Miracle-dz/UniFAR-FTUs huggingface dataset:
Incorporating Structural Diversity of Neighbors in a Diffusion Model for Social Networks

Diffusion is known to be an important process governing the behaviours observed in network environments like social networks, contact networks, etc. For modeling the diffusion process, the Independent Cascade Model (IC Model) is commonly adopted and algorithms have been proposed for recovering the hidden diffusion network based on observed cascades. However, the IC Model assumes the effects of multiple neighbors on a node to be independent and does not consider the structural diversity of nodes' neighbourhood. In this paper, we propose an extension of the IC Model with the community structure of node neighbours incorporated. We derive an expectation maximization (EM) algorithm to infer the model parameters. To evaluate the effectiveness and efficiency of the proposed method, we compared it with the IC model and its variants that do not consider the structural properties. Our empirical results based on the MemeTracker dataset, shows that after incorporating the structural diversity, there is a significant improvement in the modelling accuracy, with reasonable increase in run-time.
Quantum neural network for State of Charge estimation

State of Charge (SoC) estimation is one of the most important parts of Battery Management System (BMS). Inaccurate estimation of SoC may cause overcharge or overdischarge which could lead permanent damage to battery cells. Neural Network (NN) models can yield quite accurate SoC estimation. However, the computation effort is also quite huge and it takes long time training. To improve the performance of NN, a new battery SoC estimation method based on Quantum Neural Network (QNN) is proposed. Results show that QNN is more computation efficient and yields more accurate results, when compared to the conventional NN and other methods such as Coulometric Counting (CC) and Open Circuit Voltage (OCV) prediction methods.
Continuous optical loading of a Bose-Einstein condensate in the Thomas-Fermi regime

We discuss the optical loading of a Bose-Einstein condensate in the Thomas-Fermi regime. The condensate is loaded via spontaneous emission from a reservoir of excited-state atoms. By means of a master equation formalism, we discuss the modification of the condensate temperature during the loading. We identify the threshold temperature, Tth, above (below) which the loading process leads to cooling (heating), respectively. The consequences of our analysis for the continuous loading of an atom laser are discussed.
Women’s employment and union dissolution in a changing socio-economic context in Russia

This study examines the effect of women’s employment on the risk of union disruption within the centrally planned economy and transition period in Russia. The empirical part is based on two retrospective surveys conducted in Russia in 2004/2005, covering the years 1967-2004. These are analyzed using hazard regression. The results show that within two periods (1967-1991 and 1992-2004) the risk of union dissolution was similar among women who worked and those who did not work. No differences were found between various employment groups during socialism. In the transition period, however, a variation in the risk of union dissolution among groups of working women existed. The biggest differences are related to company ownership type, with women who worked in private enterprises having the highest risk of union dissolution.
Gaussian mixture modeling for statistical analysis of features of high-resolution CT images of diffuse pulmonary diseases

This paper presents results of statistical analysis of fractal and texture features obtained from images of diffuse pulmonary diseases (DPDs). The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissues. A Gaussian mixture model (GMM) was constructed for each feature, including all patterns. For each GMM, the six classes were identified and compared with the radiological classification of the corresponding ROIs. In 78.5% of the features, the GMM provides, for at least one class, a correct classification of at least 60%. The GMM approach facilitates detailed statistical analysis of the characteristics of each feature and assists in the development of classification strategies.
Modified Fuzzy-Based Random Early Detection for Constant Bit Rate and Poisson Traffic

The Random Early Detection (RED) mechanism, attempts to control the congestion level at a bottleneck by monitoring and updating the average queue size. The RED algorithm calculates the average queue size using a low pass filter with an exponential weighted moving average. The average queue size is compared to two thresholds: a minimum and a maximum threshold. However in this technique, the probability of dropping the packets is more which leads to data loss. This paper proposes a modified fuzzy based RED algorithm with more levels of thresholding to effectively avoid congestion. It also minimizes data loss. Keywords: Active Queue Management (AQM), Random Early Detection (RED), Gentle RED, Piecewise RED, Threshold, Fuzzy .
Rodent

Monte-Carlo Renderers must generate many color samples to produce a noise-free image, and for each of those, they must evaluate complex mathematical models representing the appearance of the objects in the scene. These models are usually in the form of shaders: Small programs that are executed during rendering in order to compute a value for the current sample. Renderers often compile and optimize shaders just before rendering, taking advantage of the knowledge of the scene. In principle, the entire renderer could benefit from a-priori code generation. For instance, scheduling can take advantage of the knowledge of the scene in order to maximize hardware usage. However, writing such a configurable renderer eventually means writing a compiler that translates a scene description into machine code. In this paper, we present a framework that allows generating entire renderers for CPUs and GPUs without having to write a dedicated compiler: First, we provide a rendering library in a functional/imperative language that elegantly abstracts the individual rendering concepts using higher-order functions. Second, we use partial evaluation to combine and specialize the individual components of a renderer according to a particular scene. Our results show that the renderers we generate outperform equivalent high-performance implementations written with state-of-the-art ray tracing libraries on the CPU and GPU.
Answering queries using views: A KRDB perspective for the semantic Web

In this article, we investigate a first step towards the long-term vision of the Semantic Web by studying the problem of answering queries posed through a mediated ontology to multiple information sources whose content is described as views over the ontology relations. The contributions of this paper are twofold. We first offer a uniform logical setting which allows us to encompass and to relate the existing work on answering and rewriting queries using views. In particular, we make clearer the connection between the problem of rewriting queries using views and the problem of answering queries using extensions of views. Then we focus on an instance of the problem of rewriting conjunctive queries using views through an ontology expressed in a description logic, for which we exhibit a complete algorithm.
Protein intake and kidney function in the middle-age population : contrast between cross-sectional and longitudinal data

Background. Protein intake is considered a determinant of glomerular filtration rate (GFR). Urinary urea is an objective marker of protein intake. The population-based study investigated, cross-sectionally and longitudinally, the association of protein intake with GFR, indexed by estimated GFR (eGFR). Methods. Data were collected on overnight urinary urea, serum creatinine (S-cr), eGFR and other variables in 1522 men and women aged 45–64 years who participated in the Gubbio study (baseline). S-Cr, eGFR and other variables were re-assessed in 1144 of the 1425 survivors after 12-year follow-up. Results. At baseline, mean ± SD was 84.0 ± 11.4 mL/min × 1.73 m for eGFR calculated by CKD-Epi equation and 1.34 ± 0.57 g/day per kg of ideal weight for protein intake assessed by measurements of overnight urine excretion of urea nitrogen. Cross-sectional analyses of baseline data indicated a positive correlation of protein intake with eGFR (R = 0.180, P < 0.001). In multi-variable regression, 1 g/day higher protein intake related to 4.7 mL/min × 1.73 m higher eGFR [95% confidence interval (CI) = 3.7/5.7]. At follow-up, mean ± SD of 12-year eGFR change was −11.6 ± 9.0 mL/min × 1.73 m. Baseline protein intake correlated with more negative eGFR change (R =−0.251, P < 0.001). In multi-variable regression, 1 g/day higher protein intake related to −4.1 mL/min × 1.73 m more negative eGFR change (95% CI =−5.1/−3.1) and to 1.78 risk for incidence of eGFR < 60 mL/min × 1.73 m (95% CI = 1.15/2.78). Conclusions. In middle-aged adults, high protein intake is associated cross-sectionally with higher GFR but longitudinally with greater GFR decline over time.
Building Robust Wireless LAN for Industrial Control with the DSSS-CDMA Cell Phone Network Paradigm

Wireless LAN for industrial control (IC-WLAN) provides many benefits, such as mobility, low deployment cost, and ease of reconfiguration. However, the top concern is robustness of wireless communications. Wireless control loops must be maintained under persistent adverse channel conditions, such as noise, large-scale path loss, fading, and many electromagnetic interference sources in industrial environments. The conventional IEEE 802.11 WLANs, originally designed for high bandwidth instead of high robustness, are therefore inappropriate for IC-WLAN. A solution lies in the direct sequence spread spectrum (DSSS) technology: by deploying the largest possible processing gain (slowest bit rate) that fully exploits the low data rate feature of industrial control, much higher robustness can be achieved. We hereby propose using DSSS-CDMA to build IC-WLAN. We carry out fine-grained physical layer simulations and Monte Carlo comparisons. The results show that DSSS-CDMA IC-WLAN provides much higher robustness than IEEE 802.11/802.15. 4 WLAN, so that reliable wireless industrial control loops become feasible. We also show that deploying larger processing gain is preferable, to deploying more intensive convolutional coding. The DSSS-CDMA IC-WLAN scheme also opens up a new problem space for interdisciplinary study, involving real-time scheduling, resource management, communication, networking, and control
' s report Title : Mobile phone tracking : in support of modelling traffic-related air pollution contribution to individual exposure and its implications for public health impact assessment Version : 1

The manuscript presents a concept for using mobile phone tracking technology for assessing human exposure to traffic related air pollution. The authors propose a system that will collect individual mobile phone data and estimate both individual trajectory and vehicle trajectory. The vehicle trajectory will be used for modeling air pollution levels after accounting for estimated vehicle type and emission rates. Juxtaposing the modeled air pollution and individual trajectories can then be used for estimating individual exposure levels.
Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction

Recently developed deep learning techniques have significantly improved the accuracy of various speech and image recognition systems. In this paper we show how to adapt some of these techniques to create a novel chained convolutional architecture with next-step conditioning for improving performance on protein sequence prediction problems. We explore its value by demonstrating its ability to improve performance on eight-class secondary structure prediction. We first establish a state-of-the-art baseline by adapting recent advances in convolutional neural networks which were developed for vision tasks. This model achieves 70.0% per amino acid accuracy on the CB513 benchmark dataset without use of standard performance-boosting techniques such as ensembling or multitask learning. We then improve upon this state-of-the-art result using a novel chained prediction approach which frames the secondary structure prediction as a next-step prediction problem. This sequential model achieves 70.3% Q8 accuracy on CB513 with a single model; an ensemble of these models produces 71.4% Q8 accuracy on the same test set, improving upon the previous overall state of the art for the eight-class secondary structure problem. Our models are implemented using TensorFlow, an open-source machine learning software library available at TensorFlow.org; we aim to release the code for these experiments as part of the TensorFlow repository.
The Relationship of Metabolic Syndrome Traits with Beta-Cell Function and Insulin Sensitivity by Oral Minimal Model Assessment in South Asian and European Families Residing in the Netherlands

Background. There are different metabolic syndrome traits among patients with different ethnicities. Methods. We investigated this by studying 44 South Asians and 54 Europeans and classified them in three groups according to the occurrence of metabolic syndrome (MetS) and Type 2 Diabetes (T2D). Insulin sensitivity index (ISI), static, dynamic, and total beta-cell responsivity indices (Φ), and disposition indices (DIs) were calculated with the use of oral minimal model (OMM). Results. In both ethnicities, ISI was lower in the subgroup with MetS and T2D as compared to the subgroup without MetS nor T2D (P < 0.004). South Asians without MetS were more insulin resistant than Europeans without MetS (P = 0.033). In the South Asians, ISI, dynamic DI, and static DI were associated significantly (P < 0.006) with high-density lipoprotein cholesterol and triglycerides. In the Europeans, ISI was associated with waist-to-hip ratio (P = 0.005) and systolic and diastolic blood pressure (P < 0.005), while static DI was related to the systolic blood pressure (P = 0.005). Conclusions. MetS was linked with insulin resistance and reduced capacity to handle glucose regardless of ethnicity. ISI and DIs were associated with lipid traits in South Asians and with blood pressure in Europeans suggesting that insulin resistance enhances different metabolic syndrome traits among different ethnicities.
Variational Particle Filter for Multi-Object Tracking

The paper proposes an edge-based multi-object tracking framework which deals with tracking multiple objects with occlusions using a variational particle filter. Object is modelled by a mixture of a non-parametric contour model and a non-parametric edge model using kernel density estimation. Visual tracking with a mixture model is formulated as a Bayesian incomplete data problem, where measurements in an image are associated with a generative model which is a mixture of mixture models including object models and a clutter model and unobservable associations of measurements to densities in the generative model are regarded as missing data. A likelihood for tracking multiple objects jointly with an exclusion principle is presented, where it is assumed that one measurement can only be generated from one density and one density can generate multiple measurements and it significantly reduces the complexity of enumerating all feasible events. To address the curse of dimensionality in tracking multiple objects jointly, a variational particle filter (VPF) is proposed for multi-object tracking, where the proposal distribution is based on the approximated posterior from variational inference rather than using the prior as the proposal distribution in sampling importance resampling (SIR) particle filter. With the variational particle filter, the number of particles needed for multi-object tracking can be significantly reduced. Experimental results in challenging sequences demonstrate the robust performance of the proposed method.
A human-oriented image retrieval system using interactive genetic algorithm

Content-based image retrieval has been actively studied in several fields. This provides more effective management and retrieval of images than the keyword-based approach. However, most of the conventional methods lack the capability to effectively incorporate human intuition and emotion into retrieving images. It is difficult to obtain satisfactory results when the user wants the image that cannot be explicitly described or can be requested only based on impression. In order to solve this problem and supplement the lack of the user's expression capability, we have developed an image retrieval system based on human preference and emotion by using an interactive genetic algorithm (IGA). This system extracts the feature from images by wavelet transform, and provides a user-friendly means to retrieve an image from a large database when the user cannot clearly define what the image must be. Therefore, this facilitates the search for the image not only with explicit queries, but also with implicit queries such as "cheerful impression," "gloomy impression," and so on. A thorough experiment with a 2000 image database shows the usefulness of the proposed system.
Antibodies inhibit the protease-mediated processing of a malaria merozoite surface protein

When merozoites of the malaria parasite Plasmodium falciparum are released from infected erythrocytes and invade new red cells, a component of a protein complex derived from the merozoite surface protein 1 (MSP-1) precursor undergoes a single proteolytic cleavage known as secondary processing. This releases the complex from the parasite surface, except for a small membrane-bound fragment consisting of two epidermal growth factor (EGF)-like domains, which is the only part of MSP-1 to be carried into invaded erythrocytes. We report that, a group of monoclonal antibodies specific for epitopes within the EGF- like domains, some interfere with secondary processing whereas others do not. Those that most effectively inhibit processing have previously been shown to prevent invasion. Other antibodies, some of which can block this inhibition, not only do not prevent invasion but are carried into the host cell bound to the merozoite surface. These observations unequivocally demonstrate that the binding of antibody to the COOH- terminal region of MSP-1 on the merozoite surface may not be sufficient to prevent erythrocyte invasion, and show that the interaction of different antibodies with adjacent epitopes within the EGF-like domains of MSP-1 can have distinct biochemical effects on the molecule. Inhibition of MSP-1 processing on merozoites may be a mechanism by which protective antibodies interrupt the asexual cycle of the malaria parasite.
A distributed broker system enabling coordinated access schemes in autonomous wireless networks

Autonomous wireless networks play an increasingly crucial role in today's society. Harsh environmental conditions, challenging deployment topologies, unreliable (or non-existing) power grids, as well as misguided control procedures of communications infrastructures by governmental authorities have made autonomous communication infrastructures a valuable asset for long-term development and wealth of societies. However, such wireless networks are in an early phase of their development and need to incorporate necessary equipment and algorithms for autonomous operation. This paper presents a concept and a concrete simulation scenario showing how a distributed context management system and the data collected and distributed by such a system can improve the performance of an autonomous, coordinated wireless mesh network (ACWMN). Research agrees that it is context awareness that will transform today's networks to incorporate autonomous, coordinated behavior. We further depict a concrete example simulation scenario where the access capacity of an ACWMN with WLAN radio access technology is significantly increased by coordinating user access with upper limits on the number of users that can concurrently request data services.
Micro Reflectance Measurements of Water Content in Melt Inclusions

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Central infusion of aldosterone synthase inhibitor attenuates left ventricular dysfunction and remodelling in rats after myocardial infarction.

AIMS Blockade of mineralocorticoid receptors in the central nervous system (CNS) prevents sympathetic hyperactivity and improves left ventricle (LV) function in rats post-myocardial infarction (MI). We examined whether aldosterone produced locally in the brain may contribute to the activation of mineralocorticoid receptors in the CNS. METHODS AND RESULTS Two days after coronary artery ligation, Wistar rats received an intra-cerebroventricular (icv) infusion via osmotic mini-pumps of the aldosterone synthase inhibitor FAD286 at 100 microg/kg/day or vehicle for 4 weeks. LV function was assessed by echocardiography at 2 and 4 weeks, and by Millar catheter at 4 weeks. At 4 weeks post-MI, aldosterone in the hippocampus was increased by 70% and tended to increase in the hypothalamus by 20%. These increases were prevented by FAD286. Across groups, aldosterone in the hippocampus and hypothalamus showed a high correlation. There were no differences in brain corticosterone levels. Compared to sham rats, at both 2 and 4 weeks post-MI rats treated with vehicle showed increased LV dimensions and decreased LV ejection fraction. Icv infusion of FAD286 attenuated these changes in LV dimensions and ejection fraction by approximately 30%. At 4 weeks post-MI, LV peak systolic pressure (LVPSP) and dP/dt(max/min) were decreased and LV end-diastolic pressure (LVEDP) was increased. In rats treated with icv FAD286, LVPSP and dP/dt(min) remained normal and LVEDP and dP/dt(max) were markedly improved. Post-MI increases in cardiac fibrosis and cardiomyocyte diameter were substantially attenuated by icv FAD286. CONCLUSION These data suggest that aldosterone produced locally in the brain acts as the main agonist of mineralocorticoid receptors in the CNS and contributes substantially to the progressive heart failure post MI.
A Programming Model for the Cloud Platform

Programming models for cloud computing has become a research focus recently. Cloud computing promises to provide on-demand and flexible IT services, which goes beyond traditional programming models and calls for new ones. Some progress has been made in cloud computing programming models for large-scale data processing, but little was done on models of predictable performance. With the advantages on predictable performance, easily programming and deadlock avoidance, the BSP model has been widely applied in parallel databases, search engines, and scientific computing. This paper targets to adapt the BSP model into cloud environment. The scheduling of computing tasks and the allocation of cloud resources will be integrated into the BSP model. A BSPCloud programming model with predictable performance is proposed.