Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonparanormal Information Estimation

We study the problem of using i.i. d. samples from an unknown multivariate probability distribution $p$ to estimate the mutual information of $p$. This problem has recently received attention in two settings: (1) where $p$ is assumed to be Gaussian and (2) where $p$ is assumed only to lie in a large nonparametric smoothness class. Estimators proposed for the Gaussian case converge in high dimensions when the Gaussian assumption holds, but are brittle, failing dramatically when $p$ is not Gaussian. Estimators proposed for the nonparametric case fail to converge with realistic sample sizes except in very low dimensions. As a result, there is a lack of robust mutual information estimators for many realistic data. To address this, we propose estimators for mutual information when $p$ is assumed to be a nonparanormal (a.k.a., Gaussian copula) model, a semiparametric compromise between Gaussian and nonparametric extremes. Using theoretical bounds and experiments, we show these estimators strike a practical balance between robustness and scaling with dimensionality.
On the Spatial Distribution of Dark Matter Halos

We study the spatial distribution of dark matter halos in the universe in terms of their number density contrast, related to the underlying dark matter fluctuation via a nonlocal and nonlinear bias random field. The description of the matter dynamics is simplified by adopting the "truncated" Zeldovich approximation to obtain both analytical results and simulated maps. The halo number density field in our maps and its probability distribution reproduce with excellent accuracy those of halos in a high-resolution N-body simulation with the same initial conditions. Our nonlinear and nonlocal bias prescription matches the N-body halo distribution better than any Eulerian linear and local bias.
Modulation of endoplasmic reticulum calcium pump expression during lung cancer cell differentiation

Cellular calcium signaling plays important roles in several signal transduction pathways that control proliferation, differentiation and apoptosis. In epithelial cells calcium signaling is initiated mainly by calcium release from endoplasmic‐reticulum‐associated intracellular calcium pools. Because calcium is accumulated in the endoplasmic reticulum by sarco/endoplasmic reticulum calcium ATPases (SERCA), these enzymes play a critical role in the control of calcium‐dependent cell activation, growth and survival. We investigated the modulation of SERCA expression and function in human lung adenocarcinoma cells. In addition to the ubiquitous SERCA2 enzyme, the SERCA3 isoform was also expressed at variable levels. SERCA3 expression was selectively enhanced during cell differentiation in lung cancer cells, and marked SERCA3 expression was found in fully differentiated normal bronchial epithelium. As studied by using a recombinant fluorescent calcium probe, induction of the expression of SERCA3, a lower calcium affinity pump, was associated with decreased intracellular calcium storage, whereas the amplitude of capacitative calcium influx remained unchanged. Our observations indicate that the calcium homeostasis of the endoplasmic reticulum in lung adenocarcinoma cells presents a functional defect due to decreased SERCA3 expression that is corrected during pharmacologically induced differentiation. The data presented in this work show, for the first time, that endoplasmic reticulum calcium storage is anomalous in lung cancer cells, and suggest that SERCA3 may serve as a useful new phenotypic marker for the study of lung epithelial differentiation.
An Analysis of Road Maps Based on Voronoi Diagram for Vehicular Broadcast

In Vehicular Ad-hoc Networks, a method of message dissemination is broadcast. It is a method of message dissemination by relaying messages between vehicles. In broadcast, vehicles cause the broadcast storm problem because of redundant relays. Thus, it is necessary to control redundant relays by broadcast protocols. Many broadcast protocols have been developed and they can disseminate with suppressing redundant relays. However, the packet reception ratio and its overhead depend on not only broadcast protocols, but also structures of road maps. Thus, it is necessary to research some relationships between radio propagation features and structures of road maps. In this paper, we propose a method of analyzing complexities of road maps. It is named Analysis Road map Complexity by Deviation of intersection distribution (ARC-DID). It focus on that there is a relationship between radio propagation features and places of intersections. Hence, we propose an analysis method of complexities of road maps based on intersection distribution. In order to analyze complexities of road maps based on a deviation of intersections, we use a Voronoi diagram. According to this method, we can analyze complexities of road maps quantitatively. In order to show utility of complexities calculated by proposed method, we execute a broadcast simulation with three parameters. According to the simulation, complexities calculated by ARC-DID can set parameters which adapt to road maps.
Optimiztion of HMM by the Tabu Search Algorithm

In this paper, a simple version of the tabu search algorithm is employed to train a Hidden Markov Model (HMM) to search out the optimal parameter structure of HMM for automatic speech recognition. The proposed TS-HMM training provides a mecha- nism that allows the search process to escape from a local optimum and obtain a near global optimum. Experimental results show that the TS-HMM training has a higher probability of finding the optimal model parameters than traditional algorithms do.
On Stochastic Self-Assembly of Underwater Robots

We present a rule-based approach which incorporates a state machine in a novel manner to carry out self-assembly in underwater environments. The rules formulated along with the state machine govern the manner in which individual robots combine to generate shapes like a straight line, a `T' alphabet and a two sided pyramid. We analyze the factors that affect the time taken to complete self-assembly via simulations. Finally, we adopt a statistical dynamics approach to calculate the combination rates of the robots and show how it yields a Markov process model. We use the Markov model to predict the assembly process and show that it is similar to carrying out the full mechanics-based simulation.
Applying Quantitative Microstructure Control in Advanced Functional Composites

Microstructure control in functional materials draws from a historical reserve rich in established theory and experimental observation of metallurgy. Methods such as rapid solidification, eutectoid reaction, and nucleation and growth precipitation have all proven to be effective means to produce microstructure relevant for a wide array of applications. Here, the available parameters to control structure morphology, size, and spacing are discussed using thermoelectric composites as an example. Moreover, exploiting different aspects of a material system's phase diagram enables a controlled introduction of nanostructures. While much of this discussion is pertinent to the rapidly developing field of thermal conductivity control in thermoelectric composites, these techniques can be applied to a variety of other material systems where their use may lead to novel electrical, optical, as well as thermal properties of semiconductors and insulators as it has in the past for the mechanical properties of metals.
Urban Landscapes and Identity

Identity has always been a popular research subject for various disciplines, such as psychology, philosophy, sociology, human geography and anthropology. However, urbanization and globalization processes, which have caused a rapid change on our environments, have brought the concept of identity on the agenda of planners and designers in the last few decades. The major concern of the identity related research is on the sustainability of place identity.
Cell-Free MicroRNA Expression Profiles in Malignant Effusion Associated with Patient Survival in Non-Small Cell Lung Cancer

Objective MicroRNAs (miRNAs) expression is altered in cancer cells, and miRNAs could serve as diagnostic and prognostic biomarker for cancer patients. This study was designed to analyze circulating miRNAs expression in the malignant pleural effusion (MPE) and their association with patient survival in non-small cell lung cancer (NSCLC). Methods Pleural effusion from 184 patients with NSCLC and MPE were collected. MiRNA microarray and bioinformatics interpretation were used to evaluate miRNA expression profiles in 10 NSCLC patients with different survival prognosis. Associations were validated in 184 patients (randomly classified into training and validation set with equal number in each group) using quantitative RT-PCR. Risk scores were formulated based on the expression signature of miRNAs. Clinical data, such as patient survival, were collected for correlation analysis. Results Thirty-three miRNAs were found to be altered more than two-fold by microarray in malignant effusions between longer-survival and shorter-survival groups, and levels of five miRNAs (miRNA-93, miRNA-100, miRNA-134, miRNA-151 and miRNA-345) were significantly associated with overall survival. High expression of miR-100 and low expression of miRNA-93, miRNA-134, miRNA-151 and miRNA-345 were associated with poor survival in both the training and validation cohort. Patients with high risk scores had overall poor survival compared to the patients with low risk scores. Risk score was an independent predictor of patient survival. Conclusions Expression patterns of miRNAs are systematically altered in MPE of patient with NSCLC. The five miRNA signature from the effusion may serve as a predictor for the overall survival of patients with lung cancers.
Playing Linear Number Board Games — But Not Circular Ones — Improves Low-Income Preschoolers ’ Numerical Understanding

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Development of an OpenSim Shoulder Model for Manual Wheelchair Users With Tetraplegia

Extended manual wheelchair use has been associated with repetitive strain injuries in the shoulder and has been identified as a contributing factor to upper limb pain experienced by manual wheelchair users with spinal cord injury (SCI) [1]. Due to the nature of their SCI, individuals with tetraplegia (formerly quadriplegia) may be at an even greater risk for developing shoulder injuries because they often have only partial innervation of their shoulder, scapular, and thoracohumeral muscles [2]. Copyright © 2011 by ASME
Smart Query Sampling with Feature Coverage and Unsupervised Machine Learning

Snowflake is a Data Cloud Platform with billions of daily customer queries. As Snowflake continues to grow with larger query volume, more diverse customers, and frequent software upgrades, there is a higher than ever demand for efficient testing. Snowtrail is a Snowflake-internal testing infrastructure that prepares, executes, and measures workloads from eligible customer queries. One of Snowtrail’s challenges is to generate concise workloads that are both representative of customer queries and thorough in testing Snowflake software features. Existing Snowtrail query workloads are either explicitly provided by users or pseudo-randomly selected. Here, we provide a novel sampling solution based on clustering feature-vectorized queries with unsupervised tasks. Variance across columns and multivariate Kullback–Leibler divergence scores show that a small K-Means sampled workload contains more spread but is less similar to the original population distribution. Column-wise Kolmogorov–Smirnov tests shows a general distinction between the K-Means sampled population from the true distribution regardless of sample size. Runtime experiments confirm that K-Means-based sampling is likely at least quadratic time with regard to the population size and linear time with regard to the sample size, making it not as practical for overly large datasets. Finally, an elbow method analogous to the K-Means elbow method is implemented to select the ideal sample size based on balancing variance and similarity to the population. Overall, this work brings practical improvements to the Snowflake Data Cloud, and the approach described in the paper is generally applicable to data management systems with feature collection capabilities.
Physical properties of blends of polycarbonate and a liquid crystalline copolyester

Blends of polycarbonate (PC) and poly(ethylene terephthalate-co-p-oxybenzoate) (PET/PHB60) were prepared by melt-blending. Physical and/or chemical interactions between the two phases of the system were studied by thermal analysis and infrared spectroscopy. Rheological measurements in shear flow were carried out both in the low and high shear rate regions in the temperature range of the existence of the mesophase. At low liquid crystalline polymer (LCP) content, the blends showed flow curves similar to those of the unfilled PC, while at higher LCP percentages the rheological behavior of the pure LCP was resembled. Moreover, in the whole shear range, the viscosity values of such blends were in between those of the pure polymers. The influence of the addition of 10% LCP on the mechanical properties of the PC was investigated. Fiber-spinning was performed under different experimental conditions, and it was found that opportune drawing conditions are necessary to improve the modulus of the matrix. Morphological analyses of the pure LCP and of the blends were related to the rheological and mechanical behavior of these systems. While the LCP exhibited an elevated dimensional stability, the inclusion of the LCP in PC matrix did not improve the dimensional stability of the blends.
N4SID and MOESP subspace identification methods

Multivariable Output Error State Space (MOESP) and Numerical algorithms for Subspace State Space System Identification (N4SID) algorithms are two well known subspace identification techniques discussed in this paper. Due to the use of robust numerical tools such as QR decomposition and singular value decomposition (SVD), these identification techniques are often implemented for multivariable systems. Subspace identification algorithms are attractive since the state space form is highly suitable to estimate, predict, filters as well as for control design. In literature, there are several simulation studies for MOESP and N4SID algorithms performed in offline and online mode. In this paper, order selection, validity and the stability for both algorithms for model identification of a glass tube manufacturing process system is considered. The weighting factor α, used in online identification is obtained from trial and error and particle swarm optimization (PSO). Utilizing PSO, the value of α is determined in the online identification and a more accurate result with lower computation time is obtained.
N,N′-Bis(1-acetylcyclohexyl)-1,8:4,5-naphthalenetetracarboximide

The title compound, C30H30N2O6, has crystallographic inversion symmetry with the nitrogen atom and the two oxygen atoms of the naphthalene diimide system deviating by −0.243 (2), 0.109 (3) and 0.247 (2) Å, respectively, from the plane defined by the carbon atoms.
Tree Kernel-based SVM with Structured Syntactic Knowledge for BTG-based Phrase Reordering

Structured syntactic knowledge is important for phrase reordering. This paper proposes using convolution tree kernel over source parse tree to model structured syntactic knowledge for BTG-based phrase reordering in the context of statistical machine translation. Our study reveals that the structured syntactic features over the source phrases are very effective for BTG constraint-based phrase reordering and those features can be well captured by the tree kernel. We further combine the structured features and other commonly-used linear features into a composite kernel. Experimental results on the NIST MT-2005 Chinese-English translation tasks show that our proposed phrase reordering model statistically significantly outperforms the baseline methods.
Non‐random reassortment in human influenza A viruses

Background The influenza A virus has two basic modes of evolution. Because of a high error rate in the process of replication by RNA polymerase, the viral genome drifts via accumulated mutations. The second mode of evolution is termed a shift, which results from the reassortment of the eight segments of this virus. When two different influenza viruses co‐infect the same host cell, new virions can be released that contain segments from both parental strains. This type of shift has been the source of at least two of the influenza pandemics in the 20th century (H2N2 in 1957 and H3N2 in 1968).
SU-Sentilab : A Classification System for Sentiment Analysis in Twitter

Sentiment analysis refers to automatically extracting the sentiment present in a given natural language text. We present our participation to the SemEval2013 competition, in the sentiment analysis of Twitter and SMS messages. Our approach for this task is the combination of two sentiment analysis subsystems which are combined together to build the final system. Both subsystems use supervised learning using features based on various polarity lexicons.
Zonal modes of Cosmic Microwave Background temperature maps

All-sky maps of the cosmic microwave background temperature fluctuations are usually represented by a spherical harmonic decomposition, involving modes labelled by their degree l and order m (where −l≤m≤+l). The zonal modes (i.e. those with m= 0) are of particular interest because they vary only with galactic latitude; any anomalous behaviour in them might therefore be an indication of erroneous foreground substraction. We perform a simple statistical analysis of the modes with low l for sky maps derived via different cleaning procedures from the Wilkinson Microwave Anisotropy Probe, and show that the zonal modes provide a useful diagnostic of possible systematics.
Dimensionality reduction for dimension-specific search

Dimensionality reduction plays an important role in efficient similarity search, which is often based on k-nearest neighbor (k-NN) queries over a high-dimensional feature space. In this paper, we introduce a novel type of k-NN query, namely conditional k-NN (ck-NN), which considers dimension-specific constraint in addition to the inter-point distances. However, existing dimensionality reduction methods are not applicable to this new type of queries. We propose a novel Mean-Std (standard deviation) guided Dimensionality Reduction (MSDR) to support a pruning based efficient ck-NN query processing strategy. Our preliminary experimental results on 3D protein structure data demonstrate that the MSDR method is promising.
Comparison of oral feeding of peptide and amino acid meals to normal human subjects

Intestinal perfusion studies performed in man have suggested that amino acid nitrogen may be absorbed more rapidly from peptides than free amino acids. The aim of the present study was to compare the effects of the oral administration of peptides and free amino acids. Two isonitrogenous liquid test meals, one containing 50 g of a partial enzymic hydrolysate of fish protein in which approximately 80% of the nitrogen content was present as small peptides (peptide meal), and the other a mixture of free amino acids (amino acid meal) the composition and molar pattern of which simulated that of the peptide meal, were administered on separate occasions to six normal subjects intubated with a triple lumen tube. Both meals contained the reference marker polyethylene glycol. Fractional absorption of amino acid residues one and two hours after ingestion of the two meals was similar at three intestinal locations situated 120, 160, and 200 cm from the mouth of each subject, and at two hours 73·8% and 72·0% of the amino acid residues had been absorbed respectively by the time the contents of the peptide and amino acid meals reached the middle sampling port of the tube. The total sum of individual amino acid increments in plasma was significantly greater 30 minutes (p < 0·025) and one hour (p < 0·05) after ingestion of the peptide than amino acid meals. By three hours the total area under the two plasma curves was similar. Normal human subjects thus appeared to be capable of assimilating orally administered mixtures of peptides and free amino acids with equal efficiency. Secretion of fluid into the lumen of the upper small intestine, assessed by reference to dilution of the polyethylene glycol, was less after ingestion of the peptide meal. In clinical situations characterised by fluid and electrolyte malabsorption consideration might be given to using small peptides rather than free amino acids as the nitrogen source in nutritional diets.
A Practical Step-by-Step Guide to the Time-Varying Loudness Model of Moore, Glasberg, and Baer (1997; 2002)

In this tutorial article we provide a condensed, practical step-by-step guide to the excitation pattern loudness model of Moore, Glasberg and Baer [J. Audio Eng. Soc. 45, 224–240, 1997; J. Audio Eng. Soc. 50, 331–342, 2002]. The various components of this model have been separately described in the well known publications of Patterson et al. [J. Acoust. Soc. Am. 72, 1788–1803, 1982], Moore [Hearing (Academic Press), 161-205, 1995], Moore et al. (1997) and Glasberg and Moore (2002). This paper provides a consolidated and concise introduction to the complete model for those who find the disparate and complex references intimidating and who wish to understand the function of each of the component parts. Furthermore, we provide a consolidated notation and integral forms. This introduction may be useful to the loudness theory beginner and to those who wish to adapt and apply the model for novel, practical purposes.
ADoCS: Automatic Designer of Conference Schedules

Distributing papers into sessions in scientific conferences is a task consisting in grouping papers with common topics and considering the size restrictions imposed by the conference schedule. This problem can be seen as a semi-supervised clustering of scientific papers based on their features. This paper presents a web tool called ADoCS that solves the problem of configuring conference schedules by an automatic clustering of articles by similarity using a new algorithm considering size constraints.