title: A study of speaker adaptation for DNN-based speech synthesis

abstract: A major advantage of statistical parametric speech synthesis (SPSS) over unit-selection speech synthesis is its adaptability and controllability in changing speaker characteristics and speaking style. Recently, several studies using deep neural networks (DNNs) as acoustic models for SPSS have shown promising results. However, the adaptability of DNNs in SPSS has not been systematically studied. In this paper, we conduct an experimental analysis of speaker adaptation for DNN-based speech synthesis at different levels. In particular, we augment a low-dimensional speaker-specific vector with linguistic features as input to represent speaker identity, perform model adaptation to scale the hidden activation weights, and perform a feature space transformation at the output layer to modify generated acoustic features. We systematically analyse the performance of each individual adaptation technique and that of their combinations. Experimental results confirm the adaptability of the DNN, and listening tests demonstrate that the DNN can achieve significantly better adaptation performance than the hidden Markov model (HMM) baseline in terms of naturalness and speaker similarity.


title: Parallel and series configurations of flyback converter for pulsed power applications

abstract: Advances in solid-state switches and power electronics techniques have led to the development of compact, efficient and more reliable pulsed power systems. Although, the power rating and operation speed of the new solid-state switches are considerably increased, their low blocking voltage level puts a limits in the pulsed power operation. This paper proposes the advantage of parallel and series configurations of pulsed power modules in obtaining high voltage levels with fast rise time (dv/dt) using only conventional switches. The proposed configuration is based on two flyback modules. The effectiveness of the proposed approach is verified by numerical simulations, and the advantages of each configuration are indicated in comparison with a single module.


Non-Uniform Random Variate Generation


title: Recent Advances in the User Evaluation Methods and Studies of Non-Photorealistic Visualization and Rendering Techniques

abstract: While traditional computer graphics research to this day still focuses on the production and assessment of photorealism, a relatively new field, the one of nonphotorealistic rendering (NPR), has produced results that focus on viewer engagement by the use of stylization, abstraction and expressiveness. In this paper we briefly describe some of the techniques used today and then proceed to identify some the most important evaluation studies associated with NPR by categorizing them and outlining the applications they have been used in, their methods and finally the results and their impact on future work in this area.


title: Estimating O–D travel time matrix by Google Maps API: implementation, advantages, and implications

abstract: Many spatial analysis tasks call for the use of travel time between multiple origins and destinations, that is, O–D travel time matrix. Commercial geographical information systems (GIS) software requires the input of a well-defined road network dataset and significant efforts in implementing the task. However, road network data are often outdated, miss critical road condition details, or are expensive to acquire; and skillful usage of related software is a major obstacle for researchers without advanced training in GIS. This research develops a desktop tool for implementing the task by calling the Google Maps Application Programming Interface (API). By doing so, we are able to tap into the dynamically updated transportation network data and the routing rules maintained by Google and obtain a reliable estimate of O–D travel time matrix. The results are compared with those computed by the ArcGIS Network Analyst module to demonstrate its advantages. A case study in accessibility analysis is presented to illu...


title: Link-Prediction Enhanced Consensus Clustering for Complex Networks

abstract: Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that “consume” the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based sampling algorithm and merges their multiple partitions into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook.


title: Guest editorial: special issue on the emerging applications of neural networks

abstract: It has been over 60 years since the first neural network model was developed by McCulloch and Pitts [1], and has been just over 50 years since the Mark I Peceptron model was developed by Rosenblatt and Wightman [2]. The downfall of Neural Network research was due to the limited capabilities of perceptron which was rigorously proved in the book ‘‘Perceptrons’’ by Minsky and Papert [3]. Neural network research has then gone through a period of quiet years from the mid sixties to the mid eighties. The take off of neural computing research was probably due to the announcement that Japan had resumed the research of neural networks. The US feared that they would be overtaken by the Japanese. This resulted in Ira Skurnick, a program funding manager in the US defense Science Office, decided to fund neural computing research again. To all neural computing researchers, the late eighties are well remembered because we saw how neural computing had been reinstated and repositioned. In 1986, we saw the first annual Neural Networks for Computing Conference, and we saw Rumelhart reported back his famous backpropagation learning algorithm. To date, we have seen the flourish of neural computing with its application stretched from rigorous mathematical proof to different engineering, physical science, and even business finance applications. This special issue on ‘‘The Emerging Applications of Neural Networks’’ is aimed at disseminating the latest applications of neural networks in the areas of engineering or computer science, business and management, geology and library science. It is exciting to see different researchers from different countries, and cultures come up with new thinking and applications. In the twenty-first century, We may have come to a stage that science theories and basic research are more than sufficient to our daily lives. This special issue delineates how the versatile neural networks are affecting us everyday. The papers solicited are selected from papers presented in the ICONIP 2009. Eventually, twelve papers from experts with innovation on new neural applications are invited and included in this issue. We include the paper:


title: An ahead pipelined alloyed perceptron with single cycle access time

abstract: The increasing pipeline depth, aggressive clock rates and execution width of modern processors require ever more accurate dynamic branch predictors to fully exploit their potential. Recent research on ahead pipelined branch predictors [11, 19] and branch predictors based on perceptrons [10, 11] have offered either increased accuracy or effective single cycle access times, at the cost of large hardware budgets and additional complexity in the branch predictor recovery mechanism. Here we show that a pipelined perceptron predictor can be constructed so that it has an effective latency of one cycle with a minimal loss of accuracy. We then introduce the concept of a precomputed local perceptron, which allows the use of both local and global history in an ahead pipelined perceptron. Both of these two techniques together allow this new perceptron predictor to match or exceed the accuracy of previous designs except at very small hardware budgets, and allow the elimination of most of the complexity in the rest of the pipeline associated with overriding predictors.


title: DanNet: the challenge of compiling a wordnet for Danish by reusing a monolingual dictionary

abstract: This paper is a contribution to the discussion on compiling computational lexical resources from conventional dictionaries. It describes the theoretical as well as practical problems that are encountered when reusing a conventional dictionary for compiling a lexical-semantic resource in terms of a wordnet. More specifically, it describes the methodological issues of compiling a wordnet for Danish, DanNet, from a monolingual basis, and not—as is often seen—by applying the translational expansion method with Princeton WordNet as the English source. Thus, we apply as our basis a large, corpus-based printed dictionary of modern Danish. Using this approach, we discuss the issues of readjusting inconsistent and/or underspecified hyponymy hierarchies taken from the conventional dictionary, sense distinctions as opposed to the synonym sets of wordnets, generating semantic wordnet relations on the basis of sense definitions, and finally, supplementing missing or implicit information.


title: Towards air quality indices in smart cities by calibrated low-cost sensors applied to networks

abstract: This experimental study focusses on air quality monitoring by low-cost and accurate sensors to provide a rank of air quality indices for citizens community in smart cities. Inter-comparison long-term measurements of CO, NO 2 , SO 2 and PM 10 are performed in a real scenario using referenced chemical analyzers at air-quality monitoring stations. The accuracy of the low-cost sensors is assessed in order to address the Data Quality Objectives (DQO) of the EU Air Quality Directive 2008/50/EC for indicative measurements. These preliminary results show that the low-cost sensors, when accurate, are suitable to define a rank of individual air quality index (AQI) to inform effectively general public and enhance environmental awareness. Finally, we show that the developed multiparametric sensor-system NASUS can be integrated both in a distributed city-network based on cost-effective fixed nodes, and in portable handheld sensor-systems to monitor air-pollution personal exposure.


title: Named Data Networking: A survey

abstract: Internet was developed as a packet data network where users and data sources (server) with specific IP addresses interacted over a pre-established communication channel. This model of client–server data communication has evolved into a peer-to-peer mode of data sharing in recent times. Applications like, YouTube, Bit Torrent, social networks have revolutionized the idea of user generated contents. Modern users care only for specific data items irrespective of their sources. So, the idea of using IP addresses to identify servers hosting a particular content is losing its importance. Moreover, want of IP addresses is a challenging issue haunting the Internet community since long. The need of the time is a content-centric networking platform where data hosts are of less importance, and Named Data Networking (NDN) has been proposed to that end. NDN allows users to float a data request without any knowledge about the hosting entity. NDN can handle user mobility, security issues more efficiently than the current Internet. Although NDN has been proposed in 2010, so far, there is no survey paper studying its architecture and various schemes proposed for its different characteristic features, like, naming, adaptive forwarding and routing, caching, security, mobility, etc. In this paper, we introduce a novel taxonomy to study NDN features in depth. We have also covered several NDN applications. We conclude our survey by identifying a set of open challenges which should be addressed by researchers in due course.


title: A Critical Assessment of E-Government in the Caribbean: Success, Challenges and Use of Emerging Technologies

abstract: There have been sustained calls on the need for countries in the Caribbean to improve public service delivery as well as the methods by which governments interface with citizens. These modernisations are increasingly being framed in terms of the role of emerging technologies and more specifically, egovernment and c-government to transform the operations and effectiveness of governments. As small and developing states, the region is faced with unique institutional and structural challenges to the adoption and implementation of these measures. The results of various e-government strategies have been varied across the board. This paper offers a critical assessment of e-government strategies across the Caribbean focusing on the challenges, success factors and opportunities for implementation. The study explores the challenges and opportunities for advancing in and beyond e-government to c-government and more generally, in utilising emerging technologies and innovations towards improved public governance.


Video Games for Entertainment and Education


title: Biomimetic 4D printing

abstract: Printed hydrogel composites with plant-inspired architectures dynamically change shape on immersion in water to yield prescribed complex morphologies.


title: Automated unit test generation for classes with environment dependencies

abstract: Automated test generation for object-oriented software typically consists of producing sequences of calls aiming at high code coverage. In practice, the success of this process may be inhibited when classes interact with their environment, such as the file system, network, user-interactions, etc. This leads to two major problems: First, code that depends on the environment can sometimes not be fully covered simply by generating sequences of calls to a class under test, for example when execution of a branch depends on the contents of a file. Second, even if code that is environment-dependent can be covered, the resulting tests may be unstable, i.e., they would pass when first generated, but then may fail when executed in a different environment. For example, tests on classes that make use of the system time may have failing assertions if the tests are executed at a different time than when they were generated. In this paper, we apply bytecode instrumentation to automatically separate code from its environmental dependencies, and extend the EvoSuite Java test generation tool such that it can explicitly set the state of the environment as part of the sequences of calls it generates. Using a prototype implementation, which handles a wide range of environmental interactions such as the file system, console inputs and many non-deterministic functions of the Java virtual machine (JVM), we performed experiments on 100 Java projects randomly selected from SourceForge (the SF100 corpus). The results show significantly improved code coverage - in some cases even in the order of +80%/+90%. Furthermore, our techniques reduce the number of unstable tests by more than 50%.


title: Bootstrapping Knowledge Representations: From Entailment Meshes Via Semantic Nets to Learning Webs

abstract: The symbol‐based epistemology used in artificial intelligence is contrasted with the constructivist, coherence epistemology promoted by cybernetics. The latter leads to bootstrapping knowledge representations, in which different parts of the system mutually support each other. Gordon Pask’s entailment meshes are reviewed as a basic application of this approach, and then extended to entailment nets: directed graphs governed by the “bootstrapping axiom”, determining which concepts are to be distinguished or merged. This allows a constant restructuring of the conceptual network. Semantic networks and frame‐like representations can be expressed in this scheme by introducing a basic ontology of node and link types. Entailment nets are then generalized to associative networks with weighted links. Learning algorithms are presented which can adapt the link strengths, based on the frequency with which links are selected by hypertext users. It is argued that such bootstrapping methods can be applied to make the World Wide Web more intelligent, allowing it to self‐organize and support inferences.


title: Training Techniques to Improve Endurance Exercise Performances

abstract: In previously untrained individuals, endurance training improves peak oxygen uptake (.VO2peak), increases capillary density of working muscle, raises blood volume and decreases heart rate during exercise at the same absolute intensity. In contrast, sprint training has a greater effect on muscle glyco(geno)lytic capacity than on muscle mitochondrial content. Sprint training invariably raises the activity of one or more of themuscle glyco(geno)lytic or related enzymes and enhances sarcolemmal lactate transport capacity. Some groups have also reported that sprint training transforms muscle fibre types, but these data are conflicting and not supported by any consistent alteration in sarcoplasmic reticulum Ca2+ATPase activity or muscle physicochemical H+ buffering capacity.


title: Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients

abstract: There is growing amount of scientific evidence that motor activity is the most consistent indicator of bipolar disorder. Motor activity includes several areas such as body movement, motor response time, level of psychomotor activity, and speech related motor activity. Studies of motor activity in bipolar disorder have typically used self-reported questionnaires with clinical observer-rated scales, which are therefore subjective and have often limited effectiveness. Motor activity information can be used to classify episode type in bipolar patients, which is highly relevant, since severe depression and manic states can result in mortality. This paper introduces a system able to classify the state of patients suffering from bipolar disorder using sensed information from smartphones. We collected audio, accelerometer and self-assessment data from five patients over a time-period of 12 weeks during their real-life activities. In this research we evaluated the performance of several classifiers, different sets of features and the role of the questionnaires for classifying bipolar disorder episodes. In particular, we have shown that it is possible to classify with high confidence ( ź 85 % ) the course of mood episodes or relapse in bipolar patients. To our knowledge, no research to date has focused on naturalistic observation of day-to-day phone conversation to classify impaired life functioning in individuals with bipolar disorder.


title: Context-based vision system for place and object recognition

abstract: While navigating in an environment, a vision system has to be able to recognize where it is and what the main objects in the scene are. We present a context-based vision system for place and object recognition. The goal is to identify familiar locations (e.g., office 610, conference room 941, main street), to categorize new environments (office, corridor, street) and to use that information to provide contextual priors for object recognition (e.g., tables are more likely in an office than a street). We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and show how such contextual information introduces strong priors that simplify object recognition. We have trained the system to recognize over 60 locations (indoors and outdoors) and to suggest the presence and locations of more than 20 different object types. The algorithm has been integrated into a mobile system that provides realtime feedback to the user.


title: 60-GHz LTCC Integrated Circularly Polarized Helical Antenna Array

abstract: A 60-GHz wideband circularly polarized (CP) helical antenna array of 4 × 4 elements is designed and fabricated using low temperature cofired ceramic (LTCC) technology. The flexible via hole distribution is fully utilized to achieve a helical antenna array to obtain good circular polarization performance. Meanwhile, grounded coplanar waveguide (GCPW) to stripline is utilized for probe station measurement. Unlike traditional helical antennas, the proposed helical antenna array is convenient for integrated applications. The fabricated antenna array has dimension of 12 × 10 × 2 mm3. The simulated and measured impedance, axial ratio (AR) and radiation pattern are studied and compared. The proposed antenna array shows a wide measured impedance bandwidth from 52.5 to 65.5 GHz for | S11| <; -10dB, wideband measured AR bandwidth from 54 to 66 GHz for AR <;3 dB, respectively.


title: Visually Meaningful Encrypted Images using Arnold Transform and Other Transforms

abstract: Images are widely used in multimedia transmission and its security is an all-time need for the public. Image encryption has provided confidentiality of its content. The possibilities of attacks and decrypting an image are more on encrypted images. The algorithm uses different transforms to encrypt a digital image. To reduce the attacks on encrypted image and to make it indistinguishable, it is been hidden into a reference image, which creates a visually meaningful encrypted image. This paper presents an experimental work with various transforms to improve the image quality of the visually meaningful encrypted images. The idea of using the DCT with Arnold transform has given better results for image quality analysis than with the algorithm using Arnold transform alone. The original image can be decrypted and obtained by an authenticated personnel with a data hiding key.


title: Study of Flower Pollination Algorithm for Continuous Optimization

abstract: Modern optimization has in its disposal an immense variety of heuristic algorithms which can effectively deal with both continuous and combinatorial optimization problems. Recent years brought in this area fast development of unconventional methods inspired by phenomena found in nature. Flower Pollination Algorithm based on pollination mechanisms of flowering plants constitutes an example of such technique. The paper presents first a detailed description of this algorithm. Then results of experimental study of its properties for selected benchmark continuous optimization problems are given. Finally, the performance the algorithm is discussed, predominantly in comparison with the well-known Particle Swarm Optimization Algorithm.


Detecting Story Analogies from Annotations of Time, Action and Agency


title: Capacity of a Mobile Multiple-Antenna Communication Link in Rayleigh Flat Fading

abstract: We analyze a mobile wireless link comprising M transmitter and N receiver antennas operating in a Rayleigh flat-fading environment. The propagation coefficients between pairs of transmitter and receiver antennas are statistically independent and unknown; they remain constant for a coherence interval of T symbol periods, after which they change to new independent values which they maintain for another T symbol periods, and so on. Computing the link capacity, associated with channel coding over multiple fading intervals, requires an optimization over the joint density of T/spl middot/M complex transmitted signals. We prove that there is no point in making the number of transmitter antennas greater than the length of the coherence interval: the capacity for M>T is equal to the capacity for M=T. Capacity is achieved when the T/spl times/M transmitted signal matrix is equal to the product of two statistically independent matrices: a T/spl times/T isotropically distributed unitary matrix times a certain T/spl times/M random matrix that is diagonal, real, and nonnegative. This result enables us to determine capacity for many interesting cases. We conclude that, for a fixed number of antennas, as the length of the coherence interval increases, the capacity approaches the capacity obtained as if the receiver knew the propagation coefficients.


Tortoise and Hares Consensus: the Meshcash Framework for Incentive-Compatible, Scalable Cryptocurrencies


title: Geographical partition for distributed web crawling

abstract: This paper evaluates scalable distributed crawling by means of the geographical partition of the Web. The approach is based on the existence of multiple distributed crawlers each one responsible for the pages belonging to one or more previously identified geographical zones. The work considers a distributed crawler where the assignment of pages to visit is based on page content geographical scope. For the initial assignment of a page to a partition we use a simple heuristic that marks a page within the same scope of the hosting web server geographical location. During download, if the analyze of a page contents recommends a different geographical scope, the page is forwarded to the well-located web server.A sample of the Portuguese Web pages, extracted during the year 2005, was used to evaluate: a) page download communication times and the b) overhead of pages exchange among servers. Evaluation results permit to compare our approach to conventional hash partitioning strategies.


title: Adaptive speed control of brushless DC (BLDC) motor based on interval type-2 fuzzy logic

abstract: To precisely control the speed of BLDC motors at high speed and with very good performance, an accurate motor model is required. As a result, the controller design can play an important role in the effectiveness of the system. The classic controllers such as PID are widely used in the BLDC motor controllers, but they are not appropriate due to non-linear model of the BLDC motor. To enhance the performance and speed of response, many studies were taken to improve the adjusting methods of PID controller gains by using fuzzy logic. Use of fuzzy logic considering approximately interpretation of the observations and determination of the approximate commands, provides a good platform for designing intelligent robust controller. Nowadays type-2 fuzzy logic is used because of more ability to model and reduce uncertainty effects in rule-based fuzzy systems. In this paper, an interval type-2 fuzzy logic-based proportional-integral-derivative controller (IT2FLPIDC) is proposed for speed control of brushless DC (BLDC) motor. The proposed controller performance is compared with the conventional PID and type-1 fuzzy logic-based PID controllers, respectively in MATLAB/Simulink environment. Simulation results show the superior IT2FLPIDC performance than two other ones.


title: Affordances as Qualities

abstract: Affordances elude ontology. They have been recognized to play a role in categorization, especially of artifacts, but also of natural features. Yet, attempts to ontologize them face problems ranging from their presumed subjective nature to the fact that they involve potential actions, not objects or properties. We take a fresh look at the ontology of affordances, based on a simple insight: affordances are perceived by agents and may lead to actions, just like qualities are perceived and may lead to observations. We understand perception as a process invoking a quale in an agent. This quale can then be expressed as an action, if it stems from an affordance, or as an observation, if it stems from an other quality. Thus, we see affordances as qualities of the environment, perceived and potentially expressed by agents. We extend our recently proposed ontology of observations to include affordances and show how the parallel between observations (producing values) and affordances (producing actions) provides a simple and powerful ontological account of both.


title: Emerging IT for Medication Adherence

abstract: Medication adherence is one of the most complex and persistent challenges in healthcare. Solutions have been designed with varying degrees of success. Here, the authors present a roadmap that shows how emerging IT can play a major role in improving medication adherence. Specifically, they discuss five major technologies for medication adherence: smartphone apps, smart medication boxes, smart pills, sensors, and implantable devices. They show how these technologies support and integrate into a roadmap for higher medication adherence. The hope is that IT professionals will address many of the challenges and opportunities identified in the article.


title: Discrete optimization of ray potentials for semantic 3D reconstruction

abstract: Dense semantic 3D reconstruction is typically formulated as a discrete or continuous problem over label assignments in a voxel grid, combining semantic and depth likelihoods in a Markov Random Field framework. The depth and semantic information is incorporated as a unary potential, smoothed by a pairwise regularizer. However, modelling likelihoods as a unary potential does not model the problem correctly leading to various undesirable visibility artifacts. We propose to formulate an optimization problem that directly optimizes the reprojection error of the 3D model with respect to the image estimates, which corresponds to the optimization over rays, where the cost function depends on the semantic class and depth of the first occupied voxel along the ray. The 2-label formulation is made feasible by transforming it into a graph-representable form under QPBO relaxation, solvable using graph cut. The multi-label problem is solved by applying α-expansion using the same relaxation in each expansion move. Our method was indeed shown to be feasible in practice, running comparably fast to the competing methods, while not suffering from ray potential approximation artifacts.


title: Chip Level Techniques for EMI Reduction in LCD Panels

abstract: This paper presents chip level techniques to improve electro-magnetic interference (EMI) characteristics of LCD-TV panels. A timing controller (TCON) uses over-driving algorithms to improve the response time of liquid crystals (LC). Since this algorithm needs previous frame data, external memory such as double data rate synchronous DRAM (DDR SDRAM) is widely used as a frame buffer. A TTL interface between the TCON and memory is used for read and write operations, generating EMI noise. For reduction of this EMI, three methods are described. The first approach is to reduce the driving current of data I/O buffers. The second is to adopt spread spectrum clock generation (SSCG), and the third is to apply a proposed algorithm which minimizes data transitions. EMI measurement of a 32" LCD-TV panel shows that these approaches are very effective for reduction of EMI, achieving 20dB reduction at 175MHz.


title: Cloak and dagger: dynamics of web search cloaking

abstract: Cloaking is a common 'bait-and-switch' technique used to hide the true nature of a Web site by delivering blatantly different semantic content to different user segments. It is often used in search engine optimization (SEO) to obtain user traffic illegitimately for scams. In this paper, we measure and characterize the prevalence of cloaking on different search engines, how this behavior changes for targeted versus untargeted advertising and ultimately the response to site cloaking by search engine providers. Using a custom crawler, called Dagger, we track both popular search terms (e.g., as identified by Google, Alexa and Twitter) and targeted keywords (focused on pharmaceutical products) for over five months, identifying when distinct results were provided to crawlers and browsers. We further track the lifetime of cloaked search results as well as the sites they point to, demonstrating that cloakers can expect to maintain their pages in search results for several days on popular search engines and maintain the pages themselves for longer still.


title: Ultra Low Power short range radios: Covering the last mile of the IoT

abstract: Short Range Radios are predicted to realize a paradigm change by 2020, similar to the revolution of mobile communication, in the nineties. These radios provide the last mile wireless connectivity between “things” and the Internet. They connect your new wearable devices to your smart phone, or enable smart energy monitoring and control in your home. In this paper, we analyze the power breakdown of such new consumer devices operating from small, advanced batteries. It is shown that the wireless connectivity has a dominant impact on the overall power budget of these devices. Today, short range radios with Ultra-Low Power (ULP) consumption, in the range of a few mW, are reported. It is shown that future small and flexible sensor nodes need wireless connectivity at sub-mW “U2LP” consumption. An overview of state-of-the-art short range transceivers reveals promising architectures and circuits to reach the U2LP target.


title: Click data as implicit relevance feedback in web search

abstract: Search sessions consist of a person presenting a query to a search engine, followed by that person examining the search results, selecting some of those search results for further review, possibly following some series of hyperlinks, and perhaps backtracking to previously viewed pages in the session. The series of pages selected for viewing in a search session, sometimes called the click data, is intuitively a source of relevance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example, the search engine could learn which documents are frequently visited when certain search queries are given. In this article, we address three issues related to using click data as implicit relevance feedback: (1) How click data beyond the search results page might be more reliable than just the clicks from the search results page; (2) Whether we can further subselect from this click data to get even more reliable relevance feedback; and (3) How the reliability of click data for relevance feedback changes when the goal becomes finding one document for the user that completely meets their information needs (if possible). We refer to these documents as the ones that are strictly relevant to the query. Our conclusions are based on empirical data from a live website with manual assessment of relevance. We found that considering all of the click data in a search session as relevance feedback has the potential to increase both precision and recall of the feedback data. We further found that, when the goal is identifying strictly relevant documents, that it could be useful to focus on last visited documents rather than all documents visited in a search session.


title: Neutrosophic Correlation and Simple Linear Regression

abstract: Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in (7, 8). Recently, Salama et al. in (14, 15, 16, 32 ), introduced the concept of correlation coefficient of neutrosophic data. In this paper, we introduce and study the concepts of correlation and correlation coefficient of neutrosophic data in probability spaces and study some of their properties. Also, we in- troduce and study the neutrosophic simple linear regression model. Possible applications to data pro- cessing are touched upon.


title: Deep g-buffers for stable global illumination approximation

abstract: We introduce a new hardware-accelerated method for constructing Deep G-buffers that is 2x-8x faster than the previous depth-peeling method and produces more stable results. We then build several high-performance shading algorithms atop our representation, including dynamic diffuse interreflection, ambient occlusion (AO), and screen-space mirror reflection effects. Our construction method is order-independent, guarantees a minimum separation between layers, operates in a (small) bounded memory footprint, and does not require per-pixel sorting. Moreover, addressing the increasingly expensive cost of pre-rasterization, our approach requires only a single pass over the scene geometry. We include the pseudocode for our Deep G-buffer construction in the paper and the full source code of our technique in our supplemental document.


title: Biogeographical contingency and the evolution of tropical anchovies (genus Cetengraulis) from temperate anchovies (genus Engraulis)

abstract: ABSTRACTAim Similar regimes of selection in different geographical settings candeterministically produce similar adaptive morphologies. We tested thehypothesis that the evolutionary trajectories of ﬁsh in upwelling zones can bealtered by biogeographic contingencies in the biological and physicalenvironment.Location Eastern Paciﬁc and western Atlantic oceans.Methods We estimated phylogenetic relationships among eastern Paciﬁctemperate anchovies (genus Engraulis) and tropical anchovies (genusCetengraulis) with neighbour-joining and Bayesian tree analysis of a 521-bpsegment of mitochondrial DNA cytochrome b. Available sequences for ﬁveadditional engraulid taxa were included to establish polarity of the tree. Bayesianestimates (BEAST) of time to most recent common ancestor (TMRCA) for thenodes in the phylogeny were calibrated with divergence between Cetengraulisedentulus and Cetengraulis mysticetus precipitated by the rise of the PanamaIsthmus 2.8–3.2 Ma.Results Neighbour-joining and Bayesian trees indicate that South AmericanEngraulis anchoita (Argentina) and Engraulis ringens (Chile) together are basalsister taxa to the California anchovy (Engraulis mordax) and Old World anchovies(Engraulis japonicas, Engraulis australis, Engraulis capensis and Engraulisencrasicolus). The two tropical species of Cetengraulis are sister-taxa toCalifornian E. mordax, even though their phenotypes and ecologies differmarkedly. A relaxed molecular clock indicates a TMRCA between CalifornianE. mordax and Cetengraulis at about 4.2 Ma (3.0–6.3 Ma 95% highest probabilitydensity).Main conclusions The TMRCA between the California anchovy, E. mordax,and tropical Cetengraulis coincides with the formation of the Gulf of California,which provided opportunities for allopatric isolation during climate oscillations.Mid-Pliocene warming (3.1–2.9 Ma) may have trapped ancestors of Cetengraulisin the Gulf of California, where they evolved digestive tract morphologies toexploit inshore tropical habitats with low plankton productivities. Whilepopulations of several other temperate ﬁshes have become isolated in the Gulfof California, few of these derived species show strong adaptive shifts fromtemperate sister taxa or range expansions into the tropical provinces of thewestern Atlantic and eastern Paciﬁc.KeywordsAdaptive shift, Bayesian analysis, determinism, Engraulidae, evolution, Gulf ofCalifornia, marine ﬁsh, mitochondrial DNA, Panama Seaway, TMRCA.


title: A Miller-compensated amplifier with Gm-boosting

abstract: A two-stage Miller-compensated amplifier with Gm-boosting (MCG) is introduced in this paper. The design involves the conventional two-stage amplifier and a proposed wide-bandwidth scalar circuit that amplifies the output signal voltage at the first stage of the conventional two-stage amplifier. The wide-bandwidth scalar circuit has very short delay so that the non-dominant pole and right-half-plane zero are moved to higher frequencies. The proposed MCG method is able to enhance the unity-gain frequency (UGF) and the phase margin (PM) of the two-stage amplifier. By deploying the current starving approach, the power consumption of proposed amplifier is also brought down. Using a standard 130-nm CMOS technology, simulation results show that the improved MCG amplifier driving a 100-pF load capacitance achieves a gain of 86.83 dB. The UGF is 2.48 MHz and the PM is 60.7°.


title: Collision detection enabled weighted path planning: a wrist and base force/torque sensors approach

abstract: This paper presents a collision-detection enabled weighted path planning approach for a robot sharing the same workspace with humans. Using base and wrist force/torque sensors, a model based method is proposed to detect the collision forces and disturbance torques on the joints of the manipulator; then a weighted path planning approach is developed to control the interaction. The experimental results illustrate the validity of the developed collision detection and planning scheme


title: WiDir: walking direction estimation using wireless signals

abstract: Despite its importance, walking direction is still a key context lacking a cost-effective and continuous solution that people can access in indoor environments. Recently, device-free sensing has attracted great attention because these techniques do not require the user to carry any device and hence could enable many applications in smart homes and offices. In this paper, we present WiDir, the first system that leverages WiFi wireless signals to estimate a human's walking direction, in a device-free manner. Human motion changes the multipath distribution and thus WiFi Channel State Information at the receiver end. WiDir analyzes the phase change dynamics from multiple WiFi subcarriers based on Fresnel zone model and infers the walking direction. We implement a proof-of-concept prototype using commercial WiFi devices and evaluate it in both home and office environments. Experimental results show that WiDir can estimate human walking direction with a median error of less than 10 degrees.


title: Estimation of V̇O2max from the ratio between HRmax and HRrest – the Heart Rate Ratio Method

abstract: The effects of training and/or ageing upon maximal oxygen uptake ( VO(2max)) and heart rate values at rest (HR(rest)) and maximal exercise (HR(max)), respectively, suggest a relationship between VO(2max) and the HR(max)-to-HR(rest) ratio which may be of use for indirect testing of VO(2max). Fick principle calculations supplemented by literature data on maximum-to-rest ratios for stroke volume and the arterio-venous O(2) difference suggest that the conversion factor between mass-specific VO(2max) (ml.min(-1).kg(-1)) and HR(max).HR(rest)(-1) is approximately 15. In the study we experimentally examined this relationship and evaluated its potential for prediction of VO(2max). VO(2max) was measured in 46 well-trained men (age 21-51 years) during a treadmill protocol. A subgroup ( n=10) demonstrated that the proportionality factor between HR(max).HR(rest)(-1) and mass-specific VO(2max) was 15.3 (0.7) ml.min(-1).kg(-1). Using this value, VO(2max) in the remaining 36 individuals could be estimated with an SEE of 0.21 l.min(-1) or 2.7 ml.min(-1).kg(-1) (approximately 4.5%). This compares favourably with other common indirect tests. When replacing measured HR(max) with an age-predicted one, SEE was 0.37 l.min(-1) and 4.7 ml.min(-1).kg(-1) (approximately 7.8%), which is still comparable with other indirect tests. We conclude that the HR(max)-to-HR(rest) ratio may provide a tool for estimation of VO(2max) in well-trained men. The applicability of the test principle in relation to other groups will have to await direct validation. VO(2max) can be estimated indirectly from the measured HR(max)-to-HR(rest) ratio with an accuracy that compares favourably with that of other common indirect tests. The results also suggest that the test may be of use for VO(2max) estimation based on resting measurements alone.


title: Principles of survey research part 2: designing a survey

abstract: This second article of our series looks at the process of designing a survey. The design process begins with reviewing the objectives, examining the target population identified by the objectives, and deciding how best to obtain the information needed to address those objectives. However, we also need to consider factors such as determining the appropriate sample size and ensuring the largest possible response rate.To illustrate our ideas, we use the three surveys described in Part 1 of this series to suggest good and bad practice in software engineering survey research.


title: How to practice person‐centred care: A conceptual framework

abstract: Background ::: Globally, health-care systems and organizations are looking to improve health system performance through the implementation of a person-centred care (PCC) model. While numerous conceptual frameworks for PCC exist, a gap remains in practical guidance on PCC implementation. ::: ::: Methods ::: Based on a narrative review of the PCC literature, a generic conceptual framework was developed in collaboration with a patient partner, which synthesizes evidence, recommendations and best practice from existing frameworks and implementation case studies. The Donabedian model for health-care improvement was used to classify PCC domains into the categories of “Structure,” “Process” and “Outcome” for health-care quality improvement. ::: ::: Discussion ::: The framework emphasizes the structural domain, which relates to the health-care system or context in which care is delivered, providing the foundation for PCC, and influencing the processes and outcomes of care. Structural domains identified include: the creation of a PCC culture across the continuum of care; co-designing educational programs, as well as health promotion and prevention programs with patients; providing a supportive and accommodating environment; and developing and integrating structures to support health information technology and to measure and monitor PCC performance. Process domains describe the importance of cultivating communication and respectful and compassionate care; engaging patients in managing their care; and integration of care. Outcome domains identified include: access to care and Patient-Reported Outcomes. ::: ::: Conclusion ::: This conceptual framework provides a step-wise roadmap to guide health-care systems and organizations in the provision PCC across various health-care sectors.


CliNER : A Lightweight Tool for Clinical Named Entity Recognition


title: Finite-Element-Based Multiobjective Design Optimization Procedure of Interior Permanent Magnet Synchronous Motors for Wide Constant-Power Region Operation

abstract: This paper proposes the design optimization procedure of three-phase interior permanent magnet (IPM) synchronous motors with minimum weight, maximum power output, and suitability for wide constant-power region operation. The particular rotor geometry of the IPM synchronous motor and the presence of several variables and constraints make the design problem very complicated. The authors propose to combine an accurate finite-element analysis with a multiobjective optimization procedure using a new algorithm belonging to the class of controlled random search algorithms. The optimization procedure has been employed to design two IPM motors for industrial application and a city electrical scooter. A prototype has been realized and tested. The comparison between the predicted and measured performances shows the reliability of the simulation results and the effectiveness, versatility, and robustness of the proposed procedure.


title: Tactical geospatial intelligence from full motion video

abstract: The current proliferation of Unmanned Aircraft Systems provides an increasing amount of full-motion video (FMV) that, among other things, encodes geospatial intelligence. But the FMV is rarely converted into useful products, thus the intel potential is wasted. We have developed four concept demonstrations of methods to convert FMV into more immediately useful products, including: more accurate coordinates for objects of interest; timely, geo-registered, orthorectified imagery; conversion of mouse-clicks to object coordinates; and first-person-perspective visualization of graphical control measures. We believe these concepts can convey valuable geospatial intelligence to the tactical user.


title: Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis

abstract: Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded, whereas computational burden is significantly relaxed. When the desired object information is known, this task can be achieved by finding the bands that contain the most information about these objects. When the desired object information is unknown, i.e., unsupervised band selection, the objective is to select the most distinctive and informative bands. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose unsupervised band selection algorithms based on band similarity measurement. The experimental result shows that our approach can yield a better result in terms of information conservation and class separability than other widely used techniques.


title: The Crack of Dawn: Perceptual Functions and Neural Mechanisms that Mark the Transition from Unconscious Processing to Conscious Vision

abstract: There is conscious vision, and there is unconscious visual processing. So far so good. But where lies the boundary between the two? What are the visual functions that shape the transition from “processing in the dark” to having a conscious visual percept? And what are the neural mechanisms that carry that transition? I review the findings on feature detection, object categorization, interference, inference, Gestalt grouping, and perceptual organization, and examine to what extent these functions correlate with the presence or absence of conscious vision. It turns out that a surprisingly large set of visual functions is executed unconsciously, indicating that unconscious vision is much “smarter” than we might intuitively think. Only when these unconscious mechanisms fail, and more elaborate and incremental processing steps are required, is consciousness necessary. The function of conscious vision may be to add a final layer to our interpretation of the world, to solve relatively “new” visual problems, and to enable visual learning.


title: doi:10.1155/2007/49389 Research Article Channel Equalization in Filter Bank Based Multicarrier Modulation for Wireless Communications

abstract: Channel equalization in filter bank based multicarrier (FBMC) modulation is addressed. We utilize an efficient oversampled filter bank concept with 2x-oversampled subcarrier signals that can be equalized independently of each other. Due to Nyquist pulse shaping, consecutive symbol waveforms overlap in time, which calls for special means for equalization. Two alternative linear low-complexity subcarrier equalizer structures are developed together with straightforward channel estimation-based methods to calculate the equalizer coefficients using pointwise equalization within each subband (in a frequency-sampled manner). A novel structure, consisting of a linear-phase FIR amplitude equalizer and an allpass filter as phase equalizer, is found to provide enhanced robustness to timing estimation errors. This allows the receiver to be operated without time synchronization before the filter bank. The coded error-rate performance of FBMC with the studied equalization scheme is compared to a cyclic prefix OFDM reference in wireless mobile channel conditions, taking into account issues like spectral regrowth with practical nonlinear transmitters and sensitivity to frequency offsets. It is further emphasized that FBMC provides flexible means for high-quality frequency selective filtering in the receiver to suppress strong interfering spectral components within or close to the used frequency band.


title: A Framework for Third Party Android Marketplaces to Identify Repackaged Apps

abstract: App security on third party Android marketplaces has been one of the most serious concerns in Android ecosystem. Without screening and integrity evaluation on uploaded Apps like the App publishing process done by Google Play market, there might exist many malicious Apps in third party Android markets. Malicious Apps can leak sensitive user information and personal data, steal user identity and conduct financial frauds accordingly. As the easiest way to generate a malicious App is to modify a released App and repackage it, therefore, how to detect repackaged Apps has become an interesting research topic. For third party Android markets to eliminate or mitigate repackaged Apps uploaded into markets, a framework based on App fingerprint and whitelist/blacklist of Apps is proposed to evaluate the integrity of an App before it is released on the market in this paper. A framework prototype, called as Secure Market, is implemented and evaluated through various test scenarios. Our evaluation results have shown that the proposed framework detected 96% of repackaged Apps in average.


title: A Novel Multiconnected Convolutional Network for Super-Resolution

abstract: Convolutional neural networks exhibit superior performance for single image super-resolution (SISR) tasks. However, as the network grows deeper, features from the earlier layers are impeded or less used in later layers. In SISR, the earlier layers are mainly composed of local features that are essential to the task. In this letter, we present a novel multiconnected convolutional network for SISR tasks by enhancing the combination of both low- and high-level features. We design a structure built on multiconnected blocks to extract diversified and complicated features via the concatenation of low-level features to high-level features. In addition to stacking multiconnected blocks, a long skip-connection is implemented to further aggregate features of the first layer and a specific later layer. Furthermore, we employ a flexible two-parameter loss function to optimize the training process. The proposed method yields state-of-the-art performance both in terms of quantitative metrics and visual quality. The method also outperforms existing methods on datasets via unknown degrading operators, indicating an excellent generalization ability.


title: Machine Learning Applied to Rotating Check Fraud Detection

abstract: Given small profit margins, independently owned and operated restaurants are highly sensitive to insider fraud and yet have scant resources to combat the problem. This paper is the first open research to apply machine learning (ML) techniques to detecting insider fraud in point-of-sales transaction data in the restaurant industry. We show that after applying under-sampling techniques and carefully engineering features, ML can deliver very high fraud-detection performance. Knowledge about engineered features, algorithm selection, performance, and tuning gained from this research can be applied in future research on fraud detection of restaurant data.


title: Event-driven anomalies in spatiotemporal taxi passseger demand

abstract: We propose a grid-based approach of mining historical taxi GPS data to study anomalous pickup patterns in spatiotemporal passenger demand caused by public events. The developed method is applied by way of example to the city of Munich on a large-scale taxi dataset. Information about occurring public events is collected from different online and offline sources. For further analysis, the data is aggregated on an equally distributed grid level based on geohashes. In a first step, an extension of robust principle component analysis (RPCA) is developed and applied to the dataset. It is shown that extended RPCA can separate anomalous pickup patterns from base demand in the given time series. Secondly, the combination of anomaly detection and knowledge about occurring public events allows the exploration of correlations in demand fluctuations. The results show that the proposed method can be used to discover the potential impact of public events on spatiotemporal passenger demand in large-scale mobility datasets.


title: How far can you get with a modern face recognition test set using only simple features?

abstract: In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously illustrated an inherent danger in using such sets, showing that an extremely basic recognition system, built on a trivial feature set, was able to take advantage of low-level regularities in popular object and face recognition sets, performing on par with many state-of-the-art systems. Recently, several groups have raised the performance “bar” for these sets, using more advanced classification tools. However, it is difficult to know whether these improvements are due to progress towards solving the core computational problem, or are due to further improvements in the exploitation of low-level regularities. Here, we show that even modest optimization of the simple model introduced by Pinto et al. using modern multiple kernel learning (MKL) techniques once again yields “state-of-the-art” performance levels on a standard face recognition set (“labeled faces in the wild”). However, at the same time, even with the inclusion of MKL techniques, systems based on these simple features still fail on a synthetic face recognition test that includes more “realistic” view variation by design. These results underscore the importance of building test sets focussed on capturing the central computational challenges of real-world face recognition.


title: Self-Injection-Locked AIA Radar Sensor Using PLL Demodulator for N oncontact Vital Sign Detection

abstract: In this paper, a new self-injection-Iocked radar sensor with a self-oscillating active-integrated-antenna (AIA) and a phase-locked-loop demodulator is presented for noncontact vital sign detection. The AIA is developed to simultaneously radiate the radio-frequency signal and receive the back-scattered injection signal, which is phase-modulated by the chest movement of the human subject. According to the injection-locking theory, the oscillation frequency of the AIA will be shifted by injecting a signal with a phase variation, which amounts to the frequency modulation. Hence, the vital sign can be demodulated by a PLL demodulator. The developed radar sensor has been experimentally verified as the human subject is located at the distances of 1.5 m away from the radar sensor.


title: Upper limb rehabilitation using robotic exoskeleton systems: a systematic review

abstract: Exoskeleton assisted therapy has been reported as a significant reduction in impairment and gain in functional abilities of stroke patients. In this paper, we conduct a systematic review on the upper limb rehabilitation using robotic exoskeleton systems. This review is based on typical mechanical structures and control strategies for exoskeletons in clinical rehabilitation conditions. A variety of upper limb exoskeletons are classified and reviewed according to their rehabilitation joints. Special attentions are paid to the performance control strategies and mechanism designs in clinical trials and to promote the adaptability to different patients and conditions. Finally, we analyze and highlight the current research gaps and the future directions in this field. We intend to offer informative resources and reliable guidance for relevant researcher’s further studies, and exert a far-reaching influence on the development of advanced upper limb exoskeleton robotic systems.


title: A shared task involving multi-label classification of clinical free text

abstract: This paper reports on a shared task involving the assignment of ICD-9-CM codes to radiology reports. Two features distinguished this task from previous shared tasks in the biomedical domain. One is that it resulted in the first freely distributable corpus of fully anonymized clinical text. This resource is permanently available and will (we hope) facilitate future research. The other key feature of the task is that it required categorization with respect to a large and commercially significant set of labels. The number of participants was larger than in any previous biomedical challenge task. We describe the data production process and the evaluation measures, and give a preliminary analysis of the results. Many systems performed at levels approaching the inter-coder agreement, suggesting that human-like performance on this task is within the reach of currently available technologies.


title: The use of constructive-developmental theory to advance the understanding of leadership

abstract: Constructive-developmental theory is a stage theory of adult development that focuses on the growth and elaboration of a person's ways of understanding the self and the world. In this article we review how the constructive-developmental frameworks of Kegan [Kegan, R. (1982). The evolving self: Problem and process in human development. Cambridge, MA: Harvard University Press], Torbert [Torbert, W. R. (1987). Managing the corporate dream: Restructuring for long-term success. Homewood, IL: Dow Jones-Irwin.], and Kohlberg [Kohlberg, L. (1969). Stage and sequence: The cognitive developmental approach to socialization. In D. Goslin (Ed.), Handbook of socialization: Theory and research. New York: Rand McNally] have been applied in the theoretical and empirical literature on leadership and management. Although the literature has produced a number of propositions, the notion that a leader's order of development should impact his or her leadership effectiveness or managerial performance has generated the most research. We found mixed support for this proposition as well as a number of limitations in the research in general. To have a greater impact on the leadership field, constructive-developmental theory needs to generate more robust research, to link more clearly with on-going streams of leadership research, and to explore the contribution of aspects of the theory beyond individual order of development.


High Gain and Wideband Microstrip Antenna using Circular Array of Square Parasitic Patches on a Superstrate Layer


Classification using Hybrid SVM and KNN Approach


title: A learning style classification mechanism for e-learning

abstract: With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student's learning style. Hence, the first step for achieving adaptive learning environments is to identify students' learning styles. This paper proposes a learning style classification mechanism to classify and then identify students' learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students' learning styles.


title: Detecting emotions in social media: a constrained optimization approach

abstract: Emotion detection can considerably enhance our understanding of users' emotional states. Understanding users' emotions especially in a real-time setting can be pivotal in improving user interactions and understanding their preferences. In this paper, we propose a constraint optimization framework to discover emotions from social media content of the users. Our framework employs several novel constraints such as emotion bindings, topic correlations, along with specialized features proposed by prior work and well-established emotion lexicons. We propose an efficient inference algorithm and report promising empirical results on three diverse datasets.


title: BRAF Fusions Define a Distinct Molecular Subset of Melanomas with Potential Sensitivity to MEK Inhibition

abstract: Purpose: Recurrent “driver” mutations at specific loci in BRAF , NRAS , KIT , GNAQ , and GNA11 define clinically relevant molecular subsets of melanoma, but more than 30% are “pan-negative” for these recurrent mutations. We sought to identify additional potential drivers in “pan-negative” melanoma. Experimental Design: Using a targeted next-generation sequencing (NGS) assay (FoundationOne™) and targeted RNA sequencing, we identified a novel PAPSS1-BRAF fusion in a “pan-negative” melanoma. We then analyzed NGS data from 51 additional melanomas genotyped by FoundationOne™, as well as melanoma RNA, whole-genome and whole-exome sequencing data in The Cancer Genome Atlas (TCGA), to determine the potential frequency of BRAF fusions in melanoma. We characterized the signaling properties of confirmed molecular alterations by ectopic expression of engineered cDNAs in 293H cells. Results: Activation of the mitogen-activated protein kinase (MAPK) pathway in cells by ectopic expression of PAPSS1-BRAF was abrogated by mitogen-activated protein kinase kinase (MEK) inhibition but not by BRAF inhibition. NGS data analysis of 51 additional melanomas revealed a second BRAF fusion (TRIM24-BRAF) in a “pan-negative” sample; MAPK signaling induced by TRIM24-BRAF was also MEK inhibitor sensitive. Through mining TCGA skin cutaneous melanoma dataset, we further identified two potential BRAF fusions in another 49 “pan-negative” cases. Conclusions: BRAF fusions define a new molecular subset of melanoma, potentially comprising 4% to 8% of “pan-negative” cases. Their presence may explain an unexpected clinical response to MEK inhibitor therapy or assist in selecting patients for MEK-directed therapy. Clin Cancer Res; 19(24); 6696–702. ©2013 AACR .


title: Securing Fingerprint Template: Fuzzy Vault with Helper Data

abstract: An important issue gaining attention in biometrics community is the security and privacy of biometric systems: How robust are these systems against attacks? What happens if the biometric template is lost or stolen? Can the privacy of the users be preserved even when a security breach occurs? Among the numerous attacks that can be launched against these systems, protecting the user template that is stored either locally (e.g., on a smart card) or centrally (e.g., on the server) is a major concern. As a possible solution to this problem, a new class of algorithms, termed biometric cryptosystems has been proposed. These systems do not store the original template but only a transformed version of the template within a cryptographic framework. An example of such systems is the fuzzy vault construct proposed by Juels and Sudan. In this construct, the biometric template is converted to a 2D point cloud, containing a secret such as a symmetric encryption key. The operation of the vault requires some "helper" data. In this paper, we present an implementation of the fuzzy fingerprint vault based on orientation field based helper data that is automatically extracted from the fingerprints. We further show that this helper data does not leak any information about fingerprint minutiae, hence complementing the increased user privacy afforded by the fuzzy fingerprint vault. We demonstrate the vault performance on a public domain fingerprint database.


title: What does the retrosplenial cortex do?

abstract: The past decade has seen a transformation in research on the retrosplenial cortex (RSC). This cortical area has emerged as a key member of a core network of brain regions that underpins a range of cognitive functions, including episodic memory, navigation, imagination and planning for the future. It is now also evident that the RSC is consistently compromised in the most common neurological disorders that impair memory. Here we review advances on multiple fronts, most notably in neuroanatomy, animal studies and neuroimaging, that have highlighted the importance of the RSC for cognition, and consider why specifying its precise functions remains problematic.


title: Detection Method for Counterfeit Currency Based on Bit-Plane Slicing Technique

abstract: Counterfeiting and forging currencies is a serious threat to any economy. Even though currency exists as a variation of coins, banknotes, and electronic data, many economies remain threatened by counterfeiting which is made possible by the ongoing technological advancements in reprographic equipment available to the general public. Clearly, counterfeit currency detection is not a task that can be neglected. Digital image processing is one of the most common and effective techniques used to distinguish counterfeit banknotes from genuine ones. A new approach is presented in this paper using the bit-plane slicing technique to extract the most significant data from counterfeit banknote images with the application of an edge detector algorithm. The proposed technique consists of decomposing original images of 256 gray levels into their equivalent 8 binary images. This is useful in analyzing the relative importance contributed by each bit of the original image. Higher order bit levels are evaluated for grayscale banknote images with the application of Canny edge detection algorithm. The results are then compared with genuine banknotes and with other existing techniques used for detecting counterfeit notes. Unlike existing research, it was observed that the edges obtained using bit-plane sliced images are more accurate and can be detected faster than obtaining them from the original image without being sliced. The detection of counterfeit currency was also achieved by following the process of using Canny edge detection, image segmentation, and feature extraction.


title: Security analysis of KEA authenticated key exchange protocol

abstract: KEA is a Diffie-Hellman based key-exchange protocol developed by NSA which provides mutual authentication for the parties. It became publicly available in 1998 and since then it was neither attacked nor proved to be secure. We analyze the security of KEA and find that the original protocol is susceptible to a class of attacks. On the positive side, we present a simple modification of the protocol which makes KEA secure. We prove that the modified protocol, called KEA+, satisfies the strongest security requirements for authenticated key-exchange and that it retains some security even if a secret key of a party is leaked. Our security proof is in the random oracle model and uses the Gap Diffie-Hellman assumption. Finally, we show how to add a key confirmation feature to KEA+ (we call the version with key confirmation KEA+C) and discuss the security properties of KEA+C.


title: The PASCAL CHiME Speech Separation and Recognition Challenge

abstract: Distant microphone speech recognition systems that operate with human-like robustness remain a distant goal. The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is reverberantly mixed into a noise background composed of multiple competing sound sources. This paper describes a recent speech recognition evaluation that was designed to bring together researchers from multiple communities in order to foster novel approaches to this problem. The task was to identify keywords from sentences reverberantly mixed into audio backgrounds binaurally recorded in a busy domestic environment. The challenge was designed to model the essential difficulties of the multisource environment problem while remaining on a scale that would make it accessible to a wide audience. Compared to previous ASR evaluations a particular novelty of the task is that the utterances to be recognised were provided in a continuous audio background rather than as pre-segmented utterances thus allowing a range of background modelling techniques to be employed. The challenge attracted thirteen submissions. This paper describes the challenge problem, provides an overview of the systems that were entered and provides a comparison alongside both a baseline recognition system and human performance. The paper discusses insights gained from the challenge and lessons learnt for the design of future such evaluations.


title: A near-optimal algorithm for computing the entropy of a stream

abstract: We describe a simple algorithm for approximating the empirical entropy of a stream of m values in a single pass, using O(e-2 log(Δ-1) log m) words of space. Our algorithm is based upon a novel extension of a method introduced by Alon, Matias, and Szegedy [1]. We show a space lower bound of Ω(e-2 / log(e-1)), meaning that our algorithm is near-optimal in terms of its dependency on e. This improves over previous work on this problem [8, 13, 17, 5]. We show that generalizing to kth order entropy requires close to linear space for all k ≥ 1, and give additive approximations using our algorithm. Lastly, we show how to compute a multiplicative approximation to the entropy of a random walk on an undirected graph.


title: Predictive complex event processing: a conceptual framework for combining complex event processing and predictive analytics

abstract: Complex Event Processing deals with the detection of complex events based on rules and patterns defined by domain experts. Many complex events require real-time detection in order to have enough time for appropriate reactions. However, there are several events (e.g. credit card fraud) that should be prevented proactively before they occur, not just responded after they happened. In this paper, we briefly describe Complex Event Processing (CEP) and Predictive Analytics (PA). Afterwards, we focus on a major future direction of CEP, namely the inclusion of PA technologies into CEP tools and applications. Involving PA opens a wide range of possibilities in several application fields. However, we have observed that only few solutions apply PA techniques. In this paper, we define a conceptual framework which combines CEP and PA and which can be the basis of generic design pattern in the future. The conceptual framework is demonstrated in a proof--of--concept experiment. Finally we provide the results and lessons learned.


Do Sexual Pictures Receive More Likes on Instagram?: Sexual Pictures Attract More Likes in both Normal and Celebrity Group


title: Limiting the reconstruction capability of generative neural network using negative learning

abstract: Generative models are widely used for unsupervised learning with various applications, including data compression and signal restoration. Training methods for such systems focus on the generality of the network given limited amount of training data. A less researched type of techniques concerns generation of only a single type of input. This is useful for applications such as constraint handling, noise reduction and anomaly detection. In this paper we present a technique to limit the generative capability of the network using negative learning. The proposed method searches the solution in the gradient direction for the desired input and in the opposite direction for the undesired input. One of the application can be anomaly detection where the undesired inputs are the anomalous data. In the results section we demonstrate the features of the algorithm using MNIST handwritten digit dataset and latter apply the technique to a real-world obstacle detection problem. The results clearly show that the proposed learning technique can significantly improve the performance for anomaly detection.


title: Effectiveness of a multifactorial handwashing program to reduce school absenteeism due to acute gastroenteritis.

abstract: BACKGROUND ::: Acute gastroenteritis (AGE) is one of the most common diseases among children and an important cause of school absenteeism. The aim of this study was to assess the effectiveness of a handwashing program using hand sanitizers for the prevention of school absenteeism due to AGE. ::: ::: ::: METHODS ::: A randomized, controlled and open study of a sample of 1341 children between 4 and 12 years of age, attending 5 state schools in Almería (Spain), with an 8-month follow up (academic year). The experimental group (EG) washed their hands with soap and water, complementing this with the use of a hand sanitizer, and the control group (CG) followed the usual handwashing procedure. Absenteeism rates due GI were compared between the 2 groups through the multivariate Poisson regression analysis. Percent days absent in both groups were compared with a Z-test. ::: ::: ::: RESULTS ::: 446 cases of school absenteeism due to AGE were registered. The school children from the EG had a 36% lower risk of absenteeism due to AGE (IRR: 0.64, 95% confidence interval: 0.52-0.78) and a decrease in absenteeism of 0.13 episodes/child/academic year (0.27 of EG vs 0.40 CG/episodes/child/academic year, P < 0.001). Pupils missed 725 school days due to AGE and absent days was significantly lower in the EG (EG: 0.31%, 95% confidence interval: 0.28-0.35 vs. CG: 0.44%, 95% confidence interval: 0.40-0.48, P < 0.001). ::: ::: ::: CONCLUSIONS ::: The use of hand sanitizer as a complement to handwashing with soap is an efficient measure to reduce absent days and the number of school absenteeism cases due to AGE.


title: Image denoising using NL-means via smooth patch ordering

abstract: In our recent work we proposed an image denoising scheme based on reordering of the noisy image pixels to a one dimensional (1D) signal, and applying linear smoothing filters on it. This algorithm had two main limitations: It did not take advantage of the distances between the noisy image patches, which were used in the reordering process; and the smoothing filters required a separate training set to be learned from. In this work, we propose an image denoising algorithm, which applies similar permutations to the noisy image, but overcomes the above two shortcomings. We eliminate the need for learning filters by employing the nonlocal means (NL-means) algorithm. We estimate each pixel as a weighted average of noisy pixels in union of neighborhoods obtained from different global pixel permutations, where the weights are determined by distances between the patches. We show that the proposed scheme achieves results which are close to the state-of-the-art.


title: A recurrent neural network that learns to count

abstract: Parallel distributed processing (PDP) architectures demonstrate a potentially radical alternative to the traditional theories of language processing that are based on serial computational models. However, learning complex structural relationships in temporal data presents a serious challenge to PDP systems. For example, automata theory dictates that processing strings from a context-free language (CFL) requires a stack or counter memory device. While some PDP models have been hand-crafted to emulate such a device, it is not clear how a neural network might develop such a device when learning a CFL. This research employs standard backpropagation training techniques for a recurrent neural network (RNN) in the task of learning to predict the next character in a simple deterministic CFL (DCFL). We show that an RNN can learn to recognize the structure of a simple DCFL. We use dynamical systems theory to identify how network states reflect that structure by building counters in phase space. The work is an empirical investigation which is complementary to theoretical analyses of network capabilities, yet original in its specific configuration of dynamics involved. The application of dynamical systems theory helps us relate the simulation results to theoretical results, and the learning task enables us to highlight some issues for understanding dynamical systems that process language with counters.


title: A Hybrid Markov/Semi-Markov Conditional Random Field For Sequence Segmentation

abstract: Markov order-1 conditional random fields (CRFs) and semi-Markov CRFs are two popular models for sequence segmentation and labeling. Both models have advantages in terms of the type of features they most naturally represent. We propose a hybrid model that is capable of representing both types of features, and describe efficient algorithms for its training and inference. We demonstrate that our hybrid model achieves error reductions of 18% and 25% over a standard order-1 CRF and a semi-Markov CRF (resp.) on the task of Chinese word segmentation. We also propose the use of a powerful feature for the semi-Markov CRF: the log conditional odds that a given token sequence constitutes a chunk according to a generative model, which reduces error by an additional 13%. Our best system achieves 96.8% F-measure, the highest reported score on this test set.


title: Combined script and page orientation estimation using the Tesseract OCR engine

abstract: This paper proposes a simple but effective algorithm to estimate the script and dominant page orientation of the text contained in an image. A candidate set of shape classes for each script is generated using synthetically rendered text and used to train a fast shape classifier. At run time, the classifier is applied independently to connected components in the image for each possible orientation of the component, and the accumulated confidence scores are used to determine the best estimate of page orientation and script. Results demonstrate the effectiveness of the approach on a dataset of 1846 documents containing a diverse set of images in 14 scripts and any of four possible page orientations. A C++ implementation of this work will be made available in a future release of the open-source Tesseract OCR engine [1].


title: An Experimental Study on the Attribution of Personality Traits to Bullies and Targets in a Workplace Setting

abstract: Previous studies on the personality of bullies and targets have exclusively been based on self-report. Against this backdrop we conducted a between group experimental vignette study with three conditions, describing a bully, a target and a control, respectively. Students (n=242) were recruited as participants and rated the target on the observer rating version of the NEO Five Factor Inventory-Revised reflecting the personality dimensions Neuroticism, Extroversion, Openness to experience, Agreeableness and Conscientiousness. A MANOVA showed an overall significant effect of the experimental conditions. On Neuroticism significant differences between all conditions were found with targets rated highest and the control lowest. In terms of Extroversion the target was rated as lower than the control and the bully. No main effect was found for Openness. On Agreeableness the bully was rated as lower than both the target and the control. The bully was rated lower on Conscientiousness than the control. The significant differences reflected medium to large effect sizes. By and large the results are in agreement with comparable self-report data. The results are discussed in terms of practical implications and directions for future research are outlined.


title: Multiscale Gradients-Based Color Filter Array Interpolation

abstract: Single sensor digital cameras use color filter arrays to capture a subset of the color data at each pixel coordinate. Demosaicing or color filter array (CFA) interpolation is the process of estimating the missing color samples to reconstruct a full color image. In this paper, we propose a demosaicing method that uses multiscale color gradients to adaptively combine color difference estimates from different directions. The proposed solution does not require any thresholds since it does not make any hard decisions, and it is noniterative. Although most suitable for the Bayer CFA pattern, the method can be extended to other mosaic patterns. To demonstrate this, we describe its application to the Lukac CFA pattern. Experimental results show that it outperforms other available demosaicing methods by a clear margin in terms of CPSNR and S-CIELAB measures for both mosaic patterns.


title: Multi-Agent Monte Carlo Go

abstract: In this paper we propose a Multi-Agent version of UCT Monte Carlo Go. We use the emergent behavior of a great number of simple agents to increase the quality of the Monte Carlo simulations, increasing the strength of the artificial player as a whole. Instead of one agent playing against itself, different agents play in the simulation phase of the algorithm, leading to a better exploration of the search space. We could significantly overcome Fuego, a top Computer Go software. Emergent behavior seems to be the next step of Computer Go development.


title: Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases

abstract: Text representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of multiword expressions (concepts/entities). However, these methods are limited to textual knowledge bases (e.g., Wikipedia). In this paper, we propose a novel and simple technique for integrating the knowledge about concepts from two large scale knowledge bases of different structure (Wikipedia and Probase) in order to learn concept representations. We adapt the efficient skip-gram model to seamlessly learn from the knowledge in Wikipedia text and Probase concept graph. We evaluate our concept embedding models on two tasks: (1) analogical reasoning, where we achieve a state-of-the-art performance of 91% on semantic analogies, (2) concept categorization, where we achieve a state-of-the-art performance on two benchmark datasets achieving categorization accuracy of 100% on one and 98% on the other. Additionally, we present a case study to evaluate our model on unsupervised argument type identification for neural semantic parsing. We demonstrate the competitive accuracy of our unsupervised method and its ability to better generalize to out of vocabulary entity mentions compared to the tedious and error prone methods which depend on gazetteers and regular expressions.


title: Local Origin of Field Potentials in Visual Cortex

abstract: Summary The local field potential (LFP) is increasingly used to measure the combined activity of neurons within a region of tissue. Yet, available estimates of the size of this region are highly disparate, ranging from several hundred microns to a few millimeters. To measure the size of this region directly, we used a combination of multielectrode recordings and optical imaging. We determined the orientation selectivity of stimulus-evoked LFP signals in primary visual cortex and were able to predict it on the basis of the surrounding map of orientation preference. The results show that >95% of the LFP signal originates within 250 μm of the recording electrode. This quantitative estimate indicates that LFPs are more local than often recognized and provides a guide to the interpretation of the increasing number of studies that rest on LFP recordings.


title: Structural Alignment Methods with Applications to Geospatial Ontologies

abstract: We consider the problem of enabling interoperability and information sharing among geospatial applications that use ontologies to describe their concepts and the relationships among them. We present two fully automatic alignment methods that use the graph structures of a pair of ontologies to establish their alignment, that is, the semantic correspondences between their concepts. We have tested our methods on geospatial ontologies pertaining to wetlands and four other pairs that belong to a repository that has been used in the Ontology Alignment Evaluation Initiative (OAEI). Using these ontologies, we have compared the effectiveness (precision and recall) of our methods against the Similarity Flooding Algorithm that was proposed by others and show that for each of the tested ontologies one of our methods is at least as effective as their method. We have tuned the performance of our methods by introducing a greedy approach that reduces the number of concepts that get compared. This approach reduces runtime by approximately 30% with a minor compromise to the effectiveness of the results. To further validate our approach, we participated in the OAEI competition to align a pair of ontologies, each with a few thousand concepts.


title: Challenges and implications of using ultrasonic communications in intra-body area networks

abstract: Body area networks (BANs) promise to enable revolutionary biomedical applications by wirelessly interconnecting devices implanted or worn by humans. However, BAN wireless communications based on radio-frequency (RF) electromagnetic waves suffer from poor propagation of signals in body tissues, which leads to high levels of attenuation. In addition, in-body transmissions are constrained to be low-power to prevent overheating of tissues and consequent death of cells. To address the limitations of RF propagation in the human body, we propose a paradigm shift by exploring the use of ultrasonic waves as the physical medium to wirelessly interconnect in-body implanted devices. Acoustic waves are the transmission technology of choice for underwater communications, since they are known to propagate better than their RF counterpart in media composed mainly of water. Similarly, we envision that ultrasound (e.g., acoustic waves at non-audible frequencies) will provide support for communications in the human body, which is composed for 65% of water. In this paper, we first assess the feasibility of using ultrasonic communications in intra-body BANs, i.e., in-body networks where the devices are biomedical sensors that communicate with an actuator/gateway device located inside the body. We discuss the fundamentals of ultrasonic propagation in tissues, and explore important tradeoffs, including the choice of a transmission frequency, transmission power, bandwidth, and transducer size. Then, we discuss future research challenges for ultrasonic networking of intra-body devices at the physical, medium access and network layers of the protocol stack.


title: Automatic aortic root landmark detection in CTA images for preprocedural planning of transcatheter aortic valve implantation

abstract: Abstract ::: Transcatheter aortic valve implantation is currently a well-established minimal invasive treatment option for patients with severe aortic valve stenosis. CT Angiography is used for the pre-operative planning and sizing of the prosthesis. To reduce the inconsistency in sizing due to interobserver variability, we introduce and evaluate an automatic aortic root landmarks detection method to determine the sizing parameters. The proposed algorithm detects the sinotubular junction, two coronary ostia, and three valvular hinge points on a segmented aortic root surface. Using these aortic root landmarks, the automated method determines annulus radius, annulus orientation, and distance from annulus plane to right and left coronary ostia. Validation is performed by the comparison with manual measurements of two observers for 40 CTA image datasets. Detection of landmarks showed high accuracy where the mean distance between the automatically detected and reference landmarks was 2.81 ± 2.08 mm, comparable to the interobserver variation of 2.67 ± 2.52 mm. The mean annulus to coronary ostium distance was 16.9 ± 3.3 and 17.1 ± 3.3 mm for the automated and the reference manual measurements, respectively, with a mean paired difference of 1.89 ± 1.71 mm and interobserver mean paired difference of 1.38 ± 1.52 mm. Automated detection of aortic root landmarks enables automated sizing with good agreement with manual measurements, which suggests applicability of the presented method in current clinical practice.


title: Tibetan-Chinese Cross Language Text Similarity Calculation Based onLDA Topic Model

abstract: Topic model building is the basis and the most critical module of cross-language topic detection and tracking. Topic model also can be applied to cross-language text similarity calculation. It can improve the efficiency and the speed of calculation by reducing the texts' dimensionality. In this paper, we use the LDA model in cross-language text similarity computation to obtain Tibetan-Chinese comparable corpora: (1) Extending Tibetan-Chinese dictionary by extracting Tibetan-Chinese entities from Wikipedia. (2) Using topic model to make the texts mapped to the feature space of topics. (3) Calculating the similarity of two texts in different language according to the characteristics of the news text. The method for text similarity calculation based on LDA model reduces the dimensions of text space vector, and enhances the understanding of the text's semantics. It also improves the speed and efficiency of calculation.


title: Multiple Sector ID Capture (MIDC): A Novel Beamforming Technique for 60-GHz Band Multi-Gbps WLAN/PAN Systems

abstract: A novel beamforming (BF) technique (MIDC: Multiple sector-ID Capture) is proposed for 60-GHz band WLAN/PAN systems. In contrast to conventional BF techniques adopted in 60-GHz band standards, where quasi-omni (Q-omni) antenna radiation patterns are utilized, MIDC precisely detects the best link even when the Q-omni pattern is imperfect. Furthermore, it can reserve multiple antenna settings corresponding to existing communication links in the initial training by making use of the quasi-optical nature of millimeter-waves. This enables fast beam switching when link blockage occurs. The training is executed in short durations by putting together DoA/DoD-estimation and “beam-combining” techniques. The basic function of MIDC is verified experimentally in a simple multipath propagation environment by using our 60-GHz CMOS transceiver LSIs integrated with planar phased-array antennas. MIDC has been adopted in the MAC/PHY specification of the primary 60-GHz band standards: WiGig (Wireless Gigabit Alliance) and IEEE 802.11ad.


title: Bayesian space conceptualization and place classification for semantic maps in mobile robotics

abstract: The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim, this work attempts to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, it details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned. Several algorithms, from naive ones using only object category presence to more sophisticated ones using both objects and relationships, are proposed. Both learning and inference use the information encoded in the underlying representation-objects and relative spatial information between them. The approaches are based on learning from exemplars, clustering and the use of Bayesian network classifiers. The approaches are generative. Further, even though they are based on learning from exemplars, they are not ontology specific; i.e. they do not assume the use of any particular ontology. The presented algorithms rely on a robots inherent high-level feature extraction capability (object recognition and structural element extraction) capability to actually form concept models and infer them. Thus, this report presents methods that could enable a robot to to link sensory information to increasingly abstract concepts (spatial constructs). Such a conceptualization and the representation that results thereof would enable robots to be more cognizant of their surroundings and yet, compatible to us. Experiments on conceptualization and place classification are reported. Thus, the theme of this work is-conceptualization and classification for representation and spatial cognition.


title: Ordinal Image Segmentation using Deep Neural Networks

abstract: Ordinal arrangement of objects is a common property in biomedical images. Traditional methods to deal with semantic image segmentation in this setting are ad-hoc and application specific. In this paper, we propose ordinal-aware deep learning architectures for image segmentation that enforce pixelwise consistency by construction. We validated the proposed architectures on several real-life biomedical datasets and achieved competitive results in all cases.


title: Real-time simulation of large bodies of water with small scale details

abstract: We present a hybrid water simulation method that combines grid based and particles based approaches. Our specialized shallow water solver can handle arbitrary underlying terrain slopes, arbitrary water depth and supports wet-dry regions tracking. To treat open water scenes we introduce a method for handling non-reflecting boundary conditions. Regions of liquid that cannot be represented by the height field including breaking waves, water falls and splashing due to rigid and soft bodies interaction are automatically turned into spray, splash and foam particles. The particles are treated as simple non-interacting point masses and they exchange mass and momentum with the height field fluid. We also present a method for procedurally adding small scale waves that are advected with the water flow. We demonstrate the effectiveness of our method in various test scene including a large flowing river along a valley with beaches, big rocks, steep cliffs and waterfalls. Both the grid and the particles simulations are implemented in CUDA. We achieve real-time performance on modern GPUs in all the examples.


title: Too much sitting: the population health science of sedentary behavior.

abstract: Even when adults meet physical activity guidelines, sitting for prolonged periods can compromise metabolic health. Television (TV) time and objective measurement studies show deleterious associations, and breaking up sedentary time is beneficial. Sitting time, TV time, and time sitting in automobiles increase premature mortality risk. Further evidence from prospective studies, intervention trials, and population-based behavioral studies is required.


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