Miracle-dz/UniFAR-FTUs huggingface dataset:
Unmasking Multivariate Outliers and Leverage Points: Comment

Detecting outliers in a multivariate point cloud is not trivial, especially when there are several outliers. The classical identification method does not always find them, because it is based on the sample mean and covariance matrix, which are themselves affected by the outliers. To avoid this masking effect, we propose to compute distances based on very robust estimates of location and covariance. In the case of regression data, the outliers also may be unmasked by using a highly robust regression method. A new display is proposed in which the robust regression residuals are plotted versus the robust distances
Identification of MicroRNAs Regulating Clostridium perfringens Type C Infection in the Spleen of Diarrheic Piglets

Clostridium perfringens (C. perfringens) type C is one of the common bacteria in piglet diarrhea, which seriously affects the swine industry’s development. The spleen plays crucial roles in the resistance and elimination of pathogenic microorganisms, and miRNAs play important roles in regulating piglet diarrhea caused by pathogens. However, the mechanism by which miRNAs in the spleen are involved in regulating C. perfringens type C causing diarrhea in piglets remains unclear. The expression profiles of the spleen miRNAs of 7-day-old piglets challenged by C. perfringens type C were studied using small RNA-sequencing in control (SC), susceptible (SS), and resistant (SR) groups. Eight-eight differentially expressed miRNAs were screened. The KEGG pathway analysis of target genes revealed that the miRNAs were involved in the MAPK, p53, and ECM–receptor interaction signaling pathways. NFATC4 was determined to be a direct target of miR-532-3p and miR-133b using a dual-luciferase reporter assay. Thus, miR-133b and miR-532-3p targeted to NFATC4 were likely involved to piglet resistance to C. perfringens type C. This paper provides the valuable resources to deeply understand the genetic basis of C. perfringens type C resistance in piglets and a solid foundation to identify novel markers of C. perfringens type C resistance.
Coastal Vegetation Dynamics in Response to Climatic and Relative Sea Level Changes in Mahanadi River Delta, NE Coast of India

ABSTRACT This research assesses the impacts of climate change and relative sea level (RSL) fluctuations on coastal vegetation during the past 2000 years along the Mahanadi River delta, northeast coast of India. Sediment samples of a 2.6 m trench from Barhatubi area located in the lower flood plain of Mahanadi River delta were studied for sedimentological and pollen analysis. Mangrove succession can be divided into three zones: (1) Freshwater marsh (∼1980–1420 cal yr BP); (2) Tidal flat (∼1420–770 cal yr BP) and (3) Landward mangrove margin (∼770 cal yr BP-Present). A transgressive shift from floodplain freshwater marsh to a tidal flat is recorded between ∼1980–1420 cal yr BP which is evidenced by upland tree taxa, Poaceae, pteridophyte and fungal spores along with low percentage of mangroves such as Avicennia, Sonneratia and Rhizophora representing the landward edge of the tidal region. Dominance of Sonneratia and a decline in terrestrial taxa reflects a rise in the relative sea level around ∼1420 cal yr BP with less freshwater input from land suggesting a weakened monsoon condition. After ∼770 cal yr BP, an overall regressive phase with small cycles of relative sea level rise/fall has been observed due to the dominance of salt tolerant Avicennia along with Rhizophora, Excoecaria agallocha and Aegialitis rotundifolia alternating with Sonneratia and other marine palynomorphs.
Integrative taxonomy reveals a new species of the water mite genus Monatractides K. Viets, 1926 from Corsica (Acari, Hydrachnidia: Torrenticolidae)

ABSTRACT In the present study, we use morphological data and DNA barcodes to describe a new species, i.e. Monatractides corsicus sp. nov. from Corsica, France. The new species belongs to the M. stadleri-complex which includes at least four distinct genetic lineages in the Western Palaearctic. Morphologically, the new species can be identified on the basis of a comparatively large palp, thick chelicera and the colour pattern of the dorsal shield. Moreover, we present the first DNA barcoding data for M. latissimus (K. Viets, 1936) and M. lusitanicus (Lundblad, 1941). http://www.zoobank.org/urn:lsid:zoobank.org:pub:461D9E10-3897-4CF0-9384-CC4A582E125A
FlexBNN: Fast Private Binary Neural Network Inference With Flexible Bit-Width

Advancements in deep learning enable neural network (NN) inference to be a service, but service providers and clients want to keep their inputs secret for privacy protection. Private Inference is the task of evaluating NN without leaking private inputs. Existing secure multiparty computation (MPC)-based solutions mainly focus on fixed bit-width methodology, such as 32 and 64 bits. Binary Neural Network (BNN) is efficient when evaluated in MPC and has achieved reasonable accuracy for commonly used datasets, but prior private BNN inference solutions, which focus on Boolean Circuits, are still costly in communication and run-time. In this paper, we introduce FLEX BNN, a fast private BNN inference framework using three-party computation (3PC) in Arithmetic Circuits against semi-honest adversaries with honest-majority. In FLEX BNN, we propose to employ flexible and small bit-width equipped with a seamless bit-width conversion method and design several specific optimizations towards the basic operations: i) We propose bit-width determination methods for Matrix Multiplication and Sign-based Activation function. ii) We integrate Batch Normalization and Max-Pooling into the Sign-based Activation function for better efficiency. iii) More importantly, we achieve seamless bit-width conversion within the Sign-based Activation function with no additional cost. Extensive experiments illustrate that FLEX BNN outperforms state-of-the-art solutions in communication, run-time, and scalability. On average, FLEX BNN is <inline-formula> <tex-math notation="LaTeX">$11\times $ </tex-math></inline-formula> faster than XONN (USENIX Security’19) in LAN, <inline-formula> <tex-math notation="LaTeX">$46\times $ </tex-math></inline-formula> (resp. <inline-formula> <tex-math notation="LaTeX">$9.3\times $ </tex-math></inline-formula>) faster than QUOTIENT (ACM CCS’19) in LAN (resp. WAN), <inline-formula> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> faster than BANNERS (ACM IH&MMSec’21) in LAN, and 1.1-<inline-formula> <tex-math notation="LaTeX">$2.9\times $ </tex-math></inline-formula> (resp. 1.5-<inline-formula> <tex-math notation="LaTeX">$2.7\times $ </tex-math></inline-formula>) faster than FALCON (semi-honest, PoPETs’21) in LAN (resp. WAN), and improves the respective communication by <inline-formula> <tex-math notation="LaTeX">$500\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$127\times $ </tex-math></inline-formula>, and 1.3-<inline-formula> <tex-math notation="LaTeX">$1.5\times $ </tex-math></inline-formula> compared to XONN, BANNERS, and FALCON.
Inference in log-alpha-power and log-skew-normal multivariate models

ABSTRACT Random vectors with positive components are common in many applied fields, for example, in meteorology, when daily precipitation is measured through a region Marchenko and Genton (2010). Frequently, the log-normal multivariate distribution is used for modeling this type of data. This modeling approach is not appropriate for data with high asymmetry or kurtosis. Consequently, more flexible multivariate distributions than the log-normal multivariate are required. As an alternative to this distribution, we propose the log-alpha-power multivariate and log-skew-normal multivariate models. The first model is an extension for positive data of the fractional order statistics model Durrans (1992). The second one is an extension of the log-skew-normal model studied by Mateu-Figueras and Pawlowsky-Glahn (2007). We study parameter estimation for these models by means of pseudo-likelihood and maximum likelihood methods. We illustrate the proposal analyzing a real dataset.
ROBUST H∞ FILTERING FOR UNCERTAIN DISCRETE-TIME STATE-DELAYED SYSTEMS： APARAMETER-DEPENDENT LYAPUNOV APPROACH

This paper is concerned with the problem of robust H∞ filtering for linear discrete-time systems with multiple state delays and polytopic uncertain parameters. Attention is focused on the design of full-order, reduced-order and zeroth-order robust H∞ filters on the basis of a recently published parameter-dependent Lyapunov stability result. Sufficient conditions for the existence of such filters are formulated in terms of linear matrix inequalities, upon which admissible filters can be obtained from convex optimization problems. The proposed methodology has been shown, via a numerical example, to be much less conservative than previous filter design methods in the quadratic framework.
Uni-temporal Sentinel-2 imagery for wildfire detection using deep learning semantic segmentation models

Abstract Wildfires are common disasters that have long-lasting climate effects and serious ecological, social, and economic effects due to climate change. Since Earth observation (EO) satellites were launched into space, remote sensing (RS) has become a more efficient technique that can be used in agriculture, environmental protection, geological exploration, and wildfires. The increasing number of EO satellites orbiting the earth provides huge amounts of data, such as Sentinel-2 with its Multi Spectral Instrument (MSI) sensor. Using uni-temporal Sentinel-2 imagery, we proposed a workflow based on deep learning (DL) semantic segmentation models to detect wildfires. In particular, we created a new big wildfire dataset suitable for semantic segmentation models. We tested our dataset using DL models such as U-Net, LinkNet, DeepLabV3+, U-Net++, and Attention ResU-Net. The results are analysed and compared in terms of the F1 score, the intersection over union (IoU) score, the precision and recall metrics, and the amount of training time each model possesses. The best results were achieved using U-Net with the ResNet50 encoder, with F1-score of 98.78% and IoU of 97.38%, and we developed it into a pre-trained DL Package (DLPK) model that is able to detect and monitor the wildfire from Sentinel-2 images automatically.
Unconventional long range triplet proximity effect in planar YBa2Cu3O7/La0.7Sr0.3MnO3/YBa2Cu3O7 Josephson junctions

The proximity effect between superconductors and ferromagnets may become long range due to the generation of triplet pairs. The recent finding of a long, one micron-range unconventional Josephson effect between YBa2Cu3O7 high Tc cuprates separated by a half metallic La0.7Sr0. 3MnO3 manganite ferromagnet has uncovered a novel unconventional triplet proximity effect. In this paper, we examine the temperature dependence of the critical current in planar Josephson junctions. We find that the critical current—normal resistance product follows the predictions of traditional superconductor-normal metal-superconductor junctions, which implies that triplet pairs in a ferromagnet are transported in the diffusive limit similarly to singlet pairs in a normal metal. This result calls for theoretical studies of the new triplet Josephson effect and underlines its potential in future superconducting spintronics.
Understanding of the Readiness for HTA Implementation in Jordan as a Step Towards Universal Health Coverage

As Jordan strives to achieve universal health coverage, the mechanism for determining which health technologies to include in the basket of reimbursed services has become increasingly important. This study sought to understand stakeholder perspectives in the Jordanian health system regarding the readiness and need to implement health technology assessment (HTA) to support decision-making quality and transparency, ensure value for money on health system spending, and support the achievement of universal health coverage. This study used a cross-sectional survey methodology, and a quantitative analysis was conducted. A questionnaire based on the HTA implementation scorecard was administered in-person to capture responses regarding fourteen dimensions. Thirty-one responses from representatives across the Ministry of Health, regulatory authority, and other stakeholders in the national health system were collected. Most respondents were familiar with HTA and there was nearly unanimous agreement on the need for HTA implementation in Jordan. While the perspectives on how the implementation would occur were varied, careful consideration may be warranted in the areas of the legal framework for HTA, the quality of available data, financial constraints, and limited human resource capacity, as Jordan progresses towards implementing HTA on the road to universal healthcare.
A Middle Model of Economic Development? Revisiting the Economic Geography of Middle-Wage Occupations in the United States

High-wage regions have frequently been models for economic development policy. However, these places have also been more likely to experience a “hollowing out” of the middle of the labor market, whereby college-educated residents experience high-wage growth, while residents without a college degree do not. This paper studies regions where—in contrast to hollowing out—the share of middle-wage jobs has grown since 1980. Its aim is to understand how—if at all—the characteristics of these regions could suggest a model of economic development that prioritizes opportunities for noncollege graduates. The authors find that regions with a growing share of middle-wage jobs have been associated with higher levels of upward mobility and wage growth for workers without a college degree. These places, such as Wausau, Wisconsin, and Manhattan, Kansas, are associated with higher local school performance and have experienced comparatively high growth in production jobs.
Socioeconomic and Behavioral Determinants of Cardiovascular Risk in Russia: A Structural Equation Modeling Approach

Purpose Despite much attention within the literature, the multiple risk factors associated with CVD mortality in Russia are still not fully understood. Drawing on the Health Belief Model as a theoretical framework, we aim to elicit socioeconomic and behavioral determinants of cardiovascular risks in Russian men and women. Methods Using the Know Your Heart project data, we utilize regression analysis and then structural equation modeling (latent class analysis and mediation analysis) to study the determinants of CVD risks. Results OLS and ordered logit regressions show that the key factors defining cardiovascular health behaviors in Russia are health-related actions to reduce the perceived threat of diseases (physical activity and GP visits), perceived barriers to behavioral change (financial constraints), and cues to action (awareness of the federal health check-up program). The latent class analysis further identifies three distinct groups of the population with different CVD risk levels. Over one-third of respondents belong to the “high CVD risk” class characterized by the highest share of smokers and alcohol abusers who evade contact with primary care and face financial constraints. In the mediation analysis, we find that employment mediates the relationship between physical activity and CVD risks: physically active individuals have a greater chance of employment, and employment further mitigates CVD risks. We also find an indication of the selection of the healthy into employment in the causal relationship between GP visits, having a job, and CVD risks. Conclusion A corresponding set of policy actions stem from these findings. These include reinforcing the change of perceptions of CVD risks and lowering barriers to health care; raising awareness of the free preventive check-up program in the “high CVD risk” group; making sports and exercise accessible to the elderly; and using off-putting labels on alcohol products as behavioral nudges among “physically active but drinking” males.
A new method for forecasting stock prices using artificial neural network and wavelet theory

Recently, a new decomposition method known as wavelet decomposition was introduced, which is accomplished through the use of an orthogonal basis consisting of so-called "wavelets". Wavelet analysis is a significant advance over Fourier analysis for two reasons. First, while Fourier analysis gives us only frequency information, wavelet analysis gives us both frequency information and time information. Secondly, wavelet analysis can represent a nonstationary process better than Fourier analysis by allowing us to look at the series through wavelets of variable sizes. While the wavelet theory has brought about significant advancements in representation of functions, not much work on its applicability to forecasting has been made. Although an initial attempt to provide the statistical framework for wavelet analysis was made by (Basseville, et al. 1992a, b), the applicability of wavelet analysis to forecasting needs to be further developed. The purpose of this paper is to introduce new methodologies based on wavelet decomposition that can forecast with greater accuracy than existing models. (1) Several models (including ARIMA, detrending and AR, random walk, and artificial neural network) are applied to the original series (Standard & Poor's 500 Index), and to the wavelet decomposed series. The results from the two approaches are compared to see whether the use of the decomposed series (which is the special smoothed version of the original series) in forecasting yields better results than applying the models directly to the original series. (2) An artificial neural network is incorporated with wavelets to forecast the Standard & Poor's 500 Index. (3) Information on the generating process (i.e. relationship from the decomposed series to the original series) is used for forecasting. The models in (1) which use decomposed series, result in smaller forecasting errors, i.e. RMSPE, MAPE, and TIC, than models where the original series are directly applied. The models described in (3) yield better results than the models described in (1) and (2). Wavelet theory is a concept that will need to be developed further for use in economics and finance. While this paper utilizes the simplest wavelets and a univariate case, it should be possible to employ more complicated wavelets and multivariate cases to see if their use can further advance the role of wavelet theory in forecasting.
Numerical modelling of multiphase subsurface ﬂow in the presence of fractures

Subsurface flow is influenced by the heterogeneity of the porous medium and in particular by the presence of faults and large fractures which act as preferential paths for the flow. In this work we present a robust numerical method for the simulation of two- phase Darcy flows in heterogeneous media and propose a possible treatment of fractures by means of the extended finite element method, XFEM, and the coupling with a reduced model for the flow inside the fracture. The use of extended finite elements allows to handle fractures that are non conforming with the underlying mesh, thus increasing the applicability of the proposed scheme to the simulation of realistic problems such as oil migration in fractured basins, CO2 storage or pollutant dispersion in groundwater flows.
Diiron Aminocarbyne Complexes with NCE− Ligands (E = O, S, Se)

Diiron μ-aminocarbyne complexes [Fe2Cp2(NCMe)(CO)(μ-CO){μ-CN(Me)(R)}]CF3SO3 (R = Xyl, [1aNCMe]CF3SO3; R = Me, [1bNCMe]CF3SO3; R = Cy, [1cNCMe]CF3SO3; R = CH2Ph, [1dNCMe]CF3SO3), freshly prepared from tricarbonyl precursors [1a–d]CF3SO3, reacted with NaOCN (in acetone) and NBu4SCN (in dichloromethane) to give [Fe2Cp2(kN-NCO)(CO)(μ-CO){μ-CN(Me)(R)}] (R = Xyl, 2a; Me, 2b; Cy, 2c) and [Fe2Cp2(kN-NCS)(CO)(μ-CO){μ-CN(Me)(CH2Ph)}], 3 in 67–81% yields via substitution of the acetonitrile ligand. The reaction of [1aNCMe–1cNCMe]CF3SO3 with KSeCN in THF at reflux temperature led to the cyanide complexes [Fe2Cp2(CN)(CO)(μ-CO){μ-CNMe(R)}], 6a–c (45–67%). When the reaction of [1aNCMe]CF3SO3 with KSeCN was performed in acetone at room temperature, subsequent careful chromatography allowed the separation of moderate amounts of [Fe2Cp2(kSe-SeCN)(CO)(μ-CO){μ-CN(Me)(Xyl)}], 4a, and [Fe2Cp2(kN-NCSe)(CO)(μ-CO){μ-CN(Me)(Xyl)}], 5a. All products were fully characterized by elemental analysis, IR, and multinuclear NMR spectroscopy; moreover, the molecular structure of trans-6b was ascertained by single crystal X-ray diffraction. DFT calculations were carried out to shed light on the coordination mode and stability of the {NCSe-} fragment.
Physics-Informed Neural Network with Fourier Features for Radiation Transport in Heterogeneous Media

Abstract Typical machine learning (ML) methods are difficult to apply to radiation transport due to the large computational cost associated with simulating problems to create training data. Physics-informed Neural Networks (PiNNs) are a ML method that train a neural network with the residual of a governing equation as the loss function. This allows PiNNs to be trained in a low-data regime in the absence of (experimental or synthetic) data. PiNNs also are trained on points sampled within the phase-space volume of the problem, which means they are not required to be evaluated on a mesh, providing a distinct advantage in solving the linear Boltzmann transport equation, which is difficult to discretize. We have applied PiNNs to solve the streaming and interaction terms of the linear Boltzmann transport equation to create an accurate ML model that is wrapped inside a traditional source iteration process. We present an application of Fourier Features to PiNNs that yields good performance on heterogeneous problems. We also introduce a sampling method based on heuristics that improves the performance of PiNN simulations. The results are presented in a suite of one-dimensional radiation transport problems where PiNNs show very good agreement when compared to fine-mesh answers from traditional discretization techniques.