title: The Logic Of Plausible Reasoning: A Core Theory

abstract: Abstract The paper presents a core theory of human plausible reasoning based on analysis of people's answers to everyday questions about the world. The theory consists of three parts: 1. 1. a formal representation of plausible inference patterns; such as deductions, inductions, and analogies, that are frequently employed in answering everyday questions; 2. 2. a set of parameters, such as conditional likelihood, typicality, and similarity, that affect the certainty of people's answers to such questions; and 3. 3. a system relating the different plausible inference patterns and the different certainty parameters. This is one of the first attempts to construct a formal theory that addresses both the semantic and parametric aspects of the kind of everyday reasoning that pervades. all of human discourse.


A Topology-Based Approach for Followees Recommendation in Twitter


title: Patients want granular privacy control over health information in electronic medical records

abstract: OBJECTIVE ::: To assess patients' desire for granular level privacy control over which personal health information should be shared, with whom, and for what purpose; and whether these preferences vary based on sensitivity of health information. ::: ::: ::: MATERIALS AND METHODS ::: A card task for matching health information with providers, questionnaire, and interview with 30 patients whose health information is stored in an electronic medical record system. Most patients' records contained sensitive health information. ::: ::: ::: RESULTS ::: No patients reported that they would prefer to share all information stored in an electronic medical record (EMR) with all potential recipients. Sharing preferences varied by type of information (EMR data element) and recipient (eg, primary care provider), and overall sharing preferences varied by participant. Patients with and without sensitive records preferred less sharing of sensitive versus less-sensitive information. ::: ::: ::: DISCUSSION ::: Patients expressed sharing preferences consistent with a desire for granular privacy control over which health information should be shared with whom and expressed differences in sharing preferences for sensitive versus less-sensitive EMR data. The pattern of results may be used by designers to generate privacy-preserving EMR systems including interfaces for patients to express privacy and sharing preferences. ::: ::: ::: CONCLUSIONS ::: To maintain the level of privacy afforded by medical records and to achieve alignment with patients' preferences, patients should have granular privacy control over information contained in their EMR.


title: Event-Driven Semantic Concept Discovery by Exploiting Weakly Tagged Internet Images

abstract: Analysis and detection of complex events in videos require a semantic representation of the video content. Existing video semantic representation methods typically require users to pre-define an exhaustive concept lexicon and manually annotate the presence of the concepts in each video, which is infeasible for real-world video event detection problems. In this paper, we propose an automatic semantic concept discovery scheme by exploiting Internet images and their associated tags. Given a target event and its textual descriptions, we crawl a collection of images and their associated tags by performing text based image search using the noun and verb pairs extracted from the event textual descriptions. The system first identifies the candidate concepts for an event by measuring whether a tag is a meaningful word and visually detectable. Then a concept visual model is built for each candidate concept using a SVM classifier with probabilistic output. Finally, the concept models are applied to generate concept based video representations. We use the TRECVID Multimedia Event Detection (MED) 2013 as our video test set and crawl 400K Flickr images to automatically discover 2, 000 visual concepts. We show significant performance gains of the proposed concept discovery method over different video event detection tasks including supervised event modeling over concept space and semantic based zero-shot retrieval without training examples. Importantly, we show the proposed method of automatic concept discovery outperforms other well-known concept library construction approaches such as Classemes and ImageNet by a large margin (228%) in zero-shot event retrieval. Finally, subjective evaluation by humans also confirms clear superiority of the proposed method in discovering concepts for event representation.


title: Study of wheel slip and traction forces in differential drive robots and slip avoidance control strategy

abstract: The effect of wheel slip in differential drive robots is investigated in this paper. We consider differential drive robots with two driven wheels and ball-type caster wheels that are used to provide balance and support to the mobile robot. The limiting values of traction forces for slip and no slip conditions are dependent on wheel-ground kinetic and static friction coefficients. The traction forces are used to determine the fraction of input torque that provides robot motion and this is used to calculate the actual position of the robot under slip conditions. The traction forces under no slip conditions are used to determine the limiting value of the wheel torque above which the wheel slips. This limiting torque value is used to set a saturation limit for the input torque to avoid slip. Simulations are conducted to evaluate the behavior of the robot during slip and no slip conditions. Experiments are conducted under similar slip and no slip conditions using a custom built differential drive mobile robot with one caster wheel to validate the simulations. Experiments are also conducted with the torque limiting strategy. Results from model simulations and experiments are presented and discussed.


title: Approximation Algorithms for Partial Covering Problems

abstract: We study the generalization of covering problems to partial covering. Here we wish to cover only a desired number of elements, rather than covering all elements as in standard covering problems. For example, in k-set cover, we wish to choose a minimum number of sets to cover at least k elements. For k-set cover, if each element occurs in at most f sets, then we derive a primal-dual f-approximation algorithm (thus implying a 2-approximation for k-vertex cover) in polynomial time. In addition to its simplicity, this algorithm has the advantage of being parallelizable. For instances where each set has cardinality at most three, we obtain an approximation of 4/3. We also present better-than-2-approximation algorithms for k-vertex cover on bounded degree graphs, and for vertex cover on expanders of bounded average degree. We obtain a polynomial-time approximation scheme for k-vertex cover on planar graphs, and for covering points in Rd by disks.


title: Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing

abstract: Crowdsourcing has become an eective and popular tool for human-powered computation to label large datasets. Since the workers can be unreliable, it is common in crowdsourcing to assign multiple workers to one task, and to aggregate the labels in order to obtain results of high quality. In this paper, we provide nite-sample exponential bounds on the error rate (in probability and in expectation) of general aggregation rules under the Dawid-Skene crowdsourcing model. The bounds are derived for multi-class labeling, and can be used to analyze many aggregation methods, including majority voting, weighted majority voting and the oracle Maximum A Posteriori (MAP) rule. We show that the oracle MAP rule approximately optimizes our upper bound on the mean error rate of weighted majority voting in certain setting. We propose an iterative weighted majority voting (IWMV) method that optimizes the error rate bound and approximates the oracle MAP rule. Its one step version has a provable theoretical guarantee on the error rate. The IWMV method is intuitive and computationally simple. Experimental results on simulated and real data show that IWMV performs at least on par with the state-of-the-art methods, and it has a much lower computational cost (around one hundred times faster) than the state-of-the-art methods.


title: A user attention model for video summarization

abstract: Automatic generation of video summarization is one of the key techniques in video management and browsing. In this paper, we present a generic framework of video summarization based on the modeling of viewer's attention. Without fully semantic understanding of video content, this framework takes advantage of understanding of video content, this framework takes advantage of computational attention models and eliminates the needs of complex heuristic rules in video summarization. A set of methods of audio-visual attention model features are proposed and presented. The experimental evaluations indicate that the computational attention based approach is an effective alternative to video semantic analysis for video summarization.


title: Exploring the "planning fallacy": Why people underestimate their task completion times.

abstract: This study tested 3 main hypotheses concerning people's predictions of task completion times: (a) People underestimate their own but not others' completion times, (b) people focus on plan-based scenarios rather than on relevant past experiences while generating their predictions, and (c) people's attributions diminish the relevance of past experiences. Results supported each hypothesis. Ss' predictions of their completion times were too optimistic for a variety of academic and nonacademic tasks. Think-aloud procedures revealed that Ss focused primarily on future scenarios when predicting their completion times. In Study 4, the optimistic bias was eliminated for Ss instructed to connect relevant past experiences with their predictions. In Studies 3 and 4, Ss attributed their past prediction failures to relatively external, transient, and specific factors. In Study 5, observer Ss overestimated others' completion times and made greater use of relevant past experiences.


title: Controlling balancer and steering for bicycle stabilization

abstract: In this paper, we propose a control algorithm for stabilizing of an unmanned bicycle at zero speed by using a nonlinear control based on an output-zeroing controller. The simplified model of the bicycle with the balancer is derived from Lagrangian and nonholonomic constraints with respect to translation and rotation relative to the ground plane. We derived a controller using a steering torque and a balancer torque to stabilize the bicycle at zero linear velocity. The output-zeroing controller is designed by using the angular momentum of two-link system and by adding the effect of steering angle to the derivative of the angular momentum. Numerical simulation and experimental results are shown to verify the effectiveness of the proposed control strategy.


title: Latency Analysis in Real LTE Networks for Vehicular Applications

abstract: In the development path to implement V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) communications, two different, but not entirely concurrent approaches, are been investigated: the use of a new technology standard, 802.11p, denoted as DSRC/WAVE (Dedicated Short Range Communications/Wireless Access in Vehicular Environments), and the use of an already established technology, UMTS/LTE (Universal Mobile Communications System/Long Term Evolution). The choice to invest in one of these two technologies to build a V2X infrastructure passes through which one will provide a minor latency communication time between network nodes. This paper presents a measure of this parameter in mobile networks of real mobile service providers, distinguishing between data obtained from UMTS and LTE servers. Furthermore, is presented an architecture where the smartphone is a gateway to provide V2X communications, in regards that this device has a widespread adoption and can in rapidly pace turn the car a node in the Internet of Things.


title: The Resource Based View of the Firm and Innovation: Identification of Critical Linkages

abstract: Over the last years the resource-based view (RBV) of strategic management has attracted wide academic and managerial attention. Drawing on RBV literature, this paper analyses the interrelationships between RBV and organizational innovation. The authors examine those aspects of RBV that critically determine the firms capacity to innovate by integrating the relevant theoretical and empirical evidence. A number of contributions setting the ground for future empirical research are provided.


title: Independent factor reinforcement learning for portfolio management

abstract: In this paper we propose to do portfolio management using reinforcement learning (RL) and independent factor model. Factors in independent factor model are mutually independent and exhibit better predictability. RL is applied to each factor to capture temporal dependence and provide investment suggestion on factor. Optimal weights on factors are found by portfolio optimization method subject to the investment suggestions and general portfolio constraints. Experimental results and analysis are given to show that the proposed method has better performance when compare to two alternative portfolio management systems.


title: A BCI motor imagery experiment based on parametric feature extraction and Fisher Criterion

abstract: An EEG-based classification method in the time domain is proposed to identify left and right hand motor imagery as part of a brain-computer interface (BCI) experiment. The feature vector is formed by sixth order autoregressive coefficients (AR) or sixth order adaptive autoregressive coefficients (AAR) representing EEG signals obtained from C3 and C4 channels, according to the EEG 10–20 standard. The signal is analyzed considering 1 second windows with a 50% overlapping. A feature selection process based on the Fisher Criterion (FC) removes irrelevant or noisy information. A Linear Discriminant Analysis (LDA) is applied to both cases: feature vectors formed with the total number of coefficients, and feature vectors formed with coefficients corresponding to larger Fisher Ratio. Classification results obtained using two AR methods, Burg and Levinson-Durbin, and one AAR LMS are presented.


title: From 3D cell culture to organs-on-chips.

abstract: 3D cell-culture models have recently garnered great attention because they often promote levels of cell differentiation and tissue organization not possible in conventional 2D culture systems. We review new advances in 3D culture that leverage microfabrication technologies from the microchip industry and microfluidics approaches to create cell-culture microenvironments that both support tissue differentiation and recapitulate the tissue-tissue interfaces, spatiotemporal chemical gradients, and mechanical microenvironments of living organs. These 'organs-on-chips' permit the study of human physiology in an organ-specific context, enable development of novel in vitro disease models, and could potentially serve as replacements for animals used in drug development and toxin testing.


title: BayesRank: A Bayesian Approach to Ranked Peer Grading

abstract: Advances in online and computer supported education afford exciting opportunities to revolutionize the classroom, while also presenting a number of new challenges not faced in traditional educational settings. Foremost among these challenges is the problem of accurately and efficiently evaluating learner work as the class size grows, which is directly related to the larger goal of providing quality, timely, and actionable formative feedback. Recently there has been a surge in interest in using peer grading methods coupled with machine learning to accurately and fairly evaluate learner work while alleviating the instructor bottleneck and grading overload. Prior work in peer grading almost exclusively focuses on numerically scored grades -- either real-valued or ordinal. In this work, we consider the implications of peer ranking in which learners rank a small subset of peer work from strongest to weakest, and propose new types of computational analyses that can be applied to this ranking data. We adopt a Bayesian approach to the ranked peer grading problem and develop a novel model and method for utilizing ranked peer-grading data. We additionally develop a novel procedure for adaptively identifying which work should be ranked by particular peers in order to dynamically resolve ambiguity in the data and rapidly resolve a clearer picture of learner performance. We showcase our results on both synthetic and several real-world educational datasets.


title: Who is this "We"? Levels of collective identity and self representations.

abstract: Cross-cultural perspectives have brought renewed interest in the social aspects of the self and the extent to which individuals define themselves in terms of their relationships to others and to social groups. This article provides a conceptual review of research and theory of the social self, arguing that the personal, relational, and collective levels of self-definition represent distinct forms of selfrepresentation with different origins, sources of self-worth, and social motivations. A set of 3 experiments illustrates haw priming of the interpersonal or collective "we" can alter spontaneous judgments of similarity and self-descriptions. Until recently, social psychological theories of the self focused on the individuated self-concept—the person's sense of unique identity differentiated from others. Cross-cultural perspectives, however, have brought a renewed interest in the social aspects of the self and the extent to which individuals define themselves in terms of their relationships to others and to social groups (Markus & Kitayama, 1991; Triandis, Bontempo, Villareal, Asai, & Lucca, 1988). Central to this new perspective is the idea that connectedness and belonging are not merely affiliations or alliances between the self and others but entail fundamental differences in the way the self is construed (Brewer, 1991; Markus & Kitayama, 1991; Singelis, 1994; Trafimow, Triandis, & Goto, 1991; Triandis, 1989; Turner, Oakes, Haslam, & McGarty, 1994). Some of these theories of the social self focus on cross-cultural differences in whether the self is typically construed as individuated or interpersonal. However, all recognize that these different self-construals may also coexist within the same individual, available to be activated at different times or in different contexts. Furthermore, in several theories, achieving an extended sense of self has the status of a fundamental human motivation (Baumeister & Leary, 1995; Brewer, 1991). In other words, individuals seek to define themselves in terms of their immersion in relationships with others and with larger collectives and derive much of their self-evaluation from such social identities (Breckler & Greenwald, 1986; Greenwald & Breckler, 1985). The motivational properties of collective identities are systematically documented in Baumeister and Leary's (1995) comprehensive review of the evidence in support of a fundamental "need to belong" as an innate feature of human nature. All of the theories mentioned above draw some kind of distinction between the individuated or personal self (those aspects of the self-concept that differentiate the self from all others) and a relational or social self (those aspects of the self-concept that


title: Prevalence of anemia and socio-demographic factors associated with anemia among pregnant women attending antenatal Hospital in Jaipur City, India.

abstract: Objective: To study the prevalence of anemia and socio-demographic factors associated with anemia among pregnant women attending antenatal hospital. Material and methods: Total 100 pregnant women were selected for the study and the study was carried out from Sep 2011 to Jan 2012. Pretested and prestructured questionnaire was used to collect general information. Blood samples were collected by qualified technician for hemoglobin estimation. Socio-economic classification by B.G. Prasad was adopted. Classification of anemia by WHO was used. Chi-square test was used for statistical analysis. Results: Overall prevalence of anemia among the pregnant women was found to be 63%. Factors such as level of education and socio-economic status were found to be significantly associated with prevalence of anemia. Conclusion: There is a need for realization that health system should focus on various factors that contribute to the occurrence of anemia and include them as important mediators in the National Health Policy.


title: A Data-Oriented M2M Messaging Mechanism for Industrial IoT Applications

abstract: Machine-to-machine (M2M) communication is a key enabling technology for the future industrial Internet of Things applications. It plays an important role in the connectivity and integration of computerized machines, such as sensors, actuators, controllers, and robots. The requirements in flexibility, efficiency, and cross-platform compatibility of the intermodule communication between the connected machines raise challenges for the M2M messaging mechanism toward ubiquitous data access and events notification. This investigation determines the challenges facing the M2M communication of industrial systems and presents a data-oriented M2M messaging mechanism based on ZeroMQ for the ubiquitous data access in rich sensing pervasive industrial applications. To prove the feasibility of the proposed solution, the EU funded PickNPack production line with a reference industrial network architecture is presented, and the communication between a microwave sensor device and the quality assessment and sensing module controller of the PickNPack line is illustrated as a case study. The evaluation is carried out through qualitative analysis and experimental studies, and the results demonstrate the feasibility of the proposed messaging mechanism. Due to the flexibility in dealing with hierarchical system architecture and cross-platform heterogeneity of industrial applications, this messaging mechanism deserves extensive investigations and further evaluations.


title: Small-sized spiral dipole antenna for RFID transponder of UHF band

abstract: In this paper, the small-sized spiral dipole antenna for UHF RFID transponder is proposed. For the expected application of the UHF RFID systems, such as a supply chain integration, industrial automation, track and trace, and so on, the small and efficient printed antenna is required. To reduce the antenna size and to minimize the efficiency lowering due to small size, the proposed antenna is designed to have the spiral structure rather than the meander structure. In this structure, the current cancellation on the antenna is much less than in the meander structures, and the total current induces an identical magnetic dipole, which makes the omni-directional radiation pattern. The proposed antenna is designed for UHF RFID systems of Korea, to have the operating frequency bandwidth of 908 MHz-913 MHz for VSWR < 2. The antenna has the physical size of 15.5 mm by 14.8 mm, which is about lambda over 16 in electrical length. This antenna is fabricated on the PET dielectric, to be utilized as an antenna of the transponder for the application of the UHF RFID systems.


title: Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

abstract: The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital’s demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses’ preferences for working shifts and weekends off by considering several important factors such as hospital’s policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.


title: Joint 2D-3D temporally consistent semantic segmentation of street scenes

abstract: In this paper we propose a novel Conditional Random Field (CRF) formulation for the semantic scene labeling problem which is able to enforce temporal consistency between consecutive video frames and take advantage of the 3D scene geometry to improve segmentation quality. The main contribution of this work lies in the novel use of a 3D scene reconstruction as a means to temporally couple the individual image segmentations, allowing information flow from 3D geometry to the 2D image space. As our results show, the proposed framework outperforms state-of-the-art methods and opens a new perspective towards a tighter interplay of 2D and 3D information in the scene understanding problem.


title: Inductance calculation of an air-core disk winding

abstract: The study of transient voltages in transformer windings has aroused interest recently with the presentation of more efficient methods of modelling. This paper presents a method of modelling the magnetic field of an air-core disk winding which has sections lumped together. Past methods have proved to be cumbersome and required detailed representations of the winding. The method presented allows direct computation of the self-and mutual inductances fop lumped sections and is shown to be accurate compared to classical methods.


title: A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems

abstract: This paper describes an effort to model a student's changing knowledge state during skill acquisition. Dynamic Bayes Nets (DBNs) provide a powerful way to represent and reason about uncertainty in time series data, and are therefore well-suited to model student knowledge. Many general-purpose Bayes net packages have been implemented and distributed; however, constructing DBNs often involves complicated coding effort. To address this problem, we introduce a tool called BNT-SM. BNT-SM inputs a data set and a compact XML specification of a Bayes net model hypothesized by a researcher to describe causal relationships among student knowledge and observed behavior. BNT-SM generates and executes the code to train and test the model using the Bayes Net Toolbox [1]. Compared to the BNT code it outputs, BNT-SM reduces the number of lines of code required to use a DBN by a factor of 5. In addition to supporting more flexible models, we illustrate how to use BNT-SM to simulate Knowledge Tracing (KT) [2], an established technique for student modeling. The trained DBN does a better job of modeling and predicting student performance than the original KT code (Area Under Curve = 0.610 > 0.568), due to differences in how it estimates parameters.


Predictive Factors of Efficacy of Sunitinib Therapy in Gastrointestinal Stromal Tumours


title: Sentiment Aggregation using ConceptNet Ontology

abstract: Sentiment analysis of reviews traditionally ignored the association between the features of the given product domain. The hierarchical relationship between the features of a product and their associated sentiment that influence the polarity of a review is not dealt with very well. In this work, we analyze the influence of the hierarchical relationship between the product attributes and their sentiments on the overall review polarity. ConceptNet is used to automatically create a product specific ontology that depicts the hierarchical relationship between the product attributes. The ontology tree is annotated with feature-specific polarities which are aggregated bottom-up, exploiting the ontological information, to find the overall review polarity. We propose a weakly supervised system that achieves a reasonable performance improvement over the baseline without requiring any tagged training data.


title: Design Choices and Their Implications for 3D Mid-Air Manipulation Techniques

abstract: Manipulation is one of the most important tasks required in virtual environments and thus it has been thoroughly studied for widespread input devices such as mice or multi-touch screens. Nowadays, the Kinect sensor has turned mid-air interaction into another affordable and popular way of interacting. Mid-air interaction enables the possibility of interacting remotely without any physical contact and in a more natural manner. Nonetheless, although some scattered manipulation techniques have been proposed for mid-air interaction, there is a lack of evaluations and comparisons that hinders the selection and development of these techniques. To solve this issue, we gathered four design choices that can be used to classify mid-air manipulation techniques. Namely, choices are based on the required number of hands, separation of translation-rotation, decomposition of rotation, and interaction metaphors. Furthermore, we developed, adapted, and compared three manipulation techniques selected for studying the implications of the design choices. These implications are useful to select among already existing techniques as well as to inform technique developers.


title: Student assessment using Bayesian nets

abstract: We describe OLAE as an assessment tool that collects data from students solving problems in introductory college physics, analyses that data with probabilistic methods that determine what knowledge the student is using, and flexibly presents the results of analysis. For each problem, OLAE automatically creates a Bayesian net that relates knowledge, represented as first-order rules, to particular actions, such as written equations. Using the resulting Bayesian network, OLAE observes a student's behavior and computes the probabilities that the student knows and uses each of the rules.


title: Glancing up or down: Mood management and selective social comparisons on social networking sites

abstract: We tracked social network site browsing after inducing negative or positive mood.The site featured upward or downward social comparison targets on two dimensions.Negative moods led to less upward and more downward social comparison exposure.Mood effect appeared for both career success and physical appearance comparisons.Results suggest online social comparisons facilitate mood management. Social networking sites (SNS) provide opportunities for mood management through selective exposure. This study tested the prediction that negative mood fosters self-enhancing social comparisons to SNS profiles. Participants were induced into positive or negative moods and then browsed manipulated profiles on an experimental SNS. Profiles varied in a 2×2 within-subjects design along two dimensions, ratings of career success and attractiveness, allowing for upward comparisons (high ratings) and downward comparisons (low ratings). Selective exposure was measured in seconds spent viewing profiles. Negative mood led to less exposure to upward comparisons and more to downward comparisons than positive mood. The comparison dimension did not influence selective exposure. Thus, in a negative mood, SNS users prefer self-enhancing social comparisons to manage their mood.


title: Experiences of implementing process management: a multiple‐case study

abstract: Purpose – Process management is becoming an essential part of contemporary organizations in all industries. However, many organizations experience problems during the implementation of a process management approach. The purpose of this paper is to explore and describe the organizational implications when implementing process management, how to handle the relationship between the functional organization and a process perspective, and the roles of managers, teams, and individuals.Design/methodology/approach – A multiple‐case study approach is used to get an extensive picture of and analyze how three Swedish organizations have worked with process management.Findings – The studied organizations have introduced a process management structure into their functional organizational structure, including the introduction of new management positions such as process owners and process leaders. A discourse is identified in earlier research between those arguing for a full transformation from a functionally oriented to ...


Modeling and simulation of feed system design of CNC machine tool based on Matlab / simulink


title: Why it is time for a HyPE: A Hybrid Query Processing Engine for Efficient GPU Coprocessing in DBMS

abstract: GPU acceleration is a promising approach to speed up query processing of database systems by using low cost graphic processors as coprocessors. Two major trends have emerged in this area: (1) The development of frameworks for scheduling tasks in heterogeneous CPU/GPU platforms, which is mainly in the context of coprocessing for applications and does not consider specifics of database-query processing and optimization. (2) The acceleration of database operations using efficient GPU algorithms, which typically cannot be applied easily on other database systems, because of their analytical-algorithm-specific cost models. One major challenge is how to combine traditional database query processing with GPU coprocessing techniques and efficient database operation scheduling in a GPU-aware query optimizer. In this thesis, we develop a hybrid query processing engine, which extends the traditional physical optimization process to generate hybrid query plans and to perform a cost-based optimization in a way that the advantages of CPUs and GPUs are combined. Furthermore, we aim at a portable solution between different GPU-accelerated database management systems to maximize applicability. Preliminary results indicate great potential.


title: A Tutorial on Deep Latent Variable Models of Natural Language

abstract: There has been much recent, exciting work on combining the complementary strengths of latent variable models and deep learning. Latent variable modeling makes it easy to explicitly specify model constraints through conditional independence properties, while deep learning makes it possible to parameterize these conditional likelihoods with powerful function approximators. While these "deep latent variable" models provide a rich, flexible framework for modeling many real-world phenomena, difficulties exist: deep parameterizations of conditional likelihoods usually make posterior inference intractable, and latent variable objectives often complicate backpropagation by introducing points of non-differentiability. This tutorial explores these issues in depth through the lens of variational inference.


title: A 15b-Linear, 20MS/s, 1.5b/Stage Pipelined ADC Digitally Calibrated with Signal-Dependent Dithering

abstract: A signal-dependent dithering concept is developed to measure the multiplying DAC (MDAC) gain error of a 1.5b/stage pipelined ADC in background. A 15b, 20MS/s prototype ADC exhibits SFDR and THD of 98 and -92dB with 14.5MHz input. The chip fabricated in 0.18mum CMOS occupies 2.3 times 1.7mm2, and consumes 285mW at 1.8V


title: Prediction of Magnetic Hysteresis in the Force of a Prebiased E-Core Reluctance Actuator

abstract: Magnetic hysteresis in the force of a pre-biased E-core reluctance actuator is mesearched using three analysis methods. Simulations are performed with a 2d/3d finite element method (FEM) and two semi-analytic methods are evaluated namely, a generalization of the classical Preisach model (CPM), which is combined with a dynamic magnetic equivalent circuit (MEC) method, and a complex impedance model, which is combined with a static MEC model. Ultimately, the FEM simulations and analytic models are examined by a comparison to force measurements performed with a piezoelectric load cell.


title: Temporo-parietal Junction Activity in Theory-of-Mind Tasks: Falseness, Beliefs, or Attention

abstract: By combining the false belief (FB) and photo (PH) vignettes to identify theory-of-mind areas with the false sign (FS) vignettes, we re-establish the functional asymmetry between the left and right temporo-parietal junction (TPJ). The right TPJ (TPJ-R) is specially sensitive to processing belief information, whereas the left TPJ (TPJ-L) is equally responsible for FBs as well as FSs. Measuring BOLD at two time points in each vignette, at the time the FB-inducing information (or lack of information) is presented and at the time the test question is processed, made clear that the FB is processed spontaneously as soon as the relevant information is presented and not on demand for answering the question in contrast to extant behavioral data. Finally, a fourth, true belief vignette (TB) required teleological reasoning, that is, prediction of a rational action without any doubts being raised about the adequacy of the actor's information about reality. Activation by this vignette supported claims that the TPJ-R is activated by TBs as well as FBs.


title: Two-stage Algorithm for Fairness-aware Machine Learning

abstract: Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive attributes of data does not solve this problem because a \textit{disparate impact} can arise when non-sensitive attributes and sensitive attributes are correlated. Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision. Inspired by the two-stage least squares method that is widely used in the field of economics, we propose a two-stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory attributes to remove reverse discrimination, and (iii) deal with numerical sensitive attributes. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real-world datasets.


title: Importance-based approach for rough drawings

abstract: We present a framework for producing rough drawings from photographs. Depicting a scene using a series of lines is one of the most effective methods of visual communication. Our framework for rough drawing is comprised of three steps: extracting lines from images, estimating line importance, and producing strokes that express various styles. To extract lines, we employ the widely used difference-of-Gaussian filter approach to devise a fault-correcting line shift scheme. Line importance is estimated by combining gradient and saliency. To obtain an efficient saliency estimation, we propose a stochastic content-based method. Various styles of rough drawings are produced by convoluting adaptive stroke texture segments, which are prepared by sampling real stroke texture images. We test our framework on various images and compare our results with real artwork and other schemes.


title: Content-based author co-citation analysis

abstract: Author co-citation analysis (ACA) has long been used as an effective method for identifying the intellectual structure of a research domain, but it relies on simple co-citation counting, which does not take the citation content into consideration. The present study proposes a new method for measuring the similarity between co-cited authors by considering author's citation content. We collected the full-text journal articles in the information science domain and extracted the citing sentences to calculate their similarity distances. We compared our method with traditional ACA and found out that our approach, while displaying a similar intellectual structure for the information science domain as the other baseline methods, also provides more details about the sub-disciplines in the domain than with traditional ACA.


title: A dual-band wide-angle scanning phased array antenna in K/Ka bands for satellite-on-the-move applications

abstract: This study presents the design of a dual-band cavity-backed phased array antenna with wide-angle scanning capability in K/Ka bands for the geostationary (GSO)-fixed satellite service (FSS). To reduce the losses and mutual coupling, a novel antenna element similar to the cavity-backed Strip-Slot-Air-Inverted-Patch (SSAIP) is proposed as an element of designed array of 81 elements. Wide-angle scanning up to 60o is achieved at both frequency bands of the operation. The developed antenna is suitable for seamless installation on both land and air vehicles for bi-directional SatCom systems.


title: A Neural Autoregressive Approach to Attention-based Recognition

abstract: Tasks that require the synchronization of perception and action are incredibly hard and pose a fundamental challenge to the fields of machine learning and computer vision. One important example of such a task is the problem of performing visual recognition through a sequence of controllable fixations; this requires jointly deciding what inference to perform from fixations and where to perform these fixations. While these two problems are challenging when addressed separately, they become even more formidable if solved jointly. Recently, a restricted Boltzmann machine (RBM) model was proposed that could learn meaningful fixation policies and achieve good recognition performance. In this paper, we propose an alternative approach based on a feed-forward, auto-regressive architecture, which permits exact calculation of training gradients (given the fixation sequence), unlike for the RBM model. On a problem of facial expression recognition, we demonstrate the improvement gained by this alternative approach. Additionally, we investigate several variations of the model in order to shed some light on successful strategies for fixation-based recognition.


title: Silicon Photonics for Exascale Systems

abstract: With the extraordinary growth in parallelism at all system scales driven by multicore architectures, computing performance is increasingly determined by how efficiently high-bandwidth data is communicated among the numerous compute resources. High-performance systems are especially challenged by the growing energy costs dominated by data movement. As future computing systems aim to realize the Exascale regime—surpassing 1018 operations per second—achieving energy efficient high-bandwidth communication becomes paramount to scaled performance. Silicon photonics offers the possibility of delivering the needed communication bandwidths to match the growing computing powers of these highly parallel architectures with extremely scalable energy efficiency. However, the insertion of photonic interconnects is not a one-for-one replacement. The lack of practical buffering and the fundamental circuit switched nature of optical data communications require a holistic approach to designing system-wide photonic interconnection networks. New network architectures are required and must include arbitration strategies that incorporate the characteristics of the optical physical layer. This paper reviews the recent progresses in silicon photonic based interconnect devices along with the system level requirements for Exascale. We present a co-design approach for building silicon photonic interconnection networks that leverages the unique optical data movement capabilities and offers a path toward realizing future Exascale systems.


title: Generalization Through the Recurrent Interaction of Episodic Memories

abstract: In this article, we present a perspective on the role of the hippocampal system in generalization, instantiated in a computational model called REMERGE (recurrency and episodic memory results in generalization). We expose a fundamental, but neglected, tension between prevailing computational theories that emphasize the function of the hippocampus in pattern separation (Marr, 1971; McClelland, McNaughton, & O’Reilly, 1995), and empirical support for its role in generalization and flexible relational memory (Cohen & Eichenbaum, 1993; Eichenbaum, 1999). Our account provides a means by which to resolve this conflict, by demonstrating that the basic representational scheme envisioned by complementary learning systems theory (McClelland et al., 1995), which relies upon orthogonalized codes in the hippocampus, is compatible with efficient generalization—as long as there is recurrence rather than unidirectional flow within the hippocampal circuit or, more widely, between the hippocampus and neocortex. We propose that recurrent similarity computation, a process that facilitates the discovery of higher-order relationships between a set of related experiences, expands the scope of classical exemplar-based models of memory (e.g., Nosofsky, 1984) and allows the hippocampus to support generalization through interactions that unfold within a dynamically created memory space.


title: Broadband Microwave Crossover Using Combination of Ring Resonator and Circular Microstrip Patch

abstract: The design of a novel wideband microwave crossover is presented. The proposed structure is fully compatible with microstrip circuits as it is printed on one side of a single-layer substrate. The structure is made up of a circular microstrip patch connected to a ring resonator. The operation of the presented design is explained by deriving the equivalent circuit model using the second-order even-odd mode excitations. The derived model is verified by full-wave electromagnetic simulations. A prototype is designed, developed, and tested. The results show a crossover with more than 18 dB of isolation, less than 0.5 dB insertion loss, and less than 0.25 ns deviation in the group delay across the band from 2.15 GHz to 3.35 GHz (44% fractional bandwidth).


title: Gelation/crystallization mechanisms of UHMWPE solutions and structures of ultradrawn gel films

abstract: The gelation speeds and temperatures for decalin and paraffin differed significantly. In decalin, gel syneresis occurred by solvent flow from the gels accelerates crystallization, and dry gel films were prepared. In contrast, in paraffin, many nucleating points appeared immediately, and few syneresis occurred even after quenching to room temperature. To prepare dry gel films, paraffin was blotted with filter paper, and residual paraffin was removed in hexane. However, the small-angle X-ray scattering (SAXS) patterns from both films provided similar profiles indicating large crystalline lamellar stacking ensuring ultradrawing.


title: Signaling pathways effecting crosstalk between cartilage and adjacent tissues: Seminars in cell and developmental biology: The biology and pathology of cartilage

abstract: Endochondral ossification, the mechanism responsible for the development of the long bones, is dependent on an extremely stringent coordination between the processes of chondrocyte maturation in the growth plate, vascular expansion in the surrounding tissues, and osteoblast differentiation and osteogenesis in the perichondrium and the developing bone center. The synchronization of these processes occurring in adjacent tissues is regulated through vigorous crosstalk between chondrocytes, endothelial cells and osteoblast lineage cells. Our knowledge about the molecular constituents of these bidirectional communications is undoubtedly incomplete, but certainly some signaling pathways effective in cartilage have been recognized to play key roles in steering vascularization and osteogenesis in the perichondrial tissues. These include hypoxia-driven signaling pathways, governed by the hypoxia-inducible factors (HIFs) and vascular endothelial growth factor (VEGF), which are absolutely essential for the survival and functioning of chondrocytes in the avascular growth plate, at least in part by regulating the oxygenation of developing cartilage through the stimulation of angiogenesis in the surrounding tissues. A second coordinating signal emanating from cartilage and regulating developmental processes in the adjacent perichondrium is Indian Hedgehog (IHH). IHH, produced by pre-hypertrophic and early hypertrophic chondrocytes in the growth plate, induces the differentiation of adjacent perichondrial progenitor cells into osteoblasts, thereby harmonizing the site and time of bone formation with the developmental progression of chondrogenesis. Both signaling pathways represent vital mediators of the tightly organized conversion of avascular cartilage into vascularized and mineralized bone during endochondral ossification.


title: Application of Locality Sensitive Hashing to realtime loop closure detection

abstract: In this work we present a new approach for detecting loop closures in a real-time online setting. The Loop Closure Detection problem is important in visual SLAM applications and different approaches exist to deal with this problem. Most of these approaches are based on the Bag-of-Words approach, and assume a fixed visual vocabulary can work in different types of environments. However BOW is known to introduce perceptual aliasing. By using Locality Sensitive Hashing (LSH) we are able to compute image similarity and detect loop closures by using visual features directly without vector quantization as in BOW and also LSH does not require a prior visual vocabulary. We show the effectiveness of our approach empirically by comparing it to the Bag of Words (BOW) approach which is the dominant method of selecting candidate loop closing images. Our method is fast enough for realtime applications and its accuracy is significantly better than the BOW approach.


title: Recursive sparse recovery in large but correlated noise

abstract: In this work, we focus on the problem of recursively recovering a time sequence of sparse signals, with time-varying sparsity patterns, from highly undersampled measurements corrupted by very large but correlated noise. It is assumed that the noise is correlated enough to have an approximately low rank covariance matrix that is either constant, or changes slowly, with time. We show how our recently introduced Recursive Projected CS (ReProCS) and modified-ReProCS ideas can be used to solve this problem very effectively. To the best of our knowledge, except for the recent work of dense error correction via l 1 minimization, which can handle another kind of large but “structured” noise (the noise needs to be sparse), none of the other works in sparse recovery have studied the case of any other kind of large noise.


title: CMOS current reference generator using integrated resistors

abstract: A novel method is proposed that can generate a stable reference current using on-chip integrated resistors without the use of trimming. By optimally combining different types of integrated resistors, the overall resistance variability is reduced below that of the most accurate available resistor. Combining such a resistance with a band-gap reference and a temperature compensation circuit, we implement an on-chip current reference. This circuit is implemented using a standard 0.35um CMOS process with 3V power supply. Monte-Carlo simulations show a nominal reference current (1uA) from a combined resistor varies by ±11.5% over a −10°C to 70°C temperature range. This variation is reduced by a factor of two compared with using a normal resistor. The measured values of the combined resistor and the normal resistor are 1.08MΩ and 1.45MΩ, respectively. This is −10% and +20% deviation from the desired value of 1.2MΩ.


title: Chapter 6: Deep Dynamic Models for Learning Hidden Representations of Speech Features

abstract: Deep hierarchical structure with multiple layers of hidden space in human speech is intrinsically connected to its dynamic characteristics manifested in all levels of speech production and perception. The desire and an attempt to capitalize on a (superficial) understanding of this deep speech structure helped ignite the recent surge of interest in the deep learning approach to speech recognition and related applications, and a more thorough understanding of the deep structure of speech dynamics and the related computational representations is expected to further advance the research progress in speech technology. In this chapter, we first survey a series of studies on representing speech in a hidden space using dynamic systems and recurrent neural networks, emphasizing different ways of learning the model parameters and subsequently the hidden feature representations of time-varying speech data. We analyze and summarize this rich set of deep, dynamic speech models into two major categories: (1) top-down, generative models adopting localist representations of speech classes and features in the hidden space; and (2) bottom-up, discriminative models adopting distributed representations. With detailed examinations of and comparisons between these two types of models, we focus on the localist versus distributed representations as their respective hallmarks and defining characteristics. Future directions are discussed and analyzed about potential strategies to leverage the strengths of both the localist and distributed representations while overcoming their respective weaknesses, beyond blind integration of the two by using the generative model to pre-train the discriminative one as a popular method of training deep neural networks.


title: QArabPro: A Rule Based Question Answering System for Reading Comprehension Tests in Arabic

abstract: Problem statement: Extensive research efforts in the area of Natural L anguage Processing (NLP) were focused on developing reading comprehension Question Answering systems (QA) for Latin based languages such as, English, French and German. Approach: However, little effort was directed towards the development of such systems for bidirec tional languages such as Arabic, Urdu and Farsi. In general, QA systems are more sophisticated and more complex than Search Engines (SE) because they seek a specific and somewhat exact answer to the query. Results: Existing Arabic QA system including the most recent described excluded one or both types of questions (How and Why) from their work because of the difficulty of handling these questions. In this study, we present a new approach and a new questio n- answering system (QArabPro) for reading comprehension texts in Arabic. The overall accuracy of our system is 84%. Conclusion/Recommendations: These results are promising compared to existing systems. Our system handles all types of questions including (How and why).


title: Scales for Measuring User Engagement with Social Network Sites: A Systematic Review of Psychometric Properties

abstract: Abstract In the past decade, various scales have been developed for measuring engagement with social network sites (SNS), but validity concerns have recently been raised about some of them. The present study thus provides a systematic review of the psychometric properties of these scales. This review included articles that aimed at either developing an SNS engagement scale or providing a systematic test of the psychometric properties of the scale. We conducted keyword-based searches of several broad multidisciplinary databases, along with reference list searches and article citation searches. These search strategies yielded a total of 14 reports, revealing validation evidence for 12 SNS engagement scales among 13,861 participants from 11 countries. There was mixed evidence for the various types of validity tests, with some scales having been validated more rigorously with multiple studies and samples while others having not yet been systematically validated. Sampling and acquiescence biases were also present for some scales. The present review provides recommendations for researchers intending to study SNS engagement. Although the literature search was multi-faceted, it may conceivably have missed studies that provided less rigorous validity evidence. Overall, this study contributes to evaluating and strengthening the methodological foundations of SNS research.


title: Heart rate and skin conductance analysis of antecendents and consequences of decision making

abstract: The current study examined the pattern of heart rate and skin conductance changes preceding risky choices and following outcome for bad, moderate, and good performers on an analogue of the Iowa gambling task (Bechara, Damasio, Damasio, & Anderson, 1994). The task required a choice between four options; two options were followed by a high reward and, unpredictably, an even higher loss (disadvantageous options) and two other options were followed by a small reward but the unpredictable loss was also small (advantageous options). Anticipatory heart rate slowing and skin conductance level were higher preceding disadvantageous relative to advantageous options, but only for good performers. In contrast, heart rate slowed and skin conductance level increased following loss relative to reward outcomes, and these changes were similar for all performance groups. These findings were interpreted to suggest that decision-making impairments in bad performers arise from a weak somatic response generated by secondary inducers (i.e., somatic markers), rather than a weak somatic response generated by primary inducers of reward and punishment.


title: Hatching egg characteristics, chick quality, and broiler performance at 2 breeder flock ages and from 3 egg weights

abstract: The objective of this study was to determine the effects of flock age and egg weight on hatching egg characteristics, fertility, hatchability, salable chick production, and broiler performance using a commercial Cobb 500 broiler breeder flock. Hatching eggs from the same breeder flock in 3 weight categories (light, average, and heavy) were obtained from a commercial hatchery when the birds were 29 and 59 wk of age. One group of eggs per age and weight category was selected to assess specific gravity and was broken open to weigh egg components. Another group of eggs was incubated for 21.5 d and incubation parameters were measured. At hatching, all salable chicks were individually weighed and placed in floor pens, where they were grown out for 41 d. Daily mortality, weekly feed consumption, and individual BW at 21 and 41 d were recorded. Irrespective of flock age and egg weight, all eggs had a specific gravity lower than 1.080, the commercial set value. Eggs from the young flock age had a smaller proportion of yolk and a greater proportion of albumen. Age affected fertility, with a lower value observed at 29 wk of age. Chicks from the flock at 59 wk hatched earlier than chicks from the flock at 29 wk, and light eggs hatched earlier than both average and heavy eggs. Broilers from the 29-wk-old breeders had a lower final BW than broilers from the 59-wk-old breeders. The smaller proportion of yolk in eggs from 29-wk-old broiler breeders may be associated with the low final BW observed in their offspring. This could mean that chicks hatching from young broiler breeders, which produce eggs with small yolks, may be at a disadvantage when reared under the same conditions as chicks produced by older broiler breeders whose eggs have larger yolks.


title: Long-term growth and yield effects of respacing natural regeneration of Sitka spruce in Britain

abstract: Stands of Sitka spruce (Picea sitchensis (Bong.) Carr.) originating from natural regeneration can be extremely dense with high mortality, poor growth form and low volume production. Although costly, respacing (pre-commercial thinning) can reduce resource competition for the remaining trees, altering the final crop produced. Three experiments were established to examine the effect of respacing 1–2 m tall trees to different target densities. The impact on stand growth and yield was measured 11–17 years after respacing, and the longer-term impact was modelled to year 50. Unrespaced control treatments self-thinned at a similar and constant rate at two sites. At the third, extremely high initial density resulted in a higher rate of self-thinning and lower density after 11 years. Wider respacing treatments resulted in larger mean tree diameter, but there was no significant effect of respacing on stand volume 11–17 years after respacing; greater diameter growth did not compensate for low tree number. Results indicate a volume penalty associated with delaying respacing until trees were 4 m tall, but this treatment was unreplicated. Modelled stand volume in year 50 was higher for the 2.6 m × 2.6 m respacing treatment than for the 1.8 m × 1.8 m, 2.1 m × 2.1 m or 3.3 m × 3.3 m treatments. However, open-grown conditions may reduce timber quality compared to closer spacing treatments. The results are presented using a stand density management diagram for Sitka spruce growing in Canada and support recent suggestions that British stands have a shallower size–density relationship than Canadian stands.


title: Types of study in medical research: part 3 of a series on evaluation of scientific publications.

abstract: BACKGROUND ::: The choice of study type is an important aspect of the design of medical studies. The study design and consequent study type are major determinants of a study's scientific quality and clinical value. ::: ::: ::: METHODS ::: This article describes the structured classification of studies into two types, primary and secondary, as well as a further subclassification of studies of primary type. This is done on the basis of a selective literature search concerning study types in medical research, in addition to the authors' own experience. ::: ::: ::: RESULTS ::: Three main areas of medical research can be distinguished by study type: basic (experimental), clinical, and epidemiological research. Furthermore, clinical and epidemiological studies can be further subclassified as either interventional or noninterventional. ::: ::: ::: CONCLUSIONS ::: The study type that can best answer the particular research question at hand must be determined not only on a purely scientific basis, but also in view of the available financial resources, staffing, and practical feasibility (organization, medical prerequisites, number of patients, etc.).


title: Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data

abstract: Metro systems have become one of the most important public transit services in cities. It is important to understand individual metro passengers’ spatio-temporal travel patterns. More specifically, for a specific passenger: what are the temporal patterns? what are the spatial patterns? is there any relationship between the temporal and spatial patterns? are the passenger’s travel patterns normal or special? Answering all these questions can help to improve metro services, such as evacuation policy making and marketing. Given a set of massive smart card data over a long period, how to effectively and systematically identify and understand the travel patterns of individual passengers in terms of space and time is a very challenging task. This paper proposes an effective data-mining procedure to better understand the travel patterns of individual metro passengers in Shenzhen, a modern and big city in China. First, we investigate the travel patterns in individual level and devise the method to retrieve them based on raw smart card transaction data, then use statistical-based and unsupervised clustering-based methods, to understand the hidden regularities and anomalies of the travel patterns. From a statistical-based point of view, we look into the passenger travel distribution patterns and find out the abnormal passengers based on the empirical knowledge. From unsupervised clustering point of view, we classify passengers in terms of the similarity of their travel patterns. To interpret the group behaviors, we also employ the bus transaction data. Moreover, the abnormal passengers are detected based on the clustering results. At last, we provide case studies and findings to demonstrate the effectiveness of the proposed scheme.


title: Evaluation of the effects of formulation and food on the pharmacokinetics of lenvatinib (E7080) in healthy volunteers

abstract: OBJECTIVE ::: To evaluate the effect of formulation and a high-fat meal on the pharmacokinetics of orally administered lenvatinib (E7080). ::: ::: ::: MATERIALS ::: Lenvatinib 10-mg capsule and tablet. ::: ::: ::: METHODS ::: Pharmacokinetics and safety of a single 10-mg lenvatinib dose were evaluated in healthy subjects in two randomized, two-period, crossover, phase 1, bioavailability trials. The first compared a new capsule formulation with an older tablet formulation (n = 20 subjects); the second evaluated the influence of a standard high-fat meal on the relative bioavailability of the capsule formulation (n = 16 subjects). Geometric least squares mean ratios of AUC0-∞, maximum observed concentration (Cmax), and AUC0-t were determined. tmax, tlag (food effect only), and t1/2,z were also calculated, and descriptive statistics were provided. ::: ::: ::: RESULTS ::: A total of 36 healthy volunteers were enrolled in the two studies (mean ages 29 and 33 years). In the formulation study, AUC0-∞ and AUC0-t of the capsule formulation were ~ 10% less than the tablet formulation, and Cmax for the capsule formulation was ~ 14% lower. 90% Confidence intervals (CIs) for both AUCs were within the 80 - 125% CI, which is generally considered to denote bioequivalence, while the lower bound of the interval for Cmax was 79.8%. tmax and t1/2,z were comparable. For the capsule formulation, the mean (%CV) t1/2,z was 27.6 hours (27.3) and the median (range) tmax was 2.0 hours (2 - 4). In the food effect study, lenvatinib's AUC0-∞ and AUC0-t increased ~ 6% and 4% with the high-fat meal. Cmax following a high-fat meal was 5% lower than following administration in the fasted state. Administration with food delayed lenvatinib's tmax (2 vs. 4 hours). 90% CIs for AUCs were within the 80 - 125% CI, while the CI for Cmax was 72.1 - 126.4%. The single 10-mg dose demonstrated an acceptable tolerability profile; treatment-emergent adverse events occurred in 9 subjects (25%) overall and were typically mild in severity. ::: ::: ::: CONCLUSIONS ::: These studies show that a new capsule formulation produces slightly lower exposure (~10 - 14%) to lenvatinib compared with the original tablet formulation, and that oral administration with a high-fat meal does not significantly affect exposure, although absorption is delayed. Thus, lenvatinib can be administered without regard to the timing of meals.


title: An intraindividual process approach to the relationship between extraversion and positive affect: Is acting extraverted as “good” as being extraverted

abstract: This article investigates whether rapid variation within a person in extraversion is associated with positive affect variation in that person. In Study 1, participants reported their extraversion and positive affect every 3 hr for 2 weeks. Each participant was happier when acting extraverted than when acting introverted. Study 2s diary methodology replicated the relationship for weekly variations in positive affect. Study 3’s experimental methodology replicated the relationship when extraversion was manipulated within a fixed situation. Thus, the relationship between extraversion and positive affect, previously demonstrated between persons, also characterizes the internal, ongoing psychological functioning of individuals and is likely to be explained by something capable of rapid intraindividual variation. Furthermore, traits and states are at least somewhat isomorphic, and acting extraverted may increase well-being. The purpose of this article is twofold. The first purpose is to investigate whether the relationship between extraversion and positive affect is evident within person over time; that is, whether short-term, fast-moving, and everyday variations in extraversion in one person are associated with fast-moving variations in that same person’s positive affect. This purpose is important for at least three reasons. First, it determines whether the extraversion–positive affect relationship observed in between-persons studies is limited to a description of co-occurrences of differences between individuals or can be included in the characterization of the ongoing, internal psychological functioning of individuals. Second, it provides a test of the density distributions model of traits—specifically, that a large part of the individual differences in traits is the frequency with which individuals enact corresponding states (e.g., the frequency of acting extraverted) and that within-person variation in states is meaningful and at least somewhat isomorphic to variation in traits (Fleeson, 2001). Third, it tests the potential implication of the between-persons correlation that individuals can become happier by acting more extraverted. That is, if this is a potential route to self-improvement, it must be the case that changes within a person in extraversion are associated with


title: Open-Source Tools for Morphology, Lemmatization, POS Tagging and Named Entity Recognition

abstract: We present two recently released opensource taggers: NameTag is a free software for named entity recognition (NER) which achieves state-of-the-art performance on Czech; MorphoDiTa (Morphological Dictionary and Tagger) performs morphological analysis (with lemmatization), morphological generation, tagging and tokenization with state-of-the-art results for Czech and a throughput around 10-200K words per second. The taggers can be trained for any language for which annotated data exist, but they are specifically designed to be efficient for inflective languages, Both tools are free software under LGPL license and are distributed along with trained linguistic models which are free for non-commercial use under the CC BY-NC-SA license. The releases include standalone tools, C++ libraries with Java, Python and Perl bindings and web services.


title: Objects that Sound

abstract: In this paper our objectives are, first, networks that can embed audio and visual inputs into a common space that is suitable for cross-modal retrieval; and second, a network that can localize the object that sounds in an image, given the audio signal. We achieve both these objectives by training from unlabelled video using only audio-visual correspondence (AVC) as the objective function. This is a form of cross-modal self-supervision from video. ::: To this end, we design new network architectures that can be trained using the AVC task for these functionalities: for cross-modal retrieval, and for localizing the source of a sound in an image. We make the following contributions: (i) show that audio and visual embedding can be learnt that enable both within-mode (e.g. audio-to-audio) and between-mode retrieval; (ii) explore various architectures for the AVC task, including those for the visual stream that ingest a single image, or multiple images, or a single image and multi-frame optical flow; (iii) show that the semantic object that sounds within an image can be localized (using only the sound, no motion or flow information); and (iv) give a cautionary tale in how to avoid undesirable shortcuts in the data preparation.


title: A review: Crime analysis using data mining techniques and algorithms

abstract: Crime analysis is a methodical approach for identifying and analyzing patterns and trends in crime. With the increasing origin of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining, we can analyze previously unknown, useful information from an unstructured data. Predictive policing means, using analytical and predictive techniques, to identify criminal and it has been found to be pretty much effective in doing the same. Because of the increased crime rate over the years, we will have to handle a huge amount of crime data stored in warehouses which would be very difficult to be analyzed manually, and also now a day's, criminals are becoming technologically advance, so there is need to use advance technologies in order to keep police ahead of them. In this paper, the main focus is on the review of algorithms and techniques used for identify the criminals.


title: A comparison of two nursing program exit exams that predict first-time NCLEX-RN outcome.

abstract: This retrospective descriptive correlational study compared the predictive accuracy of the Health Education Systems, Inc, Exit Exam (Elsevier) and Assessment Technologies Institute's RN Comprehensive Predictor, both of which were administered to nursing students in an upper-division baccalaureate nursing program during their final semester of study. Using logistic regression analyses, it was determined that the two examinations were statistically significant but weak predictors of success on the RN licensure examination. The RN Comprehensive Predictor had a slightly better odds ratio; however, both examinations had similar sensitivity, specificity, and overall accuracy. Because the RN Comprehensive Predictor was included in the Assessment Technologies Institute's Comprehensive Assessment and Review Program already being used by the BSN program, based on the results of this study, the nursing faculty decided to use only the RN Comprehensive Predictor during its NCLEX-RN preparation course.


title: Design and Analysis of a 3-Arm Spiral Antenna

abstract: A novel 3-arm spiral antenna structure is presented in this paper. This antenna similar to traditional two-arm or four-arm spiral antennas exhibits wideband radiation characteristic and circular polarization. Advantages offered by the new design are two fold. Unlike the traditional spiral antennas the three-arm spiral can be fed by an unbalanced transmission line, such as a coaxial line or coplanar waveguide, and therefore an external balun is not needed at the feed point. Also by proper choice of arms' dimensions the antenna can be directly matched to any practical transmission line characteristic impedance and therefore external matching networks are not required. This is accomplished by feeding the antenna at the outer radius by a coplanar waveguide (CPW) transmission line and tapering it towards the center. The antenna can also be fed from the center using a coaxial or CPW line perpendicular to the plane of the spiral antenna. A full-wave numerical simulation tool is used to optimize the geometry of the proposed 3-arm spiral to achieve a compact size, wide bandwidth operation, and low axial ratio. The antenna is also designed over a ground plane to achieve a unidirectional radiation and center loading is examined that improves the axial ratio. Simulated results like return loss, radiation pattern, gain, and axial ratio are compared with those obtained from measurements and good agreements are shown. Because of its unique feed structure and compact size, application of the proposed 3-arm spiral antenna for wideband array applications is demonstrated


title: Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey

abstract: Several enabling technologies are being explored for the fifth-generation (5G) mobile system era. The aim is to evolve a cellular network that remarkably pushes forward the limits of legacy mobile systems across all dimensions of performance metrics. One dominant technology that consistently features in the list of the 5G enablers is the millimeter-wave (mmWave) massive multiple-input-multiple-output (massive MIMO) system. It shows potentials to significantly raise user throughput, enhance spectral and energy efficiencies and increase the capacity of mobile networks using the joint capabilities of the huge available bandwidth in the mmWave frequency bands and high multiplexing gains achievable with massive antenna arrays. In this survey, we present the preliminary outcomes of extensive research on mmWave massive MIMO (as research on this subject is still in the exploratory phase) and highlight emerging trends together with their respective benefits, challenges, and proposed solutions. The survey spans broad areas in the field of wireless communications, and the objective is to point out current trends, evolving research issues and future directions on mmWave massive MIMO as a technology that will open up new frontiers of services and applications for next-generation cellular networks.


title: On the wafer/pad friction of chemical-mechanical planarization (CMP) processes- Part II: experiments and applications

abstract: This paper presents the experimental validation and some application examples of the proposed wafer/pad friction models for linear chemical-mechanical planarization (CMP) processes in the companion paper. An experimental setup of a linear CMP polisher is first presented and some polishing processes are then designed for validation of the wafer/pad friction modeling and analysis. The friction torques of both the polisher spindle and roller systems are used to monitor variations of the friction coefficient in situ . Verification of the friction model under various process parameters is presented. Effects of pad conditioning and the wafer film topography on wafer/pad friction are experimentally demonstrated. Finally, several application examples are presented showing the use of the roller motor current measurement for real-time process monitoring and control.


title: Regenerative Random Forest with Automatic Feature Selection to Detect Mitosis in Histopathological Breast Cancer Images

abstract: We propose a fast and accurate method for counting the mitotic figures from histopathological slides using regenerative random forest. Our method performs automatic feature selection in an integrated manner with classification. The proposed random forest assigns a weight to each feature (dimension) of the feature vector in a novel manner based on the importance of the feature (dimension). The forest also assigns a misclassification-based penalty term to each tree in the forest. The trees are then regenerated to make a new population of trees (new forest) and only the more important features survive in the new forest. The feature vector is constructed from domain knowledge using the intensity features of nucleus, features of nuclear membrane and features of the possible stroma region surrounding the cell. The use of domain knowledge improves the classification performance. Experiments show at least 4% improvement in F-measure with an improvement in time complexity on the MITOS dataset from ICPR 2012 grand challenge.


title: Intelligent software development environments: integrating natural language processing with the eclipse platform

abstract: Software engineers need to be able to create, modify, and analyze knowledge stored in software artifacts. A significant amount of these artifacts contain natural language, like version control commit messages, source code comments, or bug reports. Integrated software development environments (IDEs) are widely used, but they are only concerned with structured software artifacts - they do not offer support for analyzing unstructured natural language and relating this knowledge with the source code. We present an integration of natural language processing capabilities into the Eclipse framework, a widely used software IDE. It allows to execute NLP analysis pipelines through the Semantic Assistants framework, a service-oriented architecture for brokering NLP services based on GATE. We demonstrate a number of semantic analysis services helpful in software engineering tasks, and evaluate one task in detail, the quality analysis of source code comments.


title: Accurate Design Procedure for Waffle-Iron Low-Pass Filter

abstract: In this paper, an accurate synthesis procedure for designing classical low-pass waffle-iron waveguide filters is proposed. The novel design technique is based on the decomposition of the filter into a finite number of design entities (DEs), which are composed of a $\mathbf{waffle}$ -iron section inserted between two uniform rectangular waveguide sections. Then, the physical dimensions of each DE are calculated by means of their associated frequency responses along the device. The proposed method has been tested through the design of a Ku-band filter, which shows a remarkable accordance between the ideal and simulated frequency response. A prototype of this filter has been also fabricated to prove the feasibility of designing classical waffle-iron filters with the novel design procedure.


title: A novel deep-learning based framework for multi-subject emotion recognition

abstract: Electroencephalogram (EEG) signal based emotion recognition, as a challenging pattern recognition task, has attracted more and more attention in recent years and widely used in medical, Affective Computing and other fields. Traditional approaches often lack of the high-level features and the generalization ability is poor, which are difficult to apply to the practical application. In this paper, we proposed a novel model for multi-subject emotion classification. The basic idea is to extract the high-level features through the deep learning model and transform traditional subject-independent recognition tasks into multi-subject recognition tasks. Experiments are carried out on the DEAP dataset, and our results demonstrate the effectiveness of the proposed method.


title: Neurobiology of Aggression and Violence

abstract: Acts of violence account for an estimated 1.43 million deaths worldwide annually. While violence can occur in many contexts, individual acts of aggression account for the majority of instances. In some individuals, repetitive acts of aggression are grounded in an underlying neurobiological susceptibility that is just beginning to be understood. The failure of “top-down” control systems in the prefrontal cortex to modulate aggressive acts that are triggered by anger provoking stimuli appears to play an important role. An imbalance between prefrontal regulatory influences and hyper-responsivity of the amygdala and other limbic regions involved in affective evaluation are implicated. Insufficient serotonergic facilitation of “top-down” control, excessive catecholaminergic stimulation, and subcortical imbalances of glutamatergic/gabaminergic systems as well as pathology in neuropeptide systems involved in the regulation of affiliative behavior may contribute to abnormalities in this circuitry. Thus, pharmacol...


title: GENERIC DETECTION AND CLASSIFICATION OF POLYMORPHIC MALWARE USING NEURAL PATTERN RECOGNITION

abstract: The obsolete way in which some anti-virus products are generating malware signatures, is provoking that polymorphic malware detection becomes a tedious problem, when actually it is not so hard. This paper describes the basics of a method by which the generic classification of polymorphic malware could be considered as a trivial issue .


title: Cloud Certification Process Validation Using Formal Methods

abstract: The importance of cloud-based systems is increasing constantly as they become crucial for completing tasks in an effective and affordable manner. Yet, their use is affected by concerns about the security of the data and applications provisioned through them. Security certification provides a means of increasing confidence in such systems, by establishing that they fulfil certain security properties of interest. Certification processes involve security property assessments against specific threat models. These processes may be based on self-assessment, testing, inspection or runtime monitoring of security properties, and/or combinations of such methods (hybrid certification). One important question for all such processes is whether they actually deliver what they promise. This question is open at the moment and is the focus of our work. To address it, we have developed an approach that formalises certification processes, by translating them in the language of the Prism model-checker and uses Prism to verify properties of interest on the model of the certification process, under specific environmental assumptions.


title: A COMPREHENSIVE HEAD POSE AND GAZE DATABASE

abstract: Within the past decade, many computational approaches have been developed to estimate gaze directions of persons based on their facial appearance. Most researchers used common face datasets with only a l imited representation of different head poses to train and verify their algorithms. Moreover, in most datasets, faces have neither a defined gaze direction, nor do they incor porate different combinations of eye gaze and head pose. Therefore, we recorded an extended dataset of 20 sub jects including faces in various combinations of head pos e and eye gaze leading to a total amount of 2220 colour i mages (111 per subject). The images were produced under controlled conditions, i.e. we used a technique to make sure that the subjects adjust their head and eyes appropriately. Furthermore, all images are manually labelled with landmarks indicating important featur es of the face. Finally, we evaluate the dataset with two computational methods for head pose and gaze estimation respectively.


title: Grid Drawings of Four-Connected Plane Graphs

abstract: A grid drawing of a plane graph G is a drawing of G on the plane so that all vertices of G are put on plane grid points and all edges are drawn as straight line segments between their endpoints without any edge-intersection. In this paper we give a very simple algorithm to find a grid drawing of any given 4-connected plane graph G with four or more vertices on the outer face. The algorithm takes time O(n) and needs a rectangular grid of width ⌈n/2⌉-1 and height ⌈n/2⌉ if G has n vertices. The algorithm is best possible in the sense that there are an infinite number of 4-connected plane graphs any grid drawings of which need rectangular grids of width ⌈n/2⌉ - 1 and height ⌈n/⌉e.


title: Hybrid OFDMA/CSMA Based Medium Access Control for Next-Generation Wireless LANs

abstract: Existing medium access control (MAC) schemes for wireless local area networks (WLAN) have been shown to lack scalability in crowded networks, and efficiency in supporting heterogeneous traffic types. These issues are mostly due to the use of random multiple access techniques in the MAC layer. The design of these techniques is highly linked to the choice of the underlying physical (PHY) layer technology. The advent of new PHY schemes that are based on orthogonal frequency division multiple access (OFDMA) provides new opportunities for devising more efficient MAC protocols. We propose a new adaptive MAC design based on OFDMA technology. The design uses OFDMA to reduce collision during transmission request phases, and makes channel access more predictable. To improve efficiency, we combine the OFDMA access with a carrier sense multiple access (CSMA) scheme. Data transmission opportunities are assigned through an access point that can schedule traffic streams in both time and frequency (subchannels) domains. We demonstrate the effectiveness of the proposed MAC and compare it to existing mechanisms through simulation experiments and by deriving an analytical model for the operation of the MAC in saturation mode.


Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory


title: Fast dense stereo correspondences by binary locality sensitive hashing

abstract: The stereo correspondence problem is still a highly active topic of research with many applications in the robotic domain. Still many state of the art algorithms proposed to date are unable to reasonably handle high resolution images due to their run time complexities or memory requirements. In this work we propose a novel stereo correspondence estimation algorithm that employs binary locality sensitive hashing and is well suited to implementation on the GPU. Our proposed method is capable of processing very high-resolution stereo images at near real-time rates. An evaluation on the new Middlebury and Disney high-resolution stereo benchmarks demonstrates that our proposed method performs well compared to existing state of the art algorithms.


title: CoCoA+: an advanced congestion control mechanism for CoAP

abstract: The Constrained Application Protocol (CoAP) has been designed by the Internet Engineering Task Force (IETF) for Internet of Things (IoT) devices. Due to the limited radio channel capacities and hardware resources of such devices, congestion can be a serious problem. CoAP addresses this important issue with a basic congestion control mechanism. CoCoA, an Internet-Draft proposal, introduced alternative congestion control mechanisms for CoAP. Yet, there has been limited evaluation of these congestion control mechanisms in the literature. In this paper, we assess the methods applied in CoCoA in detail and propose improvements to address the shortcomings observed in the congestion control mechanisms. We carry out simulations to compare the congestion control performance for default CoAP, CoCoA, and our new proposal, CoCoA+, in a variety of network topologies and use cases. The results show that CoCoA+ outperforms default CoAP and achieves better results than CoCoA in the majority of considered cases.


title: Reconstruction and Simulation of Neocortical Microcircuitry

abstract: We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm3 containing ∼31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ∼8 million connections with ∼37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies.


title: Rent3D: Floor-plan priors for monocular layout estimation

abstract: The goal of this paper is to enable a 3D “virtual-tour” of an apartment given a small set of monocular images of different rooms, as well as a 2D floor plan. We frame the problem as inference in a Markov Random Field which reasons about the layout of each room and its relative pose (3D rotation and translation) within the full apartment. This gives us accurate camera pose in the apartment for each image. What sets us apart from past work in layout estimation is the use of floor plans as a source of prior knowledge, as well as localization of each image within a bigger space (apartment). In particular, we exploit the floor plan to impose aspect ratio constraints across the layouts of different rooms, as well as to extract semantic information, e.g., the location of windows which are marked in floor plans. We show that this information can significantly help in resolving the challenging room-apartment alignment problem. We also derive an efficient exact inference algorithm which takes only a few ms per apartment. This is due to the fact that we exploit integral geometry as well as our new bounds on the aspect ratio of rooms which allow us to carve the space, significantly reducing the number of physically possible configurations. We demonstrate the effectiveness of our approach on a new dataset which contains over 200 apartments.


title: E-broidery: design and fabrication of textile-based computing

abstract: Highly durable, flexible, and even washable multilayer electronic circuitry can be constructed on textile substrates, using conductive yarns and suitably packaged components. In this paper we describe the development of e-broidery (electronic embroidery, i.e., the patterning of conductive textiles by numerically controlled sewing or weaving processes) as a means of creating computationally active textiles. We compare textiles to existing flexible circuit substrates with regard to durability, conformability, and wearability. We also report on: some unique applications enabled by our work; the construction of sensors and user interface elements in textiles; and a complete process for creating flexible multilayer circuits on fabric substrates. This process maintains close compatibility with existing electronic components and design tools, while optimizing design techniques and component packages for use in textiles.


title: Process monitoring on sequences of system call count vectors

abstract: We introduce a methodology for efficient monitoring of processes running on hosts in a corporate network. The methodology is based on collecting streams of system calls produced by all or selected processes on the hosts, and sending them over the network to a monitoring server, where machine learning algorithms are used to identify changes in process behavior due to malicious activity, hardware failures, or software errors. The methodology uses a sequence of system call count vectors as the data format which can handle large and varying volumes of data. ::: Unlike previous approaches, the methodology introduced in this paper is suitable for distributed collection and processing of data in large corporate networks. We evaluate the methodology both in a laboratory setting on a real-life setup and provide statistics characterizing performance and accuracy of the methodology.


title: The 10 Vs, Issues and Challenges of Big Data

abstract: In this emerging computing and digital globe, information and Knowledge are created and then collected with a rapid approach by wide range of applications through scientific computing and commercial workloads. Over 3.8 billion people out of 7.6 billion population of the world are connected to the internet. Out of 13.4 billion devices, 8.06 billion devices have a mobile connection. In 2020, 38.5 billion devices will be connected and globally internet traffic will be 92 times greater than it was in 2005. The use of such devices and internet not only increase the data volume but the velocity of market brings in fast-track and accelerates as information is transferred and shared with light speed on optic fiber and wireless networks. This fast generation of huge data creates numerous challenges. The existing approaches addressing issues such as, Volume, Variety, Velocity and Value in big data research perspective. The objectives of the paper are to investigate and analyze the current status of Big Data and furthermore a comprehensive overview of various aspects has discussed, and additionally has been described all 10 Vs' (Issues) of Big Data.


title: Rotation Equivariance and Invariance in Convolutional Neural Networks

abstract: Performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Many image classification tasks, such as those related to cellular imaging, exhibit invariance to rotation. We present a novel scheme using the magnitude response of the 2D-discrete-Fourier transform (2D-DFT) to encode rotational invariance in neural networks, along with a new, efficient convolutional scheme for encoding rotational equivariance throughout convolutional layers. We implemented this scheme for several image classification tasks and demonstrated improved performance, in terms of classification accuracy, time required to train the model, and robustness to hyperparameter selection, over a standard CNN and another state-of-the-art method.


title: Design and evaluation of Mina: A robotic orthosis for paraplegics

abstract: Mobility options for persons suffering from paraplegia or paraparesis are limited to mainly wheeled devices. There are significant health, psychological, and social consequences related to being confined to a wheelchair. We present the Mina, a robotic orthosis for assisting mobility, which offers a legged mobility option for these persons. Mina is an overground robotic device that is worn on the back and around the legs to provide mobility assistance for people suffering from paraplegia or paraparesis. Mina uses compliant actuation to power the hip and knee joints. For paralyzed users, balance is provided with the assistance of forearm crutches. This paper presents the evaluation of Mina with two paraplegics (SCI ASIA-A). We confirmed that with a few hours of training and practice, Mina is currently able to provide paraplegics walking mobility at speeds of up to 0.20 m/s. We further confirmed that using Mina is not physically taxing and requires little cognitive effort, allowing the user to converse and maintain eye contact while walking.