Miracle-dz/UniFAR-FTUs huggingface dataset:
Metaheuristics for the Virtual Machine Mapping Problem in Clouds

This article presents sequential and parallel metaheuristics to solve the virtual machines subletting problem in cloud systems, which deals with allocating virtual machine requests into pre- booked resources from a cloud broker, maximizing the broker profit. Three metaheuristic are stud- ied: Simulated Annealing, Genetic Algorithm, and hybrid Evolutionary Algorithm. The experimen- tal evaluation over instances accounting for workloads and scenarios using real data from cloud providers, indicates that the parallel hybrid Evolutionary Algorithm is the best method to solve the problem, computing solutions with up to 368.9% profit improvement over greedy heuristics results while accounting for accurate makespan and flowtime values.
A stepwise model for double‐stranded RNA processing by ribonuclease III

RNA interference is mediated by small interfering RNAs produced by members of the ribonuclease III (RNase III) family represented by bacterial RNase III and eukaryotic Rnt1p, Drosha and Dicer. For mechanistic studies, bacterial RNase III has been a valuable model system for the family. Previously, we have shown that RNase III uses two catalytic sites to create the 2‐nucleotide (nt) 3′ overhangs in its products. Here, we present three crystal structures of RNase III in complex with double‐stranded RNA, demonstrating how Mg2+ is essential for the formation of a catalytically competent protein–RNA complex, how the use of two Mg2+ ions can drive the hydrolysis of each phosphodiester bond, and how conformational changes in both the substrate and the protein are critical elements for assembling the catalytic complex. Moreover, we have modelled a protein–substrate complex and a protein–reaction intermediate (transition state) complex on the basis of the crystal structures. Together, the crystal structures and the models suggest a stepwise mechanism for RNase III to execute the phosphoryl transfer reaction.
Using Exponential Random Graph Models to Analyze the Character of Peer Relationship Networks and Their Effects on the Subjective Well-being of Adolescents

The influences of peer relationships on adolescent subjective well-being were investigated within the framework of social network analysis, using exponential random graph models as a methodological tool. The participants in the study were 1,279 students (678 boys and 601 girls) from nine junior middle schools in Shenzhen, China. The initial stage of the research used a peer nomination questionnaire and a subjective well-being scale (used in previous studies) to collect data on the peer relationship networks and the subjective well-being of the students. Exponential random graph models were then used to explore the relationships between students with the aim of clarifying the character of the peer relationship networks and the influence of peer relationships on subjective well being. The results showed that all the adolescent peer relationship networks in our investigation had positive reciprocal effects, positive transitivity effects and negative expansiveness effects. However, none of the relationship networks had obvious receiver effects or leaders. The adolescents in partial peer relationship networks presented similar levels of subjective well-being on three dimensions (satisfaction with life, positive affects and negative affects) though not all network friends presented these similarities. The study shows that peer networks can affect an individual’s subjective well-being. However, whether similarities among adolescents are the result of social influences or social choices needs further exploration, including longitudinal studies that investigate the potential processes of subjective well-being similarities among adolescents.
Stochastic Parse Tree Selection for an Existing RBMT System

In this paper we describe our hybrid machine translation system with which we participated in the WMT11 shared translation task for the English→German language pair. Our system was able to outperform its RBMT baseline and turned out to be the best-scored participating system in the manual evaluation. To achieve this, we extended an existing, rule-based MT system with a module for stochastic selection of analysis parse trees that allowed to better cope with parsing errors during the system's analysis phase. Due to the integration into the analysis phase of the RBMT engine, we are able to preserve the benefits of a rule-based translation system such as proper generation of target language text. Additionally, we used a statistical tool for terminology extraction to improve the lexicon of the RBMT system. We report results from both automated metrics and human evaluation efforts, including examples which show how the proposed approach can improve machine translation quality.
Fast Evaluation of Multivariate Quadratic Polynomials over GF(2^32) using Grahpics Processing Units

QUAD stream cipher is a symmetric cipher based on multivariate public-key cryptography(MPKC), which uses multivariate polynomials as encryption keys. It holds the provable security property based on the computational hardness assumption. More specifically, the security of QUAD depends on the hardness of solving non-linear multivariate quadratic systems over a finite field, which is known as an NP-complete problem. However, QUAD is slower than other stream ciphers, and an efficient implementation, which has a reduced computational cost, is required. In this paper, we propose some implementations of QUAD over GF(232) on Graphics Processing Units(GPU) and compare them. Moreover, we provide fast multiplications over GF(232), the core operation of QUAD. Our implementation gives the fastest throughput of QUAD as 24.827 Mbps. We propose an efficient implementation for computing with multivariate polynomials in multivariate cryptography on GPU and evaluate the efficiency of the proposal. GPU is considered to be a commodity parallel arithmetic unit. Our proposal parallelizes an algorithm coming from multivariate cryptography, and makes it efficient by optimizing the algorithm with GPU.
Background Subtraction Based on a Robust Consensus Method

Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model per pixel. Numerous experiments on both indoor and outdoor video sequences show that the proposed method, compared with several state-of-the-art methods, can achieve very promising performance
Hardness of buy-at-bulk network design

We consider the buy-at-bulk network design problem in which we wish to design a network for carrying multicommodity demands from a set of source nodes to a set of destination nodes. The key feature of the problem is that the cost of capacity on each edge is concave and hence exhibits economies of scale. If the cost of capacity per unit length can be different on different edges then, we say that the problem is non-uniform. The problem is uniform otherwise. We show that for any constant /spl gamma/, if NP /spl nsube/ ZPTIME(n/sup polylog n/), then there is no O(log/sup 1/2 - /spl gamma//N)-approximation algorithm for non-uniform buy-at-bulk network design and there is no O(log/sup 1/4 - /spl gamma//N)-approximation algorithm for the uniform problem.
Experientially Learning and Teaching in a Student-Directed Classroom

There exists a relatively coherent body of research relevant to problem-based and transformational learning but too few studies that have empirically explored the many anecdotal claims of the attributes of experiential, student-directed pedagogy. The purpose of this present study was to explore students’ and professor experiences with/in a student-directed experiential education elective course. What successes and challenges do students and the professor experience and how? What surprises, new learnings, and pedagogical risks ensue? The primary results include (a) Student Responsibility and Accountability, (b) Structure, (3) Role of the Professor, (4) Experiences With Self-Study Participation, and 5) Informing and Transforming Practice. These results and the review of literature add to the body of knowledge relevant to “doing” student-directed experiential education and Scholarship of Teaching and Learning (SoTL) in the postsecondary classroom.
Sparse Spatial Coding: A Novel Approach to Visual Recognition

Successful image-based object recognition techniques have been constructed founded on powerful techniques such as sparse representation, in lieu of the popular vector quantization approach. However, one serious drawback of sparse space-based methods is that local features that are quite similar can be quantized into quite distinct visual words. We address this problem with a novel approach for object recognition, called sparse spatial coding, which efficiently combines a sparse coding dictionary learning and spatial constraint coding stage. We performed experimental evaluation using the Caltech 101, Caltech 256, Corel 5000, and Corel 10000 data sets, which were specifically designed for object recognition evaluation. Our results show that our approach achieves high accuracy comparable with the best single feature method previously published on those databases. Our method outperformed, for the same bases, several multiple feature methods, and provided equivalent, and in few cases, slightly less accurate results than other techniques specifically designed to that end. Finally, we report state-of-the-art results for scene recognition on COsy Localization Dataset (COLD) and high performance results on the MIT-67 indoor scene recognition, thus demonstrating the generalization of our approach for such tasks.
Methods for the analysis of incidence rates in cluster randomized trials.

BACKGROUND The published literature on cluster randomized trials focuses on outcomes that are either continuous or binary. In many trials, the outcome is an incidence rate, such as mortality, based on person-years data. In this paper we review methods for the analysis of such data in cluster randomized trials and present some simple approaches. METHODS We discuss the choice of the measure of intervention effect and present methods for confidence interval estimation and hypothesis testing which are conceptually simple and easy to perform using standard statistical software. The method proposed for hypothesis testing applies a t-test to cluster observations. To control confounding, a Poisson regression model is fitted to the data incorporating all covariates except intervention status, and the analysis is carried out on the residuals from this model. The methods are presented for unpaired data, and extensions to paired or stratified clusters are outlined. RESULTS The methods are evaluated by simulation and illustrated by application to data from a trial of the effect of insecticide-impregnated bednets on child mortality. CONCLUSIONS The techniques provide a straightforward approach to the analysis of incidence rates in cluster randomized trials. Both the unadjusted analysis and the analysis adjusting for confounders are shown to be robust, even for very small numbers of clusters, in situations that are likely to arise in randomized trials.
Community detection in dynamic social networks: A game-theoretic approach

Most real-world social networks are inherently dynamic and composed of communities that are constantly changing in membership. As a result, recent years have witnessed increased attention toward the challenging problem of detecting evolving communities. This paper presents a game-theoretic approach for community detection in dynamic social networks in which each node is treated as a rational agent who periodically chooses from a set of predefined actions in order to maximize its utility function. The community structure of a snapshot emerges after the game reaches Nash equilibrium; the partitions and agent information are then transferred to the next snapshot. An evaluation of our method on two real world dynamic datasets (AS-Internet Routers Graph and AS-Oregon Graph) demonstrates that we are able to report more stable and accurate communities over time compared to the benchmark methods.
Epidemiological Survey-Based Formulae to Approximate Incidence and Prevalence of Neurological Disorders in the United States: a Meta-Analysis

Background This study aims to create a convenient reference for both clinicians and researchers so that vis-à-vis comparisons between brain disorders can be made quickly and accurately. We report here the incidence and prevalence of the major adult-onset brain disorders in the United States using a meta-analysis approach. Material and Methods Epidemiological figures were collected from the most recent, reliable data available in the research literature. Population statistics were based on the most recent census from the US Census Bureau. Extrapolations were made only when necessary. The most current epidemiological studies for each disorder were chosen. All effort was made to use studies based on national cohorts. Studies reviewed were conducted between 1950 and 2009. The data of the leading studies for several neurological studies was compiled in order to obtain the most accurate extrapolations. Results were compared to commonly accepted values in order to evaluate validity. Results It was found that 6.75% of the American adult population is afflicted with brain disorders. This number was eclipsed by the 8.02% of Floridians with brain disorders, which is due to the large aged population residing in the state. Conclusions There was a noticeable lack of epidemiological data concerning adult-onset brain disorders. Since approximately 1 out of every 7 households is affected by brain disorders, increased research into this arena is warranted.
The truncated somatostatin receptor sst5TMD4 stimulates the angiogenic process and is associated to lymphatic metastasis and disease-free survival in breast cancer patients

The truncated somatostatin receptor sst5TMD4 is associated with poor prognosis in breast cancer and increases breast cancer cell malignancy. Here, we examined the cellular/molecular mechanisms underlying this association, aiming to identify new molecular tools to improve diagnosis, prognosis or therapy. A gene expression array comparing sst5TMD4 stably-transfected MCF-7 cells and their controls (empty-plasmid) revealed the existence of profound alterations in the expression of genes involved in key tumoral processes, such as cell survival or angiogenesis. Moreover, sst5TMD4-overexpressing MCF-7 and MDA-MB-231 cells demonstrated increased expression/production of pro-angiogenic factors and enhanced capacity to form mammospheres. Consistently, sst5TMD4-expressing MCF-7 cells induced xenografted tumors with higher VEGF levels and elevated number of blood vessels. Importantly, sst5TMD4 was expressed in a subset of breast cancers, where it correlated with angiogenic markers, lymphatic metastasis, and reduced disease-free survival. These results, coupled to our previous data, support a relevant role of sst5TMD4 in the angiogenic process and reinforce the role of sst5TMD4 in breast cancer malignancy and metastatic potential, supporting its possible utility to develop new molecular biomarkers and drug therapies for these tumors.
Reduced severity and improved control of self-reported asthma in Finland during 2001-2010

Background Asthma and allergies are common and cause substantial burden in symptoms and suffering, hospitalizations and medication costs. However, despite the high prevalence, asthma burden has already decreased in Finland in 2000s. Objective We carried out an asthma barometer survey in all Finnish pharmacies to study changes in asthma severity and control, and use of health care services from 2001 to 2010. Methods Asthma severity, comorbid allergic conditions, and use of medication and health care services were assessed in subjects who purchased asthma or allergy medication from the pharmacies all across the country during one week in 2001 and again in 2010. In 2001, 3,062 patients (mean age, 49 years), and in 2010, 1,114 patients (mean age, 51 years) participated. Results In 2001 90% and in 2010 73% of the respondents reported physician-diagnosed asthma and were entitled to special reimbursement for their drug costs, i.e., they needed regular maintenance treatment. In 2001, 10% of the asthmatics regarded their disease as severe, compared with 4% in 2010, while the figures for mild asthma were 45% and 62%, respectively (p < 0.001). The proportion of patients needing emergency care during the last year decreased from 34% (2001) to 14% (2010) (p < 0.001) and the need for hospitalizations from 18% to 6% (p < 0.001). Smoking reduced from 24% to 18% among asthmatics ( p = 0.002). In 2010, risk factors for severe asthma were older age, comorbid atopic eczema, and food allergy. Conclusion During ten years, self-reported asthma severity has reduced and disease control improved in Finland.
Detection of Wolbachia in Aedes albopictus and Their Effects on Chikungunya Virus

Wolbachia-based vector control strategies have been proposed as a means to augment the currently existing measures for controlling dengue and chikungunya vectors. Prior to utilizing Wolbachia as a novel vector control strategy, it is crucial to understand the Wolbachia–mosquito interactions. In this study, field surveys were conducted to screen for the infection status of Wolbachia in field-collected Aedes albopictus. The effects of Wolbachia in its native host toward the replication and dissemination of chikungunya virus (CHIKV) was also studied. The prevalence of Wolbachia-infected field-collected Ae. albopictus was estimated to be 98.6% (N = 142) for females and 95.1% (N = 102) for males in the population studied. The Ae. albopictus were naturally infected with both wAlbA and wAlbB strains. We also found that the native Wolbachia has no impact on CHIKV infection and minimal effect on CHIKV dissemination to secondary organs.
Impacts of group-based micronance in agriculture Evidence from Ghana's Cocoa Abrabopa Association

The rate of return on agricultural innovation in Africa, how it can be identified, and the constraints that limit investment are clearly all questions of central interest for policies towards agriculture in that continent. It has been argued, using data from the rural sector in Africa, that the returns to investing in new technologies is high; see Conley and Udry (2007) [3] for Ghana, and Duflo, Kremer and Robinson (2008) [5] for Kenya. If this is the case then the failure of growth in such areas is the failure to save and invest. Either high discount rates or information failures must characterise such markets.
Automaticity of Access to Arithmetic Knowledge in Alzheimer’s Disease and Mild Cognitive Impairment

Two skills necessary for the execution of proficient calculation, retrieving arithmetic facts from memory and accessing number magnitude information, were studied in a group of patients diagnosed with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and healthy controls to try to elucidate the locus of impairment in AD-related calculation deficits. An arithmetic production task and a number-matching task were used as measures of explicit and implicit retrieval of arithmetic facts, and a number Stroop task assessed automatic access to number magnitude representation. AD patients, but not MCI patients, showed high response latencies and a high number of errors when performing multiplications in the production task and reduced automatic retrieval of arithmetic facts in the number-matching task. Performance on the number Stroop task suggests that access to number magnitude information is relatively resistant to cognitive impairment. Results for the AD groups are consistent with a pattern of preserved and impaired cognitive processes that might mediate the reported calculation deficits in AD.
The injecting ‘event’: harm reduction beyond the human

Abstract Since the 1980s, the primary public health response to injecting drug use in the UK has been one of harm reduction. That is, reducing the harms associated with drug use without necessarily reducing consumption itself. Rooted in a post-Enlightenment idea of rationalism, interventions are premised on the rational individual who, given the right means, will choose to avoid harm. This lies in stark contrast to dominant addiction models that pervade popular images of the ‘out of control’ drug user, or worse, ‘junkie’. Whilst harm reduction has undoubtedly had vast successes, including challenging the otherwise pathologising and often stigmatising model of addiction, I argue that it has not gone far enough in addressing aspects of drug use that go beyond ‘rational’ and ‘human’ control. Drawing on my doctoral research with people who inject drugs, conducted in London, UK, this paper highlights the role of the injecting ‘event’, which far from being directed or controlled by a pre-defined individual or ‘body’ was composed by a fragile assemblage of bodies, human and nonhuman. Furthermore, in line with the ‘event’s’ heterogeneous and precarious make-up, multiple ways of ‘becoming’ through these events were possible. I look here at these ‘becomings’ as both stabilising and destabilising ways of being in the world, and argue that we need to pay closer attention to these events and what people are actually in the process of becoming in order to enact more accountable and ‘response-able’ harm reduction.
ER stress response mechanisms in the pathogenic yeast Candida glabrata and their roles in virulence

The maintenance of endoplasmic reticulum (ER) homeostasis is critical for numerous aspects of cell physiology. Eukaryotic cells respond to the accumulation of misfolded proteins in the ER (ER stress) by activating the unfolded protein response (UPR), an intracellular signaling pathway that adjusts the folding capacity of the ER. Recent studies of several pathogenic fungi have revealed that the UPR is important for antifungal resistance and virulence; therefore, the pathway has attracted much attention as a potential therapeutic target. While the UPR is highly conserved among eukaryotes, our group recently discovered that the pathogenic yeast Candida glabrata lacks the typical fungal UPR, but possesses alternative mechanisms to cope with ER stress. This review summarizes how C. glabrata responds to ER stress and discusses the impacts of ER quality control systems on antifungal resistance and virulence.