Miracle-dz/UniFAR-FTUs huggingface dataset:
Serological responses in humans to the smallpox vaccine LC16m8

In response to potential bioterrorism with smallpox, members of the Japanese Self-Defense Forces were vaccinated with vaccinia virus (VACV) strain LC16m8, an attenuated smallpox vaccine derived from VACV strain Lister. The serological response induced by LC16m8 to four virion-surface proteins and the intracellular mature virus (IMV) and extracellular enveloped virus (EEV) was investigated. LC16m8 induced antibody response against the IMV protein A27 and the EEV protein A56. LC16m8 also induced IMV-neutralizing antibodies, but unlike the VACV strain Lister, did not induce either EEV-neutralizing antibody or antibody to EEV protein B5, except after revaccination. Given that B5 is the only target for EEV-neutralizing antibody and that neutralization of both IMV and EEV give optimal protection against orthopoxvirus challenge, these data suggest that immunity induced by LC16m8 might be less potent than that deriving from strain Lister. This potential disadvantage should be balanced against the advantage of the greater safety of LC16m8.
The Dampening Effects of Perceived Teacher Enthusiasm on Class-Related Boredom: The Mediating Role of Perceived Autonomy Support and Task Value

Class-related boredom is commonly experienced by students and it has an impact on their learning engagement and achievements. Previous research has found that perceived teacher enthusiasm might contribute to reducing students’ class-related boredom. However, the mechanism through which perceived teacher enthusiasm affects class-related boredom remains unexplored. The purpose of the present study was to investigate the mediating role of perceived autonomy support and task value in the relationship between teacher enthusiasm and class-related boredom. College students (N = 734) completed questionnaires on perceived teacher enthusiasm, boredom proneness, perceived task difficulty, perceived autonomy support, perceived task value, and class-related boredom. Results showed that after controlling for the effects of demographic variables, boredom proneness, and perceived task difficulty, both perceived autonomy support and task value fully mediated the relationship between perceived teacher enthusiasm and class-related boredom. These findings suggest that students who perceive more teacher enthusiasm might perceive more autonomy support and task value, which in turn reduce the students’ class-related boredom. Limitations in the present study have also been discussed.
An Experimental Approach to speakers' Perspectives in a paired Wall Game

A new interactive "wall game" is proposed to study the emergence of rules and symbols in interaction dynamics. In this game, two human players alternately configure a pattern on a board to communicate with each other. Distinct from related studies, players in this game have no explicit game scores or tasks to optimize. Any dynamics occurring in this game are therefore ad-hoc and on-going processes. There were three major findings in this paper. (i) The subjects mainly interacted in two modes: a dynamic mode where players proceed through the game without assigning any meanings to the pattern, and a metaphoric mode, where players process with narrative reflection. (ii) Subjects spontaneously switch between the two modes, but this switching is suppressed when playing alone. (iii) A transition diagram of the board pattern can be used to label the two modes, e.g. linearity of the diagram is correlated with the metaphoric mode. One of the main features of grammar is to display subjects' intentionality in a systematic way. We argue that the switching between the two modes observed in our experiment can be taken as a grammatical aspect that emerged in the process. These modes express the speaker's perspective in the same manner as grammatical elements do in natural language. The switching behavior should be seen as a process that embodies a player's intention using the medium (in this case, the patterns in the wall game), and a player's exploration of the medium is a necessary step before generating a grammar structure.
High Glucose-Induced Increased Expression of Endothelin-1 in Human Endothelial Cells Is Mediated by Activated CCAAT/Enhancer-Binding Proteins

High glucose-induced endothelial dysfunction is partially mediated by the down-stream pathophysiological effects triggered by increased expression of endothelin-1 (ET-1). The molecular control mechanisms of ET-1 synthesis are yet to be discovered. Members of the CCAAT/enhancer-binding proteins (C/EBP) family are important regulators of key metabolic processes, cellular differentiation and proinflammatory genes. In this study, we aimed at elucidating the role of C/EBP in mediating the high glucose effect on ET-1 expression in human endothelial cells (EC). Human umbilical vein cells (EAhy926) and primary cultures of human aortic EC were exposed to high levels of glucose (16.5–25 mM). Real-time PCR, Western blot, enzyme-linked immunosorbent assay, ET-1 promoter-luciferase reporter analysis, and chromatin immunoprecipitation assays were employed to investigate ET-1 regulation. High glucose activated C/EBPα, C/EBPβ, and C/EBPδ in a dose-dependent manner. It also promoted significant increases in ET-1 gene and peptide expression. Chemical inhibition of JNK, p38MAPK and ERK1/2 diminished significantly the high glucose-induced nuclear translocation of C/EBP and ET-1 expression. Silencing of C/EBPα, C/EBPβ or C/EBPδ greatly reduced the high glucose-induced upregulation of ET-1 mRNA, pre-pro-ET-1, and ET-1 secretion. The expression of various C/EBP isoforms was selectively downregulated by siRNA-mediated gene silencing. In silico analysis indicated the existence of typical C/EBP elements within human ET-1 gene promoter. Transient overexpression of C/EBPα, C/EBPβ or C/EBPδ upregulated the luciferase level controlled by the ET-1 gene promoter. The direct interaction of C/EBPα, C/EBPβ or C/EBPδ proteins with the ET-1 promoter in high glucose-exposed EC was confirmed by chromatin immunoprecipitation assay. High glucose-induced ET-1 expression is mediated through multiple mechanisms. We present evidence that members of the C/EBP proinflammatory transcription factors are important regulators of ET-1 in high glucose-exposed human endothelial cells. High glucose-induced activation of C/EBP-related signaling pathways may induce excessive ET-1 synthesis, thus promoting vasoconstriction and dysfunction of the vascular wall cells in diabetes.
Globalization Affected Collective Bargaining ? An Empirical Test , 1980 ‐ 2009

Theoretical models predict that globalization changes the nature of collective bargaining. Yet, the extant empirical evidence is inconclusive. We investigate the influence of globalization on three aspects of collective bargaining (degree of decentralization, union density, extent of government intervention) in industrialized and transition countries. Our innovations over the existing empirical literature are (i) the reliance on a more comprehensive dataset (44 countries from 1980 to 2009) and (ii) the use of dynamic panel data estimators. We find that economic globalization has depressed unionization rates, while the extent of decentralization and government intervention in collective bargaining have not been affected. JEL classification: F66, J5, H11
Centre for Intelligent Systems and Their Applications Fuzzy-rough Data Reduction with Ant Colony Optimization Fuzzy-rough Data Reduction with Ant Colony Optimization Fuzzy-rough Data Reduction with Ant Colony Optimization

Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset pre-processor with much success, but current methods are inadequate at finding minimal reductions, the smallest sets of features possible. To alleviate this difficulty, a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, has been developed recently and has been shown to be effective. However, this method is still not able to find the optimal subsets regularly. This paper proposes a new feature selection mechanism based on Ant Colony Optimization in an attempt to combat this. The method is then applied to the problem of finding optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to complex systems monitoring and experimentally compared with the original fuzzy-rough method, an entropy-based feature selector, and a transformation-based reduction method, PCA. Comparisons with the use of a support vector classifier are also included. The authors and the University of Edinburgh retain the right to reproduce and publish this paper for non-commercial purposes. Permission is granted for this report to be reproduced by others for non-commercial purposes as long as this copyright notice is reprinted in full in any reproduction. Applications to make other use of the material should be addressed in the first instance to Copyright Permissions, Abstract Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset pre-processor with much success, but current methods are inadequate at finding minimal reductions, the smallest sets of features possible. To alleviate this difficulty, a feature …
Towards Green Business Process Management ( Pre-Publication Draft )

There is a global consensus on the need to reduce our collective carbon footprint. While much research attention has focused on developing alternative energy sources, automotive technologies or waste disposal techniques, we often ignore the fact that the ability to optimize (existing) operations to reduce their emissions impact is fundamental to this exercise. We believe that by transforming the problem into the domain of Business Process Management (BPM) we can leverage the rich expertise in this field to address issues associated with identifying areas for improvement, understanding the implication and performing carbon footprint minimization. We will use the term “Green BPM” to describe a novel class of technologies that leverage and extend existing BPM technology to enable process design, analysis, execution and monitoring in a manner informed by the carbon footprint of process designs and instances. This article describes the first steps in the development of this class of technologies.
Fractal Co-integration of RMB Exchange Rate and China's Stock Price

Nonlinear co-integration method is discussed. According to the generalized fractal co-integration relationship, one form of nonlinear co-integration is proposed based on GPH (Geweke, Porter-Hudak) method. Using the tests of the long-memory characteristic in finance time series, based on daily price series of RMB exchange rate and Shanghai stock markets from July 22, 2005 to April 24, 2009, empirical analysis of their relationship is performed with linear and nonlinear fractional co-integration method. As the results shown, Shanghai stock market and RMB/USD exchange rate series both show the characteristic of long-memory which implicates that China’s stock markets and RMB exchange rate are non-efficient. Furthermore, the empirical results ague that there tend to be a long-run linear equilibrium relationship and exist bidirectional causal relationship between China’s stock market and RMB exchange market.
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades

Understanding the diffusion of information in social networks and social media requires modeling the text diffusion process. In this work, we develop the Hawkes Topic model (HTM) for analyzing text-based cascades, such as "retweeting a post" or "publishing a follow-up blog post. " HTM combines Hawkes processes and topic modeling to simultaneously reason about the information diffusion pathways and the topics characterizing the observed textual information. We show how to jointly infer them with a mean-field variational inference algorithm and validate our approach on both synthetic and real-world data sets, including a news media dataset for modeling information diffusion, and an ArXiv publication dataset for modeling scientific influence. The results show that HTM is significantly more accurate than several baselines for both tasks.
A novel opportunistic routing protocol applied to vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) applied to specific scenarios are kinds of opportunistic networks. Introducing currently opportunistic routing protocols and actual VANETs characteristics, the paper proposed an opportunistic routing protocol based on the comprehensive utilities of forwarding nodes. The protocol conducted a dimensionless method to deal with these factors including the distance between the forwarding and the destination, the direction arriving the destination, the forwarding velocity vector and traffic conditions. It adopted an eigenvector method to determine each factor weight, and used a weighted sums method to compose each effect factor. In routing process, the node would select the highest utility node to propagate packets. It would use a store-carryforward strategy to transmit packets in spare networks. By means of the NCTUns on the basis of the electronic map of Lanzhou, simulating the working process of the new routing protocol and the testing results indicate that the protocol could apply better to various actual VANETs scenarios.
A robust state-space kinetics-guided framework for dynamic PET image reconstruction

Dynamic PET image reconstruction is a challenging issue due to the low SNR and the large quantity of spatio-temporal data. We propose a robust state-space image reconstruction (SSIR) framework for activity reconstruction in dynamic PET. Unlike statistically-based frame-by-frame methods, tracer kinetic modeling is incorporated to provide physiological guidance for the reconstruction, harnessing the temporal information of the dynamic data. Dynamic reconstruction is formulated in a state-space representation, where a compartmental model describes the kinetic processes in a continuous-time system equation, and the imaging data are expressed in a discrete measurement equation. Tracer activity concentrations are treated as the state variables, and are estimated from the dynamic data. Sampled-data H∞ filtering is adopted for robust estimation. H∞ filtering makes no assumptions on the system and measurement statistics, and guarantees bounded estimation error for finite-energy disturbances, leading to robust performance for dynamic data with low SNR and/or errors. This alternative reconstruction approach could help us to deal with unpredictable situations in imaging (e.g. data corruption from failed detector blocks) or inaccurate noise models. Experiments on synthetic phantom and patient PET data are performed to demonstrate feasibility of the SSIR framework, and to explore its potential advantages over frame-by-frame statistical reconstruction approaches.
A subspace weighting kernel method combining clustering-based grouping for feature extraction in hyperspectral imagery classification

High dimensionality of hyperspectral data and relatively limited training samples induce the Hughes phenomenon in hyperspectral image classification. To prevent this problem and decrease the computational cost, feature extraction often acts as pre-processing. In this paper, a subspace weighting kernel method combining clustering-based grouping is proposed for feature extraction in hyperspectral imagery classification. In the proposed method, spectral bands of hyperspectral data are firstly grouped into subspaces and a subspace-modulated kernel principal component analysis (SM-KPCA) is given for feature extraction, where the modulated kernel is determined by classification-oriented schemes. Support vector machine (SVM) classifier is performed on the extracted features to validate the performance. Experiments are conducted on real data and the results prove that the proposed SM-KPCA is effective on feature extraction for improving the accuracy of hyperspectral classification.
What is Workplace Incivility? An Investigation of Employee Relational Schemas

ABSTRACT This qualitative study investigated relational schemas of workplace incivility to develop an understanding of incivility from the employee perspective, refine theoretical comprehension of the incivility construct, and begin collecting data to support development of applied interventions. Two hundred and five currently employed respondents answered open-ended survey questions about schematic features of incivility and normative questions about how it should be addressed at work. Responses were analyzed with phenomenological methodology. Workplace incivility was defined in relational schemas similarly to current scholarly conceptualization, but respondents’ examples of incivility included more severe mistreatment as well. Perpetrators, targets, bystanders, and interveners were identified as the primary roles engaged with incivility. Respondents called for active prevention and intervention against incivility, directed by organizational leadership. Rich segments of illustrative qualitative data are presented throughout the article. Researchers should note results indicating construct proliferation in workplace mistreatment literature. Practitioners should consider the roles of leaders and bystanders in addressing workplace incivility.
Individual Differences in Cognitive, Musical, and Perceptual Abilities

sparked my interested in this research and who have remained a constant source of information throughout the study. I also wish to thank Shelly Bland for her hard work and assistance in gathering and entering the data for this study. I would like to thank the Attention and Working Memory Lab at Georgia Tech for the use of their cognitive tasks and for their assistance in contacting participants for this research. I would also like to thank the faculty and staff of the Georgia Institute of Technology whose advice and assistance has been invaluable throughout this research. Lastly, I would like to thank my husband, Bradley, for his constant support and patience throughout this project.
A Stakeholder View of Corporate Governance

This conceptual paper questions traditional measures of corporate governance. In considering only the shareholder, the relative power, urgency and legitimacy of the other stakeholders are ignored. The timing and particular context are also not attended to by governance measures. We propose corporate governance be viewed as a dynamic process, evolving in response to diverse stakeholder expectations, to enhance firm reputation.
Switching hybrid for cold-starting context-aware recommender systems

Finding effective solutions for cold-starting Context-Aware Recommender Systems (CARSs) is important because usually low quality recommendations are produced for users, items or contextual situations that are new to the system. In this paper, we tackle this problem with a switching hybrid solution that exploits a custom selection of two CARS algorithms, each one suited for a particular cold-start situation, and switches between these algorithms depending on the detected recommendation situation (new user, new item or new context). We evaluate the proposed algorithms in an off-line experiment by using various contextually-tagged rating datasets. We illustrate some significant performance differences between the considered algorithms and show that they can be effectively combined into the proposed switching hybrid to cope with different types of cold-start problems.
Sectoral Shocks, Reallocation Frictions, and Optimal Government Spending

What is the optimal policy response to a negative sectoral shock? How do frictions in goods and labor markets affect the nature and speed of the process of reallocating resources across alternative uses? Should government controlled inputs be allocated to compensate for frictions faced by the private sector or, rather, should they be deployed to complement private sector decisions? In this paper we make a first attempt to understand what features of an economy determine the answers to the previous questions. We study a model in which the drop in the private demand for structures frees up resources that can be used to produce government capital. For a reasonable calibration, we find that government spending increases in response to the drop in private demand, but that the size of the increase is inversely related to the level of frictions: the 1 larger the costs that the economy faces to reallocate resources (capital and labor) across sectors, the smaller the optimal level of government spending.
A new Wold ordering for image similarity

The problem of measuring perceptual similarity between images is addressed using a new image model based on the Weld decomposition. The model permits separate treatment of image components which correspond approximately to periodicity, directionality, and randomness. The authors compare its performance in an image search application to two other methods-one based on shift-invariant principle components and one based on a multiscale simultaneous autoregressive model. When textured images are ordered by distances between their Wold components, the results appear to be much closer to the human perception of similarity. The authors discuss how decoupling the three components can increase flexibility for measuring image similarity and can save computation, permitting the "quickest" matches when the features are the most perceptually "salient". <<ETX>>
Assessing the Proportionality Assumption in Default Rate Forecasting Using the Proportional Hazard Model

The first generation quantitative bankruptcy prediction models are not designed to accommodate the time dimension. To overcome this limitation, various dynamic models based on survival analysis are developed recently. Among them, Cox (1972)’s proportional hazard model has been widely used in various fields because of the advantage of being free of distributional assumptions. The proportionality assumption, however, must be applied precisely when there is a potential structural change. In this paper, we assess the violation of proportionality assumption in the firm failure prediction model built around the Cox’s proportional hazard model and proposed non-proportional hazard model. We also examine the effect of macroeconomic variables to suggested non-proportional hazard model. We perform an investigation using the Korean stock market since the market, which has experienced two well-known structural changes caused by the Asian financial crisis and 2008 Global financial crisis, is well suited for analyzing the impact of the proportionality assumption on the appropriateness and predictability of the Cox’s proportional hazard model. It is shown that a non-proportional hazard model including a change point is a proper alternative, when the proportionality assumption is violated.