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Current-induced spin-orbit torque (SOT) has emerged as a promising method for achieving energy-efficient magnetization switching in advanced spintronic devices. However, technological advancement has been inadequate because an external in-plane magnetic field is required to attain deterministic switching. Several approaches have been explored to address these challenges. In this work, we explored the potential of a newly emerged altermagnetic material RuO2 in combination with a Pt layer to achieve both field-free and low-power switching concurrently. We leveraged out-of-plane (OOP) spin polarization via the spin-splitter effect (SSE) in RuO2 for field-free switching (FFS) and in-plane spin polarization combined with spin Hall effect (SHE) in Pt for enhanced SOT efficiency. We revealed that the effective OOP magnetic field and FFS can be maximized by tuning the nominal thickness of the Pt underlayer and the direction of the applied current. We observed a significant enhancement in FFS at an optimized Pt thickness of 1.5 nm for an applied current density as low as 2.56e11 A/m2 at a crystal angle of 90 deg. Our study paves the way for energy-efficient spintronics devices for non-volatile memory, logic circuits, and neuromorphic computing., Comment: 15 pages, 5 figures

In this study, we investigate the cosmic expansion scenario by using the Gong - Zhang parameter in the framework of $f(R, L_m)$ gravity, for a specific form of $f (R,L_m)=\frac{R}{2}+L_{m}^{n}$. We get the Hubble parameters in terms of the red-shift $(z)$ obtained using the Gong-Zhang parameter $\omega = \frac{w_o}{z+1}$. Using Bayesian statistical methods based on the $\chi^{2}$-minimization technique, we obtain the median values of this model's model parameters for the cosmic chronometer and Joint (CC+Pantheon) data sets. We deliberate about the behaviour of key cosmological parameters, for instance, the deceleration parameter, and equation of state, energy density $(\rho)$, pressure $(p)$, and the effect of the energy conditions with median values of the model parameters. Additionally, we have examined the behavior of the Statefinder diagnostics and determine the universe's current age of this model., Comment: 18 pages, 11 figures

Kesterite is a leading candidate among inorganic thin-film photovoltaic technologies, offering sustainable and environmentally friendly solutions without reliance on critical raw materials. This study investigates the performance of CZTSSe-based kesterite solar cells using SCAPS-1D simulations. Four device configurations are analyzed by integrating the CZTSSe absorber layer with buffer materials, including CdS, SnS2, IGZO, and ZnSe, selected based on their energy band alignment. Moreover, key parameters influencing device efficiency, such as absorber defect density, buffer layer thickness, and the donor and defect densities of the buffer materials, are systematically examined. The thickness of the absorber layer and acceptor density are optimized, considering practical manufacturing constraints. Following optimization, the i-ZnO/SnS2/CZTSSe/Au configuration achieves a notable efficiency of 28.38%, with a VOC of 0.83 V, a JSC of 39.93 mA/cm2, and a fill factor of 85.4%. Furthermore, the stability of the optimized structures is evaluated under varying conditions, including resistances, temperature, generation and recombination dynamics, as well as JV and QE characteristics. These findings provide valuable insights for advancing the efficiency and stability of CZTSSe solar cells, contributing to the development of sustainable photovoltaic technologies., Comment: 25 pages, 10 figures, 2 tables

General matrix multiplication (GeMM) is a core operation in virtually all AI applications. Systolic array (SA) based architectures have shown great promise as GeMM hardware accelerators thanks to their speed and energy efficiency. Unfortunately, SAs incur a linear delay in filling the operands, due to unidirectional propagation via pipeline latches. In this work, we propose a novel in-array data orchestration technique in SAs where we enable data feeding on the principal diagonal followed by bi-directional propagation. This improves the runtime by up to 2X at minimal hardware overhead. In addition, the proposed data orchestration enables convolution lowering (known as im2col) using a simple hardware support to fully exploit input feature map reuse opportunity and significantly lower the off-chip memory traffic resulting in 1.2X throughput improvement and 2.17X inference energy reduction during YOLOv3 and RESNET50 workload on average. In contrast, conventional data orchestration would require more elaborate hardware and control signals to implement im2col in hardware because of the data skew. We have synthesized and conducted place and route for 16X16 systolic arrays based on the novel and conventional orchestrations using ASAP 7nm PDK and found that our proposed approach results in 0.211% area and 1.6% power overheads., Comment: Accepted for Design Automation and Test in Europe (DATE), 2025. This is preprint of the accepted version

In this letter, we have used a non-unitary mixing scheme to resolve the tension between NO$\nu$A and T2K data. It is demonstrated that the results of NO$\nu$A and T2K can be explained by the effects by non-unitary mixing arising from $\alpha_{00}$ and $\alpha_{10}$. For $\alpha_{00}$ there is a large overlap between the allowed NO$\nu$A and T2K regions for NH on the $\sin^2\theta_{23}-\delta_{\rm CP}$ plane at $1\,\sigma$. However, the tension still exists. NO$\nu$A rules out unitary mixing at a $3\,\sigma$ level, whereas T2K strongly prefers unitary mixing. For $\alpha_{10}$, the tension can be \textbf{well resolved} with the best-fit point for NH at $|\alpha_{10}|=0.06$ for both experiments., Comment: 9 pages, 8 figures, 2 tables

Germanium dioxide (r-GeO2) is an emerging new ultrawide bandgap (UWBG) semiconductor with significant potential for power electronics, thanks to its large-size substrate compatibility and ambipolar doping capability. However, phase segregation during metal-organic chemical vapor deposition (MOCVD) on substrates like r-TiO2 has posed a significant barrier to achieving high-quality films. Conventional optimization of growth parameters has been found so far not very insufficient in film coverage and film quality. To address this, a seed-driven stepwise crystallization (SDSC) growth approach was employed in this study, featuring multiple sequential deposition steps on a pre-templated substrate enriched with r-GeO2 seeds. The process began with an initial 180-minute deposition to establish r-GeO2 nucleation seeds, followed by a sequence of shorter deposition steps (90, 60, 60, 60, 60, and 60 minutes). This stepwise growth strategy progressively increased the crystalline coverage to 57.4%, 77.49%, 79.73%, 93.27%, 99.17%, and ultimately 100%. Concurrently, the crystalline quality improved substantially, evidenced by a ~30% reduction in the Full Width at Half Maximum (FWHM) of X-ray diffraction rocking curves. These findings demonstrate the potential of the SDSC approach for overcoming phase segregation and achieving high-quality, large-area r-GeO2 films., Comment: 17 pages, 5 figures, 1 Table

Identifying a full basis of operators to a given order is key to the generality of Effective Field Theory (EFT) and is by now a problem of known solution in terms of the Hilbert series. The present work is concerned with hidden symmetry in general and Higgs EFT in particular and {\it(i)} connects the counting formula presented in [1] in the CCWZ formulation with the linear frame and makes this connection explicit in HEFT {\it (ii)} outlines the differences in perturbation theory in each frame {\it (iii)} presents a new counting formula with measure in the full $SU(3)\times SU(2)\times U(1)$ group for HEFT and {\it (iv)} provides a Mathematica code that produces the number of operators at the user-specified order in HEFT., Comment: 34 pages, 1 figure, 12 tables

It has recently been conjectured \cite{Kim:2023sig} that the end point of the rotational superradiant instability of black holes in $AdS_4$ is a Grey Galaxy: an $\omega=1$ black hole sitting at the centre of $AdS_4$, surrounded by a large disk of rapidly rotating gravitons and other bulk fields. In this paper we study Grey Galaxies in $AdS_5$. In this case, the rotational group is of rank 2, and so has two distinct angular velocities $\omega_1$ and $\omega_2$. We demonstrate that $AdS_5$ hosts two qualitatively distinct Grey Galaxy phases: the first with either $\omega_1\approx 1$ or $\omega_2\approx 1$, and the second with both angular velocities $\approx 1$. We use these results to present a conjecture for a part of the phase diagram of ${\cal N}=4$ Yang-Mills (as a function of energy and the two angular momenta) that displays several phase transitions between regular black holes and various Grey Galaxy phases. We present an explicit gravitational construction of the phases in which $\omega_1$ and $\omega_2$ are both parametrically close to unity, and demonstrate that the corresponding boundary stress tensor is the sum of two pieces. The first is the stress tensor of the central black hole. The second - the contribution of the bulk gas - takes the form of the stress tensor of an equilibrated boundary conformal fluid, rotating at the given angular speeds $\omega_i$. We also briefly comment on the structure of Grey Galaxies in $AdS_D$ for $D > 5$., Comment: 71 pages, 4 figures

Considerable study has already been conducted regarding autonomous driving in modern era. An autonomous driving system must be extremely good at detecting objects surrounding the car to ensure safety. In this paper, classification, and estimation of an object's (pedestrian) position (concerning an ego 3D coordinate system) are studied and the distance between the ego vehicle and the object in the context of autonomous driving is measured. To classify the object, faster Region-based Convolution Neural Network (R-CNN) with inception v2 is utilized. First, a network is trained with customized dataset to estimate the reference position of objects as well as the distance from the vehicle. From camera calibration to computing the distance, cutting-edge technologies of computer vision algorithms in a series of processes are applied to generate a 3D reference point of the region of interest. The foremost step in this process is generating a disparity map using the concept of stereo vision.

Strong lensing directly probes the internal structure of the lensing galaxies. In this paper, we investigate the relation between the internal structure of massive elliptical galaxies and their environment using a sample of 15 strong lensing systems. We performed lens modeling for them using Lenstronomy and constrained the mass and light distributions of the deflector galaxies. We adopt the local galaxy density as a metric for the environment and test our results against several alternative definitions of it. We robustly find that the centroid offset between the mass and light is not correlated with the local galaxy density. This result supports using centroid offsets as a probe of dark matter theories since the environment's impact on it can be treated as negligible. Although we find a strong correlation between the position angle offset and the standard definition of the local galaxy density, consistent with previous studies, the correlation becomes weaker for alternative definitions of the local galaxy density. This result weakens the support for interpreting the position angle misalignment as having originated from interaction with the environment. Furthermore, we find the 'residual shear' magnitude in the lens model to be uncorrelated with the local galaxy density, supporting the interpretation of the residual shear originating, in part, from the inadequacy in modeling the angular structure of the lensing galaxy and not solely from the structures present in the environment or along the line of sight., Comment: 15 pages, 9 figures, 3 tables. Submitted to A&A

In [Phys. Rev. A 77, 060304(R),(2008)], Facchi et al. introduced absolutely maximally entangled (AME) states and also suggested ``majority-agreed key distribution"(MAKD) as a possible application for such states. In MAKD, the qubits of an AME state are distributed one each to many spatially separated parties. AME property makes it necessary that quantum key distribution(QKD) between any two parties can only be performed with the cooperation of a majority of parties. Our contributions to MAKD are, $(1)$ We recognize that stabilizer structure of the shared state is a useful addition to MAKD and prove that the cooperation of any majority of parties(including the two communicants) is necessary and sufficient for QKD between any two parties sharing AME stabilizer states. Considering the rarity of qubit AME states, we extended this result to the qudit case. $(2)$ We generalize to shared graph states that are not necessarily AME. We show that the stabilizer structure of graph states allows for QKD between any inseparable bipartition of qubits. Inseparability in graph states is visually apparent in the connectivity of its underlying mathematical graph. We exploit this connectivity to demonstrate conference keys and multiple independent keys per shared state. Recent experimental and theoretical progress in graph state preparation and self-testing make these protocols feasible in the near future., Comment: 14 pages, 8 figures, 2 tables

While AI models have demonstrated remarkable capabilities in constrained domains like game strategy, their potential for genuine creativity in open-ended domains like art remains debated. We explore this question by examining how AI can transcend human cognitive limitations in visual art creation. Our research hypothesizes that visual art contains a vast unexplored space of conceptual combinations, constrained not by inherent incompatibility, but by cognitive limitations imposed by artists' cultural, temporal, geographical and social contexts. To test this hypothesis, we present the Alien Recombination method, a novel approach utilizing fine-tuned large language models to identify and generate concept combinations that lie beyond human cognitive availability. The system models and deliberately counteracts human availability bias, the tendency to rely on immediately accessible examples, to discover novel artistic combinations. This system not only produces combinations that have never been attempted before within our dataset but also identifies and generates combinations that are cognitively unavailable to all artists in the domain. Furthermore, we translate these combinations into visual representations, enabling the exploration of subjective perceptions of novelty. Our findings suggest that cognitive unavailability is a promising metric for optimizing artistic novelty, outperforming merely temperature scaling without additional evaluation criteria. This approach uses generative models to connect previously unconnected ideas, providing new insight into the potential of framing AI-driven creativity as a combinatorial problem., Comment: NeurIPS 2024 Workshop on Creativity & Generative AI, 13 pages, 11 figures

In light of its many benefits, home automation systems are one of the subjects that are becoming ever more prevalent. The term "home automation" describes the remote monitoring and management of household equipment. The Internet and its usages are constantly expanding, which means there is a lot of room for remote access, management, and surveillance of these network-enabled systems. Nowadays, scientists and researchers are developing cutting-edge prototypes of home automation system which includes smart lighting system, smart kitchen, smart fire protection devices, smart lawn mower, smart health monitor system and so on. Each automated system's primary objective is its ability to reduce human labor, effort, time, and mistakes brought on by carelessness. The objective of this study is to provide in-depth evaluation of the newly developed home automation system. Moreover, state-of-the-art home automation topologies such as ZigBee, Z-wave, Wi-Fi and Bluetooth are also discussed here. The authors are optimistic that this study would have a major impact on the present advances in home automation technology.

We investigate the astrophysical consequences of black holes in quadratic gravity, characterized by the parameters $S_0$, $S_2$, $m_0$ and $m_2$, in addition to the black hole mass $M$. To evaluate the physical validity of the fundamental quadratic gravity black hole solutions, we analyze their gravitational lensing properties in the strong field regime. Specifically, we examine the shadow cast by the quadratic gravity black hole and constrain its parameters using observational data from the $M87^*$ and $Sgr A^*$ supermassive black holes. Our analysis reveals that, within the $1\sigma$ confidence level, a significant portion of the parameter space for quadratic gravity black holes is consistent with the Event Horizon Telescope (EHT) observations of $M87^*$ and $Sgr A^*$. This suggests that these black holes are plausible candidates for describing astrophysical black holes. As an additional observational test, we perform a detailed investigation of the strong gravitational lensing properties of these black holes. We explore the fundamental strong lensing observables in detail, including the angular positions and separations of the lensed images, the relative magnifications, the radius of the outermost Einstein ring and the relativistic time delay between images. We compare the predictions of the quadratic gravity black hole for each observable with those of the classical Schwarzschild solution using realistic astrophysical data. Our findings provide a pathway for testing quadratic gravity at the galactic and extragalactic scales, offering new insights into the observational properties of black hole solutions within this framework., Comment: 30 pages, 44 figures

Dispersion of mobile robots over the nodes of an anonymous graph is an important problem and turns out to be a crucial subroutine for designing efficient algorithms for many fundamental graph problems via mobile robots. In this problem, starting from an arbitrary initial distribution of $n$ robots across the $n$ nodes, the goal is to achieve a final configuration where each node holds at most one robot. This paper investigates the dispersion problem on an oriented grid, considering the possibility of robot failures (crashes) at any time during the algorithm's execution. We present a crash-tolerant dispersion algorithm that solves the dispersion problem on an anonymous oriented grid in $O(\sqrt{n})$ time and using $O(\log n)$ bits of memory per robot. The algorithm is optimal in terms of both time and memory per robot. We further extend this algorithm to deal with weak Byzantine robots. The weak Byzantine fault dispersion algorithm takes optimal $O(\sqrt{n})$ rounds but requires $O(n\log n)$ bits of memory per robot., Comment: arXiv admin note: substantial text overlap with arXiv:2405.02002

Ultrawide bandgap (UWBG) semiconductors are promising for next-generation power electronics, largely attributed to their substantial bandgap and exceptional breakdown electric field. Rutile GeO2 (r-GeO2) emerges as a promising alternative, particularly because of its ambipolar dopability. However, research on r-GeO2 is still in its infancy, and further investigation into its structural properties is essential for enhancing epilayer quality. In our previous work, we identified distinct surface morphologies; square-patterned and smooth regions of epitaxial r-GeO2 films grown on r-TiO2 (001) substrates using metal-organic chemical vapor deposition (MOCVD).This research employs transmission electron microscopy (TEM) to investigate the structural characteristics of the material. The findings indicate that the square-patterned regions are crystalline, whereas the smooth regions exhibit amorphous properties. The measured lattice spacing in the (110) plane is 0.324 nm, slightly exceeding the theoretical value of 0.312 nm. This discrepancy suggests the presence of tensile strain in the r-GeO2 film, resulting from lattice mismatch or thermal expansion differences with the substrate. We also observed a threading dislocation density of 1.83*10^9 cm-2, consisting of 11.76% screw-type, 29.41% edge-type, 55.89% mixed-type dislocations, and 2.94% planar defects. These findings offer valuable insights into the growth mechanisms and defect characteristics of r-GeO2., Comment: 19 pages, 5 figures

The rise of large language models (LLMs) has significantly impacted various domains, including natural language processing (NLP) and image generation, by making complex computational tasks more accessible. While LLMs demonstrate impressive generative capabilities, there is an ongoing debate about their level of "understanding," particularly in structured domains like SQL. In this paper, we evaluate the extent to which LLMs "understand" SQL by testing them on a series of key SQL tasks. These tasks, such as syntax error detection, missing token identification, query performance prediction, query equivalence checking, and query explanation, assess the models' proficiency in recognition, context awareness, semantics, and coherence, which are essential skills for SQL understanding. We generate labeled datasets from well-known workloads, and evaluate the latest LLMs, focusing on how query complexity and syntactic features influence performance. Our results indicate that while GPT4 excels at tasks requiring recognition and context, all models struggle with deeper semantic understanding and coherence, especially in query equivalence and performance estimation, revealing the limitations of current LLMs in achieving full SQL comprehension., Comment: 12 pages conference submission

While Large language model (LLM)-based programming assistants such as CoPilot and ChatGPT can help improve the productivity of professional software developers, they can also facilitate cheating in introductory computer programming courses. Assuming instructors have limited control over the industrial-strength models, this paper investigates the baseline performance of 5 widely used LLMs on a collection of introductory programming problems, examines adversarial perturbations to degrade their performance, and describes the results of a user study aimed at understanding the efficacy of such perturbations in hindering actual code generation for introductory programming assignments. The user study suggests that i) perturbations combinedly reduced the average correctness score by 77%, ii) the drop in correctness caused by these perturbations was affected based on their detectability.

Protests are public expressions of personal or collective discontent with the current state of affairs. Although traditional protests involve in-person events, the ubiquity of computers and software opened up a new avenue for activism: protestware. The roots of protestware date back to the early days of computing. However, recent events in the Russo-Ukrainian war has sparked a new wave of protestware. While news and media are heavily reporting on individual protestware as they are discovered, the understanding of such software as a whole is severely limited. In particular, we do not have a detailed understanding of their characteristics and their impact on the community. To address this gap, we first collect 32 samples of protestware. Then, with these samples, we formulate characteristics of protestware using inductive analysis. In addition, we analyze the aftermath of the protestware which has potential to affect the software supply chain in terms of community sentiment and usage. We report that: (1) protestware has three notable characteristics, namely, i) the "nature of inducing protest" is diverse, ii) the "nature of targeting users" is discriminatory, and iii) the "nature of transparency" is not always respected; (2) disruptive protestware may cause substantial adverse impact on downstream users; (3) developers of protestware may not shift their beliefs even with pushback; (4) the usage of protestware from JavaScript libraries has been seen to generally increase over time.

This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

We examine a static spherically symmetric black hole metric that originates from the vacuum solution of the traceless metric-affine bumblebee model in which spontaneous Lorentz symmetry-breaking occurs when the bumblebee fields acquire a non-vanishing vacuum expectation value. A free Lorentz-violating parameter enters into the basic formulation of the metric-affine bumblebee model. In this study, we use observations from the Event Horizon Telescope (EHT) collaboration on $M87^*$ and $SgrA^*$ to analyse the shadow of the black hole and an attempt has been made to constrain that free Lorentz-violating parameter. We also investigate particle motion over time-like geodesics and compute the corresponding epicyclic frequencies. We further constrain the Lorentz-violating parameter by using the reported high-frequency quasi-periodic oscillations (QPOs) of microquasars, offering new insights into its possible impact on astrophysical phenomena., Comment: 13 pages latex, 6 figures

CIZA J2242.8+5301, or the Sausage cluster, is well studied over a range of frequencies. Since its first discovery, a lot of interesting features and unique characteristics have been uncovered. In this work, we report some more new morphological features using the uGMRT band-3 and band-4 data. In the north relic, we observe variation in spectral index profiles across the relic width from the east to west, which may indicate a decrease in downstream cooling rate in that direction. We re-confirm the presence of an additional ~ 930 kpc relic in the north. We classify the filamentary source in the downstream region to be a narrow angle tail (NAT) radio galaxy. The bright arc in the east relic shows sub-structure in the spectral index profile, which may indicate the presence of finer filaments. We further report the presence of a double-strand structure in the east relic similar to the 'Toothbrush' relic. We categorize the bright 'L' shaped structure in the southern relic to be a NAT radio galaxy, as well as trace the actual ~ 1.1 Mpc relic component. We re-confirm the existence of the faint southern extent, measuring the relic length to be ~ 1.8 Mpc. Furthermore, we suggest the southern relic to be a union of individual component relics rather than a single giant filamentary relic. Lastly, based on the morphological symmetry between northern and southern relics, we suggest a schematic shock structure associated with the merger event in an attempt to explain their formation scenario., Comment: 23 pages, 18 figures, 3 tables, submitted to ApJ

Following the work of Castillo-Hall-Oliver-Pollack-Thompson who extended Maynard-Tao theorem on admissible tuples to number fields and function fields for tuples with monic linear forms, here we obtain the Maynard-Tao theorem for admissible tuples of linear forms with arbitrary leading coefficients in number fields and function fields. Also, we provide some applications of our results., Comment: 15 pages

The popularity of the Internet of Things (IoT) has driven its usage in our homes and industries over the past 10-12 years. However, there have been some major issues related to identity management and ownership transfer involving IoT devices, particularly for consumer IoT devices, e. g. smart appliances such as smart TVs, smart refrigerators, and so on. There have been a few attempts to address this issue; however, user-centric and effective ownership and identity management of IoT devices have not been very successful so far. Recently, blockchain technology has been used to address these issues with limited success. This article presents a Self-sovereign Identity (SSI) based system that facilitates a secure and user-centric ownership management and transfer of consumer IoT devices. The system leverages a number of emerging technologies, such as blockchain and decentralized identifiers (DID), verifiable credentials (VC), under the umbrella of SSI. We present the architecture of the system based on a threat model and requirement analysis, discuss the implementation of a Proof-of-Concept based on the proposed system and illustrate a number of use-cases with their detailed protocol flows. Furthermore, we analyse its security using ProVerif, a state-of-the art protocol verification tool and examine its performance.

For all integers $r,s>2$, there are infinitely many primes which can be written as the sum of an $r$-gonal number and an $s$-gonal number. In general, any linear combination of an $r$-gonal number and an $s$-gonal number with coprime positive integer coefficients produces infinitely many primes. However, unless $r=s=4$, such primes can not be described by finitely many congruences., Comment: 6 pages

Multiwavelength studies of galaxy clusters are crucial for understanding the complex interconnection of the thermal and non-thermal constituents of these massive structures and uncovering the physical processes involved in their formation and evolution. Here, we report a multiwavelength assessment of the galaxy cluster A384, which was previously reported to host a radio halo with a 660 kpc size at MeerKAT 1.28 GHz. The halo is slightly offset from the cluster centre. Our uGMRT observation reveals that the halo extends up to 690 kpc at 407 MHz with a nonuniform spectral index $\alpha^{1284}_{407}$ distribution varying from flat (-0.5) to steep (-1.3) values. In addition, we use legacy GMRT 608 MHz, \textit{XMM-Newton} X-ray, and the Dark Energy Survey optical observations to obtain an extensive understanding of the dynamical nature of the galaxy cluster. The X-ray surface brightness concentration parameter (c$_{SB}$ = 0.16) and centroid shift (w = 0.057) reveal an ongoing dynamical disturbance in the cluster. This is also supported by the elongated 2-D optical galaxy density distribution map of the cluster. We obtain an asymmetry parameter of 0.35 $\pm$ 0.04 from optical analysis, further supporting the dynamical disturbance in the cluster. The radio and X-ray surface brightness follows a sub-linear correlation. Our observation suggests that the cluster is currently in a merging state where particle re-acceleration in the turbulent ICM resulted in the radio halo emission., Comment: 10 pages, 8 Figures, submitted to MNRAS

Neuromorphic computing (NC) is a promising candidate for artificial intelligence applications. To realize NC, electronic analogues of brain components, such as synapses and neurons, must be designed. In spintronics, domain wall (DW) based magnetic tunnel junctions - which offer both synaptic and neuronal functionalities - are one of the promising candidates. An electronic neuron should exhibit leaky-integrate-fire functions similar to their biological counterparts. However, most experimental studies focused only on the integrate-and-fire functions, overlooking the leaky function. Here, we report on a domain wall neuron device that achieves integration using spin-orbit torque-induced domain wall motion and a leaky function via synthetic antiferromagnetic coupling. By fabricating Hall bar devices in a special geometry, we could achieve these two functionalities. During the leaky process, the maximum DW velocity achieved was 2500 {\mu}m/s. The proposed design utilizes materials used in STT-MRAM fabrication and is compatible with CMOS fabrication. Therefore, this neuron can be readily integrated into NC., Comment: 15 pages, 4 Figures, 1 Table

We prove that for every irrational number $\alpha$, real number $\beta$, real number $c$ satisfying $1

Given a quantum Markovian noise model, we study the maximum dimension of a classical or quantum system that can be stored for arbitrarily large time. We show that, unlike the fixed time setting, in the limit of infinite time, the classical and quantum capacities are characterized by efficiently computable properties of the peripheral spectrum of the quantum channel. In addition, the capacities are additive under tensor product, which implies in the language of Shannon theory that the one-shot and the asymptotic i.i.d. capacities are the same. We also provide an improved algorithm for computing the structure of the peripheral subspace of a quantum channel, which might be of independent interest., Comment: Preliminary version, comments are welcome

Occupation information can be utilized by digital assistants to provide occupation-specific personalized task support, including interruption management, task planning, and recommendations. Prior research in the digital workplace assistant domain requires users to input their occupation information for effective support. However, as many individuals switch between multiple occupations daily, current solutions falter without continuous user input. To address this, this study introduces WorkR, a framework that leverages passive sensing to capture pervasive signals from various task activities, addressing three challenges: the lack of a passive sensing architecture, personalization of occupation characteristics, and discovering latent relationships among occupation variables. We argue that signals from application usage, movements, social interactions, and the environment can inform a user's occupation. WorkR uses a Variational Autoencoder (VAE) to derive latent features for training models to infer occupations. Our experiments with an anonymized, context-rich activity and task log dataset demonstrate that our models can accurately infer occupations with more than 91% accuracy across six ISO occupation categories.

Dark photons coupling to $L_\mu-L_\tau$ lepton number difference are a highly studied light dark matter candidate, with potential to be discovered through their impact on terrestrial neutrino oscillation experiments. We re-examine this in the light of claimed tensions between the NO$\nu$A and T2K long baseline experiments, also taking into account data from the MINOS experiment. We obtain leading limits on the $L_\mu-L_\tau$ gauge coupling $g'$ versus dark photon mass $m_{A'}$, and find no statistically significant alleviation of the tension from inclusion of the new physics effect., Comment: 5 pages, 1 figure

In this paper we study a Kahler structure on finite points. In particular, we study the edge Laplacian of a graph twisted by the Kahler structure introduced in this paper. We also discuss a metric aspect from a twisted holomorphic Dolbeault-Dirac spectral triple and show that the points have a finite diameter with respect to Connes' distance., Comment: 14 pages

This study investigates the critical role of buffer layers in enhancing the efficiency of thin-film CdTe solar cells. We explore non-toxic buffer materials, specifically ZnSe, ZnMgO, 3C-SiC, and WS2, as potential replacements for the traditional Cd-based CdS buffer layer. Our analysis includes a thorough evaluation of the electrical and optical performance of these buffer materials in conjunction with CdTe absorbers. We optimize the thickness of both the buffer and absorber layers to achieve the best performance. Additionally, we examine the impact of defect density variations in the buffer materials and their corresponding temperature effects. Among the tested materials, ZnSe and ZnMgO demonstrate the highest potential, achieving power conversion efficiencies of 20.74% and 20.73%, respectively., Comment: Following feedback and co-author discussions, we identified areas needing improvement and lack confidence in some data. We aim to revise and may re-upload later

Rutile Germanium Dioxide (r-GeO$_2$) has been identified as an ultrawide bandgap (UWBG) semiconductor recently, featuring a bandgap of 4.68 eV, comparable to Ga$_2$O$_3$ but offering bipolar dopability, higher electron mobility, higher thermal conductivity, and higher Baliga's figure of merit (BFOM).These superior properties position GeO$_2$ as a promising material for various semiconductor applications. However, the epitaxial growth of r-GeO$_2$, particularly in its most advantageous rutile polymorph, is still at an early stage. This work explores the growth of r-GeO$_2$ using metal-organic chemical vapor deposition (MOCVD) on an r-TiO$_2$ (001) substrate, utilizing tetraethyl germane (TEGe) as the precursor. Our investigations reveal that higher growth temperatures significantly enhance crystalline quality, achieving a full width at half maximum (FWHM) of 0.181 degree at 925 degree C, compared to 0.54 degree at 840 degree C and amorphous structures at 725 degree C. Additionally, we found that longer growth durations increase surface roughness due to the formation of faceted crystals. Meanwhile, adjusting the susceptor rotation speed from 300 RPM to 170 RPM plays a crucial role in optimizing crystalline quality, effectively reducing surface roughness by approximately 15 times. This study offers a foundational guide for optimizing MOCVD growth conditions of r-GeO$_2$ films, emphasizing the crucial need for precise control over deposition temperature and rotation speed to enhance adatom mobility and effectively minimize the boundary layer thickness., Comment: 20 pages, 5 figures, 3 tables

Disorder is ubiquitous in any quantum many-body system and is usually considered to be an obstacle to the elucidation of the underlying physics of complex systems, but its presence can often introduce exotic phases of matter that cannot generally be realized in a clean system. We report here a detailed experimental and theoretical study of magnetic properties of highly disordered Sr$_3$CuNb$_2$O$_9$ material which exhibits random site mixing between Cu and Nb. The magnetic moments (Cu$^{2+}$) are arranged in a quasi-cubic (three-dimensional) manner, leading to a high degree of frustration with a Curie-Weiss temperature ($\theta_{CW}$) of about -60 K without any long-range magnetic ordering down to 466 mK. These observations suggest that Sr$_3$CuNb$_2$O$_9$ is a candidate for a quantum spin liquid. More interestingly, the susceptibility ($\chi = M/\mu_0H$) and the $C_m/T$ ($C_m$ is the magnetic part of the heat capacity) follow a power-law behavior with decreasing temperature. In addition, $M(T,\mu_0H)$ and $C_m(T,\mu_0H)/T$ show scaling relationships over a wide temperature and field range. This unusual behavior with respect to the conventional behavior of a QSL can be discussed qualitatively as the coexistence of a disorder-induced random spin singlet (RSS) state and a QSL state. A quantitative description has been given by numerical calculations considering a power-law probability distribution $P(J) \propto J^{-\gamma}$ ($J$ is the exchange interaction) of random spin singlets. The parameters extracted from the numerical calculations are in excellent agreement with the experimental data. Furthermore, the analytical results are also consistent with the power-law and scaling behavior of $\chi$ and $C_m(T,\mu_0H)/T$ as a whole. Thus, our comprehensive experimental and theoretical analysis provides evidence for the stabilization of the RSS state in a three-dimensional lattice., Comment: 7 pages, 4 figures. Accepted at Physical Review B (Letter)

We investigate shadows, deflection angle, quasinormal modes (QNMs), and sparsity of Hawking radiation of the Schwarzschild string cloud black hole's solution after applying quantum corrections required by the Generalised Uncertainty Principle (GUP). First, we explore the shadow's behaviour in the presence of a string cloud using three alternative GUP frameworks: linear quadratic GUP (LQGUP), quadratic GUP (QGUP), and linear GUP. We then used the weak field limit approach to determine the effect of the string cloud and GUP parameters on the light deflection angle, with computation based on the Gauss-Bonnet theorem. Next, to compute the quasinormal modes of Schwarzschild string clouds incorporating quantum correction with GUP, we determine the effective potentials generated by perturbing scalar, electromagnetic and fermionic fields, using the sixth-order WKB approach in conjunction with the appropriate numerical analysis. Our investigation indicates that string and linear GUP parameters have distinct and different effects on QNMs. We find that the greybody factor increases due to the presence of string cloud while the linear GUP parameter shows the opposite. We then examine the radiation spectrum and sparsity in the GUP corrected black hole with the cloud of string framework, which provides additional information about the thermal radiation released by black holes. Finally, our inquiries reveal that the influence of the string parameter and the quadratic GUP parameter on various astrophysical observables is comparable, however the impact of the linear GUP parameter is opposite., Comment: 32 pages, 7 tables, 14 captioned figures. Accepted for publication in Eur. Phys. J. C

In this article, we represent an even Gaussian integer with sufficiently large norm as a sum of a Gaussian prime and a Gaussian integer with at most two Gaussian prime factors akin to Chen in the rational case., Comment: 23 pages

\textbf{Objective:} Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD or remain stable. The objective of the work was to capture structural and functional modulations of brain structure and function relying on multimodal MRI data and Single Nucleotide Polymorphisms, also in case of missing views, with the twofold goal of classifying AD patients versus healthy controls and detecting MCI converters. % in two distinct tasks, dealing with also missing data.\\ \textbf{Approach:} We propose a multimodal DL-based classification framework where a generative module employing Cycle Generative Adversarial Networks was introduced in the latent space for imputing missing data (a common issue of multimodal approaches). Explainable AI method was then used to extract input features' relevance allowing for post-hoc validation and enhancing the interpretability of the learned representations. \textbf{Main results:} Experimental results on two tasks, AD detection and MCI conversion, showed that our framework reached competitive performance in the state-of-the-art with an accuracy of $0.926\pm0.02$ and $0.711\pm0.01$ in the two tasks, respectively. The interpretability analysis revealed gray matter modulations in cortical and subcortical brain areas typically associated with AD. Moreover, impairments in sensory-motor and visual resting state networks along the disease continuum, as well as genetic mutations defining biological processes linked to endocytosis, amyloid-beta, and cholesterol, were identified. \textbf{Significance:} Our integrative and interpretable DL approach shows promising performance for AD detection and MCI prediction while shedding light on important biological insights., Comment: 33 pages, 8 figures (main text + supplementary materials), submitted to a journal

Precise and prompt identification of road surface conditions enables vehicles to adjust their actions, like changing speed or using specific traction control techniques, to lower the chance of accidents and potential danger to drivers and pedestrians. However, most of the existing methods for detecting road surfaces solely rely on visual data, which may be insufficient in certain situations, such as when the roads are covered by debris, in low light conditions, or in the presence of fog. Therefore, we introduce a multimodal approach for the automated detection of road surface conditions by integrating audio and images. The robustness of the proposed method is tested on a diverse dataset collected under various environmental conditions and road surface types. Through extensive evaluation, we demonstrate the effectiveness and reliability of our multimodal approach in accurately identifying road surface conditions in real-time scenarios. Our findings highlight the potential of integrating auditory and visual cues for enhancing road safety and minimizing accident risks, Comment: 4 pages

In this work, two originally separate adjustments for the Friedmann equations are concurrently considered. Firstly, the fractal structure of the black hole horizon region is imposed by the Barrow entropy. The second adjustment is the $f(\mathcal{T})$ gravity, which is based on a teleparallel framework generalization of the Einstein-Hilbert action, where $\mathcal{T}$ is the scalar torsion. This can be considered as a Barrow entropy modification of the $f(\mathcal{T})$ gravity thus yielding a new model. Gravity thermodynamics hypothesis principles are used to integrate these two models under a unified framework. We derive the modified Friedmann equation and note the corrections obtained. To understand the implications of such dual modifications, an application is analyzed and a particular $f(\mathcal{T})$ toy model is chosen for the purpose. The equation of the state parameter of the resulting model, the dimensionless density parameters of matter and dark energy, and the deceleration parameter are explored to check the viability of the new model. A discussion of the dynamic evolution of the universe follows from these results. It is seen that the results comply with the observations. The newly developed model is promising and demands further study., Comment: 15 pages, 5 figures. arXiv admin note: text overlap with arXiv:2211.04178 by other authors