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The uniqueness and rigidity theorems assert that the asymptotically flat, vacuum, stationary rotating black hole solution in general relativity must be the Kerr solution, exhibiting novel symmetries such as axisymmetry and circularity. In our analysis of post-merger ringdown signal from coalescing black hole binary systems, we identify potential observational signatures for deviations from these Kerr symmetries. Utilizing ringdown data from the gravitational wave event GW150914, we place significant constraints on such deviations. Our analysis introduces a new and novel approach for testing spacetime symmetries through gravitational wave observations., Comment: 5+7 pages, 6 figures

Spatio-temporal dynamics of the evolution of population involving growth and diffusion processes can be modeled by class of partial diffusion equations (PDEs) known as reaction-diffusion systems. In this work, we developed a nonlinear transformations method that converts the original nonlinear Fisher-KPP class of PDEs into an exactly solvable class. We then demonstrated that the proposed nonlinear transformation method intrinsically preserves the relaxation behavior of the solutions to asymptotic values of the non-linear dynamical system. We also show that these particular transforms are very amenable to yield an exact closed form solution in terms of the heat kernel and analytical approximations through the two variable Hermite polynomials. With this proposed method, we calculated the front velocity and shape of the propagating wave and showed how the non-linear transformation affects these parameters for both short and long epochs. As applications, we focus on solving pertinent cases of the Fisher-KPP type of PDEs relating to the evolutionary dynamics by assigning fitness to the mutant gene according to zygosity conditions. We calculated the relaxation of velocity with the parameters of the initial conditions in the following cases, namely, the Fisher, the heterozygote inferior fitness, the heterozygote superior fitness, and finally a general nonlinearity case. We also verified previous conjectures through the exact solutions computed using the proposed method., Comment: 39 pages, 10 figures

The purpose of this manuscript is to derive two distinct integral representations of the generalized polylogarithm using two different techniques. The first approach involves the Dirichlet series and its Laplace representation, which leads to a single integral representation. The second approach utilizes the Hadamard convolution, resulting in a double integral representation. As a consequence, an integral representation of the Lerch transcendent function is obtained. Furthermore, we establish properties such as complete monotonicity, Turan inequality, convexity, and bounds of the generalized polylogarithm. Finally, we provide an alternative proof of an existing integral representation of the generalized polylogarithm using the Hadamard convolution., Comment: 19 pages, Rough draft

The integration of artificial intelligence (AI) in legal judgment prediction (LJP) has the potential to transform the legal landscape, particularly in jurisdictions like India, where a significant backlog of cases burdens the legal system. This paper introduces NyayaAnumana, the largest and most diverse corpus of Indian legal cases compiled for LJP, encompassing a total of 7,02,945 preprocessed cases. NyayaAnumana, which combines the words "Nyay" (judgment) and "Anuman" (prediction or inference) respectively for most major Indian languages, includes a wide range of cases from the Supreme Court, High Courts, Tribunal Courts, District Courts, and Daily Orders and, thus, provides unparalleled diversity and coverage. Our dataset surpasses existing datasets like PredEx and ILDC, offering a comprehensive foundation for advanced AI research in the legal domain. In addition to the dataset, we present INLegalLlama, a domain-specific generative large language model (LLM) tailored to the intricacies of the Indian legal system. It is developed through a two-phase training approach over a base LLaMa model. First, Indian legal documents are injected using continual pretraining. Second, task-specific supervised finetuning is done. This method allows the model to achieve a deeper understanding of legal contexts. Our experiments demonstrate that incorporating diverse court data significantly boosts model accuracy, achieving approximately 90% F1-score in prediction tasks. INLegalLlama not only improves prediction accuracy but also offers comprehensible explanations, addressing the need for explainability in AI-assisted legal decisions., Comment: Accepted on COLING 2025

This paper presents the development and implementation of a versatile ad-hoc metropolitan-range Quantum Key Distribution (QKD) network. The approach presented integrates various types of physical channels and QKD protocols, and a mix of trusted and untrusted nodes. Unlike conventional QKD networks that predominantly depend on either fiber-based or free-space optical (FSO) links, the testbed presented amalgamates FSO and fiber-based links, thereby overcoming some inherent limitations. Various network deployment strategies have been considered, including permanent infrastructure and provisional ad-hoc links to eradicate coverage gaps. Furthermore, the ability to rapidly establish a network using portable FSO terminals and to investigate diverse link topologies is demonstrated. The study also showcases the successful establishment of a quantum-secured link to a cloud server.

Longitudinal passive sensing studies for health and behavior outcomes often have missing and incomplete data. Handling missing data effectively is thus a critical data processing and modeling step. Our formative interviews with researchers working in longitudinal health and behavior passive sensing revealed a recurring theme: most researchers consider imputation a low-priority step in their analysis and inference pipeline, opting to use simple and off-the-shelf imputation strategies without comprehensively evaluating its impact on study outcomes. Through this paper, we call attention to the importance of imputation. Using publicly available passive sensing datasets for depression, we show that prioritizing imputation can significantly impact the study outcomes -- with our proposed imputation strategies resulting in up to 31% improvement in AUROC to predict depression over the original imputation strategy. We conclude by discussing the challenges and opportunities with effective imputation in longitudinal sensing studies.

We introduce a novel control mode for Scanning Tunneling Microscopy (STM) that leverages di/dz feedback. By superimposing a high-frequency sinusoidal modulation on the control signal, we extract the amplitude of the resulting tunneling current to obtain a di/dz measurement as the tip is scanned over the surface. A feedback control loop is then closed to maintain a constant di/dz, enhancing the sensitivity of the tip to subtle surface variations throughout a scan. This approach offers distinct advantages over conventional constant-current imaging. We demonstrate the effectiveness of this technique through high-resolution imaging and lithographic experiments on several Si(100)-2x1:H surfaces. Our findings, validated across multiple STM systems and imaging conditions, pave the way for a new paradigm in STM control, imaging, and lithography., Comment: 14 pages, 18 figures

After decades of experimental efforts, the DAMA/LIBRA(DL) annual modulation (AM) analysis on the ${\chi}$N (WIMP Dark Matter interactions on nucleus) channel remains the only one which can be interpreted as positive signatures. This has been refuted by numerous time-integrated (TI) and AM analysis. It has been shown that ${\chi}$e (WIMP interactions with electrons) alone is not compatible with the DL AM data. We expand the investigations by performing an AM analysis with the addition of ${\chi}$e long-range and short-range interactions to ${\chi}$N, derived using the frozen-core approximation method. Two scenarios are considered, where the ${\chi}$N and ${\chi}$e processes are due to a single ${\chi}$ (${\Gamma}^{1\chi}_{tot}$) or two different ${\chi}$s (${\Gamma}^{2\chi}_{tot}$). The combined fits with ${\chi}$N and ${\chi}$e provide stronger significance to the DL AM data which are compatible with the presence of additional physical effects beyond \c{hi}N alone. This is the first analysis which explores how ${\chi}$e AM can play a role in DL AM. The revised allowed regions as well as the exclusion contours from the other null AM experiments are presented. All DL AM allowed parameter spaces in ${\chi}$N and ${\chi}$e channels under both ${\Gamma}^{1\chi}_{tot}$ and ${\Gamma}^{2\chi}_{tot}$ are excluded at the 90\% confidence level by the combined null AM results. It can be projected that DL-allowed parameter spaces from generic models with interactions induced by two-WIMPs are ruled out., Comment: 6 pages, 5 figures

We consider the problem of constructing distributed overlay networks, where nodes in a reconfigurable system can create or sever connections with nodes whose identifiers they know. Initially, each node knows only its own and its neighbors' identifiers, forming a local channel, while the evolving structure is termed the global channel. The goal is to reconfigure any connected graph into a desired topology, such as a bounded-degree expander graph or a well-formed tree with a constant maximum degree and logarithmic diameter, minimizing the total number of rounds and message complexity. This problem mirrors real-world peer-to-peer network construction, where creating robust and efficient systems is desired. We study the overlay reconstruction problem in a network of $n$ nodes in two models: \textbf{GOSSIP-reply} and \textbf{HYBRID}. In the \textbf{GOSSIP-reply} model, each node can send a message and receive a corresponding reply message in one round. In the \textbf{HYBRID} model, a node can send $O(1)$ messages to each neighbor in the local channel and a total of $O(\log n)$ messages in the global channel. In both models, we propose protocols with $O\left(\log^2 n\right)$ round complexities and $O\left(n \log^2 n\right)$ message complexities using messages of $O(\log n)$ bits. Both protocols use $O\left(n \log^3 n\right)$ bits of communication, which we conjecture to be optimal. Additionally, our approach ensures that the total number of messages for node $v$, with degree $\deg(v)$ in the initial topology, is bounded by $O\left(\deg(v) + \log^2 n\right)$ with high probability.

Robotic wire arc additive manufacturing has been widely adopted due to its high deposition rates and large print volume relative to other metal additive manufacturing processes. For complex geometries, printing with variable height within layers offer the advantage of producing overhangs without the need for support material or geometric decomposition. This approach has been demonstrated for steel using precomputed robot speed profiles to achieve consistent geometric quality. In contrast, aluminum exhibits a bead geometry that is tightly coupled to the temperature of the previous layer, resulting in significant changes to the height of the deposited material at different points in the part. This paper presents a closed-loop approach to correcting for variations in the height of the deposited material between layers. We use an IR camera mounted on a separate robot to track the welding flame and estimate the height of deposited material. The robot velocity profile is then updated to account for the error in the previous layer and the nominal planned height profile while factoring in process and system constraints. Implementation of this framework showed significant improvement over the open-loop case and demonstrated robustness to inaccurate model parameters., Comment: 8 pages, 17 figures

The existence of dark matter has long been extensively studied in the past few decades. In this study, we investigate the emission of neutrinos and photons from neutron stars (NSs) by employing the modified theory of gravity and the corresponding Tolman-Oppenheimer-Volkoff (TOV) system of equations. The extreme matter density and magnetic field inside the NSs provide a unique laboratory for studying fundamental physics, including the interplay between gravity and quantum field effects. The impact of a strong magnetic field has also been incorporated into the corresponding TOV equations. We here attempt to see how neutrinos and photons emissions from these compact objects are impacted by the modified TOV equations due to modified theory of gravity; f(R,T) gravity or scalar-tensor theory and strong magnetic fields. Our analysis focuses on how these modifications influence the structure, cooling, and photon/neutrino luminosities of NS. We computed the surface temperature of NSs for normal Einstein gravity and modified gravity theories with and without magnetic field for three EoSs; namely APR, FPS and SLY. On comparison of our predicted values of surface temperature with the observed surface temperature for three NSs, we find that modified gravity along with inside magnetic field-based predictions shows reasonable agreement with the corresponding observed values.

The spectra of particles in disordered lattices can either be completely extended or localized or can be intermediate which hosts both the localized and extended states separated from each other. In this work, however, we show that in the case of a one dimensional lattice with long-range hopping and non-Hermitian quasiperiodic onsite potential, the localized and extended states in the spectrum are intermixed with each other rather than well separated. As a result, an atypical intermediate phase appears where consecutive pairs of extended states intermittently appear in the pool of localized states. We also argue that such anomalous spectral intermixing can be realized in the short-range hopping limit by appropriate engineering of the onsite potential. Moreover, we obtain that the nature of the spectrum also reveals non-standard scenarios in the complex energy plane where the complex energies encircle real ones. These findings shed light on the intricate interplay between the non-Hermiticity and quasiperiodic disorder in the system.

We present first-principles results on the electronic and magnetic properties of the cubic bulk $\beta$-phase of iron(III) oxide (Fe$_2$O$_3$). Given that all Fe-Fe magnetic couplings are expected to be antiferromagnetic within this high-symmetry crystal structure, the system may exhibit some signature of magnetic frustration, making it challenging to identify its magnetic ground state. We have analyzed the possible magnetic phases of the $\beta$-phase among which there are ferrimagnets, altermagnets and Kramers antiferromagnets. While the $\alpha$-phase is an altermagnet and the $\gamma$-phase is a ferrimagnet, we conclude that the magnetic ground state for the bulk $\beta$-phase of Fe$_2$O$_3$ is a Kramers antiferromagnet, moreover, we find that close in energy there is a bulk d-wave altermagnetic phase. We report the density of states and the evolution band gap as a function of the electronic correlations, for suitable values of the Coulomb repulsion the system is a charge-transfer insulator with an indirect band gap of 1.5 eV. As the opposite to the $\gamma$-phase, the magnetic configuration between first-neighbor of the same kind is always antiferromagnetic while the magnetic configuration between Fe$_a$ and Fe$_b$ is ferro or antiferro. In this magnetic arrangement, first-neighbor interactions cancel out in the mean-field estimation of the N\'eel temperature, leaving second-neighbor magnetic exchanges as the primary contributors, resulting in a N\'eel temperature lower than that of other phases. Our work paves the way toward the ab initio study of nanoparticles and alloys for the $\beta$-phase of Fe$_2$O$_3$.

We investigated the magnetic transitions in BiFeO$_3$ at low temperature (5-300 K) and observed nearly 90$^o$ rotation of magnetic domains (imaged by vertical magnetic force microscopy) across 150 K in an epitaxial thin film of thickness $\sim$36 nm. It offers a clear evidence of spin reorientation transition. It also corroborates the transition observed below $\sim$150 K in the zero-field-cooled and field-cooled magnetization versus temperature data. The field-driven 180$^o$ domain switching at room temperature, on the other hand, signifies presence of ferromagnetism. Since bulk antiferromagnetic BiFeO$_3$ does not exhibit such a transition, this observation in ferromagnetic thin film of BiFeO$_3$ indicates a radical effect because of epitaxial strain. Density functional theory based first-principles calculations too reveal that combined in- and out-of-plane epitaxial strain induces magnetic transition from G- to C-type structure in BiFeO$_3$., Comment: 12 pages, 9 figures, published in Phys. Rev. B

Cosmography has been extensively utilized to constrain the kinematic state of the Universe using measured distances. In this work, we propose a new method to reconstruct coupling theories using the first kind of Chebyshev polynomial for two variables in which the functional form of the $f(Q,T)$ theory has been obtained. Further, the unknowns that appeared in the series are constrained using the cosmographic parameters. We find the explicit form of the luminosity distance in terms of cosmographic parameters to perform MCMC analysis using the PANTHEON+SH0ES data set. Through the distance modulus function, we observe that the result comes out to be an excellent match to the standard cosmological model and data., Comment: Accepted version in Physics of the Dark Universe

In the first part of this two-part paper, we construct a family MFD$_2$ of low-correlation quaternary spreading codes having period $2046$. By quaternary, we mean that the spreading code symbols are drawn from $Z_4$ and are designed to be used in conjunction with QPSK modulation. Apart from low auto and crosscorrelation properties, we also require in addition, to our knowledge for the first time, that the spreading code family IZ4$_2$ obtained by taking the union of the component in-phase and quadrature-phase binary spreading codes associated to each quaternary spreading code in MFD$_2$, also have desirable low-correlation properties. We also investigate the balance of the quaternary and binary spreading codes. The second part is motivated by an application to the design of spreading code, (in this application termed as ranging codes), having parameters suitable for use in a lunar PNT system. Two lengths that are of particular current interest for a planned lunar PNT satellite system are $2046$ and $10230$. We study the applicability of a subset IZ4$_{2S}$ of IZ4$_2$ containing balanced binary spreading codes having length $2046$ to such a lunar PNT system. We show that the spreading codes belonging to IZ4$_{2S}$ compare favorably with the spreading codes of length $2046$ appearing in a recent issue of Inside GNSS. We also show that the IZ4$_{10}$ spreading code family in which the spreading codes have length $10230$, compares well in comparison with spreading codes of length $10230$ described in this article. In addition, the IZ4$_{10}$ and IZ4$_2$ spreading codes have been paired so as to be orthogonal at zero shift despite their different lengths and chipping rates.

Low-Rank Adaptation (LoRA) and other parameter-efficient fine-tuning (PEFT) methods provide low-memory, storage-efficient solutions for personalizing text-to-image models. However, these methods offer little to no improvement in wall-clock training time or the number of steps needed for convergence compared to full model fine-tuning. While PEFT methods assume that shifts in generated distributions (from base to fine-tuned models) can be effectively modeled through weight changes in a low-rank subspace, they fail to leverage knowledge of common use cases, which typically focus on capturing specific styles or identities. Observing that desired outputs often comprise only a small subset of the possible domain covered by LoRA training, we propose reducing the search space by incorporating a prior over regions of interest. We demonstrate that training a hypernetwork model to generate LoRA weights can achieve competitive quality for specific domains while enabling near-instantaneous conditioning on user input, in contrast to traditional training methods that require thousands of steps., Comment: 9 pages, 6 figures

The Huge Object model of property testing [Goldreich and Ron, TheoretiCS 23] concerns properties of distributions supported on $\{0,1\}^n$, where $n$ is so large that even reading a single sampled string is unrealistic. Instead, query access is provided to the samples, and the efficiency of the algorithm is measured by the total number of queries that were made to them. Index-invariant properties under this model were defined in [Chakraborty et al., COLT 23], as a compromise between enduring the full intricacies of string testing when considering unconstrained properties, and giving up completely on the string structure when considering label-invariant properties. Index-invariant properties are those that are invariant through a consistent reordering of the bits of the involved strings. Here we provide an adaptation of Szemer\'edi's regularity method for this setting, and in particular show that if an index-invariant property admits an $\epsilon$-test with a number of queries depending only on the proximity parameter $\epsilon$, then it also admits a distance estimation algorithm whose number of queries depends only on the approximation parameter., Comment: 57 Pages

We conduct the first-ever Pulsar Polarization Array (PPA) analysis to detect the ultralight Axion-Like Dark Matter (ALDM) using the polarization data of 22 millisecond pulsars from the third data release of Parkes Pulsar Timing Array. As one of the major dark matter candidates, the ultralight ALDM exhibits a pronounced wave nature on astronomical scales and offers a promising solution to small-scale structure issues within local galaxies. While the linearly polarized pulsar light travels through the ALDM galactic halo, its position angle (PA) can be subject to an oscillation induced by the ALDM Chern-Simons coupling with electromagnetic field. The PPA is thus especially suited for detecting the ultralight ALDM by correlating polarization data across the arrayed pulsars. To accomplish this task, we develop an advanced Bayesian analysis framework that allows us to construct pulsar PA residual time series, model noise contributions properly and search for pulsar cross-correlations. We find that for an ALDM density of $\rho_0=0.4\,\textrm{GeV}/\textrm{cm}^3$, the Parkes PPA offers the best global limits on the ALDM Chern-Simons coupling, namely $\lesssim 10^{-13.5}-10^{-12.2}~{\rm GeV}^{-1}$, for the mass range of $10^{-22} - 10^{-21}~{\rm eV}$. The crucial role of pulsar cross-correlation in recognizing the nature of the derived limits is also highlighted., Comment: 6+15 pages, 10 figures, 2 tables, submitted to the journal

In this paper, we investigate various cardinal properties of the space $Q_{C}X$ of all real-valued quasicontinuous functions on the topological space $X$, under the topology of uniform convergence on compact subsets. It begins by examining the relationship between tightness and other properties in the context of the space $X$, highlighting results such as the alignment of tightness $Q_{C}X$ with the compact Lindel\"of number of $X$ under Hausdorff conditions and the countable tightness of $Q_{C}X$ when $X$ is second countable. Further investigations reveal conditions for the tightness of $Q_{C}X$ relative to $k$-covers of $X$, as well as connections between density tightness, fan tightness, and other properties in Hausdorff spaces. Additionally, we discuss the implications of the Frechet-Urysohn property $Q_{C}X$ for open $k$-covers in Hausdorff spaces. We explore relationships between $Q_{C}X$'s tightness, the Frechet-Urysohn property, and the $\sigma$-compactness of locally compact Hausdorff spaces $X$. Furthermore, we examine the $k_{f}$-covering property and the existence of $k$-covers in the context of Whyburn spaces.

In this paper, we find necessary and sufficient conditions for countable fan tightness and countable strong fan tightness of the space (briefly, $C_{p}(X,G)$) of all group-valued continuous functions endowed with the topology of pointwise convergence in term of Menger property and Rothberger property respectively. Furthermore, we establish a relationship between countable fan tightness, the Reznichenko property and the Hurewicz property for the space $C_{p}(X,G)$. In addition to this we prove that the Menger property is preserve during $G$-equivalence of topological spaces. Through this paper, we establish a general result regarding fan tightness of $C_{p}(X,G)$ and Hurewicz number of the space $X^{n}$ for every natural number $n$. Finally, we study the monolithicity of the space $C_{p}(X,G)$.

Cardinal functions provide valuable insight into the topological properties of spaces, helping to analyze and compare spaces in terms of their covering, convergence and separation properties. This paper focuses on investigating cardinal functions like network weight, Lindel\"of degree, tightness, weak covering, pseudocharacter, and $i$-weight, for the spaces $Q_{P}(X)$ and $Q_{P}(X,Y)$ of quasi-continuous functions under the topology of point-wise convergence. In addition to these, we also investigate properties of restriction and induced maps associated with the spaces $Q_{P}(X)$ and $Q_{P}(X,Y)$.

A fundamental result in symplectic linear algebra states that for a given positive semi-definite quadratic form on a symplectic space there exists a symplectic in which the quadratic form reduces to a normal form. The special case of the aforementioned result for positive-definite quadratic forms is known as Williamson's theorem. In this work, we establish necessary and sufficient conditions on positive semi-definite quadratic forms on a symplectic space to be simultaneously reduced to their normal forms in a common symplectic basis. In particular, we establish conditions on 2n x 2n real symmetric positive-definite matrices to be simultaneously diagonalizable by a symplectic matrix in the sense of Williamson's theorem. We also discuss some applications of the main result in quantum information theory and statistical thermodynamics., Comment: 12 pages

The neutron capture cross-section for the $^{181}$Ta(n,$\gamma$)$^{182}$Ta reaction has been experimentally measured at the neutron energies 0.53 and 1.05 MeV using off-line $\gamma$-ray spectrometry. $^{115}$In(n,n'$\gamma$)$^{115m}$In is used as a reference monitor reaction cross-section. The neutron was produced via the $^{7}$Li(p,n)$^{7}$Be reaction. The present study measures the cross-sections with their uncertainties and correlation matrix. The self-attenuation process, $\gamma$-ray correction factor, and low background neutron energy contribution have been calculated. The measured neutron spectrum averaged cross-sections of $^{181}$Ta(n,$\gamma$)$^{182}$Ta are discussed and compared with the existing data from the EXFOR database and also with the ENDF/B-VIII.0, TENDL-2019, JENDL-5, JEFF-3.3 evaluated data libraries.

Solar Organic Rankine Cycle (ORC)-based power generation plants leverage solar irradiation to produce thermal energy, offering a highly compatible renewable technology due to the alignment between solar irradiation temperatures and ORC operating requirements. Their superior performance compared to steam Rankine cycles in small-scale applications makes them particularly relevant within the smart grid and microgrid contexts. This study explores the role of ORC in peer-to-peer (P2P) energy trading within renewable-based community microgrids, where consumers become prosumers, simultaneously producing and consuming energy while engaging in virtual trading at the distribution system level. Focusing on a microgrid integrating solar ORC with a storage system to meet consumer demand, the paper highlights the importance of combining these technologies with storage to enhance predictability and competitiveness with conventional energy plants, despite management challenges. A methodology based on operations research techniques is developed to optimize system performance. Furthermore, the impact of various technological parameters of the solar ORC on the system's performance is examined. The study concludes by assessing the value of solar ORC within the transactive energy trading framework across different configurations and scenarios. Results demonstrate an average 16\% reduction in operational costs, showcasing the benefits of implementing a predictable and manageable system in P2P transactive energy trading., Comment: This is a preprint of a paper accepted for publication to the "Energy Systems" Journal of Springer

Celiac disease is an autoimmune disorder triggered by the consumption of gluten. It causes damage to the villi, the finger-like projections in the small intestine that are responsible for nutrient absorption. Additionally, the crypts, which form the base of the villi, are also affected, impairing the regenerative process. The deterioration in villi length, computed as the villi-to-crypt length ratio, indicates the severity of celiac disease. However, manual measurement of villi-crypt length can be both time-consuming and susceptible to inter-observer variability, leading to inconsistencies in diagnosis. While some methods can perform measurement as a post-hoc process, they are prone to errors in the initial stages. This gap underscores the need for pathologically driven solutions that enhance measurement accuracy and reduce human error in celiac disease assessments. Our proposed method, MeasureNet, is a pathologically driven polyline detection framework incorporating polyline localization and object-driven losses specifically designed for measurement tasks. Furthermore, we leverage segmentation model to provide auxiliary guidance about crypt location when crypt are partially visible. To ensure that model is not overdependent on segmentation mask we enhance model robustness through a mask feature mixup technique. Additionally, we introduce a novel dataset for grading celiac disease, consisting of 750 annotated duodenum biopsy images. MeasureNet achieves an 82.66% classification accuracy for binary classification and 81% accuracy for multi-class grading of celiac disease. Code: https://github.com/dair-iitd/MeasureNet

Deep Learning methods are highly local and sensitive to the domain of data they are trained with. Even a slight deviation from the domain distribution affects prediction accuracy of deep networks significantly. In this work, we have investigated a set of techniques aimed at increasing accuracy of generator networks which perform translation from one domain to the other in an adversarial setting. In particular, we experimented with activations, the encoder-decoder network architectures, and introduced a Loss called cyclic loss to constrain the Generator network so that it learns effective source-target translation. This machine learning problem is motivated by myriad applications that can be derived from domain adaptation networks like generating labeled data from synthetic inputs in an unsupervised fashion, and using these translation network in conjunction with the original domain network to generalize deep learning networks across domains., Comment: 16 pages, 14 figures

Through gravitational decoupling using the extended minimal geometric deformation, a new family of static and rotating ``hairy'' black holes is provided. The background of these models is a generic Schwarzschild metric containing as special cases, the Schwarzschild, Schwarzschild-dS, Reissner-Nordstrom and Reissner-Nordstrom-dS black holes. Assuming the Kerr-Schild condition and a general equation of state, the unknown matter sector is solved given rise to black hole space-times without a Cauchy horizon, transforming the original time-like singularity of the Reissner-Nordstrom and Reissner-Nordstrom-dS black holes into a space-like singularity. This fact is preserved for the rotating version of all these solutions., Comment: 10 pages, 5 figures. Accepted version: Physics of the Dark Universe

Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications on graph data. Because graph data does not follow the independently and identically distributed (i.i.d.) assumption, adversarial manipulations or incorrect data can propagate to other data points through message passing, which deteriorates the model's performance. To allow model developers to remove the adverse effects of manipulated entities from a trained GNN, we study the recently formulated problem of Corrective Unlearning. We find that current graph unlearning methods fail to unlearn the effect of manipulations even when the whole manipulated set is known. We introduce a new graph unlearning method, Cognac, which can unlearn the effect of the manipulation set even when only 5% of it is identified. It recovers most of the performance of a strong oracle with fully corrected training data, even beating retraining from scratch without the deletion set while being 8x more efficient. We hope our work assists GNN developers in mitigating harmful effects caused by issues in real-world data post-training. Our code is publicly available at https://github.com/varshitakolipaka/corrective-unlearning-for-gnns

Infrastructure construction, often dubbed an "industry of industries," is closely linked with government spending and public procurement, offering significant opportunities for improved efficiency and productivity through better transparency and information access. By leveraging these opportunities, we can achieve notable gains in productivity, cost savings, and broader economic benefits. Our approach introduces an integrated software ecosystem utilizing Data Mesh and Service Mesh architectures. This system includes the largest training dataset for infrastructure and procurement, encompassing over 100 billion tokens, scientific publications, activities, and risk data, all structured by a systematic AI framework. Supported by a Knowledge Graph linked to domain-specific multi-agent tasks and Q&A capabilities, our platform standardizes and ingests diverse data sources, transforming them into structured knowledge. Leveraging large language models (LLMs) and automation, our system revolutionizes data structuring and knowledge creation, aiding decision-making in early-stage project planning, detailed research, market trend analysis, and qualitative assessments. Its web-scalable architecture delivers domain-curated information, enabling AI agents to facilitate reasoning and manage uncertainties, while preparing for future expansions with specialized agents targeting particular challenges. This integration of AI with domain expertise not only boosts efficiency and decision-making in construction and infrastructure but also establishes a framework for enhancing government efficiency and accelerating the transition of traditional industries to digital workflows. This work is poised to significantly influence AI-driven initiatives in this sector and guide best practices in AI Operations.

Reverse thinking plays a crucial role in human reasoning. Humans can reason not only from a problem to a solution but also in reverse, i.e., start from the solution and reason towards the problem. This often enhances overall reasoning performance as it enables consistency checks between their forward and backward thinking. To enable Large Language Models (LLMs) to perform reverse thinking, we introduce Reverse-Enhanced Thinking (RevThink), a framework composed of data augmentation and learning objectives. In RevThink, we augment the dataset by collecting structured forward-backward reasoning from a teacher model, consisting of: (1) the original question, (2) forward reasoning, (3) backward question, and (4) backward reasoning. We then employ three objectives to train a smaller student model in a multi-task learning fashion: (a) generate forward reasoning from a question, (b) generate a backward question from a question, and (c) generate backward reasoning from the backward question. Experiments across 12 datasets covering commonsense, math, and logical reasoning show an average 13.53% improvement over the student model's zero-shot performance and a 6.84% improvement over the strongest knowledge distillation baselines. Moreover, our method demonstrates sample efficiency -- using only 10% of the correct forward reasoning from the training data, it outperforms a standard fine-tuning method trained on 10x more forward reasoning. RevThink also exhibits strong generalization to out-of-distribution held-out datasets., Comment: 20 pages

Retrieval Augmented Generation (RAG) systems are a widespread application of Large Language Models (LLMs) in the industry. While many tools exist empowering developers to build their own systems, measuring their performance locally, with datasets reflective of the system's use cases, is a technological challenge. Solutions to this problem range from non-specific and cheap (most public datasets) to specific and costly (generating data from local documents). In this paper, we show that using public question and answer (Q&A) datasets to assess retrieval performance can lead to non-optimal systems design, and that common tools for RAG dataset generation can lead to unbalanced data. We propose solutions to these issues based on the characterization of RAG datasets through labels and through label-targeted data generation. Finally, we show that fine-tuned small LLMs can efficiently generate Q&A datasets. We believe that these observations are invaluable to the know-your-data step of RAG systems development., Comment: to be published in the 31st International Conference on Computational Linguistics (COLING 2025)

In integrated circuit design, the analysis of wafer map patterns is critical to improve yield and detect manufacturing issues. We develop Wafer2Spike, an architecture for wafer map pattern classification using a spiking neural network (SNN), and demonstrate that a well-trained SNN achieves superior performance compared to deep neural network-based solutions. Wafer2Spike achieves an average classification accuracy of 98\% on the WM-811k wafer benchmark dataset. It is also superior to existing approaches for classifying defect patterns that are underrepresented in the original dataset. Wafer2Spike achieves this improved precision with great computational efficiency.

We propose shortcut to adiabaticity protocols for Bose-Einstein condensates trapped in generalized anisotropic harmonic traps in three dimensions. These protocols enable high-fidelity tuning of trap geometries on time scales much faster than those required for adiabatic processes and are robust across a wide range of interaction strengths, from weakly interacting regimes to the Thomas-Fermi limit. Using the same approach, we also design STA paths to rapidly drive interaction strengths in both isotropic and anisotropic traps. Comparisons with standard linear ramps of system parameters demonstrate significant improvements in performance. Finally, we apply these STA techniques to a unitary engine cycle with a BEC as the working medium. The STA methods significantly enhance the engine's power output without reducing efficiency and remain highly effective even after multiple consecutive cycles., Comment: 10 pages, 5 figures

Transition metal dichalcogenides (TMDs) are a novel class of quantum materials with significant potential in spintronics, optoelectronics, valleytronics, and opto-valleytronics. TMDs exhibit strong spin-orbit coupling, enabling efficient spin-charge interconversion, which makes them ideal candidates for spin-orbit torque-driven spintronic devices. In this study, we investigated the spin-to-charge conversion through ferromagnetic resonance in MoS2/Cu/CoFeB heterostructures with varying Cu spacer thicknesses. The conversion efficiency, quantified by the spin Hall angle, was enhanced by an order of magnitude due to Cu intercalation. Magneto-optic Kerr effect microscopy confirmed that Cu did not significantly modify the magnetic domains, indicating its effectiveness in decoupling MoS2 from CoFeB. This decoupling preserves the spin-orbit coupling (SOC) of MoS2 by mitigating the exchange interaction with CoFeB, as proximity to localized magnetization can alter the electronic structure and SOC. First-principles calculations revealed that Cu intercalation notably enhances the spin Berry curvature and spin Hall conductivity, contributing to the increased spin Hall angle. This study demonstrates that interface engineering of ferromagnet/TMD-based heterostructures can achieve higher spin-to-charge conversion efficiencies, paving the way for advancements in spintronic applications.

Charge-conjugation and parity violation in strong interaction for cold dense quark matter is studied with axions of quantum chromodynamic within the three flavor Nambu--Jona-Lasinio model that includes the coupling of axions to quarks. We first calculate the effective potential for axions at finite baryon density and zero temperature including the effects of a first order chiral phase transition. Using the equation of state for quark matter with axions and a hadronic matter equation of state in the ambit of a relativistic mean field theory in quantum hadrodynamics, we discuss the hadron-quark phase transition. Inclusion of axions reduces the critical density for chiral transition. We use a Gibbs construct for the hadron-quark phase transition satisfying the constraints of beta equilibrium and charge neutrality as appropriate for the neutron star matter. The equation of state so obtained is used to investigate the structure of hybrid neutron stars. It is found that with the presence of axions, it is possible to have stable hybrid neutron stars having an inner core of quark matter both in pure quark matter phase as well as in a mixed phase with hyperonic matter along with a outer core of hyperonic matter and is in agreement with modern astrophysical constraints. We also discuss the properties of non-radial oscillations of such hybrid neutron stars. It is observed that the quadrupolar fundamental modes ($f$-modes) for such hybrid neutron stars get substantial enhancements both due to a larger quark core in the presence of axions and from the hyperons as compared to a canonical nucleonic neutron stars., Comment: 25 pages, 12 figures

The newly introduced Visual State Space Model (VMamba), which employs \textit{State Space Mechanisms} (SSM) to interpret images as sequences of patches, has shown exceptional performance compared to Vision Transformers (ViT) across various computer vision tasks. However, recent studies have highlighted that deep models are susceptible to adversarial attacks. One common approach is to embed a trigger in the training data to retrain the model, causing it to misclassify data samples into a target class, a phenomenon known as a backdoor attack. In this paper, we first evaluate the robustness of the VMamba model against existing backdoor attacks. Based on this evaluation, we introduce a novel architectural backdoor attack, termed BadScan, designed to deceive the VMamba model. This attack utilizes bit plane slicing to create visually imperceptible backdoored images. During testing, if a trigger is detected by performing XOR operations between the $k^{th}$ bit planes of the modified triggered patches, the traditional 2D selective scan (SS2D) mechanism in the visual state space (VSS) block of VMamba is replaced with our newly designed BadScan block, which incorporates four newly developed scanning patterns. We demonstrate that the BadScan backdoor attack represents a significant threat to visual state space models and remains effective even after complete retraining from scratch. Experimental results on two widely used image classification datasets, CIFAR-10, and ImageNet-1K, reveal that while visual state space models generally exhibit robustness against current backdoor attacks, the BadScan attack is particularly effective, achieving a higher Triggered Accuracy Ratio (TAR) in misleading the VMamba model and its variants.

Large Language Models (LLMs) have significantly impacted nearly every domain of human knowledge. However, the explainability of these models esp. to laypersons, which are crucial for instilling trust, have been examined through various skeptical lenses. In this paper, we introduce a novel notion of LLM explainability to laypersons, termed $\textit{ReQuesting}$, across three high-priority application domains -- law, health and finance, using multiple state-of-the-art LLMs. The proposed notion exhibits faithful generation of explainable layman-understandable algorithms on multiple tasks through high degree of reproducibility. Furthermore, we observe a notable alignment of the explainable algorithms with intrinsic reasoning of the LLMs., Comment: 35 pages, 6 figures

Vision-language foundation models, such as CLIP, have shown unprecedented zero-shot performance across a wide range of tasks. Nevertheless, these models may be unreliable under distributional shifts, as their performance is significantly degraded. In this work, we explore how to efficiently leverage class text information to mitigate these distribution drifts encountered by large pre-trained vision-language models (VLMs) during test-time inference. In particular, we propose to generate pseudo-labels for the test-time samples by exploiting generic class text embeddings as fixed centroids of a label assignment problem, which is efficiently solved with Optimal Transport. Furthermore, the proposed adaptation method (CLIP-OT) integrates a multiple template knowledge distillation approach, which replicates multi-view contrastive learning strategies in unsupervised representation learning but without incurring additional computational complexity. Extensive experiments on multiple popular test-time adaptation benchmarks presenting diverse complexity empirically show the superiority of CLIP-OT, achieving performance gains of up to 7% over recent state-of-the-art methods, yet being computationally and memory efficient.

This article describes the development of a novel U-Net-enhanced Wavelet Neural Operator (U-WNO),which combines wavelet decomposition, operator learning, and an encoder-decoder mechanism. This approach harnesses the superiority of the wavelets in time frequency localization of the functions, and the combine down-sampling and up-sampling operations to generate the segmentation map to enable accurate tracking of patterns in spatial domain and effective learning of the functional mappings to perform regional segmentation. By bridging the gap between theoretical advancements and practical applications, the U-WNO holds potential for significant impact in multiple science and industrial fields, facilitating more accurate decision-making and improved operational efficiencies. The operator is demonstrated for different pregnancy trimesters, utilizing two-dimensional ultrasound images.

Reward models (RMs) are a crucial component in the alignment of large language models' (LLMs) outputs with human values. RMs approximate human preferences over possible LLM responses to the same prompt by predicting and comparing reward scores. However, as they are typically modified versions of LLMs with scalar output heads, RMs are large black boxes whose predictions are not explainable. More transparent RMs would enable improved trust in the alignment of LLMs. In this work, we propose to use contrastive explanations to explain any binary response comparison made by an RM. Specifically, we generate a diverse set of new comparisons similar to the original one to characterise the RM's local behaviour. The perturbed responses forming the new comparisons are generated to explicitly modify manually specified high-level evaluation attributes, on which analyses of RM behaviour are grounded. In quantitative experiments, we validate the effectiveness of our method for finding high-quality contrastive explanations. We then showcase the qualitative usefulness of our method for investigating global sensitivity of RMs to each evaluation attribute, and demonstrate how representative examples can be automatically extracted to explain and compare behaviours of different RMs. We see our method as a flexible framework for RM explanation, providing a basis for more interpretable and trustworthy LLM alignment., Comment: Accepted at ICLR 2025 conference

Detecting diffuse radio emission, such as from halos, in galaxy clusters is crucial for understanding large-scale structure formation in the universe. Traditional methods, which rely on X-ray and Sunyaev-Zeldovich (SZ) cluster pre-selection, introduce biases that limit our understanding of the full population of diffuse radio sources. In this work, we provide a possible resolution for this astrophysical tension by developing a machine learning (ML) framework capable of unbiased detection of diffuse emission, using a limited real dataset like those from the Murchison Widefield Array (MWA). We generate for the first time radio halo images using Wasserstein Generative Adversarial Networks (WGANs) and Denoising Diffusion Probabilistic Models (DDPMs), and apply them to train a neural network classifier independent of pre-selection methods. The halo images generated by DDPMs are of higher quality than those produced by WGANs. The diffusion-supported classifier with a multi-head attention block achieved the best average validation accuracy of 95.93% over 10 runs, using 36 clusters for training and 10 for testing, without further hyperparameter tuning. Using our classifier, we rediscovered 9/12 halos (75% detection rate) from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) Catalogue, and 5/8 halos (63% detection rate) from the Planck Sunyaev-Zeldovich Catalogue 2 (PSZ2) within the GaLactic and Extragalactic All-sky MWA (GLEAM) survey. In addition, we identify 11 potential new halos, minihalos, or candidates in the COSMOS field using XMM-chandra-detected clusters in GLEAM data. This work demonstrates the potential of ML for unbiased detection of diffuse emission and provides labeled datasets for further study., Comment: 16 pages, Submitted to MNRAS

The AErosol RObotic NETwork (AERONET), established in 1993 with limited global sites, has grown to over 900 locations, providing three decades of continuous aerosol data. While earlier studies based on shorter time periods (10-12 years) and fewer sites (approximately 250) made significant contributions to aerosol research, the vast AERONET dataset (1993-2023) calls for a comprehensive reevaluation to refine global aerosol models and improve satellite retrievals. This is particularly important in light of major environmental changes such as industrialization, land use shifts, and natural events like wildfires and dust storms. In this study, a set of fine and coarse aerosol models called AERONET-Extended (AEROEX) models are developed based on cluster analysis of 30-years AERONET data, analyzing over 202,000 samples using Gaussian Mixture Models to classify aerosol types by season and region. Aerosols are categorized into spherical, spheroidal, and mixed types using particle linear depolarization ratio and fine mode fraction. Four fine-mode aerosol models were derived based on differences in scattering and absorption properties, revealing regional/seasonal variations, particularly in North America, Europe and Asia. Additionally, two coarse-mode aerosol models were identified, separated by their absorbing properties in dust-prone and polluted regions. We performed simulation analysis showing that the new models significantly improve satellite-based aerosol optical depth retrievals compared to widely used dark target aerosol models. A global aerosol model map, generated at 1x1 degree resolution for each season using Random Forest and expert refinement, provides valuable insights for climate and atmospheric studies, improving satellite-based aerosol retrievals at global scale.

We propose a diffusion model-based approach, FloAtControlNet to generate cinemagraphs composed of animations of human clothing. We focus on human clothing like dresses, skirts and pants. The input to our model is a text prompt depicting the type of clothing and the texture of clothing like leopard, striped, or plain, and a sequence of normal maps that capture the underlying animation that we desire in the output. The backbone of our method is a normal-map conditioned ControlNet which is operated in a training-free regime. The key observation is that the underlying animation is embedded in the flow of the normal maps. We utilize the flow thus obtained to manipulate the self-attention maps of appropriate layers. Specifically, the self-attention maps of a particular layer and frame are recomputed as a linear combination of itself and the self-attention maps of the same layer and the previous frame, warped by the flow on the normal maps of the two frames. We show that manipulating the self-attention maps greatly enhances the quality of the clothing animation, making it look more natural as well as suppressing the background artifacts. Through extensive experiments, we show that the method proposed beats all baselines both qualitatively in terms of visual results and user study. Specifically, our method is able to alleviate the background flickering that exists in other diffusion model-based baselines that we consider. In addition, we show that our method beats all baselines in terms of RMSE and PSNR computed using the input normal map sequences and the normal map sequences obtained from the output RGB frames. Further, we show that well-established evaluation metrics like LPIPS, SSIM, and CLIP scores that are generally for visual quality are not necessarily suitable for capturing the subtle motions in human clothing animations.

Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to generate such designs, which accelerates the rate of content production. An automated way of evaluating their quality becomes critical. Towards this end, we introduce Design-o-meter, a data-driven methodology to quantify the goodness of graphic designs. Further, our approach can suggest modifications to these designs to improve its visual appeal. To the best of our knowledge, Design-o-meter is the first approach that scores and refines designs in a unified framework despite the inherent subjectivity and ambiguity of the setting. Our exhaustive quantitative and qualitative analysis of our approach against baselines adapted for the task (including recent Multimodal LLM-based approaches) brings out the efficacy of our methodology. We hope our work will usher more interest in this important and pragmatic problem setting., Comment: Accepted to WACV 2025. Project page: https://sahilg06.github.io/Design-o-meter/

Real-time visual feedback is essential for tetherless control of remotely operated vehicles, particularly during inspection and manipulation tasks. Though acoustic communication is the preferred choice for medium-range communication underwater, its limited bandwidth renders it impractical to transmit images or videos in real-time. To address this, we propose a model-based image compression technique that leverages prior mission information. Our approach employs trained machine-learning based novel view synthesis models, and uses gradient descent optimization to refine latent representations to help generate compressible differences between camera images and rendered images. We evaluate the proposed compression technique using a dataset from an artificial ocean basin, demonstrating superior compression ratios and image quality over existing techniques. Moreover, our method exhibits robustness to introduction of new objects within the scene, highlighting its potential for advancing tetherless remotely operated vehicle operations., Comment: Preprint submitted to IEEE Journal of Oceanic Engineering

The 21 cm signal from the Epoch of Reionization will be observed with the up-coming Square Kilometer Array (SKA). SKA should yield a full tomography of the signal which opens the possibility to explore its non-Gaussian properties. How can we extract the maximum information from the tomography and derive the tightest constraint on the signal? In this work, instead of looking for the most informative summary statistics, we investigate how to combine the information from two sets of summary statistics using simulation-based inference. To this purpose, we train Neural Density Estimators (NDE) to fit the implicit likelihood of our model, the LICORICE code, using the Loreli II database. We train three different NDEs: one to perform Bayesian inference on the power spectrum, one to do it on the linear moments of the Pixel Distribution Function (PDF) and one to work with the combination of the two. We perform $\sim 900$ inferences at different points in our parameter space and use them to assess both the validity of our posteriors with Simulation-based Calibration (SBC) and the typical gain obtained by combining summary statistics. We find that our posteriors are biased by no more than $\sim 20 \%$ of their standard deviation and under-confident by no more than $\sim 15 \%$. Then, we establish that combining summary statistics produces a contraction of the 4-D volume of the posterior (derived from the generalized variance) in 91.5 % of our cases, and in 70 to 80 % of the cases for the marginalized 1-D posteriors. The median volume variation is a contraction of a factor of a few for the 4D posteriors and a contraction of 20 to 30 % in the case of the marginalized 1D posteriors. This shows that our approach is a possible alternative to looking for sufficient statistics in the theoretical sense., Comment: 11 pages, submitted to A&A

Remote sensing imagery offers rich spectral data across extensive areas for Earth observation. Many attempts have been made to leverage these data with transfer learning to develop scalable alternatives for estimating socio-economic conditions, reducing reliance on expensive survey-collected data. However, much of this research has primarily focused on daytime satellite imagery due to the limitation that most pre-trained models are trained on 3-band RGB images. Consequently, modeling techniques for spectral bands beyond the visible spectrum have not been thoroughly investigated. Additionally, quantifying uncertainty in remote sensing regression has been less explored, yet it is essential for more informed targeting and iterative collection of ground truth survey data. In this paper, we introduce a novel framework that leverages generic foundational vision models to process remote sensing imagery using combinations of three spectral bands to exploit multi-spectral data. We also employ methods such as heteroscedastic regression and Bayesian modeling to generate uncertainty estimates for the predictions. Experimental results demonstrate that our method outperforms existing models that use RGB or multi-spectral models with unstructured band usage. Moreover, our framework helps identify uncertain predictions, guiding future ground truth data acquisition., Comment: 11 pages, 4 figures

A recent experimental study on a spent uranium dioxide (UO2) fuel sample from Belgium Reactor 3 (BR3) identified a unique pair structure formed by the noble metal phase (NMP) and fission gas (xenon [Xe]) precipitate. However, the fundamental mechanism behind this structure remains unclear. The present study aims to provide an understanding of the interaction between five different metal precipitates (molybdenum [Mo], ruthenium [Ru], palladium [Pd], technetium [Tc], and rhodium [Rh]) and the Xe fission gas atoms in UO2, by using density functional theory (DFT) in combination with the Hubbard U correction to compute the formation energies involved. All DFT+U calculations were performed with occupation matrix control to ensure antiferromagnetic ordering of UO2. The calculated formation and binding energies of the Xe and solid fission products in the NMP reveal that these metal precipitates form stable pair structures with Xe. Notably, the formation energy of Xe-metal pairs is lower than that of the isolated single defects in all instances, with Pd and Mo showing the most favourable binding energy, likely accounting for the observed pair structure formation.

This work addresses a trust-based enhancement to the Multipath Ad hoc On-Demand Distance Vector (AOMDV) routing protocol. While AODV and its multipath variant AOMDV have been fundamental in mobile ad hoc networks, they lack mechanisms to account for node reliability. A probabilistic link-trust model is proposed that incorporates factors such as past behavior, battery levels, and node coupling to distribute data optimally to reduce delay while simultaneously transmitting through multiple paths.