Miracle-dz/UniFAR-FTUs huggingface dataset:
On the sensitivity of He I singlet lines to the Fe IV model atom in O stars

Aims. Recent calculations and analyses of O star spectra have revealed discrepancies between theory and observations, and between different theoretical calculations, for the strength of optical $\ion{He}{i}$ singlet transitions. We investigate the source of these discrepancies. Methods. Using a non-LTE radiative transfer code we have undertaken detailed test calculations for a range of O star properties. Our principal test model has parameters similar to those of the O9V star, 10 Lac. Results. We show that the discrepancies arise from uncertainties in the radiation field in the $\ion{He}{i}$ 1s 2 1 S–1s 2p 1 P° transition near 584 A. The radiation field at 584 A is influenced by model assumptions, such as the treatment of line-blanketing and the adopted turbulent velocity, and by the $\ion{Fe}{iv}$ atomic data. It is shown that two $\ion{Fe}{iv}$ transitions near 584 A can have a substantial influence on the strength of the $\ion{He}{i}$ singlet transitions. Conclusions. Because of the difficulty of modeling the $\ion{He}{i}$ singlet lines, particularly in stars with solar metalicity, the $\ion{He}{i}$ triplet lines should be preferred in spectral analyses. These lines are much less sensitive to model assumptions.
Analysis of virtual method invocation for binary translation

The University of Queensland Binary Translator (UQBT) is a static binary translation framework that allows for the translation of binary, executable programs, from one architecture to another one. Using different specification files, the UQBT can be easily tailored for either a new source or target architecture. UQBT employs sophisticated analyses in order to abstract the binary code front the features of the source architecture. In static binary translation, it is not always possible to decode and translate all paths of a program statically, as instructions like computed jumps and indexed calls rely on runtime values. An interpreter is commonly used at runtime to translate such pieces of code, at the expense of runtime. In this paper we present a technique to statically recover the code of virtual method dispatchers, which enhances the abstraction front the binary code. We also show how the statically recovered information is then used by a dynamic environment to analyse virtual tables in order to increase the code coverage for the binary translator. The technique has been tested on SPARC(R) and Pentium machines using binaries of different C++ compilers, and preliminary results are presented.
[An interaction between a polymorphism of the serotonin transporter (5HTT) gene and the clinical picture of adolescents with combined type ofADHD (hyperkinetic disorder) and youth drinking].

INTRODUCTION The combined type of ADHD and alcohol dependence are two different disorders. Research demonstrate that 45-55% of patients diagnosed with ADHD also suffer from comorbid substance abuse, and 11-55% of patients diagnosed with substance abuse suffer from undiagnosed ADHD. Alcohol is by far the most widely used psychoactive substance in the European culture. The serotonin transporter (5HHT) gene has been implicated as one of the candidate genes in both disorders in recent molecular genetic research. AIM The aim of the present study was to seek a common clinical and biological marker for hyperkinetic disorder and youth drinking. METHODS The study was conducted between 2008 and 2012. The sample consisted of 100 combined type ADHD patients: 51 adolescents youth drinking and 100 individuals without mental disorders or addiction in a population-based group. The 5HHT gene polymorphism was examined using PCR (Polymerase Chain Reaction). Statistical analysis was conducted with STATISTICA.PL software (version 5.0.97) licensed by StatSoft, Inc. USA. RESULTS A preferential trend for the "s" short allele of the investigated 5HHT gene polymorphism was observed in all the groups of adolescents compared to the population-based group of adults without alcohol dependence (p = 0.01). CONCLUSION Based on the conducted study a provisional conclusion may be drawn that the presence of the short "s" allele of the 5HTTgene polymorphism may be a prognostic factor of impulsivity in ADHD and of predisposition to alcohol dependence.
Perinatal transmission of hepatitis G virus (GB virus type C) and hepatitis C virus infections--a comparison.

Hepatitis G virus (HGV) infection is more common than hepatitis C virus (HCV) infection and is frequently found in healthy individuals. Although parenteral spread of HGV is well recognized, other routes of transmission probably occur as well. In a prospective study of mother-to-infant transmission of hepatitis viruses, 69 pregnant women with antibodies to HCV and their 81 newborn children were included. Serum levels of HCV RNA and HGV RNA were detected by polymerase chain reaction (PCR) assays, and antibodies to HCV and HGV envelope protein E2 were detected by enzyme-linked immunosorbent assay. Fifty-nine of the mothers had HCV viremia, whereas 16 had HGV viremia. HCV transmission from viremic mothers occurred in 2.8%-4.2% of the cases, whereas HGV transmission from viremic mothers occurred in 75.0%-80.0% of the cases (P < .001). Sequencing of the PCR products of HGV from the mother-infant serum pairs showed minor differences in most cases but sequence homology in each pair. Although the rate of perinatal HGV transmission highly exceeded that of perinatal HCV transmission, HGV did not seem to induce hepatitis in the children.
Superadiabatic transitions in quantum molecular dynamics

We study the dynamics of a molecule’s nuclear wave function near an avoided crossing of two electronic energy levels for one nuclear degree of freedom. We derive the general form of the Schrödinger equation in the nth superadiabatic representation for all . Using these results, we obtain closed formulas for the time development of the component of the wave function in an initially unoccupied energy subspace when a wave packet travels through the transition region. In the optimal superadiabatic representation, which we define, this component builds up monotonically. Finally, we give an explicit formula for the transition wave function away from the avoided crossing, which is in excellent agreement with high-precision numerical calculations.
Constraint solving, exclusive or and the decision of confide ntiality for security protocols assuming a bounded number of sessions.

We present decidability results for the verification of cryptographic protocols in the presence of equational theories corresponding toxor and Abelian groups. Since the perfect cryptography assumption is unrealistic for crypto graphic primitives with visible algebraic properties such as xor, we extend the conventional Dolev-Yao model by permitting the intruder to exploit these properties. We show that the reachability problem is NP-complete for the extended intruder theories in the cases of xor and Abelian groups. This result follows from a normal proof theorem. Then, we show how to lift this result in the xor case: we consider a symbolic constraint system expressing the reach ability (e.g., secrecy) problem for a finite number of sessions. We prove that such constraint system is decidable, relying in particular on an extension of combination algorithms for unification procedures. As a corollary, this enables automatic symbolic verification of cryptographic pro tocols employingxor for a fixed number of sessions.
Teaching visual conditional discriminations using errorless learning: The role of prompts requiring simple and conditional discriminative control

ABSTRACT Errorless learning procedures were first developed in Basic Research Laboratories with nonhuman subjects to teach simple (i.e., nonconditional) discriminations (SD). Today, errorless procedures are widely used for teaching both simple and conditional discriminations (CD), often to developmentally limited learners. The present article puts forward the idea that the stimulus control engendered by prompts in errorless learning procedures may also be classified as requiring simple versus conditional discriminative control. The present paper presents data comparing two errorless prompting procedures to teach visual CDs to six typically developing children and three children diagnosed with autism spectrum disorder (ASD). A matching-to-sample procedure with unfamiliar forms as visual stimuli was used. A comparison of two different prompts was conducted: prompts requiring SD control and prompts requiring conditional discriminative control. Results indicated that eight of nine participants acquired CDs in fewer trials when the prompts required conditional control; one child with ASD required fewer trials when prompts required simple discriminative control. Results suggest an advantage for teaching CDs using prompts requiring conditional discriminative control.
The research of soft tissue deformation based on mass-spring model

This paper studies the soft tissue deformation of Virtual Surgery Simulation, and using OpenGL to establish the virtual soft tissue deformation system based on mass-spring model. Discussed with fidelity and real-time of the soft tissue deformation in Virtual Surgery Simulation. By Comparing quadrilateral topology, the regular hexagon topology based on mass-spring model is proposed, discussed the dynamic model and its numerical integration method and force feedback calculation model of soft tissue deformation, according to the lack of nearest neighbor mass calculating algorithm in virtual surgical instrument contacting the soft tissue surface, a new method is proposed. The results show that, the modified algorithms have better stability and real-time in soft tissue deformation simulation.
Main-memory index structures with fixed-size partial keys

The performance of main-memory index structures is increasingly determined by the number of CPU cache misses incurred when traversing the index. When keys are stored indirectly, as is standard in main-memory databases, the cost of key retrieval in terms of cache misses can dominate the cost of an index traversal. Yet it is inefficient in both time and space to store even moderate sized keys directly in index nodes. In this paper, we investigate the performance of tree structures suitable for OLTP workloads in the face of expensive cache misses and non-trivial key sizes. We propose two index structures, pkT-trees and pkB-trees, which significantly reduce cache misses by storing partial-key information in the index. We show that a small, fixed amount of key information allows most cache misses to be avoided, allowing for a simple node structure and efficient implementation. Finally, we study the performance and cache behavior of partial-key trees by comparing them with other main-memory tree structures for a wide variety of key sizes and key value distributions.
Improving Summarization Quality with Topic Modeling

The problem of extractive text summarization for a collection of documents is defined as the problem of selecting a small subset of sentences so that the contents and meaning of the original document set are preserved in the best possible way. In this paper we describe different applications of topic modeling as it relates to summarization. We consider two summarization models - supervised and unsupervised - enhanced by integrating the topic knowledge into their standard lexicon-based models. Both summarizers strive to cover as much information of the input documents as possible when generating the summaries. The supervised summarizer operates in a standard rank-and-select-sentences manner of extractive summarization, where the best linear combination of multiple sentence features is learned by a genetic algorithm (GA). The unsupervised summarizer models the summarization task as an optimization problem. As is the case with most existing summarization approaches, both original models measure information coverage by lexical units and were enriched by topic knowledge, providing a new measure for the informaition coverage. The experimental results show that utilizing topic knowledge improves the summarization quality.
New cell culture model for aromatase inhibitor-resistant breast cancer shows sensitivity to fulvestrant treatment and cross-resistance between letrozole and exemestane

Aromatase inhibitor (AI) treatment is first-line systemic treatment for the majority of postmenopausal breast cancer patients with estrogen receptor (ER)-positive primary tumor. Although many patients benefit from treatment, some will develop resistance, and models mimicking acquired resistance will be valuable tools to unravel the resistance mechanisms and to find new treatments and biomarkers. Cell culture models for acquired resistance to the three clinically relevant AIs letrozole, anastrozole and exemestane were developed by selection and expansion of colonies of MCF-7 breast cancer cells surviving long-term AI treatment under conditions where endogenous aromatase-mediated conversion of androgen to estrogen was required for growth. Four cell lines resistant to each of the AIs were established and characterized. Maintenance of ER expression and function was a general finding, but ER loss was seen in one of twelve cell lines. HER receptor expression was increased, in particular EGFR expression in letrozole-resistant cell lines. The AI-resistant cell lines had acquired ability to grow without aromatase-mediated conversion of testosterone to estradiol, but upon withdrawal of AI treatment, testosterone induced minor growth stimulation. Letrozole, exemestane and tamoxifen were able to abrogate the testosterone stimulation but could not reduce growth to below the level in standard growth medium with AI, demonstrating cross-resistance between letrozole, exemestane and tamoxifen. In contrast, fulvestrant totally blocked growth of the AI resistant cell lines both after withdrawal of AI and with AI treatment. These data show that ER is the main driver of growth of the AI-resistant cell lines and indicate ligand-independent activation of ER. Fulvestrant is an efficient treatment option for these AI-resistant breast cancer cells, and the cell lines will be useful tools to disclose the underlying molecular mechanism for resistance to the different AIs.
Mathematical properties and analysis of Google's PageRank

To determine the order in which to display web pages, the search engine Google computes the PageRank vector, whose entries are the PageRanks of the web pages. The PageRank vector is the stationary distribution of a stochastic matrix, the Google matrix. The Google matrix in turn is a convex combination of two stochastic matrices: one matrix represents the link structure of the web graph and a second, rank-one matrix, mimics the random behaviour of web surfers and can also be used to combat web spamming. As a consequence, PageRank depends mainly the link structure of the web graph, but not on the contents of the web pages. We analyze the sensitivity of PageRank to changes in the Google matrix, including addition and deletion of links in the web graph. Due to the proliferation of web pages, the dimension of the Google matrix most likely exceeds ten billion. One of the simplest and most storage-efficient methods for computing PageRank is the power method. We present error bounds for the iterates of the power method and fo their residuals. Key words: Markov matrix, stochastic matrix, stationary distribution, power method, perturbation bounds AMS subject classifications: 15A51, 65C40, 65F15, 65F50, 65F10
A variational model of multiphase segmentation for images with Gaussian noises and its Split Bregman algorithm

In this paper, we propose a variational multiphase segmentation model for images with Gaussian noises. Its data term for parameter estimation is based on density functions of Gaussian distribution and its length term of active contours for n regions division is based on n binary labeling functions. We design the Split Bregman algorithm for the sub-problem of minimization on every labeling function based on the convexified version, and obtain the final solution via soft thresholding technique. Numerical examples validate the model and its algorithm finally.
Initial Study of Reconfigurable Neural Network Accelerators

Neural networks or NNs are widely used for many machine learning applications such as image processing and speech recognition. Since general-purpose processors such as CPUs and GPUs are energy inefficient for computing NNs, application-specific hardware accelerators for NNs (a.k.a. neural network accelerators or NNAs) have been proposed to improve the energy efficiency. However, existing NNAs are too customized for computing specific NNs, and do not allow to change NN models or learning algorithms. This limitation prevents machine-learning researchers from exploiting NNAs, so we are developing a general-purpose NNA that has the capability to compute any NN. Our NNA equips with reconfigurable logic in addition to various custom logics, which is called reconfigurable NNA or RNNA. RNNA is highly tuned for the NN computation but allows end users to customize the hardware to compute their desired NN. This paper introduces the RNNA architecture, and reports the performance analysis of RNNA with a cycle-level simulator.
Planet Formation around Stars of Various Masses: Hot Super-Earths

We consider trends resulting from two formation mechanisms for short-period super-Earths: planet-planet scattering and migration. We model scenarios where these planets originate near the snow line in “cold-finger” circumstellar disks. Low-mass planet-planet scattering excites planets to low-periastron orbits only for lower mass stars. With long circularization times, these planets reside on long-period eccentric orbits. Closer formation regions mean planets that reach short-period orbits by migration are most common around low-mass stars. Above ~1 M☉, planets massive enough to migrate to close-in orbits before the gas disk dissipates are above the critical mass for gas giant formation. Thus, there is an upper stellar mass limit for short-period super-Earths that form by migration. If disk masses are distributed as a power law, planet frequency increases with metallicity because most disks have low masses. For disk masses distributed around a relatively high mass, planet frequency decreases with increasing metallicity. As icy planets migrate, they shepherd interior objects toward the star, which grow to ~1 M⊕. In contrast to icy migrators, surviving shepherded planets are rocky. On reaching short-period orbits, planets are subject to evaporation processes. The closest planets may be reduced to rocky or icy cores. Low-mass stars have lower EUV luminosities, so the level of evaporation decreases with decreasing stellar mass.
Defects in the Fanconi Anemia Pathway in Head and Neck Cancer Cells Stimulate Tumor Cell Invasion through DNA-PK and Rac1 Signaling

Purpose: Head and neck squamous cell carcinoma (HNSCC) remains a devastating disease, and Fanconi anemia (FA) gene mutations and transcriptional repression are common. Invasive tumor behavior is associated with poor outcome, but relevant pathways triggering invasion are poorly understood. There is a significant need to improve our understanding of genetic pathways and molecular mechanisms driving advanced tumor phenotypes, to develop tailored therapies. Here we sought to investigate the phenotypic and molecular consequences of FA pathway loss in HNSCC cells. Experimental Design: Using sporadic HNSCC cell lines with and without FA gene knockdown, we sought to characterize the phenotypic and molecular consequences of FA deficiency. FA pathway inactivation was confirmed by the detection of classic hallmarks of FA following exposure to DNA cross-linkers. Cells were subjected to RNA sequencing with qRT-PCR validation, followed by cellular adhesion and invasion assays in the presence and absence of DNA-dependent protein kinase (DNA-PK) and Rac1 inhibitors. Results: We demonstrate that FA loss in HNSCC cells leads to cytoskeletal reorganization and invasive tumor cell behavior in the absence of proliferative gains. We further demonstrate that cellular invasion following FA loss is mediated, at least in part, through NHEJ-associated DNA-PK and downstream Rac1 GTPase activity. Conclusions: These findings demonstrate that FA loss stimulates HNSCC cell motility and invasion, and implicate a targetable DNA-PK/Rac1 signaling axis in advanced tumor phenotypes. Clin Cancer Res; 22(8); 2062–73. ©2015 AACR.
Path planning of construction manipulators using Genetic Algorithms

This paper presents the work done to investigate the potential of applying Genetic Algorithms for path planning of construction manipulators. Construction manipulators can take the shape of specialized equipment such as the pipe manipulator or common equipment such as cranes. The need for automating the path of construction manipulators has been identified in a number of past studies. Path planning is a well-established area in robotics research, work in this area focuses on identifying new algorithms and techniques to improve the accuracy and efficiency of the automated path planning process. The application of Genetic Algorithms for robotic path planning is a relatively new development. This paper reviews different construction situations in which path planning is required and identifies the factors influencing path selection in construction situations. Genetic Algorithm models to find satisficing paths are proposed, The GA model and initial results for a simple test problem using a 2 DOF planar manipulator are also presented.
Digital Video Broadcasting (dvb); Implementation Guidelines for the Second Generation System for Broadcasting, Interactive Services, News Gathering and Other Broadband Satellite Applications; Part 1: Dvb-s2

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Focusing the Normalised Information Distance on the Relevant Information Content for Image Similarity

This paper investigates the normalised information distance (NID) proposed by Bennet et~al~(1998) as an approach to measure the visual similarity (or dissimilarity) of images. Earlier studies suggest that compression-based approximations to the NID can yield dissimilarity measures that correlate well with visual comparisons. However, results also indicate that conventional feature-based dissimilarity measures often outperform those that are based on the NID. This paper proposes that a theoretical decomposition of the NID can help explain why the NID-based dissimilarity measures might not perform well compared to feature-based approaches. The theoretical decomposition considers the perceptually relevant and irrelevant information content for image similarity. We illustrate how the NID-based dissimilarity measures could be improved by discarding the irrelevant information, and applying the NID on only the relevant information.