Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Service Caching, Resource Allocation and Task Offloading for MEC-Based Networks: A Multi-Layer Optimization Approach

To provide reliable and elastic Multi-access edge computing services, one feasible solution is to federate geographically proximate edge servers to form a logically centralized resource pool. Optimization of such systems, however, becomes challenging. In this paper, we study the problem of maximizing users’ QoE in a MEC-based network, through jointly optimizing service caching, resource allocation and task offloading decisions. We formulate a mixed-integer nonlinear programming (MINLP) problem for the task and establish its NP-hardness. To tackle it efficiently, we propose a novel two-stage algorithmic solution based on approximation and decomposition theory. The proposed algorithm achieves high system performance while at the same time, ensures all constraints from different layers are satisfied. Meanwhile, the structure of the algorithm also fits the multi-layer optimizing feature, making it suitable to be implemented at different layers. In addition, we propose a distributed and online version of our mechanism with very limited information exchange between MEC servers, and further demonstrate how the cost of service switches from real MEC systems can be incorporated into our framework. We evaluate our mechanisms through simulations with both synthetic and real-world traces, and results indicate they are effective as compared to representative baseline algorithms.
New Homotopy Perturbation Method to Solve Non-Linear Problems

In this article, we introduce a new homotopy perturbation method (NHPM) for solving non-linear problems, such that it can be converted a non-linear differential equations to some simple linear differential. We will solve linear differential equation by using analytic method that it is better than the variational iteration method and to find parameter
Frailty and chronic kidney disease: associations and implications

ABSTRACT Introduction: Frailty and its association with chronic kidney disease (CKD) has been established previously. The present study examined this association further by studying the distribution of frailty among groups defined by different stages of the disease. It also identified associated health deficits and explored their association with estimated glomerular filtration rate (eGFR) and urine albumin creatinine ratio (UACR). Methods: A cross-sectional survey was conducted on 90 non-dialysis dependent CKD Stage 1–4 patients, recruited in three stratified groups of 30 participants each based on the stage of disease. Frailty was assessed using Fried’s frailty criteria and associated health deficits were recorded using a pre-determined list. Depression was screened using a 4-point depression scale. Results: 21.1% of the participants were frail and 43.3% were pre-frail. The proportion of frailty in CKD groups A (Stages 1 and 2), B (Stage 3a), and C (Stages 3b and 4) was 10%, 13.3%, and 40%, respectively. The association of health deficits including co-morbidities, physical parameters, mental status, daily activities, etc. with UACR, eGFR, and CKD stages was not statistically significant. Nearly one in two frail participants was depressed compared with 14% among non-frail participants. Conclusion: The skewed distribution of 21% frail subjects identified in our study indicates an association between frailty and advancing kidney disease. Frail individuals had a lower eGFR, higher UACR, were more likely to be depressed, and had higher count of health deficits and poorer performance on Barthel Index of Activities of Daily Living and WHOQOL. Early identification of depression would improve care in these patients.
Near-Exact Distributions for Likelihood Ratio Statistics Used in the Simultaneous Test of Conditions on Mean Vectors and Patterns of Covariance Matrices

The authors address likelihood ratio statistics used to test simultaneously conditions on mean vectors and patterns on covariance matrices. Tests for conditions on mean vectors, assuming or not a given structure for the covariance matrix, are quite common, since they may be easily implemented. But, on the other hand, the practical use of simultaneous tests for conditions on the mean vectors and a given pattern for the covariance matrix is usually hindered by the nonmanageability of the expressions for their exact distribution functions. The authors show the importance of being able to adequately factorize the c.f. of the logarithm of likelihood ratio statistics in order to obtain sharp and highly manageable near-exact distributions, or even the exact distribution in a highly manageable form. The tests considered are the simultaneous tests of equality or nullity of means and circularity, compound symmetry, or sphericity of the covariance matrix. Numerical studies show the high accuracy of the near-exact distributions and their adequacy for cases with very small samples and/or large number of variables. The exact and near-exact quantiles computed show how the common chi-square asymptotic approximation is highly inadequate for situations with small samples or large number of variables.
BIFURCATION PROBLEMS OF THE MODEL SYSTEM SIMILAR TO THE LORENZ EQUATIONS OF THE FLOW BETWEEN TWO CONCENTRIC ROTATING SPHERES

Bifurcation problems of the model system similar to the Lorenz equations of the Navier-Stokes equations for the flow between two concentric rotating spheres are discussed.Velocity-stream function form of the Navier-stokes equations for the flow between two concentric rotating spheres is deduced,and detection conditions of singular point and solution branches are given.
The force coupling method: A flexible approach for the simulation of particulate flows

We describe the basis of the Force Coupling Method. The flow disturbance induced by each particle in a suspension is represented by a low order multipole expansion. The finite size of the particles is related to the width of the finite localized forcing added to the Stokes equations. Particles are tracked in a Lagrangian framework while all the trajectories are coupled through the direct solutions of hydrodynamic interactions supplemented by the pairwise lubrication forces when particles are close to contact. The method is validated on analytic solutions for simple configurations of isolated particles or pairs of particles. Then, some fundamental configurations of suspension flows are investigated using the Force Coupling Method. Macroscopic quantities characterizing the dynamics of a suspension settling under gravity or embedded in a pure shear flow show that our approach is suitable for simulating particulate flows with a moderate computational cost. This method may be adapted to any numerical code solving the Eulerian equations of the carrying fluid flow equations. In conclusion, we give some examples of further developments of the method and applications to complex suspension involving interparticle forces or wall bounded geometries.
How Do They Grow

Will reading habit influence your life? Many say yes. Reading how do they grow it is a good habit; you can develop this habit to be such interesting way. Yeah, reading habit will not only make you have any favourite activity. It will be one of guidance of your life. When reading has become a habit, you will not make it as disturbing activities or as boring activity. You can gain many benefits and importances of reading.
Improving and Evaluating Topic Models and Other Models of Text

ABSTRACT An ongoing challenge in the analysis of document collections is how to summarize content in terms of a set of inferred themes that can be interpreted substantively in terms of topics. The current practice of parameterizing the themes in terms of most frequent words limits interpretability by ignoring the differential use of words across topics. Here, we show that words that are both frequent and exclusive to a theme are more effective at characterizing topical content, and we propose a regularization scheme that leads to better estimates of these quantities. We consider a supervised setting where professional editors have annotated documents to topic categories, organized into a tree, in which leaf-nodes correspond to more specific topics. Each document is annotated to multiple categories, at different levels of the tree. We introduce a hierarchical Poisson convolution model to analyze these annotated documents. A parallelized Hamiltonian Monte Carlo sampler allows the inference to scale to millions of documents. The model leverages the structure among categories defined by professional editors to infer a clear semantic description for each topic in terms of words that are both frequent and exclusive. In this supervised setting, we validate the efficacy of word frequency and exclusivity at characterizing topical content on two very large collections of documents, from Reuters and the New York Times. In an unsupervised setting, we then consider a simplified version of the model that shares the same regularization scheme with the previous model. We carry out a large randomized experiment on Amazon Mechanical Turk to demonstrate that topic summaries based on frequency and exclusivity, estimated using the proposed regularization scheme, are more interpretable than currently established frequency-based summaries, and that the proposed model produces more efficient estimates of exclusivity than the currently established models.
Problems with the Mean Sea Level Pressure Field over the Western United States

Abstract Reduction of station pressure to mean sea level (MSL) is a difficult procedure. In general, the temperature structure of the fictitious air column between station height and MSL is not known and has to be estimated somehow. Normally, station pressure is reduced to MSL only for stations with relatively low elevations above sea level (ASL). At higher stations, station pressure is usually converted to the height of the closest standard pressure surface. In the United States, however, station pressure is reduced to MSL for stations as high as 2000 m ASL. In order to reduce the amplitude of the annual MSL pressure variation at stations situated above 305 m ASL (hereinafter referred to as “plateau stations”), a so-called plateau correction is applied at these stations. The correction increases reduced MSL pressure when the actual temperature at the station is greater than the yearly mean temperature at the same station, and vice versa. The correction can therefore change both magnitude and direction of.
Change Detection in Video using Pixel based Parametric Analysis

Background/Objectives: This paper contains the change detection between frames of a video. The main objective is to show how the sequence of frames differ from one another in a video. Methods/Statistical Analysis: The amount of change between two frames is measured using the pixel based change detection parameter’s Absolute Average Difference (AAD), Peak Signal to Noise Ratio (PSNR), Normalized Correlation (NC), Signal to Noise Ratio (SNR) and Mean Squared Error (MSE). These parameters detect the changes in each and every pixel of the frame with respect to other frame and values are calculated for each frame of a video and compared in the tables followed by analysis of the values. Findings: The results indicated the changes that are not perceived by the human eyes. They depict that if the change between two frames are visible to the human eyes then they differ more based on the parameter we have considered and if the change is not visible to the eyes then minute differences is observed between the two respective frames. Application/Improvements: The change detection plays a significant role in visual media. Based on the change detection of frames, analysis of a video can be done.
Cardinal Arithmetic and the Axiom of Choice

Fairly deep results of Zermelo-Fraenkel (ZF) set theory have been mechanized using the proof assistant Isabelle. The results concern cardinal arithmetic and the Axiom of Choice (AC). A key result about cardinal multiplication is •›• = •, whereis any infinite cardinal. Proving this result required developing theories of orders, order-isomorphisms, order types, ordinal arithmetic, cardinals, etc.; this covers most of Kunen, Set Theory, Chapter I. Further- more, we have proved the equivalence of 7 formulations of the Well-ordering Theorem and 20 formulations of AC; this covers the first two chapters of Rubin and Rubin, Equivalents of the Axiom of Choice, and involves highly technical material. The definitions used in the proofs are largely faithful in style to the original mathematics.
Intrahost Genetic Diversity of Dengue Virus in Human Hosts and Mosquito Vectors under Natural Conditions Which Impact Replicative Fitness In Vitro

Dengue virus (DENV) is an arbovirus whose transmission cycle involves disparate hosts: humans and mosquitoes. The error-prone nature of viral RNA replication drives the high mutation rates, and the consequently high genetic diversity affects viral fitness over this transmission cycle. A few studies have been performed to investigate the intrahost genetic diversity between hosts, although their mosquito infections were performed artificially in the laboratory setting. Here, we performed whole-genome deep sequencing of DENV-1 (n = 11) and DENV-4 (n = 13) derived from clinical samples and field-caught mosquitoes from the houses of naturally infected patients, in order to analyze the intrahost genetic diversity of DENV between host types. Prominent differences in DENV intrahost diversity were observed in the viral population structure between DENV-1 and DENV-4, which appear to be associated with differing selection pressures. Interestingly, three single amino acid substitutions in the NS2A (K81R), NS3 (K107R), and NS5 (I563V) proteins in DENV-4 appear to be specifically acquired during infection in Ae. aegypti mosquitoes. Our in vitro study shows that the NS2A (K81R) mutant replicates similarly to the wild-type infectious clone-derived virus, while the NS3 (K107R), and NS5 (I563V) mutants have prolonged replication kinetics in the early phase in both Vero and C6/36 cells. These findings suggest that DENV is subjected to selection pressure in both mosquito and human hosts. The NS3 and NS5 genes may be specific targets of diversifying selection that play essential roles in early processing, RNA replication, and infectious particle production, and they are potentially adaptive at the population level during host switching.
Shed Roof Air Extractors and Collectors: Design Guidelines for Natural Ventilation in Generic Models

Most of the Brazilian territory is classified as a hot and humid climate, whose natural ventilation is one of the most important passive design strategies. The use of this strategy can be enhanced in the design through the shed roof air collectors or extractors. However, this strategy is not exploited by architecture design, due to the designers’ lack of knowledge about the efficiency of these devices. The article’s aim is to present guidelines for the design of shed roof air extractors and collectors, seeking to help designers to use these devices in buildings. The method is parametric studies, through CFD simulations. For the shed roof air extractors and collectors, the following is recommended: aerodynamic geometries; building with less depth and large air outlet openings. The increase in the number of sheds influences ventilation more than the change in the geometry of the sheds. For extraction, the area of the air outlet openings is the parameter that exerts the greatest influence on ventilation. For collection, the increase in the sizes of the openings of the sheds, without changing other parameters, does not significantly increase the air speed.
Handling catastrophic forgetting using cross-domain order in incremental deep learning

Abstract. In the present era of big data applications, incremental learning has emerged as the most admired area of research where ever-ending tasks from different application domains arrive temporarily, and the learning models majorly focus on newly arriving tasks while forgetting the historically acquired knowledge of old tasks. In this context, when a deep neural network (DNN) performs a sequence of learning tasks, it usually forgets the previously learned tasks. This phenomenon is known as catastrophic forgetting (CF), and the existing state-of-the-art deep learning approaches are unable to handle this issue. Following this, many DNN-based state-of-the-art solutions have been proposed to alleviate the problem that ensures a more natural way of human brain imitation. During training, deep learning algorithms are subjected to a random sequence of diversified datasets (also referred to as cross-domain datasets) that typically belong to multiple domains and have a variety of feature representation, distribution, density, etc. In such cases, CF may become a serious issue because the model tends to forget the previously learned features that affect the overall performance. Because there has been very little research in this area, a more robust incremental deep learning algorithm is proposed in our study to learn desired tasks incrementally. To overcome this situation, proper sequencing of cross-domain datasets is important so that related datasets are kept together to enhance the learnability of the model. Our research proposes a cross-domain order approach with hard attention to the task (HAT) methodology that uses an attention-gated mechanism to efficiently learn the task-specific features. Intense experiments have been performed, and the performance of the proposed approach is compared with the changing cross-domain orders with the baseline models over the following datasets: MNIST, P-MNIST, CIFAR-10, CIFAR-100, and SVHN, etc. The results of the experiments show that learning through cross-domain orders results in better incremental learning.
Single Copies of the 5S rRNA Inserted into 45S rDNA Intergenic Spacers in the Genomes of Nototheniidae (Perciformes, Actinopterygii)

In the vast majority of Animalia genomes, the 5S rRNA gene repeats are located on chromosomes outside of the 45S rDNA arrays of the nucleolar organiser (NOR). We analysed the genomic databases available and found that a 5S rDNA sequence is inserted into the intergenic spacer (IGS) between the 45S rDNA repeats in ten species of the family Nototheniidae (Perciformes, Actinopterigii). We call this sequence the NOR-5S rRNA gene. Along with Testudines and Crocodilia, this is the second case of a close association between four rRNA genes within one repetitive unit in deuterostomes. In both cases, NOR-5S is oriented opposite the 45S rDNA. None of the three nucleotide substitutions compared to the canonical 5S rRNA gene influenced the 5S rRNA secondary structure. In transcriptomes of the Patagonian toothfish, we only found NOR-5S rRNA reads in ovaries and early embryos, but not in testis or somatic tissues of adults. Thus, we consider the NOR-5S gene to be a maternal-type 5S rRNA template. The colocalization of the 5S and 45S ribosomal genes appears to be essential for the equimolar production of all four rRNAs in the species that show rDNA amplification during oogenesis. Most likely, the integration of 5S and NOR rRNA genes occurred prior to Nototheniidae lineage diversification.
Effective Combination of the Metal Centers in MOF-Based Materials toward Sustainable Oxidation Catalysts

A successful encapsulation of Keggin-type polyoxomolybdate (H3[PMo12O40], PMo12) into metal-organic framework (MOF) materials with an identical framework but distinct metal centers (ZIF-8 with Zn2+ and ZIF-67 with Co2+) was accomplished by a straightforward room-temperature procedure. The presence of Zn2+ in the composite material PMo12@ZIF-8 instead of Co2+ in PMo12@ZIF-67 caused a remarkable increase in the catalytic activity that achieved a total oxidative desulfurization of a multicomponent model diesel under moderate and friendly conditions (oxidant: H2O2 and solvent: ionic liquid, IL). Interestingly, the parent ZIF-8-based composite with the Keggin-type polyoxotungstate (H3[PW12O40], PW12), PW12@ZIF-8, did not show the relevant catalytic activity. The ZIF-type supports present an appropriate framework to accommodate active polyoxometalates (POMs) into their cavities without leaching, but the nature of the metallic center from the POM and the metal present in the ZIF framework were vital for the catalytic performance of the composite materials.
The Error Analysis of Continuously Machining Regular Polygon Parts Based on Matlab

It is a new method to process regular polygon parts using cycloid principle.This paper studies the continuously machining of regular polygon parts,analyzes the influence of cycloid parameters in the cycloid equation on its shape using Matlab,makes a research on the approached rule of the cycloid-approached beeline and the error distribution.By computing and analyzing the regular polygon parts,it computed machining error meets the requirement of precision.It has been proved feasibility by using cycloid principle to process polygon components.
LDRP: Device-Centric Latency Diagnostic and Reduction for Cellular Networks Without Root

We design and implement <monospace>LDRP</monospace>, a device-based, standard-compliant solution to latency diagnosis and reduction in mobile networks without root privilege. <monospace>LDRP</monospace> takes a data-driven approach and works with a variety of latency-sensitive applications. After identifying elements in LTE uplink latency, we design <monospace>LDRP</monospace> that can infer the critical parameter used in data transmission and infer them for diagnosis. In addition, <monospace>LDRP</monospace> designates small dummy messages, which precede uplink data transmissions, thus eliminating latency elements due to power-saving, scheduling, etc. It imposes proper timing control among dummy messages and data packets to handle various conflicts. We achieve the latency diagnosis and reduction without requiring root privilege and ensure the latency is no worse than the legacy LTE design. The design of <monospace>LDRP</monospace> is also applicable for 5G. The evaluation shows that, <monospace>LDRP</monospace> infers the latency with at most 4% error and reduces the median LTE uplink latency by a factor up to 7.4× (from 42 to 5 ms) for four apps over 4 mobile carriers.
Deep learning model based on inceptionResnet-v2 for Finger vein recognition

In recent years, The pattern of finger veins is widely recognized as an effective biometric for identifying a person. The traditional finger vein identification systems are based on handcrafted features. However, Finger vein systems has been switched toward automatic features extraction due to the emergence of deep neural networks that are capable of extracting deep features. In this paper, an inceptionResnet-v2 pre-trained deep convolution neural network model is proposed for finger vein identification. We tested the performance of the proposed model on the public Finger vein databases SDUMLA, MMCBNU and FV-USM. The obtained results indicate the effectiveness and security of the proposed network as it has achieved very small errors for all data sets