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We consider the restriction of Brownian shifts to their invariant subspaces and classify when they are unitarily equivalent. Additionally, we prove an asymptotic property stating that normalized Brownian shifts belong to the classical $C_{00}$-class., Comment: 15 pages

Magnetodielectric (MD) materials are important for their ability to spin-charge conversion, magnetic field control of electric polarization and vice versa. Among these, two-dimensional (2D) van der Waals (vdW) magnetic materials are of particular interest due to the presence of magnetic anisotropy (MA) originating from the interaction between the magnetic moments and the crystal field. Also, these materials indicate a high degree of stability in the long-range spin order and may be described using suitable spin Hamiltonians of the Heisenberg, XY, or Ising type. Recent reports have suggested effective interactions between magnetization and electric polarization in 2D magnets. However, MD coupling studies on layered magnetic materials are still few. This review covers the fundamentals of magnetodielectric coupling by explaining related key terms. It includes the necessary conditions for having this coupling and sheds light on the possible physical mechanisms behind this coupling starting from phenomenological descriptions. Apart from that, this review classifies 2D magnetic materials into several categories for reaching out each and every class of materials. Additionally, this review summarizes recent advancements of some pioneer 2D magnetodielectric materials. Last but not the least, the current review provides possible research directions for enhancing magnetodielectric coupling in those and mentions the possibilities for future developments., Comment: 19 pages, 8 figures

In the minimal gauged B-L extension of the Standard Model, we demonstrate that PeV-scale dark matter (DM) and the baryon asymmetry of the Universe (BAU) can be simultaneously explained through the three right-handed neutrinos (RHNs) present in the theory. The DM candidate undergoes decay into light neutrinos, providing an explanation for the observed IceCube events, while the other two RHNs generate the BAU via leptogenesis. The breaking of gauge symmetry gives rise to detectable gravitational waves (GWs) from decaying cosmic strings (CS), making this framework testable at several future GW detectors-despite being beyond the reach of conventional collider experiments due to the extremely weak coupling. The symmetry-breaking scale establishes a connection between particle masses, couplings, and the GW spectrum, offering a unified and predictive scenario., Comment: 7 pages+supplemental material (3 pages), 2 figures

The constituents in a class of active matter systems change their directions of motion by being influenced by the velocities of the neighbors. Such systems may undergo phase transitions, with respect to ordering in the velocity field, as well as clustering in the density field, when the strength of an externally imposed noise is varied. Via computer simulations, with a well-known model, that faithfully represents these systems, we show that evolutions in both clustering and ordering exhibit certain interesting features that were hitherto unrealized. The transformations occur quicker, following quenches to a fixed final state, below the transition point, for disordered starting states that are farther away from the ``critical" noise strength. This implies earliest arrival of the farthest, at a given destination. Detailed analysis of the results, combined with the outcomes from a similar study of para- to ferromagnetic transitions, show that the variation in critical fluctuations in the initial configurations can lead to such interesting effect. We quantify this via the Ornstein-Zernike theory., Comment: 12 pages, 5 figures

Active, responsive, nonequilibrium materials, at the forefront of materials engineering, offer dynamical restructuring, mobility and other complex life-like properties. Yet, this enhanced functionality comes with significant amplification of the size and complexity of the datasets needed to characterize their properties, thereby challenging conventional approaches to analysis. To meet this need, we present BARCODE (Biomaterial Activity Readouts to Categorize, Optimize, Design and Engineer), an open-access software that automates high throughput screening of microscopy video data to enable nonequilibrium material optimization and discovery. BARCODE produces a unique fingerprint or barcode of performance metrics that visually and quantitatively encodes dynamic material properties with minimal file size. Using three complementary material agnostic analysis branches, BARCODE significantly reduces data dimensionality and size, while providing rich, multiparametric outputs and rapid tractable characterization of activity and structure. We analyze a series of datasets of cytoskeleton networks and cell monolayers to demonstrate the ability of BARCODE to accelerate and streamline screening and analysis, reveal unexpected correlations and emergence, and enable broad non-expert data access, comparison, and sharing.

Passwords have been long used as the primary authentication method for web services. Weak passwords used by the users have prompted the use of password management tools and two-factor authentication to ensure better account security. While prior studies have studied their adoption individually, none of these studies focuses particularly on the Indian setting, which is culturally and economically different from the countries in which these studies have been done in the past. To this end, we conducted a survey with 90 participants residing in India to better understand the mindset of people on using password managers and two-factor authentication (2FA). Our findings suggest that a majority of the participants have used 2FA and password managers in some form, although they are sometimes unaware of their formal names. While many participants used some form of 2FA across all their accounts, browser-integrated and device-default password managers are predominantly utilized for less sensitive platforms such as e-commerce and social media rather than for more critical accounts like banking. The primary motivation for using password managers is the convenience of auto-filling. However, some participants avoid using password managers due to a lack of trust in these tools. Notably, dedicated third-party applications show low adoption for both password manager and 2FA. Despite acknowledging the importance of secure password practices, many participants still reuse passwords across multiple accounts, prefer shorter passwords, and use commonly predictable password patterns. Overall, the study suggests that Indians are more inclined to choose default settings, underscoring the need for tailored strategies to improve user awareness and strengthen password security practices., Comment: Submitted on 2024-10-25. Accepted at BuildSec 2024, to be published in IEEE Xplore. Original version: 8 pages

Saccharum spontaneum is a grass-type plant abundantly found in the Indian subcontinent, known for its beautiful, lustrous white flowers. Fibres were extracted from the flower and analyzed for their physical, mechanical, and chemical properties. The chemical composition of the fibre is 90.9% holocellulose, with a moisture content of 10.97%, and an average fibre length of 25 mm. FTIR spectra confirmed the presence of functional groups similar to those found in other natural cellulosic fibres. Additionally, the fibre exhibits a tensile strength of approximately 63 cN/tex, which is significantly higher than that of cotton and jute fibres. However, its crystallinity is relatively high at about 75%, resulting in a low elongation at break of 1.9%. FESEM analysis revealed a hollow structure in the fibre, indicating its potential suitability for applications requiring high thermal insulation, excellent moisture management, vapor permeability, and microbial fuel cell development.

The superheated emulsion detector consisting of the droplets of tetra-fluoroethane (C2HC$_2$H$_2$F$_4$2F4) has been fabricated at the laboratory and installed at the 555m deep underground laboratory, JUSL during July to Dec 2022. The 500ml detector ran for an effective period of 48.6 days at a threshold of 5.87 keV with an exposure of 2.47 kg-days. The acoustic signals produced due to the bubble nucleation were collected by the acoustic sensor and FPGAbased data acquisition system. The data shows a minimum sensitivity of SI-nucleon for carbon at WIMP mass of 22.81 GeV/c$^2$ and SD (p) for fluorine at 30.67 GeV/c$^2$. The threshold of WIMP mass is 5.16 GeV/c$^2$ for F and 4.44 GeV/c$^2$ for C at the operating threshold of 5.87 keV. The first result of the dark matter direct search experiment named InDEx with tetra-fluoro-ethane active liquid from JUSL underground laboratory is reported in this article.

Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and improving accuracy in the face of an increasing request of such scans and a global shortage in radiologists. This study introduces a 3D foundation model for detecting diverse neuro-trauma findings with high accuracy and efficiency. Using large language models (LLMs) for automatic labeling, we generated comprehensive multi-label annotations for critical conditions. Our approach involved pretraining neural networks for hemorrhage subtype segmentation and brain anatomy parcellation, which were integrated into a pretrained comprehensive neuro-trauma detection network through multimodal fine-tuning. Performance evaluation against expert annotations and comparison with CT-CLIP demonstrated strong triage accuracy across major neuro-trauma findings, such as hemorrhage and midline shift, as well as less frequent critical conditions such as cerebral edema and arterial hyperdensity. The integration of neuro-specific features significantly enhanced diagnostic capabilities, achieving an average AUC of 0.861 for 16 neuro-trauma conditions. This work advances foundation models in medical imaging, serving as a benchmark for future AI-assisted neuro-trauma diagnostics in emergency radiology.

We derive analytical expressions for the spin wave frequencies and precession amplitudes in monolayer and antiferromagnetically coupled bilayer CrSBr under in-plane external magnetic fields. The analysis covers the antiferromagnetic, ferromagnetic, and canted phases, demonstrating that the spin wave frequencies in all phases are tunable by the applied magnetic field. We discuss the roles of intra- and interlayer exchange interactions, triaxial anisotropy, and intralayer dynamic dipolar fields in controlling the magnetization dynamics., Comment: 14 pages, 8 figures

State space models (SSMs) have emerged as an efficient alternative to transformer-based models, offering linear complexity that scales better than transformers. One of the latest advances in SSMs, Mamba, introduces a selective scan mechanism that assigns trainable weights to input tokens, effectively mimicking the attention mechanism. Mamba has also been successfully extended to the vision domain by decomposing 2D images into smaller patches and arranging them as 1D sequences. However, it remains unclear how these patches interact with (or attend to) each other in relation to their original 2D spatial location. Additionally, the order used to arrange the patches into a sequence also significantly impacts their attention distribution. To better understand the attention between patches and explore the attention patterns, we introduce a visual analytics tool specifically designed for vision-based Mamba models. This tool enables a deeper understanding of how attention is distributed across patches in different Mamba blocks and how it evolves throughout a Mamba model. Using the tool, we also investigate the impact of different patch-ordering strategies on the learned attention, offering further insights into the model's behavior., Comment: 6 pages, 8 figures

This paper presents performance measurements of a new readout electronics system based on silicon photomultipliers for the PHENIX electromagnetic calorimeter. Installation of the lead-scintillator Shashlik style calorimeter into the SeaQuest/SpinQuest spectrometer has been proposed to broaden the experiment's dark sector search program, an upgrade known as DarkQuest. The calorimeter and electronics system were subjected to testing and calibration at the Fermilab Test Beam Facility. Detailed studies of the energy response and resolution, as well as particle identification capabilities, were performed. The background rate in the actual experimental environment was also examined. The system is found to be well-suited for a dark sector search program on the Fermilab 120 GeV proton beamline., Comment: Prepared for submission to Nuclear Instrumentation and Methods

From a leading-order unbiased renormalization group analysis we here showcase the emergence of superconductivity (including the topological ones) from purely repulsive electron-electron interactions in two-dimensional doped Dirac insulators, featuring a Fermi surface. In the absence of chemical doping, such systems describe quantum anomalous or spin Hall and normal insulators. Otherwise a simply connected Fermi surface becomes annular deep inside the topological regime. By considering all symmetry allowed repulsive local four-fermion interactions, we show that the nature of the resulting superconducting states at low temperature follows certain Clifford algebraic selection rules, irrespective of the underlying Fermi surface topology. Within the framework of a microscopic Hubbard model, on-site repulsion among fermions with opposite orbitals (spin projections) typically favors topological $p$-wave (conventional $s$-wave) pairing. Theoretically predicted superconductivity can in principle be observed in experiments once the promising candidate materials for quantum anomalous and spin Hall insulators are doped to foster Fermi surfaces., Comment: 21 Pages, 9 Figures, and 4 Tables

We study a variant of the 3HDM, referred to as the BGL-3HDM, incorporating a $U(1)_1\times U(1)_2$ symmetry, which can distinguish the primary sources of mass for different fermion generations. In the version considered here, the Yukawa matrices in the down-quark and charged lepton sectors are diagonal, thereby eliminating tree-level FCNCs in these sectors. FCNC interactions mediated by neutral nonstandard Higgses are confined to the up-quark sector only. No new BSM parameters are introduced by the Yukawa sector of the model, making it as economical as the NFC versions of 3HDM with a $U(1)_1\times U(1)_2$ symmetry in terms of the number of free parameters. However, even in the down-quark and in the charged lepton sectors, flavor diagonal but nonuniversal Higgs couplings set this model apart from the NFC versions of the 3HDM., Comment: 23 pages, 3 captioned figures

We present $I$-band time-series photometric variability studies of three known nearby ($\sim$ 140 pc) and young ( $\sim$ 1 Myr) brown dwarfs (BD) in the Taurus star-forming region in the Perseus Molecular Cloud. From 10 nights of observations over a time span of 10 years, with a typical run of 3 to 6 hours each night, we estimated that the BDs show unstable short-scale periodicity from 1.5 to 4.8 hours. Using the long-term photometry from the Transiting Exoplanet Survey Satellite (TESS), we have conducted a time-resolved variability analysis of CFHT-BD-Tau 3 and CFHT-BD-Tau 4, revealing orbital periods of $\sim$ 0.96 days and $\sim$ 3 days respectively, consistent with earlier studies. We also found two superflares in TESS sector 43 data for CFHT-BD-Tau 4 and estimated the flare energies as $7.09\times10^{35}$ erg and $3.75\times10^{36}$ erg. A magnetic field of $\sim3.39 ~kG$ is required to generate such flare energies on this BD. We performed spot modelling analysis on CFHT-BD-Tau 3 and CFHT-BD-Tau 4 to address the variability detected in the data using the package BASSMAN. Spectral energy distribution and infrared colours of the sources suggest that they have a sufficient amount of circumstellar material around them.

The Quantum Phase Difference Estimation (QPDE) algorithm, as an extension of the Quantum Phase Estimation (QPE), is a quantum algorithm designed to compute the differences of two eigenvalues of a unitary operator by exploiting the quantum superposition of two eigenstates. Unlike QPE, QPDE is free of controlled-unitary operations, and is suitable for calculations on noisy intermediate-scale quantum (NISQ) devices. We present the implementation and verification of a novel early fault-tolerant QPDE algorithm for determining energy gaps across diverse spin system configurations using NISQ devices. The algorithm is applied to the systems described by two and three-spin Heisenberg Hamiltonians with different geometric arrangements and coupling strengths, including symmetric, asymmetric, spin-frustrated, and non-frustrated configurations. By leveraging the match gate-like structure of the time evolution operator of Heisenberg Hamiltonian, we achieve constant-depth quantum circuits suitable for NISQ hardware implementation. Our results on IBM quantum processors show remarkable accuracy ranging from 85\% to 93\%, demonstrating excellent agreement with classical calculations even in the presence of hardware noise. The methodology incorporates sophisticated quantum noise suppression techniques, including Pauli Twirling and Dynamical Decoupling, and employs an adaptive framework. Our findings demonstrate the practical viability of the QPDE algorithm for quantum many-body simulations on current NISQ hardware, establishing a robust framework for future applications.

In many random phenomena, such as life-testing experiments and environmental data (like rainfall data), there are often positive values and an excess of zeros, which create modeling challenges. In life testing, immediate failures result in zero lifetimes, often due to defects or poor quality, especially in electronics and clinical trials. These failures, called zero inliers, are difficult to model using standard approaches. When studying extreme values in the above scenarios, a key issue is selecting an appropriate threshold for accurate tail approximation of the population using asymptotic models. While some extreme value mixture models address threshold estimation and tail approximation, conventional parametric and non-parametric bulk and generalised Pareto distribution (GPD) approaches often neglect inliers, leading to suboptimal results. This paper introduces a framework for modeling extreme events and inliers using the GPD, addressing threshold uncertainty and effectively capturing inliers at zero. The model's parameters are estimated using the maximum likelihood estimation (MLE) method, ensuring optimal precision. Through simulation studies and real-world applications, we demonstrate that the proposed model significantly outperforms the traditional methods, which typically neglect inliers at the origin., Comment: 35 pages, 5 figures

Considering the fact that no Standard Model (SM) particle can account as a suitable dark matter candidate in the relevant studies, several Beyond Standard Model (BSM) frameworks incorporate within itself some extra fields depending upon the model taken into account. Left-Right Symmetric Model (LRSM) is one such BSM framework which can successfully explain the origin of neutrino masses and also give an elaborative elucidation of the associated phenomenology like Neutrinoless Double Beta Decay $(0\nu\beta\beta)$, Lepton Flavor Violation (LFV), baryogenesis via leptogenesis etc. However, in spite of the presence of several new fields within the model, none of them can stand as a suitable dark matter candidate. This compels us to extend LRSM with extra scalars or fermions depending upon the type of study to be taken into account. In the current work, we extend LRSM with a sterile fermion per generation and the lightest of which acts as the Dark Matter (DM) candidate. The realization of the model has been done using modular symmetry, where we have used modular group $\Gamma(3)$ of weight 2 which is isomorphic to non-abelian discrete symmetry group $A_{4}$. We have then calculated the relic abundance as well the decay rate of the corresponding DM candidate and also studied $0\nu\beta\beta$ and LFV within the model and the results have been discussed in detail within the manuscript., Comment: 30 pages, 11 figures. arXiv admin note: text overlap with arXiv:2312.00822

Accurately mapping the radio environment (e.g., identifying wireless signal strength at specific frequency bands and geographic locations) is crucial for efficient spectrum sharing, enabling secondary users (SUs) to access underutilized spectrum bands while protecting primary users (PUs). However, current models are either not generalizable due to shadowing, interference, and fading or are computationally too expensive, limiting real-world applicability. To address the shortcomings of existing models, we derive a second-order partial differential equation (PDE) for the Received Signal Strength Indicator (RSSI) based on a statistical model used in the literature. We then propose ReVeal (Re-constructor and Visualizer of Spectrum Landscape), a novel Physics-Informed Neural Network (PINN) that integrates the PDE residual into a neural network loss function to accurately model the radio environment based on sparse RF sensor measurements. ReVeal is validated using real-world measurement data from the rural and suburban areas of the ARA testbed and benchmarked against existing methods.ReVeal outperforms the existing methods in predicting the radio environment; for instance, with a root mean square error (RMSE) of only 1.95 dB, ReVeal achieves an accuracy that is an order of magnitude higher than existing methods such as the 3GPP and ITU-R channel models, ray-tracing, and neural networks. ReVeal achieves both high accuracy and low computational complexity while only requiring sparse RF sampling, for instance, only requiring 30 training sample points across an area of 514 square kilometers.

The proliferation of hate speech on social media is one of the serious issues that is bringing huge impacts to society: an escalation of violence, discrimination, and social fragmentation. The problem of detecting hate speech is intrinsically multifaceted due to cultural, linguistic, and contextual complexities and adversarial manipulations. In this study, we systematically investigate the performance of LLMs on detecting hate speech across multilingual datasets and diverse geographic contexts. Our work presents a new evaluation framework in three dimensions: binary classification of hate speech, geography-aware contextual detection, and robustness to adversarially generated text. Using a dataset of 1,000 comments from five diverse regions, we evaluate three state-of-the-art LLMs: Llama2 (13b), Codellama (7b), and DeepSeekCoder (6.7b). Codellama had the best binary classification recall with 70.6% and an F1-score of 52.18%, whereas DeepSeekCoder had the best performance in geographic sensitivity, correctly detecting 63 out of 265 locations. The tests for adversarial robustness also showed significant weaknesses; Llama2 misclassified 62.5% of manipulated samples. These results bring to light the trade-offs between accuracy, contextual understanding, and robustness in the current versions of LLMs. This work has thus set the stage for developing contextually aware, multilingual hate speech detection systems by underlining key strengths and limitations, therefore offering actionable insights for future research and real-world applications., Comment: 6 pages, 2 figures

We present the luminosity function and volumetric rate of a sample of Type IIP supernovae (SNe) from the Zwicky Transient Facility Census of the Local Universe survey (CLU). This is the largest sample of Type IIP SNe from a systematic volume-limited survey to-date. The final sample includes 330 Type IIP SNe and 36 low-luminosity Type II (LLIIP) SNe with $M_{\textrm{r,peak}}>-16$ mag, which triples the literature sample of LLIIP SNe. The fraction of LLIIP SNe is $19^{+3}_{-4}\%$ of the total CLU Type IIP SNe population ($8^{+1}_{-2}\%$ of all core-collapse SNe). This implies that while LLIIP SNe likely represent the fate of core-collapse SNe of $8-12$ \Msun\ progenitors, they alone cannot account for the fate of all massive stars in this mass range. To derive an absolute rate, we estimate the ZTF pipeline efficiency as a function of the apparent magnitude and the local surface brightness. We derive a volumetric rate of $(3.9_{-0.4}^{+0.4}) \times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for Type IIP SNe and $(7.3_{-0.6}^{+0.6}) \times 10^{3}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for LLIIP SNe. Now that the rate of LLIIP SNe is robustly derived, the unresolved discrepancy between core-collapse SN rates and star-formation rates cannot be explained by LLIIP SNe alone., Comment: Submitted to PASP

Understanding the thermodynamic properties of quantum systems is essential for developing energy-efficient quantum technologies. In this regard, this work explores the application of quantum computational methods to study the quantum properties and work extraction processes in a dinuclear quantum battery model. Our results demonstrate that variational quantum algorithms can reproduce key trends in experimental data, making it possible to analyze the effectiveness of the presented protocol in noisy environments and providing insights into the feasibility of quantum batteries in near-term devices. We have shown that the presence of a noisy environment hinders the accuracy of the evaluation of the amount of energy stored in the system. Additionally, we analyze the work extraction precision, revealing that although the system can store energy at room temperature, the protocol is highly precise only at low temperatures, and its accuracy at ambient conditions remains limited, compromising its usability.

Text summarization plays a crucial role in natural language processing by condensing large volumes of text into concise and coherent summaries. As digital content continues to grow rapidly and the demand for effective information retrieval increases, text summarization has become a focal point of research in recent years. This study offers a thorough evaluation of four leading pre-trained and open-source large language models: BART, FLAN-T5, LLaMA-3-8B, and Gemma-7B, across five diverse datasets CNN/DM, Gigaword, News Summary, XSum, and BBC News. The evaluation employs widely recognized automatic metrics, including ROUGE-1, ROUGE-2, ROUGE-L, BERTScore, and METEOR, to assess the models' capabilities in generating coherent and informative summaries. The results reveal the comparative strengths and limitations of these models in processing various text types., Comment: 5 pages, 2 figures, 6 tables

Arctic sea ice is in reduction and has been a key significant indicator of climate change. In this paper, we explore Arctic Sea ice extent data to identify teleconnection with weather change in the polar and sub-tropical jet stream intersection in eastern United States (US) and hence the potential influence in ground level ozone pollution. Several statistical methods including Bayesian techniques such as: spatio-temporal modelling and Bayesian network are implemented to identify the teleconnection and also validated based on theories in atmospheric science. We observe that the teleconnection is relatively strong in autumn, winter and spring seasons compared to the summer. Furthermore, the sudden decremental effect of Arctic sea-ice extent in mid-2000s has a shifting influence in ozone pollutions compared to the previous years. A similar downward shift in the Arctic sea-ice extent has been projected in 2030. These findings indicate to initiate further strategic policies for the Arctic influence, ozone concentrations together the seasonal and global changing patterns of climate., Comment: 19 pages, 6 figures

Spin-phonon interactions in 2D magnetic materials are crucial in advancing next-generation spintronic devices. Therefore, identifying new materials with significant spin-phonon interactions is of great importance. In this context, MnSe, previously recognized as an exemplary non-layered p-type semiconductor emerges in this study as an intriguing material with notable spin-phonon characteristics. The complex magnetism in pristine MnSe, primarily dominated by antiferromagnetism with a weak ferromagnetic component, gives rise to both spontaneous and conventional exchange bias effects at low temperatures. In an effort to understand this intriguing magnetism, we conducted a detailed Raman spectroscopy study, which reveals unconventional deviations from the usual phonon anharmonicity around Neel temperature (170 K), in the self-energies of the P1, P2, and P3 modes. Notably, the P1 mode is most sensitive to spin-phonon coupling, while the P2 mode is particularly responsive to the structural phase transition at 250 K. Therefore, these findings provide comprehensive insights into the phase transitions of pristine MnSe, particularly highlighting the previously unobserved interplay between its magnetic behavior and phonon dynamics.

Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods can be effective, they typically rely on intuition and do not automatically refine prompts over time. In contrast, automatic prompt optimization employing heuristic-based search algorithms can systematically explore and improve prompts with minimal human oversight. This survey proposes a comprehensive taxonomy of these methods, categorizing them by where optimization occurs, what is optimized, what criteria drive the optimization, which operators generate new prompts, and which iterative search algorithms are applied. We further highlight specialized datasets and tools that support and accelerate automated prompt refinement. We conclude by discussing key open challenges pointing toward future opportunities for more robust and versatile LLM applications.

Photonic spin texture (PST), the spatial distribution of the spin angular momentum (SAM) of light, is connected to unique properties of light, such as optical skyrmions and topological optical N-invariants. There has been recent progress on the generation and manipulation of PST using various methodologies. However, a challenge remains for the sub-wavelength characterization of PST. Here, we demonstrate nitrogen-vacancy (NV) centers in diamond as nanoscale quantum sensors for imaging the PST of a beam with orbital angular momentum (OAM). Leveraging the coherent interaction between photon spin and NV center electron spin at cryogenic temperature (77 K), and using the Hahn-Echo magnetometry technique, we experimentally demonstrate the imprinting of the PST on the quantum phase of NV centers. Our work can lead to the development of a quantum imaging platform capable of characterization of the spin texture of light at sub-wavelength scales.

Let $I(\Delta)^{[k]}$ denote the $k^{\text{th}}$ square-free power of the facet ideal of a simplicial complex $\Delta$ in a polynomial ring $R$. Square-free powers are intimately related to the `Matching Theory' and `Ordinary Powers'. In this article, we show that if $\Delta$ is a Cohen-Macaulay simplicial forest, then $R/I(\Delta)^{[k]}$ is Cohen-Macaulay for all $k\ge 1$. This result is quite interesting since all ordinary powers of a graded radical ideal can never be Cohen-Macaulay unless it is a complete intersection. To prove the result, we introduce a new combinatorial notion called special leaf, and using this, we provide an explicit combinatorial formula of $\mathrm{depth}(R/I(\Delta)^{[k]})$ for all $k\ge 1$, where $\Delta$ is a Cohen-Macaulay simplicial forest. As an application, we show that the normalized depth function of a Cohen-Macaulay simplicial forest is nonincreasing., Comment: 14 pages, 1 figure, comments are welcome!

Relaxation of the magnetic field in rigidly rotating quark-gluon plasma is studied. It is shown that the infrared modes satisfying $k < |m|\Omega$ and $k< |m\pm 1|\Omega$, where integer $m$ is the projection of the orbital angular momentum along the rotating axis and $\Omega$ is the angular velocity, are unstable. The instability onset time and the magnetic field growth rate are computed for a standard initial profile of the magnetic field. Given the present phenomenological values of $\Omega$ and electrical conductivity $\sigma$ the instability is not expected to be a significant factor in the field's time evolution., Comment: 16 pages, 5 figures

The generation of pure spin current, spin angular momentum transport without charge flow, is crucial for developing energy-efficient spintronic devices with minimal Joule heating. Here, we introduce the intrinsic nonlinear pure spin Hall effect (NPSHE), where both linear and second-order charge Hall currents vanish. We show intrinsic second-order spin angular momentum transport in metals and insulators through a detailed analysis of the quantum geometric origin of different spin current contributions. Our comprehensive symmetry analysis identifies 39 magnetic point groups that support NPSHE, providing a foundation for material design and experimental realization. We predict significant nonlinear pure spin Hall currents in Kramers-Weyl metals even at room temperature, positioning them as potential candidates for NPSHE-based spin-torque devices. Our work lays a practical pathway for realizing charge-free angular momentum transport for the development of next-generation, energy-efficient spintronic devices., Comment: 9 pages, 4 figures

We identified a sample of 88 bona fide Population II Cepheids (henceforth referred to as Cepheids) and 44 candidates in Galactic globular clusters (GCs). Seventy-eight of the Cepheids in the sample align within $2\,\sigma$ of the period-luminosity relation for Milky Way Type II Cepheids (T2CEPs). Nine align with the period-luminosity relation for fundamental-mode anomalous Cepheids (ACEPs), and only one (BL Bo\"otis) follows the relation for first-overtone ACEPs, as determined from observations of ACEPs in the Large Magellanic Cloud. For sources in common between our catalog and the OGLE catalog, the classification agrees in 94% of cases. In comparison, for sources shared between the Gaia Specific Object Study (SOS) and OGLE, the agreement is 74%. In the dense environments of GCs, our analysis shows that the completeness of the Gaia catalogs for Cepheids is 64% for the SOS and 74% for the classifier of variable stars. We determined the red and blue edges of the instability strip for T2CEPs using linear MESA-RSP models. We find that the best-fit models, with $M = 0.6\,{\rm M}_\odot$ and Z = 0.0003, are able to fit $90%$ of the stars in our sample. This percentage is the same for helium abundances $Y = 0.220$ and $0.245$. Higher values of $Y$ lower this percentage, and the same effect is observed with lower values of $Z$. In the future, combining the sample of T2CEPs with the precise parallaxes obtained from GCs will strengthen the geometric calibration of a distance ladder based on Population II stars. This will be useful for determining distances within the Milky Way and for cross-checking distances to Local Group galaxies determined through other methods., Comment: Accepted for publication in A&A

Star formation through the dynamical magnetized collapse remains an active area of astrophysical research. We carry out a comprehensive exploration on the magnetized gravitational collapse of a non-rotating self-gravitating initially spherically symmetric prestellar cloud core using two-dimensional nonideal magnetohydrodynamic simulations incorporating ambipolar diffusion and Ohmic dissipation. Our study encompasses a broader range of equations of state (EOSs) in the form of $P(\rho) \propto \rho^{\Gamma}$, with the aim of constraining the choice of EOSs for allowing star formation. Our results reveal that the collapse with a $\Gamma$ no stiffer than $4/3$, complemented by magnetized virial theorem, allows the dynamical contraction of the prestellar core to happen continuously where a central point mass forms and steadily builds up its mass from the infalling envelope, with a mass accretion rate of a scale of the order of $c_{\rm s}^3/G$. The choice of an isothermal EOS most naturally facilitates the collapse as a magnetic analog of the inside-out collapse. In addition to that, our study exhibits that the nonisothermal magnetized collapse models with a $\Gamma$ no stiffer than 4/3 qualitatively demonstrate similar infall features to those of an isothermal EOS. Furthermore, the collapse models with a $\Gamma$ stiffer than $4/3$ fail to ensure the sufficient cooling to allow the direct mass growth of the central point mass, thus delaying the infall. Our work can offer deeper insights in understanding the significance of EOSs on the magnetized gravitational collapse, enabling star formation., Comment: Accepted for publication in Astrophysical Journal, 35 pages, 26 Figures

We investigate the occurrence of accretion bursts, dust accumulation, and the prospects for planetesimal formation in a gravitationally unstable magnetized protoplanetary disk with globally suppressed but episodically triggered magnetorotational instability (MRI), particularly in young intermediate-mass stars (YIMSs) but with a brief comparison to low-mass counterparts. We use numerical magnetohydrodynamics simulations in the thin-disk limit (FEOSAD code) to model the formation and long-term evolution of a gravitationally unstable magnetized protoplanetary disk, including dust dynamics and growth, since the collapse of a massive slowly-rotating prestellar cloud core. Massive gas concentrations and dust rings form within the inner disk region owing to the radially varying efficiency of mass transport by gravitational instability (GI). These rings are initially susceptible to streaming instability (SI). However, gradual warming of the dust rings, thanks to high opacity and GI-induced influx of matter increases the gas temperature above a threshold for the MRI to develop via thermal ionization of alkaline metals. The ensuing MRI bursts destroy the dust rings, making planetesimal formation via SI problematic. In the later evolution phase, when the burst activity starts to diminish, SI becomes inefficient because of growing dust drift velocity and more extended inner dead zone, both acting to reduce the dust concentration below the threshold for SI to develop. Low-mass objects appear to be less affected by these adverse effects. Our results suggest that disks around young intermediate-mass stars may be challenging environments for planetesimal formation via SI. This may explain the dearth of planets around stars with $M_\ast > 3.0 \,$$M_\odot$., Comment: Accepted for Publication in Astrophysical Journal, 24 Pages, 13 Figures

We examine the effects of thermal conduction on relativistic, magnetized, viscous, advective accretion flows around rotating black holes considering bremsstrahlung and synchrotron cooling processes. Assuming the toroidal component of magnetic fields as the dominant one, we self-consistently solve the steady-state fluid equations to derive the global transonic accretion solutions for a black hole of spin $a_{\rm k}$. Depending on the model parameters, the magnetized accretion flow undergoes shock transitions and shock-induced global accretion solutions persist over a wide range of model parameters including the conduction parameter ($\Upsilon_{\rm s}$), plasma-$\beta$, and viscosity parameter ($\alpha_{\rm B}$). We find that the shock properties -- such as shock radius ($r_{\rm s}$), compression ratio ($R$), and shock strength ($S$) -- are regulated by $\Upsilon_{\rm s}$, plasma $\beta$, and $\alpha_{\rm B}$. Furthermore, we compute the critical conduction parameter ($\Upsilon_{\rm s}^{\rm cri}$), a threshold beyond which shock formation ceases to exist, and investigate its dependence on plasma-$\beta$ and $\alpha_{\rm B}$ for both weakly rotating ($a_{\rm k} \rightarrow 0$) and rapidly rotating ($a_{\rm k} \rightarrow 1$) black holes. Finally, we examine the spectral energy distribution (SED) of the accretion disc and observe that increased thermal conduction and magnetic field strength lead to more luminous emission spectra from black hole sources., Comment: 11 pages, 5 figures, 1 table, Comments welcome

The aim of this paper is twofold. In the first part, we define the cohomology of a Nijenhuis Lie algebra with coefficients in a suitable representation. Our cohomology of a Nijenhuis Lie algebra governs the simultaneous deformations of the underlying Lie algebra and the Nijenhuis operator. Subsequently, we define homotopy Nijenhuis operators on $2$-term $L_\infty$-algebras and show that in some cases they are related to third cocycles of Nijenhuis Lie algebras. In another part of this paper, we extend our study to (generic) Nijenhuis Lie bialgebras where the Nijenhuis operators on the underlying Lie algebras and Lie coalgebras need not be the same. In due course, we introduce matched pairs and Manin triples of Nijenhuis Lie algebras and show that they are equivalent to Nijenhuis Lie bialgebras. Finally, we consider the admissible classical Yang-Baxter equation whose antisymmetric solutions yield Nijenhuis Lie bialgebras., Comment: Comments are welcome. 28 pages

In this article, we investigate parton energy loss in cold nuclear matter by studying the ratio of Drell-Yan production cross sections in fixed-target proton-nucleus (p + A) collisions. We analyze Drell-Yan production cross-section data from the Fermilab E866 and E906 experiments using two different quark energy loss parametrization models and various parton distribution functions for 800 GeV and 120 GeV proton beams incident on light and heavy nuclear targets. The sensitivity of the energy loss parameter on the employed parton distribution function has been thoroughly investigated. Our results have been used to predict the target mass dependence of Drell-Yan production in upcoming proton-induced collisions at SPS and FAIR., Comment: Already accepted in the European Physical Journal C

In this paper, we first introduce the non-abelian cohomology group of a Nijenhuis Lie algebra with values in another Nijenhuis Lie algebra and show that it parametrizes the isomorphism classes of all non-abelian extensions. In particular, we obtain a classification result for abelian extensions of a Nijenhuis Lie algebra by a given Nijenhuis representation. Next, given a non-abelian extension of Nijenhuis Lie algebras, we investigate the inducibility of a pair of Nijenhuis Lie algebra automorphisms and show that the corresponding obstruction lies in the non-abelian cohomology group. Subsequently, we also consider the inducibility of a pair of Nijenhuis Lie algebra derivations in a given abelian extension., Comment: Comments are welcome. 22 pages

Remote Sensing Image Captioning (RSIC) is the process of generating meaningful descriptions from remote sensing images. Recently, it has gained significant attention, with encoder-decoder models serving as the backbone for generating meaningful captions. The encoder extracts essential visual features from the input image, transforming them into a compact representation, while the decoder utilizes this representation to generate coherent textual descriptions. Recently, transformer-based models have gained significant popularity due to their ability to capture long-range dependencies and contextual information. The decoder has been well explored for text generation, whereas the encoder remains relatively unexplored. However, optimizing the encoder is crucial as it directly influences the richness of extracted features, which in turn affects the quality of generated captions. To address this gap, we systematically evaluate twelve different convolutional neural network (CNN) architectures within a transformer-based encoder framework to assess their effectiveness in RSIC. The evaluation consists of two stages: first, a numerical analysis categorizes CNNs into different clusters, based on their performance. The best performing CNNs are then subjected to human evaluation from a human-centric perspective by a human annotator. Additionally, we analyze the impact of different search strategies, namely greedy search and beam search, to ensure the best caption. The results highlight the critical role of encoder selection in improving captioning performance, demonstrating that specific CNN architectures significantly enhance the quality of generated descriptions for remote sensing images. By providing a detailed comparison of multiple encoders, this study offers valuable insights to guide advances in transformer-based image captioning models.

MAROON-X is a state-of-the-art extreme precision radial velocity spectrograph deployed on the 8.1-meter Gemini-N telescope on Maunakea, Hawai'i. Using a stabilized Fabry-P\'erot etalon for wavelength and drift calibration, MAROON-X has achieved a short-term precision of $\sim$\,30\,cm\,s$^{-1}$. However, due to a long-term drift in the etalon (2.2\,cm\,s$^{-1}$ per day) and various interruptions of the instrument baseline over the first few years of operation, MAROON-X experiences RV offsets between observing runs several times larger than the short-term precision during any individual run, which hinders the detection of longer-period signals. In this study, we analyze RV measurements of 11 targets that either exhibit small RV scatter or have signals that can be precisely constrained using Keplerian or Gaussian Process models. Leveraging this ensemble, we calibrate MAROON-X's run offsets for data collected between September 2020 and early January 2024 to a precision of $\sim$0.5\,m\,s$^{-1}$. When applying these calibrated offsets to HD 3651, a quiet star, we obtain residual velocities with an RMS of $<$70\,cm\,s$^{-1}$ in both the Red and Blue channels of MAROON-X over a baseline of 29 months. We also demonstrate the sensitivity of MAROON-X data calibrated with these offsets through a series of injection-recovery tests. Based on our findings, MAROON-X is capable of detecting sub m\,s$^{-1}$ signals out to periods of more than 1,000 days., Comment: Accepted in AJ; 11 figures, 24 pages

Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks. However, efficient processing of long contexts using LLMs remains a significant challenge. We introduce \textbf{EpMAN} -- a method for processing long contexts in an \textit{episodic memory} module while \textit{holistically attending to} semantically relevant context chunks. The output of \textit{episodic attention} is then used to reweigh the decoder's self-attention to the stored KV cache of the context during training and generation. When an LLM decoder is trained using \textbf{EpMAN}, its performance on multiple challenging single-hop long-context recall and question-answering benchmarks is found to be stronger and more robust across the range from 16k to 256k tokens than baseline decoders trained with self-attention, and popular retrieval-augmented generation frameworks.

In X-ray computed tomography (CT) imaging, the choice of reconstruction kernel is crucial as it significantly impacts the quality of clinical images. Different kernels influence spatial resolution, image noise, and contrast in various ways. Clinical applications involving lung imaging often require images reconstructed with both soft and sharp kernels. The reconstruction of images with different kernels requires raw sinogram data and storing images for all kernels increases processing time and storage requirements. The Display Field-of-View (DFOV) adds complexity to kernel synthesis, as data acquired at different DFOVs exhibit varying levels of sharpness and details. This work introduces an efficient, DFOV-agnostic solution for image-based kernel synthesis using model-based deep learning. The proposed method explicitly integrates CT kernel and DFOV characteristics into the forward model. Experimental results on clinical data, along with quantitative analysis of the estimated modulation transfer function using wire phantom data, clearly demonstrate the utility of the proposed method in real-time. Additionally, a comparative study with a direct learning network, that lacks forward model information, shows that the proposed method is more robust to DFOV variations., Comment: Accepted at IEEE ISBI 2025

We study the inherent trade-offs in minimizing privacy risks and maximizing utility, while maintaining high computational efficiency, when fine-tuning large language models (LLMs). A number of recent works in privacy research have attempted to mitigate privacy risks posed by memorizing fine-tuning data by using differentially private training methods (e.g., DP), albeit at a significantly higher computational cost (inefficiency). In parallel, several works in systems research have focussed on developing (parameter) efficient fine-tuning methods (e.g., LoRA), but few works, if any, investigated whether such efficient methods enhance or diminish privacy risks. In this paper, we investigate this gap and arrive at a surprising conclusion: efficient fine-tuning methods like LoRA mitigate privacy risks similar to private fine-tuning methods like DP. Our empirical finding directly contradicts prevailing wisdom that privacy and efficiency objectives are at odds during fine-tuning. Our finding is established by (a) carefully defining measures of privacy and utility that distinguish between memorizing sensitive and non-sensitive tokens in training and test datasets used in fine-tuning and (b) extensive evaluations using multiple open-source language models from Pythia, Gemma, and Llama families and different domain-specific datasets., Comment: This is a work in progress. The draft may change in future

Bipartite quantum states with higher Schmidt numbers have been shown to outperform those with lower Schmidt numbers in various information processing tasks. Therefore, to ensure the efficient use of resources in these tasks, it is essential to certify the Schmidt number of the resource states. Ideally, this certification should rely as little as possible on the certifying devices, ensuring robustness against potential imperfections. In this work, we explore the scope of fully and partially device-independent Schmidt number certification methods. We demonstrate the general impossibility of fully device-independent certification for all states. Specifically, in a restricted setting, we present a class of states with Schmidt number 3, for which it is impossible to certify that their Schmidt number is greater than 2. However, we show that the Schmidt number of all states can be certified in a measurement-device-independent manner via semi-quantum nonlocal games, which assume trust in the preparation devices. Finally, we present an explicit construction of a semi-quantum game for the measurement-device-independent certification of a class of states., Comment: Initial draft; Comments are welcome; 9 pages, 1 figure

Achieving low contact resistance in advanced electronic devices remains a significant challenge. As the demand for faster and more energy-efficient devices grows, 2D contact engineering emerges as a promising solution for next-generation electronics. Beyond graphene, 1T-WTe2 has gained attention due to its outstanding electrical transport properties, quantum phenomena, and Weyl semimetallic characteristics. We demonstrate the direct wafer-scale growth of 1T-WTe2 via molecular beam epitaxy (MBE) and use it as a 2D contact for layered materials like InSe, which exhibits broad photoresponsivity. The performance of this 2D electrode in InSe-based photodetectors is compared with conventional metal electrodes. Under near-infrared (NIR) to deep ultraviolet (DUV) illumination, the InSe/1T-WTe2 configuration shows a broad photoresponsivity range from 0.14 to 217.58 A/W, with fast rise/fall times of 42/126 ms in the visible region. In contrast, the InSe/Ti-Au configuration exhibits a peak photoresponsivity of 3.64 A/W in the DUV range, with an overall lower responsivity spanning from 0.000865 A/W to 3.64 A/W under NIR and DUV illumination, respectively. Additionally, in the visible regime, it exhibits slower rise and fall times of 150 ms and 144 ms, respectively, compared to InSe/1T-WTe2. These findings indicate that MBE-grown 1T-WTe2 serves as an effective 2D electrode, delivering higher photoresponsivity and faster photodetection compared to traditional metal contacts. This approach offers a simplified, high-performance alternative for layered material-based devices, eliminating the need for complex heterostructure configurations., Comment: 8 figures

In this paper, an automated Linear Parameter-Varying (LPV) model conversion approach is proposed for nonlinear dynamical systems. The proposed method achieves global embedding of the original nonlinear behavior of the system by leveraging the second fundamental theorem of calculus to factorize matrix function expressions without any approximation. The implementation of the proposed method in the LPVcore toolbox for Matlab is discussed, and its performance is showcased on a comprehensive example of automated LPV model conversion of an unbalanced disk system, which is then used to design an LPV controller that is deployed on the original nonlinear system. In addition, the conversion capabilities are further demonstrated by obtaining an LPV embedding of a three-degree-of-freedom control moment gyroscope. All software implementations are available at www.lpvcore.net., Comment: 8 pages, 8 figures, submitted to LPVS25

One of the main concerns while deploying machine learning models in real-world applications is fairness. Counterfactual fairness has emerged as an intuitive and natural definition of fairness. However, existing methodologies for enforcing counterfactual fairness seem to have two limitations: (i) generating counterfactual samples faithful to the underlying causal graph, and (ii) as we argue in this paper, existing regularizers are mere proxies and do not directly enforce the exact definition of counterfactual fairness. In this work, our aim is to mitigate both issues. Firstly, we propose employing Neural Causal Models (NCMs) for generating the counterfactual samples. For implementing the abduction step in NCMs, the posteriors of the exogenous variables need to be estimated given a counterfactual query, as they are not readily available. As a consequence, $\mathcal{L}_3$ consistency with respect to the underlying causal graph cannot be guaranteed in practice due to the estimation errors involved. To mitigate this issue, we propose a novel kernel least squares loss term that enforces the $\mathcal{L}_3$ constraints explicitly. Thus, we obtain an improved counterfactual generation suitable for the counterfactual fairness task. Secondly, we propose a new MMD-based regularizer term that explicitly enforces the counterfactual fairness conditions into the base model while training. We show an improved trade-off between counterfactual fairness and generalization over existing baselines on synthetic and benchmark datasets., Comment: 9 pages, 2 figures

We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions as spike trains and employs a fully connected spiking neural network to estimate the total deposited energy, as well as the position and spatial distribution of the light emissions within the sensitive material. The extracted primitives offer valuable topological information about the shower development in the material, achieved without requiring a segmentation of the active medium. A potential nanophotonic implementation using III-V semiconductor nanowires is discussed. It can be both fast and energy efficient., Comment: 15 pages, 12 figures, submitted to MDPI Particles

Understanding the impacts of U-turn configurations on intersection safety and traffic operations is essential for developing effective strategies to enhance road safety and efficiency. Extensive research has been conducted to investigate the role of geometric designs, driver behavior, and advanced technologies in mitigating crash risks and improving traffic flow at U-turn facilities. By synthesizing this collective body of work through the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this paper provides a valuable resource for transportation professionals, policymakers, and researchers seeking evidence-based solutions. This systematic review draws on studies from diverse traffic environments and regional contexts, focusing on innovative design interventions, such as restricted crossing U-turns (RCUTs) and median U-turn intersections (MUTs), as well as integrated strategies leveraging technological advancements. By presenting a comprehensive analysis of U-turn-related challenges and opportunities, this review contributes to advancing transportation safety research and guiding the development of adaptive strategies tailored to varied traffic conditions and evolving technologies., Comment: 28 pages, 8 tables, 2 figures

Models with axion like particles (ALPs) often predict the formation of a string-domain wall network in the early universe. We study how such networks of defects appear in the context of string theory, and discuss the conditions for their long-term stability. In a scenario with several axions, we show how a bias term in the potential arises naturally from the effects of multiple instantons, leading to the eventual decay of the domain walls. We find that the annihilation of the network leads to the generation of a stochastic gravitational wave background (SGWB) with a spectrum that has characteristic contributions from both walls and strings. The unique shape of the spectrum provides an opportunity to probe string theory axions at existing and upcoming observatories. The extinction of the network is also accompanied by the production of different axion mass eigenstates. In a region of the parameter space, the lightest eigenstate can be long lived and make up the dark matter in the universe., Comment: 22+6 pages, 7 figures