Miracle-dz/UniFAR-FTUs huggingface dataset:
Microlensing of close binary stars

The gravity due to a multiple-mass system has a remarkable gravitational effect: the extreme magnification of background light sources along extended so-called caustic lines. This property has been the channel for some remarkable astrophysical discoveries over the past decade, including the detection and characterization of extrasolar planets, the routine analysis of limb darkening, and, in one case, limits set on the apparent shape of a star several kiloparsec distant. In this paper, we investigate the properties of the microlensing of close binary star systems. We show that in some cases it is possible to detect flux from the Roche lobes of close binary stars. Such observations could constrain models of close binary stellar systems.
A NEW ALGORITHM FOR WORD SENSE DISAMBIGUATION

The task of disambiguation is to determine which of the senses of an ambiguous word is invoked in a particular use of the word [3]. Starting from the algorithm of Yarowsky [5, 4] and the Naive Bayes Classifier (NBC) algorithm , in this paper we propose an original algorithm which combines their elements. This algorithm preserve the advantage of principles of Yarowsky ( one sense per discourse and one sense per collocation) with the known high performance of the NBC algorithm. Moreover, an agent is constructed accomplishing this algorithm.
Selective compression technique using Variable-to-Fixed coding

Inthis paper, we propose a code based technique, Variable-to-Fixed (V-F) coding, for power efficient test data compression. The proposed scheme with the aim of achieving high compression ratio and low power consumption relies on reducing, the number of bits for representing the original test vector and the number of transitions per second. Simulation results on ISCAS'89 benchmark circuits, demonstrate that this optimization methodology helps achieve reduced test data volume than previous schemes for cases where the number of specified bits in the test set is relatively few.
An Introduction to Plan , Activity , and Intent Recognition

The ability to recognize the plans and goals of other agents enables humans to reason about what other people are doing, why they are doing it, and what they will do next. This fundamental cognitive capability is also critical to interpersonal interactions because human communications presuppose an ability to understand the motivations of the participants and subjects of the discussion. As the complexity of human-machine interactions increases and automated systems become more intelligent, we strive to provide them with comparable intent recognition capabilities. Research addressing this problem is variously referred to as plan recognition, activity recognition, goal recognition, and intent recognition. This synergistic research area combines techniques from user modeling, computer vision, natural language understanding, probabilistic reasoning, and machine learning. Plan recognition algorithms play a crucial role in a wide variety of applications including smart homes, intelligent user interfaces, personal agent assistants, human-robot interaction, and video surveillance. Plan recognition research in computer science dates back at least thirty-five years, initially defined in a paper by Schmidt, Sridharan, and Goodson [64]. In the last ten years, significant new advances have been made on this problem by researchers in artificial intelligence and related areas. These advances have been driven by three primary factors: first, the pressing need for sophisticated and efficient plan recognition systems in a wide variety of applications; second, the development of new algorithmic techniques in probabilistic modeling, machine learning, and optimization (combined with more powerful computers to use these techniques); and finally, our increased ability to gather data about human activities. Recent research in the field is often divided into two subareas. “Activity recognition” focuses on the problem of dealing directly with noisy low-level data gathered by physical sensors such as cameras, wearable sensors, and instrumented user interfaces. The primary task in this space is to discover and extract interesting patterns in noisy sensory data that can be interpreted as meaningful activities. For example, an activity recognition system processing
Progression of dysarthria and dysphagia in postmortem-confirmed parkinsonian disorders.

BACKGROUND Dysarthria and dysphagia are known to occur in parkinsonian syndromes such as Parkinson disease (PD), dementia with Lewy bodies (DLB), corticobasal degeneration (CBD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). Differences in the evolution of these symptoms have not been studied systematically in postmortem-confirmed cases. OBJECTIVE To study differences in the evolution of dysarthria and dysphagia in postmortem-confirmed parkinsonian disorders. PATIENTS AND METHODS Eighty-three pathologically confirmed cases (PD, n = 17; MSA, n = 15; DLB, n = 14; PSP, n = 24; and CBD, n = 13) formed the basis for a multicenter clinicopathological study organized by the National Institute of Neurological Disorders and Stroke, Bethesda, Md. Cases with enough clinicopathological documentation for the purpose of the study were selected from research and neuropathological files of 7 medical centers in 4 countries (Austria, France, England, and the United States). RESULTS Median dysarthria latencies were short in PSP and MSA (24 months each), intermediate in CBD and DLB (40 and 42 months), and long in PD (84 months). Median dysphagia latencies were intermediate in PSP (42 months), DLB (43 months), CBD (64 months), and MSA (67 months), and long in PD (130 months). Dysarthria or dysphagia within 1 year of disease onset was a distinguishing feature for atypical parkinsonian disorders (APDs) (specificity, 100%) but failed to further distinguish among the APDs. Survival time after onset of a complaint of dysphagia was similar in PD, MSA, and PSP (15 to 24 months, P =.7) and latency to a complaint of dysphagia was highly correlated with total survival time (rho = 0.88; P<.001) in all disorders. CONCLUSIONS Latency to onset of dysarthria and dysphagia clearly differentiated PD from the APDs, but did not help distinguish different APDs. Survival after onset of dysphagia was similarly poor among all parkinsonian disorders. Evaluation and adequate treatment of patients with PD who complain of dysphagia might prevent or delay complications such as aspiration pneumonia, which in turn may improve quality of life and increase survival time.
Study and Realization of Supplier Business Intelligence System for Chain Supermarket

With the supplier data of supermarket, the business intelligence system was developed. The paper made use of data guiding into and guiding out service tool of Microsoft SQL Server 2005 as ETL tool. A mass of data needed for analyzing and decisionmaking were separated from affair database by taking out, transition, cleanout, loading. The data warehouse facing supplier theme was established by Microsoft Analysis Services developing system. Online analytical processing system was designed. Supplier management Agent managed the data of supplier data warehouse and incepted the information that distribution center Agent sent out. It combined the data mining algorithm of clustering analysis to choose type of suppliers and informed distribution center Agent. The business intelligence system made furthest use of data in the supermarket corporation affair database, transformed them into information and then sublimed the information into knowledge. The system provides effective support for decision-making of governor in supermarket supplier management. Keywords-supplier; business intelligence; supermarket; data mining; Agent technique
The Science of Animal Behavior and Welfare: Challenges, Opportunities, and Global Perspective

Animal welfare science is a relatively new scientific discipline, evolving mostly from within veterinary medicine over the latter half of the twentieth century into an independent specialty in its own right. Originally, the field of study was heavily focused on animal behavior (ethology), but it has emerged into a truly multi- and inter-disciplinary science, encompassing such sciences as behavior, physiology, pathology, health, immunology, endocrinology, and neuroscience, and influenced by personal and societal ethics. The first academic organization devoted to the scientific study of animal welfare was established in 1966 as the society for veterinary ethology (SVE), demonstrating its veterinary roots by being then affiliated with the British Veterinary Association. The world’s first Professor of Animal Welfare was appointed 20 years later at the University of Cambridge’s Department of Clinical Veterinary Medicine, and in 1991, the SVE became the International Society for Applied Ethology, in recognition of its geographical spread and its evolution from veterinary medicine. Over the last quarter of a century, there has been further expansion of the field and now animal welfare science is represented in many universities’ veterinary medicine and animal science departments across the world. Animal welfare science has become part of the core curriculum for many veterinary degrees, is a recognized specialty qualification within the veterinary professions of Europe, USA, and Australia and courses in animal welfare science as a stand-alone discipline are offered worldwide at Bachelor’s, Master’s, and Doctorate degree levels. Within research, there have been similar patterns of expansion and a spread from a heavy focus on farm animal welfare to the welfare of zoo, laboratory, and companion animals and the impacts of humans on wildlife. There continue to be studies that compare the welfare of populations within systems, but there is also more attention given to gaining in-depth knowledge of the welfare of individual animals, knowing that populations are not homogenous and that individuals within the same system may be experiencing quite different welfare states. We not only continue to use “traditional” welfare indicators but also work to develop novel indicators for use in experimental settings or in the field. As our fundamental knowledge base increases, we look for increasing application and we respond to challenges that arise from our own research questions and findings and from societal needs. In this paper, I will focus on a number of the areas that I see represent Grand Challenges within our discipline.
Labour Taxes and Work Hours in Australia

In the 1970s, work hours in Europe were similar to work hours in America, but today Europeans work less than Americans. Prescott (2004) attributes the decline in European work hours to an increase in the effective marginal tax rate on labour income. The Australian labour market experience confirms that the taxation of labour income is an important determinant of the decision to work. In Australia taxes and work hours did not change much in the long-run, but Australian work hours rebounded after a temporary increase in taxes in the 1980s. The resilience of Australian work hours suggests that a return to the tax rates of the 1970s would restore the European labour supply.
A Method to Construct Low Delay Single Error Correction Codes for Protecting Data Bits Only

Error correction codes (ECCs) have been used for decades to protect memories from soft errors. Single error correction (SEC) codes that can correct 1-bit error per word are a common option for memory protection. In some cases, SEC codes are extended to also provide double error detection and are known as SEC-DED codes. As technology scales, soft errors on registers also became a concern and, therefore, SEC codes are used to protect registers. The use of an ECC impacts the circuit design in terms of both delay and area. Traditional SEC or SEC-DED codes developed for memories have focused on minimizing the number of redundant bits added by the code. This is important in a memory as those bits are added to each word in the memory. However, for registers used in circuits, minimizing the delay or area introduced by the ECC can be more important. In this paper, a method to construct low delay SEC or SEC-DED codes that correct errors only on the data bits is proposed. The method is evaluated for several data block sizes, showing that the new codes offer significant delay reductions when compared with traditional SEC or SEC-DED codes. The results for the area of the encoder and decoder also show substantial savings compared to existing codes.
Sex and shedding of human immunodeficiency virus.

To the Editor? By distinguishing between women who ovulated during the menstrual cycle and those who did not ovulate, Greenblat et al. [1] found that sex, contrary to generally accepted opinion, influences circulating human immunodeficiency virus (HIV) type 1 RNA levels. In ovulating women, the virus load fell during the early follicular phase through the midluteal phase. Furthermore, cervicovaginal shedding of HIV among pregnant women was higher than it was among nonpregnant women with moreadvanced HIV disease [2]. Since the hormonal balances of pregnant and nonpregnant women differ, the effects of ovulation and of pregnancy on HIV RNA levels seem to point to a connection with reproductive hormones. However, the mechanisms of both phenomena are not wholly understood. It may, therefore, be of interest to investigate similar phenomena in animals. In sheep infected experimentally with Chlamydia psittaci, shedding of the microorganism occurs only during the periovicular phase of the estrous cycle [3]. Detectable levels of chlamydiae obtained by vaginal swabs collected during different stages of the ewe's estrous cycle were restricted to the day of ovulation until ~4 days later. Of note, a persistent chlamydial infection of the ewe's reproductive tract occurred after the experimental infection. The periovicular, increased shedding of C. psittaci in sheep and the influence of ovulation in women on the fluctuation in plasma HIV RNA level [1] seem to support the significance of changes related to reproductive ability. Another comparison relates to birds. In some of 19 orders of birds examined over 4 years in Israel, shedding of C. psittaci was
Information regular and ψ-mixing channels

For channels with time structure we introduce a condition that characterizes infinite, asymptotically decreasing output memory called information regularity. We show that this condition is strictly weaker than asymptotic output-memorylessness, a property that was used to prove a coding theorem for continuous-time channels in case of infinite information capacity. We further show that the coding theorem still holds for information regular channels. We prove that Gaussian asymptotic output-memoryless channels have in fact finite output memory. This demonstrates that asymptotic output-memorylessness is quite restrictive. We consider discrete- and continuous-time channels with completely abstract alphabets in a unified framework and discuss practically relevant examples.
Journal of Theoretical and Applied Information Technology Model Based Object-oriented Software Testing

Testing is an important phase of quality control in Software development. Software testing is necessary to produce highly reliable systems. The use of a model to describe the behavior of a system is a proven and major advantage to test. In this paper, we focus on model-based testing. The term model-based testing refers to test case derivation from a model representing software behavior. We discuss model-based approach to automatic testing of object oriented software which is carried out at the time of software development. We review the reported research result in this area and also discuss recent trends. Finally, we close with a discussion of where model-based testing fits in the present and future of software engineering.
An Assessment of the Relative Influence of Environmental, Behavioral, and Attitudinal Aspects of the Pre-College Experience in Determining Students' Long-Term Goals and Aspirations. AIR 2002 Forum Paper.

The higher education literature is replete with analyses of survey data that attempt to promote insight into various college student characteristics, behaviors, and value orientations. Many studies have been conducted to understand or estimate students' short-term activities and chances for personal and academic success, but less effort has been made to identify the factors that might influence or determine students' long-term goals and aspirations. This study attempted to fill a gap in the literature by using a national, comprehensive survey to consider the relationship of different thematic or conceptual factors to the weight that students assign to various life-time goals and achievements. Using the Cooperative Institutional Research Program (CIRP) annual survey of incoming college students, the study finds that students' motivations for attending college are better predictors of postgraduate goals than are behavioral measures or demographic variables. CIRP responses from approximately 4,400 students in 1986 and about 270,000 students in 2000 were used in the analysis. (Contains 6 tables and 11 references.) (Author/SLD) Reproductions supplied by EDRS are the best that can be made from the original document. An Assessment of the Relative Influence of Environmental, Behavioral, and Attitudinal Aspects of the Pre-College Experience in Determining Students' Long-Term Goals and Aspirations PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
Efficient Sequence Matching and Path Construction for Geomagnetic Indoor Localization

Geomagnetic field is highly applicable to indoor localization due to its pervasive spatial presence, high signal stability and infrastructure-less support. Previous work in the area often fuses it with pedometer (step counter) via particles. These approaches are computationally intensive and require strong assumptions on user behavior. To overcome the weaknesses, we propose Magil, a pedometer-free approach leveraging solely upon magnetic field for indoor localization. To the best of our knowledge, this is the first piece of work using only geomagnetism for smartphone localization. Magil is applicable to any open or complex indoor environment. In the offline phase, it continuously collects and stores geomagnetic fingerprints while a surveyor is walking in pre-defined paths covering the indoor area. In the online phase, it identifies the indoor segments whose fingerprint variations highly match with the target observations. With a modified shortest path formulation, Magil selects and connects these matched segments and obtains the target locations and paths. We have implemented Magil, and conducted extensive experiments in our university campus. Our results show that Magil outperforms many state-of-the-art schemes by a wide margin (cutting localization error by more than 30%).
Weakly-Supervised Image Parsing via Constructing Semantic Graphs and Hypergraphs

In this paper, we address the problem of weakly-supervised image parsing, whose aim is to automatically determine the class labels of image regions given image-level labels only. In the literature, existing studies pay main attention to the formulation of the weakly-supervised learning problem, i.e., how to propagate class labels from images to regions given an affinity graph of regions. Notably, however, the affinity graph of regions, which is generally constructed in relatively simpler settings in existing methods, is of crucial importance to the parsing performance due to the fact that the weakly-supervised image parsing problem cannot be handled within a single image, and that the affinity graph facilitates label propagation among multiple images. Therefore, in contrast to existing methods, we focus on how to make the affinity graph more descriptive through embedding more semantics into it. We develop two novel graphs by leveraging the weak supervision information carefully: 1) Semantic graph, which is established upon a conventional graph by utilizing the proposed weakly-supervised criteria; 2) Semantic hypergraph, which explores both intra-image and inter-image high-order semantic relevance. Experimental results on two standard datasets demonstrate that the proposed semantic graphs and hypergraphs not only capture more semantic relevance, but also perform significantly better than conventional graphs in image parsing. More remarkably, due to the complementariness among the proposed semantic graphs and hypergraphs, the combination of them shows even more promising results.
Tibetan Text Clustering Based on Machine Learning

Tibetan information processing technology has been obtained some achievements. But it falls behind Chinese and English information processing. It still needs to be paid more attention. Text clustering has the potential to accelerate the development of Tibetan information processing. In this paper, we propose an approach of Tibetan text clustering based on machine learning. Firstly, the approach is to execute Tibetan word segmentation with Tibetan texts. Then feature selection and text representation are conducted. Finally, K-means and DBSCAN are adopted to deal with the text clustering. The experimental results present that DBSCAN has better performance for Tibetan text clustering. Text clustering systems are designed based on proposed approach. The study is meaningful for the Tibetan text classification, information retrieval as well as construction of high-quality Tibetan corpus.
An antibody to lymphotoxin and tumor necrosis factor prevents transfer of experimental allergic encephalomyelitis

Uncertainty regarding pathogenic mechanisms has been a major impediment to effective prevention and treatment for human neurologic diseases such as multiple sclerosis, tropical spastic paraparesis, and AIDS demyelinating disease. Here, we implicate lymphotoxin (LT) (tumor necrosis factor beta [TNF-beta]) and TNF-alpha in experimental allergic encephalomyelitis (EAE), a murine model of an autoimmune demyelinating disease. In this communication, we report that treatment of recipient mice with an antibody that neutralizes LT and TNF-alpha prevents transfer of clone-mediated EAE. LNC-8, a myelin basic protein-specific T cell line, produces high levels of LT and TNF-alpha after activation by concanavalin A, antibody to the CD-3 epsilon component of the T cell receptor, or myelin basic protein presented in the context of syngeneic spleen cells. LNC-8 cells transfer clinical signs of EAE. When LNC-8 recipient mice were also treated with TN3.19. 12, a monoclonal antibody that neutralizes LT and TNF-alpha, the severity of the transferred EAE was reduced, while control antibodies did not alter the disease. The effect of anti-LT/TNF-alpha treatment was long lived and has been sustained for 5 mo. These findings suggest that LT and TNF-alpha and the T cells that produce them play an important role in EAE.
Is There Lending Rate Stickiness in the Chilean Banking Industry

This paper provides new empirical evidence on macroeconomic policies and results in Latin America and the Caribbean (LAC), based on recent data for the region and the world at large. Our results show that: (i) both monetary and fiscal policies are counter- (pro-) cyclical when credibility is high (low), (ii) the accuracy of inflation-targeting central banks in meeting their targets rises with central bank independence and declines with country risk, (iii) intermediate exchange-rate regimes became less persistent than hard pegs and floats after the Asian crisis, (iv) exchange rate regimes do matter for inflation and growth – and regime transitions have significant output and inflation consequences (v) international differences in productivity growth do not track well real exchange rate (RER) trends and RER misalignments are not resolved by supply reforms that raise growth, (vi) LAC’s financial integration with international capital markets has increased significantly during the last decade, (vii) adverse foreign shocks explain a major part of LAC’s growth performance during the 1990s, and (viii) the composition of foreign capital flows does matter for growth.