query: A 5-GHz fully integrated full PMOS low-phase-noise LC VCO

positive: 1/f noise of NMOS and PMOS transistors and their implications to design of voltage controlled oscillators Low frequency noise of NMOS and PMOS transistors in a 0.25 /spl mu/m foundry CMOS process with a pure SiO/sub 2/ gate oxide layer is characterized for the entire range of MOSFET operation. Surprisingly, the measurement results showed that surface channel PMOS transistors have about an order of magnitude lower 1/f noise than NMOS transistors especially at V/sub GS/-V/sub TH/ less than /spl sim/0.4 V The data were used to show that a VCO using all surface channel PMOS transistors can have /spl sim/14 dB lower close-in phase noise compared to that for a VCO using all surface channel NMOS transistors.

positive: Design issues in CMOS differential LC oscillators An analysis of phase noise in differential crosscoupled inductance–capacitance(LC) oscillators is presented. The effect of tail current and tank power dissipation on the voltage amplitude is shown. Various noise sources in the complementary cross-coupled pair are identified, and their effect on phase noise is analyzed. The predictions are in good agreement with measurements over a large range of tail currents and supply voltages. A 1.8-GHz LC oscillator with a phase noise of 121 dBc/Hz at 600 kHz is demonstrated, dissipating 6 mW of power using on-chip spiral inductors.

positive: Oscillator phase noise: a tutorial Linear time-invariant (LTI) phase noise theories provide important qualitative design insights but are limited in their quantitative predictive power. Part of the difficulty is that device noise undergoes multiple frequency translations to become oscillator phase noise. A quantitative understanding of this process requires abandoning the principle of time invariance assumed in most older theories of phase noise. Fortunately, the noise-to-phase transfer function of oscillators is still linear, despite the existence of the nonlinearities necessary for amplitude stabilization. In addition to providing a quantitative reconciliation between theory and measurement, the time-varying phase noise model presented in this tutorial identifies the importance of symmetry in suppressing the upconversion of 1/f noise into close-in phase noise, and provides an explicit appreciation of cyclostationary effects and AM-PM conversion. These insights allow a reinterpretation of why the Colpitts oscillator exhibits good performance, and suggest new oscillator topologies. Tuned LC and ring oscillator circuit examples are presented to reinforce the theoretical considerations developed. Simulation issues and the accommodation of amplitude noise are considered in appendixes.

positive: Jitter and Phase Noise in Ring Oscillators A companion analysis of clock jitter and phase noise of single-ended and differential ring oscillators is presented. The impulse sensitivity functions are used to derive expressions for the jitter and phase noise of ring oscillators. The effect of the number of stages, power dissipation, frequency of oscillation, and shortchannel effects on the jitter and phase noise of ring oscillators is analyzed. Jitter and phase noise due to substrate and supply noise is discussed, and the effect of symmetry on the upconversion of 1/f noise is demonstrated. Several new design insights are given for low jitter/phase-noise design. Good agreement between theory and measurements is observed.

positive: A study of phase noise in CMOS oscillators This paper presents a study of phase noise in two inductorless CMOS oscillators. First-order analysis of a linear oscillatory system leads to a noise shaping function and a new definition of Q. A linear model of CMOS ring oscillators is used to calculate their phase noise, and three phase noise phenomena, namely, additive noise, high-frequency multiplicative noise, and low-frequency multiplicative noise, are identified and formulated. Based on the same concepts, a CMOS relaxation oscillator is also analyzed. Issues and techniques related to simulation of noise in the time domain are described, and two prototypes fabricated in a 0.5m CMOS technology are used to investigate the accuracy of the theoretical predictions. Compared with the measured results, the calculated phase noise values of a 2-GHz ring oscillator and a 900-MHz relaxation oscillator at 5 MHz offset have an error of approximately 4 dB.

negative: On the Logic and Purpose of Significance Testing There has been much recent attention given to the problems involved with the traditional approach to null hypothesis significance testing (NHST). Many have suggested that, perhaps, NHST should be abandoned altogether in favor of other bases for conclusions such as confidence intervals and effect size estimates (e.g., Schmidt, 1996). The purposes of this article are to (a) review the function that data analysis is supposed to serve in the social sciences, (b) examine the ways in which these functions are performed by NHST, (c) examine the case against NHST, and (d) evaluate interval-based estimation as an alternative to NHST.

negative: Pedestrian Motion Tracking and Crowd Abnormal Behavior Detection Based on Intelligent Video Surveillance Pedestrian tracking and detection of crowd abnormal activity under dynamic and complex background using Intelligent Video Surveillance (IVS) system are beneficial for security in public places. This paper presents a pedestrian tracking method combing Histogram of Oriented Gradients (HOG) detection and particle filter. This method regards the particle filter as the tracking framework, identifies the target area according to the result of HOG detection and modifies particle sampling constantly. Our method can track pedestrians in dynamic backgrounds more accurately compared with the traditional particle filter algorithms. Meanwhile, a method to detect crowd abnormal activity is also proposed based on a model of crowd features using Mixture of Gaussian (MOG). This method calculates features of crowd-interest points, then establishes the crowd features model using MOG, conducts self-adaptive updating and detects abnormal activity by matching the input feature with model distribution. Experiments show our algorithm can efficiently detect abnormal velocity and escape panic in crowds with a high detection rate and a relatively low false alarm rate.

negative: Flip-chip interconnection EA-DFB laser module for 100-Gbit/s/λ application We have developed a flip-chip interconnection technique to solve the problem of the degradation of the modulation bandwidth of an optical transmitter due to the parasitic inductance of bonding wire. And we have fabricated an LE-EA-DFB laser module that has a 3-dB bandwidth of 59 GHz thanks to our flip-chip interconnection technique. The fabricated optical receiver, which includes a wideband electrical amplifier, has a 3-dB bandwidth of 51 GHz. Using these optical modules with a 3-dB bandwidth of more than 50 GHz, we have obtained a bit-error rate of less than 2 × 10-4, which is an error-free condition using KP4 forward error correction, under 53.2-Gbaud 4-level pulse amplitude modulation (4-PAM) operation without an equalizer even after a 2-km transmission through single-mode fiber.

negative: Terrain surface classification for autonomous ground vehicles using a 2D laser stripe-based structured light sensor To increase autonomous ground vehicle (AGV) safety and efficiency on outdoor terrains the vehicle's control system should have settings for individual terrain surfaces. A first step in such a terrain-dependent control system is classification of the surface upon which the AGV is traversing. This paper considers vision-based terrain surface classification for the path directly in front of the vehicle (≪ 1 m). Most visionbased terrain classification has focused on terrain traversability and not on terrain surface classification. The few approaches to classifying traversable terrain surfaces, with the exception of the use of infrared cameras to classify mud, have relied on stand-alone cameras that are designed for daytime use and are not expected to perform well in the dark. In contrast, this research uses a laser stripe-based structured light sensor, which uses a laser in conjunction with a camera, and hence can work at night. Also, unlike most previous results, the classification here does not rely on color since color changes with illumination and weather, and certain terrains have multiple colors (e.g., sand may be red or white). Instead, it relies only on spatial relationships, specifically spatial frequency response and texture, which captures spatial relationships between different gray levels. Terrain surface classification using each of these features separately is conducted by using a probabilistic neural network. Experimental results based on classifying four outdoor terrains demonstrate the effectiveness of the proposed methods.

negative: The microbiota-gut-brain axis: neurobehavioral correlates, health and sociality Recent data suggest that the human body is not such a neatly self-sufficient island after all. It is more like a super-complex ecosystem containing trillions of bacteria and other microorganisms that inhabit all our surfaces; skin, mouth, sexual organs, and specially intestines. It has recently become evident that such microbiota, specifically within the gut, can greatly influence many physiological parameters, including cognitive functions, such as learning, memory and decision making processes. Human microbiota is a diverse and dynamic ecosystem, which has evolved in a mutualistic relationship with its host. Ontogenetically, it is vertically inoculated from the mother during birth, established during the first year of life and during lifespan, horizontally transferred among relatives, mates or close community members. This micro-ecosystem serves the host by protecting it against pathogens, metabolizing complex lipids and polysaccharides that otherwise would be inaccessible nutrients, neutralizing drugs and carcinogens, modulating intestinal motility, and making visceral perception possible. It is now evident that the bidirectional signaling between the gastrointestinal tract and the brain, mainly through the vagus nerve, the so called "microbiota-gut-vagus-brain axis," is vital for maintaining homeostasis and it may be also involved in the etiology of several metabolic and mental dysfunctions/disorders. Here we review evidence on the ability of the gut microbiota to communicate with the brain and thus modulate behavior, and also elaborate on the ethological and cultural strategies of human and non-human primates to select, transfer and eliminate microorganisms for selecting the commensal profile.

negative: Endurance exercise performance: the physiology of champions. Efforts to understand human physiology through the study of champion athletes and record performances have been ongoing for about a century. For endurance sports three main factors--maximal oxygen consumption (.VO(2,max)), the so-called 'lactate threshold' and efficiency (i.e. the oxygen cost to generate a given running speed or cycling power output)--appear to play key roles in endurance performance. and lactate threshold interact to determine the 'performance .VO(2)' which is the oxygen consumption that can be sustained for a given period of time. Efficiency interacts with the performance .VO(2) to establish the speed or power that can be generated at this oxygen consumption. This review focuses on what is currently known about how these factors interact, their utility as predictors of elite performance, and areas where there is relatively less information to guide current thinking. In this context, definitive ideas about the physiological determinants of running and cycling efficiency is relatively lacking in comparison with .VO(2,max) and the lactate threshold, and there is surprisingly limited and clear information about the genetic factors that might pre-dispose for elite performance. It should also be cautioned that complex motivational and sociological factors also play important roles in who does or does not become a champion and these factors go far beyond simple physiological explanations. Therefore, the performance of elite athletes is likely to defy the types of easy explanations sought by scientific reductionism and remain an important puzzle for those interested in physiological integration well into the future.

negative: Breaking Mifare DESFire MF3ICD40: Power Analysis and Templates in the Real World With the advent of side-channel analysis, implementations of mathematically secure ciphers face a new threat: by exploiting the physical characteristics of a device, adversaries are able to break algorithms such as AES or Triple-DES (3DES), for which no efficient analytical or brute-force attacks exist. In this paper, we demonstrate practical, noninvasive side-channel attacks on the Mifare DESFire MF3ICD40 contactless smartcard, a 3DES-based alternative to the cryptanalytically weak Mifare Classic [9, 25]. We detail on how to recover the complete 112-bit secret key of the employed 3DES algorithm, using non-invasive power analysis and template attacks. Our methods can be put into practice at a low cost with standard equipment, thus posing a severe threat to many real-world applications that employ the DESFire MF3ICD40 smartcard.

negative: A Survey of Algorithms for Keyword Search on Graph Data In this chapter, we survey methods that perform keyword search on graph data. Keyword search provides a simple but user-friendly interface to retrieve information from complicated data structures. Since many real life datasets are represented by trees and graphs, keyword search has become an attractive mechanism for data of a variety of types. In this survey, we discuss methods of keyword search on schema graphs, which are abstract representation for XML data and relational data, and methods of keyword search on schema-free graphs. In our discussion, we focus on three major challenges of keyword search on graphs. First, what is the semantics of keyword search on graphs, or, what qualifies as an answer to a keyword search; second, what constitutes a good answer, or, how to rank the answers; third, how to perform keyword search efficiently. We also discuss some unresolved challenges and propose some new research directions

negative: Virtual Reality and Education Virtual Reality (VR), a new computer technology, has incredible potential in the education field. The reason for this assertion is that education is a field that requires the students to understand complex data, particularly in the study of science (see "Why is science hard to learn?" by Millar) and VR makes that task easier. VR presents information in a 3-dimensional form with the participant viewing the world from inside the world (an immersed viewpoint) with the ability to interact with the information or world. People can enter a VR world that is already created or build their own. VR’s style of presentation mimics the ways that we, as humans, have learned to interact with our physical world. The requirement of learning abstract concepts (such as written language or jargon of a particular field) in order to understand data is dramatically reduced. For example, a chemistry world can show participants how electrons hover around a nucleus depending on the amount of energy involved. The abstractness of the subject would not be eliminated, but metaphors could be employed that were based solidly in reality. For instance, in the electron world the color of the world could represent the amount of energy in the system with red representing more energy than blue. For the students, they would be able to relate to hot things looking red and cold things looking blue without the concept of "joules increasing" getting in the way.

negative: Diverse and abundant antibiotic resistance genes in Chinese swine farms. Antibiotic resistance genes (ARGs) are emerging contaminants posing a potential worldwide human health risk. Intensive animal husbandry is believed to be a major contributor to the increased environmental burden of ARGs. Despite the volume of antibiotics used in China, little information is available regarding the corresponding ARGs associated with animal farms. We assessed type and concentrations of ARGs at three stages of manure processing to land disposal at three large-scale (10,000 animals per year) commercial swine farms in China. In-feed or therapeutic antibiotics used on these farms include all major classes of antibiotics except vancomycins. High-capacity quantitative PCR arrays detected 149 unique resistance genes among all of the farm samples, the top 63 ARGs being enriched 192-fold (median) up to 28,000-fold (maximum) compared with their respective antibiotic-free manure or soil controls. Antibiotics and heavy metals used as feed supplements were elevated in the manures, suggesting the potential for coselection of resistance traits. The potential for horizontal transfer of ARGs because of transposon-specific ARGs is implicated by the enrichment of transposases--the top six alleles being enriched 189-fold (median) up to 90,000-fold in manure--as well as the high correlation (r(2) = 0.96) between ARG and transposase abundance. In addition, abundance of ARGs correlated directly with antibiotic and metal concentrations, indicating their importance in selection of resistance genes. Diverse, abundant, and potentially mobile ARGs in farm samples suggest that unmonitored use of antibiotics and metals is causing the emergence and release of ARGs to the environment.

negative: Understanding microblog continuance usage intention: an integrated model Purpose – The purpose of this paper is to explore the factors affecting users’ continuous microblog usage intention. In recent years, the number of microblog users has gradually declined. This research can reveal microblog users’ needs and provide the improvement direction of microblog services. Design/methodology/approach – By integrating Wixom and Todd’s theoretical framework, the Uses and Gratifications Theory and the DeLone and McLean Information System Success Model, a conceptual model is proposed. In this model, gratification is defined as a kind of behavioral attitude, and satisfaction is viewed as an object-based attitude. The survey data were collected online and analyzed using the partial least squares method. Findings – The results suggest that users’ continuance intention (behavioral intention) is jointly determined by users’ gratification (behavioral-based attitude) and their habitual microblog usage behavior. Likewise, gratification is positively affected by satisfaction (object-based attitude) which is a joint function of system quality and information quality (object-based beliefs). Originality/value – In this research, Wixom and Todd’s principle is applied as the basic theoretical framework; gratification is viewed as a behavior attitude and user satisfaction is identified as an object-based attitude. This research model is a new lens for continuance usage research.

negative: Calibrating the COCOMO II Post-Architecture Model The COCOMO II model was created to meet the need for a cost model that accounted for future software development practices. This resulted in the formulation of three submodels for cost estimation, one for composing applications, one for early lifecycle estimation and one for detailed estimation when the architecture of the product is understood. This paper describes the calibration procedures for the last model, Post-Architecture COCOMO II model, from eighty-three observations. The results of the multiple regression analysis and their implications are discussed. Future work includes further analysis of the PostArchitecture model, calibration of the other models, derivation of maintenance parameters, and refining the effort distribution for the model output.

negative: Automatic Seed Classification by Shape and Color Features using Machine Vision Technology : In this paper the proposed system uses content based image retrieval (CBIR) technique for identification of seed e.g. wheat, rice, gram etc. on the basis of their features. CBIR is a technique to identify or recognize the image on the basis of features present in image. Basically features are classified in to four categories 1.color 2.Shape 3. texture 4. size .In this system we are extracting color, shape feature extraction. After that classifying images in to categories using neural network according to the weights and image displayed from the category for which neural network shows maximum weight. category1 belongs to wheat and category2 belongs to gram. Experiment was conducted on 200 images of wheat and gram by using Euclidean distance(ED) and artificial neural network techniques. From 200 images 150 are used for training purpose and 50 images are used for testing purpose. The precision rate of the system by using ED is 84.4 percent By using Artificial neural network precision rate is 95 percent.

negative: Commentary on Kraus' (2015) "classifying intersex in DSM-5: critical reflections on gender dysphoria". A recurrent issue in the more recent editions of the Diagnostic andStatisticalManualofMentalDisorders (DSM)oftheAmerican Psychiatric Association (APA) has been whether patientinitiated gender change and the preceding dissatisfaction with the initially assigned gender (gender dysphoria) should be included as a psychiatric diagnosis and, if yes, whether that diagnosis should be limited to such a presentation shown by persons born with a healthy reproductive system or also be applied if it is shown by persons with congenital somatic intersexuality (in the current medical nomenclature included under disorders of sex development [DSD]) (Hughes, Houk, Ahmed, Lee, & LWPES/ESPE Consensus Group, 2006). DSM-5 stipulates for the diagnosis of Gender Dysphoria (GD)‘‘Specify if: With a disorder of sex development’’ (italics and boldface removed) and adds the‘‘Coding note: Code the disorder of sex development as well as gender dysphoria’’(American Psychiatric Association, 2013, pp. 451–459). In the view of Kraus (2015), GD thereby‘‘subsumes the physical condition under themental ‘disorder’.’’Kraus sees this conceptualization as‘‘the most significant change in the revised diagnosis’’and expresses numerous misgivings. In response, as a member of the respective DSM-5 Work Group (which was dissolved with the publication of DSM-5), I want to clarify the pertinentchangesmadefromDSM-IVandDSM-IV-TR(AmericanPsychiatricAssociation,1994,2000) toDSM-5. In the interest of space, I will only address the most salient ones (for other details, see Zucker et al., 2013). (1) Kraus’ (2015) critique, which culminates in the catchy but misleading phrase,‘‘when the controversial GID diagnosis exits the DSM, disorders of sex development enter’’(p. 1154), appears to be based on a misunderstanding that is already reflected in the titleofherarticle,‘‘ClassifyingIntersex inDSM5.’’ It is not the physical condition ‘‘disorder of sex development’’ (referring here to intersexuality as illustrated by the examples given in the DSM-5 criteria table on p. 453) that is ‘‘classified in DSM-5’’ or ‘‘subsumed’’ under mental disorder. Instead, what DSM-5 categorizes here as GD is a gender identity problem occurring in a person who also has a DSD/intersexcondition,provided therespectivepsychiatric/psychologic DSM-5 criteria for GD are met. The change is not ‘‘fundamental’’as Kraus suggests, because in DSM-IV, people with what then was called‘‘Gender Identity Disorder’’(GID) in association with an intersex condition were categorized as GID Not Otherwise Specified (GIDNOS). Also GIDNOS was a psychiatric diagnosis related to gender identity, just not as stringently defined as GID. Thus, in both DSM-IV and DSM-5, the psychologic/psychiatric condition of GD was/is categorized as a psychiatric diagnosis, not the intersex condition. In this regard, DSM-5 resembles the DSM-III (APA, 1980) and DSM-III-R (APA, 1987) diagnoses of Gender Identity Disorder of Childhood (GIDC) and the DSM-III-R diagnosis of Transsexualism (in adults): they only stipulated to note‘‘physical abnormalities of the sex organs’’(with GIDC) and‘‘physical intersexuality or a genetic abnormality’’(with Transsexualism), if also present, on Axis III (Physical Disorders and Conditions, i.e., not psychiatric diagnostic categories), but not to exclude persons with the combination of GID or Transsexualism and such somatic conditions from the gender identity-related diagnoses on Axis I (Clinical [psychiatric] Syndromes and V Codes]. & Heino F. L. Meyer-Bahlburg meyerb@nyspi.columbia.edu

negative: Path planning using Matlab-ROS integration applied to mobile robots In this paper the possibilities that Matlab provides to design, implementation and monitoring programs of autonomous navigation for mobile robots, on both simulated and real platforms, through its new toolbox for robotics will be explored. Robotics System Toolbox has established itself as a solid tool of integration between Matlab and robot operating under ROS environment. It will be studied the tools available in this toolbox that allow achieving the connection between the two platforms, in addition to the generation of algorithms of location, path planning, mapping and autonomous navigation.

negative: Microfibrillated cellulose and new nanocomposite materials: a review - DTU Orbit (15/10/2018) (15/10/2018)  Microfibrillated cellulose and new nanocomposite materials: a review Due to their abundance, high strength and stiffness, low weight and biodegradability, nano-scale cellulose fiber materials (e.g., microfibrillated cellulose and bacterial cellulose) serve as promising candidates for bio-nanocomposite production. Such new high-value materials are the subject of continuing research and are commercially interesting in terms of new products from the pulp and paper industry and the agricultural sector. Cellulose nanofibers can be extracted from various plant sources and, although the mechanical separation of plant fibers into smaller elementary constituents has typically required high energy input, chemical and/or enzymatic fiber pre-treatments have been developed to overcome this problem. A challenge associated with using nanocellulose in composites is the lack of compatibility with hydrophobic polymers and various chemical modification methods have been explored in order to address this hurdle. This review summarizes progress in nanocellulose preparation with a particular focus on microfibrillated cellulose and also discusses recent developments in bio-nanocomposite fabrication based on nanocellulose.

negative: Learning state representation for deep actor-critic control Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). One advantage of DNNs is that they can cope with large input dimensions. Instead of relying on feature engineering to lower the input dimension, DNNs can extract the features from raw observations. The drawback of this end-to-end learning is that it usually requires a large amount of data, which for real-world control applications is not always available. In this paper, a new algorithm, Model Learning Deep Deterministic Policy Gradient (ML-DDPG), is proposed that combines RL with state representation learning, i.e., learning a mapping from an input vector to a state before solving the RL task. The ML-DDPG algorithm uses a concept we call predictive priors to learn a model network which is subsequently used to pre-train the first layer of the actor and critic networks. Simulation results show that the ML-DDPG can learn reasonable continuous control policies from high-dimensional observations that contain also task-irrelevant information. Furthermore, in some cases, this approach significantly improves the final performance in comparison to end-to-end learning.

negative: Forecasting market demand for new telecommunications services: an introduction The marketing team of a new telecommunications company is usually tasked with producing forecasts for diverse stakeholders with different needs. Consequently, those outside marketing often realize neither the many reasons for developing forecasts nor the marketing theory used and the challenges involved in doing so. Based on our three decades of experience working with telecommunications operators around the world we seek to redress this situation by presenting a discussion of the issues involved in demand forecasting for new communications services. 2002 Elsevier Science Ltd. All rights reserved.

negative: A Sharp PageRank Algorithm with Applications to Edge Ranking and Graph Sparsification We give an improved algorithm for computing personalized PageRank vectors with tight error bounds which can be as small as Ω(n−p) for any fixed positive integer p. The improved PageRank algorithm is crucial for computing a quantitative ranking of edges in a given graph. We will use the edge ranking to examine two interrelated problems – graph sparsification and graph partitioning. We can combine the graph sparsification and the partitioning algorithms using PageRank vectors to derive an improved partitioning algorithm.

negative: Turning telecommunications call details to churn prediction: a data mining approach As deregulation, stew technologies, and new competitors open up the mobile telecommunications industry, churn prediction and management has become of great concern to mobile service providers: A mobile service provider wishing to retain its subscribers needs to be able to predict which of them may be at-risk of changing services and will make those subscribers the focus of customer retention efforts. In response to the limitations of existing churn-prediction systems and the unavailability of customer demographics in the mobile telecommunications provider investigated, we propose, design, and experimentally evaluate a churn-prediction technique that predicts churning from subscriber contractual information and call pattern changes extracted from call details. This proposed technique is capable of identifying potential churners at the contract level for a specific prediction time-period. In addition, the proposed technique incorporates the multi-classifier class-combiner approach to address the challenge of a highly skewed class distribution between churners and non-churners. The empirical evaluation results suggest that the proposed call-behavior-based churn-prediction technique exhibits satisfactory predictive effectiveness when more recent call details are employed for the churn prediction model construction. Furthermore, the proposed technique is able to demonstrate satisfactory or reasonable predictive power within the one-month interval between model construction and churn prediction. Using a previous demographics-based churn-prediction system as a reference, the lift factors attained by our proposed technique appear largely satisfactory.

negative: The Effects of Violent Video Games on Aggression A Meta-Analysis s International-B, 49(11). (University Microfilms No. 8902398) Ballard, M. E., & Lineberger, R. (1999). Video game violence and confederate gender: Effects on reward and punishment given by college males. Sex Roles, 41, 541–558. Ballard, M. E., & Wiest, J. R. (1995, March). Mortal Kombat: The effects of violent video technology on males’ hostility and cardiovascular responding. Paper presented at the biennial meeting of the Society for Research in Child Development, Indianapolis, IN. Bandura, A. (1994). The social cognitive theory of mass communication. In J. Bryant & D. Zillmann (Eds.), Media effects: Advances in theory and research (pp. 61–90). Hillsdale, NJ: Erlbaum. Battelle, J., & Johnstone, B. (1993, December). Seizing the next level: Sega’s plan for world domination. Wired, 1, 73–78, 128–131. Berkowitz, L., & Alioto, J. (1973). 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Meaningful Play, Playful Meaning: Proceedings of the 11th Annual Meeting of the Association for the Anthropological Study of Play. Champaign, IL: Human Kinetics. Sherry / VIDEO GAME META-ANALYSIS 431 Silvern, S., & Williamson, P. (1987). The effects of video game play on young children’s aggression, fantasy, and prosocial behavior. Journal of Developmental Psychology,

negative: Designing augmented reality interfaces Most interactive computer graphics appear on a screen separate from the real world and the user's surroundings. However this does not always have to be the case. In augmented reality (AR) interfaces, three-dimensional virtual images appear superimposed over real objects. AR applications typically use head-mounted or handheld displays to make computer graphics appear in the user's environment.

negative: Security behaviors of smartphone users Purpose – This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East. Design/methodology/approach – A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems. Findings – The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft. Research limitations/implications – Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future. Practical implications – Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement. Social implications – Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk. Originality/value – The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.

negative: Pixelwise View Selection for Unstructured Multi-View Stereo This work presents a Multi-View Stereo system for robust and efficient dense modeling from unstructured image collections. Our core contributions are the joint estimation of depth and normal information, pixelwise view selection using photometric and geometric priors, and a multi-view geometric consistency term for the simultaneous refinement and image-based depth and normal fusion. Experiments on benchmarks and large-scale Internet photo collections demonstrate stateof-the-art performance in terms of accuracy, completeness, and efficiency. Fig. 1. Reconstructions for Louvre, Todai-ji, Paris Opera, and Astronomical Clock.

negative: Maximum entropy models for natural language ambiguity resolution MAXIMUM ENTROPY MODELS FOR NATURAL LANGUAGE AMBIGUITY RESOLUTION Adwait Ratnaparkhi Supervisor Professor Mitch Marcus This thesis demonstrates that several important kinds of natural language ambiguities can be resolved to state of the art accuracies using a single statistical modeling technique based on the principle of maximum entropy We discuss the problems of sentence boundary detection part of speech tagging prepo sitional phrase attachment natural language parsing and text categorization under the maximum entropy framework In practice we have found that maximum entropy models o er the following advantages State of the art Accuracy The probability models for all of the tasks discussed per form at or near state of the art accuracies or outperform competing learning algo rithms when trained and tested under similar conditions Methods which outperform those presented here require much more supervision in the form of additional human involvement or additional supporting resources Knowledge Poor Features The facts used to model the data or features are linguis tically very simple or knowledge poor but yet succeed in approximating complex linguistic relationships Reusable Software Technology The mathematics of the maximum entropy framework

negative: LTCC Packages With Embedded Phased-Array Antennas for 60 GHz Communications A low-cost, fully-integrated antenna-in-package solution for 60 GHz phased-array systems is demonstrated. Sixteen patch antennas are integrated into a 28 mm × 28 mm ball grid array together with a flip-chip attached transmitter or receiver IC. The packages have been implemented using low temperature co-fired ceramic technology. 60 GHz interconnects, including flip-chip transitions and via structures, are optimized using full-wave simulation. Anechoic chamber measurement has shown ~ 5 dBi unit antenna gain across all four IEEE 802.15.3c channels, achieving excellent model-to-hardware correlation. The packaged transmitter and receiver ICs, mounted on evaluation boards, have demonstrated beam-steered, non-line-of-sight links with data rates up to 5.3 Gb/s.

negative: A 3.5-GHz PLL for fast low-IF/zero-IF LO switching in an 802.11 transceiver A PLL technique is introduced that enables fast and accurate frequency switching, independent of the loop bandwidth. It uses separate tuning paths, each driving a separate VCO tune port. Different frequencies are produced by letting the VCO make different weighted combinations of the stable tuning voltages. The PLL converges to the stable tuning voltages by switching it a few times between the desired frequencies and tuning paths. Once the stabilized tuning voltages are found, one can switch between frequencies as fast as one can switch between K/sub VCO/s. The technique is applied to a 3.5-GHz integer-N PLL to enable fast jumping of the local oscillator (LO) frequency when an 802.11 transceiver is switched between a low and a zero intermediate frequency (LIF/ZIF). It uses dual phase/frequency detectors (PFD), charge pumps (CPs), and on-chip loop filters to control two separate low-leakage VCO tune ports. Each PFD/tune port combination can be (de)activated separately, without disturbing the loop filters' charge. The 50-kHz bandwidth PLL achieves a measured 7-MHz jump with /spl plusmn/20 kHz accuracy within 6 /spl mu/s. The measured phase noise is -123 dBc/Hz at 1-MHz offset.

negative: A robust, input voltage adaptive and low energy consumption level converter for sub-threshold logic A new level converter (LC) is proposed for logic voltage shifting between sub-threshold voltage to normal high voltage. By employing 2 PMOS diodes, the LC shows good operation robustness with sub-threshold logic input. The switching delay of the proposed LC can adapt with the input logic voltage which is more suitable for power aware systems. With a simpler circuit structure, the energy consumption of the LC is smaller than that of the existing sub-threshold LC. Simulation results demonstrate the performance improvement and energy reduction of the proposed LC. Test chip was fabricated using 0.18 mum CMOS process. Measurement results show that our proposed LC can operate correctly with an input at as low as 127 mV and an output voltage at 1.8V.

negative: A broadband compact low-loss 4×4 Butler Matrix in CMOS with stacked transformer based quadrature couplers This paper presents an ultra-broadband ultracompact Butler Matrix design scheme. The design employs stacked transformer based couplers and lumped LC π-network phase shifters for substantial size reduction. As a proof-of-concept design, a 4×4 Butler Matrix is implemented in a standard 130nm bulk CMOS process at a center frequency of 2.0 GHz. Compared with reported fully integrated 2.0 GHz 4×4 Butler Matrix designs in CMOS, the proposed design achieves the lowest insertion loss of 1.10dB, the smallest amplitude mismatch of 0.3 dB, the largest fractional bandwidth of 34.6%, and the smallest chip core area of 0.635×1.122 mm2. Based on the measured S-parameters, the four concurrent electrical array patterns of the Butler Matrix achieve array peak-to-null ratio (PNR) of 29.5 dB at 2.0 GHz and better than 15.0 dB between 1.55 GHz and 2.50 GHz.

negative: A Low-Noise Amplifier With Tunable Interference Rejection for 3.1- to 10.6-GHz UWB Systems An ultrawideband common-gate low noise amplifier with tunable interference rejection is presented. The proposed LNA embeds a tunable active notch filter to eliminate interferer at 5-GHz WLAN and employs a common-gate input stage and dual-resonant loads for wideband implementation. This LNA has been fabricated in a 0.18-¿m CMOS process. The measured maximum power gain is 13.2 dB and noise figure is 4.5-6.2 dB with bandwidth of 3.1-10.6 GHz. The interferer rejection is 8.2 dB compared to the maximum gain and 7.6 dB noise figure at 5.2 GHz , respectively. The measured input P1dB is -11 dBm at 10.3 GHz. It consumes 12.8 mA from 1.8-V supply voltage.

negative: A 5.8-GHz radar sensor chip in 0.18-μm CMOS for non-contact vital sign detection This paper presents a 5.8-GHz radar sensor chip for non-contact vital sign detection. The sensor chip is designed and fabricated in TSMC 0.18 μm CMOS 1P6M process. Except for the low-noise amplifier, all the active and passive components of the radar system are fully integrated in a single CMOS chip. With packaging on the printed-circuit board and connecting transmitting and receiving antennas, the radar sensor chip has been successfully demonstrated to detect the respiration and heart beat rates of a human adult.

negative: Noise and Bandwidth analysis for PMOS based wide swing cascode current mirror using 0.35 um CMOS-MEMS technology In this work, we propose a PMOS based wide swing cascode current mirror (WSSCM) for 0.35 um CMOS-MEMS technology. The proposed circuit shows a high bandwidth current mirror with high output resistance capability operating at low voltage levels. Noise, output resistance and bandwidth analysis are presented. CADENCE SPECTRE circuit design tool using MIMOS 0.35 um and BSIM3 transistor models is used to carry out the simulations. The circuit achieves an amplification of 120.04 uA, the input referred noise of the circuit is 75.77 nV/√Hz with a minimum power consumption of 0.33 mW and a high output resistance of 15 MO .


query: Algorithms for Large, Sparse Network Alignment Problems

positive: Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Detection of maximum common subgraph (MCS) is useful for matching, comparing and evaluate the similarity of patterns. MCS is a well known NP-complete problem for which optimal and suboptimal algorithms are known from the literature. Nevertheless, until now no effort has been done for characterizing their performance. The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of an algorithm is made on the basis of a few experimental results available. In this paper, three optimal and well-known algorithms for maximum common subgraph detection are described. Moreover a large database containing various categories of pairs of graphs (e.g. random graphs, meshes, bounded valence graphs), is presented, and the performance of the three algorithms is evaluated on this database. Article Type Communicated by Submitted Revised Regular Paper U. Brandes September 2005 January 2007 D. Conte et al., Maximum Common Subgraph, JGAA, 11(1) 99–143 (2007) 100

positive: Probabilistic Subgraph Matching Based on Convex Relaxation We present a novel approach to the matching of subgraphs for object recognition in computer vision. Feature similarities between object model and scene graph are complemented with a regularization term that measures differences of the relational structure. For the resulting quadratic integer program, a mathematically tight relaxation is derived by exploiting the degrees of freedom of the embedding space of positive semidefinite matrices. We show that the global minimum of the relaxed convex problem can be interpreted as probability distribution over the original space of matching matrices, providing a basis for efficiently sampling all close-to-optimal combinatorial matchings within the original solution space. As a result, the approach can even handle completely ambiguous situations, despite uniqueness of the relaxed convex problem. Exhaustive numerical experiments demonstrate the promising performance of the approach which – up to a single inevitable regularization parameter that weights feature similarity against structural similarity – is free of any further tuning parameters.

positive: Clustering by passing messages between data points. Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.

positive: A Comparison of String Metrics for Matching Names and Records We describe an open-source Java toolkit of methods for matching names and records. We summarize results obtained from using various string distance metrics on the task of matching entity names: these metrics include distance functions proposed by several different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. We then describe an extension to the toolkit which allows records to be compared. We discuss some issues involved in performing a similar comparison for record-matching techniques, and finally present results for some baseline record-matching algorithms which are based on string comparisons between fields.

positive: Thirty Years Of Graph Matching In Pattern Recognition A recent paper posed the question: “Graph Matching: What are we really talking about?”. Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graph-based techniques in the Pattern Recognition and Machine Vision field.

negative: Development and validation of blue ray, an optical modem for the MEDUSA class AUVs Wireless optical communications are emerging as a viable solution for high-speed data transmission over short ranges in the ocean, complementing mainstream acoustic communication systems that operate over much longer ranges, but at lower data rates. The current drive to develop cooperative autonomous vehicular systems to carry out surveying and other complex missions in the ocean critically depends on the existence of such communication links to share sensory and coordination information. This paper presents a compact, low power consumption, cost-efficient, and lightweight optical modem designed for fast data transmission between MEDUSA underwater vehicles at ranges on the order of 10 m. The transmitter uses LEDs for ON-OFF keying, while the receiver front-end adopts a transimpedance architecture with a photodiode detector. This simplistic transmission and reception technique reduces the overall design and hardware complexities compared to, e.g., laser diodes and photomultiplier tubes. The LEDs are arranged in a circular array, with a photodiode at the centre to enable half-duplex operation. These modems are designed to achieve data rates of 20-200 kb/s over short-range, line-of-sight, configurations. A transparent casing is customised to fit the Medusa vehicles, with proper alignment for inter-vehicle communication when they move in formation.

negative:
Theory application by school-based occupational therapists. OBJECTIVES
Recent literature indicates that there is an inconsistent use of theory to guide clinical actions by occupational therapists, including those working in pediatrics. The purpose of this study was to describe school-based therapists' theory application by collecting information about what frames of reference they used and why.

METHOD
Of the 72 school-based therapists in the mid-Atlantic states who agreed to respond to a questionnaire, 51 (70.8%) returned the questionnaire. Information about demographics, what frames of reference were used, and why they were used was obtained from the questionnaire.

RESULTS
Respondents reported using a multitheoretical approach, with sensory integration theory and neurodevelopmental theory being the predominate frames of reference applied but not the only ones used. The frames of reference were used on the basis of several factors, including the children's needs and the respondent's education.

CONCLUSION
Formal and continuing education seems to have a great effect on school-based occupational therapists as they develop their personal conceptual frameworks.

negative: Agnostic Estimation of Mean and Covariance We consider the problem of estimating the mean and covariance of a distribution from i.i.d. samples in the presence of a fraction of malicious noise. This is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type. The agnostic problem includes many interesting special cases, e.g., learning the parameters of a single Gaussian (or finding the best-fit Gaussian) when a fraction of data is adversarially corrupted, agnostically learning mixtures, agnostic ICA, etc. We present polynomial-time algorithms to estimate the mean and covariance with error guarantees in terms of information-theoretic lower bounds. As a corollary, we also obtain an agnostic algorithm for Singular Value Decomposition.

negative: Robotics-logistics: Challenges for automation of logistic processes Robotics-logistics can be understood as the field of activities in which applications of industrial robot-technologies are offered and demanded in order to ensure the optimization of internal material flows. According to a survey conducted by BIBA in 2007, the area of robotics-logistics has great need for modernization. Robotics-Logistics activities can be classified in several scenarios as unloading / loading of goods and palletizing / depalletizing of goods. Possible scenarios for research and development activities within theses fields are given.

negative: Clothing identification via deep learning: forensic applications Attribute-based identification systems are essential for forensic investigations because they help in identifying individuals. An item such as clothing is a visual attribute because it can usually be used to describe people. The method proposed in this article aims to identify people based on the visual information derived from their attire. Deep learning is used to train the computer to classify images based on clothing content. We first demonstrate clothing classification using a large scale dataset, where the proposed model performs relatively poorly. Then, we use clothing classification on a dataset containing popular logos and famous brand images. The results show that the model correctly classifies most of the test images with a success rate that is higher than 70%. Finally, we evaluate clothing classification using footage from surveillance cameras. The system performs well on this dataset, labelling 70% of the test images correctly.

negative: Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).

negative: Weight-averaged consistency targets improve semi-supervised deep learning results The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%.

negative: Learning partial differential equations via data discovery and sparse optimization. We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

negative: Ensembles of nested dichotomies for multi-class problems Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.

negative: A Case Study on Korean Wave: Focused on K-POP Concert by Korean Idol Group in Paris, June 2011 The study dealt with Korean Wave focusing on K-POP and analyzed its success factors, the changes in Korean Wave and the future directions for development. Also the study has compared the results of the idol groups’ performance, held by SM Entertainment in June 2011 in Paris, and the perspective of Korean and French media. Key reasons were examined to analyze what led K-POP to play a crucial part in spreading the Korean Wave: The expansion of the age of its takers ranging from teens to females in their 20s; the fusion of a variety of cultural elements including oriental dance and occidental pop; the systematic system of idol training; marketing activities based on social media, etc. For the expansion of Korean Wave including K-POP and its successful positioning in the world market, there are several suggestions to make which are inventing the differentiated contents and highly appealing stories, approaching the local customers while considering local features, operating co-marketing with other cultural products.

negative: Nonclassical Computation - A Dynamical Systems Perspective In this chapter we investigate computation from a dynamical systems perspective. A dynamical system is described in terms of its abstract state space, the system’s current state within its state space, and a rule that determines its motion through its state space. In a classical computational system, that rule is given explicitly by the computer program; in a physical system, that rule is the underlying physical law governing the behaviour of the system. So a dynamical systems approach to computation allows us to take a unified view of computation in classical discrete systems and in systems performing non-classical computation. In particular, it gives a route to a computational interpretation of physical embodied systems exploiting the natural dynamics of their material substrates. We start with autonomous (closed) dynamical systems: those whose dynamics is not an explicit function of time, in particular, those with no inputs from an external environment. We begin with computationally conventional discrete systems, examining their computational abilities from a dynamical systems perspective. The aim here is both to introduce the necessary dynamical systems concepts, and to demonstrate how classical computation can be viewed from this perspective. We then move on to continuous dynamical systems, such as those inherent in the complex dynamics of matter, and show how these too can be interpreted computationally, and see how the material embodiment can give such computation “for free”, without the need to explicitly implement the dynamics. We next broaden the outlook to open (non-autonomous) dynamical systems, where the dynamics is a function of time, in the form of inputs from an external environment, and which may be in a closely coupled feedback loop with that environment. We finally look at constructive, or developmental, dynamical systems, where the structure of the state space is changing during the computation. This includes vari-

negative: An acute bout of self-myofascial release increases range of motion without a subsequent decrease in muscle activation or force. Foam rolling is thought to improve muscular function, performance, overuse, and joint range of motion (ROM); however, there is no empirical evidence demonstrating this. Thus, the objective of the study was to determine the effect of self-myofascial release (SMR) via foam roller application on knee extensor force and activation and knee joint ROM. Eleven healthy male (height 178.9 ± 3.5 cm, mass 86.3 ± 7.4 kg, age 22.3 ± 3.8 years) subjects who were physically active participated. Subjects' quadriceps maximum voluntary contraction force, evoked force and activation, and knee joint ROM were measured before, 2 minutes, and 10 minutes after 2 conditions: (a) 2, 1-minute trials of SMR of the quadriceps via a foam roller and (b) no SMR (Control). A 2-way analysis of variance (condition × time) with repeated measures was performed on all dependent variables recorded in the precondition and postcondition tests. There were no significant differences between conditions for any of the neuromuscular dependent variables. However, after foam rolling, subjects' ROM significantly (p < 0.001) increased by 10° and 8° at 2 and 10 minutes, respectively. There was a significant (p < 0.01) negative correlation between subjects' force and ROM before foam rolling, which no longer existed after foam rolling. In conclusion, an acute bout of SMR of the quadriceps was an effective treatment to acutely enhance knee joint ROM without a concomitant deficit in muscle performance.

negative: Computer Forensics Field Triage Process Model With the proliferation of digital based evidence, the need for the timely identification, analysis and interpretation of digital evidence is becoming more crucial. In many investigations critical information is required while at the scene or within a short period of time measured in hours as opposed to days. The traditional cyber forensics approach of seizing a system(s)/media, transporting it to the lab, making a forensic image(s), and then searching the entire system for potential evidence, is no longer appropriate in some circumstances. In cases such as child abductions, pedophiles, missing or exploited persons, time is of the essence. In these types of cases, investigators dealing with the suspect or crime scene need investigative leads quickly; in some cases it is the difference between life and death for the victim(s). The Cyber Forensic Field Triage Process Model (CFFTPM) proposes an onsite or field approach for providing the identification, analysis and interpretation of digital evidence in a short time frame, without the requirement of having to take the system(s)/media back to the lab for an in-depth examination or acquiring a complete forensic image(s). The proposed model adheres to commonly held forensic principles, and does not negate the ability that once the initial field triage is concluded, the system(s)/storage media be transported back to a lab environment for a more thorough examination and analysis. The CFFTPM has been successfully used in various real world cases, Journal of Digital Forensics, Security and Law, Vol. 1(2) 20 and its investigative importance and pragmatic approach has been amply demonstrated. Furthermore, the derived evidence from these cases has not been challenged in the court proceedings where it has been introduced. The current article describes the CFFTPM in detail, discusses the model’s forensic soundness, investigative support capabilities and practical considerations.

negative: SATIRE: a software architecture for smart AtTIRE Personal instrumentation and monitoring services that collect and archive the physical activities of a user have recently been introduced for various medical, personal, safety, and entertainment purposes. A general software architecture is needed to support different categories of such monitoring services. This paper presents a software architecture, implementation, and preliminary evaluation of SATIRE, a wearable personal monitoring service transparently embedded in user garments. SATIRE records the owner's activity and location for subsequent automated uploading and archiving. The personal archive can later be searched for particular events to answer questions regarding past and present user activity, location, and behavior patterns. A short feasibility and usage study of a prototype based on MicaZ motes provides a proof of concept for the SATIRE architecture.

negative: CDN brokering Content distribution networks (CDNs) increase the capacity of individual Web sites and attempt to deliver content from caches that are located “closer” to end-users than the origin servers that provide the content. CDN brokering provides CDNs a way to interoperate by allowing one CDN to intelligently redirect clients dynamically to other CDNs. This paper describes the goals, architecture, and performance of a CDN brokerage system. Our system has been deployed on the Internet on a provisional basis, and our architectural ideas have helped advance the evolution of Internet standards for interoperating CDNs.

negative: Editing Description Logic Ontologies with the Protégé OWL Plugin The growing interest in the Semantic Web and the Web Ontology Language (OWL) will reveal the potential of Description Logics in industrial projects. The rich semantics of OWL provide powerful reasoning capabilities that help build, maintain and query domain models for many purposes. However, before OWL can unfold its full potential, user-friendly tools with a scalable architecture are required. We present the OWL Plugin, an extension of the Protégé ontology development environment, which can be used to define classes and properties, to edit logical class expressions, to invoke reasoners, and to link ontologies into the Semantic Web. We analyze some of the challenges for developers of Description Logic editors, and discuss some of our user interface design decisions.

negative: Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based representation, and inserts objects iteratively into the scene by predicting their category, location, orientation and size with separate neural network modules. Our pipeline naturally supports automatic completion of partial scenes, as well as synthesis of complete scenes. Our method is significantly faster than the previous image-based method and generates result that outperforms state-of-the-art generative scene models in terms of faithfulness to training data and perceived visual quality.

negative: When Is It Too Early for Single Sport Specialization? Over the past 15 years, there has been an increase in youth sports participation with a concomitant increase in early year-round training in a single sport. Many factors contribute to the desire of parents and coaches to encourage early single sport specialization, including the desire to give the young athlete an edge in competition, pursuit of scholarships, and potential professional status, and the ability to label a young athlete as elite at an early age. Despite these perceived advantages, some data suggest that early sport specialization does not lead to a competitive advantage over athletes who participate in multiple sports. Although the data are limited, there is some evidence that early sport specialization may put the young athlete at risk for overuse injuries. The focus of this review is to highlight the evidence regarding early sport specialization and risk for injury; discuss the risk factors for overuse injury in high-risk sports including ice hockey, swimming, gymnastics, and baseball; and discuss future potential research that would help define the risk of injury for young athletes who participate in early sport specialization.

negative: Coordinated Power Control of Variable-Speed Diesel Generators and Lithium-Battery on a Hybrid Electric Boat This paper presents coordinated power management strategies for a hybrid electric boat (HEB) based on dynamic load's power distribution approach using diesel generators and batteries. Two variable-speed diesel generator sets connecting to dc-bus via fully controlled rectifiers serve as the main power supply source; one lithium-battery pack linked to dc-bus through a bidirectional DC/DC converter serves as an auxiliary power supply source. The power requirement of the thrusters and other onboard equipment is represented by a load power profile. The main contribution of this paper is focused on the load power sharing of the HEB between the two variable-speed diesel generators and the lithium-battery pack, through the fluctuations extracted from load power and their assignation to the batteries. Another important issue is that when the total load demand is low, one of the diesel generator set will be switched off to improve the diesel-fuel efficiency. The output powers of the diesel generators and the batteries are controlled based on the power distribution strategy via the converters controlling. The performances of the proposed hybrid power system control are evaluated through simulations in MATALB/Simulink, and through reduced-scale experimental tests carried out for a specific driving cycle of the HEB.

negative: Multiple description-DASH: Pragmatic video streaming maximizing end-users' quality of experience Over the past few years, adaptive bitrate streaming protocols, such as DASH, have risen to enhance the End-Users' experience towards video consumption over the Internet. Current methods perform a smoother playback (i.e., fewer re-buffering states) trading off with quality fluctuations due to the client-server link state. The proposed work goes a step further and introduces an innovative lightweight streaming solution by taking advantage of bandwidth aggregation over multiple paths using multiple content sources at the same time. This pragmatic evolving approach outperforms the QoE delivered by current DASH-based or P2P-based solutions and leverages the scope of action made available to the media delivery chain actors. The proposed solution has been implemented and is compared to a DASH-based approach used in most existing VoD use-cases through a first set of experiments. Results demonstrate the strong advantages in terms of quality delivered at the End-User's side and buffer occupancy. Discussion on the necessary trade-offs is also tackled.

negative: Optimizing power-accuracy trade-off in approximate adders Approximate circuit design has gained significance in recent years targeting applications like media processing where full accuracy is not required. In this paper, we propose an approximate adder in which the approximate part of the sum is obtained by finding a single optimal level that minimises the mean error distance. Therefore hardware needed for the approximate part computation can be removed, which effectively results in very low power consumption. We compare the proposed adder with various approximate adders in the literature in terms of power and accuracy metrics. The power savings of our adder is shown to be 17% to 55% more than power savings of the existing approximate adders over a significant range of accuracy values. Further, in an image addition application, this adder is shown to provide the best trade-off between PSNR and power.

negative: Substrate Integrated Waveguide (SIW) Rotman Lens and Its Ka-Band Multibeam Array Antenna Applications A new type of substrate integrated waveguide (SIW) multibeam antenna is proposed for mobile satellite communications using beam switching and diversity techniques. It employs an SIW Rotman lens as the beamforming network. The prototype of a single multibeam antenna is implemented at 28.5 GHz with seven input ports and an antenna array constructed by nine SIW linear slot arrays, which can generate a corresponding number of beams along one dimension. Several such antennas are grouped in two different ways to cover a 2-D solid angle with multiple beams. Experiment results show that the 2-D solid angle around (-40deg, 40deg) X (-35deg, 35deg) or (-25deg, 25deg) x (-35deg, 35deg) are covered with 20 or 25 beams with 5 -dB beamwidth, respectively. It is demonstrated that this type of printed multibeam antenna is a good choice for communication applications where mobility and high gain are simultaneously required.

negative: From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific HAI behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward hypotheses based on this understanding that could shape a research path toward the ability to mitigate interaction behavior in the real world.

negative: Predicting Students’ Performance using Modified ID3 Algorithm The ability to predict performance of students is very crucial in our present education system. We can use data mining concepts for this purpose. ID3 algorithm is one of the famous algorithms present today to generate decision trees. But this algorithm has a shortcoming that it is inclined to attributes with many values. So , this research aims to overcome this shortcoming of the algorithm by using gain ratio(instead of information gain) as well as by giving weights to each attribute at every decision making point. Several other algorithms like J48 and Naive Bayes classification algorithm are also applied on the dataset. The WEKA tool was used for the analysis of J48 and Naive Bayes algorithms. The results are compared and presented. The dataset used in our study is taken from the School of Computing Sciences and Engineering (SCSE), VIT University. Keyworddata mining, educational data mining (EDM), decision tree, gain ratio, weighted ID3

negative: Improving read performance of STT-MRAM based main memories through Smash Read and Flexible Read Spin Transfer Torque Magnetoresistive RAM (STT-MRAM) has been recently deemed as one promising main memory alternative for high-end mobile processors. With process technology scaling, the amplitude of write current approaches that of read current in deep sub-micrometer STT-MRAM arrays. As a result, read disturbance errors (RDEs) emerge. Both high current restore required (HCRR) reads and low current long latency (LCLL) reads can guarantee read reliability and utterly remove RDEs. However, both of them degrade system performance, because of extra restores or a longer read latency. And neither of them always achieves the better performance when running a wide variety of applications. In this paper, we present two architectural techniques to boost read performance for STT-MRAM based main memories in the presence of RDEs. We first propose Smash Read (S-RD) to shorten the latency of HCRR reads by injecting a larger read current. We further introduce Flexible Read (F-RD) to dynamically adopt different types of read schemes, S-RD and LCLL, to maximize main memory system performance. On average, our techniques improve system performance by 9~13% and reduces total energy by 4~8% over all existing read schemes including HCRR and LCLL.

negative: Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management Learning from sparse and delayed reward is a central issue in reinforcement learning. In this paper, to tackle reward sparseness problem of task oriented dialogue management, we propose a curriculum based approach on the number of slots of user goals. This curriculum makes it possible to learn dialogue management for sets of user goals with large number of slots. We also propose a dialogue policy based on progressive neural networks whose modules with parameters are appended with previous parameters fixed as the curriculum proceeds, and this policy improves performances over the one with single set of parameters.

negative: Punctuation Prediction Model for Conversational Speech An ASR system usually does not predict any punctuation or capitalization. Lack of punctuation causes problems in result presentation and confuses both the human reader and off-the-shelf natural language processing algorithms. To overcome these limitations, we train two variants of Deep Neural Network (DNN) sequence labelling models a Bidirectional Long Short-Term Memory (BLSTM) and a Convolutional Neural Network (CNN), to predict the punctuation. The models are trained on the Fisher corpus which includes punctuation annotation. In our experiments, we combine time-aligned and punctuated Fisher corpus transcripts using a sequence alignment algorithm. The neural networks are trained on Common Web Crawl GloVe embedding of the words in Fisher transcripts aligned with conversation side indicators and word time infomation. The CNNs yield a better precision and BLSTMs tend to have better recall. While BLSTMs make fewer mistakes overall, the punctuation predicted by the CNN is more accurate especially in the case of question marks. Our results constitute significant evidence that the distribution of words in time, as well as pre-trained embeddings, can be useful in the punctuation prediction task.

negative: Role of mask pattern in intelligibility of ideal binary-masked noisy speech. Intelligibility of ideal binary masked noisy speech was measured on a group of normal hearing individuals across mixture signal to noise ratio (SNR) levels, masker types, and local criteria for forming the binary mask. The binary mask is computed from time-frequency decompositions of target and masker signals using two different schemes: an ideal binary mask computed by thresholding the local SNR within time-frequency units and a target binary mask computed by comparing the local target energy against the long-term average speech spectrum. By depicting intelligibility scores as a function of the difference between mixture SNR and local SNR threshold, alignment of the performance curves is obtained for a large range of mixture SNR levels. Large intelligibility benefits are obtained for both sparse and dense binary masks. When an ideal mask is dense with many ones, the effect of changing mixture SNR level while fixing the mask is significant, whereas for more sparse masks the effect is small or insignificant.

negative: Sample-Based Tree Search with Fixed and Adaptive State Abstractions Sample-based tree search (SBTS) is an approach to solving Markov decision problems based on constructing a lookahead search tree using random samples from a generative model of the MDP. It encompasses Monte Carlo tree search (MCTS) algorithms like UCT as well as algorithms such as sparse sampling. SBTS is well-suited to solving MDPs with large state spaces due to the relative insensitivity of SBTS algorithms to the size of the state space. The limiting factor in the performance of SBTS tends to be the exponential dependence of sample complexity on the depth of the search tree. The number of samples required to build a search tree is O((|A|B)), where |A| is the number of available actions, B is the number of possible random outcomes of taking an action, and d is the depth of the tree. State abstraction can be used to reduce B by aggregating random outcomes together into abstract states. Recent work has shown that abstract tree search often performs substantially better than tree search conducted in the ground state space. This paper presents a theoretical and empirical evaluation of tree search with both fixed and adaptive state abstractions. We derive a bound on regret due to state abstraction in tree search that decomposes abstraction error into three components arising from properties of the abstraction and the search algorithm. We describe versions of popular SBTS algorithms that use fixed state abstractions, and we introduce the Progressive Abstraction Refinement in Sparse Sampling (PARSS) algorithm, which adapts its abstraction during search. We evaluate PARSS as well as sparse sampling with fixed abstractions on 12 experimental problems, and find that PARSS outperforms search with a fixed abstraction and that search with even highly inaccurate fixed abstractions outperforms search without abstraction. These results establish progressive abstraction refinement as a promising basis for new tree search algorithms, and we propose directions for future work within the progressive refinement framework.

negative: Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set We address a cross-scenario clothing retrieval problem- given a daily human photo captured in general environment, e.g., on street, finding similar clothing in online shops, where the photos are captured more professionally and with clean background. There are large discrepancies between daily photo scenario and online shopping scenario. We first propose to alleviate the human pose discrepancy by locating 30 human parts detected by a well trained human detector. Then, founded on part features, we propose a two-step calculation to obtain more reliable one-to-many similarities between the query daily photo and online shopping photos: 1) the within-scenario one-to-many similarities between a query daily photo and an extra auxiliary set are derived by direct sparse reconstruction; 2) by a cross-scenario many-to-many similarity transfer matrix inferred offline from the auxiliary set and the online shopping set, the reliable cross-scenario one-to-many similarities between the query daily photo and all online shopping photos are obtained.

negative: Deep Marching Cubes: Learning Explicit Surface Representations Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (e.g., TSDF) from which 3D surface meshes must be extracted in a post-processing step (e.g., via the marching cubes algorithm). In this paper, we investigate the problem of end-to-end 3D surface prediction. We first demonstrate that the marching cubes algorithm is not differentiable and propose an alternative differentiable formulation which we insert as a final layer into a 3D convolutional neural network. We further propose a set of loss functions which allow for training our model with sparse point supervision. Our experiments demonstrate that the model allows for predicting sub-voxel accurate 3D shapes of arbitrary topology. Additionally, it learns to complete shapes and to separate an object's inside from its outside even in the presence of sparse and incomplete ground truth. We investigate the benefits of our approach on the task of inferring shapes from 3D point clouds. Our model is flexible and can be combined with a variety of shape encoder and shape inference techniques.

negative: A greedy approach to sparse canonical correlation analysis We consider the problem of sparse canonical correlation analysis (CCA), i.e., the search for two linear combi nations, one for each multivariate, that yield maximum correlation using a specified number of variables. We propose an efficient numeri cal approximation based on a direct greedy approach which bound s the correlation at each stage. The method is specifically des igned to cope with large data sets and its computational complexit y depends only on the sparsity levels. We analyze the algorith m’s performance through the tradeoff between correlation and parsimony. The results of numerical simulation suggest that a significant portion of the correlation may be captured using a relatively small number of variables. In addition, we exami ne the use of sparse CCA as a regularization method when the number of available samples is small compared to the dimensions of t he multivariates. I. I NTRODUCTION Canonical correlation analysis (CCA), introduced by Harol d Hotelling [1], is a standard technique in multivariate data n lysis for extracting common features from a pair of data sourc es [2], [3]. Each of these data sources generates a random vecto r that we call a multivariate. Unlike classical dimensionali ty reduction methods which address one multivariate, CCA take s into account the statistical relations between samples fro m two spaces of possibly different dimensions and structure. In particular, it searches for two linear combinations, one fo r each multivariate, in order to maximize their correlation. It is used in different disciplines as a stand-alone tool or as a preprocessing step for other statistical methods. Further more, CCA is a generalized framework which includes numerous classical methods in statistics, e.g., Principal Componen t Analysis (PCA), Partial Least Squares (PLS) and Multiple Linear Regression (MLR) [4]. CCA has recently regained attention with the advent of kernel CCA and its application to independent component analysis [5], [6]. The last decade has witnessed a growing interest in the search for sparse representations of signals and sparse numerical methods. Thus, we consider the problem of sparse CCA, i.e., the search for linear combinations with maximal correlation using a small number of variables. The quest for sparsity can be motivated through various reasonings. First is the ability to interpret and visualize the results. A small number of variables allows us to get the “big picture”, while sacrificing some of the small details. Moreover, spars e representations enable the use of computationally efficien t The first two authors contributed equally to this manuscript . This work was supported in part by an AFOSR MURI under Grant FA9550-06-1-0 324. numerical methods, compression techniques, as well as nois e reduction algorithms. The second motivation for sparsity i s regularization and stability. One of the main vulnerabilit ies of CCA is its sensitivity to a small number of observations. Thu s, regularized methods such as ridge CCA [7] must be used. In this context, sparse CCA is a subset selection scheme which allows us to reduce the dimensions of the vectors and obtain a stable solution. To the best of our knowledge the first reference to sparse CCA appeared in [2] where backward and stepwise subset selection were proposed. This discussion was of qualitativ e nature and no specific numerical algorithm was proposed. Recently, increasing demands for multidimensional data pr ocessing and decreasing computational cost has caused the topic to rise to prominence once again [8]–[13]. The main disadvantages with these current solutions is that there is no direct control over the sparsity and it is difficult (and nonintuitive) to select their optimal hyperparameters. In add ition, the computational complexity of most of these methods is too high for practical applications with high dimensional data sets. Sparse CCA has also been implicitly addressed in [9], [14] an d is intimately related to the recent results on sparse PCA [9] , [15]–[17]. Indeed, our proposed solution is an extension of the results in [17] to CCA. The main contribution of this work is twofold. First, we derive CCA algorithms with direct control over the sparsity in each of the multivariates and examine their performance. Our computationally efficient methods are specifically aime d at understanding the relations between two data sets of larg e dimensions. We adopt a forward (or backward) greedy approach which is based on sequentially picking (or dropping) variables. At each stage, we bound the optimal CCA solution and bypass the need to resolve the full problem. Moreover, the computational complexity of the forward greedy method does not depend on the dimensions of the data but only on the sparsity parameters. Numerical simulation results show th at a significant portion of the correlation can be efficiently cap tured using a relatively low number of non-zero coefficients. Our second contribution is investigation of sparse CCA as a regularization method. Using empirical simulations we examin e the use of the different algorithms when the dimensions of the multivariates are larger than (or of the same order of) the number of samples and demonstrate the advantage of sparse CCA. In this context, one of the advantages of the greedy approach is that it generates the full sparsity path i n a single run and allows for efficient parameter tuning using