Miracle-dz/UniFAR-FTUs huggingface dataset:
METABOLISM AND FUNCTION OF GAMMA GLOBULIN IN ALEUTIAN DISEASE OF MINK

Aleutian disease-affected mink, which are markedly hypergammaglobulinemic, show a decreased half-life of the serum gamma globulin indicating that the hyperglobulinemia is due to increased production. No evidence that the gamma globulin was antibody to the infectious agent, to autologous or isologous tissues, or to antigens the animal was responding to prior to development of the disease was obtained. The increased gamma globulin was found to be of the 6.4S variety, and gamma globulin containing protein-protein complexes of 9S to 17S and 22S to 25S classes were observed in serums of affected mink. The findings are most consistent with the Aleutian disease virus acting as a direct and somewhat selective stimulus to plasma cell proliferation. There is no evidence that the arterial and glomerular lesions of Aleutian disease have an immunologic pathogenesis. It seems possible that these vascular changes may be directly caused by the viral agent, or may be the result of the increased gamma globulin levels.
A domain independent environment for creating information extraction modules

Text-Mining is a growing area of interest within the field of Data Mining and Knowledge Discovery. Given a collection of text documents, most approaches to Text Mining perform knowledge-discovery operations either on external tags associated with each document, or on the set of all words within each document. Both approaches suffer from limitations. This paper focuses on an intermediate approach, one that we call text mining via information extraction, in which knowledge discovery takes place on focused, relevant terms, phrases and facts, as extracted from the documents.
Security and Privacy in your Smart City

With the majority of the population living in urban environments today, the concept of Smart Cities has become an urgent necessity. It refers to an urban transformation which, using latest ICT technologies, makes cities more efficient. Composed of a growing Internet of networks, such as the one connecting humans via cellular systems, computers via broadband connections, or objects and sensors via low-cost data links, the greatest challenge today is to meaningfully manage such systems. Given that these systems will greatly impact human lives, issues related to privacy and security have come into limelight. With a surge in security breaches, not only in the Internet but also with mobile phones and now sensing infrastructures, engineers are very conscious to include privacy and security requirements into architectural designs from the very beginning. This paper summarizes the key challenges, emerging technology standards, and issues to be watched out for in the context of privacy and security in smart cities. A key observations is that privacy can be achieved i) by imposing high security requirements onto the used technology to avoid third party abuses; and ii) by decoupling technical smart city data streams from the personal one to avoid abuse of data by insiders.
Affect of Vibratory Welding Process to Improve the Mechanical Properties of Butt Welded Joints

For improving mechanical properties of steels, vibration techniques have been used in the last few decades. During the welding of metals along with mechanical vibrations, uniform and finer grain structures can be produced. This increases the toughness and hardness of the metals, because of solidification effects at the weld pool surface. As the weld pool solidifies, grains are not only limited in size, but dendrites growing perpendicular to the fusion line are restricted. While the process is going on, dendrites can be broken up before they grow to become large in size. Hence, the microstructure of the weld metal is improved during the solidification process. In this work, we employed a dynamic solidification technology, by applying mechanical vibrations during the 'Arc welding' process. Analyses have been carried out for mild steel pieces having 5 mm. of thick butt joints. The results obtained from the current study pointed out that the butt welded joints fabricated with vibratory condition are found to possess relatively high hardness, without any considerable loss in its ductility.
ProbDB: Efficient Execution of Aggregate Queries over Probabilistic Data

Les requetes d'agregats sont essentielles dans de nombreuses applications qui gerent des donnees incertaines. Cependant, calculer de maniere efficace ces requetes est generalement difficile. Dans cette demonstration nous presentons ProbDB, un systeme de gestion de bases de donnees probabilistes. Nous montrons en particulier l'efficacite de ProbDB lors du calcul de requetes avec agregats tels que SUM et COUNT. L'application sur laquelle notre demonstration se repose est une application medicale, et elle montre la rapidite avec laquelle ProbDB repond aux requetes d'agregats probabilistes.
Systemic contrast and Catalan rhotics

Abstract It is argued here that an appealing approach to the distribution and behavior of rhotics in Catalan can be had by appealing to the maintenance, and the perceptual goodness, of contrast. Since the Catalan facts are very similar to those of Spanish, the relevance of these ideas to Spanish is also discussed. The analysis is cast within Dispersion Theory (Flemming, Auditory representations in phonology, Routledge, 1995, Flemming, Contrast and perceptual distinctiveness, Cambridge University Press, 2004), in a version following Padgett (Natural Language and Linguistic Theory 21: 39–87, 2003a, The emergence of contrastive palatalization in Russian, Kluwer Academic Press, 2003b). The analysis is interesting because it (i) provides novel insight into the rhotic facts, (ii) extends dispersion-theoretic thinking into the area of consonants, and (iii) contributes to a program of research showing how allophonic generalizations can be explained by appeal to contrast.
Decomposing Meeting Graph Circuits to Minimise Kernel Loop Unrolling

This article studies an important open problem in backend compilation regarding loop unrolling after periodic register allocation. Although software pipelining is a powerful technique to extract fine-grain parallelism, variables can stay alive across more than one kernel iteration, which is challenging for code generation. The classical software solution that does not alter the computation throughput consists in unrolling the loop a posteriori (13; 12). However, the resulting unrolling degree is often unacceptable and may reach absurd levels. Alternatively, loop unrolling can be avoided thanks to software register renaming. This is achieved through the insertion of move operations. However, inserting those operations may increase the initiation interval (II) and nullifies the benefits of software pipelining itself. We propose in this article a new technique to minimise the loop unrolling degree generated after periodic register allocation. In fact, this technique consists on decomposing the generated meeting graph circuits by inserting move instructions without compromising the throughput benefits of software pipelining. The different experiments showed that the execution time is acceptable and good results can be produced when we have many functional units which can execute move operations.
Systematic variations of cloud top temperature and precipitation rate with aerosols over the global tropics

Abstract. Aerosols may modify cloud properties and precipitation via a variety of mechanisms with varying and contradicting consequences. Using a large ensemble of satellite data acquired by the Moderate Resolution Imaging Spectroradiometer onboard the Earth Observing System's Aqua platform, the CloudSat cloud profiling radar and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the tropical oceans, we identified two distinct correlations of clouds and precipitation with aerosol loading. Cloud-top temperatures are significantly negatively correlated with increasing aerosol index (AI) over oceans and aerosol optical depth (AOT) over land for deep mixed-phase clouds with liquid droplets near the warm bases and ice crystals near the cold tops; no significant changes were found for uniformly liquid clouds. Precipitation rates are positively correlated with the AI for mixed-phase clouds, but negatively correlated for liquid clouds. These distinct correlations might be a manifestation of two potential mechanisms: the invigoration effect (which enhances convection and precipitation) and the microphysical effect (which suppresses precipitation). We note that the highly limited information garnered from satellite products cannot unequivocally support the causal relationships between cloud-top temperature/precipitation rate and aerosol loading. But if aerosols are indeed the causes for the observed relationships, they may change the overall distribution of precipitation, leading to a more extreme and unfavorable rainfall pattern of suppressing light rains and fostering heavy rains.
Wage Inequality in a Burdett-Mortensen World

This paper examines the development of wage inequality in the context of a Burdett- Mortensen (1998) model that is extended to incorporate worker heterogeneity through skill requirements in the production process. In this environment, wage dispersion is a natural consequence of firms pursuing different wage strategies as well as a result of worker and firm production heterogeneity. Changes in the wage distribution are then explained by changes in the productivity of heterogeneous firms. The resulting change in theoretical steady state wage distributions as a result of changes in relative productivity is consistent with many of the observed changes in distribution of wages in the US in recent decades. In particular, an increase in the productivity of less efficient firms may reduce between-group inequality while at the same time increase within-group inequality as observed during the 1970s. On the other hand, an increase in productivity of more efficient firms will tend to increase both between- and within-group inequality as observed during the 1980s and 1990s.
Hybrid TOA/AOA Schemes for Mobile Location in Cellular Communication Systems

— Wireless location is to determine the mobile station (MS) location in a wireless cellular communications system. When fewer base stations (BSs) may be available for location purposes or the measurements with large errors in non-line-of-sight (NLOS) environments, it is necessary to integrate all available heterogeneous measurements to achieve high location accuracy. This paper illustrates a hybrid proposed schemes that combine time of arrival (TOA) at three BSs and angle of arrival (AOA) information at the serving BS to give a location estimate of the MS. The proposed schemes mitigate the NLOS effect simply by the weighted sum of the intersections between three TOA circles and the AOA line without requiring a priori information about the NLOS error. Simulation results show that the proposed methods can achieve better accuracy when compare with Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).
Nonlinear subdivision processes in irregular meshes

The present article deals with convergence and smoothness analysis of geometric, nonlinear, subdivision schemes in the presence of extraordinary points. For a certain class of stationary linear subdivision schemes we can show that if a convergent linear scheme and its nonlinear analogue meet a proximity condition, then the nonlinear scheme converges for dense enough input data. Furthermore, we obtain C1 smoothness of the nonlinear limit function in the vincinity of an extraordinary point over Reif's characteristic parametrisation. The results apply to the geometric analogues of well known subdivision schemes like Doo-Sabin or Catmull-Clark schemes. keywords: nonlinear subdivision, extraordinary point, irregular mesh. 2000 MSC. Primary 65D17, Secondary 68U05, 53A99, 58C07.
REMINDER EFFECT AND ANCHORING-ADJUSTMENT IN EARNINGS ANNOUNCEMENT: IMPLEMENTATION OF PRIORPERIOD BENCHMARK DISCLOSURE STRATEGY

The purpose of this study is to provide empirical support regarding the reminder effects and anchoring-adjustment in earnings announcements. This study is important to explain the cognitive mechanism in processing the information that the consequences can affect the judgments of investors in evaluating company performance. The research of behavioral accounting often focuses on the consideration in the framework of investment decision making mechanism based on a systematic and accurate. Prior researches have described strategic disclosure of prior-period benchmark in earnings announcement that focuses on the transitory gain or loss, which, in turn, influences investor’s judgments (Schrand & Walther 2000; Krische 2005). Using strategic reference-point theory from psychology and Hogarth & Einhorn’s (1992) belief-adjustment theory, this paper extends such research by investigating how investors behave differently to reminder effect and anchoringadjustment. The experimental results suggest that reminder effects and anchoring of information can influences investor’s judgments in evaluating of company performance. Keywords: reminder effect, anchoring-adjustment, cognitive mechanism, strategic reference-point theory, belief- adjustment theory
Unsupervised Chinese Personal Name Recognition Using Search Session

Personal name recognition is an important part of named entity recognition in Web search query logs. An unsupervised method for Chinese personal name recognition in queries is proposed using search session. Based on seed personal names which are produced automatically by introducing Chinese surnames, a local expansion method is proposed by using search sessions in query logs;and by modeling the process of people establishing connections with others the authors propose a candidate filtering method using candidate contexts. P@500 of personal name recognition had reached 99% on Sogou Web search query logs, and the precision of the candidate filtering method was 81.82%. The results validate the effectiveness of the proposed personal name recognition method, and prove that the filtering method has positive impact on improving the accuracy of the recognition.
Efficient Trajectory Pattern Mining for both Sparse and Dense Dataset

comprehension of phenomena related to movement - not only of people and vehicles but also of animals and other moving objects - has always been a key issue in many areas of scientific investigation or social analysis. Many applications track the movement of mobile objects, using location- acquisition technologies such as Global Positioning System (GPS), Global System for Mobile Communications (GSM) etc., and it can be represented as sequences of time stamped locations. In this paper, we analyze the trajectories of moving vehicles and we develop an algorithm for mining the frequent patterns of Trajectory data. We use the extensions of sequential pattern mining to spatiotemporal annotated sequential patterns. The description of frequent behaviors in terms of both space (i.e., the regions of space visited during movements) and time (ie, the duration of movements). In this paper an efficient trajectory pattern mining is proposed by incorporating three key techniques. In this paper we have examined ways of partitioning data for trajectory pattern discovery. Our aim has been to identify methods that will enable efficient counting of frequent sets in cases where the data is much too large to be contained in primary memory, and also where the density of the data means that the number of candidates to be considered becomes very large. Our starting point was a method which makes use of an initial preprocessing of the data into a tree structure (the P-tree) which incorporates a partial counting of support totals
Prevalence of adult Huntington's disease in the UK based on diagnoses recorded in general practice records

Background and purpose The prevalence of Huntington's disease (HD) in the UK is uncertain. Recently, it has been suggested that the prevalence may be substantially greater than previously reported. This study was undertaken to estimate the overall UK prevalence in adults diagnosed with HD, using data from primary care. Methods The electronic medical records of patients aged 21 years or more, with recorded diagnoses of HD, were retrieved from the UK's General Practice Research Database. Prevalence was estimated from the number of persons with recorded diagnoses of HD, on 1 July each year, between 1990 and 2010. This number was divided by the total number of persons registered with participating general practices on that same date. These data were also used to estimate both age specific prevalence and prevalence in various regions of the UK. Results A total of 1136 patients diagnosed with HD, aged 21 years or more, were identified from the database. The estimated prevalence (expressed per 100 000 population) rose from 5.4 (95% CI 3.8 to 7.5) in 1990 to 12.3 (95% CI 11.2 to 13.5) in 2010. Although an increased prevalence was observed within every age group, the most dramatic was in older patients. Age specific prevalence was highest in the 51–60 year age range (15.8 95% CI 9.0 to 22.3). The prevalence of adult HD was lowest in the London region (5.4 (95% CI 3.0 to 8.9)) and highest in the North East of England (18.3 (95% CI 8.6 to 34.6)) and Scotland (16.1 (95% CI 10.8 to 22.9)). Conclusions The prevalence of diagnosed HD is clearly substantially higher in the UK than suggested from previous studies. By extrapolation to the UK as a whole, it is estimated that there are more than 5700 people, aged 21 years or more, with HD. There has also been a surprising doubling of the HD population between 1990 and 2010. Many factors may have caused this increase, including more accurate diagnoses, better and more available therapies and an improved life expectancy, even with HD. There also appears to be a greater willingness to register a diagnosis of HD in patients’ electronic medical records. Such a high prevalence of HD requires more ingenuity and responsiveness in its care. How to appropriately care for, and respond to, so many individuals and families coping with the exigencies of HD demands our greatest resolve and imagination.
Lane detection using smoothing spline

In vehicle safety research area, a Lane Departure Warning System (LDWS) is of a major interest. The system, however, needs lane detection and lane tracking as basis. This paper proposes an algorithm used to detect the lane as an initialization for lane tracking. The algorithm takes an image captured from a video stream as an input. It then segments the image into a number of small tiles and applies the Principle Component Analysis (PCA) to each tile in order to find the centroid and the principle axis. After recovering the original image from all of the tiles, the probable lines are found using k-means clustering techniques. Two out of these lines will be selected to be the lanes of the road. At the end, the corresponding centroid points are chosen to be represented as control points of smoothing splines represented the lane. This obtained splines are aimed to be used further as an initialization of the lane tracking algorithm. The method has been tested and worked fine. However, its limitation has also been stated and needed to be improved in the future.
Algorithms for predicting the secondary structure of pairs and combinatorial sets of nucleic acid strands

Secondary structure prediction of nucleic acid molecules is a very important problem in computational molecular biology. In this thesis we introduce two new algorithms for: (1) secondary structure prediction of pairs of nucleic acid molecules (PairFold), and (2) finding which sequences, formed from a combinatorial set of nucleic acid strands, have the most stable secondary structures (CombFold). Our algorithms run in polynomial time in the sequences lengths and are extensions of the free energy minimization algorithm [72] for secondary structure prediction without pseudoknots, using the nearest neighbour thermodynamic model. Predicting hybridization of pairs of molecules is motivated by important applications such as ribozyme - mRNA target duplexes, primer binding prediction and DNA code design. Finding the most stable concatenations in combinatorial sets of strands is useful for SELEX experiments and for testing whether sets in DNA computing or tag libraries concatenate without secondary structure. Our results for PairFold predictions show over 80% accuracy for sequences of up to 100 nucleotides. The performance goes down as the sequences increase in length and as the number of non-canonical base pairs, pseudoknots and tertiary interactions, none of these considered here, increases. The accuracy of CombFold is similar to that of the free energy minimization algorithm for single strands, being just a polynomial method for structure prediction of a combinatorial set of strands. We show that although complex, CombFold can quickly predict large concatenations of sets drawn from the literature. In the future, these two algorithms can be combined to predict the most stable duplexes formed by two combinatorial sets.
Classifier Combination Techniques and Support Vector Machine : An Application to Character Recognition

This paper presents a comparative evaluation of the performance of linear and non-linear techniques for combining the outputs of an ensemble of neural network classifiers, expressed as posterior probabilities, in the recognition of hand-written digits. The ensemble consists of Multi-layer Perceptron (MLP) modules, characterized by different topologies. Two different methods are used for the coefficient selection in the linear combination of the network outputs, i.e. the Bayesian and the error correlation matrix approaches. In the case of the non liner output combination, a MLP module is applied. The results obtained with different combination schemes are also compared with the performances of Support Vector Machine (SVM) classifiers. The comparison between the two sets of experiments evidences that SVMs perform better than combination schemes revealing a higher generalization capability. However, the complexity of the SVM training procedure could be considered a limitation in the analysis real-world problems.
Drosophila as a focus in olfactory research: Mapping of olfactory sensilla by fine structure, odor specificity, odorant receptor expression, and central connectivity

This review intends to integrate recent data from the Drosophila olfactory system into an up‐to‐date account of the neuronal basis of olfaction. It focuses on (1) an electron microscopic study that mapped a large proportion of fruitfly olfactory sensilla, (2) large‐scale electrophysiological recordings that allowed the classification of the odor response spectra of a complete set of sensilla, (3) the identification and expression patterns of candidate odorant receptors in the olfactory tissues, (4) central projections of neurons expressing a given odorant receptor, (5) an improved glomerular map of the olfactory center, and (6) attempts to exploit the larval olfactory system as a model of reduced cellular complexity. These studies find surprising parallels between the olfactory systems of flies and mammals, and thus underline the usefulness of the fruitfly as an olfactory model system. Both in Drosophila and in mammals, odorant receptor neurons appear to express only one type of receptor. Neurons expressing a given receptor are scattered in the olfactory tissues but their afferents converge onto a few target glomeruli only. This suggests that in both phyla, the periphery is represented in the brain as a chemotopic map. The major difference between mammals and fruitflies refers to the numbers of receptors, neurons, and glomeruli, which are largely reduced in the latter, and particularly in larvae. Yet, if activated in a combinatorial fashion, even this small set of elements could allow discrimination between a vast array of odorants. Microsc. Res. Tech. 55:284–296, 2001. © 2001 Wiley‐Liss, Inc.