Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Multi-modal Cloud Features within a Multi-dimensional Encryption Space

The problem of combining multi-modal features which extract from characteristics of given Cloud Computing Servers in the pattern recognition system is well known difficult. This paper addresses a novel efficient technique for normalizing sets of features which are highly multi-modal in nature, so as to allow them to be incorporated from a multi-dimensional feature distribution space. The intend system identify the modes of each distribution and for removing any possible correlation between the feature data to allow to be used in an encryption key generation system.
A temporal self-organizing neural network for adaptive sub-sequence clustering and case studies

Temporal neural networks such as Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM) are popular for their incremental and explicit learning abilities. However, for sub-sequence clustering TKM and RSOM may generate many fragments whose classification membership is hard to decide. Besides they have stability issues in multivariate time series processing because they model the historical neuron activities on each variable independently. To overcome the drawbacks, we propose an adaptive sub-sequence clustering method based on single layered Self-Organizing Incremental Neural Network (SOINN). A recurrent filter is proposed to model the quantizations of neuron activations each as a scalar instead of a vector like in TKM and RSOM. Then it is integrated with the single layered SOINN for adaptive clustering where fragmented clusters in TKM and RSOM is replaced by a smoothed clustering result. Experiments are carried out on two datasets, namely a traffic flow dataset from open Caltrans performance measurement systems and a part of the KDD Cup 99 intrusion detection dataset. Experimental results show that the proposed method outperforms the conventional methods by 21.3% and 9.1% on the two datasets respectively.
Flat Cosmology with Coupled Matter and Dark Energy

Three models of a flat universe of coupled matter and dark energy with different low-redshift parameterizations of the dark energy equation of state are considered. The dark energy is assumed to vary with time like the trace of the energy-momentum tensor of cosmic matter. In the radiation-dominated era the models reduce to standard cosmology. In the matter-dominated era they are, for modern values of the cosmological parameters, consistent with data from Type Ia supernova searches and with the data of Gurvits et al. for the angular sizes of ultracompact radio sources. We find that the angular size-redshift tests for our models offer a higher statistical confidence than that based on SNe Ia data. A comparison of our results with a recent revised analysis of angular size-redshift legacy data is made, and the implication of our models with optimized relativistic beaming in the radio sources is discussed. In particular, we find that relativistic beaming implies a Lorentz factor less than 6, in agreement with its values for powerful active galactic nuclei.
Almost Optimal Local Graph Clustering Using Evolving Sets

Spectral partitioning is a simple, nearly linear time algorithm to find sparse cuts, and the Cheeger inequalities provide a worst-case guarantee for the quality of the approximation found by the algorithm. A local graph partitioning algorithm finds a set of vertices with small conductance (i.e., a sparse cut) by adaptively exploring part of a large graph G, starting from a specified vertex. For the algorithm to be local, its complexity must be bounded in terms of the size of the set that it outputs, with at most a weak dependence on the number n of vertices in G. Previous local partitioning algorithms find sparse cuts using random walks and personalized PageRank [Spielman and Teng 2013; Andersen et al. 2006]. In this article, we introduce a simple randomized local partitioning algorithm that finds a sparse cut by simulating the volume-biased evolving set process, which is a Markov chain on sets of vertices. We prove that for any ϵ > 0, and any set of vertices A that has conductance at most φ, for at least half of the starting vertices in A our algorithm will output (with constant probability) a set of conductance O(√φ /ϵ). We prove that for a given run of the algorithm, the expected ratio between its computational complexity and the volume of the set that it outputs is vol(A)ϵφ-1/2polylog(n), where vol(A) = Σv ∈ Ad(v) is the volume of the set A. This gives an algorithm with the same guarantee (up to a constant factor) as the Cheeger's inequality that runs in time slightly superlinear in the size of the output. This is the first sublinear (in the size of the input) time algorithm with almost the same guarantee as the Cheeger's inequality. In comparison, the best previous local partitioning algorithm, by Andersen et al. [2006], has a worse approximation guarantee of O(√φ log n) and a larger ratio of φ-1 polylog(n) between the complexity and output volume. As a by-product of our results, we prove a bicriteria approximation algorithm for the expansion profile of any graph. For 0 < k ≤ vol(V)/2, let φ(k) : min S: vol(S) ≤ kφ(S). There is a polynomial time algorithm that, for any k, ϵ > 0, finds a set S of volume vol(S) ≤ O(k1 + ϵ) and expansion φ(S)≤ O(√φ (k)/ϵ). As a new technical tool, we show that for any set S of vertices of a graph, a lazy t-step random walk started from a randomly chosen vertex of S will remain entirely inside S with probability at least (1 - φ(S)/2)t. This itself provides a new lower bound to the uniform mixing time of any finite state reversible Markov chain.
Advantages of a modified scoring method for the rush video‐based tic rating scale

Previously, we published a video‐based objective rating scale of tics that met reliability and validity criteria for measurement of five domains of tic disability. In the original form, the scale's metric properties did not permit internal comparison of each of the five domains of impairment and did not provide a total score for use as a primary outcome measure. In this study, we retained the original scale and videotape protocol but tested whether a modified scoring system corrected these limitations. The new scoring method rated assigned tic data to ratings of 0–4 on five disability categories: number of body areas, frequency of motor tics, frequency of phonic tics, severity of motor tics, and severity of phonic tics. The sums of these ratings yielded a total score of overall tic disability (0–20). In a series of 31 patients with Gilles de la Tourette syndrome, we assessed Spearman correlation coefficients for the old and new scoring systems as well as the correlation of the new ratings with the objectively derived sections of the Yale Global Tic Severity Scale (YGTSS), another valid and reliable scale used in clinical practice and research. For each domain, the rank order for the scores on the original scale was well retained in the new scores. Likewise, for each domain, ranking with the new scoring system correlated well with scores on the comparable objective item from the YGTSS. The new total score accurately captured the rank order of the combined five domains from the original scale and correlated well with the total objective motor plus phonic tic score from the YGTSS and the YGTSS Tourette Syndrome Overall Impairment Rating. These data demonstrate that the modified videotape‐based scoring system retains the essential information gathered in the original Rush scale. The modification provides comparisons among the five assessed domains and a total objectively based disability score that can be used as a single outcome measure for assessing tic disability.
Testing the validity of the proposed ICD-11 PTSD and complex PTSD criteria using a sample from Northern Uganda

Background The International Classification of Diseases (ICD-11) is currently under development with proposed changes recommended for the posttraumatic stress disorder (PTSD) diagnosis and the inclusion of a separate complex PTSD (CPTSD) disorder. Empirical studies support the distinction between PTSD and CPTSD; however, less research has focused on non-western populations. Objective The aim of this study was to investigate whether distinct PTSD and CPTSD symptom classes emerged and to identify potential risk factors and the severity of impairment associated with resultant classes. Methods A latent class analysis (LCA) and related analyses were conducted on 314 young adults from Northern Uganda. Fifty-one percent were female and participants were aged between 18 and 25 years. Forty percent of the participants were former child soldiers (n=124) while the remaining participants were civilians (n=190). Results The LCA revealed three classes: a CPTSD class (40.2%), a PTSD class (43.8%), and a low symptom class (16%). Child soldier status was a significant predictor of both CPTSD and PTSD classes (OR=5.96 and 2.82, respectively). Classes differed significantly on measures of anxiety/depression, conduct problems, somatic complaints, and war experiences. Conclusions To conclude, this study provides preliminary support for the proposed distinction between PTSD and CPTSD in a young adult sample from Northern Uganda. However, future studies are needed using larger samples to test alternative models before firm conclusions can be made. Highlights of the article Examine the validity of CPTSD in a non-western sample Separate PTSD and CPTSD classes emerged Former child soldiers were more strongly associated with the CPTSD class CPTSD class reported significantly higher levels of anxiety, depression, somatic complaints and conduct problems
Relevance Vector Machine based Mixture of Experts

The aim of this report is to detail the implementation of a sparse Bayesian Mixture of Experts (ME) [2] for solving a one-to-many regression mapping based on the relevance vector machine architecture. Our eventual goal is to evaluate the ME framework in human body and hand pose estimation from monocular view. However, this is left for future work. The application of ME is demonstrated using a toy dataset.
Preparation of an In-House Reference Material Containing Fumonisins in Thai Rice and Matrix Extension of the Analytical Method for Japanese Rice

Mycotoxin contamination in rice is less reported, compared to that in wheat or maize, however, some Fusarium fungi occasionally infect rice in the paddy field. Fumonisins are mycotoxins mainly produced by Fusarium verticillioides, which often ruins maize. Rice adherent fungus Gibberella fujikuroi is taxonomically near to F. verticillioides, and there are sporadic reports of fumonisin contamination in rice from Asia, Europe and the United States. Therefore, there exists the potential risk of fumonisin contamination in rice as well as the need for the validated analytical method for fumonisins in rice. Although both natural and spiked reference materials are available for some Fusarium mycotoxins in matrices of wheat and maize, there are no reference materials for Fusarium mycotoxins in rice. In this study, we have developed a method for the preparation of a reference material containing fumonisins in Thai rice. A ShakeMaster grinding machine was used for the preparation of a mixed material of blank Thai rice and F. verticillioides-infected Thai rice. The homogeneity of the mixed material was confirmed by one-way analysis of variance, which led this material to serve as an in-house reference material. Using this reference material, several procedures to extract fumonisins from Thai rice were compared. Accordingly, we proved the applicability of an effective extraction procedure for the determination of fumonisins in Japanese rice.
Functional Expression in Xenopus Oocytes of Gap-junctional Hemichannels Formed by a Cysteine-less Connexin 43*

Gap-junctional channels are formed by two connexons or gap-junctional hemichannels in series, with each connexon conformed by six connexin molecules. As with other membrane proteins, structural information on connexons can potentially be obtained with techniques that take advantage of the highly specific thiol chemistry by positioning Cys residues at locations of interest, ideally in an otherwise Cys-less protein. It has been shown that conserved Cys residues located in the extracellular loops of connexins are essential for the docking of connexons from adjacent cells, preventing the formation of functional gap-junctional channels. Here we engineered a Cys-less version of connexin 43 (Cx43) and assessed its function using a Xenopus oocyte expression system. The Cys-less protein was expressed at the plasma membrane and did not form gap-junctional channels but formed hemichannels that behave similarly to those formed by Cx43 in terms of permeation to carboxyfluorescein. The carboxyfluorescein permeability of Cys-less hemichannels was increased by protein kinase C inhibition, like the wild-type Cx43 hemichannels. We generated a protein with a single Cys in a position (residue 34) thought to face the channel pore and show that thiol modification of the Cys abolishes the carboxyfluorescein permeability. We conclude that Cysless Cx43 forms regulated functional hemichannels, and therefore Cys-less Cx43 is a useful tool for future structural studies.
Mathematical Idea Analysis: What Embodied Cognitive Science Can Say about the Human Nature of Mathematics.

This paper gives a brief introduction to a discipline called the cognitive science of mathematics. The theoretical background of the arguments is based on embodied cognition and findings in cognitive linguistics. It discusses Mathematical Idea Analysis, a set of techniques for studying implicit structures in mathematics. Particular attention is paid to everyday cognitive mechanisms such as image schemas and conceptual metaphors. Some implications for mathematics education are discussed.
The optical, infrared and radio properties of extragalactic sources observed by SDSS, 2MASS and FIRST surveys

We positionally match sources observed by the Sloan Digital Sky Survey (SDSS), the Two Micron All Sky Survey (2MASS), and the Faint Images of the Radio Sky at Twenty-cm (FIRST) survey. Practically all 2MASS sources are matched to an SDSS source within 2 arcsec; ~11% of them are optically resolved galaxies and the rest are dominated by stars. About 1/3 of FIRST sources are matched to an SDSS source within 2 arcsec; ~80% of these are galaxies and the rest are dominated by quasars. Based on these results, we project that by the completion of these surveys the matched samples will include about 10^7 stars and 10^6 galaxies observed by both SDSS and 2MASS, and about 250,000 galaxies and 50,000 quasars observed by both SDSS and FIRST. Here we present a preliminary analysis of the optical, infrared and radio properties for the extragalactic sources from the matched samples. In particular, we find that the fraction of quasars with stellar colors missed by the SDSS spectroscopic survey is probably not larger than ~10%, and that the optical colors of radio-loud quasars are ~0.05 mag. redder (with 4-sigma significance) than the colors of radio-quiet quasars.
Towards Parsing Language Learner Utterances in Context

Intelligent computer-assisted language learning (ICALL) systems give feedback on grammatical errors based on the syntactic analysis of an utterance. However, parsing a language learner sentence in isolation can result in multiple interpretations, which even humans may not be able to resolve. For deriving a helpful error diagnosis, an interpretation that is consistent with the intended meaning is needed. To achieve this, we propose an approach that models information about the context of a sentence by means of conceptual relationships and integrates them into the decision procedure of a syntactic-semantic parser. This way, we obtain a syntactic structure which complies best with a given context model. We identify and categorize cases where the context inﬂuences both the structure and the error diagnosis derived from it.
New Methods for Handling Singular Sample Covariance Matrices

The estimation of a covariance matrix from an insufficient amount of data is one of the most common problems in fields as diverse as multivariate statistics, wireless communications, signal processing, biology, learning theory, and finance. In a joint work of Marzetta, Tucci, and Simon, a new approach to handle singular covariance matrices was suggested. The main idea was to use dimensionality reduction in conjunction with an average over the Stiefel manifold. In this paper, we continue with this research and we consider some new approaches to handle this problem. One of the methods is called the mean conjugate estimator under Ewens measure and uses a randomization of the sample covariance matrix over all the permutation matrices with respect to the Ewens measure. The techniques used to attack this problem are broad and run from random matrix theory to combinatorics.
A Case for Specialized Processors for Scale-Out Workloads

Emerging scale-out workloads need extensive amounts of computational resources. However, data centers using modern server hardware face physical constraints in space and power, limiting further expansion and requiring improvements in the computational density per server and in the per-operation energy. Continuing to improve the computational resources of the cloud while staying within physical constraints mandates optimizing server efficiency. In this work, we demonstrate that modern server processors are highly inefficient for running cloud workloads. To address this problem, we investigate the microarchitectural behavior of scale-out workloads and present opportunities to enable specialized processor designs that closely match the needs of the cloud.
Edge-preserving image decomposition using L1 fidelity with L0 gradient

We present an image decomposition method using L1 fidelity term with L0 norm of gradient to decompose an image into base layer and detail layer. Generally, the L1 fidelity should be preferable to the L2 norm when the erroneous measurements exist. It is also reported that the L0 norm of gradient is a better prior term than total variation and the L2 norm of gradient. Therefore, we combine these two benefits to obtain our base layer by adopting our method using L1 fidelity and L0 gradient. Our image decomposition method can be regarded as the fundamental tool to generate multiple image editing applications, such as image denoising, edge detection, detail enhancement, cartoon JPEG artifact removal, local tone mapping, and contrast enhancement under low backlight condition. Experimental results show that our proposed method is promising as compared to the existing methods.
Reference dependence, cooperation, and coordination in games

The problems of how self-interested players can cooperate despite incentives to defect, and how players can coordinate despite the presence of multiple equilibria, are among the oldest and most fundamental in game theory. In this report, we demonstrate that a plausible and even natural specification of the reference outcome in a game simultaneously predicts systematic cooperation and defection in the Prisoner’s Dilemma, as well as equilibrium selection and out-of-equilibrium play in coordination games. The predictions hold even if players are purely self-interested, there are no salient labels, the game is played only once, and there is no communication of any kind. Furthermore, the predictions are unique, as opposed to the multiplicity of equilibria in the infinitely repeated Prisoner’s Dilemma and in coordination games. We apply experimental results to test the predictions of the model.
Loss of Activating EGFR Mutant Gene Contributes to Acquired Resistance to EGFR Tyrosine Kinase Inhibitors in Lung Cancer Cells

Non-small-cell lung cancer harboring epidermal growth factor receptor (EGFR) mutations attains a meaningful response to EGFR-tyrosine kinase inhibitors (TKIs). However, acquired resistance to EGFR-TKIs could affect long-term outcome in almost all patients. To identify the potential mechanisms of resistance, we established cell lines resistant to EGFR-TKIs from the human lung cancer cell lines PC9 and11–18, which harbored activating EGFR mutations. One erlotinib-resistant cell line from PC9 and two erlotinib-resistant cell lines and two gefitinib-resistant cell lines from 11–18 were independently established. Almost complete loss of mutant delE746-A750 EGFR gene was observed in the erlotinib-resistant cells isolated from PC9, and partial loss of the mutant L858R EGFR gene copy was specifically observed in the erlotinib- and gefitinib-resistant cells from 11–18. However, constitutive activation of EGFR downstream signaling, PI3K/Akt, was observed even after loss of the mutated EGFR gene in all resistant cell lines even in the presence of the drug. In the erlotinib-resistant cells from PC9, constitutive PI3K/Akt activation was effectively inhibited by lapatinib (a dual TKI of EGFR and HER2) or BIBW2992 (pan-TKI of EGFR family proteins). Furthermore, erlotinib with either HER2 or HER3 knockdown by their cognate siRNAs also inhibited PI3K/Akt activation. Transfection of activating mutant EGFR complementary DNA restored drug sensitivity in the erlotinib-resistant cell line. Our study indicates that loss of addiction to mutant EGFR resulted in gain of addiction to both HER2/HER3 and PI3K/Akt signaling to acquire EGFR-TKI resistance.
MetaClustering : Discovery of The Di erent Sample Clusterings in Gene Expression Data

Clustering of the samples is a standard procedure for the analysis of gene expression data, for instance to discover cancer subtypes. However, more than one biologically meaningful clustering can exist, depending on the genes chosen. We propose here to group the genes in function of the clustering of the samples they t. This allows to determine directly the di erent clusterings of the samples present in the data. As a clustering is a structure, genes belonging to the same group are functions of the same structure. Hence, the determination of groups of genes which support the same clustering could also be viewed as the detection of non-linearly linked genes. MetaClustering was applied successfully to simulated data. It also recovered the known clustering of real cancer data, which was impossible using the complete set of genes. Finally, it clustered together cell-cycle genes, showing its ability to group genes related in a non-linear way.
Genome-Wide Expression Profiling of the Arabidopsis Female Gametophyte Identifies Families of Small, Secreted Proteins

The female gametophyte of flowering plants, the embryo sac, develops within the diploid (sporophytic) tissue of the ovule. While embryo sac–expressed genes are known to be required at multiple stages of the fertilization process, the set of embryo sac–expressed genes has remained poorly defined. In particular, the set of genes responsible for mediating intracellular communication between the embryo sac and the male gametophyte, the pollen grain, is unknown. We used high-throughput cDNA sequencing and whole-genome tiling arrays to compare gene expression in wild-type ovules to that in dif1 ovules, which entirely lack embryo sacs, and myb98 ovules, which are impaired in pollen tube attraction. We identified nearly 400 genes that are downregulated in dif1 ovules. Seventy-eight percent of these embryo sac–dependent genes were predicted to encode for secreted proteins, and 60% belonged to multigenic families. Our results define a large number of candidate extracellular signaling molecules that may act during embryo sac development or fertilization; less than half of these are represented on the widely used ATH1 expression array. In particular, we found that 37 out of 40 genes encoding Domain of Unknown Function 784 (DUF784) domains require the synergid-specific transcription factor MYB98 for expression. Several DUF784 genes were transcribed in synergid cells of the embryo sac, implicating the DUF784 gene family in mediating late stages of embryo sac development or interactions with pollen tubes. The coexpression of highly similar proteins suggests a high degree of functional redundancy among embryo sac genes.