title: Exposure of Children and Adolescents to Alcohol Marketing on Social Media Websites

abstract: Aims: In 2011, online marketing became the largest marketing channel in the UK, overtaking television for the first time. This study aimed to describe the exposure of children and young adults to alcohol marketing on social media websites in the UK. Methods: We used commercially available data on the three most used social media websites among young people in the UK, from December 2010 to May 2011. We analysed by age (6-14 years; 15-24 years) and gender the reach (proportion of internet users who used the site in each month) and impressions (number of individual pages viewed on the site in each month) for Facebook, YouTube and Twitter. We further analysed case studies of five alcohol brands to assess the marketer-generated brand content available on Facebook, YouTube and Twitter in February and March 2012. Results: Facebook was the social media site with the highest reach, with an average monthly reach of 89% of males and 91% of females aged 15-24. YouTube had a similar average monthly reach while Twitter had a considerably lower usage in the age groups studied. All five of the alcohol brands studied maintained a Facebook page, Twitter page and YouTube channel, with varying levels of user engagement. Facebook pages could not be accessed by an under-18 user, but in most cases YouTube content and Twitter content could be accessed by those of all ages. Conclusion: The rise in online mar- keting of alcohol and the high use of social media websites by young people suggests that this is an area requiring further monitoring and regulation.


title: WSN for traffic monitoring using Raspberry Pi board

abstract: This paper introduces low-cost non-intrusive sensory that can collect traffic data based on Raspberry Pi single board computer. Image information acquired by Raspberry Pi HD camera module is analyzed for moving objects presence. After evaluation of detected object count, size, class and motion vector object properties are sent to server node by RF transceiver. Sensor low-power consumption ensures possibility to operate from battery for an extended period of time.


title: Analysis of Secure Key Storage Solutions on Android

abstract: Mobile phones are increasingly used for security sensitive activities such as online banking or mobile payments. This usually involves some cryptographic operations, and therefore introduces the problem of securely storing the corresponding keys on the phone. In this paper we evaluate the security provided by various options for secure storage of key material on Android, using either Android's service for key storage or the key storage solution in the Bouncy Castle library. The security provided by the key storage service of the Android OS depends on the actual phone, as it may or may not make use of ARM TrustZone features. Therefore we investigate this for different models of phones. We find that the hardware-backed version of the Android OS service does offer device binding -- i.e. keys cannot be exported from the device -- though they could be used by any attacker with root access. This last limitation is not surprising, as it is a fundamental limitation of any secure storage service offered from the TrustZone's secure world to the insecure world. Still, some of Android's documentation is a bit misleading here. Somewhat to our surprise, we find that in some respects the software-only solution of Bouncy Castle is stronger than the Android OS service using TrustZone's capabilities, in that it can incorporate a user-supplied password to secure access to keys and thus guarantee user consent.


title: A Spatial Choice Model for Product Recommendations,” Working Paper

abstract: Apparatus for covering shoes on the feet of a wearer with a protective sanitary shoe wrapper formed of heat-shrinkable material is disclosed, wherein individual shoe wrappers are positioned at a heat-shrink station in an open configuration to receive and enclose a shoe on the foot of a wearer; after the insertion of the shoe, heat is applied to the wrapper to heat-shrink it and conform it to the shoe.


title: Supervised Speech Separation Based on Deep Learning: An Overview

abstract: Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised separation algorithms have been put forward. In particular, the recent introduction of deep learning to supervised speech separation has dramatically accelerated progress and boosted separation performance. This article provides a comprehensive overview of the research on deep learning based supervised speech separation in the last several years. We first introduce the background of speech separation and the formulation of supervised separation. Then we discuss three main components of supervised separation: learning machines, training targets, and acoustic features. Much of the overview is on separation algorithms where we review monaural methods, including speech enhancement (speech-nonspeech separation), speaker separation (multi-talker separation), and speech dereverberation, as well as multi-microphone techniques. The important issue of generalization, unique to supervised learning, is discussed. This overview provides a historical perspective on how advances are made. In addition, we discuss a number of conceptual issues, including what constitutes the target source.


title: A Japanese family showing high-frequency hearing loss with KCNQ4 and TECTA mutations.

abstract: CONCLUSIONS ::: We describe a Japanese family with high-frequency sensorineural hearing loss (SNHL) harboring a c.211delC mutation in the KCNQ4 gene. Families showing progressive high-frequency SNHL should be investigated for mutations in the KCNQ4 gene. ::: ::: ::: OBJECTIVE ::: To determine the responsible deafness gene in a Japanese family with dominantly inherited high-frequency SNHL of unknown etiology. ::: ::: ::: METHODS ::: We performed hearing tests for five members of the family, and the three affected with hearing loss underwent further audiological and vestibular examinations. Genetic analysis was performed to identify any possible causative mutations, as well as analysis of detailed clinical findings to determine the phenotype. ::: ::: ::: RESULTS ::: The three affected subjects showed high-frequency SNHL. Extensive audiologic evaluation suggested cochlear involvement and progressive hearing loss. As for bilateral caloric testing, two of the three affected subjects showed hyporeflexia with recurrent vestibular symptoms. We identified the c.211delC mutation in the KCNQ4 gene and the c.2967C>A (p.H989Q) mutation in the TECTA gene. Based on the genotype-phenotype correlation, the c.211delC mutation in the KCNQ4 gene was associated with high-frequency SNHL in this family.


title: Guided Local Search for the Three-Dimensional Bin-Packing Problem

abstract: The three-dimensional bin-packing problem is the problem of orthogonally packing a set of boxes into a minimum number of three-dimensional bins. In this paper we present a heuristic algorithm based on guided local search. Starting with an upper bound on the number of bins obtained by a greedy heuristic, the presented algorithm iteratively decreases the number of bins, each time searching for a feasible packing of the boxes. The process terminates when a given time limit has been reached or the upper bound matches a precomputed lower bound. The algorithm can also be applied to two-dimensional bin-packing problems by having a constant depth for all boxes and bins. Computational experiments are reported for two- and three-dimensional instances with up to 200 boxes, showing that the algorithm on average finds better solutions than do heuristics from the literature.


Active electromagnetic attacks on secure hardware


title: Wideband 120 GHz to 140 GHz MIMO radar: System design and imaging results

abstract: This paper presents a wideband FMCW MIMO radar sensor capable of working in the frequency range between 120 GHz and 140 GHz. The sensor is based on a radar chipset fabricated in SiGe technology and uses a MIMO approach to improve the angular resolution. The MIMO operation is implemented by time domain multiplexing of the transmitters. The radar is capable of producing 2D images by using FFT processing and a delay-and-sum beamformer. This paper presents the overall radar system design together with the image reconstruction algorithms as well as first imaging results.


title: Gesture Recognition Using mm-Wave Sensor for Human-Car Interface

abstract: This article details the development of a gesture recognition technique using a mm-wave radar sensor for in-car infotainment control. Gesture recognition is becoming a more prominent form of human-computer interaction and can be used in the automotive industry to provide a safe and intuitive control interface that will limit driver distraction. We use a 60 GHz mm-wave radar sensor to detect precise features of fine motion. Specific gesture features are extracted and used to build a machine learning engine that can perform real-time gesture recognition. This article discusses the user requirements and in-car environmental constraints that influenced design decisions. Accuracy results of the technique are presented, and recommendations for further research and improvements are made.


Stories from Games : Content and Focalization Selection in Narrative Composition


title: Random cell association and void probability in poisson-distributed cellular networks

abstract: This paper studied the fundamental modeling defect existing in Poisson-distributed cellular networks in which all base stations form a homogeneous Poisson point process (PPP) of intensity λ B and all users form another independent PPP of intensity λ U . The modeling defect, hardly discovered in prior works, is the void cell issue that stems from the independence between the distributions of users and BSs and “user-centric” cell association, and it could give rise to very inaccurate analytical results. We showed that the void probability of a cell under generalized random cell association is always bounded above zero and its theoretical lower bound is exp (−λ U over λ B ) that can be achieved by large association weighting. An accurate expression of the void probability of a cell was derived and simulation results validated its correctness. We also showed that the associated BSs are essentially no longer a PPP such that modeling them as a PPP to facilitate the analysis of interference-related performance metrics may detach from reality if the BS intensity is not significantly large if compared with the user intensity.


title: Predicting Driver Intent from Models of Naturalistic Driving

abstract: Modern advanced driver assistance systems (ADAS) have lead to safer vehicles. However, current ADAS are typically limited to a reactive, physical model of the vehicle. They lack the ability to understand complex traffic scenarios. One traffic scenario that has gathered interest in recent years is the problem of inferring driver behaviour at road features such as intersections. At these locations drivers may choose to perform one of many available manoeuvres. Early identification of the manoeuvre is important for the development of future safety and situational awareness systems. The objective of this paper is to develop a method for predicting which manoeuvre a driver will execute. To fulfil this objective a simple method based on quadratic discriminant analysis is proposed. The method is computationally efficient and developed with a view to being applied to complex road networks using naturalistic driving data. The proposed method is demonstrated and validated using naturalistic driving data collected at a three way T-intersection.


Can Cybersecurity Be Proactive? A Big Data Approach and Challenges


Digital Infrastructures : The Missing IS Research Agenda


title: Modeling and implementing collaborative editing systems with transactional techniques

abstract: Many collaborative editing systems have been developed for coauthoring documents. These systems generally have different infrastructures and support a subset of interactions found in collaborative environments. In this paper, we propose a transactional framework with two advantages. First, the framework is generic as demonstrated by its capability of modeling four types of existing products: RCS, MediaWiki, Google Docs, and Google Wave. Second, the framework can be layered on the top of a modern database management system to reuse its transaction processing capabilities for data consistency control in both centralized and replicated editing systems. We detail the programming interfaces and the synchronization protocol of our transactional framework and demonstrate its usage through concrete examples. We also describe a prototype implementation of this framework over Oracle Berkeley DB High Availability, a replicated transactional database management system.


Isokinetic contraction: a new concept of resistive exercise.


Reconstruction of 3 D tree stem models from low-cost terrestrial laser scanner data


title: The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology

abstract: Abstract The human orbitofrontal cortex is an important brain region for the processing of rewards and punishments, which is a prerequisite for the complex and flexible emotional and social behaviour which contributes to the evolutionary success of humans. Yet much remains to be discovered about the functions of this key brain region, and new evidence from functional neuroimaging and clinical neuropsychology is affording new insights into the different functions of the human orbitofrontal cortex. We review the neuroanatomical and neuropsychological literature on the human orbitofrontal cortex, and propose two distinct trends of neural activity based on a meta-analysis of neuroimaging studies. One is a mediolateral distinction, whereby medial orbitofrontal cortex activity is related to monitoring the reward value of many different reinforcers, whereas lateral orbitofrontal cortex activity is related to the evaluation of punishers which may lead to a change in ongoing behaviour. The second is a posterior–anterior distinction with more complex or abstract reinforcers (such as monetary gain and loss) represented more anteriorly in the orbitofrontal cortex than simpler reinforcers such as taste or pain. Finally, we propose new neuroimaging methods for obtaining further evidence on the localisation of function in the human orbitofrontal cortex.


Local and Global Feature Descriptors Combination from RGB-Depth Videos for Human Action Recognition


title: A Mosquito Inspired Strategy to Implant Microprobes into the Brain

abstract: Mosquitos are among the deadliest insects on the planet due to their ability to transmit diseases like malaria through their bite. In order to bite, a mosquito must insert a set of micro-sized needles through the skin to reach vascular structures. The mosquito uses a combination of mechanisms including an insertion guide to enable it to bite and feed off of larger animals. Here, we report on a biomimetic strategy inspired by the mosquito insertion guide to enable the implantation of intracortical microelectrodes into the brain. Next generation microelectrode designs leveraging ultra-small dimensions and/or flexible materials offer the promise of increased performance, but present difficulties in reliable implantation. With the biomimetic guide in place, the rate of successful microprobe insertion increased from 37.5% to 100% due to the rise in the critical buckling force of the microprobes by 3.8-fold. The prototype guides presented here provide a reproducible method to augment the insertion of small, flexible devices into the brain. In the future, similar approaches may be considered and applied to the insertion of other difficult to implant medical devices.


title: Gold-indium Transient Liquid Phase (TLP) wafer bonding for MEMS vacuum packaging

abstract: This paper reports the first wafer-level vacuum packages created with gold-indium transient liquid phase (TLP) wafer bonding. The packages were bonded at 200degC for 1 hour under a vacuum environment in a commercially available wafer bonder. After bonding, the integrated getters were activated for 1 hour resulting in internal pressures as low as 200 mTorr. The pressures have been stable for over 6 months as measured by integrated Pirani gauges. Although no leak rate trend has been observed, the worst case leak rate that fits within the error of the pressure measurement is 16 mTorr/year (1ldr10-16 atm.cc.s-1).


title: Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes

abstract: Recently, regression analysis has become a popular tool for face recognition. Most existing regression methods use the one-dimensional, pixel-based error model, which characterizes the representation error individually, pixel by pixel, and thus neglects the two-dimensional structure of the error image. We observe that occlusion and illumination changes generally lead, approximately, to a low-rank error image. In order to make use of this low-rank structural information, this paper presents a two-dimensional image-matrix-based error model, namely, nuclear norm based matrix regression (NMR), for face representation and classification. NMR uses the minimal nuclear norm of representation error image as a criterion, and the alternating direction method of multipliers (ADMM) to calculate the regression coefficients. We further develop a fast ADMM algorithm to solve the approximate NMR model and show it has a quadratic rate of convergence. We experiment using five popular face image databases: the Extended Yale B, AR, EURECOM, Multi-PIE and FRGC. Experimental results demonstrate the performance advantage of NMR over the state-of-the-art regression-based methods for face recognition in the presence of occlusion and illumination variations.


title: Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

abstract: Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.


title: The Work Function Behavior of Aluminum-Doped Titanium Carbide Grown by Atomic Layer Deposition

abstract: The effective work function (eWF) of Al-doped titanium carbide (TiAlC) metal electrodes prepared by atomic layer deposition shows a strong dependence on the underlying gate dielectrics. The eWF of TiAlC on HfO2 shows a low value of 4.2 eV independent of the deposition temperature and process conditions, whereas that on SiO2 shifted to a midgap value of 4.7 eV, and it was sensitive to the process conditions. The mechanism underlying this TiAlC work function dependence on different gate dielectrics is investigated in detail.


title: Ultrawideband Composite Cavity-Backed Folded Sectorial Bowtie Antenna With Stable Pattern and High Gain

abstract: A composite cavity-backed folded sectorial bowtie antenna (FSBA) is proposed and investigated in this paper, which is differentially fed by an SMA connector through a balun, i.e. a transition from a microstrip line to a parallel stripline. The composite cavity as a general case, consisting of a conical part and a cylindrical rim, can be tuned freely from a cylindrical to a cup-shaped one. Parametric studies are performed to optimize the antenna performance. Experimental results reveal that it can achieve an impedance bandwidth of 143% for SWR les 2, a broadside gain of 8-15.3 dBi, and stable radiation pattern over the whole operating band. The total electrical dimensions are 0.66lambdam in diameter and 0.16lambdam in height, where lambdam is the free-space wavelength at lower edge of the operating frequency band. The problem about the distorted patterns in the upper frequency band for wideband cavity-backed antennas is solved in our work.


title: An Overview of Deep-Structured Learning for Information Processing

abstract: In this paper, I will introduce to the APSIPA audience an emerging area of machine learning, deep-structured learning. It refers to a class of machine learning techniques, developed mostly since 2006, where many layers of information processing stages in hierarchical architectures are exploited for pattern classification and for unsupervised feature learning. First, the brief history of deep learning is discussed. Then, I develop a classificatory scheme to analyze and summarize major work reported in the deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures, and categorize them into three types: generative, discriminative, and hybrid. Two prime deep architectures, one hybrid and one discriminative, are presented in detail. Finally, selected applications of deep learning are reviewed in broad areas of information processing including audio/speech, image/video, multimodality, language modeling, natural language processing, and information retrieval.


title: Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images

abstract: Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. The idea is to compare the observation with the output of a forward process, such as a rendered image of the object of interest in a particular pose. Due to occlusion or complicated sensor noise, it can be difficult to perform this comparison in a meaningful way. We propose an approach that "learns to compare", while taking these difficulties into account. This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares observed and rendered images. The network is trained with the maximum likelihood paradigm. We observe empirically that the CNN does not specialize to the geometry or appearance of specific objects. It can be used with objects of vastly different shapes and appearances, and in different backgrounds. Compared to state-of-the-art, we demonstrate a significant improvement on two different datasets which include a total of eleven objects, cluttered background, and heavy occlusion.


title: Why do people play on-line games? An extended TAM with social influences and flow experience

abstract: On-line games have been a highly profitable e-commerce application in recent years. The market value of on-line games is increasing markedly and number of players is rapidly growing. The reasons that people play on-line games is an important area of research. This study views on-line games as entertainment technology. However, while most past studies have focused on task-oriented technology, predictors of entertainment-oriented technology adoption have seldom been addressed. ::: ::: This study applies the technology acceptance model (TAM) that incorporates social influences and flow experience as belief-related constructs to predict users' acceptance of on-line games. The proposed model was empirically evaluated using survey data collected from 233 users about their perceptions of on-line games. Overall, the results reveal that social norms, attitude, and flow experience explain about 80% of game playing. The implications of this study are discussed.


title: Is institutional improvement possible

abstract: ABSTRACTA substantial literature shows that economic prosperity is dependent on the quality of economic institutions. Countries with low-quality institutions remain poor while countries with high-quality institutions prosper. Improvement in institutional quality brings with it economic growth. Poor countries must improve their economic institutions to escape poverty; so, if a poor country’s institutional structure is unlikely to improve, that suggests dismal prospects for economic growth and an escape from poverty. An examination of institutional quality over 30 years indicates that countries with low-quality institutions have improved their institutional quality, which demonstrates that poor countries are not stuck with low-quality institutions. They can improve their institutions, and consequently, can generate economic growth and escape poverty.


title: Non-foster circuits for small broadband antennas

abstract: As is well known antenna size implies limitations in bandwidth and gain characteristics. However, if non-Foster / Negative circuits are employed such restrictions can be partially overcome. That is negative matching circuits can provide a way to construct very small efficient antennas. Non-Foster circuits have been considered since 1950's when Negative Impedance Converters (NIC) were proposed [1]. NIC's can be realized via a combination of active devices (transistors) as well as lumped capacitors and inductors. Still, inherent losses, noise issues and bandwidth limitations from available active elements have precluded their use except for narrow bandwidth applications. But, recent advancements in integrated circuits have renewed interest in non-Foster/negative matching.


title: Smart Grid Communication: Its Challenges and Opportunities

abstract: The necessity to promote smart grid (SG) has been recognized with a strong consensus. The SG integrates electrical grids and communication infrastructures and forms an intelligent electricity network working with all connected components to deliver sustainable electricity supplies. Many advanced communication technologies have been identified for SG applications with a potential to significantly enhance the overall efficiency of power grids. In this paper, the challenges and applications of communication technologies in SG are discussed. In particular, we identify three major challenges to implement SG communication systems, including standards interoperability, cognitive access to unlicensed radio spectra, and cyber security. The issues to implement SG communications on an evolutional path and its future trends are also addressed. The aim of this paper is to offer a comprehensive review of state-of-the-art researches on SG communications.


title: Compact Omnidirectional Antenna of Circular Polarization

abstract: A compact omnidirectional antenna of circular polarization (CP) is presented for 2.4-GHz WLAN access-point applications. The antenna consists of four bended monopoles, which are simultaneously excited through an associated feeding network. The radiated horizontal and vertical field components are derived based on an analytical model, and the 90° phase difference between them is shown to be naturally guaranteed. With a size of 0.22λ0 × 0.22λ0 × 0.076λ0, the antenna exhibits a bandwidth (|S11| ≤ -10 dB and AR ≤ 3 dB) of 3.56% and a left-handed CP average gain of 1.39 dB in the azimuth plane at 2.44 GHz.


title: A Crowdsourcing Approach to Collecting Tutorial Videos -- Toward Personalized Learning-at-Scale

abstract: We investigated the feasibility of crowdsourcing full- fledged tutorial videos from ordinary people on the Web on how to solve math problems related to logarithms. This kind of approach (a form of learnersourcing [9, 11]) to efficiently collecting tutorial videos and other learning resources could be useful for realizing personalized learning-at-scale, whereby students receive specific learning resources -- drawn from a large and diverse set -- that are tailored to their individual and time-varying needs. Results of our study, in which we collected 399 videos from 66 unique "teachers" on Mechanical Turk, suggest that (1) approximately 100 videos -- over 80% of which are mathematically fully correct -- can be crowdsourced per week for $5/video; (2) the average learning gains (posttest minus pretest score) associated with watching the videos was stat. sig. higher than for a control video (0.105 versus 0.045); and (3) the average learning gains (0.1416) from watching the best tested crowdsourced videos was comparable to the learning gains (0.1506) from watching a popular Khan Academy video on logarithms.


title: Current-mode full-duplex (CMFD) signaling for high-speed chip-to-chip interconnect

abstract: In this work we propose a new current-mode full-duplex (CMFD) signaling scheme for high-speed chip-to-chip data communication. In this scheme, all the internal nodes of the link are maintained at low-impedance, facilitating high-speed data communication. A new hybrid circuit topology required for separating the inbound signal from the outbound signal is presented. The proposed current-mode hybrid is realized by a source-coupled main driver, a scaled down replica stage and a common-gate (CG) transimpedance amplifier (TIA). Detailed design, analysis, noise and jitter characterization of the proposed hybrid is presented. The hybrid is realized in 1.8V, [email protected] digital CMOS technology. Using this hybrid circuit topology, CMFD signaling over a chip-to-chip interconnect is demonstrated. The post-layout performance shows 8Gb/s data transfer rate over a FR4 PCB trace of length 7.5in. for a target bit-error rate (BER) of 10^-^1^2. The FR4 PCB trace is modeled by measured 4-port S-parameters in the frequency range from 100MHz to 20GHz. The input-referred noise current of the receiver and output-noise voltage of transmitter are [email protected] and 5.34mV, respectively. The standalone power consumption of the hybrid is 14.64mW.


title: Efficient Characterization and Classification of Malware Using Deep Learning

abstract: Bad actors have embraced automation to construct malware, and current analysis systems cannot keep up with the ever-increasing load of malware being created daily. Additionally, some static analysis of malware can be computationally expensive, and not all static analysis should be considered for every sample that is part of a large malware dataset. As a result, highly expressive and inexpensive characterizations of malicious code, coupled with low resource machine learning classification platforms are required. In this paper, we use deep learning to build a meta-model that finds the simplest classifiers to characterize and assign malware into their corresponding families. Using static analysis of malware, we generate descriptive features to be used in conjunction with deep learning, in order to predict malware families. Our meta-model can determine when simple and less expensive malware characterization will suffice to accurately classify malicious executables, or when more computationally expensive descriptions are required. Finally, our meta-model is able to predict the simplest features and models to classify malware with an accuracy of up to 90%.


title: The stabilizing system of the spine. Part II. Neutral zone and instability hypothesis.

abstract: SummaryThe neutral zone is a region of intervertebral motion around the neutral posture where little resistance is offered by the passive spinal column. Several studies—in vitro cadaveric, in vivo animal, and mathematical simulations—have shown that the neutral zone is a parameter that correlates we


title: Trust-Based Requirements Traceability

abstract: Information retrieval (IR) approaches have proven useful in recovering traceability links between free-text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trust race, inspired by Web trust models to improve the precision and recall of traceability links: Trust race uses intractability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, His trace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with His trace to build Trust race in which we use Histraceas one expert adding knowledge to the traceability links extracttedfrom CVS/SVN change logs. We apply Trustrace on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that Trustrace improves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.


title: Monolithic fabrication of sensors and actuators in a soft robotic gripper

abstract: In this paper, we present a fluidically functionalized soft-bodied robot that integrates both sensing and actuation. Rather than combining these functions as an afterthought, we design sensors and actuators into the robot at the onset, both reducing fabrication complexity and optimizing component interactions. We utilize liquid metal strain sensors and pneumatic actuators embedded into a silicone robotic gripper. The robot's body is formed by curing the silicone in complex 3D printed molds. We show that the liquid metal strain gauges provide a repeatable resistance response during robotic actuation. We further show that, given sufficient control over other time-dependent variables, it is possible to determine when the robot begins gripping an object during actuation.


title: Blockage and directivity in 60 GHz wireless personal area networks: from cross-layer model to multihop MAC design

abstract: We present a cross-layer modeling and design approach for multigigabit indoor wireless personal area networks (WPANs) utilizing the unlicensed millimeter (mm) wave spectrum in the 60 GHz band. Our approach accounts for the following two characteristics that sharply distinguish mm wave networking from that at lower carrier frequencies. First, mm wave links are inherently directional: directivity is required to overcome the higher path loss at smaller wavelengths, and it is feasible with compact, low-cost circuit board antenna arrays. Second, indoor mm wave links are highly susceptible to blockage because of the limited ability to diffract around obstacles such as the human body and furniture. We develop a diffraction-based model to determine network link connectivity as a function of the locations of stationary and moving obstacles. For a centralized WPAN controlled by an access point, it is shown that multihop communication, with the introduction of a small number of relay nodes, is effective in maintaining network connectivity in scenarios where single-hop communication would suffer unacceptable outages. The proposed multihop MAC protocol accounts for the fact that every link in the WPAN is highly directional, and is shown, using packet level simulations, to maintain high network utilization with low overhead.


title: Processing Analytical Queries over Encrypted Data

abstract: MONOMI is a system for securely executing analytical workloads over sensitive data on an untrusted database server. MONOMI works by encrypting the entire database and running queries over the encrypted data. MONOMI introduces split client/server query execution, which can execute arbitrarily complex queries over encrypted data, as well as several techniques that improve performance for such workloads, including per-row precomputation, space-efficient encryption, grouped homomorphic addition, and pre-filtering. Since these optimizations are good for some queries but not others, MONOMI introduces a designer for choosing an efficient physical design at the server for a given workload, and a planner to choose an efficient execution plan for a given query at runtime. A prototype of MONOMI running on top of Postgres can execute most of the queries from the TPC-H benchmark with a median overhead of only 1.24× (ranging from 1.03×to 2.33×) compared to an un-encrypted Postgres database where a compromised server would reveal all data.


title: The Effects of Mindfulness Meditation on Cognitive Processes and Affect in Patients with Past Depression

abstract: This study describes the effects of an 8-week course in Mindfulness-Based Stress Reduction (MBSR; J. Kabat-Zinn, 1982, 1990) on affective symptoms (depression and anxiety), dysfunctional attitudes, and rumination. Given the focus of mindfulness meditation (MM) in modifying cognitive processes, it was hypothesized that the primary change in MM practice involves reductions in ruminative tendencies. We studied a sample of individuals with lifetime mood disorders who were assessed prior to and upon completion of an MBSR course. We also compared a waitlist sample matched with a subset of the MBSR completers. Overall, the results suggest that MM practice primarily leads to decreases in ruminative thinking, even after controlling for reductions in affective symptoms and dysfunctional beliefs.


title: Acquiring scattering properties of participating media by dilution

abstract: The visual world around us displays a rich set of volumetric effects due to participating media. The appearance of these media is governed by several physical properties such as particle densities, shapes and sizes, which must be input (directly or indirectly) to a rendering algorithm to generate realistic images. While there has been significant progress in developing rendering techniques (for instance, volumetric Monte Carlo methods and analytic approximations), there are very few methods that measure or estimate these properties for media that are of relevance to computer graphics. In this paper, we present a simple device and technique for robustly estimating the properties of a broad class of participating media that can be either (a) diluted in water such as juices, beverages, paints and cleaning supplies, or (b) dissolved in water such as powders and sugar/salt crystals, or (c) suspended in water such as impurities. The key idea is to dilute the concentrations of the media so that single scattering effects dominate and multiple scattering becomes negligible, leading to a simple and robust estimation algorithm. Furthermore, unlike previous approaches that require complicated or separate measurement setups for different types or properties of media, our method and setup can be used to measure media with a complete range of absorption and scattering properties from a single HDR photograph. Once the parameters of the diluted medium are estimated, a volumetric Monte Carlo technique may be used to create renderings of any medium concentration and with multiple scattering. We have measured the scattering parameters of forty commonly found materials, that can be immediately used by the computer graphics community. We can also create realistic images of combinations or mixtures of the original measured materials, thus giving the user a wide flexibility in making realistic images of participating media.


Maze Solving by Learning State Topologies


title: Imitating the human flute playing by the WF-4RII: Mechanical, perceptual and performance control systems

abstract: In this paper, the main improvements of the mechanical, perceptual and performance control systems, implemented on the Waseda Flutist Robot No.4 Refined II, are presented. Each of the systems of the flutist robot has been designed in order to imitate as near as possible the skills displayed by human during the flute performance; as a mean for clarifying the internal processes of the human motor control to perform skillful activities, enabling the robot to express ideas/feelings in musical terms, and proposing new ways of interaction between the human and the robot. A set of experiments were proposed to verify the effectiveness of the perceptual system as wells as the proposed volume control algorithm implemented on this robot. As a result, the robot has demonstrated to be able of recognizing automatically melodies from the performance of flutist beginners, detecting and tracking the presence of a human face and performing with more expressiveness by controlling the sound volume


title: Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition

abstract: The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets. Nevertheless, unsupervised image categorization (i.e., without the ground-truth labeling) is much less investigated, yet critically important and difficult when annotations are extremely hard to obtain in the conventional way of "Google Search" and crowd sourcing. We address this problem by presenting a looped deep pseudo-task optimization (LDPO) framework for joint mining of deep CNN features and image labels. Our method is conceptually simple and rests upon the hypothesized "convergence" of better labels leading to better trained CNN models which in turn feed more discriminative image representations to facilitate more meaningful clusters/labels. Our proposed method is validated in tackling two important applications: 1) Large-scale medical image annotation has always been a prohibitively expensive and easily-biased task even for well-trained radiologists. Significantly better image categorization results are achieved via our proposed approach compared to the previous state-of-the-art method. 2) Unsupervised scene recognition on representative and publicly available datasets with our proposed technique is examined. The LDPO achieves excellent quantitative scene classification results. On the MIT indoor scene dataset, it attains a clustering accuracy of 75.3%, compared to the state-of-the-art supervised classification accuracy of 81.0% (when both are based on the VGG-VD model).


title: Spontaneous EEG oscillations reveal periodic sampling of visual attention

abstract: An important effect of sustained attention is the facilitation of perception. Although the term “sustained” suggests that this beneficial effect endures continuously as long as something is attended, we present electrophysiological evidence that perception at attended locations is actually modulated periodically. Subjects detected brief light flashes that were presented peripherally at locations that were either attended or unattended. We analyzed the correlation between detection performance for attended and unattended stimuli and the phase of ongoing EEG oscillations, which relate to subsecond fluctuations of neuronal excitability. Although on average, detection performance was improved by attention—indicated by reduced detection thresholds at attended locations—we found that detection performance for attended stimuli actually fluctuated over time along with the phase of spontaneous oscillations in the θ (≈7 Hz) frequency band just before stimulus onset. This fluctuation was absent for unattended stimuli. This pattern of results suggests that “sustained” attention in fact exerts its facilitative effect on perception in a periodic fashion.


title: Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation

abstract: Different evaluation measures assess different characteristics of machine learning algorithms. The empirical evaluation of algorithms and classifiers is a matter of on-going debate among researchers. Most measures in use today focus on a classifier's ability to identify classes correctly. We note other useful properties, such as failure avoidance or class discrimination, and we suggest measures to evaluate such properties. These measures – Youden's index, likelihood, Discriminant power – are used in medical diagnosis. We show that they are interrelated, and we apply them to a case study from the field of electronic negotiations. We also list other learning problems which may benefit from the application of these measures.


title: Antibacterial Effects of Cinnamon: From Farm to Food, Cosmetic and Pharmaceutical Industries

abstract: Herbs and spices have been used since ancient times, because of their antimicrobial properties increasing the safety and shelf life of food products by acting against foodborne pathogens and spoilage bacteria. Plants have historically been used in traditional medicine as sources of natural antimicrobial substances for the treatment of infectious disease. Therefore, much attention has been paid to medicinal plants as a source of alternative antimicrobial strategies. Moreover, due to the growing demand for preservative-free cosmetics, herbal extracts with antimicrobial activity have recently been used in the cosmetic industry to reduce the risk of allergies connected to the presence of methylparabens. Some species belonging to the genus Cinnamomum, commonly used as spices, contain many antibacterial compounds. This paper reviews the literature published over the last five years regarding the antibacterial effects of cinnamon. In addition, a brief summary of the history, traditional uses, phytochemical constituents, and clinical impact of cinnamon is provided.


title: WEFEST: Word Embedding Feature Extension for Short Text Classification

abstract: Short text classification is a crucial task for information retrieval, social medial text categorization, and many other applications. In reality, due to the inherent sparsity and the limited information available in the short texts, learning and classifying short texts is a significant challenge. In this paper, we propose a new framework, WEFEST, which expands short texts using word embedding for classification. WEFEST is rooted on the deep language model, which learns a new word embedding space, by using word correlations, such that semantically related words also have close feature vectors in the new space. By using word embedding features to help expand the short tests, WEFEST can enrich the word density in the short texts for effective learning, by following three major steps. First, each short text in the training dataset is enriched by using pre-trained word feature embedding. Then the semantic similarity between two short texts is calculated by using the statistical frequency information retrieved from the trained model. Finally, we use the nearest neighbor algorithm to achieve short text classification. Experimental results on Chinese news title dataset validate the effectiveness of the proposed method.


title: Discovering Potential Effects of Dietary Supplements from Twitter Data

abstract: The U.S. Food and Drug Administration uses the Center for Food Safety and Applied Nutrition (CFSAN) Adverse Event Reporting System (CAERS) as the primary tool for identifying new and emerging dietary supplement adverse events. Despite mandatory and voluntary reporting of dietary supplement adverse events to CAERS, many continue to go unreported. Availability of social media has enabled dietary supplement consumers to freely share their concerns and experiences online. Such consumer generated information can be a useful source to further monitor the safety of dietary supplements. To study the usefulness of social media (Twitter in particular) for safety surveillance of dietary supplements, we developed a computational processing pipeline: 1) machine learning based identification of potential Twitter posts (tweets) of personal experiences related to the use of dietary supplements, 2) detection of potential supplement events from these tweets using the medpie open source tool, and 3) mapping detected events to effects through the taxonomy provided in SNOMED CT. Using our pipeline, we identified, from a group of 1,244,661 tweets collected, a total of 17,346 personal experience tweets pertaining to 4 dietary supplements. A total of 191 effects were mapped to SNOMED CT and we discovered that 48 of the 191 effects are not listed in either of the two online sources we referenced. However, the effects discovered from the social media data will need to be verified and confirmed with other sources and/or clinical evidences.


title: A Systemic Approach for IoT Security

abstract: In this paper we want to explore a new approach for security mechanisms design and deployment in the context of Internet of Things (IoT). We claim that the usual approach to security issues, typical of more classical systems and networks, does not grab all the aspects related to this new paradigm of communication, sharing and actuation. In fact, the IoT paradigm involves new features, mechanisms and dangers that cannot be completely taken into consideration through the classical formulation of security problems. The IoT calls for a new paradigm of security, which will have to consider the security problem from a holistic perspective including the new actors and their interactions. In this paper, we propose a systemic approach to security in IoT and explore the role of each actor and its interactions with the other main actors of the proposed scheme.


title: Balancing trade-offs in test-suite reduction

abstract: Regression testing is an important activity but can get expensive for large test suites. Test-suite reduction speeds up regression testing by identifying and removing redundant tests based on a given set of requirements. Traditional research on test-suite reduction is rather diverse but most commonly shares three properties: (1) requirements are defined by a coverage criterion such as statement coverage; (2) the reduced test suite has to satisfy all the requirements as the original test suite; and (3) the quality of the reduced test suites is measured on the software version on which the reduction is performed. These properties make it hard for test engineers to decide how to use reduced test suites. We address all three properties of traditional test-suite reduction: (1) we evaluate test-suite reduction with requirements defined by killed mutants; (2) we evaluate inadequate reduction that does not require reduced test suites to satisfy all the requirements; and (3) we propose evolution-aware metrics that evaluate the quality of the reduced test suites across multiple software versions. Our evaluations allow a more thorough exploration of trade-offs in test-suite reduction, and our evolution-aware metrics show how the quality of reduced test suites can change after the version where the reduction is performed. We compare the trade-offs among various reductions on 18 projects with a total of 261,235 tests over 3,590 commits and a cumulative history spanning 35 years of development. Our results help test engineers make a more informed decision about balancing size, coverage, and fault-detection loss of reduced test suites.


title: Parenting Stress and Resilience in Parents of Children With Autism Spectrum Disorder (ASD) in Southeast Asia: A Systematic Review

abstract: Background: This paper aimed to review the literature on the factors associated with parenting stress and resilience among parents of children with autism spectrum disorder (ASD) in the South East Asia (SEA) region. Methods: An extensive search of articles in multiple online databases (PsycNET, ProQuest, PudMed, EMBASE, CINAHL, Web of Science, and Google Scholar) resulted in 28 papers that met the inclusion criteria (i.e., conducted in the SEA region, specific to ASD only, published in a peer-reviewed journal, full text in English). Studies found were conducted in the following countries: Brunei, n = 1; Indonesia, n = 2; Malaysia, n = 12; Philippines, n = 5; Singapore, n = 5, Thailand, n = 2; and Vietnam, n = 1, but none from Cambodia, East Timor, Laos, and Myanmar were identified. Results: Across the studies, six main factors were found to be associated with parenting stress: social support, severity of autism symptoms, financial difficulty, parents' perception and understanding toward ASD, parents' anxiety and worries about their child's future, and religious beliefs. These six factors could also be categorized as either a source of parenting stress or a coping strategy/resilience mechanism that may attenuate parenting stress. Conclusion: The findings suggest that greater support services in Western countries may underlie the cultural differences observed in the SEA region. Limitations in the current review were identified. The limited number of studies yielded from the search suggests a need for expanded research on ASD and parenting stress, coping, and resilience in the SEA region especially in Cambodia, East Timor, Laos, and Myanmar. The identified stress and resilience factors may serve as sociocultural markers for clinicians, psychologists, and other professionals to consider when supporting parents of children with ASD.


title: Phrase similarity in humans and machines.

abstract: Computational models of semantics have emerged as powerful tools for natural language processing. Recent work has developed models to handle compositionality, but these models have typically been evaluated on large, uncontrolled corpora. In this paper, we constructed a controlled set of phrase pairs and collected phrase similarity judgments, revealing novel insights into human semantic representation. None of the computational models that we considered were able to capture the pattern of human judgments. The results of a second experiment, using the same stimuli with a transformational judgment task, support a transformational account of similarity, according to which the similarity between phrases is inversely related to the number of edits required to transform one mental model into another. Taken together, our results indicate that popular models of compositional semantics do not capture important facets of human semantic representation.


title: Quasi-static design technique for MM-wave micromachined filters with lumped elements and series stubs

abstract: This paper describes micromachined, membrane-supported low-pass and bandpass filters which are suitable for microwave and millimeter-wave (MM-wave) application. The designs are realized in coplanar-waveguide (CPW) form using short- and open-end series stubs with integrated metal-insulator-metal (MIM) capacitors, and are compact in lateral and longitudinal dimensions. A computationally efficient analysis has been developed for the design and characterization of the filters. The technique is based on a quasi-static coupled-line (CL) treatment of the series stubs, and uses normal mode impedance parameters, which are calculated with the spectral-domain approach (SDA). Due to the broad TEM-bandwidth of the membrane-supported transmission lines, the method can accurately predict filter responses well into the rejection band. To demonstrate the above claims, the measured and simulated S-parameters of a 0.3 mm /spl times/2.2 mm low-pass filter with a cutoff frequency at 17 GHz, and a second passband at 115 GHz, are presented. The new approach is also used in the design of bandpass filters which exhibit 1.5-2-dB insertion loss and bandwidths around 10%.


title: AMON: a wearable multiparameter medical monitoring and alert system

abstract: This paper describes an advanced care and alert portable telemedical monitor (AMON), a wearable medical monitoring and alert system targeting high-risk cardiac/respiratory patients. The system includes continuous collection and evaluation of multiple vital signs, intelligent multiparameter medical emergency detection, and a cellular connection to a medical center. By integrating the whole system in an unobtrusive, wrist-worn enclosure and applying aggressive low-power design techniques, continuous long-term monitoring can be performed without interfering with the patients' everyday activities and without restricting their mobility. In the first two and a half years of this EU IST sponsored project, the AMON consortium has designed, implemented, and tested the described wrist-worn device, a communication link, and a comprehensive medical center software package. The performance of the system has been validated by a medical study with a set of 33 subjects. The paper describes the main concepts behind the AMON system and presents details of the individual subsystems and solutions as well as the results of the medical validation.


title: SNORD116 deletions cause Prader-Willi syndrome with a mild phenotype and macrocephaly.

abstract: Prader-Willi syndrome is a complex condition caused by lack of expression of imprinted genes in the paternally derived region of chromosome 15 (15q11q13). A small number of patients with Prader-Willi phenotype have been discovered to have narrow deletions, not encompassing the whole critical region, but only the SNORD116 cluster, which includes genes codifying for small nucleolar RNAs. This kind of deletion usually is not detected by the classic DNA methylation analysis test. We present the case of a male patient with a mild Prader-Willi phenotype and a small deletion including SNORD116, diagnosed by methylation-sensitive multiplex ligation-dependent probe amplification (MLPA. The patient showed neonatal hypotonia, hyperphagia, obesity, central hypogonadism, hypothyroidism, strabismus. Stature and intellectual development are within the normal range. The presence of macrocephaly, observed in other cases of SNORD116 deletions as well, is uncommon for the classic phenotype of the syndrome.


title: Load Balancing in Cloud Computing

abstract: Cloud computing helps to share data and provide many resources to users. Users pay only for those resources as much they used. Cloud computing stores the data and distributed resources in the open environment. The amount of data storage increases quickly in open environment. So, load balancing is a main challenge in cloud environment. Load balancing is helped to distribute the dynamic workload across multiple nodes to ensure that no single node is overloaded. It helps in proper utilization of resources .It also improve the performance of the system. Many existing algorithms provide load balancing and better resource utilization. There are various types load are possible in cloud computing like memory, CPU and network load. Load balancing is the process of finding overloaded nodes and then transferring the extra load to other nodes.


title: Modeling Color Difference for Visualization Design

abstract: Color is frequently used to encode values in visualizations. For color encodings to be effective, the mapping between colors and values must preserve important differences in the data. However, most guidelines for effective color choice in visualization are based on either color perceptions measured using large, uniform fields in optimal viewing environments or on qualitative intuitions. These limitations may cause data misinterpretation in visualizations, which frequently use small, elongated marks. Our goal is to develop quantitative metrics to help people use color more effectively in visualizations. We present a series of crowdsourced studies measuring color difference perceptions for three common mark types: points, bars, and lines. Our results indicate that peoples' abilities to perceive color differences varies significantly across mark types. Probabilistic models constructed from the resulting data can provide objective guidance for designers, allowing them to anticipate viewer perceptions in order to inform effective encoding design.


title: Randomized structure from motion based on atomic 3D models from camera triplets

abstract: This paper presents a new efficient technique for large-scale structure from motion from unordered data sets. We avoid costly computation of all pairwise matches and geometries by sampling pairs of images using the pairwise similarity scores based on the detected occurrences of visual words leading to a significant speedup. Furthermore, atomic 3D models reconstructed from camera triplets are used as the seeds which form the final large-scale 3D model when merged together. Using three views instead of two allows us to reveal most of the outliers of pairwise geometries at an early stage of the process hindering them from derogating the quality of the resulting 3D structure at later stages. The accuracy of the proposed technique is shown on a set of 64 images where the result of the exhaustive technique is known. Scalability is demonstrated on a landmark reconstruction from hundreds of images.


title: Superscalar Instruction Execution in the 21164 Alpha Microprocessor

abstract: The 21164 is a new quad-issue, superscalar Alpha microprocessor that executes 1.2 billion instructions per second. The 300-MHz, 0.5-/spl mu/m CMOS chip delivers an estimated 345/505 SPECint32/SPECfp92 performance. The design's high clock rate, low operational latency, and high-throughput/nonblocking memory systems contribute to this performance. >


title: Probabilistic Topic Modeling in Multilingual Settings: A Short Overview of Its Methodology and Applications

abstract: Abstract Probabilistic topic models are unsupervised generative models which model document content as a two-step generation process, that is, documents are observed as mixtures of latent concepts or topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings. Novel topic models have been designed to work with parallel and comparable texts. We define multilingual probabilistic topic modeling (MuPTM) and present the first full overview of the current research, methodology, advantages and limitations in MuPTM. As a representative example, we choose a natural extension of the omnipresent LDA model to multilingual settings called bilingual LDA (BiLDA). We provide a thorough overview of this representative multilingual model from its high-level modeling assumptions down to its mathematical foundations. We demonstrate how to use the data representation by means of output sets of (i) per-topic word distributions and (ii) per-document topic distributions coming from a multilingual probabilistic topic model in various real-life cross-lingual tasks involving different languages, without any external language pair dependent translation resource: (1) cross-lingual event-centered news clustering, (2) cross-lingual document classification, (3) cross-lingual semantic similarity, and (4) cross-lingual information retrieval. We also briefly review several other applications present in the relevant literature, and introduce and illustrate two related modeling concepts: topic smoothing and topic pruning. In summary, this article encompasses the current research in multilingual probabilistic topic modeling. By presenting a series of potential applications, we reveal the importance of the language-independent and language pair independent data representations by means of MuPTM. We provide clear directions for future research in the field by providing a systematic overview of how to link and transfer aspect knowledge across corpora written in different languages via the shared space of latent cross-lingual topics, that is, how to effectively employ learned per-topic word distributions and per-document topic distributions of any multilingual probabilistic topic model in various cross-lingual applications.


The mediating role of mentalizing capacity between parents and peer attachment and adolescent borderline personality disorder


title: Voiceprint: A Novel Sybil Attack Detection Method Based on RSSI for VANETs

abstract: Vehicular Ad Hoc Networks (VANETs) enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that bring many benefits and conveniences to improve the road safety and drive comfort in future transportation systems. Sybil attack is considered one of the most risky threats in VANETs since a Sybil attacker can generate multiple fake identities with false messages to severely impair the normal functions of safety-related applications. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. To avoid the inaccurate position estimation according to predefined radio propagation models in previous RSSI-based detection methods, Voiceprint adopts the RSSI time series as the vehicular speech and compares the similarity among all received time series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without the support of the centralized infrastructure. It has more accurate detection rate in different dynamic environments. Extensive simulations and real-world experiments demonstrate that the proposed Voiceprint is an effective method considering the cost, complexity and performance.


title: Delusion and Deception in Large Infrastructure Projects: Two Models for Explaining and Preventing Executive Disaster

abstract: "Over budget, over time, over and over again" appears to be an appropriate slogan for large, complex infrastructure projects. This article explains why cost, benefits, and time forecasts for such projects are systematically over-optimistic in the planning phase. The underlying reasons for forecasting errors are grouped into three categories: delusions or honest mistakes; deceptions or strategic manipulation of information or processes; or bad luck. Delusion and deception have each been addressed in the management literature before, but here they are jointly considered for the first time. They are specifically applied to infrastructure problems in a manner that allows both academics and practitioners to understand and implement the suggested corrective procedures. The article provides a framework for analyzing the relative explanatory power of delusion and deception. It also suggests a simplified framework for analyzing the complex principal-agent relationships that are involved in the approval and construction of large infrastructure projects, which can be used to improve forecasts. Finally, the article illustrates reference class forecasting, an outside view de-biasing technique that has proven successful in overcoming both delusion and deception in private and public investment decisions.


title: Identifying Patients at High Risk of Emergency Hospital Admissions: A Logistic Regression Analysis

abstract: Objective To use routine data to identify patients at high risk of future emergency hospital admissions. Design Descriptive analysis of inpatient hospital episode statistics. Predictive model developed using multiple logistic regression. Setting National Health Service hospital trusts in England. Participants All patients with an emergency admission to an NHS hospital between 1 April 2000 and 31 March 2001. Main outcome measures ‘High-impact users’ were defined as patients who had at least one emergency inpatient admission and who then went on to have at least two further emergency hospital admissions in the 12 months following the start date of that index admission. Results 2 895 234 patients were admitted as emergencies in 2000/2001, of whom 147 725 (5.1%) did not survive their first spell. Of the 2 747 509 surviving patients, 269 686 (9.8%) subsequently had at least two or more emergency admissions within 365 days of the index date of admission. A further 236 779 (8.6%) died during this period. Risk factors for becoming a high-impact user included the number of emergencies in the 36 months before index spell, comorbidity, age, an admission for an ambulatory care sensitive condition, ethnicity, area-level socio-economic data, local admission rates, the number of episodes in the index spell, sex and the source of admission. The predictive model based on all emergency admissions produced a receiver operating characteristic curve score of 0.72. Conclusions Routine hospital episode statistics can be used to identify patients who are at high risk of suffering future multiple emergency hospital admissions. The potential cost savings in preventing a proportion of these subsequent admissions need to be compared with the costs of case management of these patients.


title: Fully Integrated, 100-mV Minimum Input Voltage Converter With Gate-Boosted Charge Pump Kick-Started by LC Oscillator for Energy Harvesting

abstract: A fully integrated step-up dc-dc converter for energy harvesting applications is presented. A minimum start-up voltage of 100 mV is achieved by the start-up mechanism based on an LC oscillator (LCO). Conventional voltage converters with the LCO-based start-up mechanism provide a significantly low conversion efficiency of around 1%. To overcome this drawback, the following two circuit techniques are proposed in this brief: 1) a gate-boosted charge pump; and 2) an LCO deactivation. The proposed voltage converter is fabricated in the 65-nm silicon-on-thin-buried-oxide process. The measurement results show that when the input voltage is 100 mV, the proposed converter can provide an output of 760 mV at a conversion efficiency of 33%, which is the highest efficiency compared with the conventional fully integrated voltage converters.


title: Lipofibromatous hamartoma of the median nerve in patients with macrodactyly: diagnosis and treatment of a rare disease causing carpal tunnel syndrome

abstract: Lipofibromatous hamartoma is a very rare benign peripheral nerve tumour. It is mostly encountered in the proximal extremities of young adults, involving the median nerve in the majority of cases. We present two patients with macrodactyly and carpal tunnel syndrome caused by lipofibromatous hamartoma of the median nerve and discuss diagnosis and treatment of the disease. A 10-year-old girl with a congenital progressive macrodactyly of her right index finger presented with a slowly growing mass in her right palm and pain and numbness, along with motor and sensory deficits in the median nerve distribution. Treatment consisted of carpal tunnel release, epineurolysis and partial excision of the fibrofatty tissue. The second patient, a 25-year-old man presented with a swelling in his left palm and findings compatible with carpal tunnel syndrome. Intraoperatively, the lesion presented as sausage-shaped enlargement of the median nerve by fibrofatty tissue. After carpal tunnel release, a partial excision of the mass with epineurolysis was performed. In both patients, histology showed nerve bundles separated by abundant fibrofatty tissue. In the girl, a proliferation of dysplastic perineurial cells could be observed. The suspected diagnosis for patients with macrodactyly and clinical signs of carpal tunnel syndrome should be lipofibromatous hamartoma. A carefully taken history, physical examination, X-ray, and MRI are important for its correct diagnosis. The surgical management remains controversial. Treatment should include decompression of the median nerve at points of compression, partial excision of the fibrofatty tissue, and debulking of soft tissue. In some cases, an epineurolysis can be additionally performed.


title: Business Continuity Plan Design 8 Steps for Getting Started Designing a Plan

abstract: This paper identifies eight steps that a company should follow to begin the development of a business continuity plan. The plan was developed by documenting the process a large transportation company used to reinforce their continuity planning in the wake of the 2004 hurricane season. This research identifies typical questions to be asked of functional departments within the organization along with some more specific question examples for the departments. The paper identifies 13 potential projects and other recommendations found from the research.


title: Measuring Critical thinking in Problem Solving through Online Discussion Forums in First Year

abstract: The first year in university offers an exciting and confusing experience for students as they transit from the secondary school environment. The adaptation is tested through new learning environments, and higher expectations. The inevitable gap created seems pronounced in Mathematics too. The present study focuses on a group of first year university students who take up a Mathematics subject. They are exposed to the rigors of open ended or ill structured problem solving through online discussion forums, as a mechanism to initiate and encourage higher order mathematical thinking and a culture of inquiry. Their thought processes are analyzed through a critical thinking model; their responses to the said approach are sought through a survey. The problem being pursued by the authors here is to measure critical thinking during problem solving in Mathematics among the first year university students. The online discussion forums were used as a medium to expose students to the facets of open ended/ill structured problem solving.


title: Customer relationship management: key components for IT success

abstract: This article is directed towards information technology (IT) and marketing managers considering implementation of a customer relationship management (CRM) solution. The goal of this article is not to provide an all‐inclusive tutorial on CRM, but rather to provide a high level insight of the fundamental principles behind CRM and critical aspects of the IT development process. The article begins with an IT manager’s introduction into the basic CRM business and marketing principles. At the heart of the article is a proposed system development lifecycle that highlights the aspects unique or critical to CRM. Finally, it concludes with some final thoughts for long‐term success. After reading this article, the reader will be mindful of the major issues needed for success and be equipped to discuss primary development matters with vendors, staff and management.


title: Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches

abstract: Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps in images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image augmentation strategies for training deep networks. We further propose two efficient post-learning methods such as, automatic false positive learning and off-line learning, both of which can be incorporated with the region based detection system for reliable polyp detection. Using the large size of colonoscopy databases, experimental results demonstrate that the suggested detection systems show better performance compared to other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos.


title: High reflection mirrors for pulse compression gratings.

abstract: We report an experimental investigation of high reflection mirrors used to fabricate gratings for pulse compression application at the wavelength of 1.053microm. Two kinds of mirrors are studied: the mixed Metal MultiLayer Dielectric (MMLD) mirrors which combine a gold metal layer with some e-beam evaporated dielectric bilayers on the top and the standard e-beam evaporated MultiLayer Dielectric (MLD) mirrors. Various samples were manufactured, damage tested at a pulse duration of 500fs. Damage sites were subsequently observed by means of Nomarski microscopy and white light interferometer microscopy. The comparison of the results evidences that if MMLD design can offer damage performances rather similar to MLD design, it also exhibits lower stresses; being thus an optimal mirror substrate for a pulse compression grating operating under vacuum.


title: Virtual Crowds: Steps Toward Behavioral Realism

abstract: This volume presents novel computational models for representing digital humans and their interactions with other virtual characters and meaningful environments. In this context, we describe efficient algorithms to animate, control, and author human-like agents having their own set of unique capabilities, personalities, and desires. We begin with the lowest level of footstep determination to steer agents in collision-free paths. Steering choices are controlled by navigation in complex environments, including multi-domain planning with dynamically changing situations. Virtual agents are given perceptual capabilities analogous to those of real people, including sound perception, multi-sense attention, and understanding of environment semantics which affect their behavior choices. The roles and impacts of individual attributes, such as memory and personality are explored. The animation challenges of integrating a number of simultaneous behavior and movement demands on an agent are addressed through an open source software system. Finally, the creation of stories and narratives with groups of agents subject to planning and environmental constraints culminates the presentation.


title: An Optimal Algorithm for Monte Carlo Estimation

abstract: A typical approach to estimate an unknown quantity $\mu$ is to design an experiment that produces a random variable Z, distributed in [0,1] with E[Z]=\mu$, run this experiment independently a number of times, and use the average of the outcomes as the estimate. In this paper, we consider the case when no a priori information about Z is known except that is distributed in [0,1]. We describe an approximation algorithm ${\cal A}{\cal A}$ which, given $\epsilon$ and $\delta$, when running independent experiments with respect to any Z, produces an estimate that is within a factor $1+\epsilon$ of $\mu$ with probability at least $1-\delta$. We prove that the expected number of experiments run by ${\cal A}{\cal A}$ (which depends on Z) is optimal to within a constant factor {for every} Z.


The Concepts of Hidden Markov Model in Speech Recognition


title: Adolescents and Cyber Bullying: The Precaution Adoption Process Model

abstract: A survey of adolescents (N?=?1,488) documented Facebook use and experience with cyber bullying. The study found that 84 % of adolescents (middle school through college undergraduates) use Facebook, and that most users log on daily. While 30 % of the sample reported being cyber bullied, only 12.5 % quit using the site, and only 18 % told a parent or school official about the abuse. Up to 75 % of middle school Facebook users have experienced cyber bullying. The current study was the first to apply the Precaution Adoption Process Model (PAPM) to cyber bullying or to test the model with children and adolescents. Results suggest that most adolescents are aware of cyber bullying and acknowledge it as a problem in their school. About half of the adolescents did not progress beyond Stage 2 of the PAPM (aware of the problem, but haven't really thought about it). Adolescents also exhibited optimistic bias, believing they were less likely than peers to become cyber bullied. Implications for prevention education are discussed.


title: Single-Chip GaN X-Band MMIC Dynamic Supply PAs

abstract: This paper discusses a fully integrated single- chip supply-modulated power amplifier (SM-PA) GaN PA for amplifying high peak-to-average power ratio (PAPR) signals at X-band carrier frequencies. Components of the MMIC are detailed. Specifically, 10-W MMIC GaN PAs with greater than 60% efficiency at 10GHz with efficient GaN supply modulators consisting of a 100-MHz switching dc-dc converter and a cascode UHF PA, for signals with envelope bandwidths of several 100 MHz and PAPR up to 10dB, are detailed. Other transmitter approaches for high PAPR broadband signals, such as outphasing transmitters and PAs with output harmonic injection are overviewed in the context of supply modulation, and the main technical challenges are discussed.


title: A RGBD SLAM algorithm combining ORB with PROSAC for indoor mobile robot

abstract: In order to enhance the instantaneity of SLAM for indoor mobile robot, a RGBD SLAM method based on Kinect was proposed. In the method, oriented FAST and rotated BRIEF(ORB) algorithm was combined with progressive sample consensus(PROSAC) algorithm to execute feature extracting and matching. More specifically, ORB algorithm which has better property than many other feature descriptors was used for extracting feature. At the same time, ICP algorithm was adopted for coarse registration of the point clouds, and PROSAC algorithm which is superior than RANSAC in outlier removal was employed to eliminate incorrect matching. To make the result more accurate, pose-graph optimization was achieved based on g2o framework. In the end, a 3D volumetric map which can be directly used to the navigation of robots was created.


title: TEP: Tehran English-Persian parallel corpus

abstract: Parallel corpora are one of the key resources in natural language processing. In spite of their importance in many multi-lingual applications, no large-scale English-Persian corpus has been made available so far, given the difficulties in its creation and the intensive labors required. In this paper, the construction process of Tehran English-Persian parallel corpus (TEP) using movie subtitles, together with some of the difficulties we experienced during data extraction and sentence alignment are addressed. To the best of our knowledge, TEP has been the first freely released large-scale (in order of million words) English-Persian parallel corpus.


title: TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning

abstract: High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training. In this work, we propose TernGrad that uses ternary gradients to accelerate distributed deep learning in data parallelism. Our approach requires only three numerical levels {-1,0,1}, which can aggressively reduce the communication time. We mathematically prove the convergence of TernGrad under the assumption of a bound on gradients. Guided by the bound, we propose layer-wise ternarizing and gradient clipping to improve its convergence. Our experiments show that applying TernGrad on AlexNet does not incur any accuracy loss and can even improve accuracy. The accuracy loss of GoogLeNet induced by TernGrad is less than 2% on average. Finally, a performance model is proposed to study the scalability of TernGrad. Experiments show significant speed gains for various deep neural networks. Our source code is available.


title: Robust and Accurate Smartphone-Based Step Counting for Indoor Localization

abstract: Robust and accurate step counting is important for indoor localization algorithms that rely on smartphone inertial sensors. Existing solutions for step counting do not consider users’ false walking state (e.g., when a user in still state uses her phone for texting, playing games, and watching movies), which results in the over-counting problem. In this paper, we propose a robust and accurate step counting algorithm to solve the overcounting problem caused by false walking. Experimental results show that the proposed algorithm outperforms the commonly-used peak detection-based method and can improve the step counting accuracy by 6.56% for normal walking case, 9.54% for free walking case, and by 58.92% for false walking case, which is a significant improvement in the accuracy and robustness. We also compare the proposed method with popular commercial step counting applications including S Health, i-Health, and Pedometer++, which shows that our method can achieve better accuracy.


title: Isolated High Step-Up DC–DC Converter With Low Voltage Stress

abstract: Fuel cell stacks and photovoltaic panels generate rather low dc voltages and these voltages need to be boosted before converted to ac voltage. Therefore, high step-up ratio dc-dc converters are preferred in renewable energy systems. A new Z-source-based topology that can boost the input voltage to desired levels with low duty ratios is proposed in this paper. The topology utilizes coupled inductor. The leakage inductance energy can efficiently be discharged. Since the device stresses are low in this topology, low-voltage MOSFETs with small RDS(on) values can be selected to reduce the conduction loss. These features improve the converter efficiency. Also, the converter has a galvanic isolation between source and load. The operating principles and steady-state analysis of continuous and discontinuous conduction modes are discussed in detail. Finally, experimental results are given for a prototype converter that converts 25 V dc to 400 V dc at various power levels with over 90% efficiency to verify the effectiveness of the theoretical analysis.


title: Connections Between Internet Use and Political Efficacy, Knowledge, and Participation

abstract: Using data from the 2000 National Annenberg Election Survey, this study looks at the relationships between Internet access and online exposure to information about the presidential campaign and political efficacy, knowledge, and participation. Results show that Internet access and online exposure to information about the presidential campaign are significantly associated with these important political variables. Several of the associations between Internet access and exposure with political efficacy, knowledge, and participation are detectable even when taking sociodemographic variables, party identification, partisan strength, political interest, and other media exposures variables into account. Although statistically significant, these associations are quite small.


title: Changes in child exposure to secondhand smoke after implementation of smoke-free legislation in Wales: a repeated cross-sectional study

abstract: BackgroundSmoke-free legislation was introduced in Wales in April 2007. In response to concerns regarding potential displacement of smoking into the home following legislation, this study assessed changes in secondhand smoke (SHS) exposure amongst non-smoking children.MethodsApproximately 1,750 year 6 (aged 10-11) children from 75 Welsh primary schools were included in cross-sectional surveys immediately pre-legislation and one year later. Participants completed self-report questionnaires and provided saliva samples for cotinine assay. Regression analyses assessed the impact of legislation on children's SHS exposure at the population level, and amongst subgroups defined by parental figures who smoke within the home.ResultsGeometric mean salivary cotinine concentrations were 0.17 ng/ml (95% CI 0.15,0.20) pre-legislation and 0.15 ng/ml (95% CI 0.13,0.17), post-legislation, although this change was not statistically significant. Significant movement was however observed from the middle (0.10-0.50 ng/ml) to lower tertile, though not from the higher end (>0.51 ng/ml) to the middle.Reported exposure to SHS was greatest within the home. Home-based exposure did not change significantly post-legislation. Reported exposure in cafés or restaurants, buses and trains, and indoor leisure facilities fell significantly.The proportion of children reporting that parent figures smoked in the home declined (P = 0.03), with children with no parent figures who smoke in the home significantly more likely to provide saliva with cotinine concentrations of <0.10 ng/ml post-legislation.Amongst children with no parent figures who smoke in the home, the likelihood of 'not knowing' or 'never' being in a place where people were smoking increased post-legislation.ConclusionSmoke-free legislation in Wales did not increase SHS exposure in homes of children aged 10-11. Reported SHS exposure in public places fell significantly. The home remained the main source of children's SHS exposure. The legislation was associated with an unexpected reduction in cotinine levels among children with lower SHS exposure pre-legislation. The findings indicate positive rather than harmful effects of legislation on children's SHS exposure, but highlight the need for further action to protect those children most exposed to SHS.


Creating a Large Benchmark for Open Information Extraction


title: Involvement of 5-HT(2) receptor in imipramine-induced hyperglycemia in mice.

abstract: Effects of imipramine on plasma glucose levels were investigated in mice. Imipramine i. p. induced dose-dependent hyperglycemia, which was enhanced by pretreatment with 5-HT (1/2/5/7) receptor antagonist methysergide and 5-HT (2A/2B/2C) receptor antagonist LY 53857. 5-HT (2C/2B) receptor antagonist SB 206553 also augmented imipramine-induced hyperglycemia although 5-HT (1A) and 5-HT (1B) receptor antagonist (-)-propranolol,5-HT (2A) receptor antagonist ketanserin and 5-HT (3/4) receptor antagonist tropisetron each had no effect. Imipramine i. p.-induced hyperglycemia was antagonized by the 5-HT (2C/2B) receptor agonist 1-(3-chlorophenyl)piperazine (mCPP), while the 5-HT (2B) receptor agonist BW 723C86 had no effect. Intracerebroventricular injection of imipramine also elevated plasma glucose levels, which is enhanced by SB 206553. Hyperglycemia elicited by central injection of imipramine was abolished by adrenalectomy. These results suggest that imipramine-induced hyperglycemia in mice is related to its inhibition of the central 5-HT (2C) receptor. Moreover, our results indicate that adrenaline release is related to imipramine-induced hyperglycemia.


title: Extending 100G transatlantic optical transmission over legacy DMF fibers using time-domain four-dimensional nonbinary LDPC-coded modulation

abstract: The transmission reach of 102.4 Gb/s Nyquist-shaped signal has been experimentally improved to 7,680 km by employing time-domain four-dimensional nonbinary LDPC-coded modulation scheme, resulting in 1,440 km extended distance compared to the counterpart nonbinary LDPC-coded PDM-QPSK.


title: Conducting Indirect-Treatment-Comparison and Network-Meta-Analysis Studies: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 2

abstract: Evidence-based health care decision making requires comparison of all relevant competing interventions. In the absence of randomized controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best treatment(s). Mixed treatment comparisons, a special case of network meta-analysis, combine direct evidence and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than traditional meta-analysis. This report from the International Society for Pharmacoeconomics and Outcomes Research Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on technical aspects of conducting network meta-analyses (our use of this term includes most methods that involve meta-analysis in the context of a network of evidence). We start with a discussion of strategies for developing networks of evidence. Next we briefly review assumptions of network meta-analysis. Then we focus on the statistical analysis of the data: objectives, models (fixed-effects and random-effects), frequentist versus Bayesian approaches, and model validation. A checklist highlights key components of network meta-analysis, and substantial examples illustrate indirect treatment comparisons (both frequentist and Bayesian approaches) and network meta-analysis. A further section discusses eight key areas for future research.


title: Q-caching: an integrated reinforcement-learning approach for caching and routing in information-centric networks

abstract: Content delivery, such as video streaming, is one of the most prevalent Internet applications. Although very popular, the continuous growth of such applications poses novel performance and scalability challenges. Information-centric networks put content at the center, and propose novel solutions to such challenges but also pose new questions on the interface between caching and routing. In this paper, building on top of Q-routing we propose a caching strategy, namely Q-caching, which leverages information that is already collected by the routing algorithm. Q-caching promotes content diversity in the network, reducing the load at custodians and average download times for clients. In stylized topologies, we show that the gains of Q-caching against state-of-the-art algorithms are significant. We then consider the RNP topology, and show that Q-caching performance is more flexible while competitive when compared against existing algorithms.


Catch-up growth strategies differ between body structures : interactions between age and structure-specific growth in wild nestling Alpine


title: Global warming potential predictions for hydrofluoroethers with two carbon atoms

abstract: Global warming potentials are predicted using computational chemistry and thermodynamics approaches for four hydrofluoroethers where no data have previously been available. We also compare results with the same methodology for six other species. We combine predictions of radiative forcing values from density functional theory computations at the B3LYP/6-31g* level of theory with previous experimentally determined or newly estimated hydroxyl radical-hydrogen abstraction rate constants to obtain these global warming potentials. We find that many of the HFEs studied have lower global warming potentials than the hydrofluorocarbons and chlorofluorocarbons they may soon replace, although other environmental and technical issues may need to be addressed first.


title: Interactive visual simulation of dynamic ink diffusion effects

abstract: This paper presents an effective method that simulates the ink diffusion process with visual plausible effects and real-time performance. Our algorithm updates the dynamic ink volume with a hybrid grid-particle representation: the fluid velocity field is calculated with a low-resolution grid structure, while the highly detailed ink effects are controlled and visualized with the particles. We propose an improved ink rendering method with particle sprites and motion blur techniques. The simulation and the rendering processes are efficiently implemented on graphics hardware for interactive frame rates. Compared to traditional simulation methods that treat water and ink as two mixable fluids, our method is simple but effective: it captures various ink effects such as pinned boundary [Chu and Tai 2005] and filament pattern [Shiny et al. 2010] with real-time performance; it allows easy interaction with the artists; it includes basic solid-fluid interaction. We believe that our method is attractive for industrial animation and art design.


title: Issuer Quality and Corporate Bond Returns

abstract: Changes in the pricing of credit risk disproportionately affect the debt financing costs faced by low credit quality firms. As a result, time-series variation in the average quality of debt issuers may be useful for forecasting excess corporate bond returns. We show that when issuance comes disproportionately from lower quality borrowers, future excess returns on high yield and investment grade bonds are low and often significantly negative. The degree of predictability is large in both economic and statistical terms, with univariate R2 statistics as high as 30% at a 3-year horizon. The results are difficult to reconcile with integrated-markets models in which the rationally determined price of risk fluctuates in a countercyclical fashion. The results can be partially explained by models in which shocks to intermediary capital or agency problems drive variation in required returns. Finally, we consider models in which investor over-extrapolation plays a role and find some evidence in favor of these models.


title: A Framework to Evaluate Omnidirectional Video Coding Schemes

abstract: Omnidirectional videos of real world environments viewed on head-mounted displays with real-time head motion tracking can offer immersive visual experiences. For live streaming applications, compression is critical to reduce the bitrate. Omnidirectional videos, which are spherical in nature, are mapped onto one or more planes before encoding to interface with modern video coding standards. In this paper, we consider the problem of evaluating the coding efficiency in the context of viewing with a head-mounted display. We extract viewport based head motion trajectories, and compare the original and coded videos on the viewport. With this approach, we compare different sphere-to-plane mappings. We show that the average viewport quality can be approximated by a weighted spherical PSNR.