Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning in compilers

Tuning a compiler so that it produces optimised code is a difficult task because modern processors are complicated; they have a large number of components operating in parallel and each is sensitive to the behaviour of the others. Building analytical models on which optimisation heuristics can be based has become harder as processor complexity increased and this trend is bound to continue as the world moves towards further heterogeneous parallelism. Compiler writers need to spend months to get a heuristic right for any particular architecture and these days compilers often support a wide range of disparate devices. Whenever a new processor comes out, even if derived from a previous one, the compiler’s heuristics will need to be retuned for it. This is, typically, too much effort and so, in fact, most compilers are out of date. Machine learning has been shown to help; by running example programs, compiled in different ways, and observing how those ways effect program run-time, automatic machine learning tools can predict good settings with which to compile new, as yet unseen programs. The field is nascent, but has demonstrated significant results already and promises a day when compilers will be tuned for new hardware without the need for months of compiler experts’ time. Many hurdles still remain, however, and while experts no longer have to worry about the details of heuristic parameters, they must spend their time on the details of the machine learning process instead to get the full benefits of the approach. This thesis aims to remove some of the aspects of machine learning based compilers for which human experts are still required, paving the way for a completely automatic, retuning compiler. First, we tackle the most conspicuous area of human involvement; feature generation. In all previous machine learning works for compilers, the features, which describe the important aspects of each example to the machine learning tools, must be constructed by an expert. Should that expert choose features poorly, they will miss crucial information without which the machine learning algorithm can never excel. We show that not only can we automatically derive good features, but that these features out perform those of human experts. We demonstrate our approach on loop unrolling, and find we do better than previous work, obtaining XXX% of the available performance, more than the XXX% of previous state of the art. Next, we demonstrate a new method to efficiently capture the raw data needed for machine learning tasks. The iterative compilation on which machine learning in compilers depends is typically time consuming, often requiring months of compute time. The underlying processes are also noisy, so that most prior works fall into two categories; those which attempt to gather clean data by executing a large number of times and those which ignore the statistical validity of their data to keep experiment times feasible. Our approach, on the other hand guarantees clean data while adapting to the experiment at hand, needing an order of magnitude less work that prior techniques.
The Hedgehog: A Novel Optical Tracking Method for Spatially Immersive Displays

Existing commercial technologies do not adequately meet the requirements for tracking in fully enclosed Virtual Reality displays. We present a novel six degree of freedom tracking system, the Hedgehog; which overcomes several limitations inherent in existing sensors and tracking technology. The system reliably estimates the pose of the user's head with high resolution and low spatial distortion. Light emitted from an arrangement of lasers projects onto the display walls. An arrangement of cameras images the walls and the two-dimensional centroids of the projections are tracked to estimate the pose of the device. The system is able to handle ambiguous laser projection configurations, static and dynamic occlusions of the lasers, and incorporates an auto-calibration mechanism due to the use of the SCAAT (single constraint at a time) algorithm. A prototype system was evaluated relative to a state-of-the-art motion tracker and showed comparable positional accuracy (12 mm RMS) and significantly better absolute angular accuracy (0.1 RMS).
Subtopic Segmentation for Small Corpus Using a Novel Fuzzy Model

Subtopic segmentation is a critical task in numerous applications, including information retrieval, automatic summarization, essay scoring, and others. Although several approaches have been developed, many are ineffective for specific domains with a small corpus because of the fuzziness of the semantics of words and sentences in the corpus. This paper explores the problem of subtopic segmentation by proposing a fuzzy model for the semantics of both words and sentences. The model has three characteristics. First, it can deal with the uncertainty in the semantics of words and sentences. Secondly, it can measure the fuzzy similarity between the fuzzy semantics of sentences. Thirdly, it can develop a fuzzy algorithm for segmenting a text into several subtopic segments. The experiments, especially for a short text with a small corpus in a specific domain, indicate that the method can efficiently increase the accuracy of subtopic segmentation over previous methods.
A chaotic local search based bacterial foraging algorithm and its application to a permutation flow-shop scheduling problem

The permutation flow-shop scheduling problem (PFSP) is a typical combinational and non-deterministic polynomial-hard problem, with discrete solution space. In this paper, a novel bacterial foraging optimisation algorithm (BFO) has been proposed to solve the PFSP. Difficulties such as slow convergent speeds and entrapment in the local optimum were incurred by the original BFO algorithm in solving a high-dimensional combinatorial optimisation problem. In order to deal with these difficulties, a differential evolution operator and a chaotic search operator were each introduced into the original BFO algorithm to enhance the activity levels of the individual bacterium and to extend the local searching space. Theoretical analysis showed that the improved algorithm obtained more motility in chemotaxis and could converge to the global optimum with a probability of 1. Simulation results and comparisons to both continuous and combinatorial benchmark problems were used to demonstrate the effectiveness of this novel optimisation algorithm.
Dimension detection via slivers

We present a novel approach to estimate the dimension m of an unknown manifold M ⊂ Rd with positive reach from a set of point samples P ⊂ M. It works by analyzing the shape of simplices formed by point samples. Suppose that P is drawn from M according to a Poisson process with an unknown parameter λ. Let k be some fixed positive integer. When λ is large enough, we prove that the dimension can be correctly output in O(kd|P|1+1/k) time with probability greater than 1-2-k. We experimented with a practical variant and showed that its performance is competitive with several previous methods.
Shrinking New Orleans: Post-Katrina Population Adjustments

The flooding caused by hurricane Katrina in 2005 acted as a catalyst for an abrupt change of the demographic landscape of the New Orleans metropolitan area. New Orleans city proper has been a shrinking city for the last half century, but its population loss was balanced by expanding suburbs until 2005. Based on a comparison of the newly released 2010 census data with the 2000 data, this paper discusses the major population adjustments that have resulted from the disaster. Hurricane Katrina has caused the entire metropolitan area to shrink much faster than previous declines. Five years after the storm, New Orleans displays some limited signs of resilience, although the storm seems to have accelerated its population decline. Furthermore, a close examination of the available data shows us that Katrina has provoked a major change in the city's ethnic landscape.
Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data

Mapping of chromosomal regions harboring genetic polymorphisms that regulate complex traits is usually followed by a search for the causative mutations underlying the observed effects. This is often a challenging task even after fine mapping, as millions of base pairs including many genes will typically need to be investigated. Thus to trace the causative mutation(s) there is a great need for efficient bioinformatic strategies. Here, we searched for genes and mutations regulating growth in the Virginia chicken lines – an experimental population comprising two lines that have been divergently selected for body weight at 56 days for more than 50 generations. Several quantitative trait loci (QTL) have been mapped in an F2 intercross between the lines, and the regions have subsequently been replicated and fine mapped using an Advanced Intercross Line. We have further analyzed the QTL regions where the largest genetic divergence between the High-Weight selected (HWS) and Low-Weight selected (LWS) lines was observed. Such regions, covering about 37% of the actual QTL regions, were identified by comparing the allele frequencies of the HWS and LWS lines using both individual 60K SNP chip genotyping of birds and analysis of read proportions from genome resequencing of DNA pools. Based on a combination of criteria including significance of the QTL, allele frequency difference of identified mutations between the selected lines, gene information on relevance for growth, and the predicted functional effects of identified mutations we propose here a subset of candidate mutations of highest priority for further evaluation in functional studies. The candidate mutations were identified within the GCG, IGFBP2, GRB14, CRIM1, FGF16, VEGFR-2, ALG11, EDN1, SNX6, and BIRC7 genes. We believe that the proposed method of combining different types of genomic information increases the probability that the genes underlying the observed QTL effects are represented among the candidate mutations identified.
How Do Earthworms, Soil Texture and Plant Composition Affect Infiltration along an Experimental Plant Diversity Gradient in Grassland?

Background Infiltration is a key process in determining the water balance, but so far effects of earthworms, soil texture, plant species diversity and their interaction on infiltration capacity have not been studied. Methodology/Principal Findings We measured infiltration capacity in subplots with ambient and reduced earthworm density nested in plots of different plant species (1, 4, and 16 species) and plant functional group richness and composition (1 to 4 groups; legumes, grasses, small herbs, tall herbs). In summer, earthworm presence significantly increased infiltration, whereas in fall effects of grasses and legumes on infiltration were due to plant-mediated changes in earthworm biomass. Effects of grasses and legumes on infiltration even reversed effects of texture. We propose two pathways: (i) direct, probably by modifying the pore spectrum and (ii) indirect, by enhancing or suppressing earthworm biomass, which in turn influenced infiltration capacity due to change in burrowing activity of earthworms. Conclusions/Significance Overall, the results suggest that spatial and temporal variations in soil hydraulic properties can be explained by biotic processes, especially the presence of certain plant functional groups affecting earthworm biomass, while soil texture had no significant effect. Therefore biotic parameters should be taken into account in hydrological applications.
A Diffusive Gradient-in-Thin-Film Technique for Evaluation of the Bioavailability of Cd in Soil Contaminated with Cd and Pb

Management of heavy metal contamination requires accurate information about the distribution of bioavailable fractions, and about exchange between the solid and solution phases. In this study, we employed diffusive gradients in thin-films (DGT) and traditional chemical extraction methods (soil solution, HOAc, EDTA, CaCl2, and NaOAc) to determine the Cd bioavailability in Cd-contaminated soil with the addition of Pb. Two typical terrestrial species (wheat, Bainong AK58; maize, Zhengdan 958) were selected as the accumulation plants. The results showed that the added Pb may enhance the efficiency of Cd phytoextraction which is indicated by the increasing concentration of Cd accumulating in the plant tissues. The DGT-measured Cd concentrations and all the selected traditional extractants measured Cd concentrations all increased with increasing concentration of the addition Pb which were similar to the change trends of the accumulated Cd concentrations in plant tissues. Moreover, the Pearson regression coefficients between the different indicators obtained Cd concentrations and plants uptake Cd concentrations were further indicated significant correlations (p < 0.01). However, the values of Pearson regression coefficients showed the merits of DGT, CaCl2, and Csol over the other three methods. Consequently, the in situ measurement of DGT and the ex situ traditional methods could all reflect the inhibition effects between Cd and Pb. Due to the feature of dynamic measurements of DGT, it could be a robust tool to predict Cd bioavaiability in complex contaminated soil.
Motivations for Youth Volunteer Participation: Types and Structure—An Analysis of Interviews with Twenty-Four Young Volunteers

The types and phasic features of the motivations of volunteers are analyzed based on information obtained by means of interviews with young volunteers. It is found that the motivations for volunteer participation may consist of three categories: the traditional motivation (focused on responsibility), the modern motivation (focused on development), and the postmodern motivation (focused on pleasure).
Galanin influences vasopressin and oxytocin release from the hypothalamo-neurohypophysial system of salt loaded rats.

Galanin is a peptide present in the nervous system and peripheral tissues which exerts a broad range of physiological functions. The influence of centrally administered galanin (Gal; 100 pM i.c.v.) on arginine vasopressin (AVP) and oxytocin (OT) content in the hypothalamus and neurohypophysis as well as on their blood plasma concentration was estimated in male Wistar rats drinking ad libitum 2% solution of natrium chloride per 48 hours. In euhydrated rats and subsequently applied i.c. v. with Gal a significant fall in the hypothalamic and neurohypophysial content of OT but not AVP was observed, however, without simultaneous changes in these neurohormones blood plasma concentration. On the contrary, i.c. v. injection of Gal to salt-loaded rats caused a marked raise in AVP and OT level in the hypothalamus and neurohypophysis with subsequent diminution of both neurohormones concentration in blood plasma. These results suggest that in euhydrated rats Gal has an inhibitory influence on the biosynthesis as well as axonal transport of OT, but not AVP. On the contrary, in salt-loaded rats galanin restricts secretion of both neurohormones into the systemic circulation.
Anti-Human Immunodeficiency Virus Drug Combination Strategies

It is now generally accepted that mono- and bitherapy for human immunodeficiency virus type 1 (HIV-1) infection are only transiently efficient mainly due to virus drug resistance. To obtain a sustained benefit from antiviral therapy, current guidelines recommend at least triple-drug combinations, or the so-called highly active antiretroviral therapy (HAART). In some patients, HAART can be problematic, either because it is difficult to remain compliant or because previous suboptimum therapies have limited the choice of drugs. For compliant drug-naive patients, HAART should be able to offer long-term virus suppression, when changing from first- to second- to third-line HAART at drug failure. Long-term treatment might ultimately result in multi-drug resistant virus leaving few options for salvage therapy. HIV drug resistance testing to guide this salvage therapy and the development of new drugs to allow new options will therefore remain priorities in anti-HIV drug research.
Prediction of Achievement of Doctoral Students in Education

The purpose of this study was to analyze associations among Graduate Record Examination scores, graduate grade point average, and faculty ratings of success for 311 doctoral students in a School of Education. Moderate correlations obtained between examination scores and the criterion variables of faculty ratings and graduate GPAs. Students in the lowest 10% of the examination score range had significantly lower criterion grades and ratings than those in the highest 10%. Examination scores and score combinations are a useful gross screening indicator of potential success for doctoral study in education.
An Acoustic Phonetic Analysis of Word-Initial Stop Production by Young Simultaneous Mandarin- English Bilingual Children

This study examined bilinguals’ acquisition of subphonemic features in English and Mandarin to investigate whether and how their two languages are differentiated and at what linguistic level. The production of wordinitial voiceless stops (/p t k/) by two Mandarin-English bilingual children was analyzed for the measure of voice onset time (VOT), which shows a substantial overlap in two languages. Both children produced English and
The Development and Validation of a Nonverbal Measure of Self-Esteem: the Sliding Person Test

This paper reports validity evidence from several studies for a nonverbal measure of self-esteem, the Sliding Person Test (SPERT). Two versions of SPERT, a wooden manipulative task and a paper-pencil measure, were correlated . 81. Convergent validity correlations were comparable to typical correlations among self-concept measures. Test-retest and alternate form reliabilities ranged from . 57 to . 95. Minimal evidence of convergent and discriminant validity was established, and correlations with IQ and social desirability measures were sufficiently low. Relationships between a measure of self-esteem and those of social adjustment and emotional adjustment supported the findings of other studies. The potential of SPERT for use by counselors in intervention programs and by classroom teachers was demonstrated.
Mild Acidosis Enhances AMPA Receptor-Mediated Intracellular Zinc Mobilization in Cortical Neurons

Overactivation of glutamate receptors and subsequent deregulation of the intraneuronal calcium ([Ca2+]i) levels are critical components of the injurious pathways initiated by cerebral ischemia. Another hallmark of stroke is parenchymal acidosis, and we have previously shown that mild acidosis can act as a switch to decrease NMDAR-dependent neuronal loss while potentiating the neuronal loss mediated by AMPARs. Potentiation of AMPAR-mediated neuronal death in an acidotic environment was originally associated only with [Ca2+]i dyshomeostasis, as assessed by Ca2+ imaging; however, intracellular dyshomeostasis of another divalent cation, Zn2+, has recently emerged as another important co-factor in ischemic neuronal injury. Rises in [Zn2+]i greatly contribute to the fluorescent changes of Ca2+-sensitive fluorescent probes, which also have great affinity for Zn2+. We therefore revisited our original findings (Mcdonald et al., 1998) and investigated if AMPAR-mediated fura-2 signals we observed could also be partially due to [Zn2+]i increases. Fura-2 loaded neuronal cultures were exposed to the AMPAR agonist, kainate, in a physiological buffer at pH 7.4 and then washed either at pH 7.4 or pH 6.2. A delayed recovery of fura-2 signals was observed at both pHs. Interestingly this impaired recovery phase was found to be sensitive to chelation of intracellular Zn2+. Experiments with the Zn2+ sensitive (and Ca2+-insensitive) fluorescent probe FluoZin-3 confirmed the idea that AMPAR activation increases [Zn2+]i, a phenomenon that is potentiated by mild acidosis. Additionally, our results show that selective Ca2+ imaging mandates the use of intracellular heavy metal chelators to avoid confounding effects of endogenous metals such as Zn2+.
Knowledge Navigators and Lifelong Learners: Producing graduates for the information society

In recent years, there has been considerable attention, at least at the policy level, to the need for graduates to be ‘lifelong learners’. Although this concept means different things in different cultural contexts, there is more or less general agreement that graduation really only marks the beginning of the graduate's need for continuing personal and professional learning, and, moreover, that it is the responsibility of universities and other institutions of higher education to equip their graduates with the skills and attitudes to help them to continue learning throughout their lives. The emergence of an information-rich ‘knowledge society’ has made this even more of an imperative. The rapid and pervasive spread of information and communication technologies, coupled with increasing globalisation, the democratisation of knowledge production—once assumed to be largely the preserve of universities—and what has been dubbed the ‘information explosion’ collectively mean that most citizens of advanced industrialised countries are, or will soon become, ‘knowledge workers’. Accordingly, many graduates, whether they work in educational or other contexts, are likely to be involved in ‘knowledge-intensive’ activities, for which they need to be prepared. But what does this mean in practice, and what are we to do about it? In 1990, the late Dr Ernest Boyer, in his book Scholarship Reconsidered: Priorities of the Professoriate, proposed a fourfold division of academic work into what he labelled the scholarship of discovery; the scholarship of application ; the scholarship of integration ; and, finally, the scholarship of teaching . The paper suggests that each of these four aspects of scholarship has a direct counterpart outside the university, and that, accordingly, they might be taken as a way of considering the attributes of graduates as well as of academics. The paper suggests a necessary symmetry between the teaching and other scholarly work carried out within the university and the development of such abilities and predispositions in graduates from a variety of fields who might not otherwise consider themselves to be destined for ‘scholarly’ work.
Secreted protein, acidic and rich in cysteine-like 1 (SPARCL1) is down regulated in aggressive prostate cancers and is prognostic for poor clinical outcome

Prostate cancer is the second leading cause of cancer death among United States men. However, disease aggressiveness is varied, with low-grade disease often being indolent and high-grade cancer accounting for the greatest density of deaths. Outcomes are also disparate among men with high-grade prostate cancer, with upwards of 65% having disease recurrence even after primary treatment. Identification of men at risk for recurrence and elucidation of the molecular processes that drive their disease is paramount, as these men are the most likely to benefit from multimodal therapy. We previously showed that androgen-induced expression profiles in prostate development are reactivated in aggressive prostate cancers. Herein, we report the down-regulation of one such gene, Sparcl1, a secreted protein, acidic and rich in cysteine (SPARC) family matricellular protein, during invasive phases of prostate development and regeneration. We further demonstrate a parallel process in prostate cancer, with decreased expression of SPARCL1 in high-grade/metastatic prostate cancer. Mechanistically, we demonstrate that SPARCL1 loss increases the migratory and invasive properties of prostate cancer cells through Ras homolog gene family, member C (RHOC), a known mediator of metastatic progression. By using models incorporating clinicopathologic parameters to predict prostate cancer recurrence after treatment, we show that SPARCL1 loss is a significant, independent prognostic marker of disease progression. Thus, SPARCL1 is a potent regulator of cell migration/invasion and its loss is independently associated with prostate cancer recurrence.