Miracle-dz/UniFAR-FTUs huggingface dataset:
Information system infrastructure planning-threshold setting based on risk analysis

Information systems infrastructure planning performed based on the data collected from network monitoring and asset management systems can be developed further to include risk assessment and analysis as an input to the threshold setting methods in the two-dimensional load-age balance graph. The two-dimensional load-age balance graph is used to present generic value for network devices load gathered as measured values through the network managements system, and age - gathered from the asset management system. Threshold lines are defined in such graph to present areas of different interest from the perspective of low-high age, low-high load, or some other selection criteria. This paper explores how the threshold values can be set to reflect risk assessment methodology of the owner/maintainer/user. The position of each networking device in the graph is unique, disregarding the threshold values. However, the total score for the system, determined based on the position of a device in the graph area, depends on the defined threshold. The paper analyzes the impact of the threshold change based on the risk assessment criteria to the total score for a system. The total score can then be used to compare different systems, or to compare the same system in time.
Association of -592 region of IL-10 polymorphisms with asthma in south-eastern Iranian patients.

BACKGROUND Cytokines are considered important factors for the pathogenesis of asthma as they play a key role in the regulation of immune responses. The aim of this study was to investigate the association between this disease and polymorphisms in the -592 region of the IL-10 gene. METHODS This study was carried out on 100 asthmatic patients and 100 healthy controls. PCR-RFLP was applied to examine the polymorphisms in the -592 region of the IL-10 gene. RESULTS Our results showed a significant difference between patients and controls in terms of genotypes and alleles of the -592 region of the IL-10 gene. CONCLUSIONS According to our results, it can be concluded that the IL-10 promoter polymorphisms may play a crucial role in the pathogenesis of asthma.
Revision of a Floating-Point Genetic Algorithm GENOCOP V for Nonlinear Programming Problems

In this paper, focusing on general nonlinear programming problems, we attempt to propose a general and high- performance approximate solution method for them. In recent years, S. Koziel et al. have proposed a floating-point ge- netic algorithm, GENOCOP V, as a general approximate solution method for nonlinear programming problems and showed its efficiency, there are left some shortcomings of the method. In this paper, incorporating ideas to cope with these shortcomings, we propose a revised GENOCOP V (RGENOCOP V). Furthermore, we show the efficiency of the pro- posed method RGENOCOP V by comparing it with two existing methods, RGENOCOP III and GENOCOP V through the application of them into the numerical examples.
TFS: A Transparent File System for Contributory Storage

Contributory applications allow users to donate unused resources on their personal computers to a shared pool. Applications such as SETI@home, Folding@home, and Freenet are now in wide use and provide a variety of services, including data processing and content distribution. However, while several research projects have proposed contributory applications that support peer-to-peer storage systems, their adoption has been comparatively limited. We believe that a key barrier to the adoption of contributory storage systems is that contributing a large quantity of local storage interferes with the principal user of the machine. To overcome this barrier, we introduce the Transparent File System (TFS). TFS provides background tasks with large amounts of unreliable storage--all of the currently available space--without impacting the performance of ordinary file access operations. We show that TFS allows a peer-to-peer contributory storage system to provide 40% more storage at twice the performance when compared to a user-space storage mechanism. We analyze the impact of TFS on replication in peer-to-peer storage systems and show that TFS does not appreciably increase the resources needed for file replication.
Mean-field Approach to the Derivation of Baryon Superpotential from Matrix Model

We discuss how to obtain the superpotential of the baryons and mesons for SU(N) gauge theories with N flavour matter fields from matrix integral. We apply the mean-field approximation for the matrix integral. Assuming the planar limit of the self-consistency equation, we show that the result almost agrees with the field theoretical result.
Roots of Diversity Relations

The species-area relationship is one of the central generalizations in ecology; however, its origin has remained a puzzle. Since ecosystems are understood as energy transduction systems, the regularities in species richness are considered to result from ubiquitous imperatives in energy transduction. From a thermodynamic point of view, organisms are transduction mechanisms that distribute an influx of energy down along the steepest gradients to the ecosystem's diverse repositories of chemical energy, that is, populations of species. Transduction machineries, that is, ecosystems assembled from numerous species, may emerge and evolve toward high efficiency on large areas that hold more matter than small ones. This results in the well-known logistic-like relationship between the area and the number of species. The species-area relationship is understood, in terms of thermodynamics, to be the skewed cumulative curve of chemical energy distribution that is commonly known as the species-abundance relationship.
Time and Data-Aware Analysis of Graphical Service Models in Reo

Reo is a graphical channel-based coordination language that enables the modeling of complex behavioral protocols using a small set of channel types with well-defined behavior. Reo has been developed for the coordination of stand-alone components and services, which makes it suitable for the modeling of service-based business processes. The formal semantic models for Reo lay the grounds for computer-aided analysis of different aspects of Reo diagrams, including their animation, simulation and verification of control ? ow and data ? ow by means of model checking techniques. In this paper, we discuss the verification of data aware Reo process models using the mCRL2 model checking toolset including time analysis. We also show how behavior abstraction can be used to minimize Reo process models and generate smaller mCRL2 specifications. A detailed auction example illustrates our approach to time-aware modeling and verification of data-centric service models.
A Novel Window Function Yielding Suppressed Mainlobe Width and Minimum Sidelobe Peak

In many applications like FIR filters, FFT, signal processing and measurements, we are required (~45 dB) or less side lobes amplitudes. However, the problem is usual window based FIR filter design lies in its side lobes amplitudes that are higher than the requirement of application. We propose a window function, which has better performance like narrower main lobe width, minimum side lobe peak compared to the several commonly used windows. The proposed window has slightly larger main lobe width of the commonly used Hamming window, while featuring 6.2\ sim 22.62 dB smaller side lobe peak. The proposed window maintains its maximum side lobe peak about -58.4 \sim -52.6 dB compared to -35.8 \sim -38.8 dB of Hamming window for M=10~14, while offering roughly equal main lobe width. Our simulated results also show significant performance upgrading of the proposed window compared to the Kaiser, Gaussian, and Lanczos windows. The proposed window also shows better performance than Dolph-Chebyshev window. Finally, the example of designed low pass FIR filter confirms the efficiency of the proposed window.
Tagged particle processes and their non-explosion criteria

We give a derivation of tagged particle processes from unlabeled interacting Brownian motions. We give a criteria of the non-explosion property of tagged particle processes. We prove the quasi-regularity of Dirichlet forms describing the environment seen from the tagged particle, which were used in previous papers to prove the invariance principle of tagged particles of interacting Brownian motions.
Proteomic responses to hypoxia at different temperatures in the great scallop (Pecten maximus)

Hypoxia and hyperthermia are two connected consequences of the ongoing global change and constitute major threats for coastal marine organisms. In the present study, we used a proteomic approach to characterize the changes induced by hypoxia in the great scallop, Pecten maximus, subjected to three different temperatures (10 °C, 18 °C and 25 °C). We did not observe any significant change induced by hypoxia in animals acclimated at 10 °C. At 18 °C and 25 °C, 16 and 11 protein spots were differentially accumulated between normoxia and hypoxia, respectively. Moreover, biochemical data (octopine dehydrogenase activity and arginine assays) suggest that animals grown at 25 °C switched their metabolism towards anaerobic metabolism when exposed to both normoxia and hypoxia, suggesting that this temperature is out of the scallops’ optimal thermal window. The 11 proteins identified with high confidence by mass spectrometry are involved in protein modifications and signaling (e.g., CK2, TBK1), energy metabolism (e.g., ENO3) or cytoskeleton (GSN), giving insights into the thermal-dependent response of scallops to hypoxia.
Image Matting for Natural Image

The image matting problem is to extract a foreground object from a still natural image with the limited help from user. The foreground layer consists of three color layers and opacity layer. In this paper we propose a user assisted image matting workflow. Starting from a manual marking trimap, we perform hard segmentation and allow the user to quickly turn the segmentation result into a trimap. The algorithm used to apply soft matting process and allow the user to refine the result by fine-tuning the matting parameters in distinct image regions.
Iterative Learning Control With Advanced Output Data

In this paper, iterative learning control using output data, which are more advanced than the relative degree of the system, is being investigated. It is known that the output error can be made zero with the conventional iterative learning control in which the input is updated with the output data advanced by the relative degree. However, the input can become too large for nonminimum phase systems. With the proposed scheme, the input can be prevented from becoming unnecessarily too large, which in turn makes it practical to apply iterative learning control for nonminimum phase systems. Simulations are performed to show the effectiveness of the scheme.
Reversible Translocation and Activity-Dependent Localization of the Calcium–Myristoyl Switch Protein VILIP-1 to Different Membrane Compartments in Living Hippocampal Neurons

Visinin-like protein-1 (VILIP-1) belongs to the family of neuronal calcium sensor (NCS) proteins, a neuronal subfamily of EI-hand calcium-binding proteins that are myristoylated at their N termini. NCS proteins are discussed to play roles in calcium-dependent signal transduction of physiological and pathological processes in the CNS. The calcium-dependent membrane association, the so-called calcium–myristoyl switch, localizes NCS proteins to a distinct cellular signaling compartment and thus may be a critical mechanism for the coordinated regulation of signaling cascades. To study whether the biochemically defined calcium–myristoyl switch of NCS proteins can occur in living neuronal cells, the reversible and stimulus-dependent translocation of green fluorescent protein (GFP)-tagged VILIP-1 to subcellular targets was examined by fluorescence microscopy in transfected cell lines and hippocampal primary neurons. In transiently transfected NG108–15 and COS-7 cells, a translocation of diffusely distributed VILIP-1–GFP but not of myristoylation-deficient VILIP-1–GFP to the plasma membrane and to intracellular targets, such as Golgi membranes, occurred after raising the intracellular calcium concentration with a calcium ionophore. The observed calcium-dependent localization was completely reversed after depletion of intracellular calcium by EGTA. Interestingly, a fast and reversible translocation of VILIP-1–GFP and translocation of endogenous VILIP-1 to specialized membrane structures was also observed after a depolarizing stimulus or activation of glutamate receptors in hippocampal neurons. These results show for the first time the reversibility and stimulus-dependent occurrence of the calcium–myristoyl switch in living neurons, suggesting a physiological role as a signaling mechanism of NCS proteins, enabling them to activate specific targets localized in distinct membrane compartments.
Applications of Scene Attributes

In this paper, we study the feasibility of scene attributes as the intermediate scene representation for automatic image captioning, tag predicting and semantic image search. we show that when used as features for these tasks, low dimensional scene attributes can compete with or improve on the state of art performance. In particular, we propose a new method of content-based image retrieval, which takes advantage of the correlation between scene attributes and caption key words. When compared to simple uni-gram tf-idf image search, our method offers more promising results.
Valproate Alters Dopamine Signaling in Association with Induction of Par-4 Protein Expression

Chromatin remodeling through histone modifications has emerged as a key mechanism in the pathophysiology of psychiatric disorders. Valproate (VPA), a first-line medication for bipolar disorder, is known to have histone deacetylase (HDAC) inhibitor activity, but the relationship between its efficacy as a mood stabilizer and HDAC inhibitory activity is unclear. Here we provide evidence that prostate apoptosis response-4 (Par-4), an intracellular binding partner of dopamine D2 receptors (DRD2), plays a role in mediating the effectiveness of VPA. We found that chronic VPA treatment enhanced the expression of Par-4 in cultured neurons and adult mouse brains. This Par-4 induction phenomenon occurred at the transcriptional level and was correlated with an increase in histone H3 and H4 acetylation of the Par-4 promoter regions. Furthermore, chronic VPA treatment potentiated the suppression of the cAMP signaling cascade upon dopamine stimulation, which was blocked by sulpiride treatment. These results indicate that VPA potentiates DRD2 activity by enhancing Par-4 expression via a chromatin remodeling mechanism.
Rate distortion Multiple Instance Learning for image classification

In this paper, we model image classification as a Multiple Instance Learning (MIL) problem, by regarding each image as a bag composed of different regions/patches (i.e., instances). Motivated by the fact that a bag is determined by the most positive instance or the least negative instance, which is also called “witness”, we propose a new algorithm to take advantage of witnesses to improve the performance of MIL for image classification task. In the frame of Rate Distortion (RD), we regard MIL as a source coding of each instance to witness or itself, and then the distortion function is measured by the loss of the discriminant model trained on these encoded instances. Hence compared with the existing algorithms, our proposed RDMIL algorithm has the following advantages. First, the probabilistic approach in source coding well illustrates the generative process of witnesses and measures the different importance of instances. Second, the discriminant model trained in a large-margin approach sufficiently considers the diverse influences from the instances, and thus has a strong discriminative ability. The resulted objective function is decomposed into two convex sub-problems, and we especially design a sequential method to effectively optimize the RD sub-problem. Experimental results on two real-world datasets demonstrate the proposed algorithm is effective and promising.
Large scale project organization networks and operational mechanism

Large scale project involves multiple distributed participating organizations, which creates requirement for flexible organization form and effective inter-organizational cooperation. How to integrate the multiple participates is a central preoccupation for present top managers and researchers. The concept of large scale project network is proposed, the influencing factors of networks are analyzed, and further a conceptual framework of network operational mechanism is presented. The proposed framework integrates hard and soft aspects, emphasizes human factors and relationship factors in organization network, and bridges the gap between the traditional project management methods and network paradigm.
Deconvolution-Based Score Tests in Measurement Error Models

Consider a generalized linear model with response Y and scalar predictor X. Instead of observing X, a surrogate W = X + Z is observed where Z represents measurement error and is independent of X and Y. The efficient score test for the absence of association depends on m(w) = E(XIW = w) which is generally unknown (Tosteson and Tsiatis, 1988). Assuming that the distribution of Z is known, asymptotically efficient tests are constructed using nonparametric estimators of m(w). Rates of convergence for the estimator of m(w) are established in the course of proving efficiency of the proposed test.
Automated Modelling and Solving in Constraint Programming

Constraint programming can be divided very crudely into modeling and solving. Modeling defines the problem, in terms of variables that can take on different values, subject to restrictions (constraints) on which combinations of variables are allowed. Solving finds values for all the variables that simultaneously satisfy all the constraints. However, the impact of constraint programming has been constrained by a lack of "user-friendliness''. Constraint programming has a major "declarative" aspect, in that a problem model can be handed off for solution to a variety of standard solving methods. These methods are embedded in algorithms, libraries, or specialized constraint programming languages. To fully exploit this declarative opportunity however, we must provide more assistance and automation in the modeling process, as well as in the design of application-specific problem solvers. Automated modelling and solving in constraint programming presents a major challenge for the artificial intelligence community. Artificial intelligence, and in particular machine learning, is a natural field in which to explore opportunities for moving more of the burden of constraint programming from the user to the machine. This paper presents technical challenges in the areas of constraint model acquisition, formulation and reformulation, synthesis of filtering algorithms for global constraints, and automated solving. We also present the metrics by which success and progress can be measured.
Evaluation of the Impacts of Land Use on Water Quality: A Case Study in The Chaohu Lake Basin

It has been widely accepted that there is a close relationship between the land use type and water quality. There have been some researches on this relationship from the perspective of the spatial configuration of land use in recent years. This study aims to analyze the influence of various land use types on the water quality within the Chaohu Lake Basin based on the water quality monitoring data and RS data from 2000 to 2008, with the small watershed as the basic unit of analysis. The results indicated that there was significant negative correlation between forest land and grassland and the water pollution, and the built-up area had negative impacts on the water quality, while the influence of the cultivated land on the water quality was very complex. Besides, the impacts of the landscape diversity on the indicators of water quality within the watershed were also analyzed, the result of which indicated there was a significant negative relationship between them. The results can provide important scientific reference for the local land use optimization and water pollution control and guidance for the formulation of policies to coordinate the exploitation and protection of the water resource.
Cell Cycle-regulated Gene Expression inArabidopsis *

Regulated gene expression is an important mechanism for controlling cell cycle progression in yeast and mammals, and genes involved in cell division-related processes often show transcriptional regulation dependent on cell cycle position. Analysis of cell cycle processes in plants has been hampered by the lack of synchronizable cell suspensions for Arabidopsis, and few cell cycle-regulated genes are known. Using a recently described synchrony system, we have analyzed RNA from sequential samples ofArabidopsis cells progressing through the cell cycle using Affymetrix Genearrays. We identify nearly 500 genes that robustly display significant fluctuation in expression, representing the first genomic analysis of cell cycle-regulated gene expression in any plant. In addition to the limited number of genes previously identified as cell cycle-regulated in plants, we also find specific patterns of regulation for genes known or suspected to be involved in signal transduction, transcriptional regulation, and hormonal regulation, including key genes of cytokinin response. Genes identified represent pathways that are cell cycle-regulated in other organisms and those involved in plant-specific processes. The range and number of cell cycle-regulated genes show the close integration of the plant cell cycle into a variety of cellular control and response pathways.
Neural Correlates of the Severity of Cocaine, Heroin, Alcohol, MDMA and Cannabis Use in Polysubstance Abusers: A Resting-PET Brain Metabolism Study

Introduction Functional imaging studies of addiction following protracted abstinence have not been systematically conducted to look at the associations between severity of use of different drugs and brain dysfunction. Findings from such studies may be relevant to implement specific interventions for treatment. The aim of this study was to examine the association between resting-state regional brain metabolism (measured with 18F-fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and the severity of use of cocaine, heroin, alcohol, MDMA and cannabis in a sample of polysubstance users with prolonged abstinence from all drugs used. Methods Our sample consisted of 49 polysubstance users enrolled in residential treatment. We conducted correlation analyses between estimates of use of cocaine, heroin, alcohol, MDMA and cannabis and brain metabolism (BM) (using Statistical Parametric Mapping voxel-based (VB) whole-brain analyses). In all correlation analyses conducted for each of the drugs we controlled for the co-abuse of the other drugs used. Results The analysis showed significant negative correlations between severity of heroin, alcohol, MDMA and cannabis use and BM in the dorsolateral prefrontal cortex (DLPFC) and temporal cortex. Alcohol use was further associated with lower metabolism in frontal premotor cortex and putamen, and stimulants use with parietal cortex. Conclusions Duration of use of different drugs negatively correlated with overlapping regions in the DLPFC, whereas severity of cocaine, heroin and alcohol use selectively impact parietal, temporal, and frontal-premotor/basal ganglia regions respectively. The knowledge of these associations could be useful in the clinical practice since different brain alterations have been associated with different patterns of execution that may affect the rehabilitation of these patients.