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In this work, we study the eccentricity spectra of zero divisor graphs (ZDGs) associated with the ring $\mathbb{Z}_n.$ While previous studies have examined the Laplacian and distance Laplacian spectra of ZDGs, the eccentricity spectra have remained largely unknown due to the unique features of the eccentricity matrix. More specifically, we prove that for a prime $p$, the ZDG and extended ZDG of $\mathbb{Z}_{p^t}$ have integral eccentricity eigenvalues for $t \geq 3$ and $t \geq 2$, respectively. We also find the eccentricity spectra for specific classes of ZDGs and the relationship between the eccentricity matrix of these ZDGs and their tree structures using matrix analysis tools. In addition, for the usefulness of the energy gap in applications, we have calculated the eccentricity energy gap of ZDGs. These findings reveal interesting behaviours of the eccentricity matrix and may contribute to a more profound understanding of the structural properties of ZDGs., Comment: 30 pages, 6 figs. We welcome comments

The sheath plasma resonance (SPR) in an inverted fireball (IFB) system is semi-analytically investigated using a generalized hydrodynamic isothermal model formalism. It incorporates the constitutive ionic fluid viscosity, inter-species collisions, and geometric curvature effects. The SPR stability is studied for an anodic (hollow, meshed) IFB for the first time against the traditional cathode-plasma arrangements of regular electrode (solid, smooth) fireballs. The SPR develops near a spherical electrode enclosed by a plasma sheath amid a given electric potential. A generalized linear quartic dispersion relation (DR) with diverse plasma multi-parametric coefficients is methodically derived using a standard normal mode analysis. The mathematical construction of the obtained DR roots confirms that only one feasible nonzero frequency mode exists (emerging in the IFB). This root existence is confirmed both analytically and numerically. This consequent SPR creates trapped acoustic fluctuations in the IFB plasmas because of the internal reflections at the sheath plasma boundary. Also, sensible parametric changes in the SPR features, with both plasma density and viscosity, are seen. A local condition for the SPR excitation and its subsequent transition to collective standing wave-like patterns in the IFBs is illustrated. A fair corroboration of our results with the earlier SPR experimental observations of standing wave-like eigenmode patterns (evanescent) strengthens the reliability of our study alongside new applicability., Comment: This original article contains 24 pages, 15 figures, etc. It has potential applications in the areas if accepted. It is for your kind consideration and needful action

Dose prediction is an area of ongoing research that facilitates radiotherapy planning. Most commercial models utilise imaging data and intense computing resources. This study aimed to predict the dose-volume of rectum and bladder from volumes of target, at-risk structure organs and their overlap regions using machine learning. Dose-volume information of 94 patients with prostate cancer planned for 6000cGy in 20 fractions was exported from the treatment planning system as text files and mined to create a training dataset. Several statistical modelling, machine learning methods, and a new fuzzy rule-based prediction (FRBP) model were explored and validated on an independent dataset of 39 patients. The median absolute error was 2.0%-3.7% for bladder and 1.7-2.4% for rectum in the 4000-6420cGy range. For 5300cGy, 5600cGy and 6000cGy, the median difference was less than 2.5% for rectum and 3.8% for bladder. The FRBP model produced errors of 1.2%, 1.3%, 0.9% and 1.6%, 1.2%, 0.1% for the rectum and bladder respectively at these dose levels. These findings indicate feasibility of obtaining accurate predictions of the clinically important dose-volume parameters for rectum and bladder using just the volumes of these structures.

The Indian summer monsoon (ISM) and associated monsoon intraseasonal oscillations (MISOs) influence the billions of people living in the Indian subcontinent. This study explores the role of autoconversion parameterization in microphysical schemes for the simulation of MISO with the coupled climate model, e.g., the Climate Forecast System version 2 (CFSv2), by conducting sensitivity experiments in two resolutions (~100 km and ~38 km). Results reveal that the modified autoconversion parameterization better simulates the active-break spells of the ISM rainfall. The main improvements include the contrasting features of rainfall over land and ocean and the MISO index, representing MISO periodicity. The improvements are qualitatively and quantitatively more significant in the higher-resolution simulations, particularly regarding rainfall spatial patterns over the Indian subcontinent during active spells. The MISO monitoring index in the revised CFSv2 also shows improvement compared to the control run. This study concludes that proper autoconversion parameterization in the coupled climate model can lead to enhanced representation of active/break spells and sub-seasonal variability of ISM.

The advent of high-intensity, high-polarization electron beams led to significantly improved measurements of the ratio of the proton's charge to electric form factors, GEp/GMp. However, high-$Q^2$ measurements yielded significant disagreement with extractions based on unpolarized scattering, raising questions about the reliability of the measurements and consistency of the techniques. Jefferson Lab experiment E01-001 was designed to provide a high-precision extraction of GEp/GMp from unpolarized cross section measurements using a modified version of the Rosenbluth technique to allow for a more precise comparison with polarization data. Conventional Rosenbluth separations detect the scattered electron which requires comparisons of measurements with very different detected electron energy and rate for electrons at different angles. Our Super-Rosenbluth measurement detected the struck proton, rather than the scattered electron, to extract the cross section. This yielded a fixed momentum for the detected particle and dramatically reduced cross section variation, reducing rate- and momentum-dependent corrections and uncertainties. We measure the cross section vs angle with high relative precision, allowing for extremely precise extractions of GEp/GMp at $Q^2$ = 2.64, 3.20, and 4.10 GeV$^2$. Our results are consistent with traditional extractions but with much smaller corrections and systematic uncertainties, comparable to the uncertainties from polarization measurements. Our data confirm the discrepancy between Rosenbluth and polarization extractions of the proton form factor ratio using an improved Rosenbluth extraction that yields smaller and less-correlated uncertainties than typical of previous Rosenbluth extractions. We compare our results to calculations of two-photon exchange effects and find that the observed discrepancy can be relatively well explained by such effects., Comment: 32 pages, 21 figures. arXiv admin note: text overlap with arXiv:nucl-ex/0610006

`Z' type neutron star low-mass X-ray binaries typically show a `Z'-like three-branched track in their hardness intensity diagram. However, a few such `Z' sources show an additional branch known as the extended flaring branch (EFB). EFB has been poorly studied, and its origin is not known. It is thought to be an extension of the flaring branch (FB) or associated with Fe K$\alpha$ complex or an additional continuum due to the radiative recombination continuum (RRC) process. Using AstroSat observations, we have detected the EFB from two `Z' sources, GX 340+0 and GX 5-1, and performed a broadband spectral analysis in the 0.5-22 keV energy range. During EFB, both sources show the presence of a significant RRC component with absorption edges at $7.91^{+0.16}_{-0.15}$ keV and $8.10^{+0.16}_{-0.17}$ keV, respectively along with blackbody radiation and thermal Comptonisation. No signature of RRC was detected during the FB, which is adjoint to the EFB. No Fe K$\alpha$ complex is detected. Interestingly, inside EFB dips of GX 5-1, for the first time, we have detected flaring events of 30-60s, which can be modelled with a single blackbody radiation. During the FB to EFB transition, an increase in the blackbody radius by a factor of 1.5-2 is observed in both sources. Our analysis strongly suggests that EFB is not an extension of FB or caused by the Fe K$\alpha$ complex. Rather, it is caused by a sudden expansion of the hot, thermalised boundary layer and subsequent rapid cooling., Comment: 14 pages, 10 figures, 6 tables, Accepted for publication in MNRAS

Vision-language models (VLMs) have improved significantly in multi-modal tasks, but their more complex architecture makes their safety alignment more challenging than the alignment of large language models (LLMs). In this paper, we reveal an unfair distribution of safety across the layers of VLM's vision encoder, with earlier and middle layers being disproportionately vulnerable to malicious inputs compared to the more robust final layers. This 'cross-layer' vulnerability stems from the model's inability to generalize its safety training from the default architectural settings used during training to unseen or out-of-distribution scenarios, leaving certain layers exposed. We conduct a comprehensive analysis by projecting activations from various intermediate layers and demonstrate that these layers are more likely to generate harmful outputs when exposed to malicious inputs. Our experiments with LLaVA-1.5 and Llama 3.2 show discrepancies in attack success rates and toxicity scores across layers, indicating that current safety alignment strategies focused on a single default layer are insufficient., Comment: Preprint, Under Review

We prove that in the algebraic metacomplexity framework, the decomposition of metapolynomials into their isotypic components can be implemented efficiently, namely with only a quasipolynomial blowup in the circuit size. This means that many existing algebraic complexity lower bound proofs can be efficiently converted into isotypic lower bound proofs via highest weight metapolynomials, a notion studied in geometric complexity theory. In the context of algebraic natural proofs, our result means that without loss of generality algebraic natural proofs can be assumed to be isotypic. Our proof is built on the Poincar\'e--Birkhoff--Witt theorem for Lie algebras and on Gelfand--Tsetlin theory, for which we give the necessary comprehensive background., Comment: 29 pages. Comments are welcome!

Kinetically constrained spin chains serve as a prototype for structured ergodicity breaking in isolated quantum systems. We show that such a system exhibits a hierarchy of degenerate steady states when driven by incoherent pump and loss at the boundary. By tuning the relative pump and loss and how local the constraints are, one can stabilize mixed steady states, noiseless subsystems, and various decoherence-free subspaces, all of which preserve large amounts of information. We also find that a dipole-conserving bulk suppresses current in steady state. These exact results based on the flow in Hilbert space hold regardless of the specific Hamiltonian or drive mechanism. Our findings show that a competition of kinetic constraints and local drives can induce different forms of ergodicity breaking in open systems, which should be accessible in quantum simulators., Comment: 8 pages, 2 figures + Appendix + Notebook to reproduce data

We propose reinterpreting copula density estimation as a discriminative task. Under this novel estimation scheme, we train a classifier to distinguish samples from the joint density from those of the product of independent marginals, recovering the copula density in the process. We derive equivalences between well-known copula classes and classification problems naturally arising in our interpretation. Furthermore, we show our estimator achieves theoretical guarantees akin to maximum likelihood estimation. By identifying a connection with density ratio estimation, we benefit from the rich literature and models available for such problems. Empirically, we demonstrate the applicability of our approach by estimating copulas of real and high-dimensional datasets, outperforming competing copula estimators in density evaluation as well as sampling., Comment: Preprint

Spiking Neural Networks (SNNs), with their inherent recurrence, offer an efficient method for processing the asynchronous temporal data generated by Dynamic Vision Sensors (DVS), making them well-suited for event-based vision applications. However, existing SNN accelerators suffer from limitations in adaptability to diverse neuron models, bit precisions and network sizes, inefficient membrane potential (Vmem) handling, and limited sparse optimizations. In response to these challenges, we propose a scalable and reconfigurable digital compute-in-memory (CIM) SNN accelerator \chipname with a set of key features: 1) It uses in-memory computations and reconfigurable operating modes to minimize data movement associated with weight and Vmem data structures while efficiently adapting to different workloads. 2) It supports multiple weight/Vmem bit precision values, enabling a trade-off between accuracy and energy efficiency and enhancing adaptability to diverse application demands. 3) A zero-skipping mechanism for sparse inputs significantly reduces energy usage by leveraging the inherent sparsity of spikes without introducing high overheads for low sparsity. 4) Finally, the asynchronous handshaking mechanism maintains the computational efficiency of the pipeline for variable execution times of different computation units. We fabricated \chipname in 65 nm Taiwan Semiconductor Manufacturing Company (TSMC) low-power (LP) technology. It demonstrates competitive performance (scaled to the same technology node) to other digital SNN accelerators proposed in the recent literature and supports advanced reconfigurability. It achieves up to 5 TOPS/W energy efficiency at 95% input sparsity with 4-bit weights and 7-bit Vmem precision., Comment: 9 pages, 17 figures

We propose a distributed control law for a heterogeneous multi-robot coverage problem, where the robots could have different energy characteristics, such as capacity and depletion rates, due to their varying sizes, speeds, capabilities, and payloads. Existing energy-aware coverage control laws consider capacity differences but assume the battery depletion rate to be the same for all robots. In realistic scenarios, however, some robots can consume energy much faster than other robots; for instance, UAVs hover at different altitudes, and these changes could be dynamically updated based on their assigned tasks. Robots' energy capacities and depletion rates need to be considered to maximize the performance of a multi-robot system. To this end, we propose a new energy-aware controller based on Lloyd's algorithm to adapt the weights of the robots based on their energy dynamics and divide the area of interest among the robots accordingly. The controller is theoretically analyzed and extensively evaluated through simulations and real-world demonstrations in multiple realistic scenarios and compared with three baseline control laws to validate its performance and efficacy., Comment: Presented at DARS 2024

We introduce and study the analytic relative Jacobian sheaf for a Lagrangian fibration of a hyperk\"ahler manifold. When the fibration has irreducible fibers in codimension 1 and a relative principal polarization, we show that it is isomorphic to the relative Albanese sheaf which acts on the Lagrangian fibration. We apply this to OG10 Lagrangian fibrations associated to a smooth cubic fourfold constructed by Laza-Sacc\`a-Voisin and Sacc\`a. For sufficiently general cubic fourfolds, we compute the Tate-Shafarevich group parametrizing twists of the original Lagrangian fibration in terms of certain degree twists and Brauer twists. Among the main tools we use are perverse sheaves, decomposition theorem, Hodge modules and Deligne cohomology., Comment: Comments are welcome

We present the Atacama Large Millimeter/submillimeter Array Band 7 observations of the CO (J=3-2) line emission of the protostellar system HH 212 at $\sim$24 au spatial resolution and compare them to those of the SiO (J=8-7) and SO (J=8-7) line emission reported in the literature. We find that the CO line traces four distinct regions: (1) an outer outflow shell, (2) a rotating wind region between the SiO and CO shells, (3) the shocked and wide-angle inner X-wind inside a SiO shell, and (4) the jet. The origin of the CO outer outflow shell could be associated with the entrained material of the envelope, or an extended disk wind. The rotating wind, which is shocked, is launched from a radius of 9-15 au, slightly exterior to that of the previously detected SO shell, which marks the boundary where the wide-angle X-wind is interacting with and shocking the disk wind. Additionally, the SO is found to be mixed with the CO emission within the thick and extended rotating wind region. The large scale CO shocked wind coexists with the SO emission near the upper portion of the inner shocked region converged on top of the inner SiO knots. The CO jet is traced by a chain of knots with roughly equal interval, exhibiting quasi-periodicity, as reported in other jets in the literature., Comment: 18 pages, 12 figures, 2 tables. Accepted by ApJ

Symbolic Regression remains an NP-Hard problem, with extensive research focusing on AI models for this task. Transformer models have shown promise in Symbolic Regression, but performance suffers with smaller datasets. We propose applying k-fold cross-validation to a transformer-based symbolic regression model trained on a significantly reduced dataset (15,000 data points, down from 500,000). This technique partitions the training data into multiple subsets (folds), iteratively training on some while validating on others. Our aim is to provide an estimate of model generalization and mitigate overfitting issues associated with smaller datasets. Results show that this process improves the model's output consistency and generalization by a relative improvement in validation loss of 53.31%. Potentially enabling more efficient and accessible symbolic regression in resource-constrained environments.

We introduce a new class of optical micromotors driven by the spin-orbit interaction of light and spin-driven fluid flows leading to simultaneous rotation and revolution of the micromotors. The micromotors are essentially birefringent liquid crystal particles (LC) that can efficiently convert the angular momentum of light into high-frequency rotational motion. By tightly focusing circularly polarized Gaussian beams through a high numerical aperture objective into a refractive index stratified medium, we create a spherically aberrated intensity profile where the spinning motion of a micromotor optically trapped at the centre of the profile induces fluid flows that causes orbiting motion of the off-axially trapped surrounding particles (secondary micromotors). In addition, the interaction between the helicity of light and the anisotropic properties of the LC medium leads to the breaking of the input helicity and drives the conversion of right to left-circular polarization and vice-versa. This spin-to-spin conversion, causes the orbiting secondary micromotors to spin in certain cases as well so that the entire system of spinning primary micromotor and revolving and spinning secondary micromotors is reminiscent of planetary motion at mesoscopic scales. Our findings, supported by both theoretical modeling and experimental validation, advance the understanding of light-matter interactions at the microscale., Comment: 14 figures and 17 pages

Hybrid quantum-classical computing algorithms offer significant potential for accelerating the calculation of the electronic structure of strongly correlated molecules. In this work, we present the first quantum simulation of conical intersections (CIs) in a biomolecule, cytosine, using a superconducting quantum computer. We apply the Contracted Quantum Eigensolver (CQE) -- with comparisons to conventional Variational Quantum Deflation (VQD) -- to compute the near-degenerate ground and excited states associated with the conical intersection, a key feature governing the photostability of DNA and RNA. The CQE is based on an exact ansatz for many-electron molecules in the absence of noise -- a critically important property for resolving strongly correlated states at CIs. Both methods demonstrate promising accuracy when compared with exact diagonalization, even on noisy intermediate-scale quantum computers, highlighting their potential for advancing the understanding of photochemical and photobiological processes. The ability to simulate these intersections is critical for advancing our knowledge of biological processes like DNA repair and mutation, with potential implications for molecular biology and medical research.

ATOMKI nuclear anomaly has suggested a new BSM (Beyond the Standard Model) boson with mass $\sim17$ MeV emitted from excited nuclei and quickly decays into a pair of $e^+e^-$. In order to search for the new particle, we propose a new approach that utilizes the ongoing Coherent CAPTAIN-Mills (CCM) 10-ton LAr (liquid argon) detectors. The neutrons from the Lujan target can scatter inelastically by the PMT glass in the CCM detector can produce the new boson which solves the ATOMKI anomaly. The new boson can be detected from its decay to a $e^+e^-$ pair. We find that CCM probe a large area of the anomaly allowed parameter space. We also show the prediction for a 100 ton LAr detector., Comment: 6 pages, 3 figures

Artificial intelligence (AI) has become indispensable for managing and processing the vast amounts of data generated during the COVID-19 pandemic. Ontology, which formalizes knowledge within a domain using standardized vocabularies and relationships, plays a crucial role in AI by enabling automated reasoning, data integration, semantic interoperability, and extracting meaningful insights from extensive datasets. The diversity of COVID-19 datasets poses challenges in comprehending this information for both human and machines. Existing COVID-19 ontologies are designed to address specific aspects of the pandemic but lack comprehensive coverage across all essential dimensions. To address this gap, CODO, an integrated ontological model has been developed encompassing critical facets of COVID-19 information such as aetiology, epidemiology, transmission, pathogenesis, diagnosis, prevention, genomics, therapeutic safety, and more. This paper reviews CODO since its inception in 2020, detailing its developments and highlighting CODO as a tool for the aggregation, representation, analysis, and visualization of diverse COVID-19 data. The major contribution of this paper is to provide a summary of the development of CODO, and outline the overall development and evaluation approach. By adhering to best practices and leveraging W3C standards, CODO ensures data integration and semantic interoperability, supporting effective navigation of COVID-19 complexities across various domains., Comment: 15 pages, 4 figures, journal

While multi-axis 3D printing can align continuous fibers along principal stresses in continuous fiber-reinforced thermoplastic (CFRTP) composites to enhance mechanical strength, existing methods have difficulty generating toolpaths with high fiber coverage. This is mainly due to the orientation consistency constraints imposed by vector-field-based methods and the turbulent stress fields around stress concentration regions. This paper addresses these challenges by introducing a 2-RoSy representation for computing the direction field, which is then converted into a periodic scalar field to generate partial iso-curves for fiber toolpaths with nearly equal hatching distance. To improve fiber coverage in stress-concentrated regions, such as around holes, we extend the quaternion-based method for curved slicing by incorporating winding compatibility considerations. Our proposed method can achieve toolpaths coverage between 87.5% and 90.6% by continuous fibers with 1.1mm width. Models fabricated using our toolpaths show up to 84.6% improvement in failure load and 54.4% increase in stiffness when compared to the results obtained from multi-axis 3D printing with sparser fibers.

The relentless pursuit of technological advancements has ushered in a new era where artificial intelligence (AI) is not only a powerful tool but also a critical economic driver. At the forefront of this transformation is Generative AI, which is catalyzing a paradigm shift across industries. Deep generative models, an integration of generative and deep learning techniques, excel in creating new data beyond analyzing existing ones, revolutionizing sectors from production and manufacturing to finance. By automating design, optimization, and innovation cycles, Generative AI is reshaping core industrial processes. In the financial sector, it is transforming risk assessment, trading strategies, and forecasting, demonstrating its profound impact. This paper explores the sweeping changes driven by deep learning models like Large Language Models (LLMs), highlighting their potential to foster innovative business models, disruptive technologies, and novel economic landscapes. As we stand at the threshold of an AI-driven economic era, Generative AI is emerging as a pivotal force, driving innovation, disruption, and economic evolution on a global scale., Comment: 25 pages, 8 figures, Accepted at National Conference on Advances in Marketing Paradigms for Research, Innovation and Technology (AMRIT 2023)

Visual Question Answer (VQA) poses the problem of answering a natural language question about a visual context. Bangla, despite being a widely spoken language, is considered low-resource in the realm of VQA due to the lack of a proper benchmark dataset. The absence of such datasets challenges models that are known to be performant in other languages. Furthermore, existing Bangla VQA datasets offer little cultural relevance and are largely adapted from their foreign counterparts. To address these challenges, we introduce a large-scale Bangla VQA dataset titled ChitroJera, totaling over 15k samples where diverse and locally relevant data sources are used. We assess the performance of text encoders, image encoders, multimodal models, and our novel dual-encoder models. The experiments reveal that the pre-trained dual-encoders outperform other models of its scale. We also evaluate the performance of large language models (LLMs) using prompt-based techniques, with LLMs achieving the best performance. Given the underdeveloped state of existing datasets, we envision ChitroJera expanding the scope of Vision-Language tasks in Bangla.

The Skinned Multi-Person Linear (SMPL) model plays a crucial role in 3D human pose estimation, providing a streamlined yet effective representation of the human body. However, ensuring the validity of SMPL configurations during tasks such as human mesh regression remains a significant challenge , highlighting the necessity for a robust human pose prior capable of discerning realistic human poses. To address this, we introduce MOPED: \underline{M}ulti-m\underline{O}dal \underline{P}os\underline{E} \underline{D}iffuser. MOPED is the first method to leverage a novel multi-modal conditional diffusion model as a prior for SMPL pose parameters. Our method offers powerful unconditional pose generation with the ability to condition on multi-modal inputs such as images and text. This capability enhances the applicability of our approach by incorporating additional context often overlooked in traditional pose priors. Extensive experiments across three distinct tasks-pose estimation, pose denoising, and pose completion-demonstrate that our multi-modal diffusion model-based prior significantly outperforms existing methods. These results indicate that our model captures a broader spectrum of plausible human poses.

DNN training is time-consuming and requires efficient multi-accelerator parallelization, where a single training iteration is split over available accelerators. Current approaches often parallelize training using intra-batch parallelization. Combining inter-batch and intra-batch pipeline parallelism is common to further improve training throughput. In this article, we develop a system, called TiMePReSt, that combines them in a novel way which helps to better overlap computation and communication, and limits the amount of communication. The traditional pipeline-parallel training of DNNs maintains similar working principle as sequential or conventional training of DNNs by maintaining consistent weight versions in forward and backward passes of a mini-batch. Thus, it suffers from high GPU memory footprint during training. In this paper, experimental study demonstrates that compromising weight consistency doesn't decrease prediction capability of a parallelly trained DNN. Moreover, TiMePReSt overcomes GPU memory overhead and achieves zero weight staleness. State-of-the-art techniques often become costly in terms of training time. In order to address this issue, TiMePReSt introduces a variant of intra-batch parallelism that parallelizes the forward pass of each mini-batch by decomposing it into smaller micro-batches. A novel synchronization method between forward and backward passes reduces training time in TiMePReSt. The occurrence of multiple sequence problem and its relation with version difference have been observed in TiMePReSt. This paper presents a mathematical relationship between the number of micro-batches and worker machines, highlighting the variation in version difference. A mathematical expression has been developed to calculate version differences for various combinations of these two without creating diagrams for all combinations.

Transparency and explainability are two extremely important aspects to be considered when employing black-box machine learning models in high-stake applications. Providing counterfactual explanations is one way of catering this requirement. However, this also poses a threat to the privacy of both the institution that is providing the explanation as well as the user who is requesting it. In this work, we propose multiple schemes inspired by private information retrieval (PIR) techniques which ensure the \emph{user's privacy} when retrieving counterfactual explanations. We present a scheme which retrieves the \emph{exact} nearest neighbor counterfactual explanation from a database of accepted points while achieving perfect (information-theoretic) privacy for the user. While the scheme achieves perfect privacy for the user, some leakage on the database is inevitable which we quantify using a mutual information based metric. Furthermore, we propose strategies to reduce this leakage to achieve an advanced degree of database privacy. We extend these schemes to incorporate user's preference on transforming their attributes, so that a more actionable explanation can be received. Since our schemes rely on finite field arithmetic, we empirically validate our schemes on real datasets to understand the trade-off between the accuracy and the finite field sizes.

The proliferation of transliterated texts in digital spaces has emphasized the need for detecting and classifying hate speech in languages beyond English, particularly in low-resource languages. As online discourse can perpetuate discrimination based on target groups, e.g. gender, religion, and origin, multi-label classification of hateful content can help in comprehending hate motivation and enhance content moderation. While previous efforts have focused on monolingual or binary hate classification tasks, no work has yet addressed the challenge of multi-label hate speech classification in transliterated Bangla. We introduce BanTH, the first multi-label transliterated Bangla hate speech dataset comprising 37.3k samples. The samples are sourced from YouTube comments, where each instance is labeled with one or more target groups, reflecting the regional demographic. We establish novel transformer encoder-based baselines by further pre-training on transliterated Bangla corpus. We also propose a novel translation-based LLM prompting strategy for transliterated text. Experiments reveal that our further pre-trained encoders are achieving state-of-the-art performance on the BanTH dataset, while our translation-based prompting outperforms other strategies in the zero-shot setting. The introduction of BanTH not only fills a critical gap in hate speech research for Bangla but also sets the stage for future exploration into code-mixed and multi-label classification challenges in underrepresented languages.

Measurements of the EMC effect in the tritium and helium-3 mirror nuclei are reported. The data were obtained by the MARATHON Jefferson Lab experiment, which performed deep inelastic electron scattering from deuterium and the three-body nuclei, using a cryogenic gas target system and the High Resolution Spectrometers of the Hall A Facility of the Lab. The data cover the Bjorken $x$ range from 0.20 to 0.83, corresponding to a squared four-momentum transfer $Q^2$ range from 2.7 to $11.9\gevsq$, and to an invariant mass $W$ of the final hadronic state greater than 1.84 GeV/${\it c}^2$. The tritium EMC effect measurement is the first of its kind. The MARATHON experimental results are compared to results from previous measurements by DESY-HERMES and JLab-Hall C experiments, as well as with few-body theoretical predictions., Comment: arXiv admin note: text overlap with arXiv:2104.05850

This paper presents an innovative large language model (LLM) agent framework for enhancing diagnostic accuracy in simulated clinical environments using the AgentClinic benchmark. The proposed automatic correction enables doctor agents to iteratively refine their reasoning and actions following incorrect diagnoses, fostering improved decision-making over time. Experiments show that the implementation of the adaptive LLM-based doctor agents achieve correct diagnoses through dynamic interactions with simulated patients. The evaluations highlight the capacity of autonomous agents to adapt and improve in complex medical scenarios. Future enhancements will focus on refining the algorithm and expanding its applicability across a wider range of tasks and different large language models.

New gauge bosons coupled to heavy neutral leptons (HNLs) are simple and well-motivated extensions of the Standard Model. In searches for HNLs in proton fixed-target experiments, we find that a large population of gauge bosons ($Z^\prime$) produced by proton bremsstrahlung may decay to HNLs, leading to a significant improvement in existing bounds on the ($m_{HNL}, U_{\alpha}$), where $U_\alpha$ represent the mixing between HNL and the active neutrinos with flavor $\alpha$. We study this possibility in fixed target experiments with the 8 GeV proton beams, including SBND, MicroBooNE, and ICARUS, as well as DUNE and DarkQuest at 120 GeV. We find the projected sensitivities to additional $Z^\prime$-mediated HNL production can bring the seesaw mechanism of the neutrino masses within a broadened experimental reach., Comment: Corrected mistakes in the calculation of the neutral meson production and detector acceptance and subsequent sensitivity. Added information on the assumed parameterization of hadronic form factors in proton bremsstrahlung

We propose a novel mechanism, dark matter internal pair production (DIPP), to detect dark matter candidates at beam dump facilities. When energetic dark matter scatters in a material, it can create a lepton-antilepton pair by exchanging a virtual photon with the nucleus, similar to the neutrino trident process. We demonstrate this process for dark matter coupled to dark photons in experiments such as DarkQuest, SBND, and DUNE ND experiments. Since the pair-produced lepton-antilepton pair carries a large fraction of the center-of-mass energy and also has similar energy profiles, they can be clearly distinguished from backgrounds. We utilize the above features to show that DIPP is effective in probing various dark matter models, especially at DUNE ND and DarkQuest, by looking for electron-positron and muon-antimuon signatures. We also consider a scenario with dark sector couplings to quarks and muons only to show that DIPP can probe a wide range of dark matter models with various final states., Comment: 7 pages, 4 figures

Invisible axion models that solve the strong CP problem via the Peccei-Quinn (PQ) mechanism typically have a quality problem that arises from quantum gravity effects which violate all global symmetries. These models therefore require extreme fine-tuning of parameters for consistency. We present a new solution to the quality problem in a unified $SO(10)\times U(1)_a$ gauge model, where $U(1)_a$ is an anomaly free axial gauge symmetry. PQ symmetry emerges as an accidental symmetry in this setup, which admits a PQ breaking scale as large as $4\times 10^{11}$ GeV, allowing for the axion to be the cosmological dark matter. We call this a hybrid axion model due to its unique feature that it interpolates between the popular KSVZ and DFSZ axion models. Its predictions for the experimentally measurable axion couplings to the nucleon and electron are distinct from those of the usual models, a feature that can be used to test it. Furthermore, the model has no domain wall problem and it provides a realistic and predictive framework for fermion masses and mixings., Comment: References added, Fig. 2 labeling fixed, 8 pages, 3 figures

While cracking is a complex dynamics that involves material intrinsic properties like grain shape and size distribution, elastic properties of grain and cementing materials, and extrinsic properties of loading, in this work, the focus has been to check the dependence on the elastic properties of the bonding material. A 3-dimensional disordered system was constructed from spherical balls of varying radii that were chosen randomly from a log-normal distribution. The growth of micro-cracks with increasing compressive strain was monitored till the limit of the percolation crack. The two parameters varied were the bond stiffness constant and the bond strength of the material. Two distinct regimes of cracking rates were observed across a critical strain $\epsilon_{knee}$ that manifested as a knee in the cumulative crack-strain plot. The critical strain $\epsilon_{knee}$ and the strain at the percolation point $\epsilon_{perc}$ showed a power law dependence on the elastic property of the bond material. Individual micro-cracks were observed to grow sharply to a maximum value $N^{k_{b}}_{max}$, after which the number of new micro-cracks decreased, showing a long tail. The maximum $N^{k_{b}}_{max}$ was found to correspond to the strain $\epsilon_{knee}$, thus indicating that pre-$N^{k_{b}}_{max}$ cracking brittle, followed by ductile cracking behaviour of system. Lastly, we show that there exists a robust relation between $\epsilon_{knee}$ and $\epsilon_{perc}$ that is a power-law where the exponent is a function of the material elastic property. As $\epsilon_{knee}$ can be determined from acoustic signals associated with micro-cracks, our proposed relation can act as a warning towards critical strain resulting in crack percolation., Comment: 15 pages, 5 figures

We present new ALMA continuum and spectral observations of the MUSE Ultra Deep Field (MUDF), a $2\times 2$ arcmin$^2$ region with ultradeep multiwavelength imaging and spectroscopy hosting two bright $z\approx 3.22$ quasars used to study intervening gas structures in absorption. Through a blind search for dusty galaxies, we identified a total of seven high-confidence sources, six of which with secure spectroscopic redshifts. We estimate galaxy dust and stellar masses ($M_{\rm dust}\simeq 10^{7.8-8.6}\,M_{\odot}$, $M_{\star}\simeq 10^{10.2-10.7}\,M_{\odot}$), as well as star formation rates (${\rm SFR}\simeq 10^{1.2-2.0}\,M_{\odot}\,{\rm yr^{-1}} $) which show that most of these galaxies are massive and dust-obscured similar to coeval (sub-)millimeter galaxies. All six spectroscopically-confirmed galaxies are within $500~\rm km~s^{-1}$ of metal absorption lines observed in the quasar sightlines, corresponding to $100\%$ association rate. We also find that four of these galaxies belong to groups in which they are among the most massive members. Within the multiple group galaxies associated to the same absorption system, the ALMA sources are not always the closest in projection, but they are often aligned with the gaseous structures in velocity space. This suggests that these massive galaxies occupy the center of the potential well of the gas structures traced in absorption. However, albeit the low number density of sources identified with ALMA, our study may indicate that absorbers seem to infrequently originate in the inner circumgalactic medium of these galaxies. Instead, they appear to be better tracers of the gas distributed in the large-scale structure that host them., Comment: 13 pages, 9 figures, 1 Appendix. Submitted to A&A

We show that memory-augmented Transformers (Memformers) can implement linear first-order optimization methods such as conjugate gradient descent, momentum methods, and more generally, methods that linearly combine past gradients. Building on prior work that demonstrates how Transformers can simulate preconditioned gradient descent, we provide theoretical and empirical evidence that Memformers can learn more advanced optimization algorithms. Specifically, we analyze how memory registers in Memformers store suitable intermediate attention values allowing them to implement algorithms such as conjugate gradient. Our results show that Memformers can efficiently learn these methods by training on random linear regression tasks, even learning methods that outperform conjugate gradient. This work extends our knowledge about the algorithmic capabilities of Transformers, showing how they can learn complex optimization methods.

Integral geometry uses four geometric invariants -- the Minkowski functionals -- to characterize certain subsets of 3-dimensional space. The question was, how is the fluid flow in a 3-dimensional porous system related to these invariants? In this work, we systematically study the dependency of permeability on the geometrical characteristics of two categories of 3-dimensional porous systems generated: (i) stochastic and (ii) deterministic. For the stochastic systems, we investigated both normal and log-normal size distribution of grains. For the deterministic porous systems, we checked for a cubic and a hexagonal arrangement of grains of equal size. Our studies reveal that for any 3-dimensional porous system, ordered or disordered, permeability $k$ follows a unique scaling relation with the Minkowski functionals: (a) volume of the pore space, (b) integral mean curvature, (c) Euler Characteristic and (d) critical cross-sectional area of the pore space. The cubic and the hexagonal symmetrical systems formed the upper and lower bounds of the scaling relations, respectively. The disordered systems lay between these bounds. Moreover, we propose a combinatoric $F$ that weaves together the four Minkowski functionals and follows a power-law scaling with permeability. The scaling exponent is independent of particle size and distribution and has a universal value of $ 0.428$ for 3-dimensional porous systems built of spherical grains., Comment: 12 pages, 5 figures

Data poisoning attacks on machine learning models aim to manipulate the data used for model training such that the trained model behaves in the attacker's favor. In classical models such as deep neural networks, large chains of dot products do indeed cause errors injected by an attacker to propagate or to accumulate. But what about quantum models? Our hypothesis is that, in quantum machine learning, error propagation is limited for two reasons. First of all, data, which in quantum computing is encoded in terms of qubits which are confined to the Bloch sphere. Second of all, quantum information processing happens via the application of unitary operators which are norm-preserving. Testing this hypothesis, we investigate how extensive error propagation and thus poisoning attacks affect quantum machine learning.

In this paper, we propose an efficient transformer architecture that uses reinforced positional embedding to obtain superior performance with half the number of encoder decoder layers. We demonstrate that concatenating positional encoding with trainable token embeddings, normalizing columns in the token embedding matrix, and using the normalized token embedding matrix as the value of the attention layer improve the training and validation loss and the training time in an encoder-decoder Transformer model for a Portuguese-English translation task with 10 epochs or 12 hours of training across 10 trials. Our method, with roughly a threefold parameter reduction compared to the baseline model, yields a mean training loss of 1.21, a mean validation loss of 1.51, and an average training time of 1352.27 seconds per epoch, surpassing the baseline model with the same embedding dimension that employs addition of positional encoding and token embeddings, which achieves a mean training loss of 1.96, a validation loss of 2.18, and an average training time of 4297.79 seconds per epoch. Additionally, we evaluated our proposed architecture and the baseline across 14 diverse translation datasets from TensorFlow. The results indicate that our method consistently achieves lower or comparable training and validation losses, suggesting enhanced learning efficiency.

In this study, we have examined the evolving wormhole solution within Einstein-massive gravity, considering traceless, barotropic, and anisotropic pressure fluids. We have conducted a comprehensive analysis of the constraints imposed by the constants derived from the wormhole solution. It is found that the wormhole throat, situated between two asymptotic universes, undergoes simultaneous expansion with acceleration. A detailed investigation of the energy conditions for traceless, barotropic, and anisotropic fluids suggests a wide range of possibilities for evolving wormhole configurations with non-exotic matter at the throat. The dependency of this feature on the various parameters arising from the study has also been examined., Comment: 17 pages, 5 figures, 4 tables. Accepted for publication in IJMPA

We demonstrate in-situ manipulation of the critical temperature ($T_S$) and upper critical field ($H_{C2}$) of conventional and unconventional superconductors via ultrasonic excitation. Utilizing DC magnetization and AC susceptibility measurements, we observed a reduction in both $T_S$ and $H_{C2}$ with increasing amplitude of the applied ultrasonic waves. This reduction exhibits a power law dependence on the excitation voltage, suggesting a non-linear coupling between the ultrasonic waves and the superconducting order parameter. Measurements on a paramagnetic material (Gd$_2$O$_3$) with quenched orbital angular momentum(L\,=\,0) revealed no change in magnetization even under extreme ultrasonic excitation amplitude, highlighting the role of spin-orbit coupling in the observed effects. Similar measurements on cuprate superconductors showed analogous behavior, suggesting a possible link between the observed modification of superconducting properties and the modulation of the antiferromagnetic network by ultrasonic excitation.

Large Language Models trained on web-scale text acquire language generation abilities that can solve a wide range of tasks, particularly when task knowledge is refined into the generative prior using in-context examples. However, spurious features learned from noisy data hinder their generalizability. Supervised finetuning can introduce task specificity, but introduce data inefficiency. Prior studies indicate that (i) noisy neural circuitries coexist with generalizable ones within LLMs, and (ii) finetuning typically enhances (or suppresses) existing abilities without introducing newer ones. Building upon these, we propose TaRot, a novel method for task adaptation. TaRot intervenes in the neural circuitries using learnable rotation matrices that are optimized using Bayesian Optimization, on labelled samples in the order of standard few-shot prompting examples. Experiments on multiple classification and generation tasks using LLMs of varying sizes reveal the efficacy of TaRot, improving upon both zero- as well as few-shot performance, with average improvements (across models and tasks) of 23.81% and 11.15%, respectively. The source code is available at https://github.com/joykirat18/TaRot

Neurosymbolic learning has emerged as a promising paradigm to incorporate symbolic reasoning into deep learning models. However, existing frameworks are limited in scalability with respect to both the training data and the complexity of symbolic programs. We propose Dolphin, a framework to scale neurosymbolic learning at a fundamental level by mapping both forward chaining and backward gradient propagation in symbolic programs to vectorized computations. For this purpose, Dolphin introduces a set of abstractions and primitives built directly on top of a high-performance deep learning framework like PyTorch, effectively enabling symbolic programs to be written as PyTorch modules. It thereby enables neurosymbolic programs to be written in a language like Python that is familiar to developers and compile them to computation graphs that are amenable to end-to-end differentiation on GPUs. We evaluate Dolphin on a suite of 13 benchmarks across 5 neurosymbolic tasks that combine deep learning models for text, image, or video processing with symbolic programs that involve multi-hop reasoning, recursion, and even black-box functions like Python eval(). Dolphin only takes 0.33%-37.17% of the time (and 2.77% on average) to train these models on the largest input per task compared to baselines Scallop, ISED, and IndeCateR+, which time out on most of these inputs. Models written in Dolphin also achieve state-of-the-art accuracies even on the largest benchmarks.

Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task in the inductive setting, where at test time one sees entities that were not present during training; the most performant models in the inductive setting have employed path encoding modules in addition to standard subgraph encoding modules. This work similarly focuses on KG completion in the inductive setting, without the explicit use of path encodings, which can be time-consuming and introduces several hyperparameters that require costly hyperparameter optimization. Our approach uses a Transformer-based subgraph encoding module only; we introduce connection-biased attention and entity role embeddings into the subgraph encoding module to eliminate the need for an expensive and time-consuming path encoding module. Evaluations on standard inductive KG completion benchmark datasets demonstrate that our Connection-Biased Link Prediction (CBLiP) model has superior performance to models that do not use path information. Compared to models that utilize path information, CBLiP shows competitive or superior performance while being faster. Additionally, to show that the effectiveness of connection-biased attention and entity role embeddings also holds in the transductive setting, we compare CBLiP's performance on the relation prediction task in the transductive setting.

In recent years, several computational tools have been developed to detect and identify sexism, misogyny, and gender-based hate speech, especially on online platforms. Though these tools intend to draw on knowledge from both social science and computer science, little is known about the current state of research in quantifying online sexism or misogyny. Given the growing concern over the discrimination of women in online spaces and the rise in interdisciplinary research on capturing the online manifestation of sexism and misogyny, a systematic literature review on the research practices and their measures is the need of the hour. We make three main contributions: (i) we present a semi-automated way to narrow down the search results in the different phases of selection stage in the PRISMA flowchart; (ii) we perform a systematic literature review of research papers that focus on the quantification and measurement of online gender-based hate speech, examining literature from computer science and the social sciences from 2012 to 2022; and (iii) we identify the opportunities and challenges for measuring gender-based online hate speech. Our findings from topic analysis suggest a disciplinary divide between the themes of research on sexism/misogyny. With evidence-based review, we summarise the different approaches used by the studies who have explored interdisciplinary approaches to bridge the knowledge gap. Coupled with both the existing literature on social science theories and computational modeling, we provide an analysis of the benefits and shortcomings of the methodologies used. Lastly, we discuss the challenges and opportunities for future research dedicated to measuring online sexism and misogyny.

Graph isomorphism, a classical algorithmic problem, determines whether two input graphs are structurally identical or not. Interestingly, it is one of the few problems that is not yet known to belong to either the P or NP-complete complexity classes. As such, intelligent search-space pruning based strategies were proposed for developing isomorphism testing solvers like nauty and bliss, which are still, unfortunately, exponential in the worst-case scenario. Thus, the polynomial-time Weisfeiler-Lehman (WL) isomorphism testing heuristic, based on colour refinement, has been widely adopted in the literature. However, WL fails for multiple classes of non-isomorphic graph instances such as strongly regular graphs, block structures, and switched edges, among others. In this paper, we propose a novel polynomial-time graph isomorphism testing heuristic, RSVP, and depict its enhanced discriminative power compared to the Weisfeiler-Lehman approach for several challenging classes of graphs. Bounded by a run-time complexity of O(m^2+mn^2+n^3) (where n and m are the number of vertices and edges respectively), we show that RSVP can identify non-isomorphism in several 'hard' graph instance classes including Miyazaki, Paulus, cubic hypohamiltonian, strongly regular, Latin series and Steiner triple system graphs, where the 3-WL test fails. Similar to the WL test, our proposed algorithm is prone to only one-sided errors, where isomorphic graphs will never be determined to be non-isomorphic, although the reverse can happen.

We describe the smallest quantum error correcting (QEC) code to correct for amplitude-damping (AD) noise, namely, a 3-qubit code that corrects up to first order in the damping strength. We generalize this construction to create a family of codes that correct AD noise up to any fixed order. We underpin the fundamental connection between the structure of our codes and the noise structure via a relaxed form of the Knill-Laflamme conditions, that are different from existing formulations of approximate QEC conditions. Although the recovery procedure for this code is non-deterministic, our codes are optimal with respect to overheads and outperform existing codes to tackle AD noise in terms of entanglement fidelity. This alternate formulation of approximate QEC in fact leads us to a new class of quantum codes tailored to AD noise and also gives rise to a noise-adapted quantum Hamming bound for AD noise., Comment: 4 pages + 5 page appendix, 6 figures

The sky-averaged brightness temperature of the 21cm line from neutral hydrogen provides a sensitive probe of the thermal state of the intergalactic medium, particularly before and during Cosmic Dawn and the Epoch of Reionisation. This `global signal' is faint, on the order of tens to hundreds of millikelvin, and spectrally relatively smooth, making it exceedingly difficult to disentangle from foreground radio emission and instrumental artefacts. In this paper, we introduce RHINO, an experiment based around a large horn antenna operating from 60-85 MHz. Horn antennas are highly characterisable and provide excellent shielding from their immediate environment, which are potentially decisive advantages when it comes to the beam measurement and modelling problems that are particularly challenging for this kind of experiment. The system also includes a novel continuous wave calibration source to control correlated gain fluctuations, allowing continuous monitoring of the overall gain level without needing to rapidly switch between the sky and a calibration source. Here, we describe the basic RHINO concept, including the antenna design, EM simulations, and receiver electronics. We use a basic simulation and analysis pipeline to study the impact of the limited bandwidth on recovery of physical 21cm global signal model parameters, and discuss a basic calibration scheme that incorporates the continuous wave signal. Finally, we report on the current state of a scaled-down prototype system under construction at Jodrell Bank Observatory., Comment: 15 pages, 14 figures. Comments welcome

The scenario of feedback-free starbursts (FFB), which predicts excessively bright galaxies at cosmic dawn as observed using JWST, may provide a natural setting for black hole (BH) growth. This involves the formation of intermediate-mass seed BHs and their runaway mergers into super-massive BHs with high BH-to-stellar mass ratios and low AGN luminosities. We present a scenario of merger-driven BH growth in FFB galaxies and study its feasibility. BH seeds form within the building blocks of the FFB galaxies, namely, thousands of compact star clusters, each starbursting in a free-fall time of a few Myr before the onset of stellar and supernova feedback. The BH seeds form by rapid core collapse in the FFB clusters, in a few free-fall times, sped up by the migration of massive stars due to the young, broad stellar mass function and stimulated by a `gravo-gyro' instability due to internal cluster rotation and flattening. BHs of $10^4 M_\odot$ are expected in $10^6 M_\odot$ FFB clusters within sub-kpc galactic disks at $z \sim 10$. The BHs then migrate to the galaxy center by dynamical friction, hastened by the compact FFB stellar galactic disk configuration. Efficient mergers of the BH seeds will produce $10^{6-8} M_\odot$ BHs with a BH-to-stellar mass ratio $\sim 0.01$ by $z \sim 4-7$, as observed. The growth of the central BH by mergers can overcome the bottleneck introduced by gravitational wave recoils if the BHs inspiral within a relatively cold disk or if the escape velocity from the galaxy is boosted by a wet compaction event. Such events, common in massive galaxies at high redshifts, can also help by speeding up the inward BH migration and by providing central gas to assist with the final parsec problem. The cold disk version of the FFB scenario provides a feasible route for the formation of supermassive BHs., Comment: 24 pages, 14 figures