Miracle-dz/UniFAR-FTUs huggingface dataset:
Five-year outcome of a primary-care-based inception cohort of patients with inflammatory polyarthritis plus psoriasis.

OBJECTIVES To establish whether patients with inflammatory arthritis plus psoriasis have a different outcome from those who do not have psoriasis. METHODS Seventy-nine patients with inflammatory arthritis plus psoriasis were recruited by the Norfolk Arthritis Register (NOAR) in 1990-94 and followed for 5 yrs. Their outcome was compared with the remainder (n = 755) of the NOAR cohort. We then restricted the analysis to subjects who were rheumatoid factor (RF)-negative, and compared those with and without psoriasis. Outcomes studied included remission, deformed joint count, the presence and extent of erosive damage and physical function. RESULTS Patients with psoriasis were younger, more likely to be male, less likely to be RF-positive and more likely to have been treated with disease-modifying drugs than patients without psoriasis. After adjustment for age, gender and treatment, the only differences between the psoriasis and non-psoriasis groups were in RF positivity (adjusted odds ratio 0.44; 95% CI 0.25, 0.78) and in the Larsen score in patients with erosions. CONCLUSIONS Patients with inflammatory arthritis plus psoriasis have a similar outcome to other RF-negative patients with arthritis.
Loop operators and the Kondo problem

We analyse the renormalisation group flow for D-branes in WZW models from the point of view of the boundary states. To this end we consider loop operators that perturb the boundary states away from their ultraviolet fixed points, and show how to regularise and renormalise them consistently with the global symmetries of the problem. We pay particular attention to the chiral operators that only depend on left-moving currents, and which are attractors of the renormalisation group flow. We check (to lowest non-trivial order in the coupling constant) that at their stable infrared fixed points these operators measure quantum monodromies, in agreement with previous semiclassical studies. Our results help clarify the general relationship between boundary transfer matrices and defect lines, which parallels the relation between (non-commutative) fields on (a stack of) D-branes and their push-forwards to the target-space bulk.
Demand Analysis Using Strategic Reports: An Application to a School Choice Mechanism

Several school districts use assignment systems that give students an incentive to misrepresent their preferences. We find evidence consistent with strategic behavior in Cambridge. Such strategizing can complicate preference analysis. This paper develops empirical methods for studying random utility models in a new and large class of school choice mechanisms. We show that preferences are nonparametrically identified under either sufficient variation in choice environments or a preference shifter. We then develop a tractable estimation procedure and apply it to Cambridge. Estimates suggest that while 83% of students are assigned to their stated first choice, only 72% are assigned to their true first choice because students avoid ranking competitive schools. Assuming that students behave optimally, the Immediate Acceptance mechanism is preferred by the average student to the Deferred Acceptance mechanism by an equivalent of 0.08 miles. The estimated difference is smaller if beliefs are biased, and reversed if students report preferences truthfully.
Devastating Decline of Forest Elephants in Central Africa

African forest elephants– taxonomically and functionally unique–are being poached at accelerating rates, but we lack range-wide information on the repercussions. Analysis of the largest survey dataset ever assembled for forest elephants (80 foot-surveys; covering 13,000 km; 91,600 person-days of fieldwork) revealed that population size declined by ca. 62% between 2002–2011, and the taxon lost 30% of its geographical range. The population is now less than 10% of its potential size, occupying less than 25% of its potential range. High human population density, hunting intensity, absence of law enforcement, poor governance, and proximity to expanding infrastructure are the strongest predictors of decline. To save the remaining African forest elephants, illegal poaching for ivory and encroachment into core elephant habitat must be stopped. In addition, the international demand for ivory, which fuels illegal trade, must be dramatically reduced.
FPGA implementation of the histogram of oriented 4D surface for real-time human activity recognition

This paper presents a system-on-chip field gate programmable array (FPGA)-based, real-time video processing platform for human activity recognition in 3D scenes. The study details the hardware implementation of real-time human action recognition in 3D scenes, with the idea of iterative computing of a histogram of oriented 4D surface normal with an FPGA circuit. Recently, the rapid growth of modern application-based computer vision algorithms has further improved research and implementation. Especially, various accelerators such as histogram-oriented gradient (HOG), histogram-oriented flow (HOF), and histogram-oriented gradient depth (HOD) have been proposed based on the FPGA platform because it has the advantages of high performance and parallel operation. Although proposed FPGA accelerators have good performance in 2D scenes, the accelerator design space has not been well-exploited in 3D scenes. This study details the hardware implementation of a real-time human action recognition algorithm in 3D scenes, including the capture, processing, and display stages.
MOT 2.0: A Case Study on the Usefuleness of Social Modeling for Personalized E-Learning Systems

In this paper, we report on our findings from the first evaluation of MOT 2.0, an Adaptive Web 2.0 e-learning tool, which supports: 1) collaborative authoring (i.e. editing content of other users, describing content using tags, rating, commenting on the content, etc); 2) authoring for collaboration (i.e., adding author activities, such as defining groups of authors, subscribing to other authors, communication between authors, etc); 3) group-based adaptive authoring via group-based privileges; 4) social annotation i.e., tagging, rating, and feedback on the content via group-based privileges; 5) adaptive authoring, by recommending related content and/or other authors; adaptive delivery based on users' activities. Our main contributions are: 1) defining a new social layer in LAOS, a five-layer model for generic adaptive hypermedia authoring; 2) removing the barrier between tutors, learners and authors, which all become authors, with different sets of privileges; 3) adding the power of group-based authoring to the course creating.
The monoidal structure of Turing machines†

Indexed monoidal algebras are introduced as an equivalent structure for self-dual compact closed categories, and a coherence theorem is proved for the category of such algebras. Turing automata and Turing graph machines are defined by generalising the classical Turing machine concept so that the collection of such machines becomes an indexed monoidal algebra. On the analogy of the von Neumann data-flow computer architecture, Turing graph machines are proposed as potentially reversible low-level universal computational devices, and a truly reversible molecular size hardware model is presented as an example.
Author's Personal Copy Identification of Differentially Expressed Unigenes in Developing Wheat Seed Using Digital Differential Display

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t The wheat UniGene sets, derived from over one million Expressed Sequence Tags (ESTs) in the NCBI GenBank, offer a platform for identifying differentially expressed genes in wheat seeds. This report illustrates a means to efficiently utilize this public database for gene expression (transcriptome) profiling of developing wheat seed. Using a data mining tool known as Digital Differential Display (DDD), thirteen pair-wise comparisons were performed on seven seed cDNA libraries from five varieties at various seed development stages. DDD identified 46 seed-specific UniGene sets, excluding the well characterized ''housekeeping'' and seed storage protein genes. Additionally, seed-and developmentally-specific UniGenes were identified. Some of these genes encode for proteins such as purothionins, serpins, a-amylase inhibitors, lipid transfer proteins, and other unknown but novel gene sequences. Specifically, the wheat serpin and b-purothionin precursor were found to be expressed at higher levels in hard varieties than soft varieties. This study supports the starting premise that by implementing in-silico analysis of the wheat UniGene database, it is possible to rapidly create transcriptional profiles of known and novel genes in developing seeds. The expression pattern of many genes is species-, tissue-or developmentally-specific. Differential expression of any given gene is determined by whether it is significantly under-or over-expressed relative to some other reference gene(s). The level of expression of a gene is commonly estimated using two analysis approaches referred to as 'analog' and 'digital' (Audic and Claverie, 1997). These methods vary considerably in their efficacy, genome coverage, data-points delivery, and cost-effectiveness. The analog methods are based on oligonucleotide probe hybridizations such as Northern blotting, mRNA differential display, and DNA microarrays. The digital methods are based on high throughput generation of gene transcripts, which vary in length from 300 to 500 bp as in the case of Expressed Sequence Tags (ESTs) or as short as 9 bp in the case of Serial Analysis of Gene Expression (SAGE) (Velculescu et al., 1995). In-silico gene expression profiling using EST database mining has a documented impact on gene discovery in mammalian systems (Huminiecki and Bicknell, 2000; Scheurle et al., 2000). The approach follows the logic that a given set of ESTs is a representation …
Dynamically Weighted Classification with Clustering to tackle non-stationarity in Brain computer Interfacing

This paper addresses an important problem known as EEG non-stationarity in Brain-computer Interfacing. We propose a novel technique called Dynamically Weighted Classification with Clustering (DWCC), which explores hidden states in non-stationary EEG using a modified k-means clustering method by combining cosine distance measure and mutual information criterion. DWCC builds a set of classifiers, one for each pair of clusters from different classes. A dynamically-weighted classifier ensemble network is trained to combine the outputs of the classifiers, where we propose to dynamically assign the weight of a classifier for each test sample based on its distances to the cluster centres associated with the classifier. Experimental results on publicly available BCI Competition IV Dataset 2a yielded a mean accuracy of 81.5% which is statistically significant (t-test p<;0.05) compared to the baseline result of 75.9% using a single classifier.
Improving Learning Achievement in Science Education for Elementary School Students via Blended Learning

Blended learning—which combines online learning with traditional face-to-face classroom instruction—is currently held in high regard. In elementary schools, science and technology education aims to help children use technology tools and to learn how disciplines such as math and science are relevant to engineering. In this study, the authors examined what type of learning profile contributes to higher achievement in science and technology in a blended learning environment. The participants consisted of 106 elementary school students (grades three to six) from two different schools. The authors adapted the Online Technologies Self-Efficacy Scale (OTSES) and the Motivated Strategies for Learning Questionnaire (MSLQ) to measure students' computer skills and learning motivation, respectively, and to understand how the blended learning environment affected their learning achievement. The results were as follows: 1) Computer skills significantly improved for all students except sixth-graders. 2) The blended learning environment had no significant effect on learning motivation. 3) In grades four and five, students in the experimental group improved more in learning achievement than students in the control group, as reflected by their higher MSLQ and OTSES scores.
Transformation between Right and Left News Based on Text Similarity and News Structure

The left right political spectrum is a common way of classifying political issues along a one-dimensional political spectrum. The perspective of Left vs. Right is a binary interpretation of complex questions, but it also highly a ects the way we look at the world, just like a lterin front our eyes, even causes some distirtions. From media, like news, can provide a good way to see how the di erent side of spectrum look at the same thing. it 's also fun to see how the opposite side present a simliar idea in di erent. So what I do is base on several known news and the news strucure, inverted pyramid, transfer a right news to left one, and vice versa.
Air Suction and Blowing as Effective Means to Increase Aerodynamic Efficiency of a Tiltrotor Wing-Fuselage Junction

The present study aimed at investigating the effect of boundary layer suction and blowing on the aerodynamic performance of the wing/fuselage junction of a tiltrotor. A series of numerical analyses were performed in order to identify the optimal shape and location of the suction/blowing slot and a preliminary cost-benefit analysis was carried out as well by comparing the increased on board installed power required by these systems with the estimated gain in propulsive power.
Auction vs. Posted-Price: Market Mechanism, Lender Behaviors, and Transaction Outcomes in Online Crowdfunding

We study the effect of different market mechanisms, specifically auctions and postedprices, on participant behaviors and transaction outcomes in an online personal loan market. We develop a game theoretic model to generate empirically testable predictions, then exploit a regime change on a peer-to-peer lending site, Prosper.com, to test them. We find that under posted-prices, loans are funded with higher probability, but only at higher initial and contract interest rates, and are more likely to default. While market-based auctions may be slow in discovering the correct price, “expert”-based posted-price selling may yield unintended, negative welfare consequences.
SPICE as a Fast and Stable Tool for Simulating a Wide Range of Dynamic Systems

This paper introduces a generalized method for simulating dynamic systems in SPICE. The proposed method is useful for students in that the necessary software is free and only an elementary knowledge of SPICE is required. The method uses a netlist description of the system that comprises little more than a set of state equations. In comparison with many of the more commonly used tools, such as MATLAB/SIMULINK, the presented method is less involved, allowing the student to stay focused on the problem rather than the software. The method is not limited only to electrical systems: it can be applied to virtually any dynamic system, making it accessible to students from other backgrounds.
The theory of joins in relational data bases

Answering queries in a relational database often requires that the natural join of two or more relations be computed. However, not all joins are semantically meaningful. This paper gives an efficient algorithm to determine whether the join of several relations is semantically meaningful (lossless) and an efficient algorithm to determine whether a set of relations has a subset with a lossy join. These algorithms assume that all data dependencies are functional. Similar techniques also apply to the case where data dependencies are multivalued.
A comparison of online and offline strategies for program adaptation

Multithreaded programs executing on modern high-end computing systems have many potential avenues to adapt their execution to improve performance, energy consumption, or both. Program adaptation occurs anytime multiple execution modes are available to the application and one is selected based on information collected during program execution. As a result, some degree of online or offline analysis is required to come to a decision of how best to adapt and there are a variety of tradeoffs to consider when deciding which form of analysis to use, as the overheads they carry with them can vary widely in degree as well as type, as can their effectiveness. In this paper, we attempt to qualitatively and quantitatively analyze the pros and cons of specific types of online and offline forms of information collection and analysis for use in dynamic program adaptation in the context of high performance computing. We focus on providing recommendations of which strategy to employ for users with specific requirements. To justify our recommendations we use data collected from two offline and three online analysis strategies used with a specific power-performance adaptation technique, concurrency throttling. We provide a two-level analysis, comparing online and offline strategies and then comparing strategies within each category. Our results show clear trends in the appropriateness of particular strategies depending on the length of application execution - more specifically the number of iterations in the program - as well as different expected use characteristics ranging from one execution to many, with fixed versus variable program inputs across executions.
Clinical Utility of Thallium-201 Single Photon Emission Computed Tomography and Cerebrospinal Fluid Epstein-Barr Virus Detection Using Polymerase Chain Reaction in the Diagnosis of AIDS-Related Primary Central Nervous System Lymphoma

Objective: To determine the diagnostic efficiency of thallium-201 single photon emission computed tomography (SPECT) and Epstein-Barr virus (EBV) polymerase chain reaction (PCR) in the differentiation of primary central nervous system lymphoma (PCNSL) from other central nervous system processes in patients with HIV/AIDS. Design/Methods: Over 10 years, 68 thallium-201 SPECT scans were performed on neurologically symptomatic HIV+ patients with focal lesions on CT or MRI at the Johns Hopkins Hospital. Diagnoses were then established by either autopsy, biopsy, or clinical response to anti-toxoplasmosis therapy. Patients were categorized prior to a prospective clinical reading of the SPECT scans by nuclear medicine physicians. Results: In our patient sample overall, the diagnostic efficiency of thallium-201 SPECT was 79%. The diagnostic accuracy of EBV PCR testing alone in a subset of 22 patients in our study that had CSF analyzed was 73%. However, when both positive EBV PCR and positive thallium-201 SPECT results were used together, the diagnostic accuracy improved to 100% based on a sample of 13 patients where EBV PCR and SPECT imaging results were concordant. Conclusion: Thallium-201 SPECT has a relatively high positive predictive value with regards to the diagnosis of PCNSL, which suggests that patients with positive results could undergo empiric radiation treatment without resorting to brain biopsy. However, the predictive value can be increased by testing for CSF EBV using PCR. Alternatively, if CSF cannot be safely obtained because of mass effect, we believe that these data still suggest that empiric radiation treatment should be considered when discussing treatment options with patients with a positive thallium-201 SPECT.
Accelerating Multi-threaded Application Simulation through Barrier-Interval Time-Parallelism

In the last decade, the microprocessor industry has undergone a dramatic change, ushering in the new era of multi-/manycore processors. As new designs incorporate increasing core counts, simulation technology has not matched pace, resulting in simulation times that increasingly dominate the design cycle. Complexities associated with the execution of code and communication between simulated cores has presented new obstacles for the simulation of manycore designs. Hence, many techniques developed to accelerate uniprocessor simulation cannot be easily adapted to accelerate manycore simulation. In this work, a novel time-parallel barrier-interval simulation methodology is presented to rapidly accelerate the simulation of certain classes of multi-threaded workloads. A program delineated into intervals by barriers may be accurately simulated in parallel. This approach avoids challenges originating from unknown thread progressions, since the program location of each executing thread is known. For the workloads tested, wall-clock speedups range from 1.22× to 596×, with an average of 13.94×. Furthermore, this approach allows the estimation of stable performance metrics such as cycle counts with minimal losses in accuracy (2%, on average, for all tested workloads). The proposed technique provides a fast and accurate mechanism to rapidly accelerate particular classes of manycore simulations.
Understanding Team Influence on Professionals' Acceptance of Large- Scale Systems

This paper highlights the importance of team influence in affecting professionals’ acceptance of large-scale information systems. A longitudinal study involving 103 physicians was conducted with two data collections performed three months apart. The results show that team influence has a significant effect on perceived usefulness, but not on perceived ease of use, and that satisfaction with using the system of interest is significantly affected by team influence, but not by perceived usefulness. The findings make contributions to both research and practice of information systems.
On the design of an IT valuation and business alignment model using Resource Based View method and COBIT version 5

Nowadays organizations in the business world and government apply technology to improve its internal business process. One phase of the business process is logistics. Seaports, as one of the centers of logistic flows in a country, contain a wide range of problems in its internal processes. The seaport is a place that connects logistic distribution between city and country ports. By viewing distribution and logistics as the business core of a port, it is observed that the information technology system which exists and is running coincides closely with the current problem solution. However the correlation of information from all components connected to the system is also important, for instance business elements, operations and finance. This paper discusses how to analyze information systems and technology of a certain company using a Resource Based View (RBV) Model framework proposed by the author, through a study case research conducted on one of a state owned seaport company in Indonesia. The purpose of this framework analysis is to extract the value of information technology and also how it is suitable with the business. From the results of this analysis, recommendations were structured in the form of strategy to fill in gaps and a risk management plan. The final outcome of this paper's research is a framework model that can be used by other companies which analyze the suitability of the business and information technology, and also the right and proper recommendations in line with the analysis results. It will also examine how RBV is successfully implemented.