Miracle-dz/UniFAR-FTUs huggingface dataset:
Abnormal Interactions between Perifollicular Mast Cells and CD8+ T-Cells May Contribute to the Pathogenesis of Alopecia Areata

Alopecia areata (AA) is a CD8+ T-cell dependent autoimmune disease of the hair follicle (HF) in which the collapse of HF immune privilege (IP) plays a key role. Mast cells (MCs) are crucial immunomodulatory cells implicated in the regulation of T cell-dependent immunity, IP, and hair growth. Therefore, we explored the role of MCs in AA pathogenesis, focusing on MC interactions with CD8+ T-cells in vivo, in both human and mouse skin with AA lesions. Quantitative (immuno-)histomorphometry revealed that the number, degranulation and proliferation of perifollicular MCs are significantly increased in human AA lesions compared to healthy or non-lesional control skin, most prominently in subacute AA. In AA patients, perifollicular MCs showed decreased TGFβ1 and IL-10 but increased tryptase immunoreactivity, suggesting that MCs switch from an immuno-inhibitory to a pro-inflammatory phenotype. This concept was supported by a decreased number of IL-10+ and PD-L1+ MCs, while OX40L+, CD30L+, 4–1BBL+ or ICAM-1+ MCs were increased in AA. Lesional AA-HFs also displayed significantly more peri- and intrafollicular- CD8+ T-cells as well as more physical MC/CD8+ T-cell contacts than healthy or non-lesional human control skin. During the interaction with CD8+ T-cells, AA MCs prominently expressed MHC class I and OX40L, and sometimes 4–1BBL or ICAM-1, suggesting that MC may present autoantigens to CD8+ T-cells and/or co-stimulatory signals. Abnormal MC numbers, activities, and interactions with CD8+ T-cells were also seen in the grafted C3H/HeJ mouse model of AA and in a new humanized mouse model for AA. These phenomenological in vivo data suggest the novel AA pathobiology concept that perifollicular MCs are skewed towards pro-inflammatory activities that facilitate cross-talk with CD8+ T-cells in this disease, thus contributing to triggering HF-IP collapse in AA. If confirmed, MCs and their CD8+ T-cell interactions could become a promising new therapeutic target in the future management of AA.
FTY720 Protects Cardiac Microvessels of Diabetes: A Critical Role of S1P1/3 in Diabetic Heart Disease

Background: Diabetes is associated with an increased risk of cardiac microvascular disease. The mechanisms by which this damage occurs are unknown. However, research suggests that signaling through the sphingosine-1-phosphates receptor 1 and 3 (S1P1/3) by FTY720, a sphiongolipid drug that is structually similar to SIP, may play a role in the treatment on cardiac microvascular dysfunction in diabetes. We hypothesized that FTY720 might exert the cardioprotective effects of S1P1 and S1P3 viaprotein kinase C-beta (PKCβ II) signaling pathway. Methodology/Principal Findings: Transthoracic echocardiography was performed to detect the change of cardiac function. Scanning and transmission electron microscope with lanthanum tracer were used to determine microvascular ultrastructure and permeability in vivo. Apoptosis was detected by TUNEL and CD31 dual labeling in paraffin-embedded sections. Laser capture miscrodissection was used to assess cardiac micovascular endothelial cells (CMECs) in vivo. RT-PCR and Western blot analysis were used to determine the mRNA levels and protein expression of S1P1, S1P3, and PKCβ II. In the diabetic rats vs. controls, cardiac capillaries showed significantly higher density; CD31 positive endothelial cells were significantly reduced; the apoptosis index of cardiac endothlial cells was significantly higher. And FTY720 could increase the expressional level of S1P1 and boost S1P3 trasnslocation from membrane to nuclear, then ameliorate cardiac microvascular barrier impairment and pathologic angiogenesis induced by diabetes. In addition, overexpression of PKCβ II significantly decreased the protective effect of FTY720. Conclusions: Our study represents that the deregulation of S1P1 and S1P3 is an important signalresponsible for cardiac microvascular dysfunction in diabetes. FTY720 might be competent to serve as a potential therapeutic approach for diabetic heart disease through ameliorating cardiac microvascular barrier impairment and pathologic angiogenesis, which might be partly dependent on PKCβII-mediated signaling pathway.
An Interlanguage Study of Thai EFL Learners’ Apology

Although a number of interlanguage studies on apologies have been conducted, there has been no study of apologies among Thai EFL learners that differentiates between learners with high and low levels of exposure to the target language. This study, differentiating between learners with high and low levels of exposure to the target language, addresses two research questions: (i) What are the similarities and differences between perception of offense context of Thai EFL learners and native speakers of English? (ii) What are the similarities and differences between apology strategies used by Thai EFL learners and native speakers of English? Data for the analysis came from a questionnaire consisting of 12 scenarios eliciting different offense contexts. The participants were divided into four groups: native speakers of English (NEs), native speakers of Thai (NTs), Thai EFL learners with high exposure to the target language (TEHs) and Thai EFL learners with low exposure to the target language (TELs). The results revealed that due to the influence of cultural background, the learners’ perception of offense context was dissimilar to the NEs in certain respects. However, the investigation of apology strategies showed that compared to the TELs, the TEHs’ apology strategies tended to be less influenced by their cultural background and more similar to the NEs’. Providing support for levels of exposure to the target language, as individual differences vary in L2 pragmatic development, the study sheds light on pedagogical intervention that may enhance learners’ pragmatic competence.
Distributed Modeling of Discrete Event Systems

Computer simulation is widely used to support the design of any kind of complex system and to create computer-generated "virtual worlds" where humans and/or physical devices are embedded (e.g. aircraft flight simulators [20]). However, both the generation of simulation models and the execution of simulations can be time and cost expensive. While there are already several ways to increase the speed of a simulation run, the scientific challenge for the simulation of complex systems still resides in the ability to model (simulate) those systems in a parallel/distributed way [35].
Video Pump Design For Interoperability With Set Top Units: The Case Against Small PDUs

The deployment of video services to the home will continue to push the development of low cost Digital Set Top Units (STUs) for decoding MPEG video. One technique for reducing cost is to minimize the bu er space in the network interface. This means that the STU will only accept small packets. Video pump design for this situation is complex when the video pump is based on a general purpose workstation. Traditional timing mechanisms cannot be used in this situation due to the high resolution required by the small packets. A novel video pump design is presented which meets the strict STU requirements. Scalability issues are addressed and performance results are presented.
Path-constrained partial music synchronization

Digital music collections often contain different versions and interpretations of a single musical work. In view of music retrieval and browsing applications, one important task, also referred to as audio synchronization, is to automatically time-align two given audio recordings of the same underlying piece. In this paper, we present a novel synchronization procedure, which can compute meaningful audio alignments even in the presence of structural variations. Such variations include the omission of repetitions, the insertion of additional parts (soli, cadenzas), or differences in the number of stanzas in popular, folk, or art songs. As one main contribution, we introduce the concept of path-constrained similarity matrices. This enables us to employ a flexible and efficiently computable partial matching procedure in the optimization step of our synchronization algorithm. Our overall strategy aims at aligning preferably long consecutive runs while avoiding an over-fragmentation of the audio material.
Practical Improvements to Simultaneous Computation of Multi-view Geometry and Radial Lens Distortion

This paper discusses practical issues related to the use of the division model for lens distortion in multi-view geometry computation. A data normalisation strategy is presented, which has been absent from previous discussions on the topic. The convergence properties of the Rectangular Quadric Eigenvalue Problem solution for computing division model distortion are examined. It is shown that the existing method can require more than 1000 iterations when dealing with severe distortion. A method is presented for accelerating convergence to less than 10 iterations for any amount of distortion. The new method is shown to produce equivalent or better results than the existing method with up to two orders of magnitude reduction in iterations. Through detailed simulation it is found that the number of data points used to compute geometry and lens distortion has a strong influence on convergence speed and solution accuracy. It is recommended that more than the minimal number of data points be used when computing geometry using a robust estimator such as RANSAC. Adding two to four extra samples improves the convergence rate and accuracy sufficiently to compensate for the increased number of samples required by the RANSAC process.
A Framework for Analyses of Use Case Descriptions

In this article a lightweight formalism based on events is introduced for use case descriptions. The behaviour of use cases is described precisely and used to check for equivalence of use cases. The definitions also allow the developer to explore various possible restructuring of use cases. A prototype system, based on the formal definitions, enabling the development team to analyse the descriptions has also been developed.
A Survey of Intelligent Language Tutoring Systems

Intelligent Languages Tutoring Systems (ILTSs) plays a significant role in evaluating students' answers through interaction with them. ILTSs implements Natural Language processing (NLP) techniques in order to allow free input of words and sentences. ILTSs have the capability of identifying the input errors and provide an immediate feedback along with the errors source. It has been observed that ILTSs were not surveyed intensively; the reason that motivates us to conduct this research. Some NLP recent trends such as Latent Sematic Analysis and entailment were demonstrated. Different ILTSs have been discussed with a dedicated section about the development of Arabic ILTSs. Arabic share many of its characteristics with Semitic and morphologically rich languages. In our presentation we point out new trends that have been emerged while conducting survey.
Comparison of bulk and SOI CMOS technologies in a DSP processor circuit implementation

Silicon-on-insulator (SOI) CMOS technologies are very attractive options for implementing high-speed digital integrated circuits for low-power applications. This paper presents the layout migration of a DSP processor chip from a 0.6 /spl mu/m bulk CMOS to a 0.5 /spl mu/m SOI CMOS technology. The layout migration and verification are described and the two CMOS designs are compared using two main criteria: circuit speed and average power consumption. For nominal supply voltages, the simulations suggest that the SOI circuit can operate at a speed of 98 MHz which is 51 % higher than that of the original (65 MHz). The average power consumption is 35 % lower in the SOI circuit by using 3.3 V and 35 MHz for both SOI and bulk CMOS designs.
Analysis of the Virus Dynamics Model Reveals That Early Treatment of HCV Infection May Lead to the Sustained Virological Response

Considerable progress has been made towards understanding hepatitis C virus, its pathogenesis and the effect of the drug therapy on the viral load, yet around 50% of patients do not achieve the sustained virological response (SVR) by the standard treatment. Although several personalized factors such as patients’ age and weight may be important, by mathematical modeling we show that the time of the start of the therapy is a significant factor in determining the outcome. Toward this end, we first performed sensitivity analysis on the standard virus dynamics model. The analysis revealed four phases when the sensitivity of the infection to drug treatment differs. Further, we added a perturbation term in the model to simulate the drug treatment period and predict the outcome when the therapy is carried out during each of the four phases. The study shows that while the infection may be difficult to treat in the late phases, the therapy is likely to result in SVR if it is carried out in the first or second phase. Thus, development of newer and more sensitive screening methods is needed for the early detection of the infection. Moreover, the analysis predicts that the drug that blocks new infections is more effective than the drug that blocks the virus production.
A Joint Analysis of the Term Structure of Credit Default Swap Spreads and the Implied Volatility Surface

This study analyzes the co-movements of the term structure of credit default swap (CDS) spreads and the implied volatility surface by performing a factor decomposition for both dynamics. In our joint analysis we compute the information flow between the credit and volatility factors, examine their contemporaneous interactions, and assess the effectiveness of cross-hedges. Using options and CDS data for U.S. and European indices, the credit market is found to be the main contributor to overall market innovations. Our methodology is parsimonious and captures the intrinsic relationships between both markets. The empirical study highlights cross-market linkages during the Global Financial Crisis. Factors with small associated eigenvalues can be of tremendous importance for effective cross-hedging. © 2013 Wiley Periodicals, Inc. Jrl Fut Mark
The State of Rough Sets for Database

The database mining problem is often cited as one of the most promising research topics in the elds of database systems and machine learning. Although many available machine learning algorithms are potentially applicable, real-world databases pose additional diiculties partly due to the nature of their contents. In this article, we describe the characteristic features of the database mining problem, a subset of data mining queries, and the approaches for designing a database mining environment. Then, in that context, we summarize the state of rough sets and present future directions.
A REPEATED-MEASUREMENTS MODEL FOR OVERSTRESSED DEGRADATION DATA

A repeated-measurements model with a functional structure in varlance-covariance is employed to analyze the degradation data collected from an accelerated life-test experiment. A transformation is utilized to permit the calculation of the generalized least-squares estimators and their covariance matrices by ordinary least-squares regression. The mean and percentile of the distribution of the time to failure at normal use condition are calibrated with a construction of their interval estimates. The techniques of the analysis of variance and the analysis of means are applied to compare several different types of materials. The model and estimation procedures are illustrated using life-test data from Takeda's and Stinebaugh's studies on the MOSFET's.
METHOD FOR LOCALIZATION OF HUMAN FACES IN COLOR- BASED FACE DETECTORS AND TRACKERS

This paper is devoted to problem of face localization in color images, more particularly, to the task of grouping pixels with high skin probability to form possible face candidate regions. Using connected components analysis for pixels grouping has a significant drawback, for the noise of skin color detection and non-face skin-colored objects can form connected regions of skin pixels with vividly not face-like shape. Two methods for extracting ellipse-shaped skin regions are described, both capable of successfully finding faces on images with considerable noise and in presence of other skin-colored objects.
Inter-Organizational Social Network Information Systems: Diagnosing and Design

v Chapter 1: Introduction . 1 1.1 Motivation . 2 1.2 Research Questions . 6 Chapter 2: Overview . 10 2.1 Problem Class and Gap Analysis . 10 2.2 A Typology (Mullarkey 2012) . 13 2.3 Emergent IO SNIS – Why? 17 2.4 Conceptual Foundations . 20 2.5 Social Networks . 23 2.6 Inter-Organizational Social Networks (IO SN) . 28 2.7 Organizing Purpose . 37 2.8 Resilient Trust . 40 2.9 Competitive Risk and Information Sharing . 43 2.10 Class of Innovative Artifact SNIS . 46 2.11 SNIS Emergence . 47 2.12 IP SNIS Design Elements . 49 2.13 Summary . 55 Chapter 3: Research Method . 59 3.1 Problem Formulation . 63 3.1.1 Identifying and Conceptualizing the Research Opportunity . 63 3.1.2 Inter-Organizational Social Network Theory . 65 3.1. 3 IP Social Networking Information Systems . 70 3.2 Relevance in an Inter-organizational Domain . 76 3.3 Summary . 79 Chapter 4: Data Collection and Analysis . 81 4.1 Session 1: Diagnosing and Knowledge Transfer . 83 ii 4.2 Session 2: Research Model Testing & Design Principle Emergence and Elaboration . 87 4.2.1 Design Principle 1: Organizing Purpose . 89 4.2.2 Design Principle 2: Resilient Trust . 92 4.2.3 Design Principle 3: Competitive Risk . 95 4.2.4 Design Principle 4: Efficient Connectivity . 98 4.2.5 Design Principle 5: Digital Proximity . 100 4.2.6 Design Principle 6: Recommender Algorithms . 101 4.2.7 Fully Elaborated IO SNIS Research Model . 103 4.3 Session 3: Test Innovativeness of Designed Artifact . 106 4.4 Summary . 111 Chapter 5: Results and Contributions . 113 5.1 Summary of Findings by Design Construct . 114 5.2 Contributions . 119 5.2.1 Innovative Artifact . 120 5.2.2 Innovative Research Model . 122 5.2.3 New Stream of IS research . 123 5.3 Limitations and Boundary Conditions . 125 5.6 Future Research Directions . 128 5.5 Summary . 129 References . 131 Appendix 1a: The Action Design Research (ADR) Method (ADR) . 141 Appendix 1b: The Cyclical Process of Action Research (Susman & Evered, 1978) . 142 Appendix 2: Researcher-Practitioner Interactive ADR Session 1 . 143 Appendix 3: Researcher-Practitioner Interactive ADR Session 2 . 144 Appendix 4: Evaluation for Existing Online Inter-Organizational Networking Web Sites Against Target IO SNIS Session 3 . 145 Appendix 5: Example Evaluation of www.axial. net . 146 Appendix 6: Literature Review . 147 Appendix 7: IRB Determination Letter . 156
Crowd Truth: Harnessing disagreement in crowdsourcing a relation extraction gold standard

Copyright is held by the author/owner(s). WebSci-13, May 2-4, 2013, Paris, France. ACM 978-1-4503-1889-1. Abstract One of the first steps in most web data analytics is creating a human annotated ground truth, typically based on the assumption that for each annotated instance there is a single right answer. From this assumption it has always followed that ground truth quality can be measured in inter-annotator agreement. We challenge this assumption by observing that for certain annotation tasks, disagreement reflects semantic ambiguity in the target instances and provides useful information. We propose a new type of ground truth, a crowd truth, which is richer in diversity of perspectives and interpretations, and reflects more realistic human knowledge. We propose a framework to exploit such diverse human responses to annotation tasks for analyzing and understanding disagreement. Our discussion centers on a use case of relation extraction from medical text, however the crowd truth idea generalizes to most crowdsourced annotation tasks.
Unmanned Marine Vehicles at CNR-ISSIA

Abstract This paper discusses the requirements, design and operational aspects of the Unmanned Marine Vehicles (UMVs), namely the Romeo Remotely Operated Vehicle (ROV) and the Charlie Unmanned Surface Vehicle (USV), developed at CNR-ISSIA, Genova, Italy for robotics research and scientific applications, pointing out the synergies between the development of underwater and surface unmanned vehicles.
Classifier-asa-Service : Online Query of Cascades and Operating Points

We introduce a classifier and parameter selection algorithm for Classifier-as-a-Service applications where there are many components (e.g., features, kernels, classifiers) available to construct classification algorithms. Queries specify varying requirements (i.e., quality and execution time), some of which may require combining classification algorithms to satisfy; each query may have a different set of quality and execution time requirements (e.g., fast and precise, slow and thorough) and the set of images to which the classifier is to be applied may be small (e.g., even a single image), necessitating a query resolution method that takes negligible time in comparison. When operating on large datasets, meeting design requirements automatically becomes essential to reducing costs associated with unnecessary computation and expert assistance. As queries specify requirements and not implementation details, additional components can be utilized naturally. Our query resolution method combines classifiers with complementary operating points (e.g., high recall algorithmic filter, followed by high precision human verification) in a rejection-chain configuration. Experiments are conducted on the SUN397[1] dataset; we achieve state-of-the-art classification results and 1 m.s. query resolution times.
Intelligent Model Learning Based on Variance for Bayesian Reinforcement Learning

We consider a modular method to reinforcement learning that represents uncertainty of model parameters by maintaining probability distributions over them. The algorithm we call MBDP (model-based Bayesian dynamic programming) can be decomposed into two parallel types of inference: model learning and policy learning. During learning a model, we update posterior distributions of a model over observations after taking an action in each state. During learning a policy, we solve MDPs by dynamic programming with greedy approximation to make an agent choose behaviors which maximize return under the estimated model. Furthermore, we propose a principled method which utilizes the variance of Dirichlet distributions for determining when to learn and relearn the model. We demonstrate that MBDP can find near optimal policies with high probability by sufficient model learning and experimental results show that MBDP performs better compared with current state-of-the-art methods in reinforcement learning.
ITect: Scalable Information Theoretic Similarity for Malware Detection

Malware creators have been getting their way for too long now. String-based similarity measures can leverage ground truth in a scalable way and can operate at a level of abstraction that is difficult to combat from the code level. We introduce ITect, a scalable approach to malware similarity detection based on information theory. ITect targets file entropy patterns in different ways to achieve 100% precision with 90% accuracy but it could target 100% recall instead. It outperforms VirusTotal for precision and accuracy on combined Kaggle and VirusShare malware.