Miracle-dz/UniFAR-FTUs huggingface dataset:
Susceptibility and Response of Human Blood Monocyte Subsets to Primary Dengue Virus Infection

Human blood monocytes play a central role in dengue infections and form the majority of virus infected cells in the blood. Human blood monocytes are heterogeneous and divided into CD16− and CD16+ subsets. Monocyte subsets play distinct roles during disease, but it is not currently known if monocyte subsets differentially contribute to dengue protection and pathogenesis. Here, we compared the susceptibility and response of the human CD16− and CD16+ blood monocyte subsets to primary dengue virus in vitro. We found that both monocyte subsets were equally susceptible to dengue virus (DENV2 NGC), and capable of supporting the initial production of new infective virus particles. Both monocyte subsets produced anti-viral factors, including IFN-α, CXCL10 and TRAIL. However, CD16+ monocytes were the major producers of inflammatory cytokines and chemokines in response to dengue virus, including IL-1β, TNF-α, IL-6, CCL2, 3 and 4. The susceptibility of both monocyte subsets to infection was increased after IL-4 treatment, but this increase was more profound for the CD16+ monocyte subset, particularly at early time points after virus exposure. These findings reveal the differential role that monocyte subsets might play during dengue disease.
Edema Disease Caused by a Clone of Escherichia coli O147

ABSTRACT Edema disease is a systemic disease of weaned pigs caused by host-adapted strains of Escherichia coli, most commonly belonging to serogroup O138, O139, or O141. In the late 1990s, E. coli O147 strains containing the virulence genes f18, sta, stb, and stx2 were recovered from outbreaks of edema disease in the United States. Pulsed-field gel electrophoresis (PFGE) was used to determine that the majority of these strains (34/43) were closely related to one another. Subsequent analysis by multilocus restriction typing confirmed the PFGE results and indicated that the cluster of edema disease strains were only distantly related to other E. coli O147 strains. Serogrouping of edema disease isolates from the Iowa State University Veterinary Diagnostic laboratory recovered between 1996 and 2000 indicated that 42% belonged to serogroup O147. Our data suggest that these strains may be a common serotype of edema disease-causing E. coli in the United States.
An Area Efficient of 0.187Mu LNA Using Power Constraint Method

This paper presents the design for Low Noise Amplifier (LNA) using Silterra 0.18Mu technology and it based on W-CDMA standard application. The LNA function is to amplify extremely low noise signal without adding noise and also preserving required signal to noise ratio of the system at extremely low power level. Cadence design tool is used to optimize the simulation performance based on resistor and capacitor distributed. The used of power constrained method to provide an area-efficient architecture for effective design at Silterra 0.18Mu CMOS technology. Result for S11 is 13.12dB and the frequencies are at 2.08GHz while S21 is observed to be -20dB and these values show that the resistance at the input of LNA is very close to 503.
Evolutionary Ensembles with Negative Correlation Learning : Improving Speciation

The EENCL algorithm has proved to be a successful mechanism for evolving an ensemble of classifiers on a number of problems when compared to single learner systems. This paper extends this analysis by providing an empirical comparison of classification rates with several other ensemble techniques: Bagging, Negative Correlation Learning and independent training. We find that EENCL gives significantly lower classification accuracy than these algorithms on some of the tested datasets, but remains of interest due to the potential to evolve more than just network weights. We suggest that implicit fitness-sharing in EENCL is partly to blame, and find that a similar method with an island model speciation technique (INCL), makes up for some of the shortfall in EENCL, proving comparable to the other tested algorithms on all but one dataset. This paper analyses the bias-variance-covariance decomposition of both EENCL and INCL to show that INCL is more effective at reducing covariance in the ensemble, and that this accounts for the improvement in performance.
SCHEDULING JOBS ON A SINGLE BPM WITH NON-AGREEABLE RELEASE TIME AND DUE

Number: 025-1195 SCHEDULING JOBS ON A SINGLE BPM WITH NON-AGREEABLE RELEASE TIME AND DUE DATES TO MINIMIZE MAKESPAN Ganeshkumar Palanichamy WIPRO Technologies Electronic city Bangalore – 560 100 Email: ganeshkumar.palanichamy@wipro.com and M Vimalarani No. 6 Muniraju Building Shikaripalya, Hulimangala Post Anekal Taluk, Bangalore 562 106 Email: vimala.ganeshkumar@yahoo.com POMS 23rd Annual Conference Chicago, Illinois, U.S.A. April 20 to April 23, 2011 ABSTRACT This paper proposes a research problem of scheduling jobs on single batch processing machine with non-agreeable release time and due dates with the objective of minimizing makespan. Our aim in the present work is to develop an efficient algorithm from the set of heuristic algorithm for solving the batch processor problem. The efficiency of the developed algorithm is then tested through extensive computational experiments and the results will be presented. Our experimentation shows that the proposed algorithm is giving promising results compared to the best of the available algorithm.
A newsvendor pricing game

This paper considers a horizontal market of multiple firms that face stochastic price-dependent demand. The firms make joint pricing/inventory decisions and use price to compete for market demand. With fairly general demand models that are price-dependent, stochastic, and substitutable among firms, we prove the existence and uniqueness of the pure-strategy Nash equilibrium. The market at the equilibrium exhibits a bias toward under-pricing caused by competition; specifically, raising prices at any equilibrium of the game increases the total system profit, and at any joint-optimal set of pricing levels each self-interested firm has an incentive to lower its price. This result closely parallels that obtained in the inventory competition games in which prices are fixed and the bias is toward overstocking.
Commentary: Sorting the wheat from the chaff: identifying demyelinating components of the myelin oligodendrocyte glycoprotein (MOG)‐specific autoantibody repertoire

Myelin oligodendrocyte glycoprotein (MOG) is the only myelin protein known to initiate a demyelinating autoantibody response in EAE, an animal model for multiple sclerosis (MS). The pathophysiological significance of MOG‐specific autoantibodies in MS is, however, controversial, as high titer antibody responses to MOG are also found in many patients with non‐demyelinating neurological diseases. In this issue of the European Journal of Immunology, von Büdingen et al. demonstrate that demyelination in a primate model of MOG‐induced EAE is mediated by MOG‐specific antibodies directed against discontinuous, rather than linear, MOG epitopes. This functional segregation of pathogenic vs. non‐pathogenic autoantibodies in terms of epitope specificity may be crucial to understand the relevance of MOG‐specific responses in human disease. This commentary discusses these findings in the context of the structure and immunobiology of MOG, and their implications with respect to antibody‐mediated demyelination in MS.
Handwritten character recognition based on SVMs for a multiplication table game

In this paper, we present a multiplication table game that automatically detects and recognizes handwritten digits written on the white board. The result of the multiplication of two numbers would not only be displayed on the monitor, but it would be also spoken by the Text To Speech (TTS). The proposed application is divided into three stages: 1) detect a white board; 2) segment candidate regions of handwritten characters on the white board; 3) recognition using SVMs. In the experiment, the MNIST database of handwritten digits is used to evaluate the performance of the proposed application and very promising results are achieved.
Embodied Cognition In Auditory Display

This paper makes a case for the use of an embodied cognition framework, based on embodied schemata and cross-domain mappings, in the design of auditory display. An overview of research that relates auditory display with embodied cognition is provided to support such a framework. It then describes research efforts towards the development this framework. By designing to support human cognitive competencies that are bound up with meaning making, it is hoped to open the door to the creation of more meaningful and intuitive auditory displays.
Using predictive analytics to target and improve first year student attrition

This article reports on the development and implementation of the Student Success Program, a strategic, University-wide, consistent approach to increasing the success and retention of first year students, during 2014. To this end, a centrally coordinated process integrated historic student, application, enrolment, academic performance and learning management data in a data warehouse. These data were used to build chi-squared automatic iterative detection (CHAID) decision tree models aimed at predicting each student’s risk of attrition. Predictions were made multiple times per year before peak attrition time points to allow for changes in student behaviour and availability of new data. An intervention using peer-to-peer phone-call communication targeted students with the largest predicted risks, to offer support, foster retention and enable a successful outcome in higher education. The accuracy of the chi-squared automatic iterative detection models benefited most from the inclusion of data representing first year student study behaviours.
Proactive malware collection and classification system: How to collect and classify useful malware samples?

To understand malware behaviors, collecting and classifying malware samples is a critical issue for system security researchers. This paper aims to develop Proactive Malware Collection and Classification System (PMCCS), which consists of Proactive Malware Collection Unit (PMCU) and Automatic Malware Classification Unit (AMCU). To collect useful samples, PMCU uses P2P software actively search suspicious samples, such as software crack tools. During a 3-year period, PMCU has collected 42300 samples. To automatically classify useful samples, AMCU uploads suspicious samples to VirusTotal, a free online virus scanner. Based on VirusTotal scanning results, 11600 suspicious samples have been alerted at least once by AntiVirusWare (AVW) and 70% of these samples are Trojans and Virus tools, which are usually threatening malwares. Moreover, these suspicious 11600 samples are classified into: Blacklist with high suspiciousness; Ambitious list with moderate suspiciousness; Whitelist with low suspiciousness. Blacklist can be used to evaluate the performance of AVW based on False Negative (FN). On the other hand, Whitelist can be used to evaluate the performance of AVW based on False Positive (FP). From Blacklist and Whitelist, AMCU selects useful malwares, which triggering high counts of FN and FP against AVW.
Review of Language, Mind and Brain: Some Psychological and Neurological Constraints on Theories of Grammar, Ewa Dabrowska;

This book presents a comprehensive survey of the various subfields of linguistics, as envisaged from a cognitive grammar perspective. It is divided into two parts; the first half is the ‘‘basic specifications’’: issues related to psycholinguistics, acquisition, language and the brain, and evolution of language. The second part elaborates on the ‘‘building blocks’’ of language, namely words, sentences, meaning, et cetera ending finally with ‘‘a crash course in cognitive grammar’’. Broadly, the book provides some critical insights into the existing rationalist trend in linguistics – the generative approach – and suggests alternatives to developing ‘‘psychologically realistic’’ grammatical models. One underlying theme of the book is the relevance of interdisciplinary research and its benefits for linguistic theorizing. As the author notes in her introduction, that though linguistics is often defined as ‘the branch of cognitive science dealing with language’, ‘‘most linguists hasten to add, it is an ‘independent branch’, by which they mean that language can, and should, be studied in its own terms, without reference to psychological or neurological concepts and theories’’ (p. 1). This problem, as the author laments, becomes more telling with generativist linguists’ obsession with linguistic competence rather than performance. This lopsidedness begets linguistic theories which are neither psychologically nor biologically real, thus endangering the (apparently) more desirable course of the discipline toward becoming an empirical science – a science whose theories base on actual usage of language, rather than being motivated by formal elegance.
Towards the determination of typical failure patterns

One possibility to make testing strategies more effective is to incorporate knowledge about the typical geometric structure of failure-causing inputs within the input domain into the test data selection. For example Adaptive Random Testing is a testing strategy which is based on the idea of failure-causing inputs being clustered within the input domain. So far, there has been no empirical quantification about the location and the geometric shape of failure-causing inputs. Thus it is currently unknown whether the encouraging results gained by Adaptive Random Testing hold true in general. This work aims at introducing an approach which makes it possible to verify the assumption of clustered failure patterns. Possibly it furthermore enables the improvement of current Adaptive Random Testing methods and the development of further black box testing strategies incorporating knowledge about location and shape of failure patterns into test data selection. Therefore metrics for location and shape of failure patterns are specified. They are based on methods from image analysis.
Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?

Diagnosis and individualized treatment of autism spectrum disorder (ASD) represent major problems for contemporary psychiatry. Tackling these problems requires guidance by a pathophysiological theory. In this paper, we consider recent theories that re-conceptualize ASD from a “Bayesian brain” perspective, which posit that the core abnormality of ASD resides in perceptual aberrations due to a disbalance in the precision of prediction errors (sensory noise) relative to the precision of predictions (prior beliefs). This results in percepts that are dominated by sensory inputs and less guided by top-down regularization and shifts the perceptual focus to detailed aspects of the environment with difficulties in extracting meaning. While these Bayesian theories have inspired ongoing empirical studies, their clinical implications have not yet been carved out. Here, we consider how this Bayesian perspective on disease mechanisms in ASD might contribute to improving clinical care for affected individuals. Specifically, we describe a computational strategy, based on generative (e.g., hierarchical Bayesian) models of behavioral and functional neuroimaging data, for establishing diagnostic tests. These tests could provide estimates of specific cognitive processes underlying ASD and delineate pathophysiological mechanisms with concrete treatment targets. Written with a clinical audience in mind, this article outlines how the development of computational diagnostics applicable to behavioral and functional neuroimaging data in routine clinical practice could not only fundamentally alter our concept of ASD but eventually also transform the clinical management of this disorder.
Face Perception and Perceptual Expertise in Adult and Developmental Populations

Both developmental and adult investigations of perception unequivocally suggest that experience shapes perceptual abilities beginning early in development and throughout the lifespan (e.g. Gauthier and Nelson, 2001 ; McKone et al., 2007 ; Bukach et al., 2006 ; Scott and Monesson, 2009 ; Scott et al., 2007 ). Investigators studying perceptual expertise typically consider how domain-general cognitive and neural mechanisms are recruited when individuals learn about and become experts at recognizing and distinguishing visual categories, including faces and objects (Bukach et al., 2006 ). The term perceptual expertise has been used in various ways, but generally refers to an increased ability to discriminate and recognize among exemplars within some categories of visual stimuli relative to others. Investigations of perceptual expertise are usually designed to uncover the underlying representational and neural changes accompanying the acquisition of expertise using a variety of methods (e.g. behavioral measures of accuracy and reaction time, functional magnetic resonance imaging (fMRI), event-related potentials (ERPs), magnetoencephalography (MEG), eye-tracking, and computational modeling) and with various study designs and populations (e.g. real-world experts, training studies, clinical populations, developmental populations, and non-human primates). This interdisciplinary and multimethod approach has significantly contributed to understanding how humans, animals, and computers solve complex visual problems. The present chapter will review how behavioral methods, ERPs, and fMRI have been used to understand the acquisition of perceptual expertise in both adult and developmental populations. As will be discussed further, in the context of specific studies reviewed below, behavioral tasks designed to assess perceptual expertise in adults include: perceptual discrimination and matching, categorization and recognition memory tasks (e.g. Tanaka and Taylor, 1991 ; Gauthier et al., 1998 ; Scott et al., 2006a ). In infants, visual preference tasks are used to measure their ability to discriminate stimuli (e.g. Pascalis et al., 2002 , 2005 ; Kelly et al., 2007 ; Scott and Monesson, 2009 ). In both adults and infants, ERPs are used to assess neural responses before and after training or in experts. ERPs are recorded non-invasively from a net or cap of recording electrodes placed on the head and have very high temporal resolution. ERP amplitude and latency responses reflect the activity of simultaneously active populations of neurons in the cerebral cortex. This activity results in electrical signals that propagate up to the scalp and can be recorded in response to the presentation of images or sounds. Researchers typically examine two ERP components, the N170 and the N250, when adults view faces, trained objects, or objects of expertise (e.g. Tanaka and Curran 2001 ; Scott et al., 2006a , 2008 ; Tanaka and Pierce, 2009 ). Analogous infant components, C11.S1
A Study of Heterogeneous Computing Design Method based on Virtualization Technology

One challenge for the heterogeneous computing with the FPGA is how to bridge the development gap between SW and HW designs. The high level synthesis (HLS) technique allows producing hardware with high level languages like C. Design tools based on the HLS like Xilinx SDSoC and SDAccel are developed to speedup SW/HW co-designs. However, the developers still require much circuit design skills to use these tools more efficiently. In this paper, we propose a heterogeneous computing platform based on the virtualization technology, namely hCODE.With the help of the virtualization, the HW and SW design can be totally separated. This brings multiple benefits like accelerating a program without modifying or recompiling it, enable high portability and scalability across different HW and operating system.
Finite-Difference Approximations to the Heat Equation

This article provides a practical overview of numerical solutions to the heat equation using the finite difference method. The forward time, centered space (FTCS), the backward time, centered space (BTCS), and Crank-Nicolson schemes are developed, and applied to a simple problem involving the one-dimensional heat equation. Complete, working Matlab codes for each scheme are presented. The results of running the codes on finer (one-dimensional) meshes, and with smaller time steps is demonstrated. These sample calculations show that the schemes realize theoretical predictions of how their truncation errors depend on mesh spacing and time step. The Matlab codes are straightforward and allow the reader to see the differences in implementation between explicit method (FTCS) and implicit methods (BTCS and Crank-Nicolson). The codes also allow the reader to experiment with the stability limit of the FTCS scheme.
Fast Partial Subset Convolution for Steiner Tree Problem

We present a new algorithm that solves the minimum Steiner tree problem on an undirected graph with n nodes, k terminals, and m edges with bounded integer weights in time Õ((2− ε)n + nm). Our algorithm beats the aesthetically appealing and seemingly inherent 2 bound of the current best algorithm for Steiner tree problem from Björklund, Husfeldt, Kaski, and Koivisto. Our algorithm is based on fast subset convolution and a variant of the classical tree-separator theorem.
The Inverted U‐Shaped Hypothesis and Firm Environmental Responsiveness: The Moderating Role of Institutional Alignment

The impact of regulatory coercion on firm environmental responsiveness is well discussed by institutional theorists. The intuitive nature of the relationship is positive and monotonic, that is, the continuous strengthening of regulatory coercion prompts top management to be more environmentally responsive. This paper shows that: (1) overall, there is an inverted U-shaped relationship between regulatory coercion and firm environmental responsiveness, that is, the continuous strengthening of regulatory coercion induces top management to bring their firms' environmental responsiveness up to a certain optimum level beyond which its ability to trigger more proactive and substantive environmental responsiveness begins to decelerate, while reactive and symbolic strategic conformity accelerates; (2) perceived institutional (mis)alignment moderates the inverted U-shaped relationship between regulatory coercion and firm environmental responsiveness. Finally, results show that the moderated inverted U-shaped hypothesis advances the long-standing and contentious debate about the relationship between regulatory coercion and firm environmental responsiveness.
We made this bed … now look who’s lying in it

In considering the ‘Populist Responses to Austerity and Cultural Change: Brexit, Trumpism and Beyond’, there is a temptation, even a degree of comfort, in depicting 2016 as a kind of deviant moment in which non-elite anger and anxiety fueled an outburst of anti-progressive energy and the electoral victory of demagogues. The proposition put forth in this essay is that leadership scholarship is itself culpable. Transformational leadership, the dominant theme of that discourse, relies on the individual leader as an exceptional individual capable, even obliged, to reshape the worldview of followers in order to align them with the leader’s approach. By favoring an appeal to emotions over a rational exchange of positions, by assuming the superiority of one set of values over any others, by denying the validity of differing and/or conflicting interests, and by asserting authority based on hierarchical position rather than informed choice and consent, transformational leadership veered closely to our understanding of demagogues. Unintended though it may be, the dominant discourse becomes engrained as a ‘regime of truth’ into a pervasive, highly problematic social norm. Leadership scholars must accept a degree of responsibility and reconsider romantic notions of leadership in order to advance alternatives, not only to Trump and his particular agenda but also to the problematic assumptions embedded in and wider effects entwined with leadership thought.