Miracle-dz/UniFAR-FTUs huggingface dataset:
Meshing Roundtable ( IMR 24 ) Defining an L 2-disparity measure to check and improve the geometric accuracy of non-interpolating curved high-order meshes

We define an L2-disparity measure between curved high-order meshes and parameterized manifolds in terms of an L2 norm. The main application of the proposed definition is to measure and improve the distance between a curved high-order mesh and a target parameterized curve or surface. The approach allows considering meshes with the nodes on top of the curve or surface (interpolative), or floating freely in the physical space (non-interpolative). To compute the disparity measure, the average of the squared point-wise differences is minimized in terms of the nodal coordinates of an auxiliary parametric high-order mesh. To improve the accuracy of approximating the target manifold with a non-interpolating curved high-order mesh, we minimize the square of the disparity measure expressed both in terms of the nodal coordinates of the physical and parametric curved high-order meshes. The proposed objective functions are continuously differentiable and thus, we are able to use minimization algorithms that require the first or the second derivatives of the objective function. Finally, we present several examples that show that the proposed methodology generates high-order approximations of the target manifold with optimal convergence rates for the geometric accuracy even when non-uniform parameterizations of the manifolds are prescribed. Accordingly, we can generate coarse curved high-order meshes significantly more accurate than finer low-order meshes that feature the same resolution. c © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the 24th International Meshing Roundtable (IMR24).
Enhancing the performance of WDM systems by using TFF in hybrid amplifiers

Hybrid amplifiers with different gain bandwidths are indensible for long haul wavelength multiplexed optical communication systems in C-band and L-band. In this paper, the gain spectrum of EDFA has been broadened and flattened by cascading EDFA with TDFA along with a dielectric Interference filter (TFF). On using this configuration we have obtained an amplification bandwidth of 100 nm ranging from 1460nm to 1560 nm with a +2.5% gain deviation.
Smoothing the wavelet periodogram using the Haar-Fisz transform

The wavelet periodogram is hard to smooth because of the low signal-to-noise ratio and non-stationary covariance structure. This article introduces a method for smoothing a local wavelet periodogram by applying a Haar-Fisz transform which approximately Gaussianizes and approximately stabilizes the variance of the periodogram. Consequently, smoothing the transformed periodogram can take advantage of the wide variety of existing techniques suitable for homogeneous Gaussian data. This article demonstrates the superiority of the new method over existing methods and supplies theory that proves the Gaussianizing, variance stabilizing and decorrelation properties of the Haar-Fisz transform.
Nuclear RanGTP is not required for targeting small nucleolar RNAs to the nucleolus

The small GTPase Ran is the central regulator of macromolecular transport between the cytoplasm and the nucleus. Recent work has suggested that RanGTP also plays an important role in regulating some intra-nuclear processes. In this study, we have investigated whether RanGTP is required for the intra-nuclear transport of RNAs. Specifically, we directly analyzed the nucleolar localization of Box C/D and Box H/ACA small nucleolar RNAs (snoRNAs) in mammalian (tsBN2) cells, Saccharomyces cerevisiae and Xenopus oocytes under conditions that deplete nuclear RanGTP and prevent RNA export to the cytoplasm. Our data suggest that depletion of nuclear RanGTP does not significantly alter the nucleolar localization of U3 snoRNA in tsBN2 cells. Complementary studies in the budding yeast S. cerevisiae using conditional Ran mutants as well as mutants in Ran regulatory proteins also indicate that disruption of the Ran gradient or of Ran itself does not detectably affect the nucleolar localization of snoRNAs. Finally, microinjection into Xenopus oocytes was used to clearly demonstrate that a specific pool of snoRNAs could still be efficiently targeted to the nucleolus even when the RanGTP gradient was disrupted by microinjection of mutant Ran proteins. Taken together, our data from three phylogenetically distinct experimental systems suggest that nuclear RanGTP, which is essential for trafficking of RNAs between the nuclear and cytoplasmic compartments, is not required for nuclear retention or nucleolar localization of snoRNAs.
Encoding transliteration variation through dimensionality reduction : FIRE Shared Task on Transliterated Search

There exist a large amount of user generated Web content in Roman script for the languages which are written in indigenous scripts for various reasons. In the light of this phenomenon, the search engines face a non-trivial problem of matching queries and documents in transliterated space where transliterated content contain extensive spelling variation. This paper describes our proposed method to handle such variation through non-linear dimensionality reduction techniques. The approach achieves MRR as high as 0.84 on the main task of ad hoc retrieval.
An uncertainty-aware query selection model for evaluation of IR systems

We propose a mathematical framework for query selection as a mechanism for reducing the cost of constructing information retrieval test collections. In particular, our mathematical formulation explicitly models the uncertainty in the retrieval effectiveness metrics that is introduced by the absence of relevance judgments. Since the optimization problem is computationally intractable, we devise an adaptive query selection algorithm, referred to as Adaptive, that provides an approximate solution. Adaptive selects queries iteratively and assumes that no relevance judgments are available for the query under consideration. Once a query is selected, the associated relevance assessments are acquired and then used to aid the selection of subsequent queries. We demonstrate the effectiveness of the algorithm on two TREC test collections as well as a test collection of an online search engine with 1000 queries. Our experimental results show that the queries chosen by Adaptive produce reliable performance ranking of systems. The ranking is better correlated with the actual systems ranking than the rankings produced by queries that were selected using the considered baseline methods.
Higher order features and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation

Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorithm methods are used as one of the approaches to perform disambiguation. Features extracted from the context of an ambiguous word are used to identify the proper sense of such a word. The type of features have an impact on machine learning methods, thus affect disambiguation performance. In this work, we have evaluated several types of features derived from the context of the ambiguous word and we have explored as well more global features derived from MEDLINE using word embeddings. Results show that word embeddings improve the performance of more traditional features and allow as well using recurrent neural networks based on Long-Short Term Memory (LSTM) nodes, which further improve the disambiguation performance. The combination of unigrams and word embeddings set a new state of the art performance with an accuracy of 95.97 in the MSH WSD data set.
Homologous gene sequences mediate transcription-domain formation

The organisation of transcription in the mammalian nucleus is a topic of particular interest because of its relevance to gene regulation. RNA polymerase II transcription occurs at hundreds of sites throughout the nucleoplasm. Recent data indicate that coordinately regulated genes can localise to shared transcription sites. Other transcribed sequences have also been shown to cluster in the nucleus. The ribosomal RNA genes cluster in the nucleoli. Similarly, transiently transfected plasmids and dsDNA viruses form transcription domains (TDs) containing multiple templates. Intriguingly, plasmids expressing β-globin gene sequences recruit the endogenous β-globin loci to their TDs. In light of this observation, we have investigated plasmid TDs as a model for gene recruitment. We find that TD formation is dependent on the presence of homologous gene sequences. Plasmids containing non-homologous gene sequences form separate TDs, independent of homology in the backbone or promoter sequences. TD formation is also favoured by low plasmid concentrations. This effect is sequence-specific and high concentrations of one plasmid do not disrupt domain formation by non-homologous plasmids in the same cell. We conclude that recruitment into TDs is an active process that is driven by homologies between transcribed sequences and becomes saturated at high copy numbers.
Java-ML: A Machine Learning Library

Java-ML is a collection of machine learning and data mining algorithms, which aims to be a readily usable and easily extensible API for both software developers and research scientists. The interfaces for each type of algorithm are kept simple and algorithms strictly follow their respective interface. Comparing different classifiers or clustering algorithms is therefore straightforward, and implementing new algorithms is also easy. The implementations of the algorithms are clearly written, properly documented and can thus be used as a reference. The library is written in Java and is available from http://java-ml.sourceforge. net/ under the GNU GPL license.
Calculation of distance measures between hidden Markov models

This paper investigates two methods to define a distance measure between any pair of Hidden Markov Models (HMM). The first one is the geometricaly motivated euclidean distance which solely incorporates the feature probabilities. The second measure is the Kulback-Liebler distance which is based on the discriminating power of the probability measure on the space of feature sequences induced by the HMMs. A method is shown, to compute the proposed measures reasonable fast and the distance measures are compared in a series of simulations involving HMMs from a real world speech recognition system.
Development of a large-scale 3D digitizing system for cultural heritage documentation

This paper proposes a large-scale three dimensionals(3D) shape acquiring and digitizing system for cultural heritage documentation by combining close range photogrammetry and structured light scan techniques. A close range photogrammetry software package named ‘XJTUDP’ was developed to capture the global reference points, a structured light scan device named ‘XJTUOM’ was constructed to sample and pre-process the detail shape data in local areas. Their operation principles is introduced, the process of raw data acquisition and data post-processing are presented. A 4m high ancient stele of over 1200 years old located in Xian, China is measured and modeled to test the availability and reliability of the proposed system. Experiment results indicate that the proposed system is suitable for documenting such antiques, at an accuracy of 0.112mm within 4m volume. The proposed 3D digitizing system is capable of recording the 3D shape of large artworks full of local details, without any physical impact on antiques.
MULTIPLE ENDOCRINE RESPONSES TO CLONIDINE IN OBSESSIVE COMPULSIVE DISSORDER

Seventy two subjects with OCD were compared with 18 normal healthy volunteers on multiple neuroendocrine responses to clonidine. Significant heterogeneity in OCD was observed in responses of growth hormone. There was also significant disturbances in Cortisol and ACTH release. An interactional model for noradrenergic and serotonergic dysfunction in OCD is discussed.
Promoting number sense of Egyptian autistic children mainstreamed in kindergartens through visual scaffolding

ABSTRACT The present study examines the effect of visual scaffolding on promoting number sense skills (including number recognition, counting, ordinality, comparison, and calculation) of Egyptian autistic children mainstreamed in kindergartens. The participants were five autistic children, enrolled in the second level of kindergarten (ages 5–6). Kindergarten Inventory of number sense was developed by the author in light of relevant previous studies. Pre- and post-tests were administered to participants. In between the two tests, the study group engaged in visual scaffolding activities designed to improve their number sense, using a strategy including a combination of scaffolding techniques integrated with multiple types of visual representations. A follow-up test was administered to the study group eight weeks after the completion of the activities. The findings suggest that children in the study group have higher number sense levels in the post-test, compared to pre-test, and they maintained the high levels in the follow-up test.
Habanero-Java library: a Java 8 framework for multicore programming

With the advent of the multicore era, it is clear that future growth in application performance will primarily come from increased parallelism. We believe parallelism should be introduced early into the Computer Science curriculum to educate students on the fundamentals of parallel computation. In this paper, we introduce the newly-created Habanero-Java library (HJlib), a pure Java 8 library implementation of the pedagogic parallel programming model [12]. HJlib has been used in teaching a sophomore-level course titled "Fundamentals of Parallel Programming" at Rice University. HJlib adds to the Java ecosystem a powerful and portable task parallel programming model that can be used to parallelize both regular and irregular applications. By relying on simple orthogonal parallel constructs with important safety properties, HJlib allows programmers with a basic knowledge of Java to get started with parallel programming concepts by writing or refactoring applications to harness the power of multicore architecture. The HJlib APIs make extensive use of lambda expressions and can run on any Java 8 JVM. HJlib runtime feedback capabilities, such as the abstract execution metrics and the deadlock detector, help the programmer to obtain feedback on theoretical performance as well as the presence of potential bugs in their program. Being an implementation of a pedagogic programming model, HJlib is also an attractive tool for both educators and researchers. HJlib is actively being used in multiple research projects at Rice and also by external independent collaborators. These projects include exploitation of homogeneous and heterogeneous multicore parallelism in big data applications written for the Hadoop platform [20, 43].
Regulation of Hypercompetence in Legionella pneumophila

ABSTRACT Although many bacteria are known to be naturally competent for DNA uptake, this ability varies dramatically between species and even within a single species, some isolates display high levels of competence while others seem to be completely nontransformable. Surprisingly, many nontransformable bacterial strains appear to encode components necessary for DNA uptake. We believe that many such strains are actually competent but that this ability has been overlooked because standard laboratory conditions are inappropriate for competence induction. For example, most strains of the gram-negative bacterium Legionella pneumophila are not competent under normal laboratory conditions of aerobic growth at 37°C. However, it was previously reported that microaerophilic growth at 37°C allows L. pneumophila serogroup 1 strain AA100 to be naturally transformed. Here we report that another L. pneumophila serogroup 1 strain, Lp02, can also be transformed under these conditions. Moreover, Lp02 can be induced to high levels of competence by a second set of conditions, aerobic growth at 30°C. In contrast to Lp02, AA100 is only minimally transformable at 30°C, indicating that Lp02 is hypercompetent under these conditions. To identify potential causes of hypercompetence, we isolated mutants of AA100 that exhibited enhanced DNA uptake. Characterization of these mutants revealed two genes, proQ and comR, that are involved in regulating competence in L. pneumophila. This approach, involving the isolation of hypercompetent mutants, shows great promise as a method for identifying natural transformation in bacterial species previously thought to be nontransformable.
Signal Processing of Noisy Short Utterance Based on Noise Separation and Multiple Features Fusion

Recognition rate of noisy short utterance is lower, the two main factors are the inadequate training data and utterance polluted by noisy seriously. In this paper, we proposed corresponding algorithms. First, noise and speech are regarded as parallel information, we use FastICA algorithm to separate pure speech and noise. And then, we use differences detecting and eliminating algorithm (DDAEA) proposed in the paper to purify speech signal further, so that we can get more pure speech. In the phase of feature extraction, we combine vocal sourceand vocal tract-related characteristics to make full use of limited information, so we get three score evaluation subsystems: MFCC D LPCC+WOWOR4, MFCC D LPCC+WOWOR6, MFCC D LPCC+WOWOR8, the highest score of the three subsystems is the final score of the system. Experiments prove that the above algorithms improve speaker recognition performance of noisy short utterance.
Drift: A highly condensed emulation framework for mobile nodes in server clusters

Prototyping application-layer algorithms in wireless networks is a lengthy and challenging process, involving either programming within a specific simulation platform, or deploying a real testbed that is neither flexible nor scalable. In this paper, we present Drift, a high-performance wireless emulation testbed that takes advantage of the benefits of both simulation and real implementation, while trying to avoid their drawbacks. As a highly condensed emulation infrastructure, Drift makes it possible to rapidly develop and validate large-scale wireless network application-layer protocols within a cluster computing environment. Unlike existing emulation testbeds, Drift features a fully decentralized architecture, an efficient message processing unit, and more accurate network models for mobile wireless nodes. It balances the fundamental trade-off between scalability and emulation accuracy, focusing on maximizing scalability with minimal loss of accuracy. Through baseline comparison and extensive experiments, we find that Drift is able to accommodate thousands of emulated nodes per server host (in contrast to only tens of nodes typically seen in existing emulation tools), while maintaining comparable accuracy to packet level simulators. Drift will be released as an open source platform.
The synthesis of complex arithmetic computation on stochastic bit streams using sequential logic

The paradigm of logical computation on stochastic bit streams has several key advantages compared to deterministic computation based on binary radix, including error-tolerance and low hardware area cost. Prior research has shown that sequential logic operating on stochastic bit streams can compute non-polynomial functions, such as the tanh function, with less energy than conventional implementations. However, the functions that can be computed in this way are quite limited. For example, high order polynomials and non-polynomial functions cannot be computed using prior approaches. This paper proposes a new finite-state machine (FSM) topology for complex arithmetic computation on stochastic bit streams. It describes a general methodology for synthesizing such FSMs. Experimental results show that these FSM-based implementations are more tolerant of soft errors and less costly in terms of the area-time product that conventional implementations.
Vector Similarity of Related Words in the Japanese Word Net

Word2vec is a tool that produces vector representation of words from a large amount of text data. In this paper, we show that only a part of the vector space produced by word2vec is enough to represent the collective sense of a set of related words in the Japanese WordNet. Further, we will show that there is a subspace in the vector space which do not relate to the collective sense. We construct a compact decision tree by using the vectors in order to distinguish whether a given word belongs to the set of related words.
A shape-based approach for leaf classification using multiscaletriangular representation

In this paper we introduce a new multiscale shape-based approach for leaf image retrieval. The leaf is represented by local descriptors associated with margin sample points. Within this local description, we study four multiscale triangle representations: the well known triangle area representation (TAR), the triangle side lengths representation (TSL) and two new representations that we denote triangle oriented angles (TOA) and triangle side lengths and angle representation (TSLA). Unlike existing TAR approaches, where a global matching is performed, the similarity measure is based on a locality sensitive hashing of local descriptors. The proposed approach is invariant under translation, rotation and scale and robust under partial occlusion. Evaluations made on four public leaf datasets show that our shape-based approach achieves a high retrieval accuracy w.r. t. state-of-art methods.
Extending the Applicability and Improving the Performance of Runtime Parallelization

When static analysis of a sequential loop fails to yield reliable information on its dependence structure, a parallelizing compiler is left with three alternatives: it can take the conservative option of emitting code for a sequential execution; it can optimistically emit code to speculatively execute the loop as a DOALL [6, 7]; or it can emit inspector-executor code to determine the actual dependence structure at runtime and to respect it in a parallel execution [8, 9]. The rst approach is certain to yield a slow execution. The second approach gives very good results when the loop can in fact be executed as a DOALL, but is of no help otherwise. In this paper we concentrate on the nal approach, runtime parallelization through the inspector-executor method. We have two goals in this work. The rst is to expand the class of loop to which the approach may be applied by removing restrictions on the loop dependence structures that it can handle. To achieve this goal, we introduce new forms of the inspector and executor that together remove all restrictions on the loop dependence structure. Thus, we show how to parallelize a class of loop that previously would have compelled the compiler to emit sequential code. Our second goal is to improve the performance of the inspector-executor approach through specializa-tions applicable when static analysis yields some (weak) information about the array indexing functions used in assignments. We validate our work through a set of examples designed to illustrate the fundamental performance tradeos characterizing the specialized implementations, using results taken from executions on 32 processors of a KSR1.
Spatial modulation: optimal detection and performance analysis

In this letter, we derive the optimal detector for the so-called spatial modulation (SM) system introduced by Mesleh et al. in (Mesleh, 2006). The new detector performs significantly better than the original (~ 4 dB gain), and we support our results by deriving a closed form expression for the average bit error probability. As well, we show that SM with the optimal detector achieves performance gains (~ 1.5 - 3 dB) over popular multiple antenna systems, making it an excellent candidate for future wireless communication standards.