Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification

As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods.
Performance of CDRT-Based Underlay Downlink NOMA Network With Combining at the Users

The performance of an underlay coordinated direct and relay transmission (CDRT) based non-orthogonal multiple access (NOMA) network is limited by the random nature of the transmit powers due to the interference temperature limit (ITL) imposed by the primary network. In this paper, we show that combining the signals at both near and far users can significantly enhance throughput. Considering the practical scenario of imperfect successive interference cancellation, the performance of near and far user nodes is analyzed in terms of throughput and outage probability. The traditional case of static ITL is considered first. Then, analysis is presented for the case when the ITL depends on the primary channel state information, and it is shown that the gain in performance can be very large over static ITL. Closed-form expressions for NOMA power apportioning are presented for a special case. Further, it is shown that how optimum parameters can be chosen to maximize the performance. Simulations validate the derived analytical expressions.
A Viscosity Approach to Infinite-Dimensional Hamilton--Jacobi Equations Arising in Optimal Control with State Constraints

We consider nonlinear optimal control problems with state constraints and nonnegative cost in infinite dimensions, where the constraint is a closed set possibly with empty interior for a class of systems with a maximal monotone operator and satisfying certain stability properties of the set of trajectories that allow the value function to be lower semicontinuous. We prove that the value function is a viscosity solution of the Bellman equation and is in fact the minimal nonnegative supersolution.
Circulating Histones to Detect and Monitor the Progression of Cancer

Liquid biopsies have emerged as a minimally invasive cancer detection and monitoring method, which could identify cancer-related alterations in nucleosome or histone levels and modifications in blood, saliva, and urine. Histones, the core component of the nucleosome, are essential for chromatin compaction and gene expression modulation. Increasing evidence suggests that circulating histones and histone complexes, originating from cell death or immune cell activation, could act as promising biomarkers for cancer detection and management. In this review, we provide an overview of circulating histones as a powerful liquid biopsy approach and methods for their detection. We highlight current knowledge on circulating histones in hematologic malignancies and solid cancer, with a focus on their role in cancer dissemination, monitoring, and tumorigenesis. Last, we describe recently developed strategies to identify cancer tissue-of-origin in blood plasma based on nucleosome positioning, inferred from nucleosomal DNA fragmentation footprint, which is independent of the genetic landscape.
Fluctuation equations about classical field configurations and supersymmetric quantum mechanics

Abstract Several (1 + 1)-dimensional nonlinear field theory models are considered and the solutions of the small fluctuation equations about their static, classical, finite energy configurations are derived. The models investigated are scalar field theories with sine-Gordon, double-well, and inverted double-well potentials, and the 0 (3) nonlinear sigma model with symmetry-breaking sine-Gordon term. All models are considered on a circle, so that the fluctuation equations are of Lame type. In each case the supersymmetric partner is then constructed and the spectra and solutions are investigated. It is found that the two fluctuation equations about the sphaleron configuration of the 0 (3) nonlinear sigma model are not supersymmetric partners except when the circle becomes of infinite extent. Further aspects investigated are the behaviour of the Witten index and the breaking of supersymmetry under antiperiodic boundary conditions.
Biocompatible wireless power transferring based on ultrasonic resonance devices

To increase application area of implantable devices for medical treatment including implantable cardiac defibrillator or deep brain stimulator, wireless power transfer is highly required. Previous technologies such as magnetic resonance and induction coupling have limited applications because of short transfer distance compared to device size and magnetic field intensity limitation for the safety of body exposure. The biocompatible wireless power transferring technology is proposed using ultrasonic resonance method. In power transfer through tissue, since the distance between transmitter and receiver is not adjustable, the optimum power transfer condition is changed. For efficient power transferring, operating frequency should be adjusted based on acoustic radiation and spatial pressure distribution. The ultrasonic resonance transmitter and receiver are manufactured with 50mm diameter and 250 kHz resonance frequency. In case of water medium, since it is easy to adjust frequency at fixed distance, it bring.
A Novel 3D-to-3D Diffeomorphic Registration Algorithm With Applications to Left Ventricle Segmentation in MR and Ultrasound Sequences

Left ventricular segmentation is a difficult and time-consuming task performed by clinicians, requiring the use of manual contours. We propose a novel three-dimensional diffeomorphic registration algorithm for endocardial segmentation of the left ventricle in magnetic resonance images and ultrasound temporal sequences. The proposed diffeomorphic registration method computes a voxel-to-voxel correspondence in three-dimensional space and is parameterized by one radial and three curl components to emulate the cardiac deformations. In addition, the proposed method allows for enforcing constraints to control the amount of deformation. The method was evaluated on 521 temporal frames from 20 patients from the Automated Cardiac Diagnosis Challenge magnetic resonance imaging dataset and 213 frames from 10 patients undergoing ultrasound scans from the Mazankowski Alberta Heart Institute. The algorithm was compared against six other registration methods, the Symmetric Normalization algorithm from the Dipy package, two variants of the Demons algorithm from the Insight Toolkit software package, two versions of RealTiTracker, and Elastix. The proposed method yielded overall Dice scores of 98.10 (0.90)% for the MRI dataset and 92.90 (2.42)% for the ultrasound dataset. The robustness of the algorithm is demonstrated by the high performance on multiple imaging modalities and various patient abnormalities.
Starling Flocks-Inspired Resource Allocation for ISAC-Aided Green Ad Hoc Networks

Efficient and intelligent environment awareness is one of the key aspects for enabling future green networks, especially for wireless ad hoc networks considering the limited resources, while poor environment awareness may lead to unbearable data transmission delay based on terrible routing strategies, which is unacceptable in some ad hoc networks. However, the sensing of communication environment and the performance of data transmission are tightly coupled with each other in physical reality. Therefore, traditional separate analyses of sensing and transmission cannot meet the low-latency and energy-efficient demand of future green ad hoc networks. In this article, we propose an environment awareness integrated sensing and communication (ISAC) network association architecture considering both the reward and cost of nodes’ sensing range and transmission strategy. In addition, the resource allocation optimization problem of the proposed ISAC architecture is formulated relying on the graph theory, which is efficiently solved by a novel low-complexity algorithm. Simulation results show that our proposed ISAC network association architecture is beneficial in terms of achieving lower transmission delay in comparison to the traditional methods which separately optimize the sensing and transmission process. Besides, a near-optimal latency performance is obtained with the aid of our optimization method with low-complexity.
An examination of legality of global abortion services and the genesis of public policy

Abstract Legality of abortion has been one of the most controversial political initiatives in modern times, which also impacts the healthcare delivery system especially for women. The debate often devolves into disagreement on either access to services on demand from healthcare providers or service refusal regardless of the circumstances. However, the reality is different from this bipolar conversation. Instead, it varies depending upon location of the potential abortion recipient and a host of factors associated with nation-states. Thus, our purpose is to reveal different legislative protocols that lead to or inhibit availability of this aspect of women's reproductive rights, and to empirically determine what are the underlying series of factors that drive these policy decisions. Together they reveal a complex mosaic of fundamental principles that are rarely considered when formulating public policy. We hope our research across nations will help healthcare providers and policy makers recognize the genealogy of options and opportunities as they continue to debate abortion's provision to women within healthcare systems.
Effect of Potassium Concentration on Triplex Stability under Molecular Crowding Conditions

The properties of non-canonical DNA structures, like G-quadruplexes and triplexes, change under cell-mimicking molecular crowding conditions relative to dilute aqueous solutions. The analysis of environmental effects on their stability is crucial since they play important roles in gene expression and regulation. In this study, three intramolecular and intermolecular triplex-forming sequences of different C+*G-C triplet content (*: Hoogsteen base pair; - : Watson–Crick base pair) were designed and their stability measured in the absence and presence of a crowding agent with different K+ concentrations. In dilute solution, the stability of the triplexes was reduced by decreasing the concentration of KCl. This reduction became smaller as the number of C+*G-C triplets increased. Under molecular crowding conditions, Watson–Crick base pairs and Hoogsteen base pairs were destabilized and stabilized, respectively. Interestingly, with lower KCl concentrations (≤1 M), the destabilization of the triplexes due to reduction of KCl concentration was significantly smaller than in dilute solutions. In addition, the C+*G-C content had greater influence on triplex stability under molecular crowding conditions. Our work provides quantitative information about the effects of K+ concentration on triplex stability under molecular crowding conditions and should further our understanding of the function and regulation of triplexes in bioprocesses.
A Critical Examination of Rural Out-Migration Studies in Ethiopia: Considering Impacts on Agriculture in the Sending Communities

Labor migration is a complex phenomenon, yet while much attention has been paid to understanding the drivers of migration, there is a huge knowledge and policy gap regarding the effects of migration on people and communities left behind. We sought to explore the impacts of rural outmigration on migrant-sending communities in Ethiopia. This remains an understudied topic when it comes to research on migration in Ethiopia. Our investigation is based on a critical review of the migration literature pertaining to Ethiopia and, more broadly. We pursued a holistic analysis of the multidimensional aspects of migration. There are indications that rural outmigration impacts involve issues related to remittances, household food security, agricultural labor use, farmland management, and rural infrastructure development. Our analysis revealed that there had been few systematic studies and limited analyses regarding the impacts of outmigration on agriculture and the livelihoods of rural people and households left behind. Instead, Ethiopia’s migration literature largely deals with migration’s causes, including environmental factors, climate variability, agricultural pressures, livelihood stresses, and changing aspirations.
Single-probe blade tip timing based on sparse Bayesian learning

Blade tip timing (BTT) has become the most promising online monitoring method for turbine blade health. Current BTT data analysis algorithms are extremely sensitive to the number of BTT probes installed and the accuracy of prior information, limiting the practical application of BTT. In this article, a BTT data analysis algorithm is proposed based on a sparse Bayesian learning framework and single-probe BTT signal model. It requires fewer BTT probes and less prior information than previous algorithms. Different degrees of noise were added to the numerically simulated BTT signal, and the results show that the method has high accuracy and reliability under high-noise conditions. The experimental results demonstrate the feasibility of the method with a single BTT probe without any prior information. Furthermore, the characteristics of the signal of a single BTT probe were investigated from a theoretical perspective.
Structured illumination microscopy with uncertainty quantification

Deep learning shows great potential for super-resolution microscopy, offering biological structures visualization with unprecedented details and high flexibility. An effective pathway toward this goal is structured illumination microscopy (SIM) augmented by deep learning because of its ability to double the resolution beyond the light diffraction limit in real-time. Although the deep-learning-based SIM technique works effectively, it is generally a black box that is difficult to explain the latent principle. Thus, the generated super-resolution biological structures contain unconvinced information for clinical diagnosis. This limitation impedes its further applications in safety-critical fields like medical imaging. In this paper, we report a reliable deep-learning-based SIM technique with uncertainty maps. These uncertainty maps characterize imperfections in various disturbances, such as measurement noise, model error, incomplete training data, and out-of-distribution testing data. Specifically, we employ a Bayesian convolutional neural network to quantify uncertainty and explore its application in SIM. The backbone of the reported neural network is the combination of U-net and Res-net with three low-resolution images from different structured illumination angles as inputs. The outputs are high-resolution images with double resolution beyond the numerical aperture and the pixel-wise confidence intervals quantification of reconstruction images. A series of simulations and experiments validate that the reported uncertainty quantification framework offers reliable uncertainty maps and high-fidelity super resolution images. Our work may promote practical applications of deep-learning-based super-resolution microscopy.
Modulated crystal structure of chimney-ladder higher manganese silicides MnSi γ (γ∼1.74)

The crystal structure of a polycrystalline sample of higher manganese silicide (HMS) has been determined by means of the $(3+1)$-dimensional superspace group approach. The structural parameters were refined with a superspace group of $I{4}_{1}/amd(00\ensuremath{\gamma})00ss$ using powder neutron-diffraction data collected at 295 K. The compound belongs to a composite crystal family consisting of [Mn] and [Si] subsystems, with an irrational $c$-axis ratio (misfit parameter) of $\ensuremath{\gamma}={c}_{\text{Mn}}/{c}_{\text{Si}}\ensuremath{\sim}1.74$. Significant in-plane rotational modulation was revealed in the ``chimney''-[Si] subsystem, while positional modulation in the ``ladder''-[Mn] subsystem was only realized along ${c}_{\text{Mn}}$. The electronic structure of the sample was calculated on the basis of a commensurate approximation of the modulated structure using the full potential linearized augmented plane-wave method. The obtained band gap of ${E}_{g}\ensuremath{\sim}0.6\text{ }\text{eV}$ agreed well with the experimentally observed one. It appears that the band gap and density of states of the HMS samples depend on the positional modulation of the Si atoms. The various controversial formulas (for example, ${\text{Mn}}_{4}{\text{Si}}_{7}$, ${\text{Mn}}_{11}{\text{Si}}_{19}$, and so on) of the HMS phases reported thus far can be regarded as commensurate cases of a series of incommensurate ${\text{MnSi}}_{\ensuremath{\gamma}}$ phases in which the $\ensuremath{\gamma}$ value ranges from $\ensuremath{\sim}1.70$ to 1.75.
UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning

Ultrawideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-LOS (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCPs) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This article derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed support vector machine (SVM)-based classification methodology leverages the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27%, and the LOS and NLOS recalls are 94.27% and 92.57%, respectively. The ranging errors in LOS (NLOS) conditions are reduced from 0.106 (1.442 m) to 0.065 (0.739 m), demonstrating an improvement of 38.85% (48.74%) with 0.041 (0.703 m) error reduction. In contrast, the average positioning accuracy is also reduced from 0.250 to 0.091 m with an improvement of 63.49% (0.159 m), which outperforms the state-of-the-art approaches of the least-squares SVM (LS-SVM) and ${K}$ -nearest neighbor (KNN) algorithms.
Intrinsic width and luminosity function of the M92 main sequence

Measurements of B and V magnitudes of approx.475 identified stars in the magnitude interval 18.0< or approx. =V< or approx. =22.0 are given. They occupy an area approx.35 square arcminutes located approx.8' north of the center of M92. The measurements were made on seven Hale 5-m reflector plates in each of two colors, relative to a faint photoelectric sequence determined earlier. The measuring errors, determined from the deviations from the mean values, are listed for each star so as to test if the observed width of the main sequence is due to measuring errors alone. The resulting distribution of errors has been used to predict the observed width of the MS. Comparison with the observations shows that the intrinsic width could be zero, with an upper limit of sigma(B-V)approx. =0.02 mag. Comparison with theoretical evolutionary models by VandenBerg shows that this gives an upper limit for the variation of the helium abundance, star-to-star, to be sigma(deltaY)< or approx. =0.1. As in M15, the variation of the metal abundance is not well measured by the sharpness of the M92 main sequence because the metal abundance at Z = 10/sup -4/ is too low. The luminosity function, obtained from the present data,more » is compared with that determined earlier by Tayler, by Hartwick, by van den Bergh, and with Fukuoka and Simoda, with good agreement. The evidence favors that phi(M/sub v/) flattens fainter than M/sub v/approx. =+6 as predicted in some dynamical models, due to loss of low mass stars. « less
Domain walls in 3d ferroelectrics: Static properties

Abstract A single domain wall was introduced into finite 3d sc Ising models by using suitable boundary conditions, and its properties were studied by means of Monte Carlo simulations. From the polarization profile the wall width and its dependence on temperature and system size were determined. Similarities and differences (especially near Tc) as compared to surface layers are discussed. Furthermore the internal energy of the wall and from it, by a special integration procedure, its free energy are determined.