Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Closed-loop H-infinity Filter for SINS/OD Integration in Land Vehicles

In SINS/OD land vehicle system, H-infinity filter (HF) has better robustness than Kalman filter (KF) in the actual environmental uncertain interferences, but its low precision confines the application field. Closed-loop feedback system (CFS) and fuzzy inference system (FIS) are introduced into the traditional HF, a new fuzzy closed-loop H-infinity filtering algorithm is proposed in this paper. The fuzzy closed-loop H-infinity filter corrects system internal state through CFS, and it adjusts filtering gain matrix adaptively through FIS to further inhibit the uncertain interferences, thereby, improves the navigational precision and reliability. Finally, the vehicle experiment results illustrated that the filter proposed in this paper had higher precision and robustness than KF and HF.
Continuous elasticity: Design and operation of elastic systems

Abstract Advancements in the areas of Cloud Computing, Internet of Things (IoT), and hybrid Human-Computer systems have made feasible the creation of a highly integrated human-machine world. The concept of elasticity plays a crucial role in fulfilling this vision, enabling systems to address various requirements reflecting performance, security, and business concerns. However, elastic systems are still in their inception, and numerous challenges need to be addressed in their development and management. In this article we present an overview of our experience on elastic systems, with a focus on elastic cloud systems. In the quest for designing and managing elastic systems, several challenges need to be addressed, such as: (i) enabling the systems to fulfill different requirements from multiple involved stakeholders, (ii) designing elastic systems considering various degrees of elasticity capabilities provided by different technologies and environments, (iii) understanding behavioral relationships in elastic systems, and their effects on stakeholder requirements, (iv) monitoring costs and analyzing cost efficiency of elastic systems, (v) controlling the elasticity of such systems at runtime in order to fulfill stakeholders' requirements, and (vi) supporting system elasticity through operations management. We present the techniques we have adopted in order to tackle the above challenges. We introduce our solution for creating elastic systems, following their complete lifecycle, from design-time to operations management.
Stress-Mediated cis-Element Transcription Factor Interactions Interconnecting Primary and Specialized Metabolism in planta

Plant specialized metabolites are being used worldwide as therapeutic agents against several diseases. Since the precursors for specialized metabolites come through primary metabolism, extensive investigations have been carried out to understand the detailed connection between primary and specialized metabolism at various levels. Stress regulates the expression of primary and specialized metabolism genes at the transcriptional level via transcription factors binding to specific cis-elements. The presence of varied cis-element signatures upstream to different stress-responsive genes and their transcription factor binding patterns provide a prospective molecular link among diverse metabolic pathways. The pattern of occurrence of these cis-elements (overrepresentation/common) decipher the mechanism of stress-responsive upregulation of downstream genes, simultaneously forming a molecular bridge between primary and specialized metabolisms. Though many studies have been conducted on the transcriptional regulation of stress-mediated primary or specialized metabolism genes, but not much data is available with regard to cis-element signatures and transcription factors that simultaneously modulate both pathway genes. Hence, our major focus would be to present a comprehensive analysis of the stress-mediated interconnection between primary and specialized metabolism genes via the interaction between different transcription factors and their corresponding cis-elements. In future, this study could be further utilized for the overexpression of the specific transcription factors that upregulate both primary and specialized metabolism, thereby simultaneously improving the yield and therapeutic content of plants.
Generalized Ambiguity Decomposition for Understanding Ensemble Diversity

Diversity or complementarity of experts in ensemble pattern recognition and information processing systems is widely-observed by researchers to be crucial for achieving performance improvement upon fusion. Understanding this link between ensemble diversity and fusion performance is thus an important research question. However, prior works have theoretically characterized ensemble diversity and have linked it with ensemble performance in very restricted settings. We present a generalized ambiguity decomposition (GAD) theorem as a broad framework for answering these questions. The GAD theorem applies to a generic convex ensemble of experts for any arbitrary twice-differentiable loss function. It shows that the ensemble performance approximately decomposes into a difference of the average expert performance and the diversity of the ensemble. It thus provides a theoretical explanation for the empirically-observed benefit of fusing outputs from diverse classifiers and regressors. It also provides a loss function-dependent, ensemble-dependent, and data-dependent definition of diversity. We present extensions of this decomposition to common regression and classification loss functions, and report a simulation-based analysis of the diversity term and the accuracy of the decomposition. We finally present experiments on standard pattern recognition data sets which indicate the accuracy of the decomposition for real-world classification and regression problems.
On the gamification of human-centric traceability tasks in software testing and coding

Traceability is the ability to trace the influence of one software artifact on another by linking dependencies. Test-to-code traceability (relationships between test and system code) plays a vital role in the production, verification, reliability and certification of highly dependable systems. In practical settings, however, traceability tasks are most often observed in the breach by human developers/testers. Prior research works on test-to-code traceability in software engineering do not provide high traceability output and accuracy as they mainly rely on sought-after approaches to recover links. Gamified Software Engineering (GSE) is a growing field that in particular taps into gamification, the application of game mechanics in non-game contexts, to address human-related concerns in the field of SE. This paper argues that a new gamified approach is necessary to tackle the human issue of capturing traceability information in a by-product manner. Thus, it advocates for the induction of gamification concepts in software traceability. We propose a conceptual framework where the gamification is infusing engagement into human-centric traceability tasks to record trace links. An empirical evaluation was performed to assess the performance of the framework compared with a state-of-the-art approach.
Down-regulation of leucocyte immunoglobulin-like receptor expression in the synovium of rheumatoid arthritis patients after treatment with disease-modifying anti-rheumatic drugs.

OBJECTIVES To compare the expression of leucocyte immunoglobulin-like receptors (LILRs) also known as ILTs and LIRs in rheumatoid arthritis (RA) synovial membrane before and after treatment with disease-modifying anti-rheumatic drugs (DMARDs) and investigate regulation of LILR-expression and function in vitro. METHODS A study was performed on serial synovial biopsies obtained from 10 RA patients before and after treatment with DMARDs. Expression of the activating LILRA2 (ILT1 or LIR-7) and inhibitory LILRB2 (ILT4 or LIR-2) and LILRB3 (ILT5 or LIR-3) was evaluated by immunohistochemical staining, and quantified by a validated scoring system. Peripheral blood mononuclear cells and in vitro derived macrophages were used to determine effects of DMARDs on expression and function of LILRs. RESULTS Abundant expression of LILRB2, B3 and A2 was found in synovial tissue of all patients before treatment. Number of inflammatory cells expressing both inhibitory and activating LILRs dramatically decreased in patients who responded to treatment, but remained high in those who did not. However, treatment of macrophages with DMARDs in vitro did not down-regulate LILR expression. On the other hand, reduction in LILR expression in RA synovia was associated with decreased inflammatory infiltrates in those who responded to treatment. Cross-linking of LILRA2 on macrophages caused substantial production of tumour necrosis factor (TNF-alpha) in a dose- and time-dependent manner that was strongly inhibited by dexamethasone. CONCLUSIONS We show that expression of LILRs in RA synovium was significantly reduced only in patients who responded to treatment. However, clinical responses may not be due to direct effects of DMARDs on LILR expression but due to partial inhibition of LIRA2-mediated TNF-alpha production by steroids leading to suppression of inflammation.
Multicentre study design in survival analysis

Survival analysis is an important part of medical statistics or for the study of failures in mechanical systems. The latter is called reliability analysis in engineering. This paper addresses the survival analysis in medical research. In this context of medical research, the survival analysis is frequently used to define prognostic indices for survival or recurrence of a disease, and to study outcome of treatment. Within the broader domain of medical survival analysis or more specifically for the study of patients with cancer disease, there are a number of clinical/prognostic techniques developed over the last half of century in many research groups, universities and hospitals around the globe. Despite numerous publications on each of these techniques, there is still a need of evaluating the numerical outputs of the different prognostic algorithms on identical medical datasets. Hence the need to benchmark the mathematical algorithms against the available medical and other cancer datasets held by various research groups from universities or hospitals. The main scope of a benchmark process is to specifically inform and to provide prognostic advice and choice of post-operative adjuvant therapy. It is also essential to define data acquisition protocols, to create the necessary databases on which the benchmarked methodologies can be tested. In case there are several research groups which are taking part in a benchmark study, then they form a multicentre study. This paper presents the steps to be followed in order to realize such a multicentre study.
Identification of orchid species using content-based flower image retrieval

In this paper, we developed the system for recognizing the orchid species by using the images of flower. We used MSRM (Maximal Similarity based on Region Merging) method for segmenting the flower object from the background and extracting the shape feature such as the distance from the edge to the centroid point of the flower, aspect ratio, roundness, moment invariant, fractal dimension and also extract color feature. We used HSV color feature with ignoring the V value. To retrieve the image, we used Support Vector Machine (SVM) method. Orchid is a unique flower. It has a part of flower called lip (labellum) that distinguishes it from other flowers even from other types of orchids. Thus, in this paper, we proposed to do feature extraction not only on flower region but also on lip (labellum) region. The result shows that our proposed method can increase the accuracy value of content based flower image retrieval for orchid species up to ± 14%. The most dominant feature is Centroid Contour Distance, Moment Invariant and HSV Color. The system accuracy is 85,33% in validation phase and 79,33% in testing phase.
Maximally equidistributed combined Tausworthe generators

Tausworthe random number generators based on a primitive trinomial allow an easy and fast implementation when their parameters obey certain restrictions. However, such generators, with those restrictions, have bad statistical properties unless we combine them. A generator is called maximally equidistributed if its vectors of successive values have the best possible equidistribution in all dimensions. This paper shows how to find maximally equidistributed combinations in an efficient manner, and gives a list of generators with that property. Such generators have a strong theoretical support and lend themselves to very fast software implementations.
Understanding the Real World through the Analysis of User Behavior and Topics in Online Social Media

Author(s): Georgiou, Theodore | Advisor(s): El Abbadi, Amr; Yan, Xifeng | Abstract: Physical events happening in the real world usually trigger reactions and discussions in the digital world; a world most often represented by Online Social Media such as Twitter or Facebook. Mining these reactions through social sensors offers a fast and low cost way to explain what is happening in the physical world. A thorough understanding of these discussions and the context behind them has become critical for many applications like business or political analysis. This context includes the characteristics of the population participating in a discussion, or when it is being discussed, or why. As an example, we demonstrate how the time of the day affects the prediction of traffic on highways through the analysis of social media content. Obtaining an understanding of what is happening online and the ramifications on the real world can be enabled through the automatic summarization of Social Media. Trending topics are offered as a high level content recommendation system where users are suggested to view related content if they deem the displayed topics interesting. However, identifying the characteristics of the users focused on each topic can boost the importance even for topics that might not be popular or bursty. We define a way to characterize groups of users that are focused in such topics and propose an efficient and accurate algorithm to extract such communities. Through qualitative and quantitative experimentation we observe that topics with a strong community focus are interesting and more likely to catch the attention of users.Consequently, as trending topic extraction algorithms become more sophisticated and report additional information like the characteristics of the users that participate in a trend, significant and novel privacy issues arise. We introduce a statistical attack to infer sensitive attribute values of Online Social Networks users that utilizes such reported community-aware trending topics. Additionally, we provide an algorithmic methodology that alters an existing community-aware trending topic algorithm so that it can preserve the privacy of the involved users while still reporting trending topics with a satisfactory level of utility. From the user’s perspective, we explore the idea of a cyborg that can constantly monitor its owner’s privacy and alert them when necessary. However, apart from individuals, the notion of privacy can also extend to a group of people (or community). We study how non-private behavior of individuals can lead to exposure of the identity of a larger group. This exposure poses certain dangers, like online harassment targeted to the members of a group, potential physical attacks, group identity shift, etc. We discuss how this new privacy notion can be modeled and identify a set of core challenges and potential solutions.
Meningococcal Porin PorB Binds to TLR2 and Requires TLR1 for Signaling1

TLR2 plays a key role in the initiation of the cellular innate immune responses by a wide range of bacterial products. TLRs signaling, including TLR2 and its coreceptors TLR1 and TLR6, is mediated by a number of specific ligands. Although many of the TLR-mediated cell signaling pathways have been elucidated in the past few years, the molecular mechanisms that lead to cell activation are still poorly understood. In this study, we investigate the interaction of PorB from Neisseria meningitidis with TLR2 and describe the direct binding of a bacterial protein to TLR2 for the first time. Using labeled PorB, we demonstrate its binding to TLR2 both in its soluble form in vitro, and when it is over-expressed on the surface of human embryonic kidney 293 cells. We also show that TLR2-mediated binding of PorB is directly related to cellular activation. In addition, using 293 cells expressing the chimeric TLR2/TLR1 and TLR2/TLR6 complexes, we report the selectivity of PorB binding to the TLR2/TLR1 heterodimer, which is required for initiating signaling in transfected 293 cells and in murine B cells. Together, these data provide new evidence that TLR2 recognizes PorB through direct binding, and that PorB-induced cell activation is mediated by a TLR2/TLR1 complex.
Non-Confrontational Extremists

Preference falsification, the act of misrepresenting one’s beliefs under social pressure, is widespread but not ubiquitous. We show that when individuals perceive a concave cost of deviating from their principles, ideological extremists are more likely to falsify preferences. Being in ideological minority, they cave-in on decisions they disagree with ideologically because once they deviate slightly, further deviations entail relatively little additional cost. To this result—ideological perfectionism—which is supported by recent lab experiments, we add the first evidence in a high-stakes field setting regarding which individuals, on an ideological scale, conform under social pressure and which stand their ground.
The Nuclear Receptor DHR3 Modulates dS6 Kinase–Dependent Growth in Drosophila

S6 kinases (S6Ks) act to integrate nutrient and insulin signaling pathways and, as such, function as positive effectors in cell growth and organismal development. However, they also have been shown to play a key role in limiting insulin signaling and in mediating the autophagic response. To identify novel regulators of S6K signaling, we have used a Drosophila-based, sensitized, gain-of-function genetic screen. Unexpectedly, one of the strongest enhancers to emerge from this screen was the nuclear receptor (NR), Drosophila hormone receptor 3 (DHR3), a critical constituent in the coordination of Drosophila metamorphosis. Here we demonstrate that DHR3, through dS6K, also acts to regulate cell-autonomous growth. Moreover, we show that the ligand-binding domain (LBD) of DHR3 is essential for mediating this response. Consistent with these findings, we have identified an endogenous DHR3 isoform that lacks the DBD. These results provide the first molecular link between the dS6K pathway, critical in controlling nutrient-dependent growth, and that of DHR3, a major mediator of ecdysone signaling, which, acting together, coordinate metamorphosis.
Role of the Brn-3 family of POU-domain genes in the development of the auditory/vestibular, somatosensory, and visual systems.

M. XIANG, 1 L. GAN, 2 D. LI, 3 L. ZHOU, 4'6 Z. -Y. CHEN, 1 D. WAGNER, 2 B . W. O'MALLEY, JR., 3 W. KLEIN, 2 J . NATHANS 4'5'6 1Center for Advanced Biotechnology and Medicine, Department of Pediatrics, UMDNJ-Robert Wood Johnson 2 Medical School, Piscataway, New Jersey 08854; Department of Biochemistry and Molecular Biology, University 3 of Texas M.D. Anderson Cancer Center, Houston, Texas 77030; Departments of Otolaryngology, Head and Neck Surgery, 4Molecular Biology and Genetics, 5Neuroscience, and ~Ophthalmology, 6Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
Can incentives mitigate student overconfidence at grade forecasts?

ABSTRACT Research shows that college students exhibit bias in their forecasts of exam performance. Most students are overconfident in their forecasts, academically weaker students are the most overconfident, and top-performing students are underconfident. The literature identifies negative repercussions of these biases, including inadequate preparation for exams. A recurring attribute of this literature is the absence of meaningful incentives for students to forecast accurately. We implement an extra credit scheme to incentivize accurate forecasts. Depending on how forecast bias is measured, the scheme mitigates bias by top-performing students and marginally mitigates bias by other students. Our results have several implications. First, we illustrate an extra credit tool instructors can use to incentivize students to make more thoughtful assessments of their future exam performance. Second, we show how the association between incentives and forecast bias differs across student groups. Finally, we show that results in this literature are sensitive to how bias is measured.
An emotional interface for a music gathering application

Listening to music while travelling is a pleasant activity. The latest MP3 players demonstrate that storage and management of music will not be a problem in the near future. Besides listening to music the user might also want to gather new music from the Internet. We propose a music gathering application that helps the user to collect music and that is able to proactively search and download music based on the users music preferences. Furthermore, we developed an emotional interface character that provides instant and natural feedback on the status of the application
Parallel Iterative Solvers for Finite-Element Methods using a Hybrid Programming Model on SMP Cluster Architectures

An efficient parallel iterative method for unstructured grids developed by the authors for SMP cluster architectures is presented. The method is based on a three-level hybrid parallel programming model, including message passing for inter-SMP node communication, loop directives by OpenMP for intra-SMP node parallelization and vectorization for each processing element (PE). Multiple reordering methods have been applied in order to attain concurrent local operations, no global dependency, continuous memory access, and sufficiently long innermost loops. Simple 3D linear elastic problems with more than 8x10^8 DOFs have been solved by 3x3 block ICCG(0) with additive Schwarz domain decomposition and parallel descending-order jagged diagonal storage for cyclic-multicoloring/reverse Cuthill-McKee reordering (PDJDS/CM-RCM) using 128 SMP nodes of the Earth Simulator, achieving performance of 2.61 TFLOPS. PDJDS for multicoloring (PDJDS/MC) provided better performance for complicated geometries. Both PDJDS/CM-RCM and PDJDS/MC methods provide excellent vector and parallel performance in SMP nodes with vector processors. REPORT DATE March 24th, 2003 TOTAL NO. OF PAGES 14 ANY OTHER IDENTIFYING INFORMATION OF THIS REPORT Submitted to SIAM Journal on Scientific Computing. Research Organization for Information Science & Technology (RIST) 2-2-54, Nakameguro, Meguro-ku Tokyo, 153-0061, Japan TEL: +81-3-3712-5321 FAX: +81-3-3712-5552 http://geofem.tokyo. rist.or. jp/ Copyright©2003 Research Organization for Information Science & Technology (RIST)
Performance analysis of WDM link using Dispersion Compensating Fiber at different wavelengths

Performance of Wavelength Division Multiplexing (WDM) System is analyzed using Dispersion Compensating Fiber. In this work, Optimized link of 8-channel WDM (Wavelength Division Multiplexing) system using OptiSystem is designed. Comparison of the link performance is investigated at different wavelength i.e. 980nm, 1300nm and 1550nm. The better Performance of the WDM link can be achieved at wavelength of 1550nm as compared to 1300 and 980nm using dispersion compensating fiber along with single mode fiber for link length of 105 km operating at data rate of 3Gb\s with 8-channel of WDM System. The BER, Q-factor and eye diagram have been used for evaluating the performance of the system.