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A generalised extraction procedure for magnetic interactions using effective Hamiltonians applicable to systems with more than two sites featuring local spins $S_i \geq 1$ is presented. To this end, closed, non-recursive expressions pertaining to chains of arbitrary equal spins are derived with the graphical method of angular momentum. The method is illustrated by extracting magnetic couplings from ab initio calculations on a [CaMn$_3^{(\text{IV})}$O$_4$] cubane. An extension to non-sequential coupling schemes is shown to be conducive to expressing additional symmetries of certain spin Hamiltonians.

Due to the saturation of the body ($m_b$) and surface ($M_S$) earthquake magnitudes, the moment magnitude ($M_W$) is a more convenient parameter for representing earthquake energies. We use the HRVD data, including 18,569 earthquakes recorded from 1976 January 1 to 2010 December 31, for five regions on the Earth (\textbf{Asia}-Oceania, Europe, Africa, North America, and South America). Applying the error propagation technique on the moment tensor, we estimate the standard error for $M_W$ showing a distribution that deviates from Gaussian errors which recommends using the Monte Carlo Markov chain (MCMC) to obtain model parameters instead of the least square approach. We investigate the linear relationship between the body and surface waves in small ($\le$ 6.1) and large ($>$6.1) magnitudes via MCMC. The slope varies from 0.811 to 1.565 ($m_b - M_W$) and 0.566 to 0.971 ($M_S - M_W$) across five regions. For 18,569 global earthquakes, we obtained the slope for $m_b \le 6.1$ ($m_b>$6.1) 0.863 (1.374), while we find the slope for $M_S\le$6.1 ($M_S >$ 6.1) 0.581 (0.921). These transition relationships of the magnitude moments are useful for increasing earthquake monitoring capacity.

Uncertainty around predictions from a model due to the finite size of the development sample has traditionally been approached using classical inferential techniques. The finite size of the sample will result in the discrepancy between the final model and the correct model that maps covariates to predicted risks. From a decision-theoretic perspective, this discrepancy might affect the subsequent treatment decisions, and thus is associated with utility loss. From this perspective, procuring more development data is associated in expected gain in the utility of using the model. In this work, we define the Expected Value of Sample Information (EVSI) as the expected gain in clinical utility, defined in net benefit (NB) terms in net true positive units, by procuring a further development sample of a given size. We propose a bootstrap-based algorithm for EVSI computations, and show its feasibility and face validity in a case study. Decision-theoretic metrics can complement classical inferential methods when designing studies that are aimed at developing risk prediction models.

This paper presents an approach for navigation and control in unmapped environments under input and state constraints using a composite control barrier function (CBF). We consider the scenario where real-time perception feedback (e.g., LiDAR) is used online to construct a local CBF that models local state constraints (e.g., local safety constraints such as obstacles) in the a priori unmapped environment. The approach employs a soft-maximum function to synthesize a single time-varying CBF from the N most recently obtained local CBFs. Next, the input constraints are transformed into controller-state constraints through the use of control dynamics. Then, we use a soft-minimum function to compose the input constraints with the time-varying CBF that models the a priori unmapped environment. This composition yields a single relaxed CBF, which is used in a constrained optimization to obtain an optimal control that satisfies the state and input constraints. The approach is validated through simulations of a nonholonomic ground robot that is equipped with LiDAR and navigates an unmapped environment. The robot successfully navigates the environment while avoiding the a priori unmapped obstacles and satisfying both speed and input constraints., Comment: Preprint submitted to 2025 American Control Conference (ACC). arXiv admin note: substantial text overlap with arXiv:2409.01458

Objective: Gadolinium-based contrast agents (GBCAs) are commonly used in MRI scans of patients with gliomas to enhance brain tumor characterization using T1-weighted (T1W) MRI. However, there is growing concern about GBCA toxicity. This study develops a deep-learning framework to generate T1-postcontrast (T1C) from pre-contrast multiparametric MRI. Approach: We propose the tumor-aware vision transformer (TA-ViT) model that predicts high-quality T1C images. The predicted tumor region is significantly improved (P < .001) by conditioning the transformer layers from predicted segmentation maps through adaptive layer norm zero mechanism. The predicted segmentation maps were generated with the multi-parametric residual (MPR) ViT model and transformed into a latent space to produce compressed, feature-rich representations. The TA-ViT model predicted T1C MRI images of 501 glioma cases. Selected patients were split into training (N=400), validation (N=50), and test (N=51) sets. Main Results: Both qualitative and quantitative results demonstrate that the TA-ViT model performs superior against the benchmark MRP-ViT model. Our method produces synthetic T1C MRI with high soft tissue contrast and more accurately reconstructs both the tumor and whole brain volumes. The synthesized T1C images achieved remarkable improvements in both tumor and healthy tissue regions compared to the MRP-ViT model. For healthy tissue and tumor regions, the results were as follows: NMSE: 8.53 +/- 4.61E-4; PSNR: 31.2 +/- 2.2; NCC: 0.908 +/- .041 and NMSE: 1.22 +/- 1.27E-4, PSNR: 41.3 +/- 4.7, and NCC: 0.879 +/- 0.042, respectively. Significance: The proposed method generates synthetic T1C images that closely resemble real T1C images. Future development and application of this approach may enable contrast-agent-free MRI for brain tumor patients, eliminating the risk of GBCA toxicity and simplifying the MRI scan protocol., Comment: arXiv admin note: text overlap with arXiv:2407.02616

We present a closed-form optimal feedback control method that ensures safety in an a prior unknown and potentially dynamic environment. This article considers the scenario where local perception data (e.g., LiDAR) is obtained periodically, and this data can be used to construct a local control barrier function (CBF) that models a local set that is safe for a period of time into the future. Then, we use a smooth time-varying soft-maximum function to compose the N most recently obtained local CBFs into a single barrier function that models an approximate union of the N most recently obtained local sets. This composite barrier function is used in a constrained quadratic optimization, which is solved in closed form to obtain a safe-and-optimal feedback control. We also apply the time-varying soft-maximum barrier function control to 2 robotic systems (nonholonomic ground robot with nonnegligible inertia, and quadrotor robot), where the objective is to navigate an a priori unknown environment safely and reach a target destination. In these applications, we present a simple approach to generate local CBFs from periodically obtained perception data., Comment: Preprint submitted to IEEE Transactions on Control Systems Technology (TCST)

Different activation functions work best for different deep learning models. To exploit this, we leverage recent advancements in gradient-based search techniques for neural architectures to efficiently identify high-performing activation functions for a given application. We propose a fine-grained search cell that combines basic mathematical operations to model activation functions, allowing for the exploration of novel activations. Our approach enables the identification of specialized activations, leading to improved performance in every model we tried, from image classification to language models. Moreover, the identified activations exhibit strong transferability to larger models of the same type, as well as new datasets. Importantly, our automated process for creating customized activation functions is orders of magnitude more efficient than previous approaches. It can easily be applied on top of arbitrary deep learning pipelines and thus offers a promising practical avenue for enhancing deep learning architectures., Comment: 10 pages, 1 figure, excluding references and appendix

The differential rotation plays a crucial role in the dynamics of the Sun. We study the solar rotation and its correlation with solar activity by applying a modified machine learning algorithm to identify and track coronal bright points (CBPs) from the Solar Dynamics Observatory/Atmospheric Imaging Assembly observations at 193 \AA\ during cycle 24. For more than 321,440 CBPs, the sidereal and meridional velocities are computed. We find the occurring height of CBPs about 5627 km above the photosphere. We obtain a rotational map for the corona by tracking CBPs at the formation height of Fe\,{\sc xii} (193 \AA) emissions. The equator rotation (14.$^{\circ}$40 to 14.$^{\circ}$54 day$^{-1}$) and latitudinal gradient of rotation ($ - $3.$^{\circ}$0 to $ - $2.$^{\circ}$64 day$^{-1}$) show very slightly positive and negative trends with solar activity (sunspots and flares), respectively. For cycle 24, our investigations show that the northern hemisphere has more differential rotation than the southern hemisphere, confirmed by the asymmetry of the midlatitude rotation parameter. The asymmetry (ranked) of the latitudinal gradient of the rotation parameter is concordant with the sunspot numbers for 7 yr within the 9 yr of the cycle; however, for only 3 yr, it is concordant with the flare index. The minimum horizontal Reynolds stress changes from about $ - $2500 m$^{2}$ s$^{-2}$ (corresponding to high activity) in 2012 and 2014 to $ - $100 m$^{2}$ s$^{-2}$ (corresponding to low activity) in 2019 over 5$^{\circ}$ to 35$^{\circ}$ latitudes within cycle 24. We conclude that the negative horizontal Reynolds stress (momentum transfer toward the Sun's equator) is a helpful indication of solar activity.

The regular behavior of the pulsations of high-amplitude Delta Sct (HADS) stars gives a greater chance to investigate the interiors of stars. We analyzed seven HADS stars showing peak-to-peak amplitudes of more than 0.3 mag that were newly observed by TESS. We obtained that TIC374753270, TIC710783, and TIC187386415 pulsate in fundamental radial mode, also, TIC130474019 and TIC160120432 show double radial modes. On the other hand, TIC148357344 and TIC 278119167 demonstrate triple-mode behavior. Our analysis shows that these seven stars are close to the red edge of the (inside) instability strip in the Hertzsprung Russell diagram. The fundamental mode of these seven targets follows the period-luminosity (PL) relation for Delta Sct stars. However, TIC278119167 deviates slightly from the fundamental PL relation. The double-mode and triple-mode HADS stars (TIC130474019, TIC160120432, TIC148357344, and TIC278119167) are in agreement with the period ratio ranges (fundamental to first and second overtones). Using the information of 176 HADS stars (Netzel and Smolec), we find a scaling relation ([Fe/H] ~log((M**7.95+-0.15)(L**-1.83+-0.11)(P0**0.79+-0.14)(Teff**0.047+-0.02))) between the metallicity ([Fe/H]) and mass (M), luminosity (L), effective temperature (Teff), and the fundamental period (P0). We estimate the metallicity of the seven newly identified HADS stars ranging from -0.62 to 0.37 dex.

Purpose: Apparent diffusion coefficient (ADC) maps derived from diffusion weighted (DWI) MRI provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images. Methods: We proposed the multiparametric residual vision transformer model (MPR-ViT) that leverages the long-range context of ViT layers along with the precision of convolutional operators. Residual blocks throughout the network significantly increasing the representational power of the model. The MPR-ViT model was applied to T1w and T2- fluid attenuated inversion recovery images of 501 glioma cases from a publicly available dataset including preprocessed ADC maps. Selected patients were divided into training (N=400), validation (N=50) and test (N=51) sets, respectively. Using the preprocessed ADC maps as ground truth, model performance was evaluated and compared against the Vision Convolutional Transformer (VCT) and residual vision transformer (ResViT) models. Results: The results are as follows using T1w + T2-FLAIR MRI as inputs: MPR-ViT - PSNR: 31.0 +/- 2.1, MSE: 0.009 +/- 0.0005, SSIM: 0.950 +/- 0.015. In addition, ablation studies showed the relative impact on performance of each input sequence. Both qualitative and quantitative results indicate that the proposed MR- ViT model performs favorably against the ground truth data. Conclusion: We show that high-quality ADC maps can be synthesized from structural MRI using a MPR- VCT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable., Comment: arXiv admin note: text overlap with arXiv:2311.15044

Purpose: To propose a self-supervised deep learning-based compressed sensing MRI (DL-based CS-MRI) method named "Adaptive Self-Supervised Consistency Guided Diffusion Model (ASSCGD)" to accelerate data acquisition without requiring fully sampled datasets. Materials and Methods: We used the fastMRI multi-coil brain axial T2-weighted (T2-w) dataset from 1,376 cases and single-coil brain quantitative magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) T1 maps from 318 cases to train and test our model. Robustness against domain shift was evaluated using two out-of-distribution (OOD) datasets: multi-coil brain axial postcontrast T1 -weighted (T1c) dataset from 50 cases and axial T1-weighted (T1-w) dataset from 50 patients. Data were retrospectively subsampled at acceleration rates R in {2x, 4x, 8x}. ASSCGD partitions a random sampling pattern into two disjoint sets, ensuring data consistency during training. We compared our method with ReconFormer Transformer and SS-MRI, assessing performance using normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Statistical tests included one-way analysis of variance (ANOVA) and multi-comparison Tukey's Honesty Significant Difference (HSD) tests. Results: ASSCGD preserved fine structures and brain abnormalities visually better than comparative methods at R = 8x for both multi-coil and single-coil datasets. It achieved the lowest NMSE at R in {4x, 8x}, and the highest PSNR and SSIM values at all acceleration rates for the multi-coil dataset. Similar trends were observed for the single-coil dataset, though SSIM values were comparable to ReconFormer at R in {2x, 8x}. These results were further confirmed by the voxel-wise correlation scatter plots. OOD results showed significant (p << 10^-5 ) improvements in undersampled image quality after reconstruction.

The fields of machine learning and artificial intelligence drive researchers to explore energy-efficient, brain-inspired new hardware. Reservoir computing encompasses recurrent neural networks for sequential data processing and matches the performance of other recurrent networks with less training and lower costs. However, traditional software-based neural networks suffer from high energy consumption due to computational demands and massive data transfer needs. Photonic reservoir computing overcomes this challenge with energy-efficient neuromorphic photonic integrated circuits or NeuroPICs. Here, we introduce a reservoir NeuroPIC used for modulation format identification in C-band telecommunication network monitoring. It is built on a silicon-on-insulator platform with a 4-port reservoir architecture consisting of a set of physical nodes connected via delay lines. We comprehensively describe the NeuroPIC design and fabrication, experimentally demonstrate its performance, and compare it with simulations. The NeuroPIC incorporates non-linearity through a simple digital readout and achieves close to 100% accuracy in identifying several configurations of quadrature amplitude modulation formats transmitted over 20 km of optical fiber at 32 GBaud symbol rate. The NeuroPIC performance is robust against fabrication imperfections like waveguide propagation loss, phase randomization, etc. and delay line length variations. Furthermore, the experimental results exceeded numerical simulations, which we attribute to enhanced signal interference in the experimental NeuroPIC output. Our energy-efficient photonic approach has the potential for high-speed temporal data processing in a variety of applications., Comment: 32 pages, including supporting information

Lead-free antiferroelectric perovskite $\rm AgNbO_3$ is nowadays attracting extensive research interests due to its promising applications in energy storage. Although great progress has been made in optimizing the material performance, fundamental questions remain regarding the mechanism stabilizing the antiferroelectric $Pbcm$ phase. Here, combining structural symmetry analysis and first-principles calculations, we identified crucial anharmonic couplings of oxygen octahedra rotations and cation antipolar motions which contribute significantly to lowering the energy of the $Pbcm$ phase. The stabilization of this phase shows close similarities with the stabilization of the $Pbam$ phase in $\rm PbZrO_3$ except that in $\rm AgNbO_3$ the octahedra rotations are the primary distortions while the antipolar cation motions appear to be secondary. The appearance and significant amplitude of the latter are explained from the combination of hybrid-improper and triggered mechanisms.

Optical wireless communication (OWC) emerges as a pivotal solution for achieving terabit-level aggregate throughput in next-generation wireless networks. With the mature high-speed transceivers and advanced (de)multiplexing techniques designed for fiber optics, fiber-coupled OWC can be seamlessly integrated into existing ultra-high-speed networks such as data centres. In particular, OWC leveraging spatial mode multiplexing (SMM) and few-mode fiber (FMF) coupling can significantly increase capacity, though misalignment may reduce performance. This paper presents a thorough investigation into the SMM-enabled FMF coupling OWC systems affected by link misalignment, specifically focusing on systems with intensity modulation with direct detection (IM/DD) receivers. A theoretical analysis is conducted to assess the fiber coupling efficiency of the considered system in the presence of both pointing error and angle of arrival (AOA) fluctuations caused by random device vibrations. Our model elucidates the dependence of coupling efficiency to the order of the incident modes, highlighting the critical role of beam properties in system performance. To mitigate the intermodal crosstalk arising from link misalignment, we employ zero-forcing beamforming (ZFBF) to enhance the overall aggregated data rate. Through extensive numerical results, we identify optimal system configurations encompassing aperture design and mode selection, leading to a capacity boost exceeding 200%., Comment: 13 pages, 15 figures

Magnetic resonance imaging (MRI) has revolutionized medical imaging, providing a non-invasive and highly detailed look into the human body. However, the long acquisition times of MRI present challenges, causing patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, researchers are exploring various techniques to reduce acquisition time and improve the overall efficiency of MRI. One such technique is compressed sensing (CS), which reduces data acquisition by leveraging image sparsity in transformed spaces. In recent years, deep learning (DL) has been integrated with CS-MRI, leading to a new framework that has seen remarkable growth. DL-based CS-MRI approaches are proving to be highly effective in accelerating MR imaging without compromising image quality. This review comprehensively examines DL-based CS-MRI techniques, focusing on their role in increasing MR imaging speed. We provide a detailed analysis of each category of DL-based CS-MRI including end-to-end, unroll optimization, self-supervised, and federated learning. Our systematic review highlights significant contributions and underscores the exciting potential of DL in CS-MRI. Additionally, our systematic review efficiently summarizes key results and trends in DL-based CS-MRI including quantitative metrics, the dataset used, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based CS-MRI in the advancement of medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based CS-MRI publications and publicly available datasets - https://github.com/mosaf/Awesome-DL-based-CS-MRI.

Performance prediction has been a key part of the neural architecture search (NAS) process, allowing to speed up NAS algorithms by avoiding resource-consuming network training. Although many performance predictors correlate well with ground truth performance, they require training data in the form of trained networks. Recently, zero-cost proxies have been proposed as an efficient method to estimate network performance without any training. However, they are still poorly understood, exhibit biases with network properties, and their performance is limited. Inspired by the drawbacks of zero-cost proxies, we propose neural graph features (GRAF), simple to compute properties of architectural graphs. GRAF offers fast and interpretable performance prediction while outperforming zero-cost proxies and other common encodings. In combination with other zero-cost proxies, GRAF outperforms most existing performance predictors at a fraction of the cost., Comment: ICML 2024. Code at https://github.com/gabikadlecova/zc_combine , blog post: https://gabikadlecova.github.io/blog/2024/graf/

In this paper, we put forward a proof of concept for sixth generation (6G) Terabit infrared (IR) laser-based indoor optical wireless networks. We propose a novel double-tier access point (AP) architecture based on an array of arrays of vertical cavity surface emitting lasers (VCSELs) to provide a seamless grid-of-beam coverage with multi-Gb/s per beam. We present systematic design and thorough analytical modeling of the AP architecture, which are then applied to downlink system modeling using non-imaging angle diversity receivers (ADRs). We propose static beam clustering with coordinated multi-beam joint transmission (CoMB-JT) for network interference management and devise various clustering strategies to address inter-beam interference (IBI) and inter-cluster interference (ICI). Non-orthogonal multiple access (NOMA) and orthogonal frequency division multiple access (OFDMA) schemes are also adopted to handle intra-cluster interference, and the resulting signal-to-interference-plus-noise ratio (SINR) and achievable data rate are derived. The network performance is studied in terms of spatial distributions and statistics of the downlink SINR and data rate through extensive computer simulations. The results demonstrate that data rates up to 15 Gb/s are achieved within the coverage area and a properly devised clustering strikes a balance between the sum rate and fairness depending on the number of users., Comment: 15 pages, 13 figures, 1 table

Early efforts to realize exotic quantum ground states in frustrated magnets focused on frustration arising from the lattice geometry alone. Attention has shifted to bond-dependent anisotropic interactions, as well as further-neighbor interactions, on non-geometrically-frustrated lattices due to their greater versatility. The honeycomb magnet BaCo$_2$(AsO$_4$)$_2$ recently emerged as a candidate host for both bond-dependent (e.g. Kitaev) and third-neighbor ($J_3$) interactions, and has become a model experimental system due to its relatively low levels of disorder. Understanding the relative importance of different exchange interactions holds the key to achieving novel ground states, such as quantum spin liquids. Here, we use the magnetotropic susceptibility to map out the intermediate and high-field phase diagram of BaCo$_2$(AsO$_4$)$_2$ as a function of the out-of-plane magnetic field direction at $T = 1.6$ K. We show that the experimental data are qualitatively consistent with classical Monte Carlo results of the XXZ-$J_1$-$J_3$ model with small Kitaev and off-diagonal exchange couplings included. However, the calculated critical fields are systematically larger than the experimental values. Infinite-DMRG computations on the quantum model reveal that quantum corrections from a nearby ferromagnetic state are likely responsible for the suppressed critical fields. Together, our experiment and theory analyses demonstrate that, while quantum fluctuations play an important role in determining the phase diagram, most of the physics of BaCo$_2$(AsO$_4$)$_2$ can be understood in terms of the classical dynamics of long-range ordered states, leaving little room for the possibility of a quantum spin liquid., Comment: 16 pages, 12 figures

Light, being massless, casts no shadow; under ordinary circumstances, photons pass right through each other unimpeded. Here, we demonstrate a laser beam acting like an object - the beam casts a shadow upon a surface when the beam is illuminated by another light source. We observe a regular shadow in the sense it can be seen by the naked eye, it follows the contours of the surface it falls on, and it follows the position and shape of the object (the laser beam). Specifically, we use a nonlinear optical process involving four atomic levels of ruby. We are able to control the intensity of a transmitted laser beam by applying another perpendicular laser beam. We experimentally measure the dependence of the contrast of the shadow on the power of the object laser beam, finding a maximum of approximately of approximately 22 percent, similar to that of a shadow of a tree on a sunny day. We provide a theoretical model that predicts the contrast of the shadow. This work opens new possibilities for fabrication, imaging, and illumination.

Fully describing light propagation in a rotating, anisotropic medium with thermal nonlinearity requires modeling the interplay between nonlinear refraction, birefringence, and the nonlinear group index. Incorporating these factors into a generalized nonlinear Schr\"odinger equation and fitting them to recent experimental results reveals two key relationships: the photon drag effect can have a nonlinear component that is dependent on the motion of the medium, and the temporal dynamics of the moving birefringent nonlinear medium create distorted figure-eight-like transverse trajectories at the output. The beam trajectory can be accurately modelled with a full understanding of the propagation effects. Efficiently modeling these effects and accurately predicting the beam's output position has implications for optimizing applications in velocimetry and beam-steering. Understanding the roles of competitive nonlinearities gives insight into the creation or suppression of nonlinear phenomena like self-action effects., Comment: 17 pages, 10 figures, 2 tables

We introduce a nonparametric estimator of the conditional survival function in the mixture cure model for right censored data when cure status is partially known. The estimator is developed for the setting of a single continuous covariate but it can be extended to multiple covariates. It extends the estimator of Beran (1981), which ignores cure status information. We obtain an almost sure representation, from which the strong consistency and asymptotic normality of the estimator are derived. Asymptotic expressions of the bias and variance demonstrate a reduction in the variance with respect to Beran's estimator. A simulation study shows that, if the bandwidth parameter is suitably chosen, our estimator performs better than others for an ample range of covariate values. A bootstrap bandwidth selector is proposed. Finally, the proposed estimator is applied to a real dataset studying survival of sarcoma patients.

The asymmetry of line profiles, i.e., the secondary component, is crucial to understanding the energy release of coronal holes (CH), quiet sun (QS), and bright points (BPs). We investigate the asymmetry of Si IV 1393.75 {\AA} of the transition-region (TR) line recorded by Interface Region Imaging Spectrometer (IRIS) and co-spatial-temporal Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) data onboard Solar Dynamics Observatory (SDO) for three time series on 26 April 2015, 24 July 2014, 26 July 2014. Most asymmetric profiles are in the complex magnetic field regions of the networks. The asymmetric profiles are fitted with both single and double Gaussian models. The mean value of Doppler velocity of the second component is almost zero (with a significant standard deviation) in QS/CH, which may indicate that the physical process to trigger the secondary Gaussian originates at the formation height of Si IV. While the mean Doppler velocity from secondary Gaussian in BPs is around +4.0 km/s (redshifted). The non-thermal velocities of the secondary Gaussian in all three regions are slightly higher than the single Gaussian. The statistical investigation leads to the prevalence of blueshifted secondary components in QS/CH. However, secondary Gaussian in the BPs redshifted, i.e., the BPs redshift behavior could be interpreted due to the site of reconnection located above the formation height of the Si IV line. The peak intensity of the second component for all three regions is likely to follow a power law that is a signature of the small-scale flaring-like trigger mechanism., Comment: published in Monthly Notices of the Royal Astronomical Society 22 pages, 7 figures in main text, 6 figures in Appendix A and 6 figures in Appendix B

In this paper, we investigate the dynamics of surface growth resulting from sedimentation of spherical granular particles in a fluid environment, using experiments and simulations. In the experimental part, spherical polystyrene particles are poured down from the top of a vertical Hele-Shaw cell and form a 1+1-dimensional growing surface. The surface roughness is obtained from the images and the growth and roughness exponents are measured. In the numerical simulation part, the surface growth process is simulated using the Molecular Dynamics method, considering the interactions between the grains; and the exponents are calculated. In this method, unlike conventional simulation models, instead of a discrete deposition law, the dynamics of the individual particles throughout the process are obtained considering different forces acting on the particles. Finally, the simulation results are compared with the experiment, and we see a very good agreement between them. We find different values for the exponents using different methods, which indicates that the system is multi-affine and does not obey scaling laws of affine models.

In this paper the time evolution of a two-level atom in the presence of medium-assisted thermal field is explored through which, the formula of decay rate of an excited atom is generalized in two aspects. The obtained formula applies for the thermal electromagnetic field as well as the presence of arbitrary arrangement of magneto-electric media. In order to be general with respect to the material environment, the Green's function approach is used. It is seen that the non-zero temperature contributes to the decay rate via an additive term that is equal to the zero-temperature result multiplied by two times of photon number at atomic transition frequency., Comment: 5 pages

Local search is a widely used technique for tackling challenging optimization problems, offering significant advantages in terms of computational efficiency and exhibiting strong empirical behavior across a wide range of problem domains. In this paper, we address a scheduling problem on two identical parallel machines with the objective of \emph{makespan minimization}. For this problem, we consider a local search neighborhood, called \emph{$k$-swap}, which is a more generalized version of the widely-used \emph{swap} and \emph{jump} neighborhoods. The $k$-swap neighborhood is obtained by swapping at most $k$ jobs between two machines in our schedule. First, we propose an algorithm for finding an improving neighbor in the $k$-swap neighborhood which is faster than the naive approach, and prove an almost matching lower bound on any such an algorithm. Then, we analyze the number of local search steps required to converge to a local optimum with respect to the $k$-swap neighborhood. For the case $k = 2$ (similar to the swap neighborhood), we provide a polynomial upper bound on the number of local search steps, and for the case $k = 3$, we provide an exponential lower bound. Finally, we conduct computational experiments on various families of instances, and we discuss extensions to more than two machines in our schedule.

In the field of medical sciences, reliable detection and classification of brain tumors from images remains a formidable challenge due to the rarity of tumors within the population of patients. Therefore, the ability to detect tumors in anomaly scenarios is paramount for ensuring timely interventions and improved patient outcomes. This study addresses the issue by leveraging deep learning (DL) techniques to detect and classify brain tumors in challenging situations. The curated data set from the National Brain Mapping Lab (NBML) comprises 81 patients, including 30 Tumor cases and 51 Normal cases. The detection and classification pipelines are separated into two consecutive tasks. The detection phase involved comprehensive data analysis and pre-processing to modify the number of image samples and the number of patients of each class to anomaly distribution (9 Normal per 1 Tumor) to comply with real world scenarios. Next, in addition to common evaluation metrics for the testing, we employed a novel performance evaluation method called Patient to Patient (PTP), focusing on the realistic evaluation of the model. In the detection phase, we fine-tuned a YOLOv8n detection model to detect the tumor region. Subsequent testing and evaluation yielded competitive performance both in Common Evaluation Metrics and PTP metrics. Furthermore, using the Data Efficient Image Transformer (DeiT) module, we distilled a Vision Transformer (ViT) model from a fine-tuned ResNet152 as a teacher in the classification phase. This approach demonstrates promising strides in reliable tumor detection and classification, offering potential advancements in tumor diagnosis for real-world medical imaging scenarios., Comment: This work has been submitted to the Elsevier for possible publication

One of the major challenges in particle physics and cosmology is understanding why there is an asymmetry between matter and antimatter in the Universe. One possible explanation for this phenomenon is thermal leptogenesis, which involves the addition of at least two right-handed neutrinos (RHNs) to the standard model. Another possible explanation is baryogenesis through the hypermagnetic fields which involves the ${\rm U}_Y(1)$ anomaly and helical hypermagnetic fields in the early Universe. In this paper, after reviewing the thermal leptogenesis and baryogenesis through the ${\rm U}_Y(1)$ anomaly, we investigate the simplest model that combines these two scenarios and explore the parameter space for optimal results. Our results show that the combined scenario permits a specific region of parameter space that is not covered by either one separately. In fact, the minimum required mass scale of the RHN and strength of initial hypermagnetic helicity are reduced by one order of magnitude in our model. Moreover, we find that in the combined scenario, leptogenesis and baryogenesis through the ${\rm U}_Y(1)$ anomaly can either amplify or reduce the effect of each other, i.e., the generated asymmetry, depending on the sign of the helical hypermagnetic fields. Finally, we show the surprising result that a drastic amplification can occur even when the initial abundance of RHN is its equilibrium value for leptogenesis., Comment: 23 pages, 3 figures

We propose a scheme for two-qubit gates between a flying photon and an atom in a cavity. The atom-photon gate setup consists of a cavity and a Mach-Zehnder interferometer with doubly degenerate ground and excited state energy levels mediating the atom-light interaction. We provide an error analysis of the gate and model important errors, including spatial mode mismatch between the photon and the cavity, spontaneous emission, cavity losses, detunings, and random fluctuations of the cavity parameters and frequencies. Error analysis shows that the gate protocol is more robust against experimental errors compared to previous atom-photon gates and achieves higher fidelity., Comment: 6 figures, final version