Miracle-dz/UniFAR-FTUs huggingface dataset:
Job-aware Scheduling in Eagle: Divide and Stick to Your Probes

We present Eagle, a new hybrid data center scheduler for data-parallel programs. Eagle dynamically divides the nodes of the data center in partitions for the execution of long and short jobs, thereby avoiding head-of-line blocking. Furthermore, it provides job awareness and avoids stragglers by a new technique, called Sticky Batch Probing (SBP). The dynamic partitioning of the data center nodes is accomplished by a technique called Succinct State Sharing (SSS), in which the distributed schedulers are informed of the locations where long jobs are executing. SSS is particularly easy to implement with a hybrid scheduler, in which the centralized scheduler places long jobs. With SBP, when a distributed scheduler places a probe for a job on a node, the probe stays there until all tasks of the job have been completed. When finishing the execution of a task corresponding to probe P, rather than executing a task corresponding to the next probe P' in its queue, the node may choose to execute another task corresponding to P. We use SBP in combination with a distributed approximation of Shortest Remaining Processing Time (SRPT) with starvation prevention. We have implemented Eagle as a Spark plugin, and we have measured job completion times for a subset of the Google trace on a 100-node cluster for a variety of cluster loads. We provide simulation results for larger clusters, different traces, and for comparison with other scheduling disciplines. We show that Eagle outperforms other state-of-the-art scheduling solutions at most percentiles, and is more robust against mis-estimation of task duration.
Down‐regulation of BDNF in cell and animal models increases striatal‐enriched protein tyrosine phosphatase 61 (STEP61) levels

Brain‐derived neurotrophic factor (BDNF) regulates synaptic strengthening and memory consolidation, and altered BDNF expression is implicated in a number of neuropsychiatric and neurodegenerative disorders. BDNF potentiates N‐methyl‐D‐aspartate receptor function through activation of Fyn and ERK1/2. STriatal‐Enriched protein tyrosine Phosphatase (STEP) is also implicated in many of the same disorders as BDNF but, in contrast to BDNF, STEP opposes the development of synaptic strengthening. STEP‐mediated dephosphorylation of the NMDA receptor subunit GluN2B promotes internalization of GluN2B‐containing NMDA receptors, while dephosphorylation of the kinases Fyn, Pyk2, and ERK1/2 leads to their inactivation. Thus, STEP and BDNF have opposing functions. In this study, we demonstrate that manipulation of BDNF expression has a reciprocal effect on STEP61 levels. Reduced BDNF signaling leads to elevation of STEP61 both in BDNF+/− mice and after acute BDNF knockdown in cortical cultures. Moreover, a newly identified STEP inhibitor reverses the biochemical and motor abnormalities in BDNF+/− mice. In contrast, increased BDNF signaling upon treatment with a tropomyosin receptor kinase B agonist results in degradation of STEP61 and a subsequent increase in the tyrosine phosphorylation of STEP substrates in cultured neurons and in mouse frontal cortex. These findings indicate that BDNF‐tropomyosin receptor kinase B signaling leads to degradation of STEP61, while decreased BDNF expression results in increased STEP61 activity. A better understanding of the opposing interaction between STEP and BDNF in normal cognitive functions and in neuropsychiatric disorders will hopefully lead to better therapeutic strategies.
A Scoping Review for Strategies to Increase Living Kidney Donation.

BACKGROUND AND OBJECTIVES The literature on strategies to increase the number of potential living kidney donors is extensive and has yet to be characterized. Scoping reviews are a novel methodology for systematically assessing a wide breadth of a given body of literature and may be done before conducting a more targeted systematic review. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS We performed a scoping review and summarized the evidence for existing strategies to increase living kidney donation. RESULTS Our review identified seven studies that tested interventions using rigorous methods (i.e., randomized, controlled trials) and outcome measures, all of which focused on using education targeted at potential recipients to increase living donation. Of these, two studies that targeted the potential recipients' close social network reported statistically significant results. Other interventions were identified, but their effect was assessed through quasiexperimental or observational study designs. CONCLUSIONS We identified an important gap in the literature for evidence-based strategies to increase living kidney donation. From the limited data available, strategies directed at potential recipients and their social networks are the most promising. These results can inform transplant programs that are considering strategies to increase living kidney donation and highlight the need for conduct of high-quality study to increase living donation.
The Use and Interpretation of Competing Risks Regression Models

Purpose: Competing risks observations, in which patients are subject to a number of potential failure events, are a feature of most clinical cancer studies. With competing risks, several modeling approaches are available to evaluate the relationship of covariates to cause-specific failures. We discuss the use and interpretation of commonly used competing risks regression models. Experimental Design: For competing risks analysis, the influence of covariate can be evaluated in relation to cause-specific hazard or on the cumulative incidence of the failure types. We present simulation studies to illustrate how covariate effects differ between these approaches. We then show the implications of model choice in an example from a Radiation Therapy Oncology Group (RTOG) clinical trial for prostate cancer. Results: The simulation studies illustrate that, depending on the relationship of a covariate to both the failure type of principal interest and the competing failure type, different models can result in substantially different effects. For example, a covariate that has no direct influence on the hazard of a primary event can still be significantly associated with the cumulative probability of that event, if the covariate influences the hazard of a competing event. This is a logical consequence of a fundamental difference between the model formulations. The example from RTOG similarly shows differences in the influence of age and tumor grade depending on the endpoint and the model type used. Conclusions: Competing risks regression modeling requires that one considers the specific question of interest and subsequent choice of the best model to address it. Clin Cancer Res; 18(8); 2301–8. ©2012 AACR.
Do multiple herbivores maintain chemical diversity of Scots pine monoterpenes?

A central issue in our understanding of the evolution of the diversity of plant secondary metabolites (PSMs) is whether or not compounds are functional, conferring an advantage to the plant, or non-functional. We examine the hypothesis that the diversity of monoterpene PSMs within a plant species (Scots pine Pinus sylvestris) may be explained by different compounds acting as defences against high-impact herbivores operating at different life stages. We also hypothesize that pairwise coevolution, with uncorrelated interactions, is more likely to result in greater PSM diversity, than diffuse coevolution. We tested whether up to 13 different monoterpenes in Scots pine were inhibitory to herbivory by slugs (Arion ater), bank voles (Clethrionomys glareolus), red deer (Cervus elaphus) and capercaillie (Tetrao urogallus), each of which attack trees at a different life stage. Plants containing more α-pinene were avoided by both slugs and capercaillie, which may act as reinforcing selective agents for this dominant defensive compound. Herbivory by red deer and capercaillie were, respectively, weakly negatively associated with δ3-carene, and strongly negatively correlated with the minor compound β-ocimene. Three of the four herbivores are probably contributory selective agents on some of the terpenes, and thus maintain some, but by no means all, of the phytochemical diversity in the species. The correlated defensive function of α-pinene against slugs and capercaillie is consistent with diffuse coevolutionary processes.
Architecting the Optics, Energetics and Geometry of Colloidal Quantum Dot Photovoltaics

Architecting the Optics, Energetics and Geometry of Colloidal Quantum Dot Photovoltaics Illan Kramer Doctor of Philosophy Graduate Department of Edward S. Rogers Department of Electrical & Computer Engineering University of Toronto 2012 Solution processed solar cells offer the promise of a low cost solution to global energy concerns. Colloidal quantum dots are one material that can be easily synthesized in and deposited from solution. These nanoparticles also offer the unique ability to select the desired optical and electrical characteristics, all within the same materials system, through small variations in their physical dimensions. These materials, unfortunately, are not without their limitations. To date, films made from colloidal quantum dots exhibit limited mobilities and short minority diffusion lengths. These limitations imply that simple device structures may not be sufficient to make an efficient solar cell. Here we show that through clever manipulation of the geometric and energetic structures, we can utilize the size-tunability of CQDs while masking their poor electrical characteristics. We further outline the physical mechanisms present within these architectures, namely the utilization of a distributed built-in electric field to extract current through drift rather than diffusion. These architectures have consequently exceeded the performance of legacy architectures such as the Schottky cell. Finally, we discuss some of the limiting modes within these architectures and within CQD films in general including the impact of surface traps and polydispersity in CQD populations.
Distributed Spectrum Management in TV White Space Networks

We investigate the distributed spectrum management problem in opportunistic TV white space (TVWS) systems using a game theoretical approach that accounts for adjacent-channel interference and spatial reuse. TV band devices (TVBDs) compete to access idle TV channels and select channel “blocks” that optimize an objective function. This function provides a tradeoff between the achieved rate and a cost factor that depends on the interference between TVBDs. We consider practical cases where contiguous or noncontiguous channels can be accessed by TVBDs, imposing realistic constraints on the maximum frequency span between the aggregated/bonded channels. We show that under general conditions, the proposed TVWS management games admit a potential function. Accordingly, a “best response” strategy allows us to determine the spectrum assignment of all players. This algorithm is shown to converge in a few iterations to a Nash equilibrium. Furthermore, we propose an effective algorithm based on Imitation dynamics, where a TVBD probabilistically imitates successful selection strategies of other TVBDs in order to improve its objective function. Numerical results show that our game theoretical framework provides a very effective tradeoff (close to optimal, centralized spectrum allocations) between efficient TV spectrum use and reduction of interference between TVBDs.
Towards Learning Relations Between User Daily Routines and Mood

The main aim of our research is to investigate whether it is possible to find a relation between the user’s mood and daily routines. To this aim we developed an application, Smart Calendar, that, thanks to its nature, allows collecting annotated datasets of mobile data, learning user daily routines and relating activities and pattern of sensor data to the user mood. Results of this preliminary phase of the research have shown that it is possible to learn routines and, if present, to find relations with the user mood.
Cibyl: an environment for language diversity on mobile devices

With an estimated installation base of around 1 billion units, the Java J2ME platform is one of the largest development targets available. For mobile devices, J2ME is often the only available environment. For the very large body of software written in C other languages, this means difficult and costly porting to another language to support J2ME devices. This paper presents the Cibyl programming environment which allows existing code written in C and other languages supported by GCC to be recompiled into Java bytecode and run with close to native Java performance on J2ME devices. Cibyl translates compiled MIPS binaries into Java bytecode. In contrast to other approaches, Cibyl supports the full C language, is based on unmodified standard tools, and does not rely on source code conversion. To achieve good performance, Cibyl employs extensions to the MIPS architecture to support low-overhead calls to native Java functionality and use knowledge of the MIPS ABI to avoid computing unused values and transfer unnecessary registers. An evaluation on multiple virtual machines shows that Cibyl achieves performance similar to native Java, with results ranging from a slowdown of around 2 to a speedup of over 9 depending on the JVM and the benchmark.
Ezetimibe Attenuates Atherosclerosis Associated with Lipid Reduction and Inflammation Inhibition

Background Ezetimibe, as a cholesterol absorption inhibitor, has been shown protecting against atherosclerosis when combined with statin. However, side by side comparison has not been made to evaluate the beneficial effects of ezetimibe alone versus statin. Herein, the study aimed to test whether ezetimibe alone would exhibit similar effects as statin and the combination therapy would be necessary in a moderate lesion size. Methods and Results ApoE-/- male mice that were fed a saturated-fat supplemented diet were randomly assigned to different therapeutic regimens: vehicle, ezetimibe alone (10 mg/kg/day), atorvastatin (20 mg/kg/day) or combination of ezetimibe and atorvastatin through the drinking water. On 28 days, mice were sacrificed and aorta and sera were collected to analyze the atherosclerotic lesion and blood lipid and cholesterol levels. As a result, ezetimibe alone exerted similar protective effects on atherosclerotic lesion sizes as atorvastatin, which was mediated by lowering serum cholesterol concentrations, inhibiting macrophage accumulation in the lesions and reducing circulatory inflammatory cytokines, such as monocyte chemoattractant protein (MCP-1) and tumor necrosis factor (TNF-α). In contrast to ezetimibe administration, atorvastatin alone attenuated atherosclerotic lesion which is dependent on its anti-inflammation effects. There were no significance differences in lesion areas and serum concentrations of cholesterol, oxidized LDL and inflammatory cytokines between combination therapy and monotherapy (either ezetimibe or atorvastatin). There were significant correlations between the lesion areas and serum concentrations of cholesterol, MCP-1 and TNF-α, respectively. However, there were no significant correlations between the lesion areas and serum concentrations of TGF-β1 and oxLDL. Conclusions Ezetimibe alone played the same protection against a moderate atherosclerotic lesion as atorvastatin, which was associated with lowering serum cholesterol, decreasing circulating inflammatory cytokines, and inhibiting macrophage accumulation in the lesions.
Self-Adapting Context Definition

Many approaches to context-aware computing have focused on providing means for developers to define application contexts. In these approaches, correct application behavior depends on the developer defining the right contexts. The application cannot adapt incorrectly defined contexts as they are statically defined by the developer. We propose a method by which an application can use feedback to evaluate its contexts and adapt them where necessary. This results in more accurate context definitions and should lead to improved application performance. We discuss how this is achieved using Q-learning, and present a scenario and experimental results to support our approach.
Air Pollution Monitoring and Mining Based on Sensor Grid in London

In this paper, we present a distributed infrastructure based on wireless sensors network and Grid computing technology for air pollution monitoring and mining, which aims to develop low-cost and ubiquitous sensor networks to collect real-time, large scale and comprehensive environmental data from road traffic emissions for air pollution monitoring in urban environment. The main informatics challenges in respect to constructing the high-throughput sensor Grid are discussed in this paper. We present a two-layer network framework, a P2P e-Science Grid architecture, and the distributed data mining algorithm as the solutions to address the challenges. We simulated the system in TinyOS to examine the operation of each sensor as well as the networking performance. We also present the distributed data mining result to examine the effectiveness of the algorithm.
Rare Variants in Ischemic Stroke: An Exome Pilot Study

The genetic architecture of ischemic stroke is complex and is likely to include rare or low frequency variants with high penetrance and large effect sizes. Such variants are likely to provide important insights into disease pathogenesis compared to common variants with small effect sizes. Because a significant portion of human functional variation may derive from the protein-coding portion of genes we undertook a pilot study to identify variation across the human exome (i.e., the coding exons across the entire human genome) in 10 ischemic stroke cases. Our efforts focused on evaluating the feasibility and identifying the difficulties in this type of research as it applies to ischemic stroke. The cases included 8 African-Americans and 2 Caucasians selected on the basis of similar stroke subtypes and by implementing a case selection algorithm that emphasized the genetic contribution of stroke risk. Following construction of paired-end sequencing libraries, all predicted human exons in each sample were captured and sequenced. Sequencing generated an average of 25.5 million read pairs (75 bp×2) and 3.8 Gbp per sample. After passing quality filters, screening the exomes against dbSNP demonstrated an average of 2839 novel SNPs among African-Americans and 1105 among Caucasians. In an aggregate analysis, 48 genes were identified to have at least one rare variant across all stroke cases. One gene, CSN3, identified by screening our prior GWAS results in conjunction with our exome results, was found to contain an interesting coding polymorphism as well as containing excess rare variation as compared with the other genes evaluated. In conclusion, while rare coding variants may predispose to the risk of ischemic stroke, this fact has yet to be definitively proven. Our study demonstrates the complexities of such research and highlights that while exome data can be obtained, the optimal analytical methods have yet to be determined.
Characterization of Waste Skins Used in Tulum Cheese Production

GÖKHAN ZENGIN, İLHAN GÜN, MEHMET METE MUTLU, NILAY ORK, ZEYNEP SEYDIM, ARIFE CANDAŞ ADIGÜZEL ZENGIN Ege University, Engineering Faculty, Leather Engineering Department, Bornova, İzmir, 35100, Turkey, +902323114401, *corresponding author: adiguzel.candas@gmail.com Mehmet Akif Ersoy University, Burdur Vocational High School, 15030, Burdur, Turkey, ilhangun15@gmail.com Süleyman Demirel University, Food Engineering Department of Engineering Faculty, 32260, Isparta, Turkey, zeynepseydim@sdu.edu. tr
A Framework for Distributed Dynamic Load Balancing in Heterogeneous Cluster

Distributed Dynamic load balancing (DDLB) is an important system function destined to distribute workload among available processors to improve throughput and/or execution times of parallel computer in Cluster Computing. Instead of balancing the load in cluster by process migration, or by moving an entire process to a less loaded computer, we make an attempt to balance load by splitting processes into separate jobs and then balance them to nodes. In order to get target, we use mobile agent (MA) to distribute load among nodes in a cluster. In this study, a multi-agent framework for load balancing in heterogeneous cluster is given. Total load on node is calculated using queue length which is measured as the total number of processes in queue. We introduce types of agents along with policies needed to meet the requirements of the proposed load-balancing. Different metrics are used to compare load balancing mechanism with the existing message passing technology. The experiment is carried out on cluster of PC's divided into multiple LAN's using PMADE (Platform for Mobile agent distribution and execution). Preliminary experimental results demonstrated that the proposed framework is effective than the existing ones.
Probabilistic Programming for Planning Problems

Probabilistic programing is an emerging field at the intersection of statistical learning and programming languages. An appealing property of probabilistic programming languages (PPL) is their support for constructing arbitrary probability distributions. This allows one to model many different domains and solve a variety of problems. We show the link between probabilistic planning and PPLs by introducing a translation that allows one to map probabilistic planning problems onto parameter learning in PPLs. The advantage of our approach is twofold. Firstly, having the expressivity of a programming language simplifies modeling compared to using existing planning languages such as PPDDL. Secondly, there exist effective general-purpose learning algorithms that - having the correct encoding - can readily be used to learn optimal policies. In this paper we use ProbLog - a probabilistic version of Prolog - as programming language, but our approach can be applied on any other PPL as well.
Muscular Side Effects of Statins

Lipid lowering has been shown to be effective in preventing primary and recurrent cardiovascular events and to save life. Statins almost exclusively used for this purpose meanwhile became one of the most widely prescribed families of drugs world-wide. Myopathies – mainly not well characterized – are the major group of side effects. We here review different types of clinical appearances, localizations, symptoms and the biochemical background. The data indicate that severe muscular side effects are rare. Patients and their doctors, however, easily overlook mild ones. Myopathic symptoms without any known biochemical correlate are not rare. No general guideline exists about exact diagnosis and differential diagnosis. Strict adherence to the measures of life-style change and performance of regular exercise can even further enhance significantly these side effects. Much more research should be directed onto the pathophysiological (genetic? ) background to finally evaluate possible therapeutic consequences rather than simply to withdraw or change the respective statin.
Biodegradability and tensile properties of compatibilised polyethylene/rice bran film

In this study, a degradable film based on polyethylene (PE) and Rice bran (RB) as filler were developed. Low density polyethylene (LDPE) and RB (0 - 5 %) were prepared by using twin screw extruder and blown film machine. The effect of 1 parts per hundred (phr) of Ultra PlastTM10 (TP10) compatibiliser on the biodegradability, tensile properties and morphological properties of LDPE/RB films was evaluated. The presence of high RB contents had an adverse effect on the tensile strength of LDPE/RB films. Incorporation of compatibiliser increased the tensile strength of LDPE/RB films. The addition of TP10 in the films has enhanced the interfacial adhesion, distribution and dispersion phase and thus, improved the tensile properties of the films. The addition of RB in LDPE/RB films has increased their biodegradability due to the attacks of microorganism in the soil. Incorporation of TP10 in LDPE/RB films led to a negative effect on the rate of biodegradation.