Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Sensitive Resource Control Approach for Delay Limited Traffic in Wireless Networks

In this paper, we consider the challenging problem of power allocation in interfering wireless networks with services having stringent delay constraints. The objective is to allocate optimally the power to each user, take advantage of the time diversity of the time varying channel and satisfy the delay constraints of the services. To this end, we model the bit rate dynamics of the users as a linear stochastic control scheme and formulate a risk-sensitive control framework that minimizes an exponential cost function of the deviation between the traffic arrivals and departures from the queues. We provide the optimal solution of the power control problem and provide simulation results to assess the superiority of our solution compared to some existing solutions.
INTEGRATION OF GENETIC ALGORITHMS AND FUZZY LOGIC FOR URBAN GROWTH MODELING

Abstract. Urban growth phenomenon as a spatio-temporal continuous process is subject to spatial uncertainty. This inherent uncertainty cannot be fully addressed by the conventional methods based on the Boolean algebra. Fuzzy logic can be employed to overcome this limitation. Fuzzy logic preserves the continuity of dynamic urban growth spatially by choosing fuzzy membership functions, fuzzy rules and the fuzzification-defuzzification process. Fuzzy membership functions and fuzzy rule sets as the heart of fuzzy logic are rather subjective and dependent on the expert. However, due to lack of a definite method for determining the membership function parameters, certain optimization is needed to tune the parameters and improve the performance of the model. This paper integrates genetic algorithms and fuzzy logic as a genetic fuzzy system (GFS) for modeling dynamic urban growth. The proposed approach is applied for modeling urban growth in Tehran Metropolitan Area in Iran. Historical land use/cover data of Tehran Metropolitan Area extracted from the 1988 and 1999 Landsat ETM + images are employed in order to simulate the urban growth. The extracted land use classes of the year 1988 include urban areas, street, vegetation areas, slope and elevation used as urban growth physical driving forces. Relative Operating Characteristic (ROC) curve as an fitness function has been used to evaluate the performance of the GFS algorithm. The optimum membership function parameter is applied for generating a suitability map for the urban growth. Comparing the suitability map and real land use map of 1999 gives the threshold value for the best suitability map which can simulate the land use map of 1999. The simulation outcomes in terms of kappa of 89.13% and overall map accuracy of 95.58% demonstrated the efficiency and reliability of the proposed model.
IL-6 Regulates In Vivo Dendritic Cell Differentiation through STAT3 Activation1

Dendritic cells (DCs) orchestrate immune responses according to their state of maturation. In response to infection, DCs differentiate into mature cells that initiate immune responses, while in the absence of infection, most of them remain in an immature form that induces tolerance to self Ags. Understanding what controls these opposing effects is an important goal for vaccine development and prevention of unwanted immune responses. A crucial question is what cytokine(s) regulates DC maturation in the absence of infection. In this study, we show that IL-6 plays a major role in maintaining immature DCs. IL-6 knockout (KO) mice had increased numbers of mature DCs, indicating that IL-6 blocks DC maturation in vivo. We examined this effect further in knockin mice expressing mutant versions of the IL-6 signal transducer gp130, with defective signaling through either Src homology region 2 domain-containing phosphatase 2/Gab/MAPK (gp130F759/F759) or STAT3 (gp130FxxQ/FxxQ), and combined gp130 and IL-6 defects (gp130F759/F759/IL-6 KO mice). Importantly, we found STAT3 activation by IL-6 was required for the suppression of LPS-induced DC maturation. In addition, STAT3 phosphorylation in DCs was regulated by IL-6 in vivo, and STAT3 was necessary for the IL-6 suppression of bone marrow-derived DC activation/maturation. DC-mediated T cell activation was enhanced in IL-6 KO mice and suppressed in gp130F759/F759 mice. IL-6 is thus a potent regulator of DC differentiation in vivo, and IL-6-gp130-STAT3 signaling in DCs may represent a critical target for controlling T cell-mediated immune responses in vivo.
Cournot Oligopoly and Concavo-Concave Demand

The N-firm Cournot model with general technologies is reviewed to derive generalized and unified conditions for existence of a pure strategy Nash equilibrium. Tight conditions are formulated alternatively (i) in terms of concavity of two-sided transforms of inverse demand, or (ii) as linear constraints on the elasticities of inverse demand and its first derivative. These conditions hold, in particular, if a firmi? ½s marginal revenue decreases in other firmsi? ½ aggregate output, or if inverse demand is logconcave. The analysis relies on lattice-theoretic methods, engaging both cardinal and ordinal notions of supermodularity. As a byproduct, a powerful test for strict quasiconcavity is obtained.
Tracking distinct objects Makovski & Jiang 1 [

To what degree can attentive tracking of objects’ motion benefit from increased distinctiveness in the objects’ surface features? To address this question, we asked observers to track 4 moving digits among a total of 8 moving digits. By varying the distinctiveness of the digits’ color and identity, we found that tracking performance improved when the 8 objects were all distinct in color, digit identity, or both, compared to when the 8 objects were identical. However, when the 8 objects were distinct in a combination of color and digit but targets and nontargets shared color or digit identity, performance enhancement was not observed. Four follow-up experiments extended the range of the feature dimensions generating the effect and ruled out alternative strategic accounts. We conclude that surface features can be used to enhance tracking performance. This enhancement is feature-based, revealing a limited degree of feature binding in attentive tracking.
Data Mining based Methodologies for Cardiac Risk Patterns Identification

Cardiovascular diseases (CVDs) are the leading cause of death in the world. The pulse wave analysis provides a new insight in the analysis of these pathologies, while data mining techniques can contribute for an efficient diagnostic method. Amongst the various available techniques, artificial neural networks (ANNs) are well established in biomedical applications and have numerous successful classification applications. Also, clustering procedures have proven to be very useful in assessing different risk groups in terms of cardiovascular function in healthy populations. In this paper, a robust data mining approach was performed for cardiac risk patterns identification. Eight classifiers were tested: C4.5, Random Forest, RIPPER, Naïve Bayes, Bayesian Network, Multy-layer perceptron (MLP) (1 and 2-hidden layers) and radial basis function (RBF). As for clustering procedures, k-means clustering (using Euclidean distance) and expectation-maximization (EM) were the chosen algorithms. Two datasets were used as case studies to perform classification and clustering analysis. The accuracy values are good with intervals between 88.05% and 97.15%. The clustering techniques were essential in the analysis of a dataset where little information was available, allowing the identification of different clusters that represent different risk group in terms cardiovascular function. The three cluster analysis has allowed the characterization of distinctive features for each of the clusters. Reflected wave time (T_RP) and systolic wave time (T_SP) were the selected features for clusters visualization. Data mining methodologies have proven their usefulness in screening studies due to its descriptive and predictive power.
An Improved Method for Automatic Placement of Spatial Saturation Planes in MR Spectroscopy

Magnetic Resonance Spectroscopy (MRS) provides information that can be a boon for the non-invasive diagnosis of disease. However, the cuboidal excitation volumes used are not well adapted to the irregularly shaped tissues of interest (TOI) often desired for signal collection. In practice, there are two common approaches to address this problem: the cuboid can be shrunk so that it is contained entirely within the TOI, thereby reducing the total volume of the TOI sampled, or it can be chosen to completely encompass the TOI, introducing contamination in the acquisition volume. The conformal voxel MRS (CV-MRS) technique attempts to better “conform” the acquisition to the TOI by automatically placing a series of spatial saturation planes that annul unwanted signal from tissues at the periphery of a cuboid voxel encompassing the TOI. The original implementation used a constrained local optimization to place the saturation planes for arbitrary TOI geometries. Recently, a similar local optimization has been used to improve outer volume suppression in whole brain MRS studies. However, the initial implementation of the method suffered from drawbacks which could limit its clinical applicability. These included the lack of a reliable, user-friendly, interface for defining the voxel and the extended time required to perform the optimization. To address these problems, a new algorithm for creating a conformal voxel has been implemented in an application with a graphical user interface for visualising and modifying the results.
Temporal classification: extending the classification paradigm to multivariate time series

Machine learning research has, to a great extent, ignored an important aspect of many real world applications: time. Existing concept learners predominantly operate on a static set of attributes; for example, classifying flowers described by leaf size, petal colour and petal count. However, many real datasets are not static; they cannot sensibly be represented as a fixed set of attributes. Rather, the examples are expressed as features that vary temporally, and it is the temporal variation itself that is used for classification. Consider a simple gesture recognition domain, in which the temporal features are the position of the hands, finger bends, and so on. Looking at the position of the hand at one point in time is not likely to lead to a successful classification; it is only by analysing changes in position that recognition is possible. This thesis presents a new technique for temporal classification. By extracting sub-events from the training instances and parameterising them to allow feature construction for a subsequent learning process, it is able to employ background knowledge and express learnt concepts in terms of the background knowledge. The novel results of the thesis are: a temporal learner capable of producing comprehensible and accurate classifiers for multivariate time series that can learn from a small number of instances and can integrate non-temporal features; a feature construction technique that parameterises sub-events of the training set and clusters them to construct features for a propositional learner; and a technique for post-processing classification rules produced by the learner to give a comprehensible description expressed in the same form as the original background knowledge. The thesis discusses the implementation of TClass, a temporal learner. Results show rules that are comprehensible in many cases and accuracy results close to or better than existing technique—over 98 per cent for sign language and 72 per cent for ECGs (equivalent to the accuracy of a human cardiologist). One further surprising result is that a small set of very primitive sub-events proves to be functional, avoiding the need for labour-intensive background knowledge if it is not available.
DETECTING ICE LAYERS IN RADAR IMAGES WITH DEEP LEARNING

This paper proposes a Deep Convolutional Neural Network approach to detect Ice surface and bottom layers from radar imagery. Radar images are capable to penetrate the earth surface and provide us with valuable information from the un-derlying layers of ice surface. In recent years, deep hierarchical learning techniques for object detection and segmentation greatly improved the performance of traditional techniques based on hand-crafted feature engineering. We designed a hybrid Deep Convolutional Network to produce the images of surface and bottom ice boundary as outputs. Our network takes advantage of undecimated wavelet transform to provide the highest level of information from radar images, as well as multi-layer and multi-scale optimized architecture. In this work, radar images from 2009-2016 NASA Operation IceBridge Mission are used to train and test the network. Our network outperformed the state-of-the art accuracy.
Quasi-polynomial mixing of critical 2D random cluster models

We study the Glauber dynamics for the random cluster (FK) model on the torus $(\mathbb{Z}/n\mathbb{Z})^2$ with parameters $(p,q)$, for $q \in (1,4]$ and $p$ the critical point $p_c$. The dynamics is believed to undergo a critical slowdown, with its continuous-time mixing time transitioning from $O(\log n)$ for $p\neq p_c$ to a power-law in $n$ at $p=p_c$. This was verified at $p\neq p_c$ by Blanca and Sinclair, whereas at the critical $p=p_c$, with the exception of the special integer points $q=2,3,4$ (where the model corresponds to the Ising/Potts models) the best-known upper bound on mixing was exponential in $n$. Here we prove an upper bound of $n^{O(\log n)}$ at $p=p_c$ for all $q\in (1,4]$, where a key ingredient is bounding the number of nested long-range crossings at criticality.
Microneedle-array patches loaded with hypoxia-sensitive vesicles provide fast glucose-responsive insulin delivery

The development of a glucose-responsive ‘closed-loop’ insulin delivery system mimicking pancreatic function has the potential to greatly improve quality of life and health in diabetics. Current systems couple implantable continuous glucose monitors to implantable pumps, which deliver insulin subcutaneously via a cannula. However, issues surrounding multiple calibrations, a ‘lag’ in blood glucose measurement and risk of infection have hampered their use in clinical practice. Yu et al. describe a glucose-responsive-microneedle patch that can be painlessly applied to the skin and that releases insulin in response to glucose. The nanoparticles in each needle contain insulin and glucose oxidase. These molecules are surrounded by hypoxia-sensitive hyaluronic acid-based vesicles which rapidly dissociate and release encapsulated insulin under the local hypoxic environment, caused by the enzymatic oxidation of glucose in the hyperglycemic state. Using mouse models of type 1 diabetes, the authors demonstrate that the patch effectively regulates glucose after dosing with a glucose load. Once blood glucose normalized, insulin release ceased. Serial administration of patches produced prolonged euglycaemia. The faster responsiveness of this approach holds promise in helping people with diabetes manage their condition more safely and effectively.
Genetic Characteristics and Pathogenic Mechanisms of Periodontal Pathogens

Periodontal disease is caused by a group of bacteria that utilize a variety of strategies and molecular mechanisms to evade or overcome host defenses. Recent research has uncovered new evidence illuminating interesting aspects of the virulence of these bacteria and their genomic variability. This paper summarizes some of the strategies utilized by the major species–Aggregatibacter actinomycetemcomitans, Tannerella forsythia, Treponema denticola, and Porphyromonas gingivalis – implicated in the pathogenesis of periodontal disease. Whole-genome sequencing of 14 diverse A. actinomycetemcomitans strains has revealed variations in their genetic content (ranging between 0.4% and 19.5%) and organization. Strikingly, isolates from human periodontal sites showed no genomic changes during persistent colonization. T. forsythia manipulates the cytokine responses of macrophages and monocytes through its surface glycosylation. Studies have revealed that bacterial surface-expressed O-linked glycans modulate T-cell responses during periodontal inflammation. Periodontal pathogens belonging to the “red complex” consortium express neuraminidases, which enables them to scavenge sialic acid from host glycoconjugates. Analysis of recent data has demonstrated that the cleaved sialic acid acts as an important nutrient for bacterial growth and a molecule for the decoration of bacteria surfaces to help evade the host immune attack. In addition, bacterial entry into host cells is also an important prerequisite for the lifestyle of periodontal pathogens such as P. gingivalis. Studies have shown that, after its entry into the cell, this bacterium uses multiple sorting pathways destined for autophagy, lysosomes, or recycling pathways. In addition, P. gingivalis releases outer membrane vesicles which enter cells via endocytosis and cause cellular functional impairment.
An Analysis of Factors Underlying E-Health Disparities

The potential public and individual health consequences of unequal access to digital technologies have been recognized in the United States for at least a decade. Unequal access to the Internet and related technologies has been characterized as a “digital divide”; naturalistic trends toward broader access across the population and targeted intervention to increase access are described as progress toward “digital inclusion. ” The problem of the digital divide has been characterized as one of healthcare justice. The idea that everyone should have access to the telecommunications grid—telephone and computer—is a central tenet of the U.S. universal service policy. With the diffusion of broadband technologies, the issue of digital access includes not only access to the Internet but also access to new levels of service, such as broadband, to support a wide range of emerging applications.
Technology Implementation Success Model Designed for Educational Organizations

Digital technologies has changed our daily lives, even they've altered the way how we operate, organize, communicate, and how we do project development. Recently academia starts to explore an efficient way of utilizing cloud education technologies among teachers and learners. The benefits of cloud environment which adopted in practicing teaching or learning by individuals or organizations also differ from their perception and intention. This paper has investigated success factors on projects development that aims to carry out cloud technology education by the organizations. Results from confirmatory factor analysis employing structural question modeling has confirmed that the collaboration and task- technology fit bring a positive impact on project development, technology implementation in organization and perceive net benefits. Based on the analyzed results collaboration as success factor has exerts a strong influence on project development in government funded schools such as elementary, middle and high schools in Republic of Bulgaria.
Collisional and radiative processes in adiabatic deceleration, deflection, and off-axis trapping of a Rydberg atom beam.

A supersonic beam of Rydberg hydrogen atoms has been adiabatically deflected by 90°, decelerated to zero velocity in less than 25 μs, and loaded into an electric trap. The deflection has allowed the suppression of collisions with atoms in the trailing part of the gas pulse. The processes leading to trap losses, i.e., fluorescence to the ground state, and transitions and ionization induced by blackbody radiation have been monitored over several milliseconds and quantitatively analyzed.
Factors Associated with Response to Lithium Prophylaxis in DSM III Major Depression and Bipolar Disorder

The relationship of some clinical, personality and biological variables to the outcome of lithium prophylaxis was investigated in two patient samples fulfilling, respectively, the DSM III definitions of "major depression, recurrent" and "bipolar disorder". In major depressives, the presence of psychomotor retardation and melancholia during the index episode was associated with a favorable response to treatment, whereas the presence of mood-incongruent psychotic features during the same episode, a high score on the "anxiety" and "phobic" subscales of the Middlesex Hospital Questionnaire and a high score on the "neuroticism" subscale of the Eysenck Personality Questionnaire were related to a poor response. These findings are discussed in the light of the heterogeneity of DSM III major depression and of the reported common occurrence of an "anxiety-phobic" personality profile in unipolar depressives. In bipolar patients, a family history of bipolar affective illness and a high lithium ratio were associated with a good response to treatment, and the presence of the HLA-A3 antigen with an unfavorable response. These findings seem to support a role of pharmacogenetic factors in conditioning response to lithium prophylaxis.
Introduction Introduction to Genetic Association Studies

Genetic association studies test for a correlation between disease status and genetic variation to identify candidate genes or genome regions that contribute to a specific disease. A higher frequency of a singlenucleotide polymorphism (SNP) allele or genotype in a series of individuals affected with a disease can be interpreted as meaning that the tested variant increases the risk of a specific disease (although several other interpretations are also valid; see the following sections). SNPs are the most widely tested markers in association studies (and this term will be used throughout), but microsatellite markers, insertion/deletions, variable-number tandem repeats (VNTRs), and copy-number variants (CNVs) are also used. Association studies are a major tool for identifying genes conferring susceptibility to complex disorders. These traits and diseases are termed “complex” because both genetic and environmental factors contribute to the susceptibility risk. Extensive experience in genetic studies for many complex disorders (such as diabetes, heart disease, autoimmune diseases, and psychiatric traits) confirms that many different genetic variants control disease risk, with each variant having only a subtle effect. Associations with polymorphisms in candidate genes have been confirmed in many different diseases (Lohmueller et al. 2003), and genome-wide association studies (GWAS) are identifying many novel associations in genes that had not been strong a priori candidates for the disease under test (Wellcome Trust Case Control Consortium 2007). However, the modest increase in risk implies that large well-designed and analyzed studies are required to detect and confirm signals for association. This article outlines the design and analysis of genetic association studies, but it focuses specifically on case–control studies in candidate genes or regions. Even in this era of genome-wide studies, case– control studies still form the majority of published reports. We illustrate the importance of quality control in performing these studies, describe basic analytical strategies for a SNP, and point the reader toward methods for analyzing haplotypes or multiple markers. We also highlight some of the pitfalls of performing powerful, accurate association studies and discuss how these challenges are reflected in the contradictory literature for many disease–gene investigations. In addition to GWAS, other approaches to genetic association studies include family-based association studies and quantitative trait locus studies; these approaches are not addressed in any detail here.