Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy efficient resource provisioning with dynamic VM placement using energy aware load balancer in cloud

Cloud Computing is merely defined as delivering IT services over the Internet. It is ubiquitous, on-demand provisioning of resources. The fundamental principle of reusability of IT capabilities is the basis of cloud computing. To meet the growing demand for computations and large volumes of data, the model of cloud computing environment provides high performance servers and persistent storage devices like hard disk drives. These resources are the major source of the enormous power consumption in cloud data center along with air conditioning and cooling equipment. Energy consumption and diffusion of carbon that contaminate the atmosphere go hand in hand. The more the energy consumed, higher is the carbon footprint, which in turn maximizes the operating cost. Energy aware load balancer like Weighted First-cum-First-Served, helps in avoiding overheating and overloading of servers, by evenly balancing the workload across all the servers in the data center, hence reducing the amount of energy consumed. Dynamic placement of virtual machines in the data center enables to ensure availability of computing resources, tries to minimize overloaded servers and save the power consumption by shutting down some under-utilized, idle servers and thereby in maximizing the usage of resources. The main aim of the dynamic VM placement ensures the mapping of virtual machines to physical machines at run time, in such a way that the physical machine can be utilized to their maximum efficiency, and is non-preemptive in nature, where the already running VMs are neither shut down nor interrupted but in turn provides a list of live VM migrations to be executed to reach the optimal solution, thereby saves energy consumption to a greater extent. The central objective of this work is dedicated to develop an energy efficient resource provisioning framework with dynamic virtual machine placement using energy aware load balancer in cloud.
Dysfunctions of Medial and Lateral Orbitofrontal Cortex in Psychopathy

Abstract: Psychopathy is a developmental disorder marked by emotional hypo‐responsiveness and an increased risk for instrumental and reactive aggression. In this paper, it will be argued that the developmental origins of psychopathy do not lie in orbitofrontal cortex (OFC) dysfunction. This is because the key functional impairments seen in psychopathy are associated with amygdala damage, not with OFC damage. However, it will be argued that the role played by the integrated functioning of the amygdala and medial OFC in stimulus–reinforcement learning and decision making is disrupted in psychopathy. Impaired learning of stimulus–reinforcement associations and representation of reinforcement expectations are thought to underlie the impairments in socialization and appropriate decision making seen in psychopathy. It is suggested that the impairment in the role of medial OFC in prediction error signaling and the detection of contingency change may underlie the impairments in flexible behavioral change seen in psychopathy.
APPLICATIONS OF 2 DPCA IN REMOTE SENSING IMAGE FUSION

2DPCA is introduced in remote sensing image fusion, and three forms of 2DPCA-based remote sensing image fusion method are proposed according to the different evaluation methods of the projection vector, including 2DPCA, L2DPCA and D2DPCA algorithms. In the paper, the new image fusion techniques are applied in multi-source remotely sensed image fusion in contrast to PCA, IHS, Brovey and Wavelet algorithms, and then qualitative and quantitative analysis were given on the approaches. Experimental results show that the performance of 2DPCA, L2DPCA and D2DPCA image fusion algorithms outperforms PCA, IHS, Brovey and Wavelet from the point of view of the quality and performance of the fused images. This proves the validity and effectiveness of the new algorithms. The most outstanding advantage of 2DPCA, L2DPCA and D2DPCA fusion algorithms is that they can effectively utilize the two-dimensional structure information of the images, so that they can not only enhance the entropy of the fused images and better improve the spatial resolution of those, but also preserve good spectral information of the lower resolution images. * Corresponding author. nihaowxm@163.com
Is individual smoking behaviour influenced by area-level ethnic density? A cross-sectional electronic health database study of inner south-east London

Tobacco smoking remains one of the greatest public health problems facing the UK today. It varies significantly by ethnic group. This study aimed to determine whether ethnic differences in smoking behaviour are related to neighbourhood-level, own-group ethnic density across south and east London. The association between ethnic density and individual smoking behaviour was assessed by multilevel logistic regression using the electronic health records of 688 397 general practitioner-registered patients. Restricted cubic splines were created to explore whether the effect of ethnic density on smoking behaviour was nonlinear. Increasing own-group ethnic density was found to be associated with a significant reduction in the odds of being a current smoker in all ethnic groups, except for Black Caribbean women. The relationship between ethnic density and current smoking was found to be nonlinear, with the strength of association varying significantly by sex and ethnic group. These novel findings point to a complex relationship between culture, neighbourhood-level experience of adversity or social support and smoking behaviour, and will allow us to target smoking cessation services differentially to individuals/groups living in relative ethnic isolation, who do not benefit from the potential cultural/social factors associated with reduced tobacco consumption. The effect of ethnic density on smoking behaviour in London http://ow.ly/NQED308q9cO
HU DB at TREC 2014 Microblog Track

Abstract : This paper describes our system for the Tweet Timeline Generation (TTG) task of the Microblog track, at the Text Retrieval Conference (TREC) 2014. Intuitively, given a collection of microblog posts (i.e., tweets), and a keyword query Q, the goal is to generate a timeline of related tweets. Such a timeline consists of representative tweets, relevant to Q. In our system we employ query expansion to identify highly relevant tweets, and then use affinity propagation to cluster the tweets based on their word similarity, hashtag similarity and temporal similarity. We then return a representative tweet from each cluster. The result is a system with relatively good precision, but unfortunately, poor recall. We discuss the techniques employed as well as the insights gleaned while developing and testing our system.
Role of Misfolded N-CoR Mediated Transcriptional Deregulation of Flt3 in Acute Monocytic Leukemia (AML)-M5 Subtype

The nuclear receptor co-repressor (N-CoR) is a key component of the generic multi-protein complex involved in transcriptional control. Flt3, a key regulator of hematopoietic cell growth, is frequently deregulated in AML (acute myeloid leukemia). Here, we report that loss of N-CoR-mediated transcriptional control of Flt3 due to misfolding, contributes to malignant growth in AML of the M5 subtype (AML-M5). An analysis of hematopoietic genes in AML cells led to the identification of Flt3 as a transcriptional target of N-CoR. Flt3 level was inversely related to N-CoR status in various leukemia cells. N-CoR was associated with the Flt3 promoter in-vivo, and a reporter driven by the Flt3 promoter was effectively repressed by N-CoR. Blocking N-CoR loss with Genistein; an inhibitor of N-CoR misfolding, significantly down-regulated Flt3 levels regardless of the Flt3 receptor mutational status and promoted the differentiation of AML-M5 cells. While stimulation of the Flt3 receptor with the Flt3 ligand triggered N-CoR loss, Flt3 antibody mediated blockade of Flt3 ligand-receptor binding led to N-CoR stabilization. Genetic ablation of N-CoR potentiated Flt3 ligand induced proliferation of BA/F3 cells. These findings suggest that N-CoR-induced repression of Flt3 might be crucial for limiting the contribution of the Flt3 signaling pathway on the growth potential of leukemic cells and its deregulation due to N-CoR loss in AML-M5, could contribute to malignant growth by conferring a proliferative advantage to the leukemic blasts. Therapeutic restoration of N-CoR function could thus be a useful approach in restricting the contribution of the Flt3 signaling pathway in AML-M5 pathogenesis.
MACHINE LEARNING APPROACHES FOR THE ANALYSIS OF COMPLEX, HIGH-DIMENSION GENOMIC DATA

The use of genomic technology has the potential to provide invaluable insight into the mechanisms of many important production and fitness traits in the animal production industry. Unfortunately this information comes at a cost, in terms of the high-dimensions and complex structure of genomic datasets. Due to the large number of features in many genomic datasets, explicit modeling of gene interactions is often infeasible. As such, models are often nested within genes, assuming independence between gene effects. Given that genes operate in interacting networks, such assumptions can limit the power to detect important gene variants. To eliminate the need for simplifying assumptions, a machine learning algorithm, referred to as the ant colony algorithm (ACA), has been adapted for analysis of highdimension genomic data. The performance of the ACA was compared to several standard methodologies using both simulated and real genomic datasets. Methodologies were evaluated based on their performance in identifying highly predictive biomarkers and causative mutations under the influence of genetic interactions. Results show that the ACA achieved substantially better performance in all scenarios evaluated.
The KLP-7 Residue S546 Is a Putative Aurora Kinase Site Required for Microtubule Regulation at the Centrosome in C. elegans

Regulation of microtubule dynamics is essential for many cellular processes, including proper assembly and function of the mitotic spindle. The kinesin-13 microtubule-depolymerizing enzymes provide one mechanism to regulate microtubule behaviour temporally and spatially. Vertebrate MCAK locates to chromatin, kinetochores, spindle poles, microtubule tips, and the cytoplasm, implying that the regulation of kinesin-13 activity and subcellular targeting is complex. Phosphorylation of kinesin-13 by Aurora kinase inhibits microtubule depolymerization activity and some Aurora phosphorylation sites on kinesin-13 are required for subcellular localization. Herein, we determine that a C. elegans deletion mutant klp-7(tm2143) causes meiotic and mitotic defects that are consistent with an increase in the amount of microtubules in the cytoplasmic and spindle regions of meiotic embryos, and an increase in microtubules emanating from centrosomes. We show that KLP-7 is phosphorylated by Aurora A and Aurora B kinases in vitro, and that the phosphorylation by Aurora A is stimulated by TPXL-1. Using a structure-function approach, we establish that one putative Aurora kinase site, S546, within the C-terminal part of the core domain is required for the function, but not subcellular localization, of KLP-7 in vivo. Furthermore, FRAP analysis reveals microtubule-dependent differences in the turnover of KLP-7(S546A) and KLP-7(S546E) mutant proteins at the centrosome, suggesting a possible mechanism for the regulation of KLP-7 by Aurora kinase.
Continuous automatic calibration for optical see-through displays

The current advent of consumer level optical see-through (OST) head-mounted displays (HMD's) has greatly broadened the accessibility of Augmented Reality (AR) to not only researchers but also the general public as well. This increased user base heightens the need for robust automatic calibration mechanisms suited for nontechnical users. We are developing a fully automated calibration system for two stereo OST HMD's, a consumer level and prototype model, based on the recently introduced interaction free display calibration (INDICA) method. Our current efforts are also focused on the development of an evaluation process to assess the performance of the system during use by non-expert subjects.
Splice diagrams. Singularity links and universal abelian covers

Splice diagrams. Singularity Links and Universal Abelian Covers Helge Møller Pedersen To a rational homology sphere graph manifold one can associate a weighted tree invariant called splice diagram. In this thesis we prove a sufficient numerical condition on the splice diagram for a graph manifold to be a singularity link. We also show that if two manifolds have the same splice diagram, then their universal abelian covers are homeomorphic. To prove the last theorem we have to generalize our notions to orbifolds.
A Regularized Minimum Cross-entropy Algorithm on Mixture of Experts for Curve Detection

Curve detection is a basic problem in image processing and remains a difficult problem. In this paper, with the help of regularization theory, we aim to solve this problem via a gradient regularized minimum cross-entropy (RMCE) algorithm on the mixture of experts (ME) model, which can automatically make model selection. It is demonstrated by the simulation and image experiments that this gradient algorithm can not only detect curves (straight lines or circles) against noise, but also automatically determine the number of curves during parameter learning
A MRI-Compatible Combined Mechanical Loading and MR Elastography Setup to Study Deformation-Induced Skeletal Muscle Damage in Rats

Deformation of skeletal muscle in the proximity of bony structures may lead to deep tissue injury category of pressure ulcers. Changes in mechanical properties have been proposed as a risk factor in the development of deep tissue injury and may be useful as a diagnostic tool for early detection. MRE allows for the estimation of mechanical properties of soft tissue through analysis of shear wave data. The shear waves originate from vibrations induced by an external actuator placed on the tissue surface. In this study a combined Magnetic Resonance (MR) compatible indentation and MR Elastography (MRE) setup is presented to study mechanical properties associated with deep tissue injury in rats. The proposed setup allows for MRE investigations combined with damage-inducing large strain indentation of the Tibialis Anterior muscle in the rat hind leg inside a small animal MR scanner. An alginate cast allowed proper fixation of the animal leg with anatomical perfect fit, provided boundary condition information for FEA and provided good susceptibility matching. MR Elastography data could be recorded for the Tibialis Anterior muscle prior to, during, and after indentation. A decaying shear wave with an average amplitude of approximately 2 μm propagated in the whole muscle. MRE elastograms representing local tissue shear storage modulus Gd showed significant increased mean values due to damage-inducing indentation (from 4.2 ± 0.1 kPa before to 5.1 ± 0.6 kPa after, p<0.05). The proposed setup enables controlled deformation under MRI-guidance, monitoring of the wound development by MRI, and quantification of tissue mechanical properties by MRE. We expect that improved knowledge of changes in soft tissue mechanical properties due to deep tissue injury, will provide new insights in the etiology of deep tissue injuries, skeletal muscle damage and other related muscle pathologies.
Large Firms and Internal Labor Markets

This paper introduces a model of internal labor markets that is consistent with the observed differences between workers in large and small firms with respect to wages and separation rates. In particular, firms constitute labor markets with no search frictions. Workers are free to move within a firm at no cost, whereas switching across firms is costly. If the quality of a match between a worker and a occupation/department/team within a firm is uncertain, then larger firms offer more opportunities for workers to find the right match. As a result, workers abandon unpromising matches more easily and are more likely to be employed in better matches. In equilibrium, workers in larger firms are more productive, earn higher wages and are less likely to quit, even conditional on their wage. Using data from the 1996 SIPP we find support for the predictions of our framework: internal mobility is higher in larger firms and depends negatively on wages and tenure; workers in larger firms switch occupations at higher wage levels and receive higher wages in their new occupation; the size-wage premium is higher for workers with longer tenure, while workers who leave large firms continue to enjoy high wages, but only if they remain in the same occupation; and finally the wage and size effects on the separation probability are significantly larger for workers who switch occupations.
Modeling non-ignorable attrition and measurement error in panel surveys: an application to travel demand modeling

Modern panel surveys frequently suffer from high and likely non-ignorable attrition, and transportation surveys suffer from poor travel time estimates. This paper examines new methods for adjusting forecasts and model estimates to account for these problems. The methods we describe are illustrated using a new panel survey of 1500 commuters in San Diego, California. These data are being collected to evaluate a federally-funded "Congestion Pricing" experiment investigating the impacts of allowing solo drivers to pay use freeway carpool lanes. The panel survey, begun in Fall 1997, collects data on travel behavior and attitudes at six-month intervals through telephone interviews. The panel sample is refreshed with new respondents at each wave to counteract the attrition between waves. Both the original and refreshment samples are stratified on commuters' mode choices (solo drive in free lanes, pay to solo drive in the carpool lanes, or carpool for free in carpool lanes) to insure sufficient sample size for estimating our models.
The complex of TFII‐I, PARP1, and SFPQ proteins regulates the DYX1C1 gene implicated in neuronal migration and dyslexia

DYX1C1 was first identified as a candidate gene for dyslexia susceptibility, and its role in controlling neuronal migration during embryogenesis and effect on learning in rodents have been verified. In contrast, genetic association studies have been ambiguous in replicating its effects on dyslexia. To better understand the regulation of DYX1C1 and the possible functional role of genetic variation in the promoter of DYX1C1, we selected three single‐nucleotide polymorphisms (SNPs) with predicted functional consequences or suggested associations to dyslexia for detailed study. Electrophoretic mobility shift assays suggested the allele‐specific binding of the transcription factors TFII‐I (to rs3743205) and Sp1 (to rs16787 and rs12899331) that could be verified by competition assays. In addition, we purified a complex of protein factors binding to the previously suggested dyslexia‐related SNP, −3G/A (rs3743205). Three proteins, TFII‐I, PARP1, and SFPQ, were unambiguously identified by mass spectrometry and protein sequencing. Two SNPs, rs16787 and rs3743205, showed significant allelic differences in luciferase assays. Our results show that TFII‐I, PARP1, and SFPQ proteins, each previously implicated in gene regulation, form a complex controlling transcription of DYX1C1. Furthermore, allelic differences in the promoter or 5′ untranslated region of DYX1C1 may affect factor binding and thus regulation of the gene. —Tapia‐Páez, I. , Tammimies, K. , Massinen, S. , Roy. A. L. , Kere, J. The complex of TFII‐I, PARP1, and SFPQ proteins regulates the DYX1C1 gene implicated in neuronal migration and dyslexia. FASEB J. 22, 3001–3009 (2008)
Implementing the Dslash Operator in OpenCL

The Dslash operator is used in Lattice Quantum Chromodymamics (LQCD) applications to implement a Wilson-Dirac sparse matrix-vector product. Typically the Dslash operation has been implemented as a parallel program. Today’s Graphics Processing Units (GPU) are designed to do highly parallel numerical calculations for 3D graphics rendering. This design works well with scientific applications such as LQCD’s implementation of the Dslash operator. The Scientific Computing group at the Thomas Jefferson National Accelerator Facility (Jefferson Lab) has implemented the Dslash operator for execution on GPUs using NVIDIA’s Compute Unified Device Architecture (CUDA). CUDA applications, however, will only run on NVIDIA hardware. OpenCL (Open Computing Language) is a new open standard for developing parallel programs across CPUs, GPUs and other processors. This paper describes the implementation of the Dslash operator using OpenCL (Open Computing Language), its performance on NVIDIA GPUs compared with CUDA, and its performance on other hardware platforms. General Terms Performance, Languages.
Carotenoid ornamentation of adult male Common Redpolls predicts probability of dying in a salmonellosis outbreak

Summary 1 Conditional handicap models of sexual selection predict that ornamentation should be positively associated with an individual's ability to withstand challenges to their health. 2 We assessed whether levels of carotenoid ornamentation were related to the probability of adult male Common Redpolls, Carduelis flammea, dying in a salmonellosis epidemic by comparing the ornamentation of surviving adult male redpolls to those found dead. Hosts suffering from salmonellosis shed bacteria in faeces, and new individuals are typically infected when they ingest faeces-contaminated food. 3 The proportion of adult males in the sample of dead birds was significantly higher than in the sample of living birds. 4 Among adult males, probability of survival was significantly predicted by the expression of their carotenoid signals: brightly ornamented adult males were more likely to die in the epidemic. 5 A probable hypothesis for these results is that if carotenoid ornamentation in redpolls functions as a status badge of dominance, bright males would have had priority access to highly preferred, and contaminated, food patches.
Performance study of some dynamic load balancing algorithms in cloud computing environment

In Cloud computing environment the utilization of computing resources may be available through the service that redernal by cloud consumers to service providers over the Internet. Due to popularity of Cloud computing environment, the cloud computing users are increasing day by day and that has become one of the important challenges for the cloud providers in terms of load balancing. The top most challenge of cloud service providers is load balancing which is mostly used for effective operation in cloud computing environment. However, load balancing distributes the traffic evenly over multiple paths by minimizing the Response time, optimizing the resources, maximizing the throughput and by avoiding single resource overloading. In this present work, we have focused on dynamic load balancing algorithms namely modified throttled, FCFS and Particle Swam Optimization based on their performance using Cloud Analyst Simulator. Simulation outcomes are recorded in terms of the Response time and datacenter processing time of the three algorithms along with its performance and arrival cost details.