Miracle-dz/UniFAR-FTUs huggingface dataset:
Tree Packing for Elastic Machine Allocation in RaaS Cloud

An Elastic Machine (EM) consists of networked fine-grained resources, such as CPU, memory, network and storage, dynamically composed from distributed resource pools offered by a RaaS (Resource-as-a-Service) cloud. EM allocation in RaaS clouds differs from VM placement in IaaS clouds in that it needs to consider the network conditions between the fine-grained resources. To address this problem, this paper presents a d-tree model to represent network conditions for both EM and resource pools and treat EM allocation as a tree packing problem. To solve this NP hard problem efficiently, this paper describes a tree packing framework that combines 12 approximate algorithms by using tree density to sort d-trees and virtual distance to filter d-trees. Using simulation tests and 6 quality measures, new algorithms that outperform the previous tree packing algorithm are discovered. Furthermore, the tests show that the algorithms that respect the network conditions outperform those that ignore them in most cases. Moreover, the top 2 algorithms are identified using a ranking function that combines the quality measures.
Phenotypic heterogeneity and associations of two aldose reductase gene polymorphisms with nephropathy and retinopathy in type 2 diabetes.

OBJECTIVE We investigated the phenotypic features of diabetic microvascular complications and their association with a (CA)(n) microsatellite and a C/T polymorphism at the 5' region of the aldose reductase gene (ALR2) in a consecutive cohort of 738 Chinese type 2 diabetic patients. RESEARCH DESIGN AND METHODS Of the entire patient cohort, 392 were free of diabetes complications, or uncomplicated, 159 had diabetic nephropathy, 66 had diabetic retinopathy, and 121 had both diabetic nephropathy and retinopathy. Nephropathy was defined as urinary albumin excretion rate (AER) >or=20 micro g/min and albumin-to-creatinine ratio >or=3.5 mg/mmol in two urine collections. Retinopathy was defined by the presence of hemorrhages, exudates, laser marks, and fibrous proliferation or by a history of vitrectomy. (CA)(n) and C/T polymorphisms were examined by PCR followed by capillary electrophoresis and digestion with BfaI, respectively. RESULTS In the whole cohort, patients with diabetic retinopathy (n = 187) had higher blood pressure and lower BMI, while those with diabetic nephropathy (n = 280) had higher blood pressure, waist-to-hip ratio, and lipid profile than those without the respective complications. The z+6 carriers of the (CA)(n) polymorphism were less common in patients with diabetic retinopathy than those without diabetic retinopathy (n = 551) (4.3 vs. 9.3%, P = 0.04). The CT/TT carriers had a higher AER than the CC carriers (30.2 x/divided by 7.2 vs. 21.9 x/divided by 6.9 micro g/min, P = 0.03). Further subgroup analysis was performed after excluding uncomplicated patients with <5 years disease duration. The group with both diabetic nephropathy and retinopathy had higher frequencies of the z-2 allele (25.7 vs. 16.9%, P = 0.03) and T allele (26.4 vs. 18.5%, P = 0.04) and a lower frequency of the z+6 allele (1.7 vs. 5.5%, P = 0.054) than the uncomplicated group. Multiple logistic regression analysis confirmed that z-2 carrying (odds ratio 2.6, 95% CI 1.20-5.83, P = 0.02) and CT/TT genotypes (OR 2.5, 95% CI 1.19-5.19, P = 0.02) were independent predictors for both diabetic nephropathy and retinopathy. CONCLUSIONS Chinese type 2 diabetic patients exhibited phenotypic differences in terms of risk factors for both diabetic nephropathy and diabetic retinopathy. Both the z-2 allele of (CA)(n) polymorphism and T allele of ALR2 were independently associated with severe diabetic microvascular complications.
The embodied intersubjective space: the role of clinical intuition in somatic psychotherapy

ABSTRACT Interpersonal neurobiology has provided insight and given importance to right brain implicit processes within the context of psychotherapy. This includes intersubjectivity, awareness of bodily felt experience, and clinical intuition. The field of somatic psychotherapy utilises the awareness of sensorial experience and embodiment as sources of knowledge through the integration of mind and body in the process of healing. This article explores the relationship between the somatic experiences of intuition and the field of somatic psychotherapy through an epistemological vision of providing insight and ground to a common, yet not fully understood phenomenon in clinical practice. A definition of clinical intuition from a somatic perspective is presented as well as a three-phase non-linear model that incorporates understandings from neuroscience and interpersonal neurobiology. The embodied intersubjective field, hemispheric integration, and embodiment practices are explored in this model to serve as a bridge between theory and practice and to hold the potential for transformation.
Logistic regression and its application in credit scoring

Credit scoring is a mechanism used to quantify the risk factors relevant for an obligors ability and willingness to pay. Credit scoring has become the norm in modern banking, due to the large number of applications received on a daily basis and the increased regulatory requirements for banks. In this study, the concept and application of credit scoring in a South African banking environment is explained, with reference to the International Bank of Settlements regulations and requirements. The steps necessary to develop a credit scoring model is looked at with focus on the credit risk context, but not restricted to it. Applications of the concept for the whole life cycle of a product are mentioned. The statistics behind credit scoring is also explained, with particular emphasis on logistic regression. Linear regression and its assumptions are rst shown, to demonstrate why it cannot be used for a credit scoring model. Simple logistic regression is rst shown before it is expanded to a multivariate view. Due to the large number of variables available for credit scoring models provided by credit bureaus, techniques for reducing the number of variables included for modeling purposes is shown, with reference to speci c credit scoring notions. Stepwise and best subset logistic regression methodologies are also discussed with mention to a study on determining the best signi cance level for forward stepwise logistic regression. Multinomial and ordinal logistic regression is briey looked at to illustrate how binary logistic regression can be expanded to model scenarios with more than two possible outcomes, whether on a nominal or ordinal scale. As logistic regression is not the only method used in credit scoring, other methods will also be noted, but not in extensive detail. The study ends with a practical application of logistic regression for a credit scoring model on data from a South African bank.
HYBRID STOCHASTIC PETRI NETS : FIRING SPEED COMPUTATION AND FMS MODELLING

In this paper we adopt the fluid approximation theory to describe the dynamic behavior of Flexible Manufacturing Systems that we model with Hybrid Stochastic Petri Nets, a class of nets in which some places may hold fluid rather than discrete tokens. The continuous transitions of the net are fired with speeds that are piecewise constants over the entire time horizon and their instantaneous values can be obtained by solving a sequence of linear programming problems. Conflicts among continuous transitions correspond to scheduling decisions, and we discuss several optimization schemes that can be used to resolve them.
Estimating Labor Market Rigidities with Heterogeneous Firms

the incentive to adjust the workforce. I relax this assumption and introduce wage bargaining with multiple workers. A reduced-form empirical decomposition suggests that transitory shocks to sales have a strong effect on wages, but that permanent shocks have a very small effect on wages. I propose and estimate a structural model of firm behavior that can account for these dynamics: firms adjust relatively less the workforce in response to temporary shocks, leading to higher wages since the labor productivity goes up. The model is estimated using French panel data, through a new indirect inference procedure which allows for heterogeneity in firm parameters. The model fits the data well. Only a small amount of adjustment costs is needed to reproduce observed job reallocation and inaction rates. Permanent shocks are the most important source of output uctuations. Ignoring measurement errors or wage exibility lead to erroneous conclusions. The model with heterogeneous coefficients greatly improves the fit of the observed dispersion of labor productivity compared to a model with homogeneous coeffcients.
TEMPORARY SHOCKS AND THE UNAVOIDABLE ROAD TO HIGH TAXES AND HIGH UNEMPLOYMENT

This paper considers a model with endogenous job destruction in which the effect of tax rates on employment can be large even if the elasticity of substitution between consumption and leisure is low. Necessary is only that the mass of marginal jobs is large. Moreover, tax-financed unemployment benefits generate an externality that leads to multiple (stable) steady states. Uniqueness of equilibrium is analysed in an example in which there is a low-unemployment low-tax steady state and a high-unemployment high-tax steady state. After a large increase in the unemployment rate, the economy can only converge towards the high-unemployment steady state, which offers an explanation to why European unemployment rates remained high after they increased during the seventies. The equilibrium time path is not always unique. Moreover, the range of unemployment levels at which self-fulfilling expectations can reduce unemployment rates typically increases when the government is allowed to borrow. A balanced-budget fiscal policy, however, is to be preferred if the expected increase in productivity for new ventures is high.
Synergy level impedance control for multifingered hands

In many robotic manipulation tasks a robotic hand is used to just grasp and fix the object of interest while the object motion is performed by the arm. Motivated by the analysis on human grasping using data reduction techniques, we applied this concept to the DLR Hand II. Therefore, we analyzed the grasp database that was grown over the past years to find suitable robotic “synergy coordinates”. 74% of these grasps can be represented by two coordinates that were originally defined by 12 joint variables. As a second step, a synergy impedance controller was derived and implemented extending the work on passivity based hand control at DLR. This controller for torque-controlled robot hands allows to imitate the behavior of a synergistic, respectively underactuated, hand. Such a controller provides furthermore a simplified interface for higher level grasping strategies and allows furthermore to manually teach new grasps easily. The controller was evaluated on the DLR Hand II by commanding steps that demonstrate the desired transient behavior. Finally, two objects were successfully grasped validating our approach.
Pinning in phase-separating systems.

Carlo computer simulations of spinodal decomposition to show that the model exhibits "pinning" of the structure factor, a behavior also seen in phase-separating polymer gels and binary alloys with impurities. The rate of strong bond formation depends on an entropic parameter 0, and we fInd both the pinned domain size and the crossover time between "normal" spinodal decomposition and the pinning scale with 0 as power laws with exponents that relate simply to the usual growth exponent. We propose a speci6c mechanism for pinning that permits the prediction of exact values for the pinning exponents. Finally, we discuss applications of the model to binary alloys with quenched disorder and polymer gels.
Förster-Induced Energy Transfer in Functionalized Graphene

Carbon nanostructures are ideal substrates for functionalization with molecules since they consist of a single atomic layer giving rise to an extraordinary sensitivity to changes in their surrounding. The functionalization opens a new research field of hybrid nanostructures with tailored properties. Here, we present a microscopic view on the substrate–molecule interaction in the exemplary hybrid material consisting of graphene functionalized with perylene molecules. First experiments on similar systems have been recently realized illustrating an extremely efficient transfer of excitation energy from adsorbed molecules to the carbon substrate, a process with a large application potential for high-efficiency photovoltaic devices and biomedical imaging and sensing. So far, there has been no microscopically founded explanation for the observed energy transfer. Based on first-principle calculations, we have explicitly investigated the different transfer mechanisms revealing the crucial importance of Förster coupling. Due to the efficient Coulomb interaction in graphene, we obtain strong Förster rates in the range of 1/fs. We investigate its dependence on the substrate–molecule distance R and describe the impact of the momentum transfer q for an efficient energy transfer. Furthermore, we find that the Dexter transfer mechanism is negligibly small due to the vanishing overlap between the involved strongly localized orbital functions. The gained insights are applicable to a variety of carbon-based hybrid nanostructures.
A MEMORY ANN COMPUTING STRUCTURE FOR NONLINEAR SYSTEMS EMULATION IDENTIFICATION

Currently, almost all efforts for using artificial neural networks for control oriented process identification are based on feed-forward networks. Provided the system order or the upper limit of the order is known, a neural network design is feasible for which all the collection of previous values of the inputs and outputs of the system to be identified can be used as input data to train in the network computing structures to learn the input-output map. This work reports on a novel technique that makes use of memory artificial neural network architecture that can learn and transform so as to emulate any non-linear input-output map for multi-input-multi-output systems when no prior knowledge on specific system features exists.
Tiotropium bromide is effective for severe asthma with noneosinophilic phenotype

To the Editor: Although currently available asthma medications are very effective in the majority of cases, there remains a subgroup for which disease control is difficult. Anticholinergics have long been used to treat asthma, although their current role in therapy is supplementary 1. Tiotropium bromide, a recently developed anticholinergic, is a long-acting bronchodilator with a well-established effectiveness for treating chronic obstructive pulmonary disease 2. Although some subgroups of asthmatics seem to respond better to anticholinergics, the pathophysiological features of these responders have yet to be established 3, 4. Previous studies that aimed to identify asthma subgroups which respond better to anticholinergics have suggested the following clinical features: patients with nocturnal symptoms, chronic asthma showing concurrent fixed airway obstruction, intrinsic asthma with longer duration of disease and nonatopic asthma 1, 5. The objective of the present study was to determine the characteristics of asthmatics which show good responses to tiotropium bromide. We investigated the efficacy of tiotropium bromide in …
Data association combined with the probability hypothesis density filter for multi-target tracking

The particle probability hypothesis density (P-PHD) filter gives estimate of target state for multi-target tracking; however, it keeps no record of target identities and is not able to generate target tracks. This paper addresses the problem of data association (track continuity) using the particle probability hypothesis Density filter based on the particle cloud aliasing method, that is, the corresponding particle clouds originated from the same target at two successive time steps overlap each other largely. Thus, suitable associated state pairs selected from estimated state sets at successive time steps can be found to generate tracks step by step. Estimated tracks obtained by the proposed approach are basically more consistent with the true tracks compared with that of particle labeling association algorithm according to the simulation results.
Classification of Satellite Images Using Partitioned-Feature Based Classifier Model

A classifier model for satellite image data by using Partitioned-Feature based Classifier (PFC)is proposed in this paper. The PFC does not use concatenated feature vectors extracted from the original data at once to classify each datum, but uses extracted feature vectors to classify data separately. In the training stage, the contribution rate calculated from each feature vector group is drawn throughout the accuracy of each feature vector group and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the contribution rate of each feature vector group. The PFC-based classifier is applied to the problem of satellite image classification on a set of image data. The results demonstrate that the PFC-based classifier scheme can optimally enhance the classification accuracy of individual classifiers that use specific feature vector group.
Adaptive neural network state predictor and tracking control for nonlinear time-delay systems

A new adaptive nonlinear state predictor (ANSP) is presented for a class of unknown nonlinear systems with input time-delay. A dynamical identification with neu- ral network (NN) is constructed to obtain NN weights and their derivatives. The future NN weights are deduced for the nonlinear state predictor design without iterative calcu- lations. The time-delay and unknown nonlinearity are compensated by a feedback control using the predicted states. Rigorous stability analysis for the identification, predictor and feedback control are provided by means of Lyapunov criterion. Simulations and practical experiments of a temperature control system are included to verify the effectiveness of the proposed scheme.
Dehydration affects brain structure and function in healthy adolescents

It was recently observed that dehydration causes shrinkage of brain tissue and an associated increase in ventricular volume. Negative effects of dehydration on cognitive performance have been shown in some but not all studies, and it has also been reported that an increased perceived effort may be required following dehydration. However, the effects of dehydration on brain function are unknown. We investigated this question using functional magnetic resonance imaging (fMRI) in 10 healthy adolescents (mean age = 16.8, five females). Each subject completed a thermal exercise protocol and nonthermal exercise control condition in a cross‐over repeated measures design. Subjects lost more weight via perspiration in the thermal exercise versus the control condition (P < 0.0001), and lateral ventricle enlargement correlated with the reduction in body mass (r = 0.77, P = 0.01). Dehydration following the thermal exercise protocol led to a significantly stronger increase in fronto‐parietal blood‐oxygen‐level‐dependent (BOLD) response during an executive function task (Tower of London) than the control condition, whereas cerebral perfusion during rest was not affected. The increase in BOLD response after dehydration was not paralleled by a change in cognitive performance, suggesting an inefficient use of brain metabolic activity following dehydration. This pattern indicates that participants exerted a higher level of neuronal activity in order to achieve the same performance level. Given the limited availability of brain metabolic resources, these findings suggest that prolonged states of reduced water intake may adversely impact executive functions such as planning and visuo‐spatial processing. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.
Journal of Theoretical and Applied Information Technology Efficient Colour Image Segmentation Using Multi-elitist-exponential Particle Swarm Optimization

Multi-Elitist Particle Swarm Optimization Algorithm (MEPSO) for Image cluster classification and segmentation was proposed in [2]. It employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original input space into homogeneous groups in a transformed high-dimensional feature space. This particle representation scheme has been adopted for selecting the optimal number of clusters from several possible choices. In [3] we proposed exponential particle swarm optimization (EPSO). EPSO has a great impact on global and local exploration it is supposed to bring out the search behaviour quickly and intelligently as it avoid the particles from stagnation of local optima by varying inertia weight exponentially, so that the movement of the particles will be faster and distant from each other. The aim of the above two enhanced Particle swarm techniques were to produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. The enhanced PSO techniques are used to find optimal fuzzy rules and membership functions. The best fuzzy rule is selected for image segmentation. The enhanced PSO techniques give best rule set than standard PSO.In this paper we combined both the technique to produce better result than the two individual techniques. We call this PSO as MEEPSO (Multi elitist exponential particle swarm optimization). We implement and evaluate the effectiveness of this new enhanced PSO techniques and prove that the proposed PSO technique is better than existing two enhanced PSO techniques.
Exploiting Non-Determinism in Set Iterators to Reduce I/O Latency

A key goal of distributed systems is to provide prompt access to shared information repositories. The high latency of remote access is a serious impediment to this goal. We propose a new file system abstraction called dynamic sets that allows the system to transparently reduce I/O latency without relying on reference locality, without modifying DFS servers and protocols, and without unduly complicating the programming model. We present this abstraction, and describe an implementation of it that runs on local and distributed file systems, as well as the World Wide Web. Substantial performance gains are demonstrated -- up to 50\% savings in runtime for search on NFS, and up to 90\% reduction in I/O latency for Web searches.
Power conservation strategy for mobile computers using load sharing

Power management is an important aspect of mobile computing. Previous works on power conservation have concentrated on the hardware approach. In this paper, we propose a different approach of power conservation strategy for mobile computers which is based on the concept of load sharing. User jobs are transferred from a mobile host to a fixed host to reduce power consumption by the CPU. Simulation results show that under suitable conditions, transferring job can extend battery lifetime by up to 20%. Transferring jobs to a fixed host does not only extend battery lifetime but also gives users access to faster machines, hence improving job response time.
Networks of non-homogeneous M/G/oo

For a network of G/oo service facilities, the transient joint distribution of the facility populations is shown by new simple methods to have a simple Poisson product form with simple explicit formulas for the means. In the network it is assumed that: (a) each facility has an infinite number of servers; (b) the service time distributions are general; (c) external traffic is non-homogeneous in time; (d) arrivals have random or deterministic routes through the network possibly returning to the same facility more than once; (e) arrivals use the facilities on their route sequentially or in parallel (as in the case of a circuitswitched telecommunication network). The results have relevance to communication networks and manufacturing systems.