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Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB correction method that achieves at least 35 times faster processing with equivalent or superior performance on high-resolution images for the current state-of-the-art methods. Inspired by deterministic color style transfer, our approach introduces deterministic illumination color mapping, leveraging learnable projection matrices for both canonical illumination form and AWB-corrected output. It involves feeding high-resolution images and corresponding latent representations into a mapping module to derive a canonical form, followed by another mapping module that maps the pixel values to those for the corrected version. This strategy is designed as resolution-agnostic and also enables seamless integration of any pre-trained AWB network as the backbone. Experimental results confirm the effectiveness of our approach, revealing significant performance improvements and reduced time complexity compared to state-of-the-art methods. Our method provides an efficient deep learning-based AWB correction solution, promising real-time, high-quality color correction for digital imaging applications. Source code is available at https://github.com/birdortyedi/DeNIM/, Comment: 9 pages, 5 figures, ICCV 2023 Workshops (RCV 2023)

Wavefront shaping allows focusing light through or inside strongly scattering media, but the background intensity also increases due to long-range correlations, reducing the target's contrast. By manipulating non-local intensity correlations of scattered waves in a disordered system with input wavefront shaping, we create high-contrast patterns behind strongly scattering media and targeted energy delivery into a diffusive system with minimal change in the surrounding intensity. These are achieved by introducing the contrast operator and the difference operator, and utilizing their eigenstates to maximize the target-to-background intensity contrast and energy difference. This work opens the door to coherent control of non-local effects in wave transport for practical applications.

Deep generative models have gained recent traction in accelerated MRI reconstruction. Diffusion priors are particularly promising given their representational fidelity. Instead of the target transformation from undersampled to fully-sampled data required for MRI reconstruction, common diffusion priors are trained to learn a task-agnostic transformation from an asymptotic start-point of Gaussian noise onto the finite end-point of fully-sampled data. During inference, data-consistency projections are injected in between reverse diffusion steps to reach a compromise solution within the span of both the trained diffusion prior and the imaging operator for an accelerated MRI acquisition. Unfortunately, performance losses can occur due to the discrepancy between target and learned transformations given the asymptotic normality assumption in diffusion priors. To address this discrepancy, here we introduce a novel Fourier-constrained diffusion bridge (FDB) for MRI reconstruction that transforms between a finite start-point of moderately undersampled data and an end-point of fully-sampled data. We derive the theoretical formulation of FDB as a generalized diffusion process based on a stochastic degradation operator that performs random spatial-frequency removal. We propose an enhanced sampling algorithm with a learned correction term for soft dealiasing across reverse diffusion steps. Demonstrations on brain MRI indicate that FDB outperforms state-of-the-art methods including non-diffusion and diffusion priors.

Perfect synchronization in LoRa communications between Low Earth Orbit (LEO) satellites and ground base stations is still challenging, despite the potential use of atomic clocks in LEO satellites, which offer high precision. Even by incorporating atomic clocks in LEO satellites, their inherent precision can be leveraged to enhance the overall synchronization process, perfect synchronization is infeasible due to a combination of factors such as signal propagation delay, Doppler effects, clock drift and atmospheric effects. These challenges require the development of advanced synchronization techniques and algorithms to mitigate their effects and ensure reliable communication from / to LEO satellites. However, maintaining acceptable levels of synchronization rather than striving for perfection, quasisynchronous (QS) communication can be adopted which maintains communication reliability, improves resource utilization, reduces power consumption, and ensures scalability as more devices join the communication. Overall, QS communication offers a practical, adaptive, and robust solution that enables LEO satellite communications to support the growing demands of IoT applications and global connectivity. In our investigation, we explore different chip waveforms such as rectangular and raised cosine. Furthermore, for the first time, we study the Symbol Error Rate (SER) performance of QS LoRa communication, for different spreading factors (SF), over Additive White Gaussian Noise (AWGN) channels., Comment: 5 pages, 4 figures, IEEE International Black Sea Conference on Communications and Networking 2023

Black hole X-ray binaries are ideal environments to study the accretion phenomena in strong gravitational potentials. These systems undergo dramatic accretion state transitions and analysis of the X-ray spectra is used to probe the properties of the accretion disc and its evolution. In this work, we present a systematic investigation of $\sim$1800 spectra obtained by RXTE PCA observations of GRO J1655-40 and LMC X-3 to explore the nature of the accretion disc via non-relativistic and relativistic disc models describing the thermal emission in black-hole X-ray binaries. We demonstrate that the non-relativistic modelling throughout an outburst with the phenomenological multi-colour disc model DISKBB yields significantly lower and often unphysical inner disc radii and correspondingly higher ($\sim$50-60\%) disc temperatures compared to its relativistic counterparts KYNBB and KERRBB. We obtained the dimensionless spin parameters of $a_{*}=0.774 \pm 0.069 $ and $a_{*}=0.752 \pm 0.061 $ for GRO J1655-40 with KERRBB and KYNBB, respectively. We report a spin value of $a_{*}=0.098 \pm 0.063$ for LMC X-3 using the updated black hole mass of 6.98 ${M_{\odot}}$. Both measurements are consistent with the previous studies. Using our results, we highlight the importance of self-consistent modelling of the thermal emission, especially when estimating the spin with the continuum-fitting method which assumes the disc terminates at the innermost stable circular orbit at all times., Comment: Accepted for publication in MNRAS, 23 pages 17 figures

Multinucleon transfer mechanism in collision of ${}^{160}\mathrm{Gd}+{}^{186}\mathrm{W}$ system is investigated in the framework of quantal transport description, based on the stochastic mean-field (SMF) theory. The SMF theory provides a microscopic approach for nuclear dynamics beyond the time-dependent Hartree-Fock (TDHF) approach by including mean-field fluctuations. Cross-sections for the primary fragment production are determined in the quantal transport description and compared with the available data., Comment: 15 pages, 10 figures

In this paper, we study the rectifying curves in multiplicative Euclidean space of dimension 3, i.e., those curves for which the position vector always lies in its rectifying plane. Since the definition of rectifying curve is affine and not metric, we are directly able to perform multiplicative differential-geometric concepts to investigate such curves. Having presented several characterizations, we completely classify the multiplicative rectifying curves by means of the multiplicative spherical curves., Comment: 18 Pages, 4 figures

The changing level of haze is one of the main factors which affects the success of the proposed dehazing methods. However, there is a lack of controlled multi-level hazy dataset in the literature. Therefore, in this study, a new multi-level hazy color image dataset is presented. Color video data is captured for two real scenes with a controlled level of haze. The distance of the scene objects from the camera, haze level, and ground truth (clear image) are available so that different dehazing methods and models can be benchmarked. In this study, the dehazing performance of five different dehazing methods/models is compared on the dataset based on SSIM, PSNR, VSI and DISTS image quality metrics. Results show that traditional methods can generalize the dehazing problem better than many deep learning based methods. The performance of deep models depends mostly on the scene and is generally poor on cross-dataset dehazing., Comment: 13 pages

The STAR collaboration has observed an excess production of electron-positron pairs which have transverse momenta pperp < 150 MeV/c in peripheral gold-gold and uranium-uranium collisions. ALICE has also reported on an excess of mu mu pairs at low pperp in very peripheral lead-lead collisions at a center-of-mass energy of 2.76 TeV/nucleon pair. In literature, there are number of studies about the electromagnetic lepton pair productions in peripheral heavy-ion collisions. Almost all of them have a prediction that unitary is violated in these high energies so that probability of producing electron-positron pairs is greater than one. It is clear that at RHIC and LHC energies, probabilities of producing electron-positron pairs for impact parameters smaller than Compton-wavelength of electron is greater than one. This means that for small impact parameters where the electric field is strongest multiple electron-positron pair productions is inescapable and it must be included in calculations. In literature, there are number of explanations about the pperp broadening however none of them has included the multi-pair production processes in their calculations. The aim of this paper is to show that electron-positron multi-pair production cross section is large and it can not be ignored in calculations.

One of the main objectives of modern cosmology is to explain the origin and evolution of cosmic structures at different scales. The principal force responsible for the formation of such structures is gravity. In a general relativistic framework, we have shown that matter density contrasts do not grow over time at scales exceeding the cosmic screening length, which corresponds to a cosmological scale of the order of two to three gigaparsecs at the present time, at which gravitational interactions exhibit an exponential cut-off. This is a purely relativistic effect. To demonstrate the suppression of density growth, we have performed N-body simulations in a box with a comoving size of $5.632\,{\rm Gpc}/h$ and obtained the power spectrum of the mass density contrast. We have shown that it becomes independent of time for scales beyond the cosmic screening length as a clear manifestation of the cosmic screening effect., Comment: 9 pages, 3 figures. This is the Accepted Manuscript version of an article accepted for publication in Journal of Cosmology and Astroparticle Physics. Neither SISSA Medialab Srl nor IOP Publishing Ltd is responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at 10.1088/1475-7516/2024/05/083

The ability to reversibly toggle between two distinct states in a non-volatile method is important for information storage applications. Such devices have been realized for phase-change materials, which utilizes local heating methods to toggle between a crystalline and an amorphous state with distinct electrical properties. To expand such kind of switching between two topologically distinct phases requires non-volatile switching between two crystalline phases with distinct symmetries. Here we report the observation of reversible and non-volatile switching between two stable and closely-related crystal structures with remarkably distinct electronic structures in the near room temperature van der Waals ferromagnet Fe$_{5-\delta}$GeTe$_2$. From a combination of characterization techniques we show that the switching is enabled by the ordering and disordering of an Fe site vacancy that results in distinct crystalline symmetries of the two phases that can be controlled by a thermal annealing and quenching method. Furthermore, from symmetry analysis as well as first principle calculations, we provide understanding of the key distinction in the observed electronic structures of the two phases: topological nodal lines compatible with the preserved global inversion symmetry in the site-disordered phase, and flat bands resulting from quantum destructive interference on a bipartite crystaline lattice formed by the presence of the site order as well as the lifting of the topological degeneracy due to the broken inversion symmetry in the site-ordered phase. Our work not only reveals a rich variety of quantum phases emergent in the metallic van der Waals ferromagnets due to the presence of site ordering, but also demonstrates the potential of these highly tunable two-dimensional magnets for memory and spintronics applications.

Both data ferrying with disruption-tolerant networking (DTN) and mobile cellular base stations constitute important techniques for UAV-aided communication in situations of crises where standard communication infrastructure is unavailable. For optimal use of a limited number of UAVs, we propose providing both DTN and a cellular base station on each UAV. Here, DTN is used for large amounts of low-priority data, while capacity-constrained cell coverage remains reserved for emergency calls or command and control. We optimize cell coverage via a novel optimal transport-based formulation using alternating minimization, while for data ferrying we periodically deliver data between dynamic clusters by solving quadratic assignment problems. In our evaluation, we consider different scenarios with varying mobility models and a wide range of flight patterns. Overall, we tractably achieve optimal cell coverage under quality-of-service costs with DTN-based data ferrying, enabling large-scale deployment of UAV swarms for crisis communication., Comment: Accepted to IEEE LCN 2023 as full paper, pre-final version

Magnetism in two-dimensional (2D) materials has attracted considerable attention recently for both fundamental understanding of magnetism and their tunability towards device applications. The isostructural Fe$_3$GeTe$_2$ and Fe$_3$GaTe$_2$ are two members of the Fe-based van der Waals (vdW) ferromagnet family, but exhibit very different Curie temperatures (T$_C$) of 210 K and 360 K, respectively. Here, by using angle-resolved photoemission spectroscopy and density functional theory, we systematically compare the electronic structures of the two compounds. Qualitative similarities in the Fermi surface can be found between the two compounds, with expanded hole pockets in Fe$_3$GaTe$_2$ suggesting additional hole carriers compared to Fe$_3$GeTe$_2$. Interestingly, we observe no band shift in Fe$_3$GaTe$_2$ across its T$_C$ of 360 K, compared to a small shift in Fe$_3$GeTe$_2$ across its T$_C$ of 210 K. The weak temperature-dependent evolution strongly deviates from the expectations of an itinerant Stoner mechanism. Our results suggest that itinerant electrons have minimal contributions to the enhancement of T$_C$ in Fe$_3$GaTe$_2$ compared to Fe$_3$GeTe$_2$, and that the nature of ferromagnetism in these Fe-based vdW ferromagnets must be understood with considerations of the electron correlations.

Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it is an inherently slow imaging modality. Promising deep learning methods have recently been proposed to reconstruct accelerated MRI scans. However, existing methods still suffer from various limitations regarding image fidelity, contextual sensitivity, and reliance on fully-sampled acquisitions for model training. To comprehensively address these limitations, we propose a novel self-supervised deep reconstruction model, named Self-Supervised Diffusion Reconstruction (SSDiffRecon). SSDiffRecon expresses a conditional diffusion process as an unrolled architecture that interleaves cross-attention transformers for reverse diffusion steps with data-consistency blocks for physics-driven processing. Unlike recent diffusion methods for MRI reconstruction, a self-supervision strategy is adopted to train SSDiffRecon using only undersampled k-space data. Comprehensive experiments on public brain MR datasets demonstrates the superiority of SSDiffRecon against state-of-the-art supervised, and self-supervised baselines in terms of reconstruction speed and quality. Implementation will be available at https://github.com/yilmazkorkmaz1/SSDiffRecon.

The lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances the state-of-the-art by proposing an adaptive motion-compensation model for end-to-end rate-distortion optimized hierarchical bi-directional video compression. In particular, we propose two novelties: i) a multi-scale deformable alignment scheme at the feature level combined with multi-scale conditional coding, ii) motion-content adaptive inference. In addition, we employ a gain unit, which enables a single model to operate at multiple rate-distortion operating points. We also exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding., Comment: Accepted for publication in IEEE International Conference on Image Processing (ICIP) 2023

Wide-angle cameras are widely used in photogrammetry and autonomous systems which rely on the accurate metric measurements derived from images. To find the geometric relationship between incoming rays and image pixels, geometric camera calibration (GCC) has been actively developed. Aiming to provide practical calibration guidelines, this work surveys the existing GCC tools and evaluates the representative ones for wide-angle cameras. The survey covers camera models, calibration targets, and algorithms used in these tools, highlighting their properties and the trends in GCC development. The evaluation compares six target-based GCC tools, namely, BabelCalib, Basalt, Camodocal, Kalibr, the MATLAB calibrator, and the OpenCV-based ROS calibrator, with simulated and real data for wide-angle cameras described by four parametric projection models. These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools. In view of the survey and evaluation, future research directions of wide-angle GCC are also discussed., Comment: 18 pages, 12 figures

Let $k$ be an algebraically closed field of characteristic $2$, let $G$ be a finite group and let $B$ be the principal $2$-block of $kG$ with a dihedral or a generalised quaternion defect group $P$. Let also $\mathcal{T}(B)$ denote the group of splendid Morita auto-equivalences of $B$. We show that \begin{align*} \mathcal{T}(B)\cong \mathrm{Out}_P(A)\rtimes \mathrm{Out}(P,\mathcal{F})\,, \end{align*} where $\mathrm{Out}(P,\mathcal{F})$ is the group of outer automorphisms of $P$ which stabilize the fusion system $\mathcal{F}$ of $G$ on $P$ and $\mathrm{Out}_P(A)$ is the group of algebra automorphisms of a source algebra $A$ of $B$ fixing $P$ modulo inner automorphisms induced by $(A^P)^\times$.

In this work, cross section calculations of bound-free pair production (BFPP) are done for the mechanism in Pb-p collisions at LHC. BFPP cross section for the asymmetric collisions of Pb-p at the center of mass energies of $\sqrt{s_{NN}}=5,02 TeV$ and $\sqrt{s_{NN}}=8,16 TeV$ is computed. In order to reach the exact results, Monte Carlo integration techniques are utilized to calculate the lowest-order Feynman diagrams amplitudes via the lowest order perturbation theory. Also, in this work our cross section results for BFPP mechanism in Pb-p collisions at LHC are compared with BFPP cross section results obtained in literature, which are reached for Pb-p collisions by using a simple scaling applied to scale BFPP cross section results in Pb-Pb collisions at LHC.

In previous work, we deployed IssueTAG, which uses the texts present in the one-line summary and the description fields of the issue reports to automatically assign them to the stakeholders, who are responsible for resolving the reported issues. Since its deployment on January 12, 2018 at Softtech, i.e., the software subsidiary of the largest private bank in Turkey, IssueTAG has made a total of 301,752 assignments (as of November 2021). One observation we make is that a large fraction of the issue reports submitted to Softtech has screenshot attachments and, in the presence of such attachments, the reports often convey less information in their one-line summary and the description fields, which tends to reduce the assignment accuracy. In this work, we use the screenshot attachments as an additional source of information to further improve the assignment accuracy, which (to the best of our knowledge) has not been studied before in this context. In particular, we develop a number of multi-source (using both the issue reports and the screenshot attachments) and single-source assignment models (using either the issue reports or the screenshot attachments) and empirically evaluate them on real issue reports. In the experiments, compared to the currently deployed single-source model in the field, the best multi-source model developed in this work, significantly (both in the practical and statistical sense) improved the assignment accuracy for the issue reports with screenshot attachments from 0.843 to 0.858 at acceptable overhead costs, a result strongly supporting our basic hypothesis., Comment: Preprint for EMSE journal

Tensor robust principal component analysis (robust PCA) has been applied to the lightning images. Robust PCA aims to classify the images into low-rank and sparse components. The low rank and sparse components correspond to static background separation and the dynamic (lightning) part of the images correspondingly. After classification, Singular Value Decomposition (SVD) unfold technique has been applied to sparse tensor, which transforms the tensor to spatial-temporal spaces in the form of vector matrixes. Spectral evolution shows the evolution of the polarization of the UV-Vis spectra. The contour maps of 2D energy density plots reveal the zero-point energy fluctuations of the bosons, fermions, and virtual matters from early to late stages. Detection of such fluctuations in the early stages can help to remote sensing of the lightning. 3D vector(ether) field representation of the sparse tensor of the brightest lightning reveals the butterfly effect of the black holes with the signatures of flapping. There is a correlation between the approximate entropy and the chaos trends of the 3D vector field analyzed by the Lyapunov exponent. In the brightest frame, the fractal dimension measures 0.8004, which is close to the Fibonacci Hamiltonian.