Miracle-dz/UniFAR-FTUs huggingface dataset:
The ionizing radiation from massive stars and its impact on H ii regions: results from modern model atmospheres

We present a detailed comparison of the ionizing spectral energy distributions (SEDs) predicted by four modern stellar atmosphere codes, TLUSTY, CMFGEN, wM-basic and FASTWIND. We consider three sets of stellar parameters representing a late O-type dwarf (09.5 V), a mid-O-type (07 V) dwarf and an early O-type dwarf (05.5 V). We explore two different possibilities for such a comparison, following what we called evolutionary and observational approaches: in the evolutionary approach, one compares the SEDs of stars defined by the same values of T eff and log g; in the observational approach, the models to be compared do not necessarily have the same T eff and log g, but produce similar H and He I-IIoptical lines. We find that there is a better agreement, in terms of Q(H°), the ratio Q(He°)/Q(H°) and the shape of the SEDs predicted by the four codes in the spectral range between 13 and 30 eV, when models are compared following the observational approach. However, even in this case, large differences are found at higher energies. We then discuss how the differences in the SEDs may affect the overall properties of surrounding nebulae in terms of temperature and ionization structure. We find that the effect over the nebular temperature is not larger than 300-350 K. Contrarily, the different SEDs produce significantly different nebular ionization structures. This will lead to important consequences on the establishment of the ionization correction factors that are used in the abundance determination of H ii regions, as well as in the characterization of the ionizing stellar population from nebular line ratios.
Developmental Neuronal Death in Hippocampus Requires the Microglial CD11b Integrin and DAP12 Immunoreceptor

In several brain regions, microglia actively promote neuronal apoptosis during development. However, molecular actors leading microglia to trigger death remain mostly unknown. Here, we show that, in the developing hippocampus, apoptotic neurons are contacted by microglia expressing both the integrin CD11b and the immunoreceptor DAP12. We demonstrate that developmental apoptosis decreases in mice deficient for CD11b or DAP12. In addition, function-blocking antibodies directed against CD11b decrease neuronal death when injected into wild-type neonates, but have no effect when injected into DAP12-deficient littermates. This demonstrates that DAP12 and CD11b act in converging pathways to induce neuronal death. Finally, we show that DAP12 and CD11b control the production of microglial superoxide ions, which kill the neurons. Thus, our data show that the process of developmental neuronal death triggered by microglia is similar to the elimination of pathogenic cells by the innate immune cells.
Nonparametric eigenvalue-regularized precision or covariance matrix estimator

We introduce nonparametric regularization of the eigenvalues of a sample covariance matrix through splitting of the data (NERCOME), and prove that NERCOME enjoys asymptotic optimal nonlinear shrinkage of eigenvalues with respect to the Frobenius norm. One advantage of NERCOME is its computational speed when the dimension is not too large. We prove that NERCOME is positive definite almost surely, as long as the true covariance matrix is so, even when the dimension is larger than the sample size. With respect to the Stein’s loss function, the inverse of our estimator is asymptotically the optimal precision matrix estimator. Asymptotic efficiency loss is defined through comparison with an ideal estimator, which assumed the knowledge of the true covariance matrix. We show that the asymptotic efficiency loss of NERCOME is almost surely 0 with a suitable split location of the data. We also show that all the aforementioned optimality holds for data with a factor structure. Our method avoids the need to first estimate any unknowns from a factor model, and directly gives the covariance or precision matrix estimator, which can be useful when factor analysis is not the ultimate goal. We compare the performance of our estimators with other methods through extensive simulations and real data analysis.
Electron-phonon scattering at the intersection of two Landau levels

We predict a double-resonant feature in the magnetic field dependence of the phonon-mediated longitudinal conductivity xx of a two-subband quasi-two-dimensional electron system in a quantizing magnetic field. The two sharp peaks in xx appear when the energy separation between two Landau levels belonging to different size-quantization subbands is favorable for acoustic-phonon transitions. One-phonon and two-phonon mechanisms of electron conductivity are calculated and compared. The phonon-mediated interaction between the intersecting Landau levels is considered and no avoided crossing is found at thermal equilibrium.
Adaptive inverse control for settling performance improvements

Single-track hard disk drive (HDD) seek performance is measured by settling time, ts, defined as the time from the arrival of a seek command until the measured position reaches and stays within an acceptable distance from the target track. Previous work has shown feedforward dynamic inversion, coupled with an aggressive desired trajectory yd, is capable of achieving high performance settling times when the closed-loop dynamics are time-invariant and accurately modeled. In contrast, we describe an adaptive inversion procedure in this paper which removes the requirement for accurate initial models and tracks the position-variant dynamics present in our Servo Track Writer (STW) experimental apparatus. The proposed indirect adaptive inversion algorithm relies on a recursive least squares (RLS) estimate of the closed-loop dynamics. Pre-filtering of the RLS input signals and covariance resetting are necessary additions to the baseline adaptive algorithm in order to achieve fast settling times. Compared to the nonadaptive solution with accurate system identification, we show the adaptive algorithm achieves a 22% reduction in average settling time and a 53% reduction in settling time standard deviation.
Noninvasive Magnetic Resonance Spectroscopic Imaging Biomarkers to Predict the Clinical Grade of Pediatric Brain Tumors

The diagnosis and therapy of childhood brain tumors, most of which are low grade, can be complicated because of their frequent adjacent location to crucial structures, which limits diagnostic biopsy. Also, although new prognostic biomarkers identified by molecular analysis or DNA microarray gene profiling are promising, they too depend on invasive biopsy. Here, we test the hypothesis that combining information from biologically important intracellular molecules (biomarkers), noninvasively obtained by proton magnetic resonance spectroscopic imaging, will increase the diagnostic accuracy in determining the clinical grade of pediatric brain tumors. We evaluate the proton magnetic resonance spectroscopic imaging exams for 66 children with brain tumors. The intracellular biomarkers for choline-containing compounds (Cho), N-acetylaspartate, total creatine, and lipids and/or lactate were measured at the highest Cho region and normalized to the surrounding healthy tissue total creatine. Neuropathological grading was done with WHO criteria. Normalized Cho and lipids and/or lactate were elevated in high-grade (n = 23) versus low-grade (n = 43) tumors, which multiple logistic regression confirmed are independent predictors of tumor grade (for Cho, odds ratio 24.8, P < 0.001; and for lipids and/or lactate, odds ratio 4.4, P < 0.001). A linear combination of normalized Cho and lipids and/or lactate that maximizes diagnostic accuracy was calculated by maximizing the area under the receiver operating characteristic curve. Proton magnetic resonance spectroscopic imaging, although not a proxy for histology, provides noninvasive, in vivo biomarkers for predicting clinical grades of pediatric brain tumors.
The monopole moment of the three-point correlation function of the two-degree Field Galaxy Redshift Survey

We measure the monopole moment of the three-point correlation function on scales 1-70 h -1 Mpc in the two-degree Field Galaxy Redshift Survey (2dFGRS). Volume-limited samples are constructed using a series of integral magnitude bins between M = -18 to -22. Our measurements with a novel edge-corrected estimator represent most, if not all, three-point level monopole or angular averaged information in the catalogue. We fit a perturbative non-linear bias model to a joint data vector formed from the estimated two- and three-point correlation functions. Two different models are used: an analytic model based on Eulerian perturbation theory including bias and redshift distortions and a phenomenological bias model based on the direct redshift space measurements in the large Virgo simulations. To interpret the clustering results, we perform a three-parameter Gaussian maximum-likelihood analysis. In the canonical -21 to -20 volume-limited sample, we find σ 8 = 0.93 +0.06 -0.2 , b = 1.04 +0.23 -0.09 and b 2 =-0.06 +0.003 -0.001 . Our estimate of σ 8 is robust across the different volume-limited samples constructed. These results, based solely on the large-scale clustering of galaxies, are in excellent agreement with previous analyses using the Wilkinson Anisotropy Probe; this is a spectacular success of the concordance model. We also present two-parameter fits for the bias parameters, which are in excellent agreement with the previous findings of the bias evolution in the 2dFGRS.
Impact of war on growth patterns in school children in Croatia.

The objective of this paper is to present the growth patterns of school children in Osijek--the city which was exposed to severe attacks during the aggression on Croatia. The mean height and weight of Osijek schoolchildren aged 7 to 18 and the menarcheal age in girls in academic year 1995/96 were compared to the analogous data collected in 1980/81. The secular changes in height were heterogeneous. In older age groups from 12 in girls and 13 in boys, the mean height in 1995/96 increased markedly, whereas from 9 to 11 or 12, changes were undulating. In the youngest groups--at the age of 7 in both genders, and at 8 in boys, negative changes were observed. Markedly smaller height in this cohort was still pronounced in 1999/2000 when these children reached the age of 11. However, one year later (2000/01), at the age of 12, boys and girls caught up with their peers in the previous generations. These children during the war were approximately at the age of 2.5 to 4, a period when growth patterns are highly sensitive to adverse environmental influences. It might be possible that the emotional stress caused by a change of environment and separation from home, contributed to the deceleration of growth rate, i.e. the smaller height in a large part of childhood.
Global Purchasing as Labor Regulation: The Missing Middle

Do purchasing practices support or undermine the regulation of labor standards in global supply chains? This study offers the first analysis of the full range of supply chain regulatory efforts, integrating records of factory labor audits with purchase order microdata. Studying an apparel and equipment retailer with a strong reputation for addressing labor conditions in its suppliers, the authors show that the retailer persuaded factories to improve and terminated factories with poor labor compliance. However, the authors also find that purchase orders did not increase when labor standards improved. If anything, factories whose standards worsened tended to see their orders increase. Contrary to the conventional wisdom, this “missing middle” in incentives for compliance appears unrelated to any cost advantage of noncompliant factories. Instead, lack of flexibility in supplier relationships created obstacles to reallocating orders in response to compliance findings.
ATR pathway inhibition is synthetically lethal in cancer cells with ERCC1 deficiency.

The DNA damage response kinase ATR and its effector kinase CHEK1 are required for cancer cells to survive oncogene-induced replication stress. ATR inhibitors exhibit synthetic lethal interactions, with deficiencies in the DNA damage response enzymes ATM and XRCC1 and with overexpression of the cell cycle kinase cyclin E. Here, we report a systematic screen to identify synthetic lethal interactions with ATR pathway-targeted drugs, rationalized by their predicted therapeutic utility in the oncology clinic. We found that reduced function in the ATR pathway itself provided the strongest synthetic lethal interaction. In addition, we found that loss of the structure-specific endonuclease ERCC1-XPF (ERCC4) is synthetic lethal with ATR pathway inhibitors. ERCC1-deficient cells exhibited elevated levels of DNA damage, which was increased further by ATR inhibition. When treated with ATR or CHEK1 inhibitors, ERCC1-deficient cells were arrested in S-phase and failed to complete cell-cycle transit even after drug removal. Notably, triple-negative breast cancer cells and non-small cell lung cancer cells depleted of ERCC1 exhibited increased sensitivity to ATR pathway-targeted drugs. Overall, we concluded that ATR pathway-targeted drugs may offer particular utility in cancers with reduced ATR pathway function or reduced levels of ERCC4 activity.
Synchronization of brain electrical activity between two hemispheres in different frequency bands

In this paper we address the problem of synchronization in electroencephalogram (EEG) recorded from two brain hemispheres, where the goal is to study if the synchrony takes place for symmetric and asymmetric channels, and to analyze synchronization characteristics for all main EEG rhythms. In our approach we use filtration to separate rhythm's frequency content, followed by calculation of Single-Trial Phase-Locking Value (SPLV) to quantify phase synchronization. We analyzed background EEG from eight matched healthy volunteers, first comparing synchronization between symmetric/asymmetric channels and surrogate signals, and then comparing synchronization between symmetric and asymmetric channels itself. The presence of synchronization was validated by Kruskal-Wallis test to compare SPLV in symmetric vs. asymmetric channels, resulting in statistically significant distinction (p≤0.001) in all frequency bands, except gamma rhythm for which p-value was p≤0.05.
Barra, a Modular Functional GPU Simulator for GPGPU

The use of GPUs for general-purpose applications promises huge performance returns for a small investment. However the internal design of such processors is undocumented and many details are unknown, preventing developers to optimize their code for these architectures. One solution is to use functional simulation to determine program behavior and gather statistics when counters are missing or unavailable. Functional simulators are made of several parts. In this article we present a GPU functional simulator targeting GPGPU based on the UNISIM framework which takes a Nvidia cubin file as input.
WaveQ: Combining Wavelet Analysis and Clustering for Effective Image Retrieval

This paper proposes WaveQ, a content-based image retrieval system that classifies images as texture or non-texture, then uses a Daubechies wavelet decomposition to extract feature vector information from the images, and finally applies the OPTICS clustering algorithm to cluster the extracted data into groups of similar images. Queries are submitted to WaveQ in the form of an example image. WaveQ has been thoroughly tested and the results are very promising. The achieved results demonstrate that the classification of images is extremely fast and accurate.
Reserve price strategy for seller agent in multiple simultaneous auctions

Internet auction is popular due to the flexibility and convenience that it offers to consumers. In online auctions, sellers are confronted with the dilemma of deciding the best reserve price for the items to be auctioned off. In an auction site such as eBay, one can always find the same item being sold by multiple sellers in different auctions. Determining this reserve price is not a straightforward decision process due to the complexity and vagueness of the online auction environment. Setting the reserve price too high may result in no sale whilst setting the reserve price too low may result in a sale with low profit. The main focus of this paper is to analyze the performance of the selling agents with varying pricing strategy when offering item for auctions. The strategy could be categorized as low, intermediate, high, and random based on its sellers' types. Our study showed that the use of these strategies produced significant drawbacks with negative impacts towards the overall selling upshots. To counteract these shortcomings, we develop an autonomous seller agent using a heuristic decision making framework. To derive the best reserve price, several constraints are considered including the number of competitors, the number of bidders, the auction length, and the profit that the seller desires. This paper presents the design, implementation and evaluation of our selling algorithm that a seller agent can use for auctioning homogeneous goods among multiple overlapping English auctions. In this work, we modeled our market simulation for a single unit auction using an independent private value framework with dynamic participation entry.
Metabonomic changes from pancreatic intraepithelial neoplasia to pancreatic ductal adenocarcinoma in tissues from rats

Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors and is difficult to diagnose in the early phase. This study was aimed at obtaining the metabolic profiles and characteristic metabolites of pancreatic intraepithelial neoplasia (PanIN) and PDAC tissues from Sprague–Dawley (SD) rats to establish metabonomic methods used in the early diagnosis of PDAC. In the present study, the animal models were established by embedding 7,12‐dimethylbenzanthracene (DMBA) in the pancreas of SD rats to obtain PanIN and PDAC tissues. After the preprocessing of tissues, 1H nuclear magnetic resonance (NMR) spectroscopy combined with multivariate and univariate statistical analysis was applied to identify the potential metabolic signatures and the corresponding metabolic pathways. Pattern recognition models were successfully established and differential metabolites, including glucose, amino acids, carboxylic acids and coenzymes, were screened out. Compared with the control, the trends in the variation of several metabolites were similar in both PanIN and PDAC. Kynurenate and methionine levels were elevated in PanIN but decreased in PDAC, thus, could served as biomarkers to distinguish PanIN from PDAC. Our results suggest that NMR‐based techniques combined with multivariate statistical analysis can distinguish the metabolic differences among PanIN, PDAC and normal tissues, and, therefore, present a promising approach for physiopathologic metabolism investigations and early diagnoses of PDAC.
CEWES MSRC / PET TR / 98-17 Structure Significant Representation of Structured Datasets by Raghu

Numerical simulation of physical phenomena is now an accepted way of scientific inquiry. However, the field is still evolving with a profusion of new solution and grid generation techniques being continuously proposed. Concurrent and retrospective visualization are being used to validate the results, compare them among themselves and with experimental data, and browse through large scientific databases. There exists a need for representation schemes which allow access of structures in an increasing order of smoothness (or decreasing order of significance). We describe our methods on datasets obtained from curvilinear grids. Our target application required visualization of a computational simulation performed on a very remote supercomputer. Since no grid adaptation was performed, it was not deemed necessary to simplify or compress the grid. In essence, we treat the solution as if it were in the computational domain. Inherent to the identification of significant structures is determining the location of the scale coherent structures and assigning saliency values to them [22][23]. Scale coherent structures are obtained as result of combining across scales the coefficients of a wavelet transform. The result of this operation is a correlation mask that delineates regions containing significant structures. A spatial subdivision (e.g., octree) is used to delineate regions of interest. The mask values in these sub-divided regions are used as a measure of information content. Later, another wavelet transform is conducted within each sub-divided region and the coefficients are sorted based on a perceptual function with bandpass characteristics. This allows for ranking of structures based on the order of significance, giving rise to an adaptive and embedded representation scheme. We demonstrate our methods on two datasets from computational field simulations. Essentially we show how our methods allow the ranked access of significant structures. We also compare our adaptive representation scheme with a fixed blocksize scheme. CR
ReMoRo; A mobile robot platform based on distributed I/O modules for research and education

We present our recent work on the electrical and hardware design of the mobile robot platform ReMoRo that is based on distributed input/output modules. We have designed three generation of this platform with different specifications, which it help us to design more compatible and applied mobile robot. Nevertheless, the goal of this project was to develop a low-cost and robust but extensible modular robot platform for research and educational purposes. In this paper we describe a new affordable robot structure that enables large-scale innovative, new curriculum, multi robot research and multi-robotics outreach to computer and artificial intelligent students. We introduce the ReMoRo platform, which offers a balance between capabilities, accessibility, cost and an opendesign. All of electrical devices like sensors module, motor drivers and device communication manager are designed based on ARM Cortex M3 microcontrollers that runs under Real-Time Operating System (freeRTOS) for manages each modules internal scheduling and activation control in communication bus. With a range of different sensors, cylindrical manipulator and omnidirectional locomotion system, RoMeRo can interact with environment in multiple ways, handle common objects and therefore be used in various service robot scenarios like warehouse robots or multi agent mobile robots. We demonstrate the usability of our concept by quantifying the object-handling task and also briefly describe the software design based on ROS framework for educational usage.
Dual-band step-impedance bandpass filter for multimode wireless LANs

A step-impedance bandpass filter is presented for multimode wireless LANs. The filter has a new dual-band feature of two tunable passbands at desired frequencies and high out-of-band suppression, generated by incorporating step-impedance resonators in a comb-filter topology. It saves more than half the circuit size compared with the switch-type dual-band topology. The simulation and measurement results show the dual-band feature of two passbands at 2.45 and 5.75 GHz with 85 dB suppression at 3.5 GHz.
econstor Make Your Publications Visible . A Service of zbw

The purpose of this paper is to examine determinants of financial information disclosure by Tunisian companies. The methodology is based on qualitative approach, using the cognitive mapping technique. To take into account the specificities of the Tunisian economic, we felt that it is essential to conduct a qualitative analysis in the light of which we can identify the factors motivating the disclosure of financial information. The qualitative analysis is based on the census via a set of cases carried out in several Tunisian companies to understand their perceptions regarding the determinants of financial disclosure. Subjects: Behavioral Sciences; Communication Studies; Economics, Finance, Business & Industry