title: The relationship of motor skills and adaptive behavior skills in young children with autism spectrum disorders

abstract: Abstract Objective To determine the relationship of motor skills and adaptive behavior skills in young children with autism. Design A multiple regression analysis tested the relationship of motor skills on the adaptive behavior composite, daily living, adaptive social and adaptive communicative skills holding constant age, non-verbal problem solving, and calibrated autism severity. Setting Majority of the data collected took place in an autism clinic. Participants A cohort of 233 young children with ASD ( n = 172), PDD-NOS ( n = 22) and non-ASD (developmental delay, n = 39) between the ages of 14–49 months were recruited from early intervention studies and clinical referrals. Children with non-ASD (developmental delay) were included in this study to provide a range of scores indicted through calibrated autism severity. Interventions Not applicable. Main outcome measures The primary outcome measures in this study were adaptive behavior skills. Results Fine motor skills significantly predicted all adaptive behavior skills ( p p Conclusions The fine and gross motor skills are significantly related to adaptive behavior skills in young children with autism spectrum disorder. There is more to focus on and new avenues to explore in the realm of discovering how to implement early intervention and rehabilitation for young children with autism and motor skills need to be a part of the discussion.


Special Issue of Machine Learning on Information Retrieval Introduction


title: Neural Evidence for Non-conscious Working Memory

abstract: Recent studies have found that non-consciously perceived information can be retained for several seconds, a feat that has been attributed to non-conscious working memory processes. However, these studies have mainly relied on subjective measures of visual experience, and the neural processes responsible for non-conscious short-term retention remains unclear. Here we used continuous flash suppression to render stimuli non-conscious in a delayed match-to-sample task together with fMRI to investigate the neural correlates of non-conscious short-term (5-15 s) retention. The participants' behavioral performance was at chance level when they reported no visual experience of the sample stimulus. Critically, multivariate pattern analyses of BOLD signal during the delay phase could classify presence versus absence of sample stimuli based on signal patterns in frontal cortex, and its spatial position based on signal patterns in occipital cortex. In addition, univariate analyses revealed increased BOLD signal change in prefrontal regions during memory recognition. Thus, our findings demonstrate short-term maintenance of information presented non-consciously, defined by chance performance behaviorally. This non-consciously retained information seems to rely on persistent neural activity in frontal and occipital cortex, and may engage further cognitive control processes during memory recognition.


title: 5'-YRNA fragments derived by processing of transcripts from specific YRNA genes and pseudogenes are abundant in human serum and plasma

abstract: Small noncoding RNAs carry out a variety of functions in eukaryotic cells, and in multiple species they can travel between cells, thus serving as signaling molecules. In mammals multiple small RNAs have been found to circulate in the blood, although in most cases the targets of these RNAs, and even their functions, are not well understood. YRNAs are small (84-112 nt) RNAs with poorly characterized functions, best known because they make up part of the Ro ribonucleoprotein autoantigens in connective tissue diseases. In surveying small RNAs present in the serum of healthy adult humans, we have found YRNA fragments of lengths 27 nt and 30-33 nt, derived from the 5'-ends of specific YRNAs and generated by cleavage within a predicted internal loop. Many of the YRNAs from which these fragments are derived were previously annotated only as pseudogenes, or predicted informatically. These 5'-YRNA fragments make up a large proportion of all small RNAs (including miRNAs) present in human serum. They are also present in plasma, are not present in exosomes or microvesicles, and circulate as part of a complex with a mass between 100 and 300 kDa. Mouse serum contains far fewer 5'-YRNA fragments, possibly reflecting the much greater copy number of YRNA genes and pseudogenes in humans. The function of the 5'-YRNA fragments is at present unknown, but the processing and secretion of specific YRNAs to produce 5'-end fragments that circulate in stable complexes are consistent with a signaling function.


title: Fine-Grained Entity Typing with High-Multiplicity Assignments

abstract: As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data sources such as Wikipedia that have semi-open type systems. We introduce a set-prediction approach to this problem and show that our model outperforms unstructured baselines on a new Wikipedia-based fine-grained typing corpus.


title: Renewable Energy Systems With Photovoltaic Power Generators: Operation and Modeling

abstract: A substantial increase of photovoltaic (PV) power generators installations has taken place in recent years, due to the increasing efficiency of solar cells as well as the improvements of manufacturing technology of solar panels. These generators are both grid-connected and stand-alone applications. We present an overview of the essential research results. The paper concentrates on the operation and modeling of stand-alone power systems with PV power generators. Systems with PV array-inverter assemblies, operating in the slave-and-master modes, are discussed, and the simulation results obtained using a renewable energy power system modular simulator are presented. These results demonstrate that simulation is an essential step in the system development process and that PV power generators constitute a valuable energy source. They have the ability to balance the energy and supply good power quality. It is demonstrated that when PV array- inverters are operating in the master mode in stand-alone applications, they well perform the task of controlling the voltage and frequency of the power system. The mechanism of switching the master function between the diesel generator and the PV array-inverter assembly in a stand-alone power system is also proposed and analyzed. Finally, some experimental results on a practical system are compared to the simulation results and confirm the usefulness of the proposed approach to the development of renewable energy systems with PV power generators.


Towards Viable Intrusion Detection Methods For The Automotive Controller Area Network


Search for Establishing Nationally Integrated Optimum Arc Welding Process


title: Single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp

abstract: A single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp is proposed. In the proposed converter, the operation principle is quite different from other boost bridgeless three-level converters. The charging stage is presented in the low side while the discharging stage in the high side and vice versa. As a result, nature voltage clamp is achieved. And the slow diodes are ‘shared’. They operate not only as rectifying diodes, but also as voltage clamping diodes. Besides, only single inductor is required and the device utilization factor is high. Besides, the common-mode(CM) noise is rather low. First, the operational principle are analyzed in detail. Then the performance evaluation and design consideration are presented. Finally, a 1kW experimental prototype is built. The result shows that the efficiency is above 94% at full load under 90V and the maximum efficiency is 98.4%.


title: Frontomaxillary and mandibulomaxillary facial angles at 11 + 0 to 13 + 6 weeks in fetuses with trisomy 18

abstract: Objective ::: ::: To define the relative position of the maxilla and mandible in fetuses with trisomy 18 at 11 + 0 to 13 + 6 weeks of gestation. ::: ::: ::: ::: Methods ::: ::: A three-dimensional (3D) volume of the fetal head was obtained before karyotyping at 11 + 0 to 13 + 6 weeks of gestation in 36 fetuses subsequently found to have trisomy 18, and 200 chromosomally normal fetuses. The frontomaxillary facial (FMF) angle and the mandibulomaxillary facial (MMF) angle were measured in a mid-sagittal view of the fetal face. ::: ::: ::: ::: Results ::: ::: In the chromosomally normal group both the FMF and MMF angles decreased significantly with crown–rump length (CRL). In the trisomy 18 fetuses the FMF angle was significantly greater and the angle was above the 95th centile of the normal range in 21 (58.3%) cases. In contrast, in trisomy 18 fetuses the MMF angle was significantly smaller than that in normal fetuses and the angle was below the 5th centile of the normal range in 12 (33.3%) cases. ::: ::: ::: ::: Conclusions ::: ::: Trisomy 18 at 11 + 0 to 13 + 6 weeks of gestation is associated with both mid-facial hypoplasia and micrognathia or retrognathia that can be documented by measurement of the FMF angle and MMF angle, respectively. Copyright © 2007 ISUOG. Published by John Wiley & Sons, Ltd.


title: Wire drawing process simulation of Polycrystalline diamond wire drawing die

abstract: The wire drawing process of Polycrystalline diamond (PCD) wire drawing die is simulated by ABAQUS FEA software, which is used in drawing solar wafer cutting wire. According to the requirements of structure and sizes of shaper hole in the actual production, the wire drawing die whose reduction angle is 8 degrees is used in this simulation firstly. And the axial stress distribution and maximum axial tensile stress curve of cutting wire, and the equivalent stress distribution and equivalent stress curve of PCD drawing die can be obtained. From the stress curve of PCD drawing die, it can be seen that the equivalent stress maximum of die blank is 5826Mpa. Finally, several wire drawing die with different reduction angles are simulated to analyze the effects of different reduction angles on drawing force in theory. This simulation on PCD wire drawing die provides some theoretical basis for the design of reduction angle.


title: Analysis of Reinforced Concrete Slab Structures

abstract: The paper deals with the designing and analysing of concrete structures. A particular attention is paid to a multi-segment slab made from reinforced concrete. The purpose of the paper is to evaluate, in a non-linear analysis, impacts of input parameters of the concrete on the real load-carrying capacity of the ceiling which has been designed originally in DeMKP. FEM software applications have been used in the analysis. This is an in-house application DeMKP for designing the systems in line with standardised procedures. Another software is ATENA Science which can be used for non-linear analyses.


title: Visualization of Innovation in Global Supply Chain Networks

abstract: This methodological note identifies and describes a data-driven visualization approach to study innovations in supply chain networks (ISCN). We demonstrate its value and applicability with illustrative examples to pertinent structure-related ISCN research questions in the global electronics industry. Our visualization approach can be used to reveal and understand important clusters, patterns, trends, and outliers of ISCN not necessarily identified with traditional methods. The broader aim of this note is to demonstrate the complementary value of emerging visual analytic approaches in managerial decision-making contexts and describe how actionable insights can be achieved.


title: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

abstract: The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice, however, there are significant algorithmic and performance challenges. In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example. We apply the MoE to the tasks of language modeling and machine translation, where model capacity is critical for absorbing the vast quantities of knowledge available in the training corpora. We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost.


title: Fuzzy model of dominance emotions in affective computing

abstract: To date, most of the human emotion recognition systems are intended to sense the emotions and their dominance individually. This paper discusses a fuzzy model for multilevel affective computing based on the dominance dimensional model of emotions. This model can detect any other possible emotions simultaneously at the time of recognition. One hundred and thirty volunteers from various countries with different cultural backgrounds were selected to record their emotional states. These volunteers have been selected from various races and different geographical locations. Twenty-seven different emotions with their strengths in a scale of 5 were questioned through a survey. Recorded emotions were analyzed with the other possible emotions and their levels of dominance to build the fuzzy model. Then this model was integrated into a fuzzy emotion recognition system using three input devices of mouse, keyboard and the touch screen display. Support vector machine classifier detected the other possible emotions of the users along with the directly sensed emotion. The binary system (non-fuzzy) sensed emotions with an incredible accuracy of 93 %. However, it only could sense limited emotions. By integrating this model, the system was able to detect more possible emotions at a time with slightly lower recognition accuracy of 86 %. The recorded false positive rates of this model for four emotions were measured at 16.7 %. The resulted accuracy and its false positive rate are among the top three accurate human emotion recognition (affective computing) systems.


title: The main and accessory olfactory systems of female mice are activated differentially by dominant versus subordinate male urinary odors

abstract: Previous studies have shown that female preferences for male pheromones depend on the female's reproductive condition and the dominance status of the male. However, it is unknown which olfactory system detects the odors that result in a preference for a dominant male. Therefore, in the present study, we asked whether dominant versus subordinate male urinary odors differentially activate the main and accessory olfactory systems in female (C57Bl/6j) mice by monitoring the induction of the immediate early gene, c-fos. A more robust induction of Fos was observed in female mice which had direct nasal contact with dominant male urinary odors in four specific segments of the accessory olfactory system, i.e., the posteroventral part of the medial amygdala, the bed nucleus of the stria terminalis, the medial part of the preoptic nucleus and the ventrolateral part of the ventromedial hypothalamus, compared to females that were exposed to subordinate male urine. This greater activation of the accessory olfactory pathway by dominant male urine suggests that there are differences in the nonvolatile components of dominant versus subordinate male urine that are detected by the vomeronasal organ. By contrast, subordinate male urinary odors induced a greater activation in the piriform cortex which is part of the main olfactory system, suggesting that female mice discriminate between dominant and subordinate male urine using their main olfactory system as well.


title: Detecting sensitive information from textual documents: an information-theoretic approach

abstract: Whenever a document containing sensitive information needs to be made public, privacy-preserving measures should be implemented. Document sanitization aims at detecting sensitive pieces of information in text, which are removed or hidden prior publication. Even though methods detecting sensitive structured information like e-mails, dates or social security numbers, or domain specific data like disease names have been developed, the sanitization of raw textual data has been scarcely addressed. In this paper, we present a general-purpose method to automatically detect sensitive information from textual documents in a domain-independent way. Relying on the Information Theory and a corpus as large as the Web, it assess the degree of sensitiveness of terms according to the amount of information they provide. Preliminary results show that our method significantly improves the detection recall in comparison with approaches based on trained classifiers.


title: User trends modeling for a content-based recommender system

abstract: Proposing a novel evolutionary model-based recommender system.Introducing the concept of trend to capture dynamics in user interests.Proposing a Bayesian nonparametric model to construct the trend distributions.Adapting the trend-based user model in line with temporal activities of user. Recommender systems have been developed to overcome the information overload problem by retrieving the most relevant resources. Constructing an appropriate model to estimate the user interests is the major task of recommender systems. The profile matching and latent factors are two main approaches for user modeling. Although a notion of timestamps has already been applied to address the temporary nature of recommender systems, the evolutionary behavior of such systems is less studied. In this paper, we introduce the concept of trend to capture the interests of user in selecting items among different group of similar items. The trend based user model is constructed by incorporating user profile into a new extension of Distance Dependent Chines Restaurant Process (dd-CRP). dd-CRP which is a Bayesian Nonparametric model, provides a framework for constructing an evolutionary user model that captures the dynamics of user interests. We evaluate the proposed method using a real-world data-set that contains news tweets of three news agencies (New York Times, BBC and Associated Press). The experimental results and comparisons show the superior recommendation accuracy of the proposed approach, and its ability to effectively evolve over time.


title: 13.2 A 3.7mW-RX 4.4mW-TX fully integrated Bluetooth Low-Energy/IEEE802.15.4/proprietary SoC with an ADPLL-based fast frequency offset compensation in 40nm CMOS

abstract: This paper presents an ultra-low-power (ULP) fully-integrated Bluetooth Low-Energy(BLE)/IEEE802.15.4/proprietary RF SoC for Internet-of-Things applications. Ubiquitous wireless sensors connected through cellular devices are becoming widely used in everyday life. A ULP RF transceiver [1-3] is one of the most critical components that enables these emerging applications, as it can consume up to 90% of total battery energy. Furthermore, a low-cost radio design with an area-efficient fully integrated RF SoC is an important catalyst for developing such applications. By employing a low-voltage digital-intensive architecture, the presented SoC is fully compliant with BLE and IEEE802.15.4 PHY/Data-link requirements and achieves state-of-the-art power consumption of 3.7mWforRXand4.4mWforTX.


title: Ranking Universities Based on Career Outcomes of Graduates

abstract: Every year, millions of new students enter higher educational programs. Publicly available rankings of academic programs play a key role in prospective students' decisions regarding which universities to apply to and enroll in. While surveys indicate that majority of freshmen enter college to get good jobs after graduation, established methodologies for ranking universities rely on indirect indicators of career outcomes such as reputational assessments of the universities among academic peers, acceptance and graduation rates, learning environment, and availability of research funding. In addition, many of these methodologies rely on arbitrary choices of weighting factors for the different ranking indicators, and suffer from lack of analyses of statistical stability. In this paper, we addresses these challenges holistically by developing a novel methodology for ranking and recommending universities for different professions on the basis of career outcomes of professionals who graduated from those schools. Our methodology incorporates a number of techniques for achieving statistical stability, and represents a step towards personalized educational recommendations based on interests and ambitions of individuals. We have applied this methodology on LinkedIn's Economic Graph data of over 400 million professional from around the world. The resulting university rankings have been made available to the public and demonstrate that there are valuable insights to be gleaned from professional career data on LinkedIn.


title: Justifying and Generalizing Contrastive Divergence

abstract: We study an expansion of the log likelihood in undirected graphical models such as the restricted Boltzmann machine (RBM), where each term in the expansion is associated with a sample in a Gibbs chain alternating between two random variables (the visible vector and the hidden vector in RBMs). We are particularly interested in estimators of the gradient of the log likelihood obtained through this expansion. We show that its residual term converges to zero, justifying the use of a truncation---running only a short Gibbs chain, which is the main idea behind the contrastive divergence (CD) estimator of the log-likelihood gradient. By truncating even more, we obtain a stochastic reconstruction error, related through a mean-field approximation to the reconstruction error often used to train autoassociators and stacked autoassociators. The derivation is not specific to the particular parametric forms used in RBMs and requires only convergence of the Gibbs chain. We present theoretical and empirical evidence linking the number of Gibbs steps k and the magnitude of the RBM parameters to the bias in the CD estimator. These experiments also suggest that the sign of the CD estimator is correct most of the time, even when the bias is large, so that CD-k is a good descent direction even for small k.


title: Gesture spotting with body-worn inertial sensors to detect user activities

abstract: We present a method for spotting sporadically occurring gestures in a continuous data stream from body-worn inertial sensors. Our method is based on a natural partitioning of continuous sensor signals and uses a two-stage approach for the spotting task. In a first stage, signal sections likely to contain specific motion events are preselected using a simple similarity search. Those preselected sections are then further classified in a second stage, exploiting the recognition capabilities of hidden Markov models. Based on two case studies, we discuss implementation details of our approach and show that it is a feasible strategy for the spotting of various types of motion events.


title: A Very Rapid Prediction of IM Performance Combining Analytical and Finite-Element Analysis

abstract: A rapid and accurate analysis of the induction motor (IM) is the basis of any design process. This paper deals with an analysis procedure of the IM that combines a set of finite-element simulations with an analytical model. Both three- and single-phase machines are considered. Experimental tests on some IMs confirm the accuracy of the prediction achieved by the proposed analysis. The choice of specific quantities releases the results from the actual motor length and extends them to any motor adopting the same lamination and winding distribution.


title: Seven Fears and the Science of How Mobile Technologies May Be Influencing Adolescents in the Digital Age

abstract: Close to 90% of U.S. adolescents now own or have access to a mobile phone, and they are using them frequently. Adolescents send and receive an average of over 60 text messages per day from their devices, and over 90% of adolescents now access the Internet from a mobile device at least occasionally. Many adults are asking how this constant connectivity is influencing adolescents’ development. In this article, we examine seven commonly voiced fears about the influence of mobile technologies on adolescents’ safety (e.g., cyberbullying and online solicitation), social development (e.g., peer relationships, parent–child relationships, and identity development), cognitive performance, and sleep. Three sets of findings emerge. First, with some notable exceptions (e.g., sleep disruption and new tools for bullying), most online behaviors and threats to well-being are mirrored in the offline world, such that offline factors predict negative online experiences and effects. Second, the effects of mobile technologies ...


title: Algorithm 933: Reliable calculation of numerical rank, null space bases, pseudoinverse solutions, and basic solutions using suitesparseQR

abstract: The SPQR_RANK package contains routines that calculate the numerical rank of large, sparse, numerically rank-deficient matrices. The routines can also calculate orthonormal bases for numerical null spaces, approximate pseudoinverse solutions to least squares problems involving rank-deficient matrices, and basic solutions to these problems. The algorithms are based on SPQR from SuiteSparseQR (ACM Transactions on Mathematical Software 38, Article 8, 2011). SPQR is a high-performance routine for forming QR factorizations of large, sparse matrices. It returns an estimate for the numerical rank that is usually, but not always, correct. The new routines improve the accuracy of the numerical rank calculated by SPQR and reliably determine the numerical rank in the sense that, based on extensive testing with matrices from applications, the numerical rank is almost always accurately determined when our methods report that the numerical rank should be correct. Reliable determination of numerical rank is critical to the other calculations in the package. The routines work well for matrices with either small or large null space dimensions.


title: A simplified methodology for matched filter design with constraints - filter-antenna subsystem for space application

abstract: A new simplified methodology for the design of microwave cavity filters has been presented. The presented work is based on the optimization of Belevitch polynomials and inspired from the Simplified Real Frequency Technique (SRFT). one can bypass the use of computationally expensive circuit simulation softwares which are used to optimize the lumped element circuit model of the cavity filters. The procedure is applied for the design of filters for a multi-feed filter-antenna subsystem for space application.


title: Growing network with local rules: Preferential attachment, clustering hierarchy, and degree correlations

abstract: The linear preferential attachment hypothesis has been shown to be quite successful in explaining the existence of networks with power-law degree distributions. It is then quite important to determine if this mechanism is the consequence of a general principle based on local rules. In this work it is claimed that an effective linear preferential attachment is the natural outcome of growing network models based on local rules. It is also shown that the local models offer an explanation for other properties like the clustering hierarchy and degree correlations recently observed in complex networks. These conclusions are based on both analytical and numerical results for different local rules, including some models already proposed in the literature.


title: Exploiting time-based synonyms in searching document archives

abstract: Query expansion of named entities can be employed in order to increase the retrieval effectiveness. A peculiarity of named entities compared to other vocabulary terms is that they are very dynamic in appearance, and synonym relationships between terms change with time. In this paper, we present an approach to extracting synonyms of named entities over time from the whole history of Wikipedia. In addition, we will use their temporal patterns as a feature in ranking and classifying them into two types, i.e., time-independent or time-dependent. Time-independent synonyms are invariant to time, while time-dependent synonyms are relevant to a particular time period, i.e., the synonym relationships change over time. Further, we describe how to make use of both types of synonyms to increase the retrieval effectiveness, i.e., query expansion with time-independent synonyms for an ordinary search, and query expansion with time-dependent synonyms for a search wrt. temporal criteria. Finally, through an evaluation based on TREC collections, we demonstrate how retrieval performance of queries consisting of named entities can be improved using our approach.


title: LNTgate: How scientific misconduct by the U.S. NAS led to governments adopting LNT for cancer risk assessment

abstract: Abstract This paper provides a detailed rebuttal to the letter of Beyea (2016) which offered a series of alternative interpretations to those offered in my article in Environmental Research (Calabrese, 2015a) concerning the role of the U.S. National Academy of Sciences (NAS) Biological Effects of Atomic Radiation (BEAR) I Committee Genetics Panel in the adoption of the linear dose response model for cancer risk assessment. Significant newly uncovered evidence is presented which supports and extends the findings of Calabrese (2015a), reaffirming the conclusion that the Genetics Panel should be evaluated for scientific misconduct for deliberate misrepresentation of the research record in order to enhance an ideological agenda. This critique documents numerous factual errors along with extensive and deliberate filtering of information in the Beyea letter (2016) that leads to consistently incorrect conclusions and an invalid general perspective.


title: Chaos and confusion in DSM-5 diagnosis of Internet Gaming Disorder: Issues, concerns, and recommendations for clarity in the field

abstract: BackgroundThe umbrella term “Internet addiction” has been criticized for its lack of specificity given the heterogeneity of potentially problematic behaviors that can be engaged in online as well as different underlying etiological mechanisms. This has led to the naming of specific online addictions, the most notable being Internet Gaming Disorder (IGD).MethodsUsing the contemporary literature concerning IGD and cognate topics, issues and concerns relating to the concept of IGD are examined.ResultsInternet addiction and IGD are not the same, and distinguishing between the two is conceptually meaningful. Similarly, the diagnosis of IGD as proposed in the appendix of the latest (fifth) edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) remains vague regarding whether or not games need to be engaged in online, stating that IGD typically involves specific Internet games, but can also include offline games, adding to the lack of clarity. A number of authors have voiced concerns regard...


title: CrowdInside: automatic construction of indoor floorplans

abstract: The existence of a worldwide indoor floorplans database can lead to significant growth in location-based applications, especially for indoor environments. In this paper, we present CrowdInside: a crowdsourcing-based system for the automatic construction of buildings floorplans. CrowdInside leverages the smart phones sensors that are ubiquitously available with humans who use a building to automatically and transparently construct accurate motion traces. These accurate traces are generated based on a novel technique for reducing the errors in the inertial motion traces by using the points of interest in the indoor environment, such as elevators and stairs, for error resetting. The collected traces are then processed to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment. Implementation of the system in two testbeds, using different Android phones, shows that CrowdInside can detect the points of interest accurately with 0.2% false positive rate and 1.3% false negative rate. In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a a relatively small number of traces. This number is amortized over the number of users of the building. We also discuss possible extensions to CrowdInside for inferring even higher level semantics about the discovered floorplans.


title: Browser Fingerprinting as user tracking technology

abstract: The Web has become an indispensable part of our society and is currently most commonly used mode of information delivery. Millions of users access the free services provided by the websites on daily basis and while providing these free services websites track and profile their web users. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. The objective of this paper is to study about the increasingly common yet hardly discussed technique of identifying individual Web users and tracking them across multiple websites known as “Browser Fingerprinting”. A unique browser fingerprint is derived by the unique pattern of information visible whenever a computer visits a website. The permutations thus collected are sufficiently distinct that they can be used as a tool for tracking. Unlike cookies, Fingerprints are generated on server side and are difficult for a user to influence. The main objective of this research is study about how the fingerprinting was evolved, its positives and negatives, what threat it poses to users' online privacy and what countermeasures could be used to prevent it. This paper will also analyse which different properties the browsers send to the server, allowing a unique fingerprint of those browsers to be created.


title: Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application

abstract: Abstract In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.


title: A New SLNR-Based Linear Precoding for Downlink Multi-User Multi-Stream MIMO Systems

abstract: Signal-to-leakage-and-noise ratio (SLNR) is a promising criterion for linear precoder design in multi-user (MU) multiple-input multiple-output (MIMO) systems. It decouples the precoder design problem and makes closed-form solution available. In this letter, we present a new linear precoding scheme by slightly relaxing the SLNR maximization for MU-MIMO systems with multiple data streams per user. The precoding matrices are obtained by a general form of simultaneous diagonalization of two Hermitian matrices. The new scheme reduces the gap between the per-stream effective channel gains, an inherent limitation in the original SLNR precoding scheme. Simulation results demonstrate that the proposed precoding achieves considerable gains in error performance over the original one for multi-stream transmission while maintaining almost the same achievable sum-rate.


title: Comparative study on software development methodologies

abstract: ion increased productivity delivers products with a high degree of compatibility and portability shorter time to market requires considerable technical expertise documentation is readable only by domain experts is difficult to implement version control on modelling environment 54 Comparative Study on Software Development Methodologies lowers maintenance costs


title: Domain Adaptation From Multiple Sources: A Domain-Dependent Regularization Approach

abstract: In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referred to as target classifier) for label prediction of instances from the target domain by leveraging a set of base classifiers which are prelearned by using labeled instances either from the source domains or from the source domains and the target domain. With the base classifiers, we propose a new domain-dependent regularizer based on smoothness assumption, which enforces that the target classifier shares similar decision values with the relevant base classifiers on the unlabeled instances from the target domain. This newly proposed regularizer can be readily incorporated into many kernel methods (e.g., support vector machines (SVM), support vector regression, and least-squares SVM (LS-SVM)). For domain adaptation, we also develop two new domain adaptation methods referred to as FastDAM and UniverDAM. In FastDAM, we introduce our proposed domain-dependent regularizer into LS-SVM as well as employ a sparsity regularizer to learn a sparse target classifier with the support vectors only from the target domain, which thus makes the label prediction on any test instance very fast. In UniverDAM, we additionally make use of the instances from the source domains as Universum to further enhance the generalization ability of the target classifier. We evaluate our two methods on the challenging TRECIVD 2005 dataset for the large-scale video concept detection task as well as on the 20 newsgroups and email spam datasets for document retrieval. Comprehensive experiments demonstrate that FastDAM and UniverDAM outperform the existing multiple source domain adaptation methods for the two applications.


title: Layout-Induced Video Representation for Recognizing Agent-in-Place Actions

abstract: We address scene layout modeling for recognizing agent-in-place actions, which are actions associated with \textit{agents} who perform them and the \textit{places} where they occur, in the context of outdoor home surveillance. We introduce a novel representation to model the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training scenes to unseen scenes in the test set. This Layout-Induced Video Representation (LIVR) abstracts away low-level appearance variance and encodes geometric and topological relationships of places to explicitly model scene layout. LIVR partitions the semantic features of a scene into different places to force the network to learn generic place-based feature descriptions which are independent of specific scene layouts; then, LIVR dynamically aggregates features based on connectivities of places in each specific scene to model its layout. We introduce a new Agent-in-Place Action (APA) dataset\footnote{The dataset is pending legal review and will be released upon the acceptance of this paper.} to show that our method allows neural network models to generalize significantly better to unseen scenes.


title: Design and implementation of proportional-resonant controller for 3-phase current source inverter in Dspace DS1104

abstract: In this paper is presented the design of PR controller for 3-phase current source inverter. The converter is implemented with IGBT transistors and is modelled as a CSI (Current Source Inverter). Current control loops based on resonant techniques and instantaneous power p-q theory are used to control the DC/AC converter. This control strategy achieves active and reactive power generation in a three-phase system. During the development of the present work a PEBB (Power Electronic Building Block) is built. The control strategy is implemented in a Dspace DS1104.


title: Low-grade inflammation as a key mediator of the pathogenesis of osteoarthritis

abstract: Osteoarthritis (OA) has long been viewed as a degenerative disease of cartilage, but accumulating evidence indicates that inflammation has a critical role in its pathogenesis. Furthermore, we now appreciate that OA pathogenesis involves not only breakdown of cartilage, but also remodelling of the underlying bone, formation of ectopic bone, hypertrophy of the joint capsule, and inflammation of the synovial lining. That is, OA is a disorder of the joint as a whole, with inflammation driving many pathologic changes. The inflammation in OA is distinct from that in rheumatoid arthritis and other autoimmune diseases: it is chronic, comparatively low-grade, and mediated primarily by the innate immune system. Current treatments for OA only control the symptoms, and none has been FDA-approved for the prevention or slowing of disease progression. However, increasing insight into the inflammatory underpinnings of OA holds promise for the development of new, disease-modifying therapies. Indeed, several anti-inflammatory therapies have shown promise in animal models of OA. Further work is needed to identify effective inhibitors of the low-grade inflammation in OA, and to determine whether therapies that target this inflammation can prevent or slow the development and progression of the disease.


title: Electromagnetic and structural analysis of a Synchronous Reluctance Machine

abstract: The intent of this paper is to present an electromagnetic and structural study of a Synchronous Reluctance Machine. While most literature on the SynRM only considers the influence of rotor and stator geometry on the electromagnetic behavior of the machine, this paper introduces a combined approach, considering also the stress field in the machine's rotor due to inertial loads. The analysis is made for different construction solutions at different rotation speeds. The validation of static design is carried out with FEA solution. Some recommendations are made regarding possible solutions.


title: In-depth resistome analysis by targeted metagenomics

abstract: BackgroundAntimicrobial resistance is a major global health challenge. Metagenomics allows analyzing the presence and dynamics of “resistomes” (the ensemble of genes encoding antimicrobial resistance in a given microbiome) in disparate microbial ecosystems. However, the low sensitivity and specificity of available metagenomic methods preclude the detection of minority populations (often present below their detection threshold) and/or the identification of allelic variants that differ in the resulting phenotype. Here, we describe a novel strategy that combines targeted metagenomics using last generation in-solution capture platforms, with novel bioinformatics tools to establish a standardized framework that allows both quantitative and qualitative analyses of resistomes.MethodsWe developed ResCap, a targeted sequence capture platform based on SeqCapEZ (NimbleGene) technology, which includes probes for 8667 canonical resistance genes (7963 antibiotic resistance genes and 704 genes conferring resistance to metals or biocides), and 2517 relaxase genes (plasmid markers) and 78,600 genes homologous to the previous identified targets (47,806 for antibiotics and 30,794 for biocides or metals). Its performance was compared with metagenomic shotgun sequencing (MSS) for 17 fecal samples (9 humans, 8 swine). ResCap significantly improves MSS to detect “gene abundance” (from 2.0 to 83.2%) and “gene diversity” (26 versus 14.9 genes unequivocally detected per sample per million of reads; the number of reads unequivocally mapped increasing up to 300-fold by using ResCap), which were calculated using novel bioinformatic tools. ResCap also facilitated the analysis of novel genes potentially involved in the resistance to antibiotics, metals, biocides, or any combination thereof.ConclusionsResCap, the first targeted sequence capture, specifically developed to analyze resistomes, greatly enhances the sensitivity and specificity of available metagenomic methods and offers the possibility to analyze genes related to the selection and transfer of antimicrobial resistance (biocides, heavy metals, plasmids). The model opens the possibility to study other complex microbial systems in which minority populations play a relevant role.


title: A transport-layer network for distributed FPGA platforms

abstract: We present a transport-layer network that aids developers in building safe, high-performance distributed FPGA applications. Two essential features of such a network are virtual channels and end-to-end flow control. Our network implements these features, taking advantage of the low error characteristic of a rack level FPGA network to implement a low overhead credit based end-to-end flow control. Our design has many parameters in the source code which can be set at the time of FPGA synthesis, to provide flexibility in setting buffer size and flow control credits to make best use of scarce on-chip memory resources and match the traffic pattern of a virtual channel. Our prototype cluster, which is composed of 20 Xilinx VC707 boards, each with 4 20Gb/s serial links, achieves effective bandwidth of 85% of the maximum physical bandwidth, and a latency of 0.5us per hop. User feedback suggest that these features make distributed application development significantly easier.


title: A new approach to cross-modal multimedia retrieval

abstract: The problem of joint modeling the text and image components of multimedia documents is studied. The text component is represented as a sample from a hidden topic model, learned with latent Dirichlet allocation, and images are represented as bags of visual (SIFT) features. Two hypotheses are investigated: that 1) there is a benefit to explicitly modeling correlations between the two components, and 2) this modeling is more effective in feature spaces with higher levels of abstraction. Correlations between the two components are learned with canonical correlation analysis. Abstraction is achieved by representing text and images at a more general, semantic level. The two hypotheses are studied in the context of the task of cross-modal document retrieval. This includes retrieving the text that most closely matches a query image, or retrieving the images that most closely match a query text. It is shown that accounting for cross-modal correlations and semantic abstraction both improve retrieval accuracy. The cross-modal model is also shown to outperform state-of-the-art image retrieval systems on a unimodal retrieval task.


title: Inference during reading

abstract: Most current theories of text processing assume a constructionist view of inference processing. In this article, an alternative view is proposed, labeled the minimalist hypothesis. According to this hypothesis, the only inferences that are encoded automatically during reading are those that are based on easily available information, either from explicit statements in the text or from general knowledge, and those that are required to make statements in the text locally coherent. The minimalist hypothesis is shown to be supported by previous research and by the results of several new experiments. It is also argued that automatically encoded minimalist inferences provide the basic representation of textual information from which more goal-directed, purposeful inferences are constructed.


title: Maternal Food Provisioning in a Substrate-Brooding African Cichlid

abstract: Fish demonstrate the greatest variety of parental care strategies within the animal kingdom. Fish parents seldom provision food for offspring, with some exceptions predominantly found in substrate-brooding Central American cichlids and mouth-brooding African cichlids. Here, we provide the first evidence of food provisioning in a substrate-brooding African cichlid Neolamprologus mondabu. This fish is a maternal substrate-brooding cichlid endemic to Lake Tanganyika, and feeds on benthic animals using unique techniques-individuals typically feed on the surface of sandy substrates, but also expose prey by digging up substrates with vigorous wriggling of their body and fins. Young also feed on benthos on the substrate surface, but only using the first technique. We observed that feeding induced by digging accounted for 30% of total feeding bouts in adult females, demonstrating that digging is an important foraging tactic. However, parental females fed less frequently after digging than non-parental females, although both females stayed in pits created by digging for approximately 30 s. Instead, young gathered in the pit and fed intensively, suggesting that parental females provision food for young by means of digging. We tested this hypothesis by comparing the feeding frequency of young before and after digging that was simulated by hand, and observed that young doubled their feeding frequency after the simulated digging. This suggests that parental females engage in digging to uncover food items that are otherwise unavailable to young, and provision food for them at the expense of their own foraging. This behavior was similar to what has been observed in Central American cichlids.


Data-Efficient Temporal Regression with MultiTask Recurrent Neural Networks


title: Addressing Information Security Risks by Adopting Standards

abstract: Modern society depends on information technology in nearly every facet of human activity including, finance, transportation, education, government, and defense. Organizations are exposed to various kinds of risks, including information technology risks. Several standards, best practices, and frameworks have been created to help organizations manage these risks. The purpose of this research work is to highlight the challenges facing enterprises in their efforts to properly manage information security risks when adopting international standards and frameworks. To assist in selecting the best framework to use in risk management, the article presents an overview of the most popular and widely used standards and identifies selection criteria. It suggests an approach to proper implementation as well. A set of recommendations is put forward with further research opportunities on the subject.


title: Classification of massive noisy image using auto-encoders and convolutional neural network

abstract: Image processing tasks has found a new dimension with the improvement of learning feature representation from images using deep networks. Most of the research works are conducted over pre-possessed image data in the lab. But, these methods fail in the real world scenario as most of the time the image required to classify is subject to noise and other disfigurement. For the last three decades, many researches has been conducted and numerus algorithms have been proposed with varying performances to classify noisy images. But in recent times, various autoencoders have outperformed all traditional methods for reconstructing native image from it's noisy form and opened a new door for the research of noisy image classification. In this paper, we studied various auto encoders for reconstructing native images from noisy input images. We have applied convolutional neural network as classifier. Before classification task we have rectified noisy images using denoising autoencoder, convolutional denoising autoencoder and finally a hybrid of them as proposed in this paper. The proposed methods are evaluated by experimenting over benchmark dataset adulterated with noises of different proportionate. This method has outperformed some other prominent methods achieving satisfying classification accuracy even when the image is too much noisy (50% noise is added with the image data).


title: Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis

abstract: In the community of sentiment analysis, supervised learning techniques have been shown to perform very well. When transferred to another domain, however, a supervised sentiment classifier often performs extremely bad. This is so-called domain-transfer problem. In this work, we attempt to attack this problem by making the maximum use of both the old-domain data and the unlabeled new-domain data. To leverage knowledge from the old-domain data, we proposed an effective measure, i.e., Frequently Co-occurring Entropy (FCE), to pick out generalizable features that occur frequently in both domains and have similar occurring probability. To gain knowledge from the new-domain data, we proposed Adapted Naive Bayes (ANB), a weighted transfer version of Naive Bayes Classifier. The experimental results indicate that proposed approach could improve the performance of base classifier dramatically, and even provide much better performance than the transfer-learning baseline, i.e. the Naive Bayes Transfer Classifier (NTBC).


title: A review of fault management in WDM mesh networks: basic concepts and research challenges

abstract: The article first presents a broad overview of the fault management mechanisms involved in deploying a survivable optical mesh network which employs optical crossconnects. We review various protection and restoration schemes, primary and back-up route computation methods, shareability optimization, and dynamic restoration. We then describe different parameters that can measure the quality of service provided by a WDM mesh network to upper protocol layers (e.g., IP network backbones, ATM network backbones, leased lines, virtual private networks), such as service availability, service reliability, restoration time, and service restorability. We review these concepts, the factors that affect them, and how to improve them. In particular, we present a framework for cost-effective availability-aware connection provisioning to provide differentiated services in WDM mesh networks. Through the framework, the more realistic scenario of multiple near-simultaneous failures can be handled. In addition, the emerging problem of protecting low-speed connections of different bandwidth granularities is also reviewed.


title: Maze Solving Algorithms for Micro Mouse

abstract: The problem of micro-mouse is 30 years old but its importance in the field of robotics is unparalleled, as it requires a complete analysis & proper planning to be solved. This paper covers one of the most important areas of robot, ldquodecision making algorithmrdquo or in lay-manpsilas language, ldquorobot intelligencerdquo. For starting in the field of micro-mouse it is very difficult to begin with highly sophisticated algorithms. This paper begins with very basic wall follower logic to solve the maze. And gradually improves the algorithm to accurately solve the maze in shortest time with some more intelligence. The Algorithm is developed up to some sophisticated level as flood-fill algorithm. The paper would help all the beginners in this fascinating field, as they proceed towards development of the ldquobrain of the systemrdquo, particularly for robots concerned with path planning and navigation.


Draft : Deep Learning in Neural Networks : An Overview


title: Selectivity principle of the ligand escape process from a two-gate tunnel in myoglobin: Molecular dynamics simulation

abstract: We proposed a selectivity principle for the ligand escape process from two fluctuating bottlenecks in a cavity with a multigate inside a myoglobin pocket. Our previous analytical theory proposed a fluctuating bottleneck model for a Brownian particle passing through two gates on a cavity surface of an enzyme protein and has determined the escape rate in terms of the time-dependent gate function and the competition effect. It illustrated that with two (or more than two) gates on a cavity surface the gate modulation, which is controlled by protein fluctuation, dominates the ligand escape pathway. We have performed a molecular dynamics simulation to investigate the selectivity principle of the ligand escape process from two-gate tunnel in myoglobin. The simulation results confirm our theoretical conjecture. It indicates that the escape process is actually entropy driven, and the ligand escape pathway is chosen via the gate modulation. This suggests an interesting intrinsic property, that is, the oxymyoglobin ...


title: A Single Photon Avalanche Diode Implemented in 130-nm CMOS Technology

abstract: We report on the first implementation of a single photon avalanche diode (SPAD) in 130 nm complementary metal-oxide-semiconductor (CMOS) technology. The SPAD is fabricated as p+/n-well junction with octagonal shape. A guard ring of p-well around the p+ anode is used to prevent premature discharge. To investigate the dynamics of the new device, both active and passive quenching methods have been used. Single photon detection is achieved by sensing the avalanche using a fast comparator. The SPAD exhibits a maximum photon detection probability of 41% and a typical dark count rate of 100 kHz at room temperature. Thanks to its timing resolution of 144 ps full-width at half-maximum (FWHM), the SPAD has several uses in disparate disciplines, including medical imaging, 3D vision, biophotonics, low-light illumination imaging, etc.


title: Visual Analytics for Spatial Clustering: Using a Heuristic Approach for Guided Exploration

abstract: We propose a novel approach of distance-based spatial clustering and contribute a heuristic computation of input parameters for guiding users in the search of interesting cluster constellations. We thereby combine computational geometry with interactive visualization into one coherent framework. Our approach entails displaying the results of the heuristics to users, as shown in Figure 1, providing a setting from which to start the exploration and data analysis. Addition interaction capabilities are available containing visual feedback for exploring further clustering options and is able to cope with noise in the data. We evaluate, and show the benefits of our approach on a sophisticated artificial dataset and demonstrate its usefulness on real-world data.


title: Androidleaks: automatically detecting potential privacy leaks in android applications on a large scale

abstract: As mobile devices become more widespread and powerful, they store more sensitive data, which includes not only users' personal information but also the data collected via sensors throughout the day. When mobile applications have access to this growing amount of sensitive information, they may leak it carelessly or maliciously. ::: ::: Google's Android operating system provides a permissions-based security model that restricts an application's access to the user's private data. Each application statically declares the sensitive data and functionality that it requires in a manifest, which is presented to the user upon installation. However, it is not clear to the user how sensitive data is used once the application is installed. To combat this problem, we present AndroidLeaks, a static analysis framework for automatically finding potential leaks of sensitive information in Android applications on a massive scale. AndroidLeaks drastically reduces the number of applications and the number of traces that a security auditor has to verify manually. ::: ::: We evaluate the efficacy of AndroidLeaks on 24,350 Android applications from several Android markets. AndroidLeaks found 57,299 potential privacy leaks in 7,414 Android applications, out of which we have manually verified that 2,342 applications leak private data including phone information, GPS location, WiFi data, and audio recorded with the microphone. AndroidLeaks examined these applications in 30 hours, which indicates that it is capable of scaling to the increasingly large set of available applications.


title: Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

abstract: Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.


title: Flocking for multi-agent dynamic systems: algorithms and theory

abstract: In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called /spl alpha/-lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or /spl alpha/-agents, and virtual agents associated with /spl alpha/-agents called /spl beta/- and /spl gamma/-agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split/rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms.


title: 3D People Tracking with Gaussian Process Dynamical Models

abstract: We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of human motion data, with a density function that gives higher probability to poses and motions close to the training data. With Bayesian model averaging a GPDM can be learned from relatively small amounts of data, and it generalizes gracefully to motions outside the training set. Here we modify the GPDM to permit learning from motions with significant stylistic variation. The resulting priors are effective for tracking a range of human walking styles, despite weak and noisy image measurements and significant occlusions.


title: Top-k Multiclass SVM

abstract: Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a direct method to optimize for top-k performance. Our generalization of the well-known multiclass SVM is based on a tight convex upper bound of the top-k error. We propose a fast optimization scheme based on an efficient projection onto the top-k simplex, which is of its own interest. Experiments on five datasets show consistent improvements in top-k accuracy compared to various baselines.


A Flexible Hybrid Method for IT Project Management


title: Adaptive Concentration Inequalities for Sequential Decision Problems

abstract: A key challenge in sequential decision problems is to determine how many samples are needed for an agent to make reliable decisions with good probabilistic guarantees. We introduce Hoeffding-like concentration inequalities that hold for a random, adaptively chosen number of samples. Our inequalities are tight under natural assumptions and can greatly simplify the analysis of common sequential decision problems. In particular, we apply them to sequential hypothesis testing, best arm identification, and sorting. The resulting algorithms rival or exceed the state of the art both theoretically and empirically.


title: Clustering with Hypergraphs: The Case for Large Hyperedges

abstract: The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many grouping problems require an affinity measure that must involve a subset of data of size more than two, i.e., a hyperedge. Almost all previous works, however, have considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both theoretical and empirical standpoints. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate pure large hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. In the important applications of face clustering and motion segmentation, our method demonstrates substantially better accuracy and efficiency.


title: Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining

abstract: As an interesting and emerging topic, cosaliency detection aims at simultaneously extracting common salient objects in multiple related images. It differs from the conventional saliency detection paradigm in which saliency detection for each image is determined one by one independently without taking advantage of the homogeneity in the data pool of multiple related images. In this paper, we propose a novel cosaliency detection approach using deep learning models. Two new concepts, called intrasaliency prior transfer and deep intersaliency mining, are introduced and explored in the proposed work. For the intrasaliency prior transfer, we build a stacked denoising autoencoder (SDAE) to learn the saliency prior knowledge from auxiliary annotated data sets and then transfer the learned knowledge to estimate the intrasaliency for each image in cosaliency data sets. For the deep intersaliency mining, we formulate it by using the deep reconstruction residual obtained in the highest hidden layer of a self-trained SDAE. The obtained deep intersaliency can extract more intrinsic and general hidden patterns to discover the homogeneity of cosalient objects in terms of some higher level concepts. Finally, the cosaliency maps are generated by weighted integration of the proposed intrasaliency prior, deep intersaliency, and traditional shallow intersaliency. Comprehensive experiments over diverse publicly available benchmark data sets demonstrate consistent performance gains of the proposed method over the state-of-the-art cosaliency detection methods.


title: A decade’s perspective on DNA sequencing technology

abstract: The decade since the Human Genome Project ended has witnessed a remarkable sequencing technology explosion that has permitted a multitude of questions about the genome to be asked and answered, at unprecedented speed and resolution. Here I present examples of how the resulting information has both enhanced our knowledge and expanded the impact of the genome on biomedical research. New sequencing technologies have also introduced exciting new areas of biological endeavour. The continuing upward trajectory of sequencing technology development is enabling clinical applications that are aimed at improving medical diagnosis and treatment.


title: A Fuzzy Logic Based Certain Trust Model for E-Commerce

abstract: Trustworthiness especially for service oriented system is very important topic now a day in IT field of the whole world. There are many successful E-commerce organizations presently run in the whole world, but E-commerce has not reached its full potential. The main reason behind this is lack of Trust of people in e-commerce. Again, proper models are still absent for calculating trust of different e-commerce organizations. Most of the present trust models are subjective and have failed to account vagueness and ambiguity of different domain. In this paper we have proposed a new fuzzy logic based Certain Trust model which considers these ambiguity and vagueness of different domain. Fuzzy Based Certain Trust Model depends on some certain values given by experts and developers. can be applied in a system like cloud computing, Internet, Web site, e-commerce, etc. to ensure trustworthiness of these platforms. In this paper we show, although fuzzy works with uncertainties, proposed model works with some certain values. Some experimental results and validation of the model with linguistics terms are shown at the last part of the paper.


title: Design of a Wideband Bandpass Filter With Composite Short- and Open-Circuited Stubs

abstract: A new wideband bandpass filter (BPF) with composite short- and open-circuited stubs has been proposed in this letter. With the two kinds of stubs, two pairs of transmission zeros (TZs) can be produced on the two sides of the desired passband. The even-/odd-mode analysis method is used to derive the input admittances of its bisection circuits. After the Richard's transformation, these bisection circuits are in the same format of two LC circuits. By combining these two LC circuits, the equivalent circuit of the proposed filter is obtained. Through the analysis of the equivalent circuit, the open-circuited stubs introduce transmission poles in the complex frequencies and one pair of TZs in the real frequencies, and the short-circuited stubs generate one pair of TZs to block the dc component. A wideband BPF is designed and fabricated to verify the proposed design principle.


Artifacts for Agile User-Centered Design: A Systematic Mapping


title: AxNN: energy-efficient neuromorphic systems using approximate computing

abstract: Neuromorphic algorithms, which are comprised of highly complex, large-scale networks of artificial neurons, are increasingly used for a variety of recognition, classification, search and vision tasks. However, their computational and energy requirements can be quite high, and hence their energy-efficient implementation is of great interest. We propose a new approach to design energy-efficient hardware implementations of large-scale neural networks (NNs) using approximate computing. Our work is motivated by the observations that (i) NNs are used in applications where less-than-perfect results are acceptable, and often inevitable, and (ii) they are highly resilient to inexactness in many (but not all) of their constituent computations. We make two key contributions. First, we propose a method to transform any given NN into an Approximate Neural Network (AxNN). This is performed by (i) adapting the backpropagation technique, which is commonly used to train these networks, to quantify the impact of approximating each neuron to the overall network quality (e.g., classification accuracy), and (ii) selectively approximating those neurons that impact network quality the least. Further, we make the key observation that training is a naturally error-healing process that can be used to mitigate the impact of approximations to neurons. Therefore, we incrementally retrain the network with the approximations in-place, reclaiming a significant portion of the quality ceded by approximations. As a second contribution, we propose a programmable and quality-configurable neuromorphic processing engine (qcNPE), which utilizes arrays of specialized processing elements that execute neuron computations with dynamically configurable accuracies and can be used to execute AxNNs from diverse applications. We evaluated the proposed approach by constructing AXNNs for 6 recognition applications (ranging in complexity from 12–47,818 neurons and 160–3,155,968 connections) and executing them on two different platforms - qcNPE implementation containing 272 processing elements in 45nm technology and a commodity Intel Xeon server. Our results demonstrate 1.14X–1.92X energy benefits for virtually no loss (< 0.5%) in output quality, and even higher improvements (upto 2.3X) when some loss (upto 7.5%) in output quality is acceptable.


title: A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems

abstract: A hybrid indirect and direct adaptive fuzzy output tracking control schemes are developed for a class of nonlinear multiple-input-multiple-output (MIMO) systems. This hybrid control system consists of observer and other different control components. Using the state observer, it does not require the system states to be available for measurement. Assisted by observer-based state feedback control component, the adaptive fuzzy system plays a dominant role to maintain the closed-loop stability. Being the auxiliary compensation, H/sup /spl infin// control and sliding mode control are designed to suppress the influence of external disturbance and remove fuzzy approximation error, respectively. Thus, the system performance can be greatly improved. The simulation results demonstrate that the proposed hybrid fuzzy control system can guarantee the system stability and also maintain a good tracking performance.


title: Abstract A paint-brush laser range scanner

abstract: A new hand-held laser range scanner is introduced that can capture multi-view range images of an object and integrate the images without registering them. The scanner uses a reference double-frame that acts as the coordinate system of the object. Range images captured from different views of the object are in the coordinate system of the double-frame and, thus, automatically come together. A single-view image is obtained by sweeping a laser line over the object while keeping the camera fixed and analyzing the acquired laser stripes. The laser line generator and the camera can move independently, making it possible to conveniently scan an object just like painting over it with a paintbrush while viewing it from different views. The hardware and software organization of the scanner are described, the characteristics of the scanner are investigated, and example images captured by the scanner are presented.


title: A 1.2 V Low-Noise-Amplifier with Double Feedback for High Gain and Low Noise Figure

abstract: In this paper we present a balun low noise amplifier (LNA) in which the gain is boosted using a double feedback structure. The circuit is based in a conventional Balun LNA with noise and distortion cancellation. The LNA is based in two basic stages: common-gate (CG) and common-source (CS). We propose to replace the resistors by active loads, which have two inputs that will be used to provide the feedback (in the CG and CS stages). This proposed methodology will boost the gain and reduce the NF. Simulation results, with a 130 nm CMOS technology, show that the gain is 23.8 dB and the NF is less than 1.8 dB. The total power dissipation is only 5.3(since no extra blocks are required), leading to an FOM of 5.7 mW− 1 from a nominal 1.2 supply.


title: Bed exiting monitoring system with fall detection for the elderly living alone

abstract: A bed exiting monitoring system with fall detection function for the elderly living alone is proposed in this paper. By separating the process of exiting or getting on the bed into several significant movements, the sensor system composed of infrared and pressure sensors attached to the bed will correspondingly respond to these movements. Using the finite state machine (FSM) method, the bed exiting state and fall events can be detected according to specific transitions recognized by the sensor system. Experiments with plausible assessment are conducted to find the optimal sensor combination solution and to verify the FSM algorithm, which is demonstrated feasible and effective in practical use.


title: A 95% Efficient Normally-Off GaN-on-Si HEMT Hybrid-IC Boost-Converter with 425-W Output Power at 1 MHz

abstract: A 2:1 351 V hard-switched boost converter was constructed using high-voltage GaN high-electron-mobility transistors grown on Si substrates and GaN Schottky diodes grown on Sapphire substrates. The high speed and low on-resistance of the GaN devices enables extremely fast switching times and low losses, resulting in a high conversion efficiency of 95% with 425-W output power at 1 MHz. The boost converter has a power density of 175 W/in3. To our knowledge, these results are the best reported on GaN devices, and the highest for 1MHz switching.


title: Multi-document summarization based on two-level sparse representation model

abstract: Multi-document summarization is of great value to many real world applications since it can help people get the main ideas within a short time. In this paper, we tackle the problem of extracting summary sentences from multi-document sets by applying sparse coding techniques and present a novel framework to this challenging problem. Based on the data reconstruction and sentence denoising assumption, we present a two-level sparse representation model to depict the process of multi-document summarization. Three requisite properties is proposed to form an ideal reconstructable summary: Coverage, Sparsity and Diversity. We then formalize the task of multi-document summarization as an optimization problem according to the above properties, and use simulated annealing algorithm to solve it. Extensive experiments on summarization benchmark data sets DUC2006 and DUC2007 show that our proposed model is effective and outperforms the state-of-the-art algorithms.


title: Enterprise Content Management - A Literature Review

abstract: Managing information and content on an enterprise-wide scale is challenging. Enterprise content management (ECM) can be considered as an integrated approach to information management. While this concept received much attention from practitioners, ECM research is still an emerging field of IS research. Most authors that deal with ECM claim that there is little scholarly literature available. After approximately one decade of ECM research, this paper provides an in-depth review of the body of academic research: the ECM domain, its evolution, and main topics are characterized. An established ECM research framework is adopted, refined, and explained with its associated elements and working definitions. On this basis, 68 articles are reviewed, classified, and concepts are derived. Prior research is synthesized and findings are integrated in a conceptcentric way. Further, implications for research and practice, including future trends, are drawn.


title: Biometrics based observer free transferable E-cash

abstract: This paper proposes a transaction strategy to implement transferable E-cash. It does not involve any third party observer at the time of transaction and possesses strong anonymity property. It uses biometric features of the legitimate holder of e-cash to progress the transaction. It works on the transfer of coin ownership and is built around the restrictive blinding and biometric digital signature scheme. Its mathematical model utilizes the hardness of discrete log problem and representation problem in groups. It guarantees anonymity and unlinkability for a genuine user and non repudiation to a fraudulent one.


title: Beam-scanning improvement of reflectarrays using single-layered sub-wavelength elements

abstract: This paper studies the use of double concentric circular ring elements arranged in a sub-wavelength grid on a single layer of substrate, in an effort to enhance the beam-scanning capability of reflectarrays. The work is carried out at 10 GHz on a 405mm×405mm aperture. In addition to a broad gain bandwidth, the reflectarray with a sub-wavelength grid demonstrates a superior beam-scanning performance compared to the one with X/2 grid. The measured results show that the reflectarray with 0.3X grid spacing realizes a 1-dB gain bandwidth of 22% with an aperture efficiency of 56.6%. With the reflectarray fixed, this paper investigates the possibility of beam scanning by displacing the feed horn laterally. The results show that beam scanning of ±15o is possible while retaining the broadband characteristics.


title: The Gig Economy Gig jobs have become a structural aspect of contemporary economic landscape , creating unique social and technological challenges . How can policies and design solutions better protect gig workers and mitigate the risks participants face ?

abstract: Gig jobs have become a structural aspect of contemporary economic landscape, creating unique social and technological challenges. How can policies and design solutions better protect gig workers and mitigate the risks participants face?


title: Authentication and Access Control in the Internet of Things

abstract: Due to the inherent vulnerabilities of the Internet, security and privacy issues should be considered and addressed before the Internet of Things is widely deployed. This paper mainly analyzes existing authentication and access control methods, and then, it designs a feasible one for the Internet of Things.


title: Characterization of Traffic Analysis based video stream source identification

abstract: This paper presents the concept and characterization of Traffic Analysis (TA) for identifying sources of tunneled video streaming traffic. Such identification can be used in enterprise firewalls for blocking unauthorized viewing of tunneled video. We attempt to characterize and evaluate the impacts of the primary TA-influencing factors, namely, streaming protocol, codec, and the actual video content. A test environment is built to study the influence of those factors while Packet Size Distribution is used as the classification feature during Traffic Analysis. Analysis done on data obtained from the test environment has shown that the streaming protocols provide the most dominant source identification distinction. Also, while the codecs provide some weak distinctions, the influence of video content is marginal. In addition to in-laboratory experiments, a real-world verification for corroborating those observations is also made with commercial streaming service providers. Such long-haul experiments indicate that the end-to-end network conditions between the streaming server and video client can act as an additional influencing factor for traffic analysis towards video stream source identification. Overall, the results suggest the feasibility of TA for unknown video stream source identification with sufficiently diverse video examples.


title: An improved calculation system for phase-functioned neural network and implementation in unreal engine

abstract: The problem attempting to be solved in this paper is optimizing phase-functioned neural network to support generated animation for game engine. The approach adopted is using CUDA and parallel programming to improve large prediction of matrices calculation. The results of this research included a 4-layer architecture of PFNN prediction framework, a CUDA calculation solution and a showcase binding in unreal engine. As for the effects of the results obtained, PFNN calculation has been sped up from 1.8 ms to 1.0, 1.1 ms. And according to the result of performance test of the utility of PFNN in real game development, its optimization has been proven.


title: The Use of Bright and Dark Types of Humour is Rooted in the Brain

abstract: The ways in which humour can be used are related to the manifold interpersonal functions humour can serve, some of which are positive, and some negative. In the present study, phasic changes in the functional coupling of prefrontal and posterior cortex (EEG coherence) during other people's auditory displays of happy and sad mood were recorded to predict people's typical use of humour in social interactions. Greater use of benevolent humour, the intentions of which are in keeping with the characteristics of "laughing-with" humour, was associated with greater decreases of prefrontal-posterior coupling during the processing of happy laughter. More loose prefrontal-posterior coupling indicates loosening of control of the prefrontal cortex over the incoming perceptual information, thereby opening up the perceptual gate and allowing the brain to become more affected by the social-emotional signals. Greater use of humour styles linked to malicious intentions of "laughing-at" humour was associated with responses indicating a wider opened perceptual gate during the processing of other people's crying. The findings are consistent with the idea that typical humour styles develop in line with the rewarding values of their outcomes (e.g., interaction partners are happy or hurt), which in turn are defined through the individuals' latent interpersonal goals.


title: An accurate and efficient navigation system for omnidirectional robots in industrial environments

abstract: Enhanced logistics is widely regarded as a key technology to increase flexibility and cost efficiency of today's factories. For example, fully autonomous transport vehicles aim to gradually replace conveyor belts, guided vehicles, and manual labor. In this context, especially omnidirectional robots are appealing thanks to their advanced maneuvering capabilities. In industrial applications, however, accuracy as well as safety and efficiency are key requirements for successful navigation systems. In this paper, we present an accurate navigation system for omnidirectional robots. Our system includes dedicated modules for mapping, localization, trajectory generation and robot control. It has been designed for accurate execution by devising smooth, curvature continuous trajectories, by planning appropriate velocities and by accounting for platform and safety constraints. In this way, it completely utilizes the maneuvering capabilities of omnidirectional robots and optimizes trajectories with respect to time of travel. We present extensive experimental evaluations in simulation and in changing real-world environments to demonstrate the robustness and accuracy of our system.


title: Outcome of primary root canal treatment: systematic review of the literature – Part 2. Influence of clinical factors

abstract: AIMS ::: (i) To carry out meta-analyses to quantify the influence of the clinical factors on the efficacy of primary root canal treatment and (ii) to identify the best treatment protocol based on the current evidence. ::: ::: ::: METHODOLOGY ::: The evidence for the effect of each clinical factor on the success rate (SR) of primary root canal treatment was gathered in three different ways: (i) intuitive synthesis of reported findings from individual studies; (ii) weighted pooled SR by each factor under investigation was estimated using random-effect meta-analysis; (iii) weighted effect of the factor under investigation on SR were estimated and expressed as odds ratio for the dichotomous outcomes (success or failure) using fixed- and random-effects meta-analysis. Statistical heterogeneity amongst the studies was assessed by Cochran's (Q) test. Potential sources of statistical heterogeneity were investigated by exploring clinical heterogeneity using meta-regression models which included study characteristics in the regression models. ::: ::: ::: RESULTS ::: Out of the clinical factors investigated, pre-operative pulpal and periapical status were most frequently investigated, whilst the intra-operative factors were poorly studied in the 63 studies. Four factors were found to have a significant effect on the primary root canal treatment outcome, although the data heterogeneity was substantial, some of which could be explained by some of the study characteristics. ::: ::: ::: CONCLUSIONS ::: Four conditions (pre-operative absence of periapical radiolucency, root filling with no voids, root filling extending to 2 mm within the radiographic apex and satisfactory coronal restoration) were found to improve the outcome of primary root canal treatment significantly. Root canal treatment should therefore aim at achieving and maintaining access to apical anatomy during chemo-mechanical debridement, obturating the canal with densely compacted material to the apical terminus without extrusion into the apical tissues and preventing re-infection with a good quality coronal restoration.


title: Performance Comparison of Reflector- and Planar-Antenna Based Digital Beam-Forming SAR

abstract: The trend in the conception of future spaceborne radar remote sensing is clearly toward the use of digital beamforming techniques. These systems will comprise multiple digital channels, where the analog-to-digital converter is moved closer to the antenna. This dispenses the need for analog beam steering and by this the used of transmit/receive modules for phase and amplitude control. Digital beam-forming will enable Synthetic Aperture Radar (SAR) which overcomes the coverage and resolution limitations applicable to state-of-the-art systems. On the other hand, new antenna architectures, such as reflectors, already implemented in communication satellites, are being considered for SAR applications. An open question is the benefit of combining digital beam-forming techniques with reflector antennas. The paper answers this question by comparing the system architecture and digital beam-forming requirements of a planar and a reflector antenna SAR. Further elaboration yields the resulting SAR performance of both systems. This paper considers multiple novel aspects of digital beam-forming SAR system design, which jointly flow into the presented system performance.


title: Classification of crystal structure using a convolutional neural network

abstract: A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.


title: Insulin Inclusion into a Tragacanth Hydrogel: An Oral Delivery System for Insulin

abstract: Nanoparticles or microparticles created by physical complexation between two polyelectrolytes may have a prospective use as an excipient for oral insulin administration. Natural polymers such as tragacanth, alginate, dextran, pullulan, hyaluronic acid, gelatin and chitosan can be potential candidates for this purpose. In this research, insulin particles were prepared by the inclusion of insulin into a tragacanth hydrogel. The effect of the pH and concentration relationship involving polyelectrolytes offering individual particle size and zeta potential was assessed by zetasizer and scanning electron microscopy (SEM). Insulin–tragacanth interactions at varying pH (3.7, 4.3, 4.6, or 6), and concentration (0.1%, 0.5%, or 1% w/w) were evaluated by differential scanning calorimetry (DSC) and ATR Fourier transform infrared (ATR-FTIR) analysis. Individual and smaller particles, approximately 800 nm, were acquired at pH 4.6 with 0.5% of tragacanth. The acid gelation test indicated that insulin could be entrapped in the physical hydrogel of tragacanth. DSC thermograms of insulin–tragacanth showed shifts on the same unloaded tragacanth peaks and suggested polyelectrolyte–protein interactions at a pH close to 4.3–4.6. FTIR spectra of tragacanth–insulin complexes exhibited amide absorption bands featuring in the protein spectra and revealed the creation of a new chemical substance.


title: Securing the Internet of Things: New Perspectives and Research Challenges

abstract: The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. IoT challenges require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges to address existing and next-generation IoT security threats.


title: Generalized Neural Graph Embedding with Matrix Factorization

abstract: Recent advances in language modeling such as word2vec motivate a number of graph embedding approaches by treating random walk sequences as sentences to encode structural proximity in a graph. However, most of the existing principles of neural graph embedding do not incorporate auxiliary information such as node content flexibly. In this paper we take a matrix factorization perspective of graph embedding which generalizes to structural embedding as well as content embedding in a natural way. For structure embedding, we validate that the matrix we construct and factorize preserves the high-order proximities of the graph. Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding. In addition, we generalize the Skip-Gram Negative Sampling model to integrate the content of the graph in a matrix factorization framework. As a consequence, graph embedding can be learned in a unified framework integrating graph structure and node content as well as label information simultaneously. We demonstrate the efficacy of the proposed model with the tasks of semi-supervised node classification and link prediction on a variety of real-world benchmark network datasets.


MaF: An Ontology Matching Framework.


From biological to social networks : Link prediction based on multiway spectral clustering 3 Q 1 4 5 O O


title: The Statistical Analysis of Circadian Phase and Amplitude in Constant-Routine Core-Temperature Data

abstract: Accurate estimation of the phases and amplitude of the endogenous circadian pacemaker from constant-routine core-temperature series is crucial for making inferences about the properties of the human biological clock from data collected under this protocol. This paper presents a set of statistical methods based on a harmonic-regression-plus-correlated-noise model for estimating the phases and the amplitude of the endogenous circadian pacemaker from constant-routine core-temperature data. The methods include a Bayesian Monte Carlo procedure for computing the uncertainty in these circadian functions. We illustrate the techniques with a detailed study of a single subject's core-temperature series and describe their relationship to other statistical methods for circadian data analysis. In our laboratory, these methods have been successfully used to analyze more than 300 constant routines and provide a highly reliable means of extracting phase and amplitude information from core-temperature data.