Miracle-dz/UniFAR-FTUs huggingface dataset:
Birth rates of SGRs and AXPs: delayed amplification of magnetic field

We present new determination of the birth rate of AXPs and SGRS and their associated SNRs. We find a high birth rate of 1/(500 yr) to 1/(1000 yr) for AXPs/SGRs and their associated SNRs. These high rates suggest that all mas- sive stars (greater than � (23-32)M⊙) give rise to remnants with magnetar-like fields. Observations indicate a limited fraction of high magnetic fields in these pro- genitors thus our study necessarily implies magnetic field amplification. Dynamo mechanisms during the birth of the neutron stars require spin rates much faster than either observations or theory indicate. Here, we propose that neutron stars form with normal (� 10 12 G) magnetic fields, which are then amplified to 10 14 - 10 15 G after a delay of hundreds of years. The amplification is speculated to be a consequence of color ferromagnetism and to occur after the neutron star core reaches quark-deconfinement density. This delayed amplification alleviates many difficulties in interpreting simultaneously the high birth r high magnetic fields, and state of isolation of AXPs/SGRs and their link to massive stars.
Impact of quercetin-induced changes in drug-metabolizing enzyme and transporter expression on the pharmacokinetics of cyclosporine in rats

The aim of the present study was to evaluate whether quercetin (Que) modulates the mRNA and protein expression levels of drug-metabolizing enzymes (DMEs) and drug transporters (DTs) in the small intestine and liver, and thus modifies the pharmacokinetic profile of cyclosporine (CsA) in rats. This two-part study evaluated the pharmacokinetic profiles of CsA in the presence or absence of Que (experiment I) and the involvement of DMEs and DTs (experiment II). In experiment I, 24 rats received single-dose CsA (10 mg/kg) on day 1, single-dose Que (25, 50 and 100 mg/kg/day; eight rats in each group) on days 3–8, and concomitant CsA/Que on day 9. In experiment II, the mRNA and protein expression levels of cytochrome P (CYP)3A1, CYP3A2, UDP glucuronosyltransferase family 1 member A complex locus, organic anion-transporting polypeptide (OATP)2B1, OATP1B2, P-glycoprotein, breast cancer resistance protein, and multidrug resistance-associated protein 2 in the small intestine and liver of rats were analyzed following oral administration of Que at 25, 50 and 100 mg/kg in the presence or absence of CsA (10 mg/kg) for seven consecutive days. Co-administration of Que (25,50 and 100 mg/kg) decreased the maximum serum concentration of CsA by 46, 50 and 47% in a dose-independent manner. In addition, the area under the curve to the last measurable concentration and area under the curve to infinite time were decreased, by 21 and 16%, 30 and 33%, and 33 and 34% (P<0.01), respectively. However, the mRNA and protein expression levels of the above-mentioned DMEs and DTs were inhibited by Que in a dose-dependent manner (P<0.01) to a similar extent in the small intestine and liver. It was demonstrated that Que was able to reduce the bioavailability of CsA following multiple concomitant doses in rats. Overlapping modulation of intestinal and hepatic DMEs and DTs, as well as the DME-DT interplay are potential explanations for these observations.
Robust multi-patch tracking

In this paper, we propose a robust and fast multi-patch visual tracking algorithm within the Bayesian inference framework. The target template is initialized by selecting the object in the first frame manually and dividing it into small patches. For one certain frame, target candidates are sampled with the state transition model. Each candidate is divided into patches in the same way as the target template. By comparing the candidate's patches with the corresponding template patches, we can get the candidate's likelihood. The tracking result is the candidate which Maximum a Posteriori estimation. After that, tracking is continued using the Bayesian state inference and template update. Our approach can handle appearance variation, occlusion, illumination change, scale variation, rotation and cluttered background. The tracker is fast and performs favorably against several state-of-the-art trackers on challenging sequences.
Nucleolin Regulates Phosphorylation and Nuclear Export of Fibroblast Growth Factor 1 (FGF1)

Extracellular fibroblast growth factor 1 (FGF1) acts through cell surface tyrosine kinase receptors, but FGF1 can also act directly in the cell nucleus, as a result of nuclear import of endogenously produced, non-secreted FGF1 or by transport of extracellular FGF1 via endosomes and cytosol into the nucleus. In the nucleus, FGF1 can be phosphorylated by protein kinase C δ (PKCδ), and this event induces nuclear export of FGF1. To identify intracellular targets of FGF1 we performed affinity pull-down assays and identified nucleolin, a nuclear multifunctional protein, as an interaction partner of FGF1. We confirmed a direct nucleolin-FGF1 interaction by surface plasmon resonance and identified residues of FGF1 involved in the binding to be located within the heparin binding site. To assess the biological role of the nucleolin-FGF1 interaction, we studied the intracellular trafficking of FGF1. In nucleolin depleted cells, exogenous FGF1 was endocytosed and translocated to the cytosol and nucleus, but FGF1 was not phosphorylated by PKCδ or exported from the nucleus. Using FGF1 mutants with reduced binding to nucleolin and a FGF1-phosphomimetic mutant, we showed that the nucleolin-FGF1 interaction is critical for the intranuclear phosphorylation of FGF1 by PKCδ and thereby the regulation of nuclear export of FGF1.
Using Q-learning to model bidding behaviour in electricity market simulation

While the choice between two pricing rules, namely Uniform pricing rule and Pay-as-bid pricing rule, has led to a continuous debate in the electricity market establishment process, little attention has been paid to the Vickrey pricing rule. This paper presents an agent-based model to examine the employment of Uniform and Vickrey pricing rules in a deregulated electricity market. Using Q-learning in repetitive trading process, generator agents learn the market characteristics and seek to maximise their revenue by exploring bidding strategies. A look up table is utilised to memorise agents' bidding experience that help the agents improve their strategies. Supply quantity withholding and generators' collusion phenomenon have been observed in this study under certain market arrangements. The implication of these two pricing rules on the total dispatch costs and generators' profit are discussed in this paper.
Risk Attribution and Portfolio Optimizations Under Tracking-Error Constraints

This paper examines wether risk attribution process is consistent with portfolio optimizations under tracking-error constraints. Since Mina (2003), Bertrand (2005) and Menchero and Hu (2006), risk attribution has been widely used in the performance attribution process. This article presents an extension of our previous work on risk attribution to others portfolio optimization contexts. It is shown that only optimization under the tracking-error constraint alone is consistent with the risk attribution process. Indeed, as soon as additional constraints (e.g. on total risk) are introduced, the risk attribution method conflicts with the performance attribution process, preventing us from legitimating all the optimal decisions taken by a portfolio manager.
Mining framework usage changes from instantiation code

Framework evolution may break existing users, which need to be migrated to the new framework version. This is a tedious and error-prone process that benefits from automation. Existing approaches compare two versions of the framework code in order to find changes caused by refactorings. However, other kinds of changes exist, which are relevant for the migration. In this paper, we propose to mine framework usage change rules from already ported instantiations, the latter being applications build on top of the framework, or test cases maintained by the framework developers. Our evaluation shows that our approach finds usage changes not only caused by refactorings, but also by conceptual changes within the framework. Further, it copes well with some issues that plague tools focusing on finding refactorings such as deprecated program elements or multiple changes applied to a single program element.
Immune-based particle swarm algorithm for variable speed limits control

The steady increase of traffic demand over the past decades has led to a high rate of congestion and traffic accidents on many parts of road networks. It is essential to solve traffic congestion and traffic accidents. Variable speed limit control is an effective method to relieve traffic congestion, improve traffic safety and steady traffic flow. In this paper, we propose a novel variable speed limit control method based on immune particle swarm algorithm. The algorithm can maintain the balance of vehicle speed. It also can alleviate the occurrence of traffic congestion and steady traffic flow. Immune-based particle swarm algorithm for variable speed limit control provides a new approach to solve the unbalance of the speed and avoid traffic accidents.
Learning Translations for Tagged Words: Extending the Translation Lexicon of an ITG for Low Resource Languages

We tackle the challenge of learning part-of-speech classified translations as part of an inversion transduction grammar, by learning translations for English words with known part-of-speech tags, both from existing translation lexica and from parallel corpora. When translating from a low resource language into English, we can expect to have rich resources for English, such as treebanks, and small amounts of bilingual resources, such as translation lexica and parallel corpora. We solve the problem of integrating these heterogeneous resources into a single model using stochastic Inversion Transduction Grammars, which we augment with wildcards to handle unknown translations.
User Feedback as a First Class Citizen in Information Integration Systems

User feedback is gaining momentum as a means of addressing the diculties underlying information integration tasks. It can be used to assist users in building information integration systems and to improve the quality of existing systems, e.g., in dataspaces. Existing proposals in the area are conned to specic integration sub-problems considering a specic kind of feedback sought, in most cases, from a single user. We argue in this paper that, in order to maximize the benets that can be drawn from user feedback, it should be considered and managed as a rst class citizen. Accordingly, we present generic operations that underpin the management of feedback within information integration systems, and that are applicable to feedback of dierent kinds, potentially supplied by multiple users with dierent expectations. We present preliminary solutions that can be adopted for realizing such operations, and sketch a research agenda for the information integration community.
Social TV: real-time social media response to TV advertising

We link the content of real-time social media response to characteristics of the Super Bowl 2012 TV advertisements, including their advertisers' social media strategies and the categories of the products being advertised. We analyze millions of social media posts about approximately forty-five advertisements during the 2012 Super Bowl. While there are many studies that focus on the popularity of and sentiment of response to Super Bowl ads, we believe ours is the first to go deeper and analyze the textual "content" to understand better the factors contributing to specific types of social media responses among a number of TV advertising dimensions. In this study, we show that the level of online consumer engagement, measured by attracting new followers on Twitter during the Superbowl, can be linked to whether the brand that advertised had a social media strategy or not. In addition, we show that sentiment response, a measure often use to quantify the effectiveness of TV ads, varies by demographic category.
Natural language processing in a Japanese text-to-speech system

A Japanese text-to-speech system is developed that provides continuous generation of natural speech from newspaper articles and other Japanese texts written in an unrestricted mixture of Kanji (ideograms. — 7000 Chinese characters), Kana (two types of phonograms, — 200 characters), alphanumerics and marks (including ¥, &, $, etc.). In the newly developed system, the Japanese texts are analyzed in terms of both grammar and meaning where texts have ambiguities, but in terms of grammar only where there is no ambiguity. The dictionary in this system contains ordinarily segmented words for various open texts and special compound words, most of which are proper nouns that have multiple readings when segmented. This reduces the percentage of erroneous readings from 2.2% for the conventional method to 0.2% and of erroneous accentuation from 20% to 5%. The dictionary containing approximately 430,000 words uses a resident index consists of the first two characters of the words. This approach for accessing is adopted because many Japanese words consists of two basic characters. This improved the systems efficiency in maintaining a higher level of accuracy. These improvements will provide advanced information exchanges to subscriber networks.
A Genome Browser Integrating Client-side Private Data and Server-side Large-scale Public Annotations

Complete genome sequences of model organisms have provided a researcher a platform to integrate various sorts of biological data in the same coordinate; it helps to investigate the profound biological mechanism which is difficult to be validated by each piece. There are several web genome browsers, such as Ensembl [1] and University of California Santa Cruz Genome Browser [2], that visualize a genome sequence along with multifarious omic datasets: gene, marker, EST, cDNA, and protein entries. A user can explore the genomic region of one’s interest on them; however they cannot import client-side local data onto them to make a direct comparison between the private data and public databases. The Distributed Annotation System (DAS) [3] has been released to share annotations among DAS servers, and to produce an integrated view on a DAS client. Yet it requires building and maintaining a server to incorporate a private data. A user often gets confused by the difference of interfaces and functions between the HTML-based web-browser client and a native-application client. It is important for any genome browser to provide a consistent interface, whether it is running as a web-browser-hosted client or a native application, and can integrate the user’s private data without housing a server. We have developed a genome browser, GPS_client, which is a native-application running on Macintosh and Windows OS, and integrates, on the user’s client machine, a private data and the data from a public biological database: Genome Phenome Superhighway (GPS) [4, 5]. GPS_client supplies the same level of analytical functions and visualization as the web GPS service. A user needs no server arrangements but his/her own data file, which is directly loaded to the GPS_client on the client-side machine.
q-gram based database searching using a suffix array (QUASAR)

With the increasing amount of DNA sequence information deposited in public databases, searching for similarity to a query sequence has become a basic operation in molecular biology. But even today's fast algorithms reach their limits when applied to all-versus-all comparisons of large databases. Here we present a new database searching al-gorithm called QUASAR (Q-gram Alignment based on Suf-(cid:12)x ARrays) which was designed to quickly detect sequences with strong similarity to the query in a context where many searches are conducted on one database. Our algorithm applies a modi(cid:12)cation of q-tuple (cid:12)ltering implemented on top of a su(cid:14)x array. Two versions were developed, one for a RAM resident su(cid:14)x array and one for access to the su(cid:14)x array on disk. We compared our implementation with BLAST and found that our approach is an order of magnitude faster. It is, however, restricted to the search for strongly similar DNA sequences as is typically required, e.g., in the context of clustering expressed sequence tags (ESTs).
On the Effect of the Fukushima Nuclear Accident on European Power Futures

The March 2011 accident at the Fukushima Daiichi nuclear power plant (the “event”) in Japan has had global repercussions. We conduct a detailed event study on German and French power futures’ prices and returns and find that price responses for different underlyings are not significantly different between the two countries. This is a surprising result, as the anticipated policies in the aftermath of the event regarding nuclear power were markedly different for France and Germany. Furthermore, we identify a cumulative abnormal return of approximately 10 percent during the event window (5 days), whereby futures with delivery dates further in the future are less affected than futures with shorter time to maturity. We conclude that the decision to subsequently phase out nuclear power in Germany was expected by market participants and efficiently processed by the electricity market institutions, as price reactions were instantaneous.
Efficient maximum-likelihood noncoherent orthogonal STBC detection

Orthogonal space-time block coding (STBC) offers linear-complexity one-shot maximum-likelihood (ML) reception when the channel coefficients are known to the receiver. However, when the channel coefficients are unknown, the optimal receiver takes the form of sequence detection. In this work, we prove that ML noncoherent sequence detection can always be performed in polynomial time with respect to the block length for orthogonal STBC and Rayleigh distributed channel coefficients. Using recent results on efficient maximization of reduced-rank quadratic forms over finite alphabets, we develop a novel algorithm that performs ML noncoherent orthogonal STBC detection with polynomial complexity in the block length. The order of the polynomial complexity of the proposed receiver is determined by the number of transmit and receive antennas.
Applying Complementary Neural Networks to Porosity Prediction in Well Log Data Analysis

This paper presents a novel approach to the regression problem using complementary neural networks (CMTNN). The experiment is realized to the problem of porosity prediction in well log data analysis. Two sets of complementary neural networks are created. The first set is a pair of opposite neural networks trained to predict degree of truth porosity and degree of falsity porosity values. The second set is also a pair of opposite neural networks trained with the same input and parameters as the first set; however, they are trained to predict degree of truth gap values and degree of falsity gap values. The gap is the difference between the truth and falsity porosity values. The predicted truth and falsity gap values are combined and considered as the predicted gap. After that, the predicted gap is compared to the gap which is the difference between the predicted truth and falsity porosity values. The difference between both gaps is considered as an uncertainty value. Moreover, uncertainty of type error occurred in the prediction is also considered in this study. In this paper, the aggregation based on dynamic weight combination is proposed. Uncertainty values are used as weights associated with the truth and falsity porosity values. The results obtained from our approach are compared to results obtained from traditional models that use only the truth porosity values in the prediction. These existing models are based on backpropagation neural network and support vector regression. We found that our approach improves performance compared to those existing models.
Dyskinesias and associated psychiatric disorders following streptococcal infections

Background: The classical extrapyramidal movement disorder following β haemolytic streptococcus (BHS) infection is Sydenham’s chorea (SC). Recently, other post-streptococcal movement disorders have been described, including motor tics and dystonia. Associated emotional and behavioural alteration is characteristic. Aims: To describe experience of post-streptococcal dyskinesias and associated co-morbid psychiatric features presenting to a tertiary referral centre 1999–2002. Methods: In all patients, dyskinetic movement disorders followed BHS pharyngeal infection. BHS infection was defined by pharyngeal culture of the organism, or paired streptococcal serology. Movement disorders were classified according to international criteria, and validated by experienced child neurologists. Psychiatric complications were defined using ICD-10 criteria using a validated psychiatric interview. Results: In the 40 patients, the following dyskinetic movement disorders were present: chorea (n = 20), motor tics (n = 16), dystonia (n = 5), tremor (n = 3), stereotypies (n = 2), opsoclonus (n = 2), and myoclonus (n = 1). Sixty five per cent of the chorea patients were female, whereas 69% of the tic patients were male. ICD-10 psychiatric diagnoses were made in 62.5%. Using the same psychiatric instrument, only 8.9% of UK children would be expected to have an ICD-10 psychiatric diagnosis. Emotional disorders occurred in 47.5%, including obsessive-compulsive disorder (27.5%), generalised anxiety (25%), and depressive episode (17.5%). Additional psychiatric morbidity included conduct disorders (27.5%) and hyperkinetic disorders (15%). Psychiatric, movement, and post-streptococcal autoimmune disorders were commonly observed in family members. At a mean follow up of 2.7 years, 72.5% had continuing movement and psychiatric disorders. Conclusion: Post-streptococcal dyskinesias occur with significant and disabling psychiatric co-morbidity and are potential autoimmune models of common “idiopathic” movement and psychiatric disorders in children. Multiple factors may be involved in disease expression including genetic predisposition, developmental status, and the patient’s sex.
A wavelet-based anomaly detection for outbound network traffic

Monitoring and detecting network anomalies are indispensable activities for network administrators. Most anomaly detection techniques focus on inbound traffic (traffic from the Internet entering a customer network) rather than outbound traffic. However, anomalous inbound traffic patterns will be significantly different from anomalous outbound traffic. For network operators, outbound traffic is as important as inbound traffic because they can monitor unwanted activities in their networks to prevent it from affecting other networks.
No need to talk to strangers Cooperation of Interactive Software with R as Moderator

Interactive software is very helpful for exploratory analysis of data, but most packages are optimized only for a specific field of work. Interaction between the various projects is limited. On the other hand very flexible statistical software, such as R, S or S-plus exist covering the needs of almost all fields, but without support for interactive graphics. This paper describes our experience with different approaches linking R with interactive software using flat files and the Omegahat interface. Such linking directly extends capabilities of the interactive software without the need for special communication layers such as CORBA. We also show how such an interface can be used to implement cooperation between different interactive software packages with R as moderator. Practical examples are given based on our current projects Klimt and Mondrian.