Miracle-dz/UniFAR-FTUs huggingface dataset:
Event Detection Based on Noisy Object Information

This paper proposes an event detection method using noisy object information. Some events have a close connection with objects, and the objects related to the event often appear with the event in a video. For example, if an event "Grooming an animal" appears in a video, an animal and people should appear in the video. If we detect the objects that have a close connection with the events, we can detect the events based on the object detection results. However, it is inevitable that the object detector gives false alarms and the object information would be noisy. Thus, we use the information about objects which is robust against false alarms of object detection. In our experiments, we evaluated how the information on objects is effective for event detection of videos. From the results, the proposed method showed better or equivalent detection results than a state-of-theart method in some events, even the object detector gave false alarms.
The Implications of the Local Configuration of a Standard eProcurement System on the Organisation Power Circuits

The paper presents a case study of the configuration and use of a standard e-procurement packaged software. It focuses on reviewing the new business processes that the system introduces. It, then, applies the Actor Network Theory’s (ANT) notions of associations and power in order to illustrate that the new business processes introduce a new power circuit effect. In so doing, it aims to provide insight into the performing social and technical network that the newly introduced system constructs. The paper then suggests that the system configuration should not only focus on the technical functionality of the system but it should also consider how the system’s new business processes would affect the social, organisational, and political architecture of the organisation and its established performing power networks.
Characterization of a male reproductive transcriptome for Peromyscus eremicus (Cactus mouse)

Rodents of the genus Peromyscus have become increasingly utilized models for investigations into adaptive biology. This genus is particularly powerful for research linking genetics with adaptive physiology and behaviors, and recent research has capitalized on the unique opportunities afforded by the ecological diversity of these rodents. However, well characterized genomic and transcriptomic data is intrinsic to explorations of the genetic architecture responsible for ecological adaptations. This study characterizes a reproductive transcriptome of male Peromyscus eremicus (Cactus mouse), a desert specialist with extreme physiological adaptations to water limitation. We describe a reproductive transcriptome comprising three tissues in order to expand upon existing research in this species and to facilitate further studies elucidating the genetic basis of potential desert adaptations in male reproductive physiology.
Building an Open-Source Development Infrastructure for Language Technology Projects

The article presents the Giellatekno & Divvun language technology resources, more specifically the effort to utilise open-source tools to improve the build infrastructure, and the solutions to help adapt to best practices for software development. The article especially discusses how the infrastructure has been remade to cope with an increasing number of languages without incurring extra overhead for the maintainers, and at the same time let the linguists concentrate on the linguistic work. Finally, the article discusses how a uniform infrastructure like the one presented can be used to easily compare languages in terms of morphological or computational complexity, coverage or for cross-lingual applications.
Pharmacokinetics of mycophenolic acid in liver transplant patients after intravenous and oral administration of mycophenolate mofetil

The bioavailability of mycophenolic acid (MPA) after oral administration of mycophenolate mofetil (MMF) has been reported to be more than 90% in healthy volunteers, and in kidney and thoracic organ transplant patients. Such information is limited in liver transplant (LTx) patients. The present study compares the pharmacokinetics of MPA after intravenous (IV) and oral administrations of MMF in LTx recipients. Pharmacokinetic parameters were calculated using WinNonlin software. A total of 12 deceased donor LTx patients initially received IV MMF and were switched to oral MMF after 2‐7 days (mean, 3.3 ± 1.7) when oral feeds were started. Multiple blood samples were drawn immediately prior to and after IV or oral MMF and the plasma concentration of MPA was measured. The mean peak plasma concentrations and the area under the plasma concentration vs. time curve (AUC) were significantly higher after IV MMF compared to oral MMF (peak plasma concentrations of 10.7 ± 2.1 μg/mL for IV vs. 4.5 ± 2.8 μg/mL for oral; P = 0.0001; and AUC of 28.9 ± 7.1 μg · hr/mL for IV vs. 12.8 ± 4.2 μg · hr/mL for oral; P = 0.0001). The oral bioavailability of MPA was 48.5 ± 18.7%. The systemic clearance, half‐life, and steady state volume of distribution of MPA were 26.9 ± 6 L/hour, 5.5 hours, and 85 liters, respectively. The terminal disposition half‐life was not significantly different between the 2 routes of administration. In conclusion, during the early postoperative period, LTx recipients have MPA exposure with oral MMF of less than half that of IV MMF. Use of IV MMF immediately post‐LTx may provide an immunological advantage. Liver Transpl 13:791–796, 2007. © 2007 AASLD.
Extracting verified decision procedures: DPLL and Resolution

This article is concerned with the application of the program extraction technique to a new class of problems: the synthesis of decision procedures for the classical satisfiability problem that are correct by construction. To this end, we formalize a completeness proof for the DPLL proof system and extract a SAT solver from it. When applied to a propositional formula in conjunctive normal form the program produces either a satisfying assignment or a DPLL derivation showing its unsatisfiability. We use non-computational quantifiers to remove redundant computational content from the extracted program and translate it into Haskell to improve performance. We also prove the equivalence between the resolution proof system and the DPLL proof system with a bound on the size of the resulting resolution proof. This demonstrates that it is possible to capture quantitative information about the extracted program on the proof level. The formalization is carried out in the interactive proof assistant Minlog.
The Value of Trading Consolidation: Evidence from the Exercise of Warrants

Abstract We study the effect of trading consolidation by examining the response of liquidity and stock price to the exercise of deep in-the-money corporate warrants. This enables a relatively clean test of the value of trading consolidation. The exercise at the warrant expiration is fully anticipated and has no information content. An effect can come from the value of trading consolidation that improves liquidity. Indeed, we find that liquidity and stock prices both increase significantly at warrant expiration. Further, the price increase is positively related to the pre-exercise extent of fragmentation, to post-exercise improvement in stock liquidity, and to the proportional increase in the number of shares following the warrant exercise.
Oxidative Metabolites of Diisononyl Phthalate as Biomarkers for Human Exposure Assessment

Diisononyl phthalate (DINP) is a complex mixture of predominantly nine-carbon branched-chain dialkyl phthalate isomers. Similar to di(2-ethylhexyl) phthalate, a widely used phthalate, DINP causes antiandrogenic effects on developing rodent male fetuses. Traditionally, assessment of human exposure to DINP has been done using monoisononyl phthalate (MINP), the hydrolytic metabolite of DINP, as a biomarker. However, MINP is only a minor urinary metabolite of DINP. Oxidative metabolites, including mono(carboxyisooctyl) phthalate (MCIOP), mono(oxoisononyl) phthalate (MOINP), and mono(hydroxyisononyl) phthalate (MHINP) are the major urinary metabolites in DINP-dosed rats. The urinary concentrations of MINP, MCIOP, MOINP, and MHINP were measured in 129 adult anonymous human volunteers with no known exposure to DINP. Although MINP was not present at detectable levels in any of the samples analyzed, MCIOP, MHINP, and MOINP were detected in 97, 100, and 87% of the urine samples at geometric mean levels equal to 8.6, 11.4, and 1.2 ng/mL, respectively. The concentrations of all three oxidative metabolites were highly correlated with each other (p < 0.0001), which confirms a common precursor. MCIOP was excreted predominantly as a free species, whereas MOINP was excreted mostly in its glucuronidated form. The percentage of MHINP excreted either glucuronidated or in its free form was similar. The significantly higher frequency of detection and urinary concentrations of oxidative metabolites than of MINP suggest that these oxidative metabolites are better biomarkers of exposure assessment of DINP than is MINP. Therefore, we concluded that the prevalence of human exposure to DINP is underestimated by using MINP as the sole DINP urinary biomarker.
The topoisomerase I gene from Ustilago maydis: sequence, disruption and mutant phenotype.

The Ustilago maydis genomic TOP1 gene encoding DNA topoisomerase I was cloned by amplifying a gene fragment using the polymerase chain reaction, and using this fragment to search a genomic DNA library by hybridization. The predicted peptide sequence exhibited 30-40% identity to other eukaryotic TOP1 genes, yet differed in several features. First, an unusually long acidic region was identified near the amino terminus (28/29 residues are acidic), which resembles other nucleolar peptide motifs. Second, an atypical carboxy-terminal 'tail', absent in other TOP1 genes, followed the active site tyrosine residue. A top1 gene disruption mutant was constructed by replacing the genomic TOP1 gene, with a top1::HygR null allele. This mutant lost the abundant topoisomerase I activity evident in wild-type U.maydis, and displayed a subtle coloration phenotype evident during cell senescence.
TopRecs+: Pushing the Envelope on Recommender Systems

Spurred by the advances in collaborative filtering, by applications that form the core business of companies such as Amazon and Netflix, and indeed by incentives such as the famous Netflix Prize, research on recommender systems has become quite mature and sophisticated algorithms that enjoy high prediction accuracy have been developed [1]. Most of this research has been concerned with what we regard as first generation recommender systems. Ever since the database community got interested in recommender systems, people have begun asking questions related to functionality. This includes developing flexible recommender systems which can efficiently compute top-k items within their framework [18] and using recommender systems to design packages subject to user specified constraints [11]. Significant effort has been dedicated to improving accuracy of recommendations. Many of the recommendation algorithms, while highly accurate, have scalability issues. The number of items managed by modern information systems is growing rapidly. Therefore, scalability is one of the serious issues for future generation recommender systems. Recommendation methods try to capture personalized patterns in user feedback data by making assumptions and keeping dense summaries of data. User feedback is typically represented in the form of a sparse matrix that stores existing ratings of users on items. There are two groups of methods – modelbased and memory-based [1]. Model-based methods assume there is a lower dimensional underlying parametric model that has generated the ratings matrix. These methods aim at finding optimal parameter values for the model, given the observed data. Memory-based methods, on the other hand, calculate similarities between users or items and use these similarities for aggregating existing ratings and predicting unknown ratings. Thus, any item recommendation process has two steps: (1) an off-line training phase that captures personalized profiles (either as a model or as a similarity matrix); (2) an online recommendation generation process that uses the latest up-to-date model or similarity matrix to return top-k recommendations for a user. Any approach for improving scalability of item recommendation needs to pay attention to both profile building and recommendation generation. In section 2, we show how better scalability can be achieved in both aspects for one of the most popular and practical recommendation algorithms. In addition to efficiency and scalability, an important limitation of classical recommender systems is that they only provide recommendations consisting of single items, e.g., books or DVDs. It has been recognized several applications call for composite recommendations consisting of sets, lists or other collections. For example, in trip planning, a user is interested in suggestions for a set of places to visit, or points of interest (POI). If the recommender system only provides a ranked list of POIs, the user has to manually figure out the most suitable set of POIs, which is often non-trivial as there may be a cost to visiting each place (time, price, etc.), and the
Auditing the defense against cross site scripting in web applications

Majority attacks to web applications today are mainly carried out through input manipulation in order to cause unintended actions of these applications. These attacks exploit the weaknesses of web applications in preventing the manipulation of inputs. Among these attacks, cross site scripting attack — malicious input is submitted to perform unintended actions on a HTML response page — is a common type of attacks. This paper proposes an approach for thorough auditing of code to defend against cross site scripting attack. Based on the possible methods of implementing defenses against cross site scripting attack, the approach extracts all such defenses implemented in code so that developers, testers or auditors could check the extracted output to examine its adequacy. We have also evaluated the feasibility and effectiveness of the proposed approach by applying it to audit a set of real-world applications.
A New Real Differentiator with Low-Pass Filter in Control Loop

—The paper offers a new real differentiator realized as a closed loop system with a low-pass filter (LPF) within the control loop. The cascade of the LPF, with a considerable time constant, and an integrator is treated as the controlled object. The controller is of derivative (D) type with a limiter and a high gain. The closed loop time constant of the obtained real differentiator is small. The limiter and the LPF significantly attenuate noises. Simulation comparison between the proposed differentiator and the known differentiators reveals certain advantages of the proposed solution under appropriate conditions.
Relationship between demographic variables and collegiate athletes’ perceptions of social support from head coaches

Previous research has demonstrated the importance of social support during the rehabilitation of an injury. In particular, athletes view coaches as a primary source of social support. However, coaches have been an inconsistent or insufficient source of social support. Certain demographic variables may be related to differences in perceived social support from coaches. The purpose of this study was to describe injured collegiate athletes’ perceptions of social support and explore whether differences in perceived social support from coaches existed due to contextual or situational factors. A survey on perceived social support from coaches was completed by athletes (n = 246) at five NCAA institutions. Results support previous findings that injured athletes perceive coaches as sources of moderate to high amounts of social support. Additionally, significant differences emerged in perceived social support based on the level of competition and type of sport. These results reaffirm the role head coaches play in the rehabilitation of injured athletes. Understanding variables that influence how an athlete perceives social support from a coach could improve rehabilitation outcomes.
Association of the novel aminoglycoside resistance determinant RmtF with NDM carbapenemase in Enterobacteriaceae isolated in India and the UK.

OBJECTIVES 16S rRNA methyltransferases are an emerging mechanism conferring high-level resistance to clinically relevant aminoglycosides and have been associated with important mechanisms such as NDM-1. We sought genes encoding these enzymes in isolates highly resistant (MIC >200 mg/L) to gentamicin and amikacin from an Indian hospital and we additionally screened for the novel RmtF enzyme in 132 UK isolates containing NDM. METHODS All highly aminoglycoside-resistant isolates were screened for armA and rmtA-E by PCR, with cloning experiments performed for isolates negative for these genes. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry was used to determine the methylation target of the novel RmtF methyltransferase. RmtF-bearing strains were characterized further, including susceptibility testing, PFGE, electroporation, PCR-based replicon typing and multilocus sequence typing of rmtF-bearing plasmids. RESULTS High-level aminoglycoside resistance was detected in 140/1000 (14%) consecutive isolates of Enterobacteriaceae from India. ArmA, RmtB and RmtC were identified among 46%, 20% and 27% of these isolates, respectively. The novel rmtF gene was detected in 34 aminoglycoside-resistant isolates (overall prevalence 3.4%), most (59%) of which also possessed a bla(NDM) gene; rmtF was detected in 6 NDM producers from the UK. It was found on different plasmid backbones. Four and two isolates showed resistance to tigecycline and colistin, respectively. CONCLUSIONS RmtF was often found in association with NDM in members of the Enterobacteriaceae and on diverse plasmids. It is of clinical concern that the RmtF- and NDM-positive strains identified here show additional resistance to tigecycline and colistin, current drugs of last resort for the treatment of serious bacterial infections.
iAVATAR

Tags associated with social images are valuable information source for superior image search and retrieval experiences. Due to the nature of tagging, many tags associated with images are not visually descriptive. Consequently, presence of these noisy tags may reduce the effectiveness of tags' role in image retrieval. To address this problem, we demonstrate iAvatar (interActive VisuAl-representative TAgs Relationship) system that uses the notion of Normalized Image Tag Clarity (nitc) to find visual-representative tags. A visual-representative tag effectively describes the visual content of the images. Further, we visually demonstrate relationships between popular tags and visual-representative tags as well as co-occurrence likelihood of a pair of tags associated with a search tag or image using tag relationship graph (trg). We demonstrate various innovative features of iAvatar with a real-world dataset and show that it enriches users' understanding of various important tag features during image search.
A Study of π Aquarii during a Quasi-normal Star Phase: Refined Fundamental Parameters and Evidence for Binarity

We present the results of recent multicolor photometric and high-resolution spectroscopic observations of the bright Be star π Aqr. Observational data collected from the literature were used to study the star's variations over the last four decades. The star is identified with the IR sources F22227+0107 in the IRAS Faint Point Source Catalog and MSX5 G066.0066-44.7392 in the MSX Catalog. The variations in near-IR brightness of π Aqr are found to be among the largest reported for Be stars. Since 1996, the star has shown only weak signs of circumstellar emission, which has allowed us to refine the fundamental stellar parameters: AV = 0.15 mag, Teff = 24,000 K, log g = 3.9, and MV = -2.95 mag. A weak emission component of the Hα line has been detected during the recent quasi-normal star phase. From analysis of the Hα line profiles, we find antiphased radial velocity variations of the emission component and the photospheric absorption, with a period of 84.1 days and semiamplitudes of 101.4 and 16.7 km s-1, respectively. This result suggests that π Aqr may be a binary system consisting of stars with masses of M1 i = 12.4 M☉, M2 i = 2.0 M☉. We also estimate the orbital inclination angle to be between 50° and 75°. We suggest that the photometric, spectroscopic, and polarimetric variations observed during the second half of the 20th century may be due to variable mass transfer between the binary components.
Efficient search for peer-to-peer information retrieval using semantic small world

This paper proposes a semantic overlay based on the small world phenomenon that facilitates efficient search for information retrieval in unstructured P2P systems. In the semantic overlay, each node maintains a number of short-range links which are semantically similar to each other, together with a small collection of long-range links that help increasing recall rate of information retrieval and reduce network traffic as well. Experimental results show that our model can improve performance by 150% compared to Gnutella and by up to 60% compared to the Interest-based model - a similar shortcut-based search technique.
Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data.
Iterative learning control with extended state observer for iteration-varying disturbance rejection

Iterative learning control (ILC) is an effective strategy to deal with repetitive tasks and has been widely applied in industrial systems. Up to now, many control schemes have been proposed to improve the performance of ILC system against iteration-varying disturbances. However, most schemes do not directly utilize disturbance information to attenuate disturbances, which limits the performance of control scheme. In this article, a composite control scheme combining a P-type ILC scheme with disturbance compensation is proposed to improve the performance of systems with iteration-varying disturbances. An extended state observer (ESO) is proposed for disturbance estimates. Then by properly choosing the disturbance compensation gain, the disturbances can be attenuated from the system output. Finally, simulations are carried out to demonstrate the efficiency of the proposed control scheme.