Miracle-dz/UniFAR-FTUs huggingface dataset:
Eléments ad-finis de certains groupes quantiques

Soit U l'algebre enveloppante quantifiee d'une algebre de Lie semi-simple. Un theoreme de A. Joseph et G. Letzter, [3], assure que le sous-espace des elements ad-finis sous l'action adjointe de U est somme directe de sous-adU-modules isomorphes a l'espace des endomorphismes de modules simples de dimension finie. Nous donnons ici une demonstration differente de ce resultat. Elle s'appuie sur la forme introduite par M. Rosso [4], qui permet de transposer le probleme dans le dual de U
Multi-frame-based Cross-domain Denoising for Low-dose Spiral Computed Tomography

Computed tomography (CT) has been used worldwide as a non-invasive test in assisting diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the effectiveness of learning-based methods, most were developed on the simulated data collected using the Radon transform. However, the real-world scenario differs significantly from the simulation domain, especially when using the multi-slice spiral scanner geometry. This paper proposes a two-stage method for the commercially available third-generation multi-slice spiral CT scanners that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our approach makes good use of the high redundancy of the multi-slice projections and the volumetric reconstructions while leveraging the over-smoothing of high-frequency information in conventional cascaded frameworks due to aggressive denoising. The dedicated design also provides a more explicit interpretation of the data flow. Extensive experiments on various datasets showed that the proposed method could remove up to 70% of noise without compromised spatial resolution, while subjective evaluations by two radiologists further supported its superior performance against state-of-the-art methods in clinical practice.
Strains of negation in Norwegian

Scandinavian has a class of negative determiners which correspond in meaning to English no (e.g. in ingen poeng 'no points'). However, the distribution of DPs formed from these determiners is severely restricted. In general, they may license sentence negation only from a position outside the VP. In this paper, I examine different contexts in which ingen-phrases may appear VP-internally in Norwegian, and develop a descriptive taxonomy which sheds some light on the formal properties of negation.
Multivariate and Multi-step Traffic Prediction for NextG Networks with SLA Violation Constraints

This paper focuses on predicting downlink (DL) traffic volume in mobile networks while minimizing overprovisioning and meeting a given service-level agreement (SLA) violation rate. We present a multivariate, multi-step, and SLA-driven approach that incorporates 20 different radio access network (RAN) features, a custom feature set based on peak traffic hours, and handover-based clustering to leverage the spatiotemporal effects. In addition, we propose a custom loss function that ensures the SLA violation rate constraint is satisfied while minimizing overprovisioning. We also perform multi-step prediction up to 24 steps ahead and evaluate performance under both single-step and multi-step prediction conditions. Our study makes several contributions, including the analysis of RAN features, the custom feature set design, a custom loss function, and a parametric method to satisfy SLA constraints.
Spectra of Dirichlet Laplacian in 3-dimensional polyhedral layers

The structure of the spectrum of the three-dimensional Dirichlet Laplacian in the 3D polyhedral layer of fixed width is studied. It appears that the essential spectrum is defined by the smallest dihedral angle that forms the boundary of the layer while the discrete spectrum is always finite. An example of a layer with the empty discrete spectrum is constructed. The spectrum is proved to be nonempty in regular polyhedral layer.
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader.
Emergence of Rashba spin valley state in two-dimensional strained bismuth oxychalcogenides 
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The experimental evidence of the ultra-high electron mobility and strong spin-orbit coupling in the two-dimensional (2D) layered bismuth-based oxyselenide, Bi$_{2}$O$_{2}$Se, makes it a potential material for spintronic devices. However, its spin-related properties have not been extensively studied due to the centrosymmetric nature of its crystal structure. By using first-principles density-functional theory calculation, this study reports the emergence of Rashba-spin-valley states in Bi$_{2}$O$_{2}$Se monolayer (ML). Breaking the crystal inversion symmetry of Bi$_{2}$O$_{2}$Se ML using an external electric field enables the Rashba-spin-valley formation, causing the appearance of the Rashba-type splitting around the $\Gamma$ valley and spin-valley coupling at the $D$ valleys located near the middle of $\Gamma-M$ line. In addition to the typical Rashba-type spin textures around the $\Gamma$ valley, the study also observed in-plane unidirectional spin textures around the $D$ valleys, which is a rare phenomenon in 2D materials. The observed Rashba-spin-valley states are driven by the lowering point group symmetry of the crystal from $D_{4h}$ to $C_{4v}$ enforced by the electric field, as clarified through $\vec{k}\cdot\vec{p}$ model derived from symmetry analysis. More importantly, tuning the Rashba and spin-valley states by using biaxial strain offers a promising route to regulate the spin textures and spin splitting preventing the electron from back-scattering in spin transport. Finally, we proposed a more realistic system, namely, Bi$_{2}$O$_{2}$Se ML/SrTiO$_{3}$ (001) heterointerface that supports the strong Rashba-spin-valley states and highlighting the potential of the Bi$_{2}$O$_{2}$Se ML for future spintronics and valleytronics-based devices.
Implicit Neural Head Synthesis via Controllable Local Deformation Fields

High-quality reconstruction of controllable 3D head avatars from 2D videos is highly desirable for virtual human applications in movies, games, and telepresence. Neural implicit fields provide a powerful representation to model 3D head avatars with personalized shape, expressions, and facial parts, e.g., hair and mouth interior, that go beyond the linear 3D morphable model (3DMM). However, existing methods do not model faces with fine-scale facial features, or local control of facial parts that extrapolate asymmetric expressions from monocular videos. Further, most condition only on 3DMM parameters with poor(er) locality, and resolve local features with a global neural field. We build on part-based implicit shape models that decompose a global deformation field into local ones. Our novel formulation models multiple implicit deformation fields with local semantic rig-like control via 3DMM-based parameters, and representative facial landmarks. Further, we propose a local controlloss and attention mask mechanism that promote sparsity of each learned deformation field. Our formulation renders sharper locally controllable nonlinear deformations than previous implicit monocular approaches, especially mouth interior, asymmetric expressions, and facial details. Project page: https://imaging.cs. cmu. edullocal_deformation_fieldsl
Exploring Low Cost Non-Contact Detection of Biosignals for HCI

In an effort to make biosignal integration more accessible to explore for more HCI researchers, this paper presents our investigation of how well a standard, near ubiquitous webcam can support remote sensing of heart rate and respiration rate across skin tone ranges. The work contributes: how the webcam can be used for this purpose, its limitations, and how to mitigate these limitations affordably, including how the skin tone range affect the estimation results.
Superposed multiquantum barriers for InGaAlP heterojunctions

In a visible-light laser using In/sub 0.5/(Ga/sub 1-x/Al/sub x/)/sub 0.5/P (0<or=x<or=1), the conduction-band discontinuity at the interface between the active layer and clad layer is not large enough to strongly suppress carriers overflowing into the clad layer, unlike in the GaAlAs system. A multiquantum barrier (MQB) is used to overcome this problem, and the electron-reflecting powers are calculated to determine the characteristics of this suppression. Electron-reflecting powers are calculated for a high-power, high-temperature laser with an In/sub 0.5/Ga/sub 0.5/P active layer, an In/sub 0.5/Ga/sub 0.15/ clad layer, and a set of superposed MQBs, and for a short-wavelength ( lambda :586-nm) laser composed of an In/sub 0.5/Ga/sub 0.33/Al/sub 0.17/P active layer, an In/sub 0.5/Ga/sub 0.15/Al/sub 0.35/P clad layer, and a set of superposed MQBs.
Persistently trained, diffusion‐assisted energy‐based models

Maximum likelihood (ML) learning for energy‐based models (EBMs) is challenging, partly due to nonconvergence of Markov chain Monte Carlo. Several variations of ML learning have been proposed, but existing methods all fail to achieve both posttraining image generation and proper density estimation. We propose to introduce diffusion data and learn a joint EBM, called diffusion‐assisted EBMs, through persistent training (i.e. using persistent contrastive divergence) with an enhanced sampling algorithm to properly sample from complex, multimodal distributions. We present results from a 2D illustrative experiment and image experiments and demonstrate that for the first time for image data, persistently trained EBMs can simultaneously achieve long‐run stability, post‐training image generation and superior out‐of‐distribution detection.
Numerical Comparison of Methods for Hirota-Satsuma Model

This paper outlines the implementation of the modified decomposition method (MDM) to solve a very important physical model namely Hirota-Satsuma model which occurs quite often in applied sciences. Numerical results and comparisons with homotopy perturbation (HPM) and Adomian’s decomposition (ADM) methods explicitly reveal the complete reliability of the proposed MDM. It is observed that the suggested algorithm (MDM) is more user-friendly and is easier to implement compared to HPM and ADM.
The difference characteristics of two type double-patch antennas

This paper presents an application of method of moments (MoM) with Rao-Wilton-Glisson (RWG) basis functions to the analysis of microstrip patch antennas. The frequency dependent characteristics of a single patch and two double-patch antennas are analysed. The results show us that the VSWR of double-patch antennas are quite different. One type's characteristic is excellent, and the other one is very bad.
Deep Metric Learning Assisted by Intra-variance in A Semi-supervised View of Learning

Deep metric learning aims to construct an embedding space where samples of the same class are close to each other, while samples of different classes are far away from each other. Most existing deep metric learning methods attempt to maximize the difference of inter-class features. And semantic related information is obtained by increasing the distance between samples of different classes in the embedding space. However, compressing all positive samples together while creating large margins between different classes unconsciously destroys the local structure between similar samples. Ignoring the intra-class variance contained in the local structure between similar samples, the embedding space obtained from training receives lower generalizability over unseen classes, which would lead to the network overfitting the training set and crashing on the test set. To address these considerations, this paper designs a self-supervised generative assisted ranking framework that provides a semi-supervised view of intra-class variance learning scheme for typical supervised deep metric learning. Specifically, this paper performs sample synthesis with different intensities and diversity for samples satisfying certain conditions to simulate the complex transformation of intra-class samples. And an intra-class ranking loss function is designed using the idea of self-supervised learning to constrain the network to maintain the intra-class distribution during the training process to capture the subtle intra-class variance. With this approach, a more realistic embedding space can be obtained in which global and local structures of samples are well preserved, thus enhancing the effectiveness of downstream tasks. Extensive experiments on four benchmarks have shown that this approach surpasses state-of-the-art methods
Weakly supervised spatial relation extraction from radiology reports

Abstract Objective Weak supervision holds significant promise to improve clinical natural language processing by leveraging domain resources and expertise instead of large manually annotated datasets alone. Here, our objective is to evaluate a weak supervision approach to extract spatial information from radiology reports. Materials and Methods Our weak supervision approach is based on data programming that uses rules (or labeling functions) relying on domain-specific dictionaries and radiology language characteristics to generate weak labels. The labels correspond to different spatial relations that are critical to understanding radiology reports. These weak labels are then used to fine-tune a pretrained Bidirectional Encoder Representations from Transformers (BERT) model. Results Our weakly supervised BERT model provided satisfactory results in extracting spatial relations without manual annotations for training (spatial trigger F1: 72.89, relation F1: 52.47). When this model is further fine-tuned on manual annotations (relation F1: 68.76), performance surpasses the fully supervised state-of-the-art. Discussion To our knowledge, this is the first work to automatically create detailed weak labels corresponding to radiological information of clinical significance. Our data programming approach is (1) adaptable as the labeling functions can be updated with relatively little manual effort to incorporate more variations in radiology language reporting formats and (2) generalizable as these functions can be applied across multiple radiology subdomains in most cases. Conclusions We demonstrate a weakly supervision model performs sufficiently well in identifying a variety of relations from radiology text without manual annotations, while exceeding state-of-the-art results when annotated data are available.
A HIGH-PRECISION NEAR-INFRARED SURVEY FOR RADIAL VELOCITY VARIABLE LOW-MASS STARS USING CSHELL AND A METHANE GAS CELL

We present the results of a precise near-infrared (NIR) radial velocity (RV) survey of 32 low-mass stars with spectral types K2–M4 using CSHELL at the NASA InfraRed Telescope Facility in the K band with an isotopologue methane gas cell to achieve wavelength calibration and a novel, iterative RV extraction method. We surveyed 14 members of young (≈25–150 Myr) moving groups, the young field star ε Eridani, and 18 nearby (<25 pc) low-mass stars and achieved typical single-measurement precisions of 8–15 m s−1with a long-term stability of 15–50 m s−1 over longer baselines. We obtain the best NIR RV constraints to date on 27 targets in our sample, 19 of which were never followed by high-precision RV surveys. Our results indicate that very active stars can display long-term RV variations as low as ∼25–50 m s−1 at ≈2.3125 μm, thus constraining the effect of jitter at these wavelengths. We provide the first multiwavelength confirmation of GJ 876 bc and independently retrieve orbital parameters consistent with previous studies. We recovered RV variabilities for HD 160934 AB and GJ 725 AB that are consistent with their known binary orbits, and nine other targets are candidate RV variables with a statistical significance of 3σ–5σ. Our method, combined with the new iSHELL spectrograph, will yield long-term RV precisions of ≲5 m s−1 in the NIR, which will allow the detection of super-Earths near the habitable zone of mid-M dwarfs.
On the stability of ring modes in a trailing line vortex: the upper neutral points

The inviscid near-neutral stability of a trailing-vortex flow is investigated by using a normal-mode analysis in which all perturbation quantities exhibit a factor exp[i(nβz−nθ−ω)]. The problem is treated as a timewise-stability problem. The dependence of the eigenvalues ω on the axial wavenumber β, which has been normalized with respect to the azimuthal wavenumber n, is found both numerically and analytically for large values of n in the upper range of values of β near 1/q, where near-neutral modes are anticipated to occur. Here q, the swirl parameter of the flow, effectively compares the 'strengths’ of the swirl and axial components of motion in the undisturbed flow. Previous normal-mode analyses based on the same form of perturbation quantities have shown that for columnar vortices the unstable modes for large values of n are ring modes, and this feature is shown to persist near the upper neutral points. In fact this work on near-neutral ring modes supplements the earlier asymptotic theory for large n, which is known to fail near β= 1/q. Our numerical and asymptotic results are in excellent agreement and are also shown to be consistent with the earlier asymptotic theory through matching. It is found that ω→0 as β→(1/q)−.
Retrospective evaluation of noncardiogenic pulmonary edema in dogs and cats (2000-2021): 31 cases.

OBJECTIVE To characterize the clinical features of noncardiogenic pulmonary edema (NCPE), etiology, and outcome in dogs and cats. The study also aimed to evaluate associations with mortality. DESIGN Retrospective study. SETTING University teaching hospital. ANIMALS Thirty dogs and 1 cat, all client owned. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Data collected included animal characteristics, clinical history, evaluation of oxygenation, radiographic features, treatments, and outcomes. Causes of NCPE included upper airway obstruction, electrocution, drowning, neurogenic etiology, and unknown. The etiology was known in 21 of 31 cases (68%), while the etiology of the remaining 10 cases (32%) was classified as unknown. The most common cause for NCPE was upper airway obstruction, found in 14 of 31 cases (45%). The majority of thoracic radiographs showed a mixed interstitial-to-alveolar pulmonary pattern with a diffuse distribution (52%). Oxygen therapy was administered to 27 dogs (90%). Furosemide was administered to 12 dogs (40%). The median duration of hospitalization was 48 hours (range: 1-192). Twenty-three animals (74%) survived to discharge. Six dogs were mechanically ventilated, with only 2 of them (33%) surviving to discharge. The requirement for mechanical ventilation was the only parameter associated with mortality (P = 0.03). CONCLUSIONS NCPE is a heterogenous disease process that is most common in dogs. There are a variety of causes, but upper airway obstruction appears to be the most common. The overall prognosis is good in animals that do not require mechanical ventilation.
Computers And Intractability A Guide To The Theory Of Np Completeness

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Development of a Two-Channel Aerosol Retrieval Algorithm on a Global Scale Using NOAA AVHRR

Abstract This study proposes a two-channel satellite remote sensing algorithm for retrieving the aerosol optical thickness and the Angstrom exponent, which is an index for the aerosol size distribution. An efficient lookup table method is adopted in this algorithm to generate spectral radiances in channels 1 and 2 of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) over ocean areas. Ten-day composite maps of the aerosol optical thickness and the Angstrom exponent have been obtained from AVHRR global area coverage data in January and July of 1988. Aerosol optical thickness maps show that the major aerosol sources are located off the west coast of northern and southern Africa, and the Arabian Peninsula. The most important contributor is soil-derived particles from the Sahara Desert that cross the Atlantic Ocean. The authors’ optical thickness values tend to be larger than values given by the NOAA operational algorithm. A 10-day composite map of Angstrom.