title: Model-free preference-based reinforcement learning

abstract: Specifying a numeric reward function for reinforcement learning typically requires a lot of hand-tuning from a human expert. In contrast, preference-based reinforcement learning (PBRL) utilizes only pairwise comparisons between trajectories as a feedback signal, which are often more intuitive to specify. Currently available approaches to PBRL for control problems with continuous state/action spaces require a known or estimated model, which is often not available and hard to learn. In this paper, we integrate preference-based estimation of the reward function into a model-free reinforcement learning (RL) algorithm, resulting in a model-free PBRL algorithm. Our new algorithm is based on Relative Entropy Policy Search (REPS), enabling us to utilize stochastic policies and to directly control the greediness of the policy update. REPS decreases exploration of the policy slowly by limiting the relative entropy of the policy update, which ensures that the algorithm is provided with a versatile set of trajectories, and consequently with informative preferences. The preference-based estimation is computed using a sample-based Bayesian method, which can also estimate the uncertainty of the utility. Additionally, we also compare to a linear solvable approximation, based on inverse RL. We show that both approaches perform favourably to the current state-of-the-art. The overall result is an algorithm that can learn non-parametric continuous action policies from a small number of preferences.


title: GENEFIS: Toward an Effective Localist Network

abstract: Nowadays, there is increasing demand for an integrated system usable to real-time environments under limited computational resources and minimum operator supervision. In contrast, the model is also supposed to actualize high predictive quality in order to confirm the process safety and attractive working framework allowing the user to grasp how the particular task is settled. A holistic concept of a fully data-driven modeling tool namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS) is proposed in this paper. The major spotlight of GENEFIS is in delivering a sensible tradeoff between high predictive accuracy and parsimonious rule base while reckoning tractable rule semantics. The viability of GENEFIS is numerically validated via a series of experimentations using real world and artificial datasets and is compared against state of the art of the evolving neuro-fuzzy systems (ENFSs), where GENEFIS not only showcases higher predictive accuracies but also lands on more frugal structures than other algorithms.


title: CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

abstract: Robust face detection in the wild is one of the ultimate components to support various facial related problems, i.e. unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenarios due to numerous challenges, e.g. heavy facial occlusions, extremely low resolutions, strong illumination, exceptionally pose variations, image or video compression artifacts, etc. In this paper, we present a face detection approach named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN) to robustly solve the problems mentioned above. Similar to the region-based CNNs, our proposed network consists of the region proposal component and the region-of-interest (RoI) detection component. However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. Firstly, the multi-scale information is grouped both in region proposal and RoI detection to deal with tiny face regions. Secondly, our proposed network allows explicit body contextual reasoning in the network inspired from the intuition of human vision system. The proposed approach is benchmarked on two recent challenging face detection databases, i.e. the WIDER FACE Dataset which contains high degree of variability, as well as the Face Detection Dataset and Benchmark (FDDB). The experimental results show that our proposed approach trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE Dataset by a large margin, and consistently achieves competitive results on FDDB against the recent state-of-the-art face detection methods.


title: Evaluating the impact of a targeted brief HIV intervention on multiple inter-related HIV risk factors of knowledge and attitudes among incarcerated drug users

abstract: AbstractIncreases in HIV prevalence indicate ongoing need for HIV interventions. A brief manualized intervention called the Texas Christian University (TCU) WaySafe, which addresses multiple HIV ri...


title: Electro-Tactile Feedback System to Enhance Virtual Reality Experience

abstract: Virtual Reality (VR) has been proven to be capable of enabling its users to experience immersive virtual environments and interact with the computer. Most VR applications utilize stereo visual perception, however, stereo visual perception alone is not enough to fully immerse users in VR environments. Haptic feedback technology when applied to VR applications can offer improved immersion and interactivity and can extend the range of sensations experienced by the user. For example, tactile feedback can enable the user to feel an object they touch with their virtual hand or body. To provide tactile feedback in VR applications most researchers construct cumbersome mechanical actuators and linkages for applying feedback forces or vibrations to the user's real hand or body. Having to wear cumbersome feedback hardware can have the negative effect of making the user more aware of their physical body which can reduce the sense of being immersed in the VR world. To overcome this drawback we propose an alternative feedback system which is comprised of a hand gesture interface, a stereo visual head-set and an electro-tactile feedback system. Our electro-tactile feedback system is compact, simple, inexpensive and capable of delivering a variety of feedback sensations to the user. Experimental results are provided which demonstrate how the electro-tactile feedback system can enhance both the sense of immersion and interactivity for VR users.


title: UiLog: Improving Log-Based Fault Diagnosis by Log Analysis

abstract: In modern computer systems, system event logs have always been the primary source for checking system status. As computer systems become more and more complex, the interaction between software and hardware increases frequently. The components will generate enormous log information, including running reports and fault information. The sheer quantity of data is a great challenge for analysis relying on the manual method. In this paper, we implement a management and analysis system of log information, which can assist system administrators to understand the real-time status of the entire system, classify logs into different fault types, and determine the root cause of the faults. In addition, we improve the existing fault correlation analysis method based on the results of system log classification. We apply the system in a cloud computing environment for evaluation. The results show that our system can classify fault logs automatically and effectively. With the proposed system, administrators can easily detect the root cause of faults.


title: Double CO(2) fixation in photosynthesis-fermentation model enhances algal lipid synthesis for biodiesel production.

abstract: In this study, a photosynthesis-fermentation model was proposed to merge the positive aspects of autotrophs and heterotrophs. Microalga Chlorella protothecoides was grown autotrophically for CO(2) fixation and then metabolized heterotrophically for oil accumulation. Compared to typical heterotrophic metabolism, 69% higher lipid yield on glucose was achieved at the fermentation stage in the photosynthesis-fermentation model. An elementary flux mode study suggested that the enzyme Rubisco-catalyzed CO(2) re-fixation, enhancing carbon efficiency from sugar to oil. This result may explain the higher lipid yield. In this new model, 61.5% less CO(2) was released compared with typical heterotrophic metabolism. Immunoblotting and activity assay further showed that Rubisco functioned in sugar-bleaching cells at the fermentation stage. Overall, the photosynthesis-fermentation model with double CO(2) fixation in both photosynthesis and fermentation stages, enhances carbon conversion ratio of sugar to oil and thus provides an efficient approach for the production of algal lipid.


title: Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems

abstract: This paper proposes a multi agent system that provides visualization and prediction tools for bike sharing systems (BSS). The presented multi-agent system includes an agent that performs data collection and cleaning processes, it is also capable of creating demand forecasting models for each bicycle station. Moreover, the architecture offers API (Application Programming Interface) services and provides a web application for visualization and forecasting. This work aims to make the system generic enough for it to be able to integrate data from different types of bike sharing systems. Thus, in future studies it will be possible to employ the proposed system in different types of bike sharing systems. This article contains a literature review, a section on the process of developing the system and the built-in prediction models. Moreover, a case study which validates the proposed system by implementing it in a public bicycle sharing system in Salamanca, called SalenBici. It also includes an outline of the results and conclusions, a discussion on the challenges encountered in this domain, as well as possibilities for future work.


Machine Learning for Market Microstructure and High Frequency Trading


title: Recognizing Emotion From Facial Expressions: Psychological and Neurological Mechanisms

abstract: Recognizing emotion from facial expressions draws on diverse psychological processes implemented in a large array of neural structures. Studies using evoked potentials, lesions, and functional imaging have begun to elucidate some of the mechanisms. Early perceptual processing of faces draws on cortices in occipital and temporal lobes that construct detailed representations from the configuration of facial features. Subsequent recognition requires a set of structures, including amygdala and orbitofrontal cortex, that links perceptual representations of the face to the generation of knowledge about the emotion signaled, a complex set of mechanisms using multiple strategies. Although recent studies have provided a wealth of detail regarding these mechanisms in the adult human brain, investigations are also being extended to nonhuman primates, to infants, and to patients with psychiatric disorders.


title: Comparison of contractile and extensile pneumatic artificial muscles

abstract: Pneumatic Artificial Muscles (PAMs) are used in robotic and prosthetic applications due to the high power to weight ratio, controllable compliance, and simple design. Contractile PAMs are typically used in traditional hard robotics in place of heavy electric motors. As the field of soft robotics grows, extensile PAMs are beginning to have increased usage. This work experimentally tests, models, and compares contractile and extensile PAMs to demonstrate the advantages and disadvantages of each type of PAM and applications for which they are best suited.


title: Design of Highly Efficient Three-Stage Inverted Doherty Power Amplifier

abstract: This letter reports the highly efficient three-stage inverted Doherty power amplifier (IDPA) using 30 W and 50 W Si LDMOSFETs. The characteristic impedances of the output combiner are derived to achieve high efficiency at a large back-off power (BOP). The output matching networks and offset lines of the carrier and peaking cells are used to modulate the load impedance. The transmission line in the input path of the carrier cell is inserted to adjust the delay among the carrier and peaking cells. The drain efficiency (DE) of 40.3% with a gain of 9 dB is achieved at output power of 42 dBm (8.5 dB BOP) and the DE above 40% is maintained in wide output range for a 2.14 GHz continuous wave signal. For a one-carrier WCDMA signal at an output power of 40 dBm (10.5 dB BOP), the DE of 35% with the gain of 9.2 dB is achieved.


title: Time-parameterized queries in spatio-temporal databases

abstract: Time-parameterized queries (TP queries for short) retrieve (i) the actual result at the time that the query is issued, (ii) the validity period of the result given the current motion of the query and the database objects, and (iii) the change that causes the expiration of the result. Due to the highly dynamic nature of several spatio-temporal applications, TP queries are important both as standalone methods, as well as building blocks of more complex operations. However, little work has been done towards their efficient processing. In this paper, we propose a general framework that covers time-parameterized variations of the most common spatial queries, namely window queries, k-nearest neighbors and spatial joins. In particular, each of these TP queries is reduced to nearest neighbor search where the distance functions are defined according to the query type. This reduction allows the application and extension of well-known branch and bound techniques to the current problem. The proposed methods can be applied with mobile queries, mobile objects or both, given a suitable indexing method. Our experimental evaluation is based on R-trees and their extensions for dynamic objects.


title: Deep Variational Reinforcement Learning for POMDPs

abstract: Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past.


title: Few-Shot Segmentation Propagation with Guided Networks

abstract: Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors. To remedy the rigidity and annotation burden of standard approaches, we address the problem of few-shot segmentation: given few image and few pixel supervision, segment any images accordingly. We propose guided networks, which extract a latent task representation from any amount of supervision, and optimize our architecture end-to-end for fast, accurate few-shot segmentation. Our method can switch tasks without further optimization and quickly update when given more guidance. We report the first results for segmentation from one pixel per concept and show real-time interactive video segmentation. Our unified approach propagates pixel annotations across space for interactive segmentation, across time for video segmentation, and across scenes for semantic segmentation. Our guided segmentor is state-of-the-art in accuracy for the amount of annotation and time. See http://github.com/shelhamer/revolver for code, models, and more details.


title: Variational Implicit Processes

abstract: This paper introduces the variational implicit processes (VIPs), a Bayesian nonparametric method based on a class of highly flexible priors over functions. Similar to Gaussian processes (GPs), in implicit processes (IPs), an implicit multivariate prior (data simulators, Bayesian neural networks, etc.) is placed over any finite collections of random variables. A novel and efficient variational inference algorithm for IPs is derived using wake-sleep updates, which gives analytic solutions and allows scalable hyper-parameter learning with stochastic optimization. Experiments on real-world regression datasets demonstrate that VIPs return better uncertainty estimates and superior performance over existing inference methods for GPs and Bayesian neural networks. With a Bayesian LSTM as the implicit prior, the proposed approach achieves state-of-the-art results on predicting power conversion efficiency of molecules based on raw chemical formulas.


title: The effects of Zumba training on cardiovascular and neuromuscular function in female college students

abstract: AbstractThe present study examined the effects of Zumba training (group fitness based on salsa and aerobics) on endurance, trunk strength, balance, flexibility, jumping performance and quality of life (QoL) in female college students. Thirty female participants were randomly assigned (strata: age, BMI and physical activity) to an intervention (INT, n = 15: age: 21.0 ± 2.3 years; BMI: 21.8 ± 3.0 kg/m2; physical activity (PA): 7.6 ± 4.6 h/week) or control group (CON, n = 14: age: 21.0 ± 2.8 years; BMI: 21.0 ± 2.1 kg/m2; PA: 7.3 ± 3.6 h/week). Instructed Zumba training was provided twice a week for 8 weeks (training attendance: 100%). QoL was assessed using the WHO-QoL-BREF questionnaire. Endurance was measured with the 6-min walking test (6MWT). Trunk flexibility was assessed with the stand-and-reach-test and lower-extremity strength with the jump-and-reach-test. The star excursion balance test (SEBT) was employed to assess dynamic balance. Trunk strength endurance was examined using the Swiss global trunk ...


title: Prediction of the cause of accident and accident prone location on roads using data mining techniques

abstract: Road Accident is a specific instance of traumatic events that constitute major loss. Data mining tools and techniques are used to predict the likelihood of accident and accident prone locations. This paper sheds light on predicting the probability of accidents on roads with special emphasis on STATE HIGHWAYS (SHs) and ORDINARY DISTRICT ROADS (ODRs) by estimating the severity of accidents based on the type of accident, type of spot using the R tool. Pointing out the traffic collision data of roads the frequency of traffic collision of roads is analyzed using correlation analysis and exploratory visualization techniques. Finally, methodology has been proposed to analyze road traffic accidents. Using this methodology, improvement can be promised at the level and extent of road traffic safety management effectively and efficiently. The present study modeled accident and incident data gathered from the traffic data and data related to construction sectors.


title: A Cochlear Cell Line as an in vitro System for Drug Ototoxicity Screening

abstract: Aminoglycoside antibiotics, loop diuretics, antineoplastic agents and other commonly used pharmacological drugs are ototoxic. Understanding of the cellular and molecular mechanisms underlying drug ototoxicity, however, has been hampered by the limited availability of inner ear tissues and drug side effects on laboratory animals. Immortalized cell lines derived from the auditory sensory organ, sensitive to ototoxic drugs and growing in environments that can be systematically manipulated, would facilitate the research directed at elucidating these mechanisms. Such immortalized cell lines could also be used to discover novel therapeutic agents for preventing drug-induced sensorineural hearing loss. Here, we report a conditionally immortalized organ of Corti-derived epithelial cell line, which shows evidence of activation of apoptosis when exposed to known ototoxic drugs. This cell line may be an excellent in vitro system to investigate the cellular and molecular mechanisms involved in ototoxicity and for screening of the potential ototoxicity or otoprotective properties of new pharmacological drugs.


Acoustic Word Embeddings for ASR Error Detection.


title: Point Light Source Position Estimation From RGB-D Images by Learning Surface Attributes

abstract: Light source position (LSP) estimation is a difficult yet an important problem in computer vision. A common approach for estimating the LSP assumes Lambert's law. However, in real-world scenes, Lambert's law does not hold for all different types of surfaces. Instead of assuming all that surfaces follow Lambert's law, our approach classifies image surface segments based on their photometric and geometric surface attributes (i.e. glossy, matte, curved, and so on) and assigns weights to image surface segments based on their suitability for LSP estimation. In addition, we propose the use of the estimated camera pose to globally constrain LSP for RGB-D video sequences. Experiments on Boom and a newly collected RGB-D video data sets show that the state-of-the-art methods are outperformed by the proposed method. The results demonstrate that weighting image surface segments based on their attributes outperform the state-of-the-art methods in which the image surface segments are considered to equally contribute. In particular, by using the proposed surface weighting, the angular error for LSP estimation is reduced from 12.6° to 8.2° and 24.6° to 4.8° for Boom and RGB-D video data sets, respectively. Moreover, using the camera pose to globally constrain LSP provides higher accuracy (4.8°) compared with using single frames (8.5°).


title: Structure-antioxidant activity relationships of flavonoids and phenolic acids

abstract: Abstract The recent explosion of interest in the bioactivity of the the flavonoids of higher plants is due, at least in part, to the potential health benefits of these polyphenolic components of major dietary constituents. This review article discusses the biological properties of the flavonoids and focuses on the relationship between their antioxidant activity, as hydrogen donating free radical scavengers, and their chemical structures. This culminates in a proposed hierarchy of antioxidant activity in the aqueous phase. The cumulative findings concerning structure-antioxidant activity relationships in the lipophilic phase derive from studies on fatty acids, liposomes, and low-density lipoproteins; the factors underlying the influence of the different classes of polyphenols in enhancing their resistance to oxidation are discussed and support the contention that the partition coefficients of the flavonoids as well as their rates of reaction with the relevant radicals define the antioxidant activities in the lipophilic phase.


A Disposable Haptic Palpation Probe for Locating Subcutaneous Blood Vessels in Robot-Assisted Minimally Invasive Surgery


title: A new algorithm to correct fish-eye- and strong wide-angle-lens-distortion from single images

abstract: The use of super-wide angle and fish-eye lenses causes strong distortions in the resulting images. A methodology for the correction of distortions in these cases using only single images and linearity of imaged objects is presented. Contrary to most former algorithms, the algorithm discussed here does not depend on information about the real world co-ordinates of matching points. Moreover reference points determination and camera calibration is not required in this case. The algorithm is based on circle fitting. It requires only the possibility of the extraction of distorted image points from straight lines in the 3D scene. Further, the actual distortion must approximately fit the chosen distortion model. For most fish-eye lenses appropriate distortion correction results can be obtained.


title: Political Economy of Foreign Direct Investment: Globalized Production in the Twenty-First Century

abstract: This article reviews scholarly research on the political economy of foreign direct investment (FDI) over the past 20 years. FDI research during this period reflects FDI's rapid growth, particularly in developing countries, and the emergence of intense competition among countries to attract investments. Countries have grown more open to FDI as evidenced by FDI deregulation, generous financial investment incentives, and the adoption of international agreements. Although extensive research shows that multinational corporations prefer to invest in countries with strong property rights protections, whether incentives and international agreements help countries attract FDI remains contested. Scholars can advance research by disaggregating FDI into multinational firms' specific production activities because the scope for countries to compete for FDI and firms' sensitivity to property rights vary widely. More generally, scholars should recast the separate study of trade and FDI into the study of global production...


title: Morphological study of cephalothoracopagus deradelphus type conjoined twins. A case report.

abstract: Cephalopagus is a rare variety of conjoined twins. They are fused with their heads, thoracic and upper abdominal cavities. The exact mechanism for development of conjoined twins cannot be clearly explained. It appears that there is an alteration in the normal developmental process of monozygotic twins, which fail to separate from each other. We present the morphology of a cephalothoracopagus, revealed through anatomical dissection, emphasizing the arrangement of the viscera in the thoracic and abdominal cavities. They are fused with their heads, thoracic and upper abdominal cavities. The lower abdomen and pelvic cavities are free. Each twin has two upper and lower limbs, normally shaped. Each twin has a heart and two lungs. There is a single pharynx, esophagus and stomach, but normal lower abdominal systems. The genital and urinary systems are apparently normal. Due to the fusion of the heads and abnormal arrangement of the superior central nervous system, surgery is not attempted in these cases, the prognosis being very poor.


title: Integrating spatial and temporal contexts into a factorization model for POI recommendation

abstract: ABSTRACT Matrix factorization is one of the most popular methods in recommendation systems. However, it faces two challenges related to the check-in data in point of interest (POI) recommendation: data scarcity and implicit feedback. To solve these problems, we propose a Feature-Space Separated Factorization Model (FSS-FM) in this paper. The model represents the POI feature spaces as separate slices, each of which represents a type of feature. Thus, spatial and temporal information and other contexts can be easily added to compensate for scarce data. Moreover, two commonly used objective functions for the factorization model, the weighted least squares and pairwise ranking functions, are combined to construct a hybrid optimization function. Extensive experiments are conducted on two real-life data sets: Gowalla and Foursquare, and the results are compared with those of baseline methods to evaluate the model. The results suggest that the FSS-FM performs better than state-of-the-art methods in terms of precision and recall on both data sets. The model with separate feature spaces can improve the performance of recommendation. The inclusion of spatial and temporal contexts further leverages the performance, and the spatial context is more influential than the temporal context. In addition, the capacity of hybrid optimization in improving POI recommendation is demonstrated.


title: Fashion Analysis: Current Techniques and Future Directions

abstract: Driven by the huge profit potential in the fashion industry, intelligent fashion analysis based on techniques for clothing and makeover analysis is receiving much attention in the multimedia and computer vision literature. This article surveys the state-of-the-art clothing analysis techniques (clothing modeling, recognition, and parsing) that can be applied in many real applications, such as clothing retrieval and recommendation. The authors then introduce several makeover-related research directions, such as facial attractiveness prediction, facial makeup synthesis, and hair segmentation. Lastly, they discuss promising future directions for clothing and makeover analysis.


title: Is Denoising Dead?

abstract: Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable. A pertinent question then to ask is whether there is a theoretical limit to denoising performance and, more importantly, are we there yet? As camera manufacturers continue to pack increasing numbers of pixels per unit area, an increase in noise sensitivity manifests itself in the form of a noisier image. We study the performance bounds for the image denoising problem. Our work in this paper estimates a lower bound on the mean squared error of the denoised result and compares the performance of current state-of-the-art denoising methods with this bound. We show that despite the phenomenal recent progress in the quality of denoising algorithms, some room for improvement still remains for a wide class of general images, and at certain signal-to-noise levels. Therefore, image denoising is not dead--yet.


title: Public Key Compression and Modulus Switching for Fully Homomorphic Encryption over the Integers

abstract: We describe a compression technique that reduces the public key size of van Dijk, Gentry, Halevi and Vaikuntanathan's (DGHV) fully homomorphic scheme over the integers from O(λ7) to O(λ5). Our variant remains semantically secure, but in the random oracle model. We obtain an implementation of the full scheme with a 10.1 MB public key instead of 802 MB using similar parameters as in [7]. Additionally we show how to extend the quadratic encryption technique of [7] to higher degrees, to obtain a shorter public-key for the basic scheme. ::: ::: This paper also describes a new modulus switching technique for the DGHV scheme that enables to use the new FHE framework without bootstrapping from Brakerski, Gentry and Vaikuntanathan with the DGHV scheme. Finally we describe an improved attack against the Approximate GCD Problem on which the DGHV scheme is based, with complexity O(2ρ) instead of O(23ρ/2).


title: Simulation Analysis of Tree and Mesh Topologies in Zigbee Network

abstract: Recently wireless sensor Network (WSN) and wireless personal area network (WPAN) has gained lot of interest from the research community because of its diverse applications of controlling, monitoring and automation of home, offices or any rescue area. Zigbee based on IEEE 802.15.4 standard is key enabling technology for the success of both WSN and WPAN. Zigbee has some inherent powerful characteristics like very low power consumption, localization and low cost that has drawn the attention of research community recently. Along with merits come the challenges for the success of Zigbee based networks at various layers of the network model. Network topologies in Zigbee are to be selected based on applications and performance requirements. So this paper investigates the performance of Zigbee for tree and mesh topologies. In doing so parameters like MAC throughput, MAC load, MAC delay, end to end delay have been evaluated through extensive simulations using OPNET. The intricate behavior of these topologies in Zigbee shows optimum bounds of performance for these topologies have been computed in this work


title: Streaming graph partitioning for large distributed graphs

abstract: Extracting knowledge by performing computations on graphs is becoming increasingly challenging as graphs grow in size. A standard approach distributes the graph over a cluster of nodes, but performing computations on a distributed graph is expensive if large amount of data have to be moved. Without partitioning the graph, communication quickly becomes a limiting factor in scaling the system up. Existing graph partitioning heuristics incur high computation and communication cost on large graphs, sometimes as high as the future computation itself. Observing that the graph has to be loaded into the cluster, we ask if the partitioning can be done at the same time with a lightweight streaming algorithm. We propose natural, simple heuristics and compare their performance to hashing and METIS, a fast, offline heuristic. We show on a large collection of graph datasets that our heuristics are a significant improvement, with the best obtaining an average gain of 76%. The heuristics are scalable in the size of the graphs and the number of partitions. Using our streaming partitioning methods, we are able to speed up PageRank computations on Spark, a distributed computation system, by 18% to 39% for large social networks.


title: Potentials and limits of high-speed PM motors

abstract: This paper illustrates potentials and limits of high-speed permanent-magnet (PM) motors. The influence of materials chosen for PM, stator core, and retaining sleeve is highlighted. Slotted and slotless topologies are considered and compared, computing magnetic, electrical, mechanical, and thermal quantities by means of both analytical and finite-element approach. Thermal and PM demagnetization limits as well as rotor losses are evaluated. A criteria of optimization of the motor structure is described, with the diameter ratio and the iron flux density as main design variables.


title: Computations underlying sensorimotor learning

abstract: The study of sensorimotor learning has a long history. With the advent of innovative techniques for studying learning at the behavioral and computational levels new insights have been gained in recent years into how the sensorimotor system acquires, retains, represents, retrieves and forgets sensorimotor tasks. In this review we highlight recent advances in the field of sensorimotor learning from a computational perspective. We focus on studies in which computational models are used to elucidate basic mechanisms underlying adaptation and skill acquisition in human behavior.


title: BabyOS: a fresh start

abstract: In this paper, we present BabyOS, a compact educational kernel designed to facilitate the preliminary learning of operating systs. It is an in-mory syst running on real hardware, with a multi-phase project covering concurrency, scheduling, inter-process communication, mory management, and networking. Compared with other educational operating systs, BabyOS is a nontraditional project frame; an important distinction lies in that it reflects some design ideas in bedded operating systs. In this way, BabyOS provides a complentary approach to educational operating syst projects.


title: Classification and Generation of Grammatical Errors

abstract: The misuse of grammar is a common and natural nuisance, and a strategy for automatically detecting mistakes in grammatical syntax is warranted. This research defines and implements a unique approach that combines machine-learning and statistical natural language processing techniques. Several important methods are established: (1) the automated and systematic generation of grammatical errors and parallel error corpora; (2) the definition and extraction of over 150 features of a sentence; and (3) the application of various machine-learning classification algorithms on extracted feature data, in order to classify and predict the presence of grammatical errors in a sentence.


title: Over-optimization of academic publishing metrics: observing Goodhart’s Law in action

abstract: BACKGROUND ::: The academic publishing world is changing significantly, with ever-growing numbers of publications each year and shifting publishing patterns. However, the metrics used to measure academic success, such as the number of publications, citation number, and impact factor, have not changed for decades. Moreover, recent studies indicate that these metrics have become targets and follow Goodhart's Law, according to which, "when a measure becomes a target, it ceases to be a good measure." ::: ::: ::: RESULTS ::: In this study, we analyzed >120 million papers to examine how the academic publishing world has evolved over the last century, with a deeper look into the specific field of biology. Our study shows that the validity of citation-based measures is being compromised and their usefulness is lessening. In particular, the number of publications has ceased to be a good metric as a result of longer author lists, shorter papers, and surging publication numbers. Citation-based metrics, such citation number and h-index, are likewise affected by the flood of papers, self-citations, and lengthy reference lists. Measures such as a journal's impact factor have also ceased to be good metrics due to the soaring numbers of papers that are published in top journals, particularly from the same pool of authors. Moreover, by analyzing properties of >2,600 research fields, we observed that citation-based metrics are not beneficial for comparing researchers in different fields, or even in the same department. ::: ::: ::: CONCLUSIONS ::: Academic publishing has changed considerably; now we need to reconsider how we measure success.


A Review And Evaluations Of Shortest Path Algorithms


title: Binary Black Holes Algorithm

abstract: In this paper we adapt black holes optimization algorithms for binary search spaces by applying a sigmoid transformation to the gravity and electrical forces. Black holes algorithm is a Swarm Algorithm inspired of Black Holes for Optimization Problems. We supposes each solution of problem as a binary black hole and after calculating the gravity and electrical forces use a mapping function for set it. The proposed method is verified using several benchmark problems used in the area of optimization. The experimental results on different benchmarks show that the performance of the proposed algorithm is better than BPSO (Binary Particle Swarms Optimization), BAFS (Binary Artificial Fish Swarm Algorithm) and GA (Genetic Algorithm). General Terms Artificial intelligence, Swarm algorithms, Black holes algorithm.


title: Analysis and Modeling of Threshold Voltage Mismatch for CMOS at 65 nm and Beyond

abstract: This letter investigates random dopant fluctuation transistor mismatch. The dominance of the halo implant is demonstrated experimentally and with simulation, and a compact model form is developed for improved representation of the phenomenon.


title: Permittivity and Loss Tangent Characterization for Garment Antennas Based on a New Matrix-Pencil Two-Line Method

abstract: The emergence of wearable antennas to be integrated into garments has revealed the need for a careful electromagnetic characterization of textile materials. Therefore, we propose in this paper a new matrix-pencil two-line method that removes perturbations in the calculated effective permittivity and loss tangent which are caused by imperfect deembedding and inhomogeneities of the textile microstrip line structure. The approach has been rigorously validated for high-frequency laminates by comparing measured and simulated data for the resonance frequency of antennas designed using the calculated parameters. The method has been successfully applied to characterize the permittivity and loss tangent of a variety of textile materials and up to a frequency of 10 GHz. Furthermore it is shown that the use of electrotextiles in antenna design influences the effective substrate permittivity.


title: KSF-OABE: Outsourced Attribute-Based Encryption with Keyword Search Function for Cloud Storage

abstract: Cloud computing becomes increasingly popular for data owners to outsource their data to public cloud servers while allowing intended data users to retrieve these data stored in cloud. This kind of computing model brings challenges to the security and privacy of data stored in cloud. Attribute-based encryption (ABE) technology has been used to design fine-grained access control system, which provides one good method to solve the security issues in cloud setting. However, the computation cost and ciphertext size in most ABE schemes grow with the complexity of the access policy. Outsourced ABE (OABE) with fine-grained access control system can largely reduce the computation cost for users who want to access encrypted data stored in cloud by outsourcing the heavy computation to cloud service provider (CSP). However, as the amount of encrypted files stored in cloud is becoming very huge, which will hinder efficient query processing. To deal with above problem, we present a new cryptographic primitive called attribute-based encryption scheme with outsourcing key-issuing and outsourcing decryption, which can implement keyword search function (KSF-OABE). The proposed KSF-OABE scheme is proved secure against chosen-plaintext attack (CPA). CSP performs partial decryption task delegated by data user without knowing anything about the plaintext. Moreover, the CSP can perform encrypted keyword search without knowing anything about the keywords embedded in trapdoor.


A Content-Based User Model Generation and Optimization Approach for Movie Recommendation


title: Application of RRT-based local Path Planning Algorithm in Unknown Environment

abstract: Path planning for mobile robots has received a great deal of attention over the past two decades. The basic rapidly-exploring random tree (RRT) algorithm is famous in this field because it excels at exploring free space in the large environments and it is parallelizable. In this paper, we applied modified RRT algorithm to local navigation of a mobile robot in unknown environment. The biased direction from RRT algorithm is used to reduce the nodes and therefore the amount of calculation is decreased for real-time computation. We conducted the simulation and simple real-time experiments to verify the proposed method in real unknown environment.


Angulated Styloid Process-A Case Report


DPSN ET : E ND-TO-END D EEP P LANE S WEEP S TEREO


title: Telefetalcare: A first prototype of a wearable fetal electrocardiograph

abstract: Fetal heart rate monitoring is fundamental to infer information about fetal health state during pregnancy. The cardiotocography (CTG) is the most common antepartum monitoring technique. Abdominal ECG recording represents the most valuable alternative to cardiotocography, as it allows passive, non invasive and long term fetal monitoring. Unluckily fetal ECG has low SNR and needs to be extracted from abdominal recordings using ad hoc algorithms. This work describes a prototype of a wearable fetal ECG electrocardiograph. The system has flat band frequency response between 1–60Hz and guarantees good signal quality. It was tested on pregnant women between the 30th and 34th gestational week. Several electrodes configurations were tested, in order to identify the best solution. Implementation of a simple algorithm for FECG extraction permitted the reliable detection of maternal and fetal QRS complexes. The system will allow continuative and deep screening of fetal heart rate, introducing the possibility of home fetal monitoring.


Software Cost Estimation


title: A stacked SONOS technology, up to 4 levels and 6nm crystalline nanowires, with Gate-All-Around or independent gates (φ-Flash), suitable for full 3D integration

abstract: We present the first experimental study of a Gate-All-Around (GAA) SONOS memory architecture with 4-level crystalline nanowire channels (down to 6nm-diameter). The technology is also extended to an independent double gate memory architecture, called φ-Flash. The experimental results with 6nm nanowires show high programming windows (up to 7.4V), making the structure compatible with multilevel operation. Excellent retention even after 104 cycles is achieved. The independent double gate option has otherwise been successfully integrated with 4-level stacked nanowires for multibit applications. The φ-Flash exhibits up to 1.8V ΔV Th between its two gates, demonstrating multibits operation. The basic process to fully disconnect the different nanowires in view of a full 3D integration of a memory array is discussed.


Modeling and Simulation Using Circular Spiral Antenna Array for RF Harvesting


title: Flocculation improves uptake of 90Sr and 137Cs from radioactive effluents

abstract: Abstract This paper presents the results of investigation on uptake of radioactive species 90 Sr and 137 Cs present in the liquid effluents from nuclear processing plants. Chemical precipitation process is adopted to remove radioactivity from the effluents with low and intermediate level of activity. In this process, radioactive 90 Sr and 137 Cs are co-precipitated along with copper ferrocyanide, ferric hydroxide and either calcium phosphate or barium sulphate. These precipitates being fine in size require flocculation for enhanced settling rate in clarifier/thickener. The flocculation by some selected high molecular weight polyacrylamide based polymers has improved the uptake of radioactive metal ions. The adsorption of these radioactive species has been found to increase in the presence of these flocculants thereby improving the decontamination factor (DF). While flocculating the precipitates, there may be some complex formation with Sr 2+ /Cs + , flocculant and the substrate. This has enhanced the uptake of the radioactive metal ions from the liquid component. The plant trials have indicated the improvement of DF value due to flocculation by cationic flocculant.


title: Primal-dual subgradient methods for convex problems

abstract: In this paper we present a new approach for constructing subgradient schemes for different types of nonsmooth problems with convex structure. Our methods are primal-dual since they are always able to generate a feasible approximation to the optimum of an appropriately formulated dual problem. Besides other advantages, this useful feature provides the methods with a reliable stopping criterion. The proposed schemes differ from the classical approaches (divergent series methods, mirror descent methods) by presence of two control sequences. The first sequence is responsible for aggregating the support functions in the dual space, and the second one establishes a dynamically updated scale between the primal and dual spaces. This additional flexibility allows to guarantee a boundedness of the sequence of primal test points even in the case of unbounded feasible set (however, we always assume the uniform boundedness of subgradients). We present the variants of subgradient schemes for nonsmooth convex minimization, minimax problems, saddle point problems, variational inequalities, and stochastic optimization. In all situations our methods are proved to be optimal from the view point of worst-case black-box lower complexity bounds.


Identifying Individual Differences in Gender, Ethnicity, and Personality from Dialogue for Deception Detection


title: HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management

abstract: Radio spectrum resource is of fundamental importance for wireless communication. Recent reports show that most available spectrum has been allocated. While some of the spectrum bands (e.g., unlicensed band, GSM band) have seen increasingly crowded usage, most of the other spectrum resources are underutilized. This drives the emergence of open spectrum and dynamic spectrum access concepts, which allow unlicensed users equipped with cognitive radios to opportunistically access the spectrum not used by primary users. Cognitive radio has many advanced features, such as agilely sensing the existence of primary users and utilizing multiple spectrum bands simultaneously. However, in practice such capabilities are constrained by hardware cost. In this paper, we discuss how to conduct efficient spectrum management in ad hoc cognitive radio networks while taking the hardware constraints (e.g., single radio, partial spectrum sensing and spectrum aggregation limit) into consideration. A hardware-constrained cognitive MAC, HC-MAC, is proposed to conduct efficient spectrum sensing and spectrum access decision. We identify the issue of optimal spectrum sensing decision for a single secondary transmission pair, and formulate it as an optimal stopping problem. A decentralized MAC protocol is then proposed for the ad hoc cognitive radio networks. Simulation results are presented to demonstrate the effectiveness of our proposed protocol.


title: Rapid modulation of human cortical motor outputs following ischaemic nerve block.

abstract: The amplitudes of motor evoked potentials to transcranial magnetic stimulation from muscles immediately proximal to a temporarily anaesthetized (Bier's block) human forearm increase in minutes after the onset of anaesthesia and return to control values after the anaesthesia subsides. In order to determine the level at which the early modulation of human motor outputs takes place, we recorded maximal H reflexes, peripheral M responses, motor evoked potentials to transcranial magnetic stimulation, and motor evoked potentials to transcranial electrical stimulation and spinal electrical stimulation from a muscle immediately proximal to a limb segment made ischaemic by a pneumatic tourniquet. The amplitudes of motor evoked potentials to transcranial magnetic stimulation, but not to transcranial electrical stimulation and spinal electrical stimulation, were larger during ischaemia, implying that the site of change was in the motor cortex. The maximal H/M ratios were unaffected by ischaemia, indicating that alpha-motor neuron excitability to segmental Ia inputs remained unchanged. The map of cortical representation areas for this muscle obtained with transcranial magnetic stimulation was also enlarged. Taken together, our findings suggest that the temporary removal by ischaemic nerve block of myelinated afferent inputs reduces inhibition at the motor cortical level and that this disinhibition is responsible for the increased excitability of the corticospinal system.


title: A Graph-Based Vehicle Proposal Location and Detection Algorithm

abstract: The majority of the existing appearance-based vehicle-detection systems make use of a sliding-window paradigm for vehicle-candidate regions location. In order to locate all vehicle regions with various sizes and shapes, a large number of search windows are generated by a sliding-window paradigm in most vehicle-detection systems. It is desirable to obtain fewer and more precisely located vehicle candidate regions for further detection. For this purpose, a novel graph-based algorithm is proposed to locate the vehicle proposal regions, which estimates the possibility of a vehicle contained in a bounding box. Experimental results on the public traffic analysis data set (KITTI) and PASCAL VOC 2007 show that the proposed region proposal approach leads to better performances compared with popular bottom-up region proposal methods. Moreover, the proposed vehicle-detection system is evaluated on the KITTI data set, which are determined to be satisfactory, even for the images containing vehicles that have undergone scale variations and camera viewpoint changes, as well as for images that were photographed with complex backgrounds.


title: Audio Texture Synthesis with Scattering Moments

abstract: We introduce an audio texture synthesis algorithm based on scattering moments. A scattering transform is computed by iteratively decomposing a signal with complex wavelet filter banks and computing their amplitude envelop. Scattering moments provide general representations of stationary processes computed as expected values of scattering coefficients. They are estimated with low variance estimators from single realizations. Audio signals having prescribed scattering moments are synthesized with a gradient descent algorithms. Audio synthesis examples show that scattering representation provide good synthesis of audio textures with much fewer coefficients than the state of the art.


title: Design of a Magnetic-Geared Outer-Rotor Permanent-Magnet Brushless Motor for Electric Vehicles

abstract: This paper proposes a novel in-wheel motor, which artfully integrates a magnetic gear into a permanent-magnet brushless (PMBL) DC motor so that they can share a common PM rotor, hence offering both high efficiency and high power density. Moreover, the low-speed requirement for direct driving and the high-speed requirement for compact motor design can be achieved simultaneously. A 2-kW 600/4400-rpm magnetic-geared outer-rotor PMBL DC motor is designed and analyzed, which is particularly suitable for battery-powered electric motorcycles


title: Page As You Go: Piecewise Columnar Access In SAP HANA

abstract: In-memory columnar databases such as SAP HANA achieve extreme performance by means of vector processing over logical units of main memory resident columns. The core in-memory algorithms can be challenged when the working set of an application does not fit into main memory. To deal with memory pressure, most in-memory columnar databases evict candidate columns (or tables) using a set of heuristics gleaned from recent workload. As an alternative approach, we propose to reduce the unit of load and eviction from column to a contiguous portion of the in-memory columnar representation, which we call a page. In this paper, we adapt the core algorithms to be able to operate with partially loaded columns while preserving the performance benefits of vector processing. Our approach has two key advantages. First, partial column loading reduces the mandatory memory footprint for each column, making more memory available for other purposes. Second, partial eviction extends the in-memory lifetime of partially loaded column. We present a new in-memory columnar implementation for our approach, that we term page loadable column. We design a new persistency layout and access algorithms for the encoded data vector of the column, the order-preserving dictionary, and the inverted index. We compare the performance attributes of page loadable columns with those of regular in-memory columns and present a use-case for page loadable columns for cold data in data aging scenarios. Page loadable columns are completely integrated in SAP HANA, and we present extensive experimental results that quantify the performance overhead and the resource consumption when these columns are deployed.


title: Funnel libraries for real-time robust feedback motion planning

abstract: We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios where these plans must be generated in real-time, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Our approach is to pre-compute a library of "funnels" along different maneuvers of the system that the state is guaranteed to remain within (despite bounded disturbances) when the feedback controller corresponding to the maneuver is executed. We leverage powerful computational machinery from convex optimization (sums-of-squares programming in particular) to compute these funnels. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. ::: We demonstrate and validate our method using extensive hardware experiments on a small fixed-wing airplane avoiding obstacles at high speed (~12 mph), along with thorough simulation experiments of ground vehicle and quadrotor models navigating through cluttered environments. To our knowledge, these demonstrations constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in real-time in environments with complex geometric constraints.


title: Asking "Good" Questions: Questionnaire Design and Analysis in Interactive Information Retrieval Research

abstract: Asking questions is a central element of Interactive Information Retrieval (IIR) studies, yet the challenges associated with asking "good" questions is are rarely discussed. This perspectives paper examines the development, evaluation, and application of questionnaires in IIR research. The authors draw upon prior literature and their own research to explore how to best construct and extract meaning from questionnaires. Since building IIR knowledge and theories requires the employment of valid, reliable, dimensional, and sensitive self-report measures, this paper's contribution is an overview of best practices for developing, administering, and analyzing questionaires.


title: Deeper cascaded peak-piloted network for weak expression recognition

abstract: Facial expression recognition is in general a challenging problem, especially in the presence of weak expression. Most recently, deep neural networks have been emerging as a powerful tool for expression recognition. However, due to the lack of training samples, existing deep network-based methods cannot fully capture the critical and subtle details of weak expression, resulting in unsatisfactory results. In this paper, we propose Deeper Cascaded Peak-piloted Network (DCPN) for weak expression recognition. The technique of DCPN has three main aspects: (1) Peak-piloted feature transformation, which utilizes the peak expression (easy samples) to supervise the non-peak expression (hard samples) of the same type and subject; (2) the back-propagation algorithm is specially designed such that the intermediate-layer feature maps of non-peak expression are close to those of the corresponding peak expression; and (3) an novel integration training method, cascaded fine-tune, is proposed to prevent the network from overfitting. Experimental results on two popular facial expression databases, CK\(+\) and Oulu-CASIA, show the superiority of the proposed DCPN over state-of-the-art methods.


title: Morphology and biology of Laetmonice producta producta Grube (Polychaeta: Aphroditidae) in the Bellingshausen Sea and Antarctic Peninsula (Southern Ocean, Antarctica)

abstract: The present paper reports on some morphological and biological features of the scale-worm Laetmonice producta producta Grube, 1877 (Polychaeta: Aphroditidae) in the northwestern sector of Antarctica. A total of 114 specimens were collected during the 2003 and 2006 cruises of the BENTART project on the shelf and upper slope of the Antarctic Peninsula and the Bellingshausen Sea. Specimens were collected using an Agassiz trawl at 22 stations covering a depth ranging from 115 to 1431 m. Bathymetric distribution, abundance and sizes, diet, presence of endosymbionts and epibionts, and some observations related to parapodial structure and chaetal types are discussed in relation to taxonomy and environmental characteristics and compared to previous data.


title: Rgbd mapping: Using depth cameras for dense 3d modeling of indoor environments

abstract: RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop closure detection, followed by pose optimization to achieve globally consistent maps.We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras.


title: Vibration Energy Harvesting: Machinery Vibration, Human Movement and Flow Induced Vibration

abstract: With the development of low power electronics and energy harvesting technology, selfpowered systems have become a research hotspot over the last decade. The main advantage of self-powered systems is that they require minimum maintenance which makes them to be deployed in large scale or previously inaccessible locations. Therefore, the target of energy harvesting is to power autonomous ‘fit and forget’ electronic systems over their lifetime. Some possible alternative energy sources include photonic energy (Norman, 2007), thermal energy (Huesgen et al., 2008) and mechanical energy (Beeby et al., 2006). Among these sources, photonic energy has already been widely used in power supplies. Solar cells provide excellent power density. However, energy harvesting using light sources restricts the working environment of electronic systems. Such systems cannot work normally in low light or dirty conditions. Thermal energy can be converted to electrical energy by the Seebeck effect while working environment for thermo-powered systems is also limited. Mechanical energy can be found in instances where thermal or photonic energy is not suitable, which makes extracting energy from mechanical energy an attractive approach for powering electronic systems. The source of mechanical energy can be a vibrating structure, a moving human body or air/water flow induced vibration. The frequency of the mechanical excitation depends on the source: less than 10Hz for human movements and typically over 30Hz for machinery vibrations (Roundy et al., 2003). In this chapter, energy harvesting from various vibration sources will be reviewed. In section 2, energy harvesting from machinery vibration will be introduced. A general model of vibration energy harvester is presented first followed by introduction of three main transduction mechanisms, i.e. electromagnetic, piezoelectric and electrostatic transducers. In addition, vibration energy harvesters with frequency tunability and wide bandwidth will be discussed. In section 3, energy harvesting from human movement will be introduced. In section 4, energy harvesting from flow induced vibration (FIV) will be discussed. Three types of such generators will be introduced, i.e. energy harvesting from vortex-induced vibration (VIV), fluttering energy harvesters and Helmholtz resonator. Conclusions will be given in section 5.


title: DEMATEL-ANP Risk Assessment in Oil and Gas Construction Projects

abstract: Oil and gas construction projects are complex and risky because of their dynamic environment. Furthermore, rising global energy demand has increased the need for trustworthy risk assessment models for such projects that can provide adequate and precise policy planning. Traditional risk assessments in oil and gas construction projects do not consider the interrelationships of factors in the best-fit models. The Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Processes (ANP), called the DEMATEL-ANP approach, have been applied to other research disciplines to address this shortcoming. This method is able to construct a structural relationship among the different influence factors to visualize complex correlations. Thus, the purpose of this study is to showcase the DEMTAL-ANP risk assessment model to assess the overall risk factors of OGC projects. This study thus identifies the crucial risk criteria of such projects. Data were collected in 2016 through interviews with experts active in OGC projects in Iran. DEMATEL in this situation is used to determine the interdependencies’ relative strengths among the risks. The ANP method is applied to assess the relative importance of the risk factors and to determine the best strategy for implementation of a risk management program. The results presented in this study are a novel adaptation of the risk assessment methodology to OGC projects that determines the important risk factors that directly affect the project success, which in turn helps in formulation of policies for ensuring reliable energy supply planning.


title: Mixing local and global information for community detection in large networks

abstract: The problem of clustering large complex networks plays a key role in several scientific fields ranging from Biology to Sociology and Computer Science. Many approaches to clustering complex networks are based on the idea of maximizing a network modularity function. Some of these approaches can be classified as global because they exploit knowledge about the whole network topology to find clusters. Other approaches, instead, can be interpreted as local because they require only a partial knowledge of the network topology, e.g., the neighbors of a vertex. Global approaches are able to achieve high values of modularity but they do not scale well on large networks and, therefore, they cannot be applied to analyze on-line social networks like Facebook or YouTube. In contrast, local approaches are fast and scale up to large, real-life networks, at the cost of poorer results than those achieved by local methods. In this article we propose a glocal method to maximizing modularity, i.e., our method uses information at the global level, yet its scalability on large networks is comparable to that of local methods. The proposed method is called COmplex Network CLUster DEtection (or, shortly, CONCLUDE.) It works in two stages: in the first stage it uses an information-propagation model, based on random and non-backtracking walks of finite length, to compute the importance of each edge in keeping the network connected (called edge centrality.) Then, edge centrality is used to map network vertices onto points of an Euclidean space and to compute distances between all pairs of connected vertices. In the second stage, CONCLUDE uses the distances computed in the first stage to partition the network into clusters. CONCLUDE is computationally efficient since in the average case its cost is roughly linear in the number of edges of the network.


title: “My Religion Picked My Birth Control”: The Influence of Religion on Contraceptive Use

abstract: This research investigates the influence of religious preference and practice on the use of contraception. Much of earlier research examines the level of religiosity on sexual activity. This research extends this reasoning by suggesting that peer group effects create a willingness to mask the level of sexuality through the use of contraception. While it is understood that certain religions, that is, Catholicism does not condone the use of contraceptives, this research finds that Catholics are more likely to use certain methods of contraception than other religious groups. With data on contraceptive use from the Center for Disease Control’s Family Growth Survey, a likelihood probability model is employed to investigate the impact religious affiliation on contraception use. Findings suggest a preference for methods that ensure non-pregnancy while preventing feelings of shame and condemnation in their religious communities.


Disorders Associated with Cleft Palate


title: Beam forming and Papr Reduction using MRT and EGT in MIMO OFDM System

abstract: In Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing system using beamforming improve the receive Signal to Noise Ratio. In the transmission signals after beam forming the Peak-to-Average Power Ratio (PAPR) is a major issue in Orthogonal Frequency Division Multiplexing systems, High Peak to Average Power Ratio is complicated to design the power amplifier and also increases the power consumption. Analysis of Peak to Average Power Ratio in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing systems has beam forming schemes, MRT (Maximum Ratio Transmission) and EGT (Equal Gain Transmission). Maximum Ratio Transmission systems have better improvement compare to that of Equal Gain Transmission systems in Peak to Average Power Ratio reduction. In Maximum Ratio Transmission OFDM systems improve both PAPR and bit error rate, also EGT OFDM systems improves PAPR reduction and lesser improvement in bit error rate when compare to Maximum Ratio Transmission system.


Questions about the Meaning of Life


title: Control and Monitoring System Communications for Effective Energy Use

abstract: The application of integrated, distributed systems techniques to energy management, while offering substantial benefits in terms of improved effectiveness of energy use, is too expensive for small commercial and residential buildings, because of the high cost of custom installation. This paper presents ways in which the use of standatdization and commonality of hardware and software can reduce costs. Layers of commonality are identified which, if adopted, would permit automatic interfacing of mechanical, electrical, topological, logical, software and human elements of "off-the-shelf" energy management components. A serial multi-drop data link, COMONET, is described which is specifically developed for control and monitoring tasks within buildings.


title: Learning Spread-Out Local Feature Descriptors

abstract: We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors. The idea is that in order to fully utilize the expressive power of the descriptor space, good local feature descriptors should be sufficiently "spread-out" over the space. In this work, we propose a regularization term to maximize the spread in feature descriptor inspired by the property of uniform distribution. We show that the proposed regularization with triplet loss outperforms existing Euclidean distance based descriptor learning techniques by a large margin. As an extension, the proposed regularization technique can also be used to improve image-level deep feature embedding.


title: Multipath Estimation in the Global Positioning System for Multicorrelator Receivers

abstract: In urban areas, multipath (MP) is one of the main error sources when tracking signals used in global navigation satellite systems. The received signals subjected to MP are the sum of several delayed replicas leading to biased estimations. This paper studies a sequential Monte Carlo (SMC) algorithm which mitigates MP effects. The proposed algorithm is based on a state-space model associated to a multicorrelator GPS receiver and on a Rao Blackwellized technique which allows to achieve good performance.


title: Embedded 3D Printing of Strain Sensors within Highly Stretchable Elastomers

abstract: A new method, embedded-3D printing (e-3DP), is reported for fabricating strain sensors within highly conformal and extensible elastomeric matrices. e-3DP allows soft sensors to be created in nearly arbitrary planar and 3D motifs in a highly programmable and seamless manner. Several embodiments are demonstrated and sensor performance is characterized.


title: ICDAR 2013 Table Competition

abstract: Table understanding is a well studied problem in document analysis, and many academic and commercial approaches have been developed to recognize tables in several document formats, including plain text, scanned page images and born-digital, object-based formats such as PDF. Despite the abundance of these techniques, an objective comparison of their performance is still missing. The Table Competition held in the context of ICDAR 2013 is our first attempt at objectively evaluating these techniques against each other in a standardized way, across several input formats. The competition independently addresses three problems: (i) table location, (ii) table structure recognition, and (iii) these two tasks combined. We received results from seven academic systems, which we have also compared against four commercial products. This paper presents our findings.


title: Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

abstract: Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image analysis tasks, the majority of training examples are easy to classify and therefore contribute little to the CNN learning process. In this paper, we propose a method to improve and speed-up the CNN training for medical image analysis tasks by dynamically selecting misclassified negative samples during training. Training samples are heuristically sampled based on classification by the current status of the CNN. Weights are assigned to the training samples and informative samples are more likely to be included in the next CNN training iteration. We evaluated and compared our proposed method by training a CNN with (SeS) and without (NSeS) the selective sampling method. We focus on the detection of hemorrhages in color fundus images. A decreased training time from 170 epochs to 60 epochs with an increased performance—on par with two human experts—was achieved with areas under the receiver operating characteristics curve of 0.894 and 0.972 on two data sets. The SeS CNN statistically outperformed the NSeS CNN on an independent test set.


title: A 105-GHz VCO With 9.5% Tuning Range and 2.8-mW Peak Output Power in a 65-nm Bulk CMOS Process

abstract: To demonstrate high tuning range and large output power, we propose a loop of coupled oscillators in this work. Two different tuning mechanisms are simultaneously exploited. First, each core oscillator is tuned using a variable capacitor. Next, by controlling the phase/delay between the coupled oscillators, the entire loop dynamics, and hence, its frequency is tuned. In this paper, we analyze a loop of “n” coupled oscillators using Adler's equation and derive the expression for the maximum tuning range. Perturbation analysis is used to study the stability conditions of the loop of coupled system. The analysis is extended toward a nested loop of coupled oscillators. The activity condition from two-port theory is used to squeeze maximum power out of active devices. The proposed system is designed and implemented using four coupled Colpitts voltage-controlled oscillators (VCOs) in a 65-nm bulk CMOS process. The VCO achieves continuous tuning range of 9.5% at the center frequency of 105 GHz with the peak output power of 2.8 mW. The circuit consumes 54 mW from a 1.2-V supply. To the best of our knowledge, this VCO has the highest output power and tuning range among all the CMOS oscillators at or above 100 GHz.


title: A STATE-OF-THE-ART REVIEW OF CLOUD FORENSICS

abstract: Cloud computing and digital forensics are emerging elds of technology. Unlike traditional digital forensics where the target environment can be almost completely isolated, acquired and can be under the investigators control; in cloud environments, the distribution of computation and storage poses unique and complex challenges to the investigators. Recently, the term \cloud forensics" has an increasing presence in the eld of digital forensics. In this state-of-the-art review, we included the most recent research eorts that used \cloud forensics" as a keyword and then classify the literature into three dimensions: (1) survey-based, (2) technology-based and (3) forensics-procedural-based. We discuss widely accepted standard bodies and their eorts to address the current trend of cloud forensics. Our aim is not only to reference related work based on the discussed dimensions, but also to analyse them and generate a mind map that will help in identifying research gaps. Finally, we summarize existing digital forensics tools and the available simulation environments that can be used for evidence acquisition, examination and cloud forensics test purposes.


On the security of the ZigBee Light Link touchlink commissioning procedure


title: Privacy-Preserving Deep Inference for Rich User Data on The Cloud

abstract: Deep neural networks are increasingly being used in a variety of machine learning applications applied to rich user data on the cloud. However, this approach introduces a number of privacy and efficiency challenges, as the cloud operator can perform secondary inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep models for cooperative, privacy-preserving analytics. We do this by breaking down the popular deep architectures and fine-tune them in a particular way. We then evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also asses the local inference cost of different layers on a modern handset for mobile applications. Our evaluations show that by using certain kind of fine-tuning and embedding techniques and at a small processing costs, we can greatly reduce the level of information available to unintended tasks applied to the data feature on the cloud, and hence achieving the desired tradeoff between privacy and performance.


title: Classification of Imbalanced Documents by Feature Selection

abstract: We previously worked on category classification problem of reuter 's newspaper article using SVM and feature selection. In the study, feature selection by SVM-score [Sakai, Hirokawa, 2012] showed high accuracy. It was also expected to be superior to other standard indicators in case data is imbalanced. This study aimed to show the effectiveness of feature selection by SVM-score in machine learning with imbalanced data. For the reuter's data, F-measure was calculated in the classification experiment of all 13 categories. As a result, feature selection by SVM-score shows high f-measure and precision. In addition, we found feature words of negative example improve the classification performance.


Quantum computers ready to leap out of the lab in 2017


Forward Integrity for Secure Audit Logs


title: Financial Sentiment Analysis for Risk Prediction

abstract: This paper attempts to identify the importance of sentiment words in financial reports on financial risk. By using a financespecific sentiment lexicon, we apply regression and ranking techniques to analyze the relations between sentiment words and financial risk. The experimental results show that, based on the bag-of-words model, models trained on sentiment words only result in comparable performance to those on origin texts, which confirms the importance of financial sentiment words on risk prediction. Furthermore, the learned models suggest strong correlations between financial sentiment words and risk of companies. As a result, these findings are of great value for providing us more insight and understanding into the impact of financial sentiment words in financial reports.


title: 3D modeling of historic sites using range and image data

abstract: Preserving cultural heritage and historic sites is an important problem. These sites are subject to erosion, vandalism, and as long-lived artifacts, they have gone through many phases of construction, damage and repair. It is important to keep an accurate record of these sites using 3-D model building technology as they currently are, so preservationists can track changes, foresee structural problems, and allow a wider audience to "virtually" see and tour these sites. Due to the complexity of these sites, building 3-D models is time consuming and difficult, usually involving much manual effort. This paper discusses new methods that can reduce the time to build a model using automatic methods. Examples of these methods are shown in reconstructing a model of the Cathedral of Ste. Pierre in Beauvais, France.


title: Multitask learning improves prediction of cancer drug sensitivity

abstract: Precision oncology seeks to predict the best therapeutic option for individual patients based on the molecular characteristics of their tumors. To assess the preclinical feasibility of drug sensitivity prediction, several studies have measured drug responses for cytotoxic and targeted therapies across large collections of genomically and transcriptomically characterized cancer cell lines and trained predictive models using standard methods like elastic net regression. Here we use existing drug response data sets to demonstrate that multitask learning across drugs strongly improves the accuracy and interpretability of drug prediction models. Our method uses trace norm regularization with a highly efficient ADMM (alternating direction method of multipliers) optimization algorithm that readily scales to large data sets. We anticipate that our approach will enhance efforts to exploit growing drug response compendia in order to advance personalized therapy.


title: The Ant System Applied To The Quadratic Assignment

abstract: In recent years, there has been growing interest in algorithms inspired by the observation of natural phenomena to define computational procedures that can solve complex problems. We describe a distributed heuristic algorithm that was inspired by the observation of the behavior of ant colonies, and we propose its use for the quadratic assignment problem. The results obtained in solving several classical instances of the problem are compared with those obtained from other evolutionary heuristics to evaluate the quality of the proposed system.


title: Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

abstract: Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on an average across all subjects.


Sentiment Analysis and Deep Learning : A Survey


title: Warped Gaussian Processes

abstract: We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP. This can be seen as including a preprocessing transformation as an integral part of the probabilistic modelling problem, rather than as an ad-hoc step. We demonstrate on several real regression problems that learning the transformation can lead to significantly better performance than using a regular GP, or a GP with a fixed transformation.


title: Class-Specific Word Embedding through Linear Compositionality

abstract: English linguist John Rupert Firth has a famous saying "you shall know a word by the company it keeps." Most word representation learning models are based on this assumption that a word's semantic meaning can be learned from the context in which it resides. The context is defined as a small unordered number of words surrounding the target word. Research has shown that context alone provides limited information because the context contains only neighboring words. Thus only local information is learned in the word embeddings. Some research tries to improve this by utilizing outside information sources such as a knowledge base. We observe that the meaning of a word in a sentence can be better interpreted when the class information or label of the sentence is presented. We propose three approaches to train class-specific embeddings to encode class information by utilizing the linear compositionality property of word embeddings. We present a general framework consisting of a pair of convolutional neural networks for text classification tasks where the learned class-specific embeddings serve as features. We evaluate our approach and framework on topic classification of a disaster-focused Twitter dataset and a benchmark Twitter sentiment classification dataset from SemEval 2013. Our results show a potential relative accuracy improvement of more than 5% over a recent baseline.


title: Safety and walking ability of KAFO users with the C-Brace® Orthotronic Mobility System, a new microprocessor stance and swing control orthosis

abstract: BACKGROUND ::: There are clear indications for benefits of stance control orthoses compared to locked knee ankle foot orthoses. However, stance control orthoses still have limited function compared with a sound human leg. ::: ::: ::: OBJECTIVES ::: The aim of this study was to evaluate the potential benefits of a microprocessor stance and swing control orthosis compared to stance control orthoses and locked knee ankle foot orthoses in activities of daily living. ::: ::: ::: STUDY DESIGN ::: Survey of lower limb orthosis users before and after fitting of a microprocessor stance and swing control orthosis. ::: ::: ::: METHODS ::: Thirteen patients with various lower limb pareses completed a baseline survey for their current orthotic device (locked knee ankle foot orthosis or stance control orthosis) and a follow-up for the microprocessor stance and swing control orthosis with the Orthosis Evaluation Questionnaire, a new self-reported outcome measure devised by modifying the Prosthesis Evaluation Questionnaire for use in lower limb orthotics and the Activities of Daily Living Questionnaire. ::: ::: ::: RESULTS ::: The Orthosis Evaluation Questionnaire results demonstrated significant improvements by microprocessor stance and swing control orthosis use in the total score and the domains of ambulation ( p = .001), paretic limb health ( p = .04), sounds ( p = .02), and well-being ( p = .01). Activities of Daily Living Questionnaire results showed significant improvements with the microprocessor stance and swing control orthosis with regard to perceived safety and difficulty of activities of daily living. ::: ::: ::: CONCLUSION ::: The microprocessor stance and swing control orthosis may facilitate an easier, more physiological, and safer execution of many activities of daily living compared to traditional leg orthosis technologies. Clinical relevance This study compared patient-reported outcomes of a microprocessor stance and swing control orthosis (C-Brace) to those with traditional knee ankle foot orthosis and stance control orthosis devices. The C-Brace offers new functions including controlled knee flexion during weight bearing and dynamic swing control, resulting in significant improvements in perceived orthotic mobility and safety.


title: Inhibition of PIKfyve by YM-201636 Dysregulates Autophagy and Leads to Apoptosis-Independent Neuronal Cell Death

abstract: The lipid phosphatidylinositol 3,5-bisphosphate (PtdIns(3,5)P2), synthesised by PIKfyve, regulates a number of intracellular membrane trafficking pathways. Genetic alteration of the PIKfyve complex, leading to even a mild reduction in PtdIns(3,5)P2, results in marked neurodegeneration via an uncharacterised mechanism. In the present study we have shown that selectively inhibiting PIKfyve activity, using YM-201636, significantly reduces the survival of primary mouse hippocampal neurons in culture. YM-201636 treatment promoted vacuolation of endolysosomal membranes followed by apoptosis-independent cell death. Many vacuoles contained intravacuolar membranes and inclusions reminiscent of autolysosomes. Accordingly, YM-201636 treatment increased the level of the autophagosomal marker protein LC3-II, an effect that was potentiated by inhibition of lysosomal proteases, suggesting that alterations in autophagy could be a contributing factor to neuronal cell death.


title: Inertia parameter and attitude estimation of space noncooperative tumbling target based on a two-step method

abstract: This paper presents a new two-step method to estimate the ratios of the moment of inertia, the direction of the angular momentum in inertial frame, the attitude and angular velocity of the space noncooperative tumbling target, based on the measured relative attitude from the servicing spacecraft. At the beginning, the target's body frame should be determined based on its geometrical shapes. In the first step, by utilizing the conservation of angular momentum and kinematic equations, the ratios of inertia matrix in the body frame is first obtained using least square method and singular value decomposition, then the principal axis of inertia and the ratios between each principal moment is determined though coordinate transformation. In the second step, an Extended Kalman Filter (EKF) is proposed to estimate the direction of the angular momentum, attitude and angular velocity in the body-fixed coordinate frame which is rebuilded based on the direction of the principal axes from the first step. Numerical simulation results are finally presented to demonstrate the effectiveness of the proposed two-step estimation method.


title: Investigation on the Breakdown Failure in Stripe Trench-Gate Field-Stop Insulated Bipolar Transistor With Low-Saturation Voltage

abstract: In this paper, we designed a 1.5-kV trench-gate field-stop IGBT (FS-IGBT) and its on-state saturation voltage, which is smaller than 1.6 V. However, during the avalanche test, when the current limiting value of the tester is large, we have found a breakdown failure phenomenon in the designed IGBT structure although the breakdown voltage in the termination is higher than that in the cell region. The failure mechanism is investigated. It is illustrated that a filament is triggered due to some hole carriers, which can flow beneath the trenches from the cell region and then accumulate in the transition region if the trenches are ending at the deep p- well region in the termination, which is different from that in the VDMOS. To improve the avalanche reliability, a new termination structure with segregated deep p-well regions and the isolated trench is proposed and verified by numerous measurements. This paper has a significant meaning for the design of the termination of the trench-gate FS-IGBT with very low saturation voltage.


title: Latent variable decomposition of spectrograms for single channel speaker separation

abstract: In this paper we present an algorithm for the separation of multiple speakers from mixed single-channel recordings by latent variable decomposition of the speech spectrogram. We model each magnitude spectral vector in the short-time Fourier transform of a speech signal as the outcome of a discrete random process that generates frequency bin indices. The distribution of the process is modelled a mixture of multinomial distributions, such that the mixture weights of the component multinomials vary from analysis window to analysis window. The component multinomials are assumed to be speaker specific and are learnt from training signals for each speaker. The distributions representing magnitude spectral vectors for the mixed signal are decomposed into mixtures of the multinomials for all component speakers. The frequency distribution, i.e. the spectrum for each speaker is reconstructed from this decomposition. Experimental results show that the proposed method is very effective at separating mixed signals.


title: 'Hitting You Over the Head': Oncologists' Disclosure of Prognosis to Advanced Cancer Patients

abstract: The disclosure of prognosis to terminally ill patients has emerged as a recent concern given greater demands for patient involvement in medical decision-making in the United States. As part of the informed consent process, American physicians are legally and ethically obligated to provide information to such patients about the risks, benefits, and alternatives of all available treatment options including the use of experimental therapies. Although not legally required, the disclosure of a terminal prognosis is ethically justified because it upholds the principle of self-determination and enables patients to make treatment decisions consistent with their life goals. To understand oncologists' attitudes about disclosing prognostic information to cancer patients with advanced disease, we interviewed fourteen oncologists and conducted one focus group of medical fellows. Although oncologists reported to disclose prognosis in terms of cancer not being curable, they tend to avoid using percentages to convey prognosis. Oncologists' reported reluctance to disclosing prognosis was conveyed through the use of metaphors depicting the perceived violent impact of such information on patients. Oncologists' reluctance to disclose prognosis and preserve patient hope are held in check by their need to ensure that patients have 'realistic expectations' about therapy. We discuss these data in light of the cultural, ethical, and legal dimensions of prognosis disclosure, patient hope and the doctor-patient relationship, and recommend ways to enhance the communication process.


title: A Review of 25 Years of CSCW Research in Healthcare: Contributions, Challenges and Future Agendas

abstract: CSCW as a field has been concerned since its early days with healthcare, studying how healthcare work is collaboratively and practically achieved and designing systems to support that work. Reviewing literature from the CSCW Journal and related conferences where CSCW work is published, we reflect on the contributions that have emerged from this work. The analysis illustrates a rich range of concepts and findings towards understanding the work of healthcare but the work on the larger policy level is lacking. We argue that this presents a number of challenges for CSCW research moving forward: in having a greater impact on larger-scale health IT projects; broadening the scope of settings and perspectives that are studied; and reflecting on the relevance of the traditional methods in this field - namely workplace studies - to meet these challenges.


title: Smooth muscle contraction and relaxation.

abstract: This brief review serves as a refresher on smooth muscle physiology for those educators who teach in medical and graduate courses of physiology. Additionally, those professionals who are in need of an update on smooth muscle physiology may find this review to be useful. Smooth muscle lacks the striations characteristic of cardiac and skeletal muscle. Layers of smooth muscle cells line the walls of various organs and tubes in the body, and the contractile function of smooth muscle is not under voluntary control. Contractile activity in smooth muscle is initiated by a Ca(2+)-calmodulin interaction to stimulate phosphorylation of the light chain of myosin. Ca(2+) sensitization of the contractile proteins is signaled by the RhoA/Rho kinase pathway to inhibit the dephosphorylation of the light chain by myosin phosphatase, thereby maintaining force generation. Removal of Ca(2+) from the cytosol and stimulation of myosin phosphatase initiate the process of smooth muscle relaxation.


title: Clipping Noise in OFDM-Based Optical Wireless Communication Systems

abstract: In this paper, the impact of clipping noise on optical wireless communication (OWC) systems employing orthogonal frequency division multiplexing (OFDM) is investigated. The two existing optical OFDM (O-OFDM) transmission schemes, asymmetrically clipped optical OFDM (ACO-OFDM) and direct-current-biased optical OFDM (DCO-OFDM), are studied. Time domain signal clipping generally results from direct current (DC) biasing and/or from physical limitations of the transmitter front-end. These include insufficient forward biasing and the maximum power driving limit of the emitter. The clipping noise can be modeled according to the Bussgang theorem and the central limit theorem (CLT) as attenuation of the data-carrying subcarriers at the receiver and addition of zero-mean complex-valued Gaussian noise. Analytical expressions for the attenuation factor and the clipping noise variance are determined in closed-form and employed in the derivation of the electrical signal-to-noise ratio (SNR). The validity of the model is verified through a Monte Carlo bit-error ratio (BER) simulation. Finally, the BER performance of ACO-OFDM with DCO-OFDM is compared for different clipping levels and multi-level quadrature amplitude modulation (M-QAM) schemes.


Cognitive neural prosthetics


title: Shielded coil structure suppressing leakage magnetic field from 100W-class wireless power transfer system with higher efficiency

abstract: In this paper, the shielded coil structure using the ferrites and the metallic shielding is proposed. It is compared with the unshielded coil structure (i.e. a pair of circular loop coils only) to demonstrate the differences in the magnetic field distributions and system performance. The simulation results using the 3D Finite Element Analysis (FEA) tool show that it can considerably suppress the leakage magnetic field from 100W-class wireless power transfer (WPT) system with the enhanced system performance.