Miracle-dz/UniFAR-FTUs huggingface dataset:
CMA-ES with Optimal Covariance Update and Storage Complexity

The covariance matrix adaptation evolution strategy (CMA-ES) is arguably one of the most powerful real-valued derivative-free optimization algorithms, finding many applications in machine learning. The CMA-ES is a Monte Carlo method, sampling from a sequence of multi-variate Gaussian distributions. Given the function values at the sampled points, updating and storing the covariance matrix dominates the time and space complexity in each iteration of the algorithm. We propose a numerically stable quadratic-time covariance matrix update scheme with minimal memory requirements based on maintaining triangular Cholesky factors. This requires a modification of the cumulative step-size adaption (CSA) mechanism in the CMA-ES, in which we replace the inverse of the square root of the covariance matrix by the inverse of the triangular Cholesky factor. Because the triangular Cholesky factor changes smoothly with the matrix square root, this modification does not change the behavior of the CMA-ES in terms of required objective function evaluations as verified empirically. Thus, the described algorithm can and should replace the standard CMA-ES if updating and storing the covariance matrix matters.
Childhood attachment and behavioral inhibition: Predicting intolerance of uncertainty in adulthood

Abstract Intolerance of uncertainty (IU), the tendency to react negatively to uncertain situations, has been identified as an important cognitive component of anxiety disorders, yet little is known about its etiology. Links to temperament, particularly behavioral inhibition (BI), and insecure attachment have been proposed in the development of IU, but no prospective empirical investigation has been performed thus far. In the current study, attachment to caregiver and BI of 60 children were assessed at age 6, using observational measures. Mother's anxiety symptoms were assessed when participants were 14 years old. IU was reported by participants when they were 21 years old, as was neuroticism. Two types of insecure attachment (ambivalent and disorganized–controlling) and BI were positively related to IU over a 15-year span, even after controlling for participants’ neuroticism and maternal anxiety. Attachment and BI had no significant interacting effect on the development of IU. Maternal anxiety was positively related to child BI and insecure attachment, but not IU. This study is the first to provide empirical support for a link between ambivalent and disorganized–controlling attachment and BI in preschool children to the development of IU in adulthood. Results have etiological and preventative implications not only for anxiety disorders but also for all disorders related to IU.
Discovery of the Optical Transient of GRB 990308

The optical transient of the faint gamma-ray burst GRB 990308 was detected by the QUEST camera on the Venezuelan 1 m Schmidt telescope starting 3.28 hr after the burst. Our photometry gives V = 18.32 ± 0.07, R = 18.14 ± 0.06, B = 18.65 ± 0.23, and R = 18.22 ± 0.05 for times ranging from 3.28 to 3.47 hr after the burst. The colors correspond to a spectral slope of close to fν ∝ ν1/3. Within the standard synchrotron fireball model, this requires that the external medium be less dense than 104 cm-3, the electrons contain more than 20% of the shock energy, and the magnetic field energy be less than 24% of the energy in the electrons for normal interstellar or circumstellar densities. We also report upper limits of V > 12.0 at 132 s (with LOTIS), V > 13.4 from 132 to 1029 s (with LOTIS), V > 15.3 at 28.2 minutes (with Super-LOTIS), and a 8.5 GHz flux of less than 114 μJy at 110 days (with the Very Large Array). Wisconsin-Indiana-Yale-NOAO 3.5 m and Keck 10 m telescopes reveal this location to be empty of any host galaxy to R > 25.7 and K > 23.3. The lack of a host galaxy likely implies that it is either substantially subluminous or more distant than a redshift of ~1.2.
Bayesian Analysis of Binary Data Subject to Misclassification

This paper considers estimation of success probabilities of categorical binary data subject to mis-classiication errors from the Bayesian point of view. It has been shown by Bross (1954) that sample proportions are in general biased estimates. This bias is a function of the amount of misclassiication and can be substantial. Tenenbein (1970) proposed to eliminate the bias by subjecting a portion of the sample to both true and fallible classiiers, resulting in a 2 x 2 table, from which the misclassi-cation rates can be estimated. The rationale is that fallible classiiers are inexpensive relative to infallible ones. Hence if only a part of the sample is measured by the infallible classiier one can obtain a more eecient estimate, for a given sampling budget, than by measuring the whole sample using the infallible classiier. In many contexts an infallible classiier is unavailable or prohibitively expensive. Bayesian methods then provide a useful approach for dealing with the consequent nonidentiiability problems which arise when we want to carry out inference. In this paper we treat both the single measurement and the repeated measurements (where the former is a special case of the latter) from a Bayesian point of view. The posterior analyses are carried out using both Gauss-Jacobi quadrature and Gibbs sampling. Through examples it is shown that in most cases Gauss-Jacobi quadrature produces very good approximations, both in terms of accuracy and speed of computation. The Gibbs sampler requires more computation to reach the same level of accuracy as the Gauss-Jacobi.
DIRECT Distances to Nearby Galaxies Using Detached Eclipsing Binaries and Cepheids. I. Variables in the Field M31B

We have undertaken a long-term project, DIRECT, to obtain the direct distances to two important galaxies in the cosmological distance ladder—M31 and M33—using detached eclipsing binaries (DEBs) and Cepheids. While rare and difficult to detect, DEBs provide us with the potential to determine these distances with an accuracy better than 5%. The extensive photometry obtained in order to detect DEBs provides us with good light curves for the Cepheid variables. These are essential to the parallel project to derive direct Baade-Wesselink distances to Cepheids in M31 and M33. For both Cepheids and eclipsing binaries, the distance estimates will be free of any intermediate steps. As a first step in the DIRECT project, between 1996 September and 1997 January we obtained 36 full nights on the Michigan-Dartmouth-MIT Observatory 1.3 m telescope and 45 full/partial nights on the F. L. Whipple Observatory 1.2 m telescope to search for DEBs and new Cepheids in the M31 and M33 galaxies. In this paper, first in a series, we present the catalog of variable stars, most of them newly detected, found in the field M31B [(α, δ) = (11.°20, 41.°59), J2000.0]. We have found 85 variable stars: 12 eclipsing binaries, 38 Cepheids, and 35 other periodic, possible long-period or nonperiodic variables. The catalog of variables, as well as their photometry and finding charts, is available via anonymous ftp and the World Wide Web.
A pattern language for parallelizing irregular algorithms

In irregular algorithms, data set's dependences and distributions cannot be statically predicted. This class of algorithms tends to organize computations in terms of data locality instead of parallelizing control in multiple threads. Thus, opportunities for exploiting parallelism vary dynamically, according to how the algorithm changes data dependences. This paper presents the first part of a pattern language for creating parallel implementations of irregular algorithms and applications. Four patterns are proposed: Amorphous Data-Parallelism, Data-Parallel Graph, Optimistic Iteration and In-Order Iteration.
Using Crowdsourcing to get Representations based on Regular Expressions

Often the bottleneck in document classification is finding good representations that zoom in on the most important aspects of the documents. Most research uses n-gram representations, but relevant features often occur discontinuously, e.g., not. good in sentiment analysis. In this paper we present experiments getting experts to provide regular expressions, as well as crowdsourced annotation tasks from which regular expressions can be derived. Somewhat surprisingly, it turns out that these crowdsourced feature combinations outperform automatic feature combination methods, as well as expert features, by a very large margin and reduce error by 24-41% over n-gram representations.
Social Situation Awareness: Empathic Accuracy in the Aircraft Cockpit

The present study assesses the innovative concept of empathic accuracy within a crew-aircraft-system in a realistic approach scenario. Empathy, one of the key skills of social situation awareness (SSA), was found to be altered in stressful situations. Challenging and surprising events lead to a decrease in empathic accuracy in both pilot flying and pilot monitoring. Stress therefore significantly impacts SSA and modifications in training, procedures and system design could help crews better manage their workload during surprising and challenging situations, leading to increased empathic accuracy and better crew interaction.
Mining frequent itemsets without support threshold: with and without item constraints

In classical association rules mining, a minimum support threshold is assumed to be available for mining frequent itemsets. However, setting such a threshold is typically hard. We handle a more practical problem; roughly speaking, it is to mine N k-itemsets with the highest supports for k up to a certain k/sub max/ value. We call the results the N-most interesting itemsets. Generally, it is more straightforward for users to determine N and k/sub max/. We propose two new algorithms, LOOPBACK and BOMO. Experiments show that our methods outperform the previously proposed Itemset-Loop algorithm, and the performance of BOMO can be an order of magnitude better than the original FP-tree algorithm, even with the assumption of an optimally chosen support threshold. We also propose the mining of "N-most interesting k-itemsets with item constraints. " This allows user to specify different degrees of interestingness for different itemsets. Experiments show that our proposed Double FP-trees algorithm, which is based on BOMO, is highly efficient in solving this problem.
Information-Theoretic Mask Analysis of Rainfall Time Series Data

This study explores an information-theoretic/log-linear approach to multivariate time series analysis. The method is applied to daily rainfall data (4 sites, 9 years), originally quantitative but here treated as dichotomous. The analysis ascertains which lagged variables are most predictive of future rainfall and how season can be optimally de ned as an auxiliary predicting parameter. Call the rainfall variables at the four sites A.D, and collectively, Z, the lagged site variables at t-1, E.H, at t-2, I.L, etc., and the seasonal parameter, S. The best model, reducing the Shannon uncertainty, u(Z), by 22%, is HGFSJK Z, where the independent variables, H through K, are given in the order of their predictive power and S is dichotomous with unequal winter and summer lengths.
Integrating spatial concepts into a probabilistic concept web

In this paper, we study the learning and representation of grounded spatial concepts in a probabilistic concept web that connects them with other noun, adjective, and verb concepts. Specifically, we focus on the prepositional spatial concepts, such as “on”, “below”, “left”, “right”, “in front of” and “behind”. In our prior work (Celikkanat et al., 2015), inspired from the distributed highly-connected conceptual representation in human brain, we proposed using Markov Random Field for modeling a concept web on a humanoid robot. For adequately expressing the unidirectional (i.e., non-symmetric) nature of the spatial propositions, in this work, we propose a extension of the Markov Random Field into a simple hybrid Markov Random Field model, allowing both undirected and directed connections between concepts. We demonstrate that our humanoid robot, iCub, is able to (i) extract meaningful spatial concepts in addition to noun, adjective and verb concepts from a scene using the proposed model, (ii) correct wrong initial predictions using the connectedness of the concept web, and (iii) respond correctly to queries involving spatial concepts, such as ball-left-of-the-cup.
Study on in-situ Mg2Si/Al-Si composites with different compositions

微观结构和机械性质上的化学作文和热处理的效果在原处， Mg2Si/Al-Si composites 被调查。 它被发现那，在与8. 2wt%合成的Al-5.7wt% Mg2Si 的微观结构额外的 Si ，二进制最容易溶解的 Mg2Si 与不发达的中国像手迹的形态学在谷物边界定位，并且主要 -Al 由于 mischmetal 和锶盐的修饰词的选择修正效果被变成房间结构；而在里面与一篇近的 Al-Mg2Si 最容易溶解的作文和小额外的 Si 内容合成，最容易溶解的 Mg2Si 与二进制最容易溶解的 a-Al 形成了的 intercrescence 长成综合汉语像手迹的形状。 作为 Si 内容增加，最容易溶解的 Mg2Si 树突在 volum e 部分在形态学，但是少些变得更粗糙。 坚硬和演员组 Mg2Si/Al-Si composites 的张力的力量不与 Mg 内容增加增加，但是他们与最容易溶解、主要的 Mg2Si 阶段的尺寸和形态学有关。 有最佳的参数的热处理是改进性质的一个有效方法在原处 composites。
Transcriptional Profiling and miRNA-Target Network Analysis Identify Potential Biomarkers for Efficacy Evaluation of Fuzheng-Huayu Formula-Treated Hepatitis B Caused Liver Cirrhosis

Fuzheng-Huayu (FZHY) formula has been found to have a satisfactory effect on hepatitis B-caused cirrhosis (HBC) treatment. However, the efficacy evaluation of FZHY is often challenging. In this study, a randomized, double-blind and placebo-controlled trial was used to evaluate the therapeutic efficacy of FZHY in HBC treatment. In the trial, 35 medical indexes were detected, and 14 indexes had a statistically-significant difference before compared to after the trial. Importantly, the Child-Pugh score also demonstrated FZHY having therapeutic efficacy. Furthermore, the microRNA (miRNA) profiles of 12 serum samples were detected in FZHY groups, and 112 differential-expressed (DE) miRNAs were determined. Using predicted miRNA targets, 13 kernel miRNAs were identified from the established miRNA-target network. Subsequently, quantitative Real-time Polymerase Chain Reaction (qRT-PCR) was used to validate the expression level of 13 identified miRNAs in the trials. The results showed that nine miRNAs have a statistically-significant difference before compared to after FZHY treatment. By means of a logistic regression model, a miRNA panel with hsa-miR-18a-5p, -326, -1182 and -193b-5p was established, and it can clearly improve the accuracy of the efficacy evaluation of FZHY. This study suggested that the particular miRNAs can act as potential biomarkers and obviously increase the diagnostic accuracy for drug evaluation in HBC treatment progression.
MapReduce-based k-prototypes clustering method for big data

Big data clustering is one of the recently challenging tasks that is used in many application domains. Traditional clustering methods are not able to deal with large-scale of data. Furthermore, Big data are often characterized by the mixed type of data, including numerical and categorical attributes. Thus, we propose in this paper the parallelization of k-prototypes clustering method (MR-KP) using MapReduce model to handle large-scale of mixed data. Experiments results show that MR-KP scales well with increasing data set sizes and achieves a close to linear speedup while maintaining the clustering accuracy.
Microstructural dielectric elastomer actuator with uniaxial in-plane contraction

A micromachined dielectric elastomer actuator with uniaxial in-plane contraction was proposed. The modeling, fabrication, and testing of the actuator were carried out. When a bias voltage was applied, the resulting electrostatic force compressed the dielectric elastomer that then shrank in area due to its embedded microstructures. The proposed dielectric elastomer actuator consisted of two electrode layers, two flexible layers, and a microstructural layer. The microstructural layer possessed the grating patterns that served as the spacers to define the gap between the top and bottom flexible layers. The grating patterns also determined the direction of the in-plane contraction. When the applied electrostatic force pulled together the bottom and top flexible layers, these two layers bent inwardly and shortened the distance between the spacers. The design of the bending actuation was demonstrated utilizing the asymmetric thickness design of the flexible layers.
Regulation of ykrL (htpX) by Rok and YkrK, a Novel Type of Regulator in Bacillus subtilis

ABSTRACT Expression of ykrL of Bacillus subtilis, encoding a close homologue of the Escherichia coli membrane protein quality control protease HtpX, was shown to be upregulated under membrane protein overproduction stress. Using DNA affinity chromatography, two proteins were found to bind to the promoter region of ykrL: Rok, known as a repressor of competence and genes for extracytoplasmic functions, and YkrK, a novel type of regulator encoded by the gene adjacent to ykrL but divergently transcribed. Electrophoretic mobility shift assays showed Rok and YkrK binding to the ykrL promoter region as well as YkrK binding to the ykrK promoter region. Comparative bioinformatic analysis of the ykrL promoter regions in related Bacillus species revealed a consensus motif, which was demonstrated to be the binding site of YkrK. Deletion of rok and ykrK in a PykrL-gfp reporter strain showed that both proteins are repressors of ykrL expression. In addition, conditions which activated PykrL (membrane protein overproduction, dissipation of the membrane potential, and salt and phenol stress) point to the involvement of YkrL in membrane protein quality control.
Visualization methods for time-dependent data - an overview

Visualization has been successfully applied to analyse time-dependent data for a long time now. Lately, a number of new approaches have been introduced, promising more effective graphs especially for large datasets and multi-parameter data. In this paper, we give an overview on the visualization of time-series data and the available techniques. We provide a taxonomy and discuss general aspects of time-dependent data. After an overview on conventional techniques we discuss techniques for analysing time-dependent multivariate data sets in more detail. After this, we give an overview on dynamic presentation techniques and event-based visualization.
Nanoscale SiO2/ZrO2 Particulate-Reinforced Titanium Composites for Bone Implant Materials

The mechanical property of porous pure titanium (Ti) scaffold with high porosity might become poorer than that of natural bone. In this study, new Ti-based biocompatible composites were developed to simultaneously meet the requirements of low elastic modulus and appropriate strength for implant materials when they are scaffolded into a porous structure. The nanoscale particulate-reinforced Ti-based composites with different concentrations of oxide particles such as SiO2 and ZrO2 were prepared using a powder metallurgical method. The strengths of the new nanoscale particulate-reinforced titanium composites were found to be significantly higher than that of pure Ti. Cell culture results revealed that the nanoscale particulate-reinforced titanium composites showed excellent biocompatibility and cell adhesion. Human SaOS2 osteoblast-like cells grew and spread well on the surfaces of the new titanium composites. The nanoscale SiO2 and ZrO2 particulate-reinforced titanium composites are promising materials that have great potential for use as an orthopedic implant material.
A Probability Model to Improve Word Alignment

Word alignment plays a crucial role in statistical machine translation. Word-aligned corpora have been found to be an excellent source of translation-related knowledge. We present a statistical model for computing the probability of an alignment given a sentence pair. This model allows easy integration of context-specific features. Our experiments show that this model can be an effective tool for improving an existing word alignment.
Towards domain independence in machine aided human translation

This paper presents an approach for integrating statistical machine translation and automatic speech recognition for machine aided human translation (MAHT). It is applied to the problem of improving ASR performance for a human translator dictating translations in a target language while reading from a source language document. The approach addresses the issues associated with task independent ASR including out of vocabulary words and mismatched language models. We show in this paper that by obtaining domain information from the document in the form of labelled named entities from the source language text the accuracy of the ASR system can be improved by 34.5%.
LDA-based Personalized Document Recommendation

Accompanying with the rapid growth of Internet, people around the world can easily distribute, browse, and share as much information as possible through the Internet. The enormous amount of information, however, causes the information overload problem that is beyond users’ limited information processing ability. Therefore, recommender systems arise to help users to look for useful information when they cannot describe the requirements precisely. The filtering techniques in recommender systems can be categorized into content-based filtering (CBF) and collaborative filtering (CF). Although CF is shown to be superior over CBF in literature, personalized document recommendation relies more on CBF simply because of its text content in nature. Nevertheless, document recommendation task provides a good chance to integrate both techniques into a hybrid one with the aim to enhance the overall recommendation performance. The objective of this research is thus to propose a hybrid filtering approach for personalized document recommendation. Particularly, latent Dirichlet allocation to uncover latent semantic structure in documents is incorporated to help users obtain robust document similarity in CF. Two experiments are conducted accordingly. The results show that our proposed approach outperforms other counterparts on the recommendation performance, which justifies the feasibility of our proposed approach in applications.
Age and ethnic differences in the onset, persistence and recurrence of alcohol use disorder.

AIMS To estimate ethnic differences in three components of alcohol use disorder and alcohol dependence course (onset, persistence and recurrence) in a developmental framework. DESIGN Longitudinal data from The National Epidemiologic Survey of Alcohol and Related Conditions (NESARC), collected using face-to-face interviews. SETTING Civilian non-institutionalized US population aged 18 years and older, with oversampling of Hispanics, blacks and those aged 18-24 years. PARTICIPANTS Individuals who completed both NESARC assessments, were not life-long abstainers and were either white (n = 17,458), black (n = 4995), US-born Hispanic (n = 2810) or Hispanic-born outside the United States (n = 2389). MEASUREMENTS Alcohol dependence (AD) and alcohol use disorder (AUD; abuse or dependence) onset, persistence and recurrence were examined using the Alcohol Use Disorders and Associated Disabilities Interview Schedule, DSM-IV version. FINDINGS Among men: relative to whites aged 18-29, AUD onset and persistence were elevated only in US-born Hispanics aged 40 years and older; odds were reduced for all non-US-born Hispanics, older whites, most blacks and US-born Hispanics aged 30-39. For AD, onset risk was elevated for all younger minority men and only reduced among non-US-born aged Hispanics 40 or older. For women: compared to young whites, non-US-born Hispanics were at decreased AUD and AD onset risk; AUD and AD onset and persistence were increased for older blacks and US-born Hispanics. CONCLUSIONS In the United States, ethnic differences in alcohol disorder transitions (onset, persistence, and recurrence) vary across age, gender and whether a broad (alcohol use disorder) or narrow (alcohol dependence) alcohol definition is used. Evidence of increased risk for some transitions in minority groups suggests that attention should be paid to the course of alcohol use disorders, and that differences in prevalence should not be assumed to reflect differences in specific transitions.