nils-herrmann/clean_scirepeval huggingface dataset:
Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management

This paper introduces a new resource virtualization framework, Zorua, that decouples the GPU programming model from the management of key on-chip resources in hardware to enhance programming ease, portability, and performance. The application resource specification—a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block—forms a critical component of the existing GPU programming models. This specification determines the parallelism, and hence performance, of the application during execution because the corresponding on-chip hardware resources are allocated and managed purely based on this specification. This tight coupling between the software-provided resource specification and resource management in hardware leads to significant challenges in programming ease, portability, and performance, as we demonstrate in this work using real data obtained on state-of-the-art GPU systems. Our goal in this work is to reduce the dependence of performance on the software-provided static resource specification to simultaneously alleviate the above challenges. To this end, we introduce Zorua, a new resource virtualization framework, that decouples the programmerspecified resource usage of a GPU application from the actual allocation in the on-chip hardware resources. Zorua enables this decoupling by virtualizing each resource transparently to the programmer. The virtualization provided by Zorua builds on two key concepts—dynamic allocation of the on-chip resources, and their oversubscription using a swap space in memory. Zorua provides a holistic GPU resource virtualization strategy designed to (i) adaptively control the extent of oversubscription, and (ii) coordinate the dynamic management of multiple on-chip resources to maximize the effectiveness of virtualization. We demonstrate that by providing the illusion of more resources than physically available via controlled and coordinated virtualization, Zorua offers several important benefits: (i) Programming Ease. Zorua eases the burden on the programmer to provide code that is tuned to efficiently utilize the physically available on-chip resources. (ii) Portability. Zorua alleviates the necessity of re-tuning an application’s resource usage when porting the application across GPU generations. (iii) Performance. By dynamically allocating resources and carefully oversubscribing them when necessary, Zorua improves or retains the performance of applications that are already highly tuned to best utilize the resources. The holistic virtualization provided by Zorua has many other potential uses, e.g., fine-grained resource sharing among multiple kernels, low-latency preemption of GPU programs, and support for dynamic parallelism, which we describe in this paper.
Combining Breast Reduction Techniques to Treat Gigantomastia in Ghana

In this presentation of 2 consecutive cases of symptomatic juvenile breast hypertrophy in Ghana, we review the patient presentation, workup, and discuss outcomes following a combined technique of inferior pedicle stump with free nipple graft reduction mammoplasty. Surgical goals for treatment of gigantomastia were 2-fold: to resect adequate tissue to obtain symptomatic relief with improved quality of life, while avoiding a flat, boxy-appearing breast shape.
Brain correlates of music-evoked emotions

Music is a universal feature of human societies, partly owing to its power to evoke strong emotions and influence moods. During the past decade, the investigation of the neural correlates of music-evoked emotions has been invaluable for the understanding of human emotion. Functional neuroimaging studies on music and emotion show that music can modulate activity in brain structures that are known to be crucially involved in emotion, such as the amygdala, nucleus accumbens, hypothalamus, hippocampus, insula, cingulate cortex and orbitofrontal cortex. The potential of music to modulate activity in these structures has important implications for the use of music in the treatment of psychiatric and neurological disorders.
Face-to-face or Facebook: Can social connectedness be derived online?

0747-5632/$ see front matter 2012    School of Psychology, Australian Catholic University, 1100 Nudgee Rd., Banyo, QLD 4014, ; fax: +61 7 3623 7277. E-mail address: rachel.grieve@acu.edu.au (R. Grieve). Rachel Grieve ⇑, Michaelle Indian, Kate Witteveen, G. Anne Tolan, Jessica Marrington
A double-blind, randomized study of minocycline for the treatment of negative and cognitive symptoms in early-phase schizophrenia.

Current antipsychotics have only a limited effect on 2 core aspects of schizophrenia: negative symptoms and cognitive deficits. Minocycline is a second-generation tetracycline that has a beneficial effect in various neurologic disorders. Recent findings in animal models and human case reports suggest its potential for the treatment of schizophrenia. These findings may be linked to the effect of minocycline on the glutamatergic system, through inhibition of nitric oxide synthase and blocking of nitric oxide-induced neurotoxicity. Other proposed mechanisms of action include effects of minocycline on the dopaminergic system and its inhibition of microglial activation.



To examine the efficacy of minocycline as an add-on treatment for alleviating negative and cognitive symptoms in early-phase schizophrenia.



A longitudinal double-blind, randomized, placebo-controlled design was used, and patients were followed for 6 months from August 2003 to March 2007. Seventy early-phase schizophrenia patients (according to DSM-IV) were recruited and 54 were randomly allocated in a 2:1 ratio to minocycline 200 mg/d. All patients had been initiated on treatment with an atypical antipsychotic < or = 14 days prior to study entry (risperidone, olanzapine, quetiapine, or clozapine; 200-600 mg/d chlorpromazine-equivalent doses). Clinical, cognitive, and functional assessments were conducted, with the Scale for the Assessment of Negative Symptoms (SANS) as the primary outcome measure.



Minocycline was well tolerated, with few adverse events. It showed a beneficial effect on negative symptoms and general outcome (evident in SANS, Clinical Global Impressions scale). A similar pattern was found for cognitive functioning, mainly in executive functions (working memory, cognitive shifting, and cognitive planning).



Minocycline treatment was associated with improvement in negative symptoms and executive functioning, both related to frontal-lobe activity. Overall, the findings support the beneficial effect of minocycline add-on therapy in early-phase schizophrenia.
Patent Claim Translation based on Sublanguage-specific Sentence Structure

Patent claim sentences, despite their legal importance in patent documents, still pose difficulties for state-of-the-art statistical machine translation (SMT) systems owing to their extreme lengths and their special sentence structure. This paper describes a method for improving the translation quality of claim sentences, by taking into account the features specific to the claim sublanguage. Our method overcomes the issue of special sentence structure, by transferring the sublanguage-specific sentence structure (SSSS) from the source language to the target language, using a set of synchronous context-free grammar rules. Our method also overcomes the issue of extreme lengths by taking the sentence components to be the processing unit for SMT. The results of an experiment demonstrate that our SSSS transfer method, used in conjunction with pre-ordering, significantly improves the translation quality in terms of BLEU scores by five points, in both English-to-Japanese and Japanese-to-English directions. The experiment also shows that the SSSS transfer method significantly improves structural appropriateness in the translated sentences in both translation directions, which is indicated by substantial gains over 30 points in RIBES scores.
The role of interface organizations in science communication and understanding

www.frontiersinecology.org  In this paper, we address how “interface” or “boundary” organizations – organizations created to foster the use of science knowledge in environmental policy making and environmental management, as well as to encourage changes in behavior, further learning, inquiry, discovery, or enjoyment – can help scientists improve and facilitate effective communication and the application of scientific information (Gieryn 1999). Interface organizations are synergistic and operate across a range of scales, purposes, and intensities of information flow between scientists and audiences. Considerable attention has focused on how to involve scientists in the decision-making process regarding natural resource management issues related to their area of expertise (Andersson 2004; Roth et al. 2004; Rinaudo and Garin 2005; Bacic et al. 2006; Olsson and Andersson 2007). These efforts have resulted in scientific input to environmental issues, including ecosystem management (Meffe et al. 2002), adaptive collaborative management (Buck et al. 2001; Colfer 2005), and integrated watershed management (Jeffrey and Gearey 2006). A common element of many of these approaches is the use of an organization or group to manage and facilitate the interaction between the scientists and the “users” or “managers” of a natural resource. Cash et al. (2003) identified key functions of successful “boundary management” organizations. These functions include communication, translation, and mediation (convening groups, as well as resolving differences). Successful efforts are characterized by having clear lines of responsibility and accountability on both sides of the boundary, and by providing a forum in which information can be co-produced by scientists and information users. Interface organizations typically: (1) Engage: seeking out scientists with important findings and then building or filling a demand for their insights among different communities and for various niches, contexts, and scales. The organization usually serves as a convener.
LBP-Based Segmentation of Defocus Blur

Defocus blur is extremely common in images captured using optical imaging systems. It may be undesirable, but may also be an intentional artistic effect, thus it can either enhance or inhibit our visual perception of the image scene. For tasks, such as image restoration and object recognition, one might want to segment a partially blurred image into blurred and non-blurred regions. In this paper, we propose a sharpness metric based on local binary patterns and a robust segmentation algorithm to separate in- and out-of-focus image regions. The proposed sharpness metric exploits the observation that most local image patches in blurry regions have significantly fewer of certain local binary patterns compared with those in sharp regions. Using this metric together with image matting and multi-scale inference, we obtained high-quality sharpness maps. Tests on hundreds of partially blurred images were used to evaluate our blur segmentation algorithm and six comparator methods. The results show that our algorithm achieves comparative segmentation results with the state of the art and have big speed advantage over the others.
Impact Modelling for Circular Economy : Geodesign Discussion Support Environment

Transitioning towards circular economy requires changes in the current system which yield a number of impacts on such fundamental values as human health, natural environment, exhaustible resources, social well-being and prosperity. Moreover, this process involves multiple actors and requires careful considerations of ample spatial information. While plenty of systems have already been developed to support the decision-making process, up to date no standardized framework exists for spatial modelling of impacts. The poster is based on the ongoing research and aims to suggest a standardized, yet flexible approach for impact modelling using the core concepts of GIS. The framework is expected to ensure consistency and comparability between outputs of automated simulations and elicitation of impacts defined by stakeholders and prepare impact modelling guidelines that aim to overcome the characteristics of a single project and be reused in other GDSEs.
Iron, brain ageing and neurodegenerative disorders

There is increasing evidence that iron is involved in the mechanisms that underlie many neurodegenerative diseases. Conditions such as neuroferritinopathy and Friedreich ataxia are associated with mutations in genes that encode proteins that are involved in iron metabolism, and as the brain ages, iron accumulates in regions that are affected by Alzheimer's disease and Parkinson's disease. High concentrations of reactive iron can increase oxidative-stress induced neuronal vulnerability, and iron accumulation might increase the toxicity of environmental or endogenous toxins. By studying the accumulation and cellular distribution of iron during ageing, we should be able to increase our understanding of these neurodegenerative disorders and develop new therapeutic strategies.
A hybrid model for GEFCom 2014 probabilistic electricity price forecasting

This paper provides detailed information on Team Poland’s approach in the electricity price forecasting track of GEFCom2014. A new hybrid model is proposed, consisting of four major blocks: point forecasting, pre-filtering, quantile regression modeling and post-processing. This universal model structure enables independent development of a single block, without affecting performance of the remaining ones. The four-block model design in complemented by including expert judgements, which may be of great importance in periods of unusually high or low electricity demand.
Development of chromatographic methods for determination of agrimoniin and related polyphenols in pharmaceutical products.

Thin-layer chromatography (TLC) and liquid chromatography (LC) methods were developed for the qualitative and quantitative determination of agrimoniin, pedunculagin, ellagic acid, gallic acid, and catechin in selected herbal medicinal products from Rosaceae: Anserinae herba, Tormentillae rhizoma, Alchemillae herba, Agrimoniae herba, and Fragariae folium. Unmodified silica gel (TLC Si60, HPTLC LiChrospher Si60) and silica gel chemically modified with octadecyl or aminopropyl groups (HPTLC RP18W and HPTLC NH2) were used for TLC. The best resolution and selectivity were achieved with the following mobile phases: diisopropyl ether-acetone-formic acid-water (40 + 30 + 20 + 10, v/v/v/v), tetrahydrofuran-acetonitrile-water (30 + 10 + 60, v/v/v), and acetone-formic acid (60 + 40, v/v). Concentrations of the studied herbal drugs were determined by using a Chromolith Performance RP-18e column with acetonitrile-water-formic acid as the mobile phase. Determinations of linearity, range, detection and quantitation limits, accuracy, precision, and robustness showed that the HPLC method was sufficiently precise for estimation of the tannins and related polyphenols mentioned above. Investigations of suitable solvent selection, sample extraction procedure, and short-time stability of analytes at storage temperatures of 4 and 20 degrees C were also performed. The percentage of agrimoniin in pharmaceutical products was between 0.57 and 3.23%.
The Concept of Lexical Platform

The paper presents an idea of Lexical Platform proposed as a means for a lightweight integration of various lexical resources into one complex (from the perspective of non-technical users). All LRs will be represented as software web components implementing a minimal set of predefined programming interfaces providing functionality for querying and generating simple common presentation format. A common data format for the resources will not be required. Users will be able to search, browse and navigate via resources on the basis of anchor elements of a limited set of types. Lexical resources linked to the platform via components will preserve their identity.
Improving Neural Translation Models with Linguistic Factors

This paper presents an extension of neural machine translation (NMT) model to incorporate additional word-level linguistic factors. Adding such linguistic factors may be of great benefits to learning of NMT models, potentially reducing language ambiguity or alleviating data sparseness problem (Koehn and Hoang, 2007). We explore different linguistic annotations at the word level, including: lemmatization, word clusters, Part-ofSpeech tags, and labeled dependency relations. We then propose different neural attention architectures to integrate these additional factors into the NMT framework. Evaluating on translating between English and German in two directions with a low resource setting in the domain of TED talks, we obtain promising results in terms of both perplexity reductions and improved BLEU scores over baseline methods.
Effect of TIPT catalyst on insulation rejuvenation of water-tree aged cables

In order to understand the role of titanium isopropoxide (TIPT) catalyst on insulation rejuvenation for water tree aged cables, dielectric properties and micro structure changes are investigated for the rejuvenated cables. Needle-shape defects are made inside cross-linked polyethylene (XLPE) cable samples to form water tree in the XLPE layer. The water tree aged samples are injected by the liquid with phenylmethyldimethoxy silane (PMDMS) catalyzed by TIPT for rejuvenation, and the breakdown voltage of the rejuvenated samples is significantly higher than that of the new samples. By the observation of scanning electronic microscope (SEM), the nano-TiO2 particles are observed inside the breakdown channels of the rejuvenated samples. Accordingly, the insulation performance of rejuvenated samples is significantly enhanced by the nano-TiO2 particles. Through analyzing the products of hydrolysis from TIPT, the nano-scale TiO2 particles are observed, and its micro-morphology is consistent with that observed inside the breakdown channels. According to the observation, the insulation enhancement mechanism is described. Therefore, the dielectric property of the rejuvenated cables is improved due to the nano-TiO2 produced by the hydrolysis from TIPT.
Support Vector Machines and Area Under ROC curve

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a SVMs based algorithm for AUC maximization and show that under certain conditions this algorithm is related to 2-norm soft margin Support Vector Machines. We present experiments that compare SVMs performances to those of other AUC maximization based algorithms and provide empirical analysis of SVMs behavior with regards to ROCbased metrics. Our main conclusion is that SVMs can maximize both AUC and accuracy.  Support vector machines, quadratic programming, AUC, ROC curve.
Backtrack Search Algorithms and the Maximal Common Subgraph Problem

Backtrack algorithms are applicable to a wide variety of problems. An efficient but readable version of such an algorithm is presented and its use in the problem of finding the maximal common subgraph of two graphs is described. Techniques available in this application area for ordering and pruning the backtrack search are discussed. This algorithm has been used successfully as a component of a program for analysing chemical reactions and enumerating the bond changes which have taken place.
Multiple impacts: A state transition diagram approach

Impact happens when two or more bodies collide, generating very large impulsive forces in a very short period of time during which kinetic energy is first absorbed and then released after some loss. This paper introduces a state transition diagram to model a frictionless multibody collision. Each state describes a different topology of the collision characterized by the set of instantaneously active contacts. A change of state happens when a contact disappears at the end of restitution, or when a disappeared contact reappears as the relative motion of two bodies goes from separation into penetration. Within a state, (normal) impulses are coupled differentially subject to relative stiffnesses at the active contact points and the strain energies stored there. Such coupling may cause restart of compression from restitution during a single impact. Impulses grow along a bounded curve with first-order continuity, and converge during the state transitions. To solve a multibody collision problem with friction and tangential compliance, the above impact model is integrated with a compliant impact model. The paper compares model predictions to a physical experiment for the massé shot, which is a difficult trick in billiards, with a good result.
Gray matter differences correlate with spontaneous strategies in a human virtual navigation task.

Young healthy participants spontaneously use different strategies in a virtual radial maze, an adaptation of a task typically used with rodents. Functional magnetic resonance imaging confirmed previously that people who used spatial memory strategies showed increased activity in the hippocampus, whereas response strategies were associated with activity in the caudate nucleus. Here, voxel based morphometry was used to identify brain regions covarying with the navigational strategies used by individuals. Results showed that spatial learners had significantly more gray matter in the hippocampus and less gray matter in the caudate nucleus compared with response learners. Furthermore, the gray matter in the hippocampus was negatively correlated to the gray matter in the caudate nucleus, suggesting a competitive interaction between these two brain areas. In a second analysis, the gray matter of regions known to be anatomically connected to the hippocampus, such as the amygdala, parahippocampal, perirhinal, entorhinal and orbitofrontal cortices were shown to covary with gray matter in the hippocampus. Because low gray matter in the hippocampus is a risk factor for Alzheimer's disease, these results have important implications for intervention programs that aim at functional recovery in these brain areas. In addition, these data suggest that spatial strategies may provide protective effects against degeneration of the hippocampus that occurs with normal aging.