Search

Miniaturization of electronic components has led to overheating, increasing power consumption and causing early circuit failures. Conventional heat dissipation methods are becoming inadequate due to limited surface area and higher short-circuit risks. This study presents a fast, low-cost, and scalable technique using 2D hexagonal boron nitride (hBN) coatings to enhance heat dissipation in commercial electronics. Inexpensive hBN layers, applied by drop casting or spray coating, boost thermal conductivity at IC surfaces from below 0.3 W/m-K to 260 W/m-K, resulting in over double the heat flux and convective heat transfer. This significantly reduces operating temperatures and power consumption, as demonstrated by a 17.4% reduction in a coated audio amplifier circuit board. Density functional theory indicates enhanced interaction between 2D hBN and packaging materials as a key factor. This approach promises substantial energy and cost savings for large-scale electronics without altering existing manufacturing processes., Comment: 27 Pages, 5 Figures

The atomic and molecular compounds of cometary ices serve as valuable knowledge into the chemical and physical properties of the outer solar nebula, where comets are formed. From the cometary atmospheres, the atoms and gas-phase molecules arise mainly in three ways: (i) the outgassing from the nucleus, (ii) the photochemical process, and (iii) the sublimation of icy grains from the nucleus. In this paper, we present the radio and millimeter wavelength observation results of Oort cloud non-periodic comet C/2022 E3 (ZTF) using the Giant Metrewave Radio Telescope (GMRT) band L and the Atacama Large Millimeter/Submillimeter Array (ALMA) band 6. We do not detect continuum emissions and an emission line of atomic hydrogen (HI) at rest frequency 1420 MHz from this comet using the GMRT. Based on ALMA observations, we detect the dust continuum emission and rotational emission lines of methanol (CH$_{3}$OH) from comet C/2022 E3 (ZTF). From the dust continuum emission, the activity of dust production (Af$\rho$) of comet ZTF is 2280$\pm$50 cm. Based on LTE spectral modelling, the column density and excitation temperature of CH$_{3}$OH towards C/2022 E3 (ZTF) are (4.50$\pm$0.25)$\times$10$^{14}$ cm$^{-2}$ and 70$\pm$3 K. The integrated emission maps show that CH$_{3}$OH was emitted from the coma region of the comet. The production rate of CH$_{3}$OH towards C/2022 E3 (ZTF) is (7.32$\pm$0.64)$\times$10$^{26}$ molecules s$^{-1}$. The fractional abundance of CH$_{3}$OH with respect to H$_{2}$O in the coma of the comet is 1.52%. We also compare our derived abundance of CH$_{3}$OH with the existence modelled value, and we see the observed and modelled values are nearly similar. We claim that CH$_{3}$OH is formed via the subsequential hydrogenation of formaldehyde (H$_{2}$CO) on the grain surface of comet C/2022 E3 (ZTF)., Comment: Accepted for publication in Research in Astronomy and Astrophysics (RAA)

We revisit the phenomenology of dark-matter (DM) scenarios within radius-stabilized Randall-Sundrum models. Specifically, we consider models where the dark matter candidates are Standard Model (SM) singlets confined to the TeV brane and interact with the SM via spin-2 and spin-0 gravitational Kaluza-Klein (KK) modes. We compute the thermal relic density of DM particles in these models by applying recent work showing that scattering amplitudes of massive spin-2 KK states involve an intricate cancellation between various diagrams. Considering the resulting DM abundance, collider searches, and the absence of a signal in direct DM detection experiments, we show that spin-2 KK portal DM models are highly constrained. We confirm that within the usual thermal freeze-out scenario, scalar dark matter models are essentially ruled out. In contrast, we show that fermion and vector dark matter models are viable in a region of parameter space in which dark matter annihilation through a KK graviton is resonant. Specifically, vector models are viable for dark matter masses ranging from 1.1 TeV to 5.5 TeV for theories in which the scale of couplings of the KK modes is of order 40 TeV or lower. Fermion dark matter models are viable for a similar mass region, but only for KK coupling scales of order 20 TeV. In this work, we provide a complete description of the calculations needed to arrive at these results and, in an appendix, a discussion of new KK-graviton couplings needed for the computations, which have not previously been discussed in the literature. Here, we focus on models in which the radion is light, and the back-reaction of the radion stabilization dynamics on the gravitational background can be neglected. The phenomenology of a model with a heavy radion and the consideration of the effects of the radion stabilization dynamics on the DM abundance are being addressed in forthcoming work., Comment: 42 pages, 24 figures, We dedicate this work to the memory of Rohini Godbole (1952-2024) role model, mentor, and friend

In this study, we propose a novel and robust framework, Self-DenseMobileNet, designed to enhance the classification of nodules and non-nodules in chest radiographs (CXRs). Our approach integrates advanced image standardization and enhancement techniques to optimize the input quality, thereby improving classification accuracy. To enhance predictive accuracy and leverage the strengths of multiple models, the prediction probabilities from Self-DenseMobileNet were transformed into tabular data and used to train eight classical machine learning (ML) models; the top three performers were then combined via a stacking algorithm, creating a robust meta-classifier that integrates their collective insights for superior classification performance. To enhance the interpretability of our results, we employed class activation mapping (CAM) to visualize the decision-making process of the best-performing model. Our proposed framework demonstrated remarkable performance on internal validation data, achieving an accuracy of 99.28\% using a Meta-Random Forest Classifier. When tested on an external dataset, the framework maintained strong generalizability with an accuracy of 89.40\%. These results highlight a significant improvement in the classification of CXRs with lung nodules., Comment: 31 pages

In this study, we investigate the performance of few-shot classification models across different domains, specifically natural images and histopathological images. We first train several few-shot classification models on natural images and evaluate their performance on histopathological images. Subsequently, we train the same models on histopathological images and compare their performance. We incorporated four histopathology datasets and one natural images dataset and assessed performance across 5-way 1-shot, 5-way 5-shot, and 5-way 10-shot scenarios using a selection of state-of-the-art classification techniques. Our experimental results reveal insights into the transferability and generalization capabilities of few-shot classification models between diverse image domains. We analyze the strengths and limitations of these models in adapting to new domains and provide recommendations for optimizing their performance in cross-domain scenarios. This research contributes to advancing our understanding of few-shot learning in the context of image classification across diverse domains.

In this paper, we prove the existence of weak, veryweak and duality solutions to a class of elliptic problems involving singularity and measure data which is given by: $-\Delta u+(-\Delta)^s u = \frac{f(x)}{u^\gamma} +\mu$ in $\Omega$ with the zero Dirichlet boundary data $u=0$ in $\mathbb R^N \setminus \Omega$. The existence of weak solutions is obtained by approximating a sequence of problems for $0<\gamma\leq1$ and $\gamma>1$. We employ Schauder's fixed point theorem and embeddings of Marcinkiewicz spaces. The novelty of our work is that we prove the existence of a duality solution and its equivalence with weak solutions to the problem $\mathcal{L}u=\mu$. Moreover, we prove a veryweak maximum principle and a Kato-type inequality for the mixed local-nonlocal operator $\mathcal{L}=-\Delta +(-\Delta)^s$, which are crucial tools to guarantee the existence of veryweak solutions to the problem. Using a Kato-type inequality, maximum principle together with sub-super solution method, we prove the existence of veryweak solution for $0<\gamma<1$. Our work extends the studies due to Oliva and Petitta [ESAIM Control Optim. Calc. Var., 22(1):289--308, 2016.] and Petitta [Adv. Nonlinear Stud., 16(1):115--124, 2016.] for the mixed local-nonlocal operator., Comment: 32 pages

Generative Artificial Intelligence (GenAI) is becoming ubiquitous in our daily lives. The increase in computational power and data availability has led to a proliferation of both single- and multi-modal models. As the GenAI ecosystem matures, the need for extensible and model-agnostic risk identification frameworks is growing. To meet this need, we introduce the Python Risk Identification Toolkit (PyRIT), an open-source framework designed to enhance red teaming efforts in GenAI systems. PyRIT is a model- and platform-agnostic tool that enables red teamers to probe for and identify novel harms, risks, and jailbreaks in multimodal generative AI models. Its composable architecture facilitates the reuse of core building blocks and allows for extensibility to future models and modalities. This paper details the challenges specific to red teaming generative AI systems, the development and features of PyRIT, and its practical applications in real-world scenarios.

Let $K$ be a number field with algebraic closure $\bar{K}$, let $S$ be a finite set of places of $K$ containing the archimedean places, and let $\varphi$ be Chebyshev polynomial. In this paper we prove uniformity results on the number of $S$-integral preperiodic points relative to a non-preperiodic point $\beta$, as $\beta$ varies over number fields of bounded degree., Comment: 14 pages. arXiv admin note: text overlap with arXiv:2302.01562, arXiv:0808.2679, arXiv:0805.1554 by other authors

In e-commerce platforms, coupons play a crucial role in boosting transactions. In the customer-to-customer (C2C) marketplace, ensuring the satisfaction of both buyers and sellers is essential. While buyer-focused marketing strategies often receive more attention, addressing the needs of sellers is equally important. Additionally, the existing strategies tend to optimize each promotion independently, resulting in a lack of continuity between promotions and unnecessary costs in the pursuit of short-term impact within each promotion period. We introduce a Dynamic Sequential Coupon Allocation Framework (DSCAF) to optimize item coupon allocation strategies across a series of promotions. DSCAF provides sequential recommendations for coupon configurations and timing to target items. In cases where initial suggestions do not lead to sales, it dynamically adjusts the strategy and offers subsequent solutions. It integrates two predictors for estimating the sale propensity in the current and subsequent rounds of coupon allocation, and a decision-making process to determine the coupon allocation solution. It runs iteratively until the item is sold. The goal of the framework is to maximize Return on Investment (ROI) while ensuring lift Sell-through Rate (STR) remains above a specified threshold. DSCAF aims to optimize sequential coupon efficiency with a long-term perspective rather than solely focusing on the lift achieved in each individual promotion. It has been applied for item coupon allocation in Mercari.

A resonant electro-optic (EO) frequency comb is generated through electro-optic modulation of laser light within an optical resonator. Compared to cavity-less EO combs generated in a single pass through a modulator, resonant EO combs can produce broader spectra with lower radio frequency (RF) power and offer a measure of noise filtering beyond the cavity's linewidth. Understanding, measuring, and suppressing the sources of phase noise in resonant EO combs is crucial for their applications in metrology, astrophotonics, optical clock generation, and fiber-optic communication. According to the standard phase noise model of frequency combs, only two variables - the common mode offset and repetition rate phase noise - are needed to fully describe the phase noise of comb lines. However, in this work we demonstrate analytically, numerically, and experimentally that this standard model breaks down for resonant EO combs at short timescales (high frequencies) and under certain comb parameters. Specifically, a third phase noise component emerges. Consequently, resonant EO combs feature qualitatively different phase noise from their cavity-less counterparts and may not exhibit the anticipated noise filtering. A more complete description of the deviations from the standard phase noise model is critical to accurately predict the performance of frequency combs. The description presented here paves the way for improved designs tailored to applications such as super-continuum generation and optical communication.

In this paper, we investigate the subelliptic nonlocal Brezis-Nirenberg problem on stratified Lie groups involving critical nonlinearities, namely, \begin{align*} (-\Delta_{\mathbb{G}, p})^s u&= \mu |u|^{p_s^*-2}u+\lambda h(x, u) \quad \text{in}\quad \Omega, \\ u&=0\quad \text{in}\quad \mathbb{G}\backslash \Omega, \end{align*} where $(-\Delta_{\mathbb{G}, p})^s$ is the fractional $p$-sub-Laplacian on a stratified Lie group $\mathbb{G}$ with homogeneous dimension $Q,$ $\Omega$ is an open bounded subset of $\mathbb{G},$ $s \in (0,1)$, $\frac{Q}{s}>p\geq2,$ $p_s^*:=\frac{pQ}{Q-ps}$ is subelliptic fractional Sobolev critical exponent, $\mu, \lambda>0$ are real parameters and $h$ is a lower order perturbation of the critical power $|u|^{p_s^*-2}u$. Utilising direct methods of the calculus of variation, we establish the existence of at least one weak solution for the above problem under the condition that the real parameter $\lambda$ is sufficiently small. Additionally, we examine the problem for $\mu = 0$, representing subelliptic nonlocal equations on stratified Lie groups depending on one real positive parameter and involving a subcritical nonlinearity. We demonstrate the existence of at least one solution in this scenario as well. We emphasize that the results obtained here are also novel for $p=2$ even for the Heisenberg group., Comment: 31 pages

We study an {\it indefinite weighted eigenvalue problem} for an operator of {\it mixed-type} (that includes both the classical {\it $p$-Laplacian} and the {\it fractional $p$-Laplacian}) in a bounded open subset $\Omega\subset \mathbb{R}^N \,(N\geq2)$ with {\it Lipschitz boundary} $\partial \Omega$, which is given by \begin{align*} -\Delta_p u + (-\Delta_p)^su+V(x)|u|^{p-2}u&=\lambda g(x)|u|^{p-2}u~\text{in}~\Omega, u&=0~\text{in}~\mathbb{R}^N\setminus\Omega, \end{align*} where $\lambda >0$ is a parameter, exponents $0 0$ a.e. in $\Omega$. Using the variational tools together with a {\it weak comparison} and {\it strong maximum principles}, we investigate the existence and uniqueness of {\it principal eigenvalue} and discuss its qualitative properties. Moreover, with the help of {\it Ljusternik-Schnirelman category theory}, it is proved that there exists a {\it nondecreasing sequence of positive eigenvalues} which goes to infinity. Further, we show that {\it the set of all positive eigenvalues is closed}, and {\it eigenfunctions} associated with every {\it positive eigenvalue} are bounded., Comment: 28 pages

We present the second data release of the MeerKAT Absorption Line Survey (MALS), consisting of wideband continuum catalogues of 391 pointings observed at L~band. The full wideband catalogue covers 4344 deg$^2$ of sky, reaches a depth of 10 $\mu$Jy beam$^{-1}$, and contains 971,980 sources. With its balance between survey depth and sky coverage, MALS DR2 covers five orders of magnitude of flux density, presenting a robust view of the extragalactic radio source population down to 200 $\mu$Jy. Using this catalogue, we perform a measurement of the cosmic radio dipole, an anisotropy in the number counts of radio sources with respect to the cosmic background, analogous to the dipole found in the cosmic microwave background (CMB). For this measurement, we present the characterisation of completeness and noise properties of the catalogue, and show that a declination-dependent systematic affects the number density of faint sources. In the dipole measurement on the MALS catalogue, we recover reasonable dipole measurements once we model the declination systematic with a linear fit between the size of the major axis of the restoring beam and the amount of sources of each pointing. The final results are consistent with the CMB dipole in terms of direction and amplitude, unlike many recent measurements of the cosmic radio dipole made with other centimetre wavelength catalogues, which generally show a significantly larger amplitude. This result demonstrates the value of dipole measurements with deeper and more sparse radio surveys, as the population of faint sources probed may have had a significant impact on the measured dipole., Comment: 25 pages, 14 figures. Accepted for publication in Astronomy & Astrophysics. The MALS wideband catalogues and images are publicly available at https://mals.iucaa.in

The current standard for detecting human epidermal growth factor receptor 2 (HER2) status in breast cancer patients relies on HER2 amplification, identified through fluorescence in situ hybridization (FISH) or immunohistochemistry (IHC). However, hematoxylin and eosin (H\&E) tumor stains are more widely available, and accurately predicting HER2 status using H\&E could reduce costs and expedite treatment selection. Deep Learning algorithms for H&E have shown effectiveness in predicting various cancer features and clinical outcomes, including moderate success in HER2 status prediction. In this work, we employed a customized weak supervision classification technique combined with MoCo-v2 contrastive learning to predict HER2 status. We trained our pipeline on 182 publicly available H&E Whole Slide Images (WSIs) from The Cancer Genome Atlas (TCGA), for which annotations by the pathology team at Yale School of Medicine are publicly available. Our pipeline achieved an Area Under the Curve (AUC) of 0.85 across four different test folds. Additionally, we tested our model on 44 H&E slides from the TCGA-BRCA dataset, which had an HER2 score of 2+ and included corresponding HER2 status and FISH test results. These cases are considered equivocal for IHC, requiring an expensive FISH test on their IHC slides for disambiguation. Our pipeline demonstrated an AUC of 0.81 on these challenging H&E slides. Reducing the need for FISH test can have significant implications in cancer treatment equity for underserved populations.

This paper presents a study on few-shot classification in the context of histopathology images. While few-shot learning has been studied for natural image classification, its application to histopathology is relatively unexplored. Given the scarcity of labeled data in medical imaging and the inherent challenges posed by diverse tissue types and data preparation techniques, this research evaluates the performance of state-of-the-art few-shot learning methods for various scenarios on histology data. We have considered four histopathology datasets for few-shot histopathology image classification and have evaluated 5-way 1-shot, 5-way 5-shot and 5-way 10-shot scenarios with a set of state-of-the-art classification techniques. The best methods have surpassed an accuracy of 70%, 80% and 85% in the cases of 5-way 1-shot, 5-way 5-shot and 5-way 10-shot cases, respectively. We found that for histology images popular meta-learning approaches is at par with standard fine-tuning and regularization methods. Our experiments underscore the challenges of working with images from different domains and underscore the significance of unbiased and focused evaluations in advancing computer vision techniques for specialized domains, such as histology images.

This study proposes and explores a linear hydrodynamic thermo-elasticity system within mixture models, comprising fluid and solid phases, with a focus on biological tissues, particularly tumor-related phenomena. Although tumor growth is not yet incorporated, this work aims to comprehend the interaction between thermal effects and hydrodynamics on short-time scales where the tumor size typically remains stable. We establish the existence of a unique weak solution within the framework of implicit evolution equations, overcoming challenges posed by intricate coupling mechanisms within the system. To further investigate the model, we then study the one-dimensional model and explore in detail the complex interplay between fluid flow, solid deformation, and heat transfer. This complex coupled system of equations is reduced for the short time scale to obtain the semi-analytical solution.

Nowadays, various stakeholders involved in the analysis of electric power distribution grids face difficulties in the data acquisition related to the grid topology and parameters of grid assets. To mitigate the problem and possibly accelerate the accomplishment of grid studies without access to real data, we propose a novel approach for generating synthetic distribution grids (Syngrids) of (almost) arbitrary size replicating the characteristics of real medium- and low-voltage distribution networks. The method enables large-scale testing without incurring the burden of retrieving and pre-processing real-world data. The proposed algorithm exploits the publicly available information of Open Street Map (OSM). By leveraging geospatial data of real buildings and road networks, the approach allows to construct a Syngrid of chosen size with realistic topology and electrical parameters. It is shown that typical power-flow and short-circuit calculations can be performed on Syngrids ensuring convergence. Within the context of validating the effectiveness of the algorithm and the meaningful similarity of the output to real grids, the topological and electrical characteristics of a Syngrid are compared to their real-world counterparts. Finally, an open-source web platform named as Synthetic Grid Generator (SGG) and based on the proposed algorithm can be used by various stakeholders for the creation of synthetic grids., Comment: 8 pages, 8 figures

The search for earth-like exoplanets with the Doppler radial velocity technique is an extremely challenging and multifaceted precision spectroscopy problem. Currently, one of the limiting instrumental factors in reaching the required long-term $10^{-10}$ level of radial velocity precision is the defect-driven sub-pixel quantum efficiency variations in the large-format detector arrays used by precision echelle spectrographs. Tunable frequency comb calibration sources that can fully map the point spread function across a spectrograph's entire bandwidth are necessary for quantifying and correcting these detector artifacts. In this work, we demonstrate a combination of laser frequency and mode spacing control that allows full and deterministic tunability of a 30 GHz electro-optic comb together with its filter cavity. After supercontinuum generation, this gives access to any optical frequency across 700 - 1300 nm. Our specific implementation is intended for the comb deployed at the Habitable Zone Planet Finder spectrograph and its near-infrared Hawaii-2RG array, but the techniques apply to all laser frequency combs used for precision astronomical spectrograph calibration and other applications that require broadband tuning., Comment: 6 pages, 5 figures

Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone. As these models are deployed in an increasing number of domains, it is critical to ensure that they are aligned with human preferences and safety considerations. In this report, we present our methodology for safety aligning the Phi-3 series of language models. We utilized a "break-fix" cycle, performing multiple rounds of dataset curation, safety post-training, benchmarking, red teaming, and vulnerability identification to cover a variety of harm areas in both single and multi-turn scenarios. Our results indicate that this approach iteratively improved the performance of the Phi-3 models across a wide range of responsible AI benchmarks. Finally, we include additional red teaming strategies and evaluations that were used to test the safety behavior of Phi-3.5-mini and Phi-3.5-MoE, which were optimized for multilingual capabilities.

Large language models (LLMs) demonstrated transformative capabilities in many applications that require automatically generating responses based on human instruction. However, the major challenge for building LLMs, particularly in Indic languages, is the availability of high-quality data for building foundation LLMs. In this paper, we are proposing a large pre-train dataset in Hindi useful for the Indic language Hindi. We have collected the data span across several domains including major dialects in Hindi. The dataset contains 1.28 billion Hindi tokens. We have explained our pipeline including data collection, pre-processing, and availability for LLM pre-training. The proposed approach can be easily extended to other Indic and low-resource languages and will be available freely for LLM pre-training and LLM research purposes., Comment: Accepted as a book chapter in the book Title "APPLIED SPEECH AND TEXT PROCESSING FOR LOW RESOURCE LANGUAGES"

The molecular-to-atomic gas ratio is crucial to the evolution of the interstellar medium in galaxies. We investigate the balance between the atomic ($\Sigma_{\rm HI}$) and molecular gas ($\Sigma_{\rm H2}$) surface densities in eight nearby star-forming galaxies using new high-quality observations from MeerKAT and ALMA (for HI and CO, respectively). We define the molecular gas ratio as $R_{\rm mol} = \Sigma_{\rm H2} / \Sigma_{\rm HI}$ and measure how it depends on local conditions in the galaxy disks using multi-wavelength observations. We find that, depending on the galaxy, HI is detected at $>3\sigma$ out to 20-120 kpc in galactocentric radius ($r_{\rm gal}$). The typical radius at which $\Sigma_{\rm HI}$ reaches 1~$\rm M_\odot~pc^{-2}$ is $r_{\rm HI}\approx22$~kpc, which corresponds to 1-3 times the optical radius ($r_{25}$). $R_{\rm mol}$ correlates best with the dynamical equilibrium pressure, P$_{\rm DE}$, among potential drivers studied, with a median correlation coefficient of $<\rho>=0.89$. Correlations between $R_{\rm mol}$ and star formation rate, total gas and stellar surface density, metallicity, and $\Sigma_{\rm SFR}$/P$_{\rm DE}$ are present but somewhat weaker. Our results also show a direct correlation between P$_{\rm DE}$ and $\Sigma_{\rm SFR}$, supporting self-regulation models. Quantitatively, we measure similar scalings as previous works and attribute the modest differences that we find to the effect of varying resolution and sensitivity. At $r_{\rm gal} {\gtrsim}0.4~r_{25}$, atomic gas dominates over molecular gas, and at the balance of these two gas phases, we find that the baryon mass is dominated by stars, with $\Sigma_{*} > 5~\Sigma_{\rm gas}$. Our study constitutes an important step in the statistical investigation of how local galaxy properties impact the conversion from atomic to molecular gas in nearby galaxies., Comment: accepted for publication in A&A; 20 pages, 12 Figures (+4 appendix pages)

In this paper we investigate the scattering amplitudes of spin-2 Kaluza-Klein (KK) states in Randall-Sundrum models with brane-localized curvature terms. We show that the presence of brane-localized curvature interactions modifies the properties of (4D) scalar fluctuations of the metric, resulting in scattering amplitudes of the massive spin-2 KK states which grow as ${\cal O}(s^3)$ instead of ${\cal O}(s)$. We discuss the constraints on the size of the brane-localized curvature interactions based on the consistency of the Sturm-Liouville mode systems of the spin-2 and spin-0 metric fluctuations. We connect the properties of the scattering amplitudes to the diffeomorphism invariance of the compactified KK theory with brane-localized curvature interactions. We verify that the scattering amplitudes involving brane-localized external sources (matter) are diffeomorphism-invariant, but show that those for matter localized at an arbitrary point in the bulk are not. We demonstrate that, in Feynman gauge, the spin-0 Goldstone bosons corresponding to helicity-0 states of the massive spin-2 KK bosons behave as a tower of Galileons, and that it is their interactions that produce the high-energy behavior of the scattering amplitudes. We also outline the correspondence between our results and those in the Dvali-Gabadadze-Porrati (DGP) model. In an appendix we discuss the analogous issue in extra-dimensional gauge theory, and show that the presence of a brane-localized gauge kinetic-energy term does not change the high-energy behavior of corresponding KK vector boson scattering amplitudes., Comment: 36 pages, 2 figures. Minor changes, new reference added

A vast majority of electrical devices have integrated magnetic units, which generate constant magnetic fields with noticeable vibrations. The majority of existing nanogenerators acquire energy through friction/mechanical forces and most of these instances overlook acoustic vibrations and magnetic fields. Magnetic two-dimensional (2D) tellurides present a wide range of possibilities for devising a potential flexible energy harvester. We have synthesized two-dimensional chromium telluride (2D CrTe3) which exhibits ferromagnetic (FM) nature with a Tc of 224 K. The structure exhibits stable high remnant magnetization, making 2D CrTe3 flakes a potential material for harvesting of magneto-acoustic waves at room temperature. A magneto-acoustic nanogenerator (MANG) was fabricated composing of 2D CrTe3 dispersed in a polymer matrix. Basic mechanical stability and sensitivity of the device with change in load conditions were tested. A high surface charge density of 2.919 mC m-2 was obtained for the device. The thermal strain created in the lattice structure was examined using in-situ Raman spectroscopic measurements. The magnetic anisotropy energy (MAE) responsible for long-range FM ordering was calculated with the help of theoretical modelling. The theoretical calculations also showed opening of electronic bandgap which enhances the flexoelectric effects. The MANG can be a potential energy harvester to synergistically tap into the magneto-acoustic vibrations generated from the frequency changes of a vibrating device such as loudspeakers.

Two-dimensional (2D) chromium telluride Cr2Te3 exhibits strong ferromagnetic ordering with high coercivity at low temperatures and paramagnetic behaviour when approaching room temperature. The spin states of monolayer Cr2Te3 show ferromagnetic ordering in the ground state, and in-situ Raman analysis shows reversible structure transformation and hence a ferromagnetic to paramagnetic transition during low-temperature heating cycles (0 - 25 {\deg}C). The magnetic phase transition near room temperature in the 2D Cr2Te3 prompted the exploration of these layered materials for energy application. We demonstrate that the low-temperature ferromagnetic behavior can be used to magnetically deice material surfaces using an external magnetic source, avoiding the use of harsh chemicals and high temperatures. The hydrophobic nature and dipole interactions of H2O molecules with the surface of the 2D Cr2Te3 coating aid in the condensation of ice droplets formed on the surface. First-principles calculations also confirm the observed crystal structure, surface interaction, and magnetic properties of 2D Cr2Te3.

While diffusion models excel at generating high-quality images, prior work reports a significant performance gap between diffusion and autoregressive (AR) methods in language modeling. In this work, we show that simple masked discrete diffusion is more performant than previously thought. We apply an effective training recipe that improves the performance of masked diffusion models and derive a simplified, Rao-Blackwellized objective that results in additional improvements. Our objective has a simple form -- it is a mixture of classical masked language modeling losses -- and can be used to train encoder-only language models that admit efficient samplers, including ones that can generate arbitrary lengths of text semi-autoregressively like a traditional language model. On language modeling benchmarks, a range of masked diffusion models trained with modern engineering practices achieves a new state-of-the-art among diffusion models, and approaches AR perplexity. We provide the code, along with a blog post and video tutorial on the project page: https://s-sahoo.com/mdlm, Comment: NeurIPS 2024. We provide the code at https://github.com/kuleshov-group/mdlm

Community and core-periphery are two widely studied graph structures, with their coexistence observed in real-world graphs (Rombach, Porter, Fowler \& Mucha [SIAM J. App. Math. 2014, SIAM Review 2017]). However, the nature of this coexistence is not well understood and has been pointed out as an open problem (Yanchenko \& Sengupta [Statistics Surveys, 2023]). Especially, the impact of inferring the core-periphery structure of a graph on understanding its community structure is not well utilized. In this direction, we introduce a novel quantification for graphs with ground truth communities, where each community has a densely connected part (the core), and the rest is more sparse (the periphery), with inter-community edges more frequent between the peripheries. Built on this structure, we propose a new algorithmic concept that we call relative centrality to detect the cores. We observe that core-detection algorithms based on popular centrality measures such as PageRank and degree centrality can show some bias in their outcome by selecting very few vertices from some cores. We show that relative centrality solves this bias issue and provide theoretical and simulation support, as well as experiments on real-world graphs. Core detection is known to have important applications with respect to core-periphery structures. In our model, we show a new application: relative-centrality-based algorithms can select a subset of the vertices such that it contains sufficient vertices from all communities, and points in this subset are better separable into their respective communities. We apply the methods to 11 biological datasets, with our methods resulting in a more balanced selection of vertices from all communities such that clustering algorithms have better performance on this set.

We have performed Density Functional Theory (DFT) based calculations to investigate the effects of stacking patterns on the electronic and magnetic properties of several Nitride MXenes. MXenes, a relatively new addition to the family of two-dimensional materials, have exhibited fascinating properties on several occasions, primarily due to their compositional flexibility. However, compared to Carbide MXenes, Nitride MXenes are much less explored. Moreover, the structural aspects of MXenes and the tunability it may offer have not been explored until recently. In this work, we have combined these two less-explored aspects to examine the structure-property relations in the field of magnetism. We find that in the family of M$_{2}$NT$_{2}$ (M=Sc, Ti, V, Cr, Mn; T=O, F) MXenes, the stacking of transition metal planes has a substantial effect on the ground state and finite temperature magnetic properties. We also find that the electronic ground states can be tuned by changing the stacking pattern in these compounds, making the materials appropriate for applications as magnetic devices. Through a detailed analysis, we have connected the unconventional stacking pattern-driven tunability of these compounds with regard to electronic and magnetic properties to the local symmetry, inhomogeneity (or lack of it) of structural parameters, and electronic structures., Comment: Supplementary information available upon request

Memory protection units (MPUs) are hardware-assisted security features that are commonly used in embedded processors such as the ARM 940T, Infineon TC1775, and Xilinx Zynq. MPUs partition the memory statically, and set individual protection attributes for each partition. MPUs typically define two protection domains: user mode and supervisor mode. Normally, this is sufficient for protecting the kernel and applications. However, we have discovered a way to access a process memory due to a vulnerability in Xilinx MPU (XMPU) implementation that we call Resurrection Attack. We find that XMPU security policy protects user memory from unauthorized access when the user is active. However, when a user's session is terminated, the contents of the memory region of the terminated process are not cleared. An attacker can exploit this vulnerability by gaining access to the memory region after it has been reassigned. The attacker can read the data from the previous user's memory region, thereby compromising the confidentiality. To prevent the Resurrection Attack, the memory region of a terminated process must be cleared. However, this is not the case in the XMPU implementation, which allows our attack to succeed. The Resurrection Attack is a serious security flaw that could be exploited to steal sensitive data or gain unauthorized access to a system. It is important for users of Xilinx FPGAs to be aware of this vulnerability until this flaw is addressed.

Linear and weakly nonlinear stability analyses of an externally shear-imposed, gravity-driven falling film over a uniformly heated wavy substrate are studied. The longwave asymptotic expansion technique is utilized to formulate a single nonlinear free surface deflection equation. The linear stability criteria for the onset of instability are derived using the normal mode form in the linearized portion of the surface deformation equation. Linear stability theory reveals that the flow-directed sturdy external shear grows the surface wave instability by increasing the net driving force. On the contrary, the upstream-directed imposed shear may reduce the surface mode instability by restricting the gravity-driving force, which has the consequence of weakening the bulk velocity of the liquid film. However, the surface mode can be stabilized/destabilized by increasing the temperature-dependent density/surface-tension variation. Further, the bottom steepness shows dual behaviour on the surface instability depending upon the wavy wall's portion (uphill/downhill). At the downhill portion, the surface wave becomes more unstable than at the bottom substrate's uphill portion. Moreover, the multi-scale method is incorporated to obtain the complex Ginzburg-Landau equation in order to study the weakly nonlinear stability, confirming the existence of various flow regions of the liquid film. At any bottom portion (uphill/downhill), the flow-directed external shear expands the super-critical stable zones, which causes an amplification in the nonlinear wave amplitude, and the backflow-directed shear plays a counterproductive role. On the other hand, the super-critical stable region decreases or increases as long as the linear variation of density or surface tension increases with respect to the temperature, whereas the sub-critical unstable region exhibits an inverse trend., Comment: 29 pages, 46 figures, and 5 tables

The high ionic conducting sodium superionic conductor Na3Zr2Si2PO12 -NaI composites have been prepared successfully via solid state reaction route. As the NaI content increases, the ionic conductivity significantly changes. The composites with 4% NaI show a maximum ionic conductivity of ~ 3 x 10-4 {\Omega}-1cm-1 at 200oC which is one order of magnitude rise as compared to pristine sample. Further crystal structure and surface morphology also reveal the formation of a glassy phase due to the presence of Na-Si-P-O-I interaction., Comment: 8 pages, 4 figures, Presented at 9th IAPT National Student Symposium on Physics (NSSP-2022)

Medical image encryption could aid in preserving patient privacy. In this article, we provide a chaotic system-based medical picture encryption method. The diffusion and permutation architecture was used. The permutation based on plain image and chaotic keys is offered to shuffle the plain picture's pixels to other rows and columns, weakening the strong connections between neighboring pixels. Diffusion is suggested to spread small changes of plain images to all of the pixels in cipher images to enhance the encryption effect. We analyze the chaotic behavior of the proposed system using various techniques and tests such as bifurcation plots, Lyapunov exponents, MSE, PSNR tests, and histogram analysis., Comment: Original Research Review

In the past decades, deep neural networks, particularly convolutional neural networks, have achieved state-of-the-art performance in a variety of medical image segmentation tasks. Recently, the introduction of the vision transformer (ViT) has significantly altered the landscape of deep segmentation models. There has been a growing focus on ViTs, driven by their excellent performance and scalability. However, we argue that the current design of the vision transformer-based UNet (ViT-UNet) segmentation models may not effectively handle the heterogeneous appearance (e.g., varying shapes and sizes) of objects of interest in medical image segmentation tasks. To tackle this challenge, we present a structured approach to introduce spatially dynamic components to the ViT-UNet. This adaptation enables the model to effectively capture features of target objects with diverse appearances. This is achieved by three main components: \textbf{(i)} deformable patch embedding; \textbf{(ii)} spatially dynamic multi-head attention; \textbf{(iii)} deformable positional encoding. These components were integrated into a novel architecture, termed AgileFormer. AgileFormer is a spatially agile ViT-UNet designed for medical image segmentation. Experiments in three segmentation tasks using publicly available datasets demonstrated the effectiveness of the proposed method. The code is available at \href{https://github.com/sotiraslab/AgileFormer}{https://github.com/sotiraslab/AgileFormer}.

Let $(X_{k})_{k\geq 1}$ and $(Y_k)_{k\geq 1}$ be the sequence of $X$ and $Y$-coordinates of the positive integer solutions $(x, y)$ of the equation $x^2 - dy^2 = t$. In this paper we completely describe those recurrence sequences such that sums of two terms recurrence sequences in the solution sets of generalized Pell equations are infinitely many. Further, we give an upper bound for the number of such terms when there are only finitely many of them. This work is motivated by the recent paper Hajdu and Sebesty\'en (Int. J. Number Theory 18 (2022), 1605-1612)., Comment: 10 pages

Training a computer vision system to segment a novel class typically requires collecting and painstakingly annotating lots of images with objects from that class. Few-shot segmentation techniques reduce the required number of images to learn to segment a new class, but careful annotations of object boundaries are still required. On the other hand, interactive segmentation techniques only focus on incrementally improving the segmentation of one object at a time (typically, using clicks given by an expert) in a class-agnostic manner. We combine the two concepts to drastically reduce the effort required to train segmentation models for novel classes. Instead of trivially feeding interactive segmentation masks as ground truth to a few-shot segmentation model, we propose IFSENet, which can accept sparse supervision on a single or few support images in the form of clicks to generate masks on support (training, at least clicked upon once) as well as query (test, never clicked upon) images. To trade-off effort for accuracy flexibly, the number of images and clicks can be incrementally added to the support set to further improve the segmentation of support as well as query images. The proposed model approaches the accuracy of previous state-of-the-art few-shot segmentation models with considerably lower annotation effort (clicks instead of maps), when tested on Pascal and SBD datasets on query images. It also works well as an interactive segmentation method on support images.

This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human). We analyze three use cases of LLMs: interactions over web content, code, and mathematical texts. By analyzing attention distance, dispersion, and interdependency across these domains, we highlight the unique challenges posed by conversational data. Notably, conversations require nuanced handling of long-term contextual relationships and exhibit higher complexity through their attention patterns. Our findings reveal that while language models exhibit domain-specific attention behaviors, there is a significant gap in their ability to specialize in human conversations. Through detailed attention entropy analysis and t-SNE visualizations, we demonstrate the need for models trained with a diverse array of high-quality conversational data to enhance understanding and generation of human-like dialogue. This research highlights the importance of domain specialization in language models and suggests pathways for future advancement in modeling human conversational nuances.

We present the first measurement of HI mass of star-forming galaxies in different large scale structure environments from a blind survey at $z\sim 0.37$. In particular, we carry out a spectral line stacking analysis considering $2875$ spectra of colour-selected star-forming galaxies undetected in HI at $0.23 < z < 0.49$ in the COSMOS field, extracted from the MIGHTEE-HI Early Science datacubes, acquired with the MeerKAT radio telescope. We stack galaxies belonging to different subsamples depending on three different definitions of large scale structure environment: local galaxy overdensity, position inside the host dark matter halo (central, satellite, or isolated), and cosmic web type (field, filament, or knot). We first stack the full star-forming galaxy sample and find a robust HI detection yielding an average galaxy HI mass of $M_{\rm HI}=(8.12\pm 0.75)\times 10^9\, {\rm M}_\odot$ at $\sim 11.8\sigma$. Next, we investigate the different subsamples finding a negligible difference in $M_{\rm HI}$ as a function of the galaxy overdensity. We report an HI excess compared to the full sample in satellite galaxies ($M_{\rm HI}=(11.31\pm1.22)\times 10^9$, at $\sim 10.2 \sigma$) and in filaments ($M_{\rm HI}=(11.62\pm 0.90)\times 10^9$. Conversely, we report non-detections for the central and knot galaxies subsamples, which appear to be HI-deficient. We find the same qualitative results also when stacking in units of HI fraction ($f_{\rm HI}$). We conclude that the HI amount in star-forming galaxies at the studied redshifts correlates with the large scale structure environment., Comment: Accepted for publication in MNRAS. 15 figures, 3 tables