Miracle-dz/UniFAR-FTUs huggingface dataset:
CSHARP: Coherence and SHaring Aware Cache Replacement Policies for Parallel Applications

Parallel applications are becoming mainstream and architectural techniques for multicores that target these applications are the need of the hour. Sharing of data by multiple threads and issues due to data coherence are unique to parallel applications. We propose CSHARP, a hardware framework that brings coherence and sharing awareness to any shared last level cache replacement policy. We use the degree of sharing of cache lines and the information present in coherence vectors to make replacement decisions. We apply CSHARP to a state-of-the-art cache replacement policy called TA-DRRIP to show its effectiveness. Our experiments on four core simulated system show that applying CSHARP on TA-DRRIP gives an extra 10% reduction in miss-rate at the LLC. Compared to LRU policy, CSHARP on TA-DRRIP shows a 18% miss-rate reduction and a 7% performance boost. We also show the scalability of our proposal by studying the hardware overhead and performance on a 8-core system.
A cooperative biomimetic approach for high dimensional data mining

We propose in this paper an original alternative to solve the problem of search space visualization to discover the complex structure of data, while respecting topology. Our cooperative approach provided a multi-dimensional visualization from the data. The first method is the subspace selection from whole data space. This selection is obtained by a genetic algorithm reducing the data dimension space by simply determining the most relevant dimensions evaluated by a distribution measure. Once a subspace selected we construct a neighborhood graph using artificial ants algorithm.
Protein Tyrosine Nitration during Development and Abiotic Stress Response in Plants

In recent years, the study of nitric oxide (NO) in plant systems has attracted the attention of many researchers. A growing number of investigations have shown the significance of NO as a signal molecule or as a molecule involved in the response against (a)biotic processes. NO can be responsible of the post-translational modifications (NO-PTM) of target proteins by mechanisms such as the nitration of tyrosine residues. The study of protein tyrosine nitration during development and under biotic and adverse environmental conditions has increased in the last decade; nevertheless, there is also an endogenous nitration which seems to have regulatory functions. Moreover, the advance in proteome techniques has enabled the identification of new nitrated proteins, showing the high variability among plant organs, development stage and species. Finally, it may be important to discern between a widespread protein nitration because of greater RNS content, and the specific nitration of key targets which could affect cell-signaling processes. In view of the above point, we present a mini-review that offers an update about the endogenous protein tyrosine nitration, during plant development and under several abiotic stress conditions.
The Anti-Sigma Factor TcdC Modulates Hypervirulence in an Epidemic BI/NAP1/027 Clinical Isolate of Clostridium difficile

Nosocomial infections are increasingly being recognised as a major patient safety issue. The modern hospital environment and associated health care practices have provided a niche for the rapid evolution of microbial pathogens that are well adapted to surviving and proliferating in this setting, after which they can infect susceptible patients. This is clearly the case for bacterial pathogens such as Methicillin Resistant Staphylococcus aureus (MRSA) and Vancomycin Resistant Enterococcus (VRE) species, both of which have acquired resistance to antimicrobial agents as well as enhanced survival and virulence properties that present serious therapeutic dilemmas for treating physicians. It has recently become apparent that the spore-forming bacterium Clostridium difficile also falls within this category. Since 2000, there has been a striking increase in C. difficile nosocomial infections worldwide, predominantly due to the emergence of epidemic or hypervirulent isolates that appear to possess extended antibiotic resistance and virulence properties. Various hypotheses have been proposed for the emergence of these strains, and for their persistence and increased virulence, but supportive experimental data are lacking. Here we describe a genetic approach using isogenic strains to identify a factor linked to the development of hypervirulence in C. difficile. This study provides evidence that a naturally occurring mutation in a negative regulator of toxin production, the anti-sigma factor TcdC, is an important factor in the development of hypervirulence in epidemic C. difficile isolates, presumably because the mutation leads to significantly increased toxin production, a contentious hypothesis until now. These results have important implications for C. difficile pathogenesis and virulence since they suggest that strains carrying a similar mutation have the inherent potential to develop a hypervirulent phenotype.
A comparative study between polyclass and multiclass language models

In this work, we introduce the concept of Multiclass for language modeling and we compare it to the Polyclass model. The originality of the Multiclass is its capability to parse a string of classes/tags into variable length independent sequences. A few experimental tests were carried out on a class corpus extracted from the French « Le Monde » word corpus labeled automatically. This corpus contains a set of 43 million of words. In our experiments, Multiclass outperform first-order Polyclass but are slightly outperformed by second-order Polyclass.
Text Mining and Temporal Trend Detection on the Internet for Technology Assessment: Model and Tool

In today’s world, organizations conduct technology assessment (TAS) prior to decision making about investments in existing, emerging, and hot technologies to avoid costly mistakes and survive in the hyper-competitive business environment. Relying on web search engines in looking for relevant information for TAS processes, decision makers face abundant unstructured information that limit their ability to assess technologies within a reasonable time frame. Thus the following question arises: how to extract valuable TAS knowledge from a diverse corpus of textual data on the web? To cope with this question, this paper presents a web-based model and tool for knowledge mapping. The proposed knowledge maps are constructed on the basis of a novel method of co-word analysis, based on webometric web counts and a temporal trend detection algorithm which employs the vector space model (VSM). The approach is demonstrated and validated for a spectrum of information technologies. Results show that the research model assessments are highly correlated with subjective expert (n=136) assessment (r > 0.91), and with predictive validity value above 85%. Thus, it seems safe to assume that this work can probably be generalized to other domains. The model contribution is emphasized by the current growing attention to the big-data phenomenon.
Why Crowdfunding Projects Can Succeed: The Role of Proponents’ Individual and Territorial Social Capital

Purpose – Crowdfunding implies mobilizing the crowd to finance projects which are posted on dedicated websites, known as crowdfunding platforms. Funding is provided by web users in exchange for some claims on the project revenues, or for a reward, or simply for donation. In this paper, we aim at learning more on what determines the probability of a project to reach the target funding. We apply the lens of social capital, defined by the goodwill available to her/him from the structure and content of his/her social relations (Adler and Kwon 2002). In particular we distinguish between ‘individual’ (exclusive) and ‘territorial’ (locally shared) social capital. Design/methodology/approach – We test our hypotheses by running Probit estimates on a sample of 461 crowdfunding projects posted by 699 proponents and hosted on 11 Italian crowdfunding platforms. The focal dependent variables deal with the individual social capital and geographically localized social capital. The former is proxied by the number of contacts of proponents on digital social networks (i.e. Facebook). To account for the latter, we refer to the municipality of residence of each proponent and resort to the traditional measure of localized social capital used in the literature. We control also for a set of project-specific variables. Originality/value –This methodology puts in evidence that in a framework with information asymmetry individual social capital (ISC) is positively and significantly correlated with the probability of success of a crowdfunding project, providing a positive signal to crowdfunders. On the contrary, we do not find any significant correlation with the diffused territorial social capital (TSC). By jointly considering the impact of ISC and TSC, we find that TSC weakens the signal provided by ISC. We posit that such result is a consequence of an ‘adverse-selection’ problem. Good-quality projects may more easily collect money on a territorial basis if TSC is large. Therefore crowdfunding projects originated in large-TSC milieus ceteris paribus are less easily financed by crowdfunders. Practical implications – Our work has interesting managerial implications. Individual social capital impacts on the success of crowdfunding projects and interacts with geolocalized territorial social capital. Accordingly, proponents, funders and managers of crowdfunding platforms who are interested in selecting potentially successful projects must consider such interaction, and carefully evaluate both individual and territorial social capital.
Aligning Parallel English-chinese Texts Statistically with Lexical Criteria

We describe our experience with automatic alignment of sentences in parallel English-Chinese texts. Our report concerns three related topics: (1) progress on the HKUST English-Chinese Parallel Bilingual Corpus; (2) experiments addressing the applicability of Gale & Church's (1991) length-based statistical method to the task of alignment involving a non-Indo-European language; and (3) an improved statistical method that also incorporates domain-speciic lexical cues.
An Analysis of Multiobjective Optimization within Genetic Algorithms

This paper focuses on the problem of how to rank a population of solutions into order of fitness within a genetic algorithm for multiobjective optimization applications. Attention is paid to the fact that the set of acceptable solutions to a problem is usually only a small sub-set of all Paretooptimal solutions to the problem. Two key concepts essential to the solution of this problem are identified and explained: range-independence and importance. Six methods (three old and three new) for solving this multi-fitness ranking problem are described in detail and applied to five test problems for comparison: four established test functions and an example solid object design task with ten separate constraints and objectives. Results show that all five methods allow the generation of Pareto-optimal solutions, but all have different distributions of solutions within the Pareto-optimal range(s). The bias of each distribution and the resulting quality of solutions generated by each method is examined and compared. The paper concludes that the new ranking method 'Sum of Weighted Global Ratios' (SWGR) created as part of this work allows the most consistent generation of acceptable solutions, whilst also being fully independent of the multiobjective problem.
Detection of Pancreatic Cancer Biomarkers Using Mass Spectrometry

Background Pancreatic cancer is the fourth leading cause of cancer-related deaths. Therefore, in order to improve survival rates, the development of biomarkers for early diagnosis is crucial. Recently, diabetes has been associated with an increased risk of pancreatic cancer. The aims of this study were to search for novel serum biomarkers that could be used for early diagnosis of pancreatic cancer and to identify whether diabetes was a risk factor for this disease. Methods Blood samples were collected from 25 patients with diabetes (control) and 93 patients with pancreatic cancer (including 53 patients with diabetes), and analyzed using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF/MS). We performed preprocessing, and various classification methods with imputation were used to replace the missing values. To validate the selection of biomarkers identified in pancreatic cancer patients, we measured biomarker intensity in pancreatic cancer patients with diabetes following surgical resection and compared our results with those from control (diabetes-only) patients. Results By using various classification methods, we identified the commonly splitting protein peaks as m/z 1,465, 1,206, and 1,020. In the follow-up study, in which we assessed biomarkers in pancreatic cancer patients with diabetes after surgical resection, we found that the intensities of m/z at 1,465, 1,206, and 1,020 became comparable with those of diabetes-only patients.
Trends in eating patterns, physical activity and socio-demographic factors in relation to postpartum body weight development

‘The Stockholm Pregnancy and Weight Development Study’ was conducted to identify risk factors for postpartum weight retention, such as dietary habits, physical activity and socio-demographic factors. The body weight development of 1423 pregnant women was studied prospectively from the beginning of the pregnancy until 1 year postpartum. Data were collected from routine pregnancy records and from questionnaires 6 and 12 months postpartum. Mean weight retention 1 year postpartum was 0.5 kg compared with the prepregnancy body weight. A ‘trend method’ was constructed to identify a number of pre-defined major patterns of behaviour. The weight retention 1 year postpartum was greater in women who (a) increased their energy intake during and after pregnancy, (b) increased their snack eating after pregnancy tc three or more snacks/d, and (c) decreased their lunch frequency starring during or after the pregnancy. Women who had retained > 5 kg 1 year postpartum were more seldom physically active in their leisure time throughout the study period compared with women with a smaller weight gain. Postpartum weight retention correlated negatively with the degree of physical activity in the second half year postpartum. These results indicate that postpartum weight retention is more affected by a change in lifestyle during, and above all after, pregnancy than by factors before pregnancy.
An Agent-Based Framework for Artificial Stock Markets

Stock markets strive to provide an efficient trading platform for investors. Trading rules and mechanisms issued to accomplish this differ among stock markets, and are subject to modification over time. Furthermore, market participants assume a broad range of roles and trading strategies. Such variation poses problems to those involved in the study of market dynamics, when developing an artificial stock market for experimentation and analysis. More than once, the resulting artificial stock markets, and thus the experimental results, are based on very restrictive assumptions. This paper introduces an agent-based framework for artificial stock market development and experimentation. The framework is flexible in the sense that multiple market structures are supported, and an infinite range of trading strategies by market participants can be captured. Such features are accomplished through the configuration of framework properties, and the appropriate hooks for extension of the framework’s components.
Optimizing Strategic Safety Stock Placement in Two-Layer Supply Chains

In this paper, we minimize the holding cost of the safety stock in the supply chain subject to linear constraints on the service times between the nodes of the network. In the problem, the objective function is concave as we assume the demand to be bounded by a concave function. The optimal solutions of the problem belong to the set of extreme points of the polyhedron, specified by the constraints of the problem. We first characterize the extreme points for the two-layer networks and then provide bounds to use in a branch and bound algorithm.
Correlation between microleakage and screw loosening at implant-abutment connection

PURPOSE This study aimed to evaluate the correlation between microleakage and screw loosening at different types of implant-abutment connections and/or geometries measuring the torque values before and after the leakage tests. MATERIALS AND METHODS Three different abutment types (Intenal hex titanium, internal hex zirconium, morse tapered titaniuım) with different geometries were connected to its own implant fixture. All the abutments were tightened with a standard torque value then the composition was connected to the modified fluid filtration system. After the measurements of leakage removal torque values were re-measured. Kruskal-wallis test was performed for non-parametric and one-way ANOVA was performed for parametric data. The correlation was evaluated using Spearman Correlation Test (α=0.05). RESULTS Significantly higher microleakage was found at the connection of implant-internal hex zirconium abutment. Observed mean torque value loss was also significantly higher than other connection geometries. Spearman tests revealed a significant correlation between microleakage and screw loosening. CONCLUSION Microleakage may provoke screw loosening. Removing torque values rationally decrease with the increase of microleakage.
A homogeneous many-core x86 processor full system framework based on NoC

With the development of processor technique, more and more processor cores are integrated in a single chip to achieve higher performance requirements. Network-on-Chip (NoC) is the substitute for bus interconnection on many-core processor framework. In this paper, we propose a NoC based homogeneous many-core x86 processor architecture with distributed shared memory and high speed Network Interface (NI). By integrating the GEMS computer system simulator and the Booksim NoC simulator, we design a many-core x86 processor full system framework. The experiment result of PARSEC benchmark shows that the framework is working efficiently with high performance.
NET Task Parallelization in the Cloud Runtime Support for Seamless Distribution of Shared Memory Parallel Tasks

The native parallelization support in predominant programming languages focuses on local cores, while mostly neglecting the huge parallelization potential of distributed execution. We have therefore developed a . NET runtime system extension that automatically distributes ordinary shared memory parallel tasks into the cloud, to execute them on a large number of cores, e.g. on a cluster. The runtime mechanism takes care of transmitting the necessary task code and data to the service, as well as of propagating task results and side-effect changes back to the client memory. For programs with long-running tasks or a high amount of tasks, the system is able to soon compensate the network transmission overheads and thereafter scale with the number of tasks up to the available serverside cores.
An Alternative Approach to Fast Implied Volatility Calculation

This paper has the task of identifying an alternative approach (in terms of a mathematical algorithm) which can determine with speed (i.e. to converge within a few iterations) the value of the implied volatility for the options. This value is of particular importance since it is the main component of the option’s price. The paper after, an initial explanation of the objectives, illustrates various alternatives and proposes the adoption of an algorithm able to quickly converge to the desired solution.
Design and stability analysis of a Variable Structure Adaptive Pole Placement Controller for first order systems

This paper shows the design and the stability analysis of a Variable Structure Adaptive Pole Placement Controller (VS-APPC) for first order plants, both for regulation and tracking. The VS-APPC was proposed in previous papers and it provides a fast and non-oscillatory transient. Besides, it suggests robustness to parameter uncertainties and disturbances, by using switching adaptive laws for the plant parameters, instead of the traditional integral ones, as the gradient method. Additionally, simulations results are shown.
Ca2+ store depletion causes STIM1 to accumulate in ER regions closely associated with the plasma membrane

Stromal interacting molecule 1 (STIM1), reported to be an endoplasmic reticulum (ER) Ca2+ sensor controlling store-operated Ca2+ entry, redistributes from a diffuse ER localization into puncta at the cell periphery after store depletion. STIM1 redistribution is proposed to be necessary for Ca2+ release–activated Ca2+ (CRAC) channel activation, but it is unclear whether redistribution is rapid enough to play a causal role. Furthermore, the location of STIM1 puncta is uncertain, with recent reports supporting retention in the ER as well as insertion into the plasma membrane (PM). Using total internal reflection fluorescence (TIRF) microscopy and patch-clamp recording from single Jurkat cells, we show that STIM1 puncta form several seconds before CRAC channels open, supporting a causal role in channel activation. Fluorescence quenching and electron microscopy analysis reveal that puncta correspond to STIM1 accumulation in discrete subregions of junctional ER located 10–25 nm from the PM, without detectable insertion of STIM1 into the PM. Roughly one third of these ER–PM contacts form in response to store depletion. These studies identify an ER structure underlying store-operated Ca2+ entry, whose extreme proximity to the PM may enable STIM1 to interact with CRAC channels or associated proteins.
Spatial Orientation and Wayfinding in Large-Scale Virtual Spaces: An Introduction

‘‘I have coasted this lake, in search of skins, afore the war, and have been here already; not at this very spot, for we landed yonder, where you may see the oak that stands above the cluster of hemlocks. ’’ ‘‘How, Pathfinder, can you remember all these trifles so accurately? ’’ ‘‘These are our streets and houses—our churches and palaces. Remember them, indeed! ’’ The Pathfinder, James Fenimore Cooper (1840)
i-Sound: Interactive gpu-based sound auralization in dynamic scenes

We present an auralization algorithm for interactive virtual environments with dynamic objects, sources, and listener. Our approach uses a modified image source method that computes propagation paths combining direct transmission, specular reflections, and edge diffractions up to a specified order. We use a novel multi-view raycasting algorithm for parallel computation of image sources on GPUs. Rays that intersect near diffracting edges are detected using barycentric coordinates and further propagated. In order to reduce the artifacts in audio rendering of dynamic scenes, we use a high order interpolation scheme that takes into account attenuation, crossfading, and delay. The resulting system can perform auralization at interactive rates on a high-end PC with NVIDIA GTX 280 GPU with 2–3 orders of reflections and 1 order of diffraction. Overall, our approach can generate plausible sound rendering for game-like scenes with tens of thousands of triangles. We observe more than an order of magnitude improvemen.