Miracle-dz/UniFAR-FTUs huggingface dataset:
Faster Work Stealing With Return Barriers

Work-stealing is a promising approach for effectively exploiting software parallelism on parallel hardware. A programmer who uses work-stealing explicitly identifies potential parallelism and the runtime then schedules work, keeping otherwise idle hardware busy while relieving overloaded hardware of its burden. However, work-stealing comes with substantial overheads. Our prior work demonstrates that using the exception handling mechanism of modern VMs and gathering the runtime information directly from the victim’s execution stack can significantly reduce these overheads. In this paper we identify the overhead associated with managing the work-stealing related information on a victim’s execution stack. A return barrier is a mechanism for intercepting the popping of a stack frame, and thus is a powerful tool for optimizing mechanisms that involve scanning of stack state. We present the design and preliminary findings of using return barriers on a victim’s execution stack to reduce these overheads. We evaluate our design using classical work-stealing benchmarks. On these benchmarks, compared to our prior design, we are able to reduce the overheads by as much as 58%. These preliminary findings give further hope to an already promising technique of harnessing rich features of a modern VM inside a work-stealing scheduler.
Dynamic sink assignment for efficient energy consumption in wireless sensor networks

Efficient energy consumption is a challenging problem in wireless sensor networks (WSNs) and closely related to extending network lifetime. The usual way of tackling this issue for topologies with fixed link weight and fixed sink location, has been shown to be severely affected by the energy hole problem. In this paper, the energy consumption problem is initially studied for WSNs with fixed sink assignment and it is analytically shown that energy consumption is minimized when the sink is assigned to the node that is the solution of a suitably formulated 1-median problem. This motivates the introduction of a dynamic environment where link weights change based on the energy level and the aggregate traffic load of the adjacent nodes. Then, the sink is adaptively allowed to move among neighbor nodes, according to a scalable sink migration strategy. Simulation results support the analytical claims demonstrating energy consumption reduction and an additional network lifetime increment when migration is employed in the dynamic environment.
Performance evaluation of a particle-enhanced turbidimetric cystatin C assay using the Abbott Aeroset analyser and assessment of cystatin C-based equations for estimating glomerular filtration rate in chronic kidney disease.

INTRODUCTION Measurement of glomerular filtration rate (GFR) is critical for the diagnosis and stratification of chronic kidney disease (CKD). Recent studies have shown that cystatin C is superior to creatinine for the detection of impaired GFR, and several cystatin C-based equations for estimating GFR have been developed for this clinical application. We conducted the present study to assess the applicability of cystatin C as a routine clinical laboratory index and to determine the performance of cystatin C-based equations in estimating GFR in CKD patients in China. METHODS Performance evaluation of particle-enhanced turbidimetric cystatin C assay on the Abbott Aeroset analyser was carried out according to the National Committee for Clinical Laboratory document EP10-A2. Estimated GFR, which was generated from cystatin C-based equations, was compared with measured GFR, which was detected by plasma clearance of 99mTc-DTPA. RESULTS Our cystatin C assay showed a very low total imprecision and linearity drift. All eight cystatin C-based GFR estimating equations underestimated or overestimated GFR as compared with GFR determined by 99mTc-DTPA clearance. CONCLUSION Although the cystatin C assay is acceptable for routine clinical laboratory monitoring, none of the existing cystatin C-based equations were ideal for estimating GFR in Chinese CKD patients.
Learning Science in Immersive Virtual Reality

The goals of the study were (a) to compare the instructional effectiveness of immersive virtual reality (VR) versus a desktop slideshow as media for teaching scientific knowledge, and (b) to examine the efficacy of adding a generative learning strategy to a VR lesson. In Experiment 1, college students viewed a biology lesson about how the human body works either in immersive VR or via a self-directed PowerPoint slideshow on a desktop computer. Based on interest theory, it was predicted that students who learned in immersive VR would report more positive ratings of interest and motivation and would score higher on a posttest covering material in the lesson. In contrast, based on the cognitive theory of multimedia learning, it was predicted that students who learned with a well-designed slideshow would score higher on a posttest, although they might not report higher levels of interest and motivation. The results showed that students who viewed the slideshow performed significantly better on the posttest than the VR group, but reported lower motivation, interest, and engagement ratings. In Experiment 2, students either viewed a segmented VR lesson and produced a written summary after each segment or viewed the original, continuous VR lesson as in Experiment 1. Students who summarized the lesson after each segment performed significantly better on the posttest and the groups did not differ on reported interest, engagement, and motivation. These results support the cognitive theory of multimedia learning and demonstrate the value of generative learning strategies in immersive VR environments.
An FPGA based platform for real time robot localization

The paper presents a hardware architecture for implementing a mobile robot localization approach through encoders' measurements and absolute localization using camera tracking images. This technique has been developed and implemented for the motion of the robot from an initial position towards another desired position, taking into account the tradeoff between complexity (reducing computation time) and performance (reducing traveling time). The proposed method is implemented on FPGA (Field Programmable Gate Array) board through mixed hardware / software implementation in order to ensure fluid robot navigation. Experimental tests on Xilinx Virtex 5 ML507 proved the effectiveness of the proposed architecture since we have obtained a reduction of 85 % in execution time compared to initial MATLAB version developed on a PC platform.
The variable cyclotron line of GX 301–2

We present a 200 ksec observation of the High Mass X-ray Binary GX 301–2 taken in 2000 November with the Rossi X-ray Timing Explorer during the pre-periastron flare and the actual periastron passage of the neutron star. To model the spectrum we use a power law with the Fermi Dirac cutoff and a cyclotron line at higher energies plus either a reflection component or a heavily absorbed partial covering component. Although completely different, both models describe the data equally well. Phase resolved spectra show that the energy and the depth of the cyclotron resonant scattering feature vary strongly with pulse phase: It is deepest in the fall of the main pulse, the rise of the secondary pulse, and the pulse minimum in-between with τC∼0.3. In the other phase bins the line is much less deep with τC∼0.1. The energy of the line correlates strongly with its depth and varies by 25 % from 30.1 keV in the fall of the secondary pulse to 37.9 keV in the fall of the main pulse.
PSO with Predatory Escaping Behavior and Its Application on Shortest Path Routing Problems

Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have been extended to solve various types of optimization problems. However, straightforward application of PSO suffers from premature convergence and lacks of intensification around the local best locations. In this paper, we propose a new particle swarm optimization strategy, namely, particle swarm optimization with predatory escaping behavior (PSO-PE), to solve shortest path routing problems (SPR). PSO-PE uses the predatory particles to enlarge the escaping particles' predation risk. After taking a tradeoff between predation risk and their energy, escaping particles would take different escaping behaviors. This disturbance makes particle swarm achieve social cognition symmetrically, keep the diversity, balance the exploration and exploitation, and avoid the premature convergence. Simulation experiments of solving SPR using PSO-PE have been carried out on the different network topologies consisting of 15-50 nodes. Our approach can find out the optimal path with high success rates. The performance of proposed PSO-PE-based searching method surpasses the straightforward application of PSO and genetic algorithm (GA) for this problem.
Human Microbiota and Ophthalmic Disease

The human ocular surface, consisting of the cornea and conjunctiva, is colonized by an expansive, diverse microbial community. Molecular-based methods, such as 16S rRNA sequencing, has allowed for more comprehensive and precise identification of the species composition of the ocular surface microbiota compared to traditional culture-based methods. Evidence suggests that the normal microbiota plays a protective immunological role in preventing the proliferation of pathogenic species and thus, alterations in the homeostatic microbiome may be linked to ophthalmic pathologies. Further investigation of the ocular surface microbiome, as well as the microbiome of other areas of the body such as the oral mucosa and gut, and their role in the pathophysiology of diseases is a significant, emerging field of research, and may someday enable the development of novel probiotic approaches for the treatment and prevention of ophthalmic diseases.
Influence of DPYD Genetic Polymorphisms on 5-Fluorouracil Toxicities in Patients with Colorectal Cancer: A Meta-Analysis

Our meta-analysis aggregated existing results from relevant studies to comprehensively investigate the correlations between genetic polymorphisms in dihydropyrimidine dehydrogenase (DPYD) gene and 5-fluorouracil (5-FU) toxicities in patients with colorectal cancer (CRC). The MEDLINE (1966∼2013), the Cochrane Library Database (Issue 12, 2013), EMBASE (1980∼2013), CINAHL (1982∼2013), Web of Science (1945∼2013), and the Chinese Biomedical Database (CBM) (1982∼2013) were searched without language restrictions. Meta-analyses were conducted with the use of STATA software (Version 12.0, Stata Corporation, College Station, TX, USA). Seven clinical cohort studies with a total of 946 CRC patients met our inclusion criteria, and NOS scores of each of the included studies were ≥5. Our findings showed that DPYD genetic polymorphisms were significantly correlated with high incidences of 5-FU-related toxicity in CRC patients. SNP-stratified analysis indicated that there were remarkable connections of IVS14+1G>A, 464T>A, and 2194G>A polymorphisms with the incidence of marrow suppression in CRC patients receiving 5-FU chemotherapy. Furthermore, we found that IVS14+1G>A, 496A>G, and 2194G>A polymorphisms were correlated with the incidence of gastrointestinal reaction. Ethnicity-stratified analysis also revealed that DPYD genetic polymorphisms might contribute to the development of marrow suppression and gastrointestinal reaction among Asians, but not among Caucasians. The present meta-analysis suggests that DPYD genetic polymorphisms may be correlated with the incidence of 5-FU-related toxicity in CRC patients.
Designing an E-Learning Platform for Reading Comprehension

In this paper we present an innovative e-learning platform that was specifically designed to support the teaching of reading comprehension skills. First, we describe the principal difficulties of reading in a foreign language and then detail how the multitude of exercise types the presented system has at its disposal, gives support in acquiring reading comprehension skills. We show a few more supportive features of our platform and furthermore present the intuitive WYSIWYG style authoring tool which constitutes a major benefit for language tutors in creating and modifying the contents of the platform. Finally, some technical details are sketched and future developments of the platform are discussed that may also be of particular interest for the maintainers of other, similar e-learning systems.
Internationalisation in higher education –an internationalist perspective

Purpose This paper aims to argue for the significance of internationalism for the internationalisation of higher education. It analyses some conceptualisations and definitions of internationalisation before explaining the concept of internationalism, and variations of it, to demonstrate that internationalism has a moral dimension which could, and the author argues, provide a normative value base for the processes of internationalisation. Design/methodology/approach This paper is a cross-disciplinary, conceptual exploration. Findings The argument concludes with a listing of principles which should give a moral direction to internationalisation. Research limitations/implications The approach proposed is the basis for evaluations of different aspects of internationalisation such as the design and implementation of curricula. Practical implications The approach taken here, if implemented, would lead to changes in curricula and processes of internationalisation. Social implications The impact of internationalisation, and particularly of student mobility as an aspect of it, is already significant, and the perspective presented here would lead to more coherent interactions in mobility situations. Originality/value Using the neglected concept of internationalism brings a new perspective and challenge to internationalisation.
Circumscribing an Ellipsoid about the Intersection of Two Ellipsoids

An ellipsoid G is associated uniquely with a positive definite matrix A via Note that all ellipsoids discussed here are centred at 0. Given G1, and G2 we seek another ellipsoid circumscribed about G1 ∩ G2. It is easy to see that if and only if x'hx ≤ maxi x'aix for all vectors x.
Effects of Mass Media Exposure and Social Network Site Involvement on Risk Perception of and Precautionary Behavior Toward the Haze Issue in China

This study examines the effects of mass media exposure and social network site (SNS) involvement on risk perception of and precautionary behavior toward the haze issue in China. It also tests the impersonal impact hypothesis and the differential impact hypothesis in the context of social media communication. SNS involvement is found to be a stronger predictor of risk perception than mass media exposure. Both mass media exposure and SNS involvement mediate the effect of direct experience on risk perception. The impersonal impact hypothesis, which assumes that media differentially affects social risk perception and personal risk perception, is not confirmed, while the differential impact hypothesis is supported. Personal risk perception mediates the effect of mass media exposure and SNS involvement on precautionary behavior. The findings clarify the inconsistency in the effect of interpersonal communication on perceived personal risk and suggest an interplay of the predictors of precautionary behavior.
Semi-supervised Learning with Concept Drift Using Particle Dynamics Applied to Network Intrusion Detection Data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.
Deuterated molecular hydrogen in the Galactic ISM - New observations along seven translucent sightlines

We present column density measurements of the HD molecule in the interstellar gas toward 17 Galactic stars. The values for the seven most heavily reddened sightlines, with $E(B-V) = 0.38{-}0.72$, are derived from observations with the Far Ultraviolet Spectroscopic Explorer ( FUSE ). The other ten values are from a reanalysis of spectra obtained with Copernicus . In all cases, high-resolution ground-based observations of $\ion{K}{i}$ and/or the CH molecule were used to constrain the gas velocity structure and to correct for saturation effects. Comparisons of the column densities HD, CH, CN, and $\ion{K}{i}$ in these 17 sightlines indicate that HD is most tightly correlated with CH. Stringent lower limits to the interstellar D/H ratio, derived from the HD/2H 2 ratio, range from $3.7 \times 10^{-7}$ to $4.3 \times 10^{-6}$. Our results also suggest that the HD/H 2 ratio increases with the molecular fraction f (H 2 ) and that the interstellar D/H ratio might be obtained from HD by probing clouds with f (H$_2) \sim 1$. Finally, we note an apparent relationship between the molecular fractions of hydrogen and deuterium.
Interplanetary coronal mass ejection and ambient interplanetary magnetic field correlations during the Sun‐Earth connection events of October–November 2003

[1] Magnetic field observations made during 28 October to 1 November 2003, which included two fast interplanetary coronal mass ejections (ICMEs), allow a study of correlation lengths of magnetic field parameters for two types of interplanetary (IP) structures: ICMEs and ambient solar wind. Further, they permit the extension of such investigations to the magnetosheath and to a distance along the Sun-Earth line (X) of about 400 RE. Data acquired by three spacecraft are examined: ACE, in orbit around the L1 point; Geotail, traveling eastward in the near-Earth solar wind (at R ∼ 30 RE); and Wind, nominally in the distant geomagnetic tail (R ∼ −160 RE) but making repeated excursions into the magnetosheath/solar wind due to the flapping of the tail. Analyses are presented in both time and frequency domains. We find significant differences in the cross-correlation/coherence properties of the ambient interplanetary magnetic field (IMF) and ICME parameters. For the ambient IMF, we find high coherence to be confined to low frequencies, consistent with other studies. In contrast, ICME magnetic field parameters remain generally coherent up to much higher frequencies. Scale lengths of ICME magnetic field parameters are in excess of 400 RE. High speeds of ∼1700 km s−1 are inferred from the plot of phase difference versus frequency, consistent with that obtained from plasma instruments. To strengthen these results and to extend them to include dependence on the distance perpendicular to the Sun-Earth line (Y), we examine a 28-day interval in year 2001 characterized by a sequence of 10 ICMEs and containing roughly equal ambient solar wind and ICME time intervals. ACE-Wind X and Y separations were ∼220 and ∼250 RE, respectively. We find good coherence/correlation alternating with poor values. In particular, we find that in general ICME coherence/correlation lengths along Y are larger by a factor of 3–5 than those quoted in the literature for ambient solar wind parameters. Our findings are good news for the space weather effort, which depends crucially on predicting the arrival of large events, since they make possible the placement of upstream monitors to give a longer lead time than at L1.
Fmrp Interacts with Adar and Regulates RNA Editing, Synaptic Density and Locomotor Activity in Zebrafish

Fragile X syndrome (FXS) is the most frequent inherited form of mental retardation. The cause for this X-linked disorder is the silencing of the fragile X mental retardation 1 (fmr1) gene and the absence of the fragile X mental retardation protein (Fmrp). The RNA-binding protein Fmrp represses protein translation, particularly in synapses. In Drosophila, Fmrp interacts with the adenosine deaminase acting on RNA (Adar) enzymes. Adar enzymes convert adenosine to inosine (A-to-I) and modify the sequence of RNA transcripts. Utilizing the fmr1 zebrafish mutant (fmr1-/-), we studied Fmrp-dependent neuronal circuit formation, behavior, and Adar-mediated RNA editing. By combining behavior analyses and live imaging of single axons and synapses, we showed hyperlocomotor activity, as well as increased axonal branching and synaptic density, in fmr1-/- larvae. We identified thousands of clustered RNA editing sites in the zebrafish transcriptome and showed that Fmrp biochemically interacts with the Adar2a protein. The expression levels of the adar genes and Adar2 protein increased in fmr1-/- zebrafish. Microfluidic-based multiplex PCR coupled with deep sequencing showed a mild increase in A-to-I RNA editing levels in evolutionarily conserved neuronal and synaptic Adar-targets in fmr1-/- larvae. These findings suggest that loss of Fmrp results in increased Adar-mediated RNA editing activity on target-specific RNAs, which, in turn, might alter neuronal circuit formation and behavior in FXS.
Modelling out-of-vocabulary words for robust speech recognition

This thesis concerns the problem of unknown or out-of-vocabulary (OOV) words in continuous speech recognition. We propose a novel approach for handling OOV words within a single-stage recognition framework. To achieve this goal, an explicit and detailed model of OOV words is constructed and then used to augment the closed-vocabulary search space of a standard speech recognizer. This OOV model achieves open-vocabulary recognition through the use of more flexible subword units that can be concatenated during recognition to form new phone sequences corresponding to potential new words. Examples of such subword units are phones, syllables, or some automatically-learned multi-phone sequences. Subword units have the attractive property of being a closed set, and thus are able to cover any new words, and can conceivably cover most utterances with partially spoken words as well. The main challenge with such an approach is ensuring that the OOV model does not absorb portions of the speech signal corresponding to in-vocabulary (IV) words. In dealing with this challenge, we explore several research issues related to designing the subword lexicon, language model, and topology of the OOV model. We present a dictionary-based approach for estimating subword language models. Such language models are utilized within the subword search space to help recognize the underlying phonetic transcription of OOV words. We also propose a data-driven iterative bottom-up procedure for automatically creating a multi-phone subword inventory. Starting with individual phones, this procedure uses the maximum mutual information principle to successively merge phones to obtain longer subword units. The thesis also extends this OOV approach to modelling multiple classes of OOV words. In addition, the thesis examines an approach for combining OOV modelling with recognition confidence scoring. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)