Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Traffic Measurement Algorithm Based on Sampling for Big Network Data

Network traffic measurement is significant for network security and network management. As network bandwidth increases and internet applications varies, network big data is bringing new challenge for network traffic measurement. Because the existing network traffic measurement mainly processes network traffic data by the centralized method, it is very difficult to meet the application needs of massive data. According to scalability of network traffic measurement and load imbalance, the network traffic measurement based on MapReduce is researched. Elephant flow identification is an important application field in network traffic measurement, so the elephant flow identification algorithm based on sampling is proposed. Hadoop cluster test environment is built and the real network traffic is used to validate performance of the elephant flow identification algorithm on Hadoop cluster. The experimental results illustrate that the proposed algorithm has good scalability and load balancing.
Nitrogen Content in the Earth's Outer Core

Using first principles molecular dynamic simulations, we explore the effects of nitrogen (N) on the density and sound velocity of liquid iron and evaluate its potential as a light element in the Earth's outer core. Our results suggest that Fe‐N melt cannot simultaneously explain the density and seismic velocity of the Earth's outer core. Although ~2.0 wt. % N can explain the bulk sound velocity of the outer core, such N content only lowers the density of liquid Fe by ~3%. Matching both the velocity and density by the other light elements limits the N in the core to ≪2.0 wt. %. Our finding suggests that nitrogen is a minor to trace element in the Earth's core and is consistent with the geochemical mass balance with terrestrial abundance of N and alloy‐silicate partitioning data, which suggest that there cannot be significant N in the core.
Reviews and syntheses: the GESAMP atmospheric iron deposition model intercomparison study

Abstract. This work reports on the current status of the global modeling of iron (Fe) deposition fluxes and atmospheric concentrations and the analyses of the differences between models, as well as between models and observations. A total of four global 3-D chemistry transport (CTMs) and general circulation (GCMs) models participated in this intercomparison, in the framework of the United Nations Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP) Working Group 38, “The Atmospheric Input of Chemicals to the Ocean”. The global total Fe (TFe) emission strength in the models is equal to ∼72 Tg Fe yr−1 (38–134 Tg Fe yr−1) from mineral dust sources and around 2.1 Tg Fe yr−1 (1.8–2.7 Tg Fe yr−1) from combustion processes (the sum of anthropogenic combustion/biomass burning and wildfires). The mean global labile Fe (LFe) source strength in the models, considering both the primary emissions and the atmospheric processing, is calculated to be 0.7 (±0.3) Tg Fe yr−1, accounting for both mineral dust and combustion aerosols. The mean global deposition fluxes into the global ocean are estimated to be in the range of 10–30 and 0.2–0.4 Tg Fe yr−1 for TFe and LFe, respectively, which roughly corresponds to a respective 15 and 0.3 Tg Fe yr−1 for the multi-model ensemble model mean. The model intercomparison analysis indicates that the representation of the atmospheric Fe cycle varies among models, in terms of both the magnitude of natural and combustion Fe emissions as well as the complexity of atmospheric processing parameterizations of Fe-containing aerosols. The model comparison with aerosol Fe observations over oceanic regions indicates that most models overestimate surface level TFe mass concentrations near dust source regions and tend to underestimate the low concentrations observed in remote ocean regions. All models are able to simulate the tendency of higher Fe concentrations near and downwind from the dust source regions, with the mean normalized bias for the Northern Hemisphere (∼14), larger than that of the Southern Hemisphere (∼2.4) for the ensemble model mean. This model intercomparison and model–observation comparison study reveals two critical issues in LFe simulations that require further exploration: (1) the Fe-containing aerosol size distribution and (2) the relative contribution of dust and combustion sources of Fe to labile Fe in atmospheric aerosols over the remote oceanic regions.
Simpler, faster and shorter labels for distances in graphs

We consider how to assign labels to any undirected graph with n nodes such that, given the labels of two nodes and no other information regarding the graph, it is possible to determine the distance between the two nodes. The challenge in such a distance labeling scheme is primarily to minimize the maximum label length and secondarily to minimize the time needed to answer distance queries (decoding). Previous schemes have offered different tradeoffs between label lengths and query time. This paper presents a simple algorithm with shorter labels and shorter query time than any previous solution, thereby improving the state-of-the-art with respect to both label length and query time in one single algorithm. Our solution addresses several open problems concerning label length and decoding time and is the first improvement of label length for more than three decades. More specifically, we present a distance labeling scheme with labels of length log3/2n + o(n) bits1 and constant decoding time. This outperforms all existing results with respect to both size and decoding time, including Winkler's (Combinatorica 1983) decade-old result, which uses labels of size (log 3)n and O(n/log n) decoding time, and Gavoille et al. (SODA'01), which uses labels of size 11n + o(n) and O(log log n) decoding time. In addition, our algorithm is simpler than the previous ones. In the case of integral edge weights of size at most W, we present almost matching upper and lower bounds for the label size e: 1/2(n -- 1) log [W/2 + l] ≤ e ≤ 1/2n log (2W + 1) + O(log n · log(nW)). Furthermore, for r-additive approximation labeling schemes, where distances can be off by up to an additive constant r, we present both upper and lower bounds. In particular, we present an upper bound for 1-additive approximation schemes which, in the unweighted case, has the same size (ignoring second order terms) as an adjacency labeling scheme, namely n/2. We also give results for bipartite graphs as well as for exact and 1-additive distance oracles.
Linearly constrained generalized Lloyd algorithm for reduced codebook vector quantization

As linearly constrained vector quantization (LCVQ) is efficient for block-based compression of images that require low complexity decompression, it is a "de facto" standard for three-dimensional (3-D) graphics cards that use texture compression. Motivated by the lack of an efficient algorithm for designing LCVQ codebooks, the generalized Lloyd (1982) algorithm (GLA) for vector quantizer (VQ) codebook improvement and codebook design is extended to a new linearly constrained generalized Lloyd algorithm (LCGLA). This LCGLA improves VQ codebooks that are formed as linear combinations of a reduced set of base codewords. As such, it may find application wherever linearly constrained nearest neighbor (NN) techniques are used, that is, in a wide variety of signal compression and pattern recognition applications that require or assume distributions that are locally linearly constrained. In addition, several examples of linearly constrained codebooks that possess desirable properties such as good sphere packing, low-complexity implementation, fine resolution, and guaranteed convergence are presented. Fast NN search algorithms are discussed. A suggested initialization procedure halves iterations to convergence when, to reduce encoding complexity, the encoder considers the improvement of only a single codebook for each block. Experimental results for image compression show that LCGLA iterations significantly improve the PSNR of standard high-quality lossy 6:1 LCVQ compressed images.
Synthesis and characterization of three-dimensional graphene foams by chemical vapor deposition

As a three-dimensional porous structure made of two-dimensional graphene building blocks, graphene foam, has gained enormous attention in recent years. Such graphene foam integrates graphene sheets into macroscopic structures meanwhile inheriting most of the fascinating intrinsic properties of graphene. Together with its ultralow density, high porosity and flexibility, graphene foam has been proposed in many applications, such as supercapacitors, microwave shielding, electrochemical sensing and lithium-ion batteries. In this paper, three-dimensional graphene foams were synthesized by low pressure chemical vapor deposition. The obtained graphene foams were characterized by scanning electron microscopy and Raman spectroscopy. The results show that nickel foam surface was fully covered by graphene. The Raman spectra show that most graphene were multilayer, but monolayer and bilayer graphene were also found in some areas. In addition to this, it was also found that the synthesized graphene has very small D peak, indicating high quality of the synthesized graphene.
Mixed Gaussian-impulse noise removal from highly corrupted images via adaptive local and nonlocal statistical priors

The motivation of this paper is to introduce a novel framework for the restoration of images corrupted by mixed Gaussian-impulse noise. To this aim, first, an adaptive curvelet thresholding criterion is proposed which tries to adaptively remove the perturbations appeared during denoising process. Then, a new statistical regularization term, called joint adaptive statistical prior (JASP), is established which enforces both the local and nonlocal statistical consistencies, simultaneously, in a unified manner. Furthermore, a novel technique for mixed Gaussian plus impulse noise removal using JASP in a variational scheme is developed-we refer to it as De-JASP. To efficiently solve the above variational scheme, an efficient alternating minimization algorithm is developed based on split Bregman iterative framework. Extensive experimental results manifest the effectiveness of the proposed method comparing with the current state-of-the-art methods in mixed Gaussian-impulse noise removal.
Maximum Random Fuzzy Weighted Matching Models and Hybrid Genetic Algorithm

The maximum weighted matching problem is to find a maximum matching in a given graph such that the sum of weights of edges in it is maximum. In this paper, the concept of maximum random fuzzy weighted matching is proposed firstly, and then the maximum random fuzzy weighted matching problem is formulated as expected value model, chance-constrained programming and dependent-chance programming according to various decision criteria. Furthermore, a hybrid genetic algorithm is designed to solve the proposed random fuzzy programming models and finally a corresponding numerical example is presented.
Mechanisms for Risk Averse Agents, Without Loss

Auctions in which agents' payoffs are random variables have received increased attention in recent years. In particular, recent work in algorithmic mechanism design has produced mechanisms employing internal randomization, partly in response to limitations on deterministic mechanisms imposed by computational complexity. For many of these mechanisms, which are often referred to as truthful-in-expectation, incentive compatibility is contingent on the assumption that agents are risk-neutral. These mechanisms have been criticized on the grounds that this assumption is too strong, because "real" agents are typically risk averse, and moreover their precise attitude towards risk is typically unknown a-priori. In response, researchers in algorithmic mechanism design have sought the design of universally-truthful mechanisms --- mechanisms for which incentive-compatibility makes no assumptions regarding agents' attitudes towards risk. We show that any truthful-in-expectation mechanism can be generically transformed into a mechanism that is incentive compatible even when agents are risk averse, without modifying the mechanism's allocation rule. The transformed mechanism does not require reporting of agents' risk profiles. Equivalently, our result can be stated as follows: Every (randomized) allocation rule that is implementable in dominant strategies when players are risk neutral is also implementable when players are endowed with an arbitrary and unknown concave utility function for money.
Review of the fundamental theories behind small angle X-ray scattering, molecular dynamics simulations, and relevant integrated application

In this paper, the fundamental concepts and equations necessary for performing small angle X-ray scattering (SAXS) experiments, molecular dynamics (MD) simulations, and MD-SAXS analyses were reviewed. Furthermore, several key biological and non-biological applications for SAXS, MD, and MD-SAXS are presented in this review; however, this article does not cover all possible applications. SAXS is an experimental technique used for the analysis of a wide variety of biological and non-biological structures. SAXS utilizes spherical averaging to produce one- or two-dimensional intensity profiles, from which structural data may be extracted. MD simulation is a computer simulation technique that is used to model complex biological and non-biological systems at the atomic level. MD simulations apply classical Newtonian mechanics’ equations of motion to perform force calculations and to predict the theoretical physical properties of the system. This review presents several applications that highlight the ability of both SAXS and MD to study protein folding and function in addition to non-biological applications, such as the study of mechanical, electrical, and structural properties of non-biological nanoparticles. Lastly, the potential benefits of combining SAXS and MD simulations for the study of both biological and non-biological systems are demonstrated through the presentation of several examples that combine the two techniques.
Characterization of silver nanoparticles synthesized on titanium dioxide fine particles

Silver nanoparticles with a narrow size distribution were synthesized over the surface of two different commercial TiO2 particles using a simple aqueous reduction method. The reducing agent used was NaBH4; different molar ratios TiO2:Ag were also used. The nanocomposites thus prepared were characterized using transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), x-ray photoelectron spectroscopy (XPS), x-ray diffraction (XRD), dynamic light scattering (DLS) and UV–visible (UV–vis) absorption spectroscopy; the antibacterial activity was assessed using the standard microdilution method, determining the minimum inhibitory concentration (MIC) according to the National Committee for Clinical Laboratory Standards. From the microscopy studies (TEM and STEM) we observed that the silver nanoparticles are homogeneously distributed over the surface of TiO2 particles and that the TiO2:Ag molar ratio plays an important role. We used three different TiO2Ag molar ratios and the size of the silver nanoparticles is 10, 20 and 80 nm, respectively. It was found that the antibacterial activity of the nanocomposites increases considerably comparing with separated silver nanoparticles and TiO2 particles.
Endometrial Stromal Sarcomas With Sex Cord Differentiation Are Associated With PHF1 Rearrangement

Endometrial stromal tumors may pose diagnostic challenges particularly when they exhibit variant histologic appearances, involve extrauterine sites, or present as metastatic disease. In such cases, use of immunohistochemical markers and identification of specific nonrandom chromosomal rearrangements may be helpful. Over the last decade, fluorescence in situ hybridization (FISH) has been progressively incorporated as a diagnostic tool for the evaluation of endometrial stromal tumors. The purpose of this study was to review a series of these tumors and compare the results of FISH analysis with the clinicopathologic characteristics. Three endometrial stromal nodules (ESNs), 13 endometrial stromal sarcomas (ESSs), and 7 undifferentiated endometrial sarcomas (UESs) were reviewed. Three metastases from 1 of the ESS cases were also analyzed. Nine of these tumors (1 ESN, 8 ESSs, and 1 UES) exhibited unusual histologic features, including smooth muscle (3), sex cord (7), epithelioid (1), fibromyxoid (1), and skeletal muscle (2) differentiation. A tissue microarray was prepared, and FISH analysis was performed using break-apart and fusion probes for JAZF1, SUZ12, EPC1, and PHF1 genes. FISH was successful in 22 cases, and rearrangements involving JAZF1, SUZ12, EPC1, and PHF1 genes were detected in 10 of the 22 (45%) uterine tumors, including 2 of the 3 ESNs and 8 of 12 ESSs. Genetic rearrangements were found neither in the 3 metastases of the ESS nor in any of the UESs. It is noteworthy that a correlation between sex cord differentiation and PHF1 rearrangement was encountered in ESSs (P=0.008). In our series, all ESSs showing sex cords had PHF1 genetic rearrangement, suggesting that such rearrangements may induce sex cord differentiation.
Protein Molecular Function Prediction by Bayesian Phylogenomics

We present a statistical graphical model to infer specific molecular function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER (Statistical Inference of Function Through Evolutionary Relationships) accurately predicts molecular function for members of a protein family given a reconciled phylogeny and available function annotations, even when the data are sparse or noisy. Our method produced specific and consistent molecular function predictions across 100 Pfam families in comparison to the Gene Ontology annotation database, BLAST, GOtcha, and Orthostrapper. We performed a more detailed exploration of functional predictions on the adenosine-5′-monophosphate/adenosine deaminase family and the lactate/malate dehydrogenase family, in the former case comparing the predictions against a gold standard set of published functional characterizations. Given function annotations for 3% of the proteins in the deaminase family, SIFTER achieves 96% accuracy in predicting molecular function for experimentally characterized proteins as reported in the literature. The accuracy of SIFTER on this dataset is a significant improvement over other currently available methods such as BLAST (75%), GeneQuiz (64%), GOtcha (89%), and Orthostrapper (11%). We also experimentally characterized the adenosine deaminase from Plasmodium falciparum, confirming SIFTER's prediction. The results illustrate the predictive power of exploiting a statistical model of function evolution in phylogenomic problems. A software implementation of SIFTER is available from the authors.
The Relational Way To Dam The Flood Of Genome Data

Mutations in genomes can indicate a predisposition for diseases such as cancer or cardiovascular disorder. Genome analysis is an established procedure to determine mutations and deduce their impact on living organisms. The first step in genome analysis is DNA sequencing that makes the biochemically stored hereditary information in DNA digitally readable. The cost and time to sequence a whole genome decreases rapidly and leads to an increase of available raw genome data that must be stored and integrated to be analyzed. Damming this flood of genome data requires efficient and effective analysis as well as data management solutions. State-of-the-art in genome analysis are flat-file-based storage and analysis solutions. Consequently, every analysis application is responsible to manage data on its own, which leads to implementation and process overhead. Database systems have already shown their ability to reduce data management overhead for analysis applications in various domains. However, current approaches using relational database systems for genome-data management lack scalable performance on increasing amounts of genome data. In this thesis, we investigate the capabilities of relational main-memory database systems to store and query genome data efficiently, while enabling flexible data access.
The pathogenesis of spinocerebellar ataxia

Six forms of spinocerebellar ataxia (SCA) are caused by pathological cytosine-adenine-guanine (CAG) trinucleotide repeat expansions in the coding region of the mutated genes. The translated proteins contain abnormally long polyglutamine stretches, and SCA-1, SCA-2, SCA-3/Machado-Joseph disease (MJD), SCA-6, SCA-7, and SCA-17 are “polyglutamine diseases”. Despite their clinical and genetic heterogeneity, the ataxia-causing lesions in the brain invariably affect the “cerebellar module” that is defined as a reciprocal circuitry between the cerebellar cortex, the dentate nuclei, and the inferior olivary nuclei. While the neurons of the basis pontis do not properly belong to this module, pontine atrophy is an important additional lesion in SCA-1, SCA-2, and SCA-7. The descriptive term olivopontocerebellar atrophy (OPCA) applies to these forms whereas SCA-6 is the prototype of “pure” cerebellar cortical or cerebello-olivary atrophy. Purkinje cells have an elaborate dendritic tree, and atrophy of these most remarkable cells has captured the attention of many morphologists. Almost invariably, the loss of Purkinje cells entails retrograde neuronal degeneration in the inferior olivary nuclei. However, SCA-6 is an exception, and many olivary neurons survive. Similarly, stellate, basket, and granule cells do not undergo commensurate retrograde atrophy when Purkinje cells disappear. The dentate nucleus displays “grumose” degeneration in SCA-3/MJD while the cerebellar cortex and the inferior olivary nuclei remain largely unaffected. The role of polyglutamine-containing intranuclear and cytoplasmic inclusion bodies in SCA remains unknown but protein aggregation may be the common step in the pathogenesis of these otherwise rather heterogeneous disorders.
A Crisis Management Quality Improvement Initiative in a Children's Psychiatric Hospital: Design, Implementation, and Outcome

Behavioral crisis management, including the use of seclusion and restraint, is the most high risk process in the psychiatric care of children and adolescents. The authors describe hospital-wide programmatic changes implemented at a children’s psychiatric hospital that aimed to improve the quality of crisis management services. Pre/post quantitative and qualitative data suggest reduced restraint and seclusion use, reduced patient and staff injury related to crisis management, and increased patient satisfaction during the post-program period. Factors deemed beneficial in program implementation are discussed. (Journal of Psychiatric Practice 2012;18:304–311)