA I units showed brief burst discharges (release response). The FA II units responded distinctly to the mechanical transients associated with the start of the vertical movement and especially with the sudden cessation of movement at the terminal table contact. FA II units whose end organs were remotely located in relation to the skin areas in contact with the object also responded. Most FA II units also discharged at the initial touch and at the release of the object, albeit less reliably than the type I units. In addition to weak dynamic responses during the phase of isometric force increase, the SA II units showed comparatively strong tonic responses while the object was held during static conditions. High firing rates also were maintained during long-lasting lifts. Moreover, it was established that the signals in SA II afferents were related to the three dimensional force profile in the grip. The results are discussed with regard to the possible implications for the control of precise manipulative movements.


title: Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods

abstract: We present an algorithm for minimizing a sum of functions that combines the computational efficiency of stochastic gradient descent (SGD) with the second order curvature information leveraged by quasi-Newton methods. We unify these disparate approaches by maintaining an independent Hessian approximation for each contributing function in the sum. We maintain computational tractability and limit memory requirements even for high dimensional optimization problems by storing and manipulating these quadratic approximations in a shared, time evolving, low dimensional subspace. This algorithm contrasts with earlier stochastic second order techniques that treat the Hessian of each contributing function as a noisy approximation to the full Hessian, rather than as a target for direct estimation. Each update step requires only a single contributing function or minibatch evaluation (as in SGD), and each step is scaled using an approximate inverse Hessian and little to no adjustment of hyperparameters is required (as is typical for quasi-Newton methods). We experimentally demonstrate improved convergence on seven diverse optimization problems. The algorithm is released as open source Python and MATLAB packages.


title: A De-compositional Approach to Regular Expression Matching for Network Security Applications

abstract: Regular expressions are a very common tool for network security applications because they can match precisely and maintain high matching speed even for many simultaneous patterns. Their core feature is efficient representation as an automaton, where much of the interaction between patterns can be pre-computed and aggregated. Many algorithms have been devised to try and improve this pre-computation to not take exponential space while keeping high performance, but none has met all the requirements of fast, automated construction, small memory image, and high matching speed. We present Match Filtering, a technique for de-composing regular expressions into segments that can be matched independently, while a stateful post-processing engine filters these matches to eliminate those that do not correspond to matches of the original regular expression. Using standard CPU instructions, the post-processing engine can more efficiently represent constructs that would require a multiplicative increase in automaton states. Because the pre-processing is simple, automaton construction can be automated and fast, and because most on-line processing is done by a DFA, its matching speed is close to that of a DFA alone. We demonstrate experimentally 30x smaller, fast (seconds, not minutes) automaton construction and 43% faster matching speeds than state-of-the-art software algorithms.


title: Deep Learning Approach for Very Similar Objects Recognition Application on Chihuahua and Muffin Problem

abstract: We address the problem to tackle the very similar objects like Chihuahua or muffin problem to recognize at least in human vision level. Our regular deep structured machine learning still does not solve it. We saw many times for about year in our community the problem. Today we proposed the state-of-the-art solution for it. Our approach is quite tricky to get the very high accuracy. We propose the deep transfer learning method which could be tackled all this type of problems not limited to just Chihuahua or muffin problem. It is the best method to train with small data set not like require huge amount data.


title: Brushless DC motor drive based on multi-input DC boost converter supplemented by hybrid PV/FC/battery power system

abstract: This paper describes a novel operation performance of a brushless DC motor (BLDC), fed by a Hybrid Distributed Generation System (HDGS). The HDGS is accompanied with photovoltaic (PV) array, fuel cell stacks and a battery to supply a fixed power for driving a BLDC motor. The HDGS employs a novel multi-input DC boost converter for utilizing DG sources in a unified structure. In this structure each switching cycle of proposed boost converter is divided into five switching periods that introduce five different duty ratios. Utilizing these duty ratios result in regulating the PV/FC/Battery powers. Hence, a fixed electrical power for the BLDC motor is achieved. Due to fixed power capability of the system in different environmental conditions, the BLDC motor will be able to run at its nominal power, particle swarm optimization (PSO) has been used to regulate the PID parameter of speed controller in order to realize better dynamic response. Reducing the torque ripple and improving current control capability of BLDC motor, hysteresis current control with current shaping is applied. The proposed system is suitable in remote areas for applications such as cooling fan, agricultural water pumping systems and related home applications.


title: Matching Natural Language Relations to Knowledge Graph Properties for Question Answering

abstract: Research has seen considerable achievements concerning translation of natural language patterns into formal queries for Question Answering (QA) based on Knowledge Graphs (KG). One of the main challenges in this research area is about how to identify which property within a Knowledge Graph matches the predicate found in a Natural Language (NL) relation. Current approaches for formal query generation attempt to resolve this problem mainly by first retrieving the named entity from the KG together with a list of its predicates, then filtering out one from all the predicates of the entity. We attempt an approach to directly match an NL predicate to KG properties that can be employed within QA pipelines. In this paper, we specify a systematic approach as well as providing a tool that can be employed to solve this task. Our approach models KB relations with their underlying parts of speech, we then enhance this with extra attributes obtained from Wordnet and Dependency parsing characteristics. From a question, we model a similar representation of query relations. We then define distance measurements between the query relation and the properties representations from the KG to identify which property is referred to by the relation within the query. We report substantive recall values and considerable precision from our evaluation.


title: Controlling propagation and coupling of waveguide modes using phase-gradient metasurfaces

abstract: Research on two-dimensional designer optical structures, or metasurfaces, has mainly focused on controlling the wavefronts of light propagating in free space. Here, we show that gradient metasurface structures consisting of phased arrays of plasmonic or dielectric nanoantennas can be used to control guided waves via strong optical scattering at subwavelength intervals. Based on this design principle, we experimentally demonstrate waveguide mode converters, polarization rotators and waveguide devices supporting asymmetric optical power transmission. We also demonstrate all-dielectric on-chip polarization rotators based on phased arrays of Mie resonators with negligible insertion losses. Our gradient metasurfaces can enable small-footprint, broadband and low-loss photonic integrated devices.