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We explore semi-pre-C*-algebras in the context of rigid semisimple C*-tensor categories and using techniques from annular representations, we extend Ozawa's criterion for property (T) in groups to this context, Comment: 10 pages, many tikZ figures, comments are welcome

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

We study the Leptogenesis and Dark Matter in the presence of an extra singlet complex scalar field in an extended discrete $\mathcal{Z_{\rm 3}}$ symmetry. The vacuum expectation value of the new scalar spontaneously breaks the $\mathcal{Z_{\rm 3}}$ symmetry. A remnant CP-like $\mathcal{Z_{\rm 2}}$ symmetry stabilizes the imaginary part of the complex scalar field which can act as a pseudo-Goldstone DM. The real part of the complex scalar couples to RHN opens up new decay channels which can lead to a larger CP-violation in generating the lepton asymmetry. Thus the singlet complex scalar plays a crucial role in understanding the Leptogenesis and Dark Matter parameter space. This singlet complex scalar is also responsible for the First-Order Phase Transition (FOPT) which may provide observable stochastic Gravitational wave signatures. We discuss the possible correlations among these three phenomena., Comment: 5 tables and 18 figures

Scent marks play a crucial role in both territorial and sexual communication in many species. We investigated how free-ranging dogs respond to scent marks from individuals of different identities in terms of sex and group, across varying strategic locations within their territory. Both male and female dogs showed heightened interest in scent marks compared to control, exhibiting stronger territorial responses,. with males being more territorial than females. Overmarking behaviour was predominantly observed in males, particularly in response to male scent marks and those from neighbouring groups. Behavioural cluster analysis revealed distinct responses to different scent marks, with neighbouring group male scents eliciting the most distinct reactions. Our findings highlight the multifaceted role of scent marks in free-ranging dog communication, mediating both territorial defence and intrasexual competition. The differential responses based on the identity and gender of the scent-marker emphasize the complexity of olfactory signalling in this species. This study contributes to understanding the social behaviour of dogs in their natural habitat, and opens up possibilities for future explorations in the role of olfactory cues in the social dynamics of the species.

Quantum machine learning (QML) is a rapidly emerging area of research, driven by the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. With the progress in the research of QML models, there is a rise in third-party quantum cloud services to cater to the increasing demand for resources. New security concerns surface, specifically regarding the protection of intellectual property (IP) from untrustworthy service providers. One of the most pressing risks is the potential for reverse engineering (RE) by malicious actors who may steal proprietary quantum IPs such as trained parameters and QML architecture, modify them to remove additional watermarks or signatures and re-transpile them for other quantum hardware. Prior work presents a brute force approach to RE the QML parameters which takes exponential time overhead. In this paper, we introduce an autoencoder-based approach to extract the parameters from transpiled QML models deployed on untrusted third-party vendors. We experiment on multi-qubit classifiers and note that they can be reverse-engineered under restricted conditions with a mean error of order 10^-1. The amount of time taken to prepare the dataset and train the model to reverse engineer the QML circuit being of the order 10^3 seconds (which is 10^2x better than the previously reported value for 4-layered 4-qubit classifiers) makes the threat of RE highly potent, underscoring the need for continued development of effective defenses., Comment: 7 pages, 4 figures

Side channel attacks (SCAs) remain a significant threat to the security of cryptographic systems in modern embedded devices. Even mathematically secure cryptographic algorithms, when implemented in hardware, inadvertently leak information through physical side channel signatures such as power consumption, electromagnetic (EM) radiation, light emissions, and acoustic emanations. Exploiting these side channels significantly reduces the search space of the attacker. In recent years, physical countermeasures have significantly increased the minimum traces to disclosure (MTD) to 1 billion. Among them, signature attenuation is the first method to achieve this mark. Signature attenuation often relies on analog techniques, and digital signature attenuation reduces MTD to 20 million, requiring additional methods for high resilience. We focus on improving the digital signature attenuation by an order of magnitude (MTD 200M). Additionally, we explore possible attacks against signature attenuation countermeasure. We introduce a Voltage drop Linear region Biasing (VLB) attack technique that reduces the MTD to over 2000 times less than the previous threshold. This is the first known attack against a physical side-channel attack (SCA) countermeasure. We have implemented an attack detector with a response time of 0.8 milliseconds to detect such attacks, limiting SCA leakage window to sub-ms, which is insufficient for a successful attack., Comment: Extended from CICC. Now under revision at Journal of Solid-State Circuits

Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning models have achieved state-of-the-art results of streamflow prediction, their end-to-end single-task learning approach often fails to capture the causal relationships within these systems. To address this, we propose Hierarchical Conditional Multi-Task Learning (HCMTL), a hierarchical approach that jointly models soil water and snowpack processes based on their causal connections to streamflow. HCMTL utilizes task embeddings to connect network modules, enhancing flexibility and expressiveness while capturing unobserved processes beyond soil water and snowpack. It also incorporates the Conditional Mini-Batch strategy to improve long time series modeling. We compare HCMTL with five baselines on a global dataset. HCMTL's superior performance across hundreds of drainage basins over extended periods shows that integrating domain-specific causal knowledge into deep learning enhances both prediction accuracy and interpretability. This is essential for advancing our understanding of complex hydrological systems and supporting efficient water resource management to mitigate natural disasters like droughts and floods.

Teleoperated driving (ToD) systems are a special type of cyber-physical system (CPS) where the operator remotely controls the steering, acceleration, and braking actions of the vehicle. Malicious actors may inject false data into communication channels to manipulate the teleoperator's driving commands to cause harm. Hence, protection of this communication is necessary for a safe operation of the target vehicle. However, according to the National Institute of Standards and Technology (NIST) cybersecurity framework, protection is not enough, and detecting an attack is necessary. Moreover, UN R155 mandates that vehicle fleets detect and log security incidents. Thus, the cyber-physical threats of ToD are modeled using the attack-centric approach in this paper. Then, an attack model with false data injection (FDI) on the steering control command is created from real vehicle data. A risk of this attack model is assessed for a last-mile delivery (LMD) application. Finally, a physics-based context-aware anomaly detection system (PCADS) is proposed to detect such false injection attacks, and preliminary experimental results are presented to validate the model., Comment: 27 pages, 14 figures, Submitted to IET Intelligent Transport Systems

Battery geometry (shape and size) is one of the important parameters which governs the battery capacity and thermal behavior. In the dynamic conditions or during the operation, the performance of batteries become much more complex. Herein, the changes in thermal behavior of lithium-ion battery (LIB)by altering the geometry i.e., length to diameter ratio (l/d), is investigated. The geometries considered are named as large geometry (LG), datum geometry (DG) and small geometry (SG) with the l/d ratio of 5.25, 3.61, and 2.38, respectively. A three-dimensional (3D) multi-partition thermal model is adopted, and the numerical results are validated by the published experimental data. For three different cooling approaches such as radial, both-tab and mixed cooling, the average battery temperature and temperature heterogeneity are thoroughly examined considering the heat transfer coefficients (h) of50 and 100 W/m2K at discharge rates of 1, 2 and 3C. Amongst, the minimum average battery temperature is exhibited by DG, the minimum radial temperature heterogeneity is obtained from LG, and substantial outperformance in terms of faster cooling rate is identified for SG, irrespective of the cooling approach employed, Comment: 39 pages, 12 Figures

While the kinetics of domain growth, even for conserved order-parameter dynamics, is widely studied for short-range inter-particle interactions, systems having long-range interactions are receiving attention only recently. Here we present results, for such dynamics, from a Monte Carlo (MC) study of the two-dimensional long-range Ising model, with critical compositions of up and down spins. The order parameter in the MC simulations was conserved via the incorporation of the Kawasaki spin-exchange method. The simulation results for domain growth, following quenches of the homogeneous systems to temperatures below the critical values $T_c$, were analyzed via finite-size scaling and other advanced methods. The outcomes reveal that the growths follow power-laws, with the exponent having interesting dependence on the range of interaction. Quite interstingly, when the range is above a cut-off, the exponent, for any given range, changes from a larger value to a smaller one, during the evolution process. While the corresponding values at late times match with certain theoretical predictions for the conserved order-parameter dynamics, the ones at the early times appear surprisingly high, quite close to the theoretical values for the nonconserved case., Comment: 10 figures

Generative Error Correction (GEC) has emerged as a powerful post-processing method to enhance the performance of Automatic Speech Recognition (ASR) systems. However, we show that GEC models struggle to generalize beyond the specific types of errors encountered during training, limiting their ability to correct new, unseen errors at test time, particularly in out-of-domain (OOD) scenarios. This phenomenon amplifies with named entities (NEs), where, in addition to insufficient contextual information or knowledge about the NEs, novel NEs keep emerging. To address these issues, we propose DARAG (Data- and Retrieval-Augmented Generative Error Correction), a novel approach designed to improve GEC for ASR in in-domain (ID) and OOD scenarios. We augment the GEC training dataset with synthetic data generated by prompting LLMs and text-to-speech models, thereby simulating additional errors from which the model can learn. For OOD scenarios, we simulate test-time errors from new domains similarly and in an unsupervised fashion. Additionally, to better handle named entities, we introduce retrieval-augmented correction by augmenting the input with entities retrieved from a database. Our approach is simple, scalable, and both domain- and language-agnostic. We experiment on multiple datasets and settings, showing that DARAG outperforms all our baselines, achieving 8\% -- 30\% relative WER improvements in ID and 10\% -- 33\% improvements in OOD settings., Comment: Preprint. Under Review

We investigate strong gravitational lensing by two static black hole models (Model-1 and Model-2) within the Effective Quantum Gravity (EQG) framework, characterized by mass $M$ and parameter $\zeta$. For $\zeta = 0$, they reduce to the Schwarzschild solution, and depending on the parameters, they describe black holes with an event and Cauchy horizon (Model-1), a single horizon (Model-2), or no horizons. Using SMBHs Sgr A* and M87* as lenses and integrating theoretical predictions with recent EHT data, we identify significant differences in lensing signatures due to quantum corrections. For Model-1, the deviations of the lensing observables: $|\delta\theta_{\infty}|$ of black holes in EQG from Schwarzschild black hole, for SMBHs Sgr A* and M87, can reach as much as $1.75~\mu$as and $1.32~\mu$as, while $|\delta s|$ is about $30.12$~nas for Sgr A* and $22.63$~nas for M87*. The flux ratio of the first image to all subsequent packed images indicates that EQG black hole images are brighter than their Schwarzschild counterparts, with a deviation in the brightness ratio $|\delta r_{mag}|$ reaching up to 2.02. The time delays between the second and first images, denoted $|\delta T_{2,1}|$, exhibit substantial deviations from the GR counterpart, reaching up to 1.53 min for Sgr A* and 1159.9 min for M87*. The EHT constraints on $\theta_{sh}$ of Sgr A* and M87* within the $1\sigma$ region limit the parameters $\zeta$. Our analysis concludes that EQG black holes are consistent with the EHT observations within this finite space., Comment: The paper is submitted to EPJC on 2024-10-03

In this paper, we present EH-MAM (Easy-to-Hard adaptive Masked Acoustic Modeling), a novel self-supervised learning approach for speech representation learning. In contrast to the prior methods that use random masking schemes for Masked Acoustic Modeling (MAM), we introduce a novel selective and adaptive masking strategy. Specifically, during SSL training, we progressively introduce harder regions to the model for reconstruction. Our approach automatically selects hard regions and is built on the observation that the reconstruction loss of individual frames in MAM can provide natural signals to judge the difficulty of solving the MAM pre-text task for that frame. To identify these hard regions, we employ a teacher model that first predicts the frame-wise losses and then decides which frames to mask. By learning to create challenging problems, such as identifying harder frames and solving them simultaneously, the model is able to learn more effective representations and thereby acquire a more comprehensive understanding of the speech. Quantitatively, EH-MAM outperforms several state-of-the-art baselines across various low-resource speech recognition and SUPERB benchmarks by 5%-10%. Additionally, we conduct a thorough analysis to show that the regions masked by EH-MAM effectively capture useful context across speech frames.

Robust classification of the operational environment of wireless devices is becoming increasingly important for wireless network optimization, particularly in a shared spectrum environment. Distinguishing between indoor and outdoor devices can enhance reliability and improve coexistence with existing, outdoor, incumbents. For instance, the unlicensed but shared 6 GHz band (5.925 - 7.125 GHz) enables sharing by imposing lower transmit power for indoor unlicensed devices and a spectrum coordination requirement for outdoor devices. Further, indoor devices are prohibited from using battery power, external antennas, and weatherization to prevent outdoor operations. As these rules may be circumvented, we propose a robust indoor/outdoor classification method by leveraging the fact that the radio-frequency environment faced by a device are quite different indoors and outdoors. We first collect signal strength data from all cellular and Wi-Fi bands that can be received by a smartphone in various environments (indoor interior, indoor near windows, and outdoors), along with GPS accuracy, and then evaluate three machine learning (ML) methods: deep neural network (DNN), decision tree, and random forest to perform classification into these three categories. Our results indicate that the DNN model performs the best, particularly in minimizing the most important classification error, that of classifying outdoor devices as indoor interior devices.

We analyze the Large Hadron Collider potential to study triple couplings of the electroweak gauge bosons using their boosted hadronic decays. Deviations from Standard Model predictions spoil cancelations present in the Standard Model leading to the growth of the electroweak diboson production cross section at high center-of-mass energies. In this kinematical limit, $W$'s and $Z$'s are highly boosted, and consequently, their hadronic decays give rise to fat jets. Here, we show that the study of boosted hadronically decaying $W$ and $Z$ leads to limits on triple gauge couplings that are comparable to the ones originating from the leptonic decay channels., Comment: 9 pages, 2 figures

The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying dynamics. Emerging data-centric artificial intelligence (AI) techniques tackle these tasks using deep neural networks (DNNs), which are model-agnostic. Recent developments illustrate the possibility of fusing DNNs with classic Kalman-type filtering, obtaining systems that learn to track in partially known dynamics. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms. We review both generic and dedicated DNN architectures suitable for state estimation, and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented. The usefulness of each approach in preserving the individual strengths of model-based KFs and data-driven DNNs is investigated in a qualitative and quantitative study, whose code is publicly available, illustrating the gains of hybrid model-based/data-driven designs. We also discuss existing challenges and future research directions that arise from fusing AI and Kalman-type algorithms., Comment: Submitted to IEEE Signal Processing Magazine

Quantum sensors based on critical many-body systems are known to exhibit enhanced sensing capability. Such enhancements typically scale algebraically with the probe size. Going beyond algebraic advantage and reaching exponential scaling has remained elusive when all the resources, such as the preparation time, are taken into account. In this work, we show that systems with first order quantum phase transitions can indeed achieve exponential scaling of sensitivity, thanks to their exponential energy gap closing. Remarkably, even after considering the preparation time using local adiabatic driving, the exponential scaling is sustained. Our results are demonstrated through comprehensive analysis of three paradigmatic models exhibiting first order phase transitions, namely Grover, $p$-spin, and biclique models. We show that this scaling survives moderate decoherence during state preparation and also can be optimally measured in experimentally available basis., Comment: 9 pages, 7 figures. Comments are welcome

We study gravitational lensing in the strong-field limit using the rotating quantum-corrected black hole (RQCBH) with an additional parameter $\alpha$ besides mass $M$ and spin parameter $a$. We discover a decrease in the deflection angle $\alpha_D$, the photon sphere radius $x_{ps}$, and the angular position $\theta_{\infty}$. The flux ratio of the first image to all subsequent images, $r_{mag}$, decreases rapidly as $\alpha$ increases. We compare RQCBH observables with those of Kerr black holes, using Sgr A* and M87* as lenses to observe the effect of the quantum-corrected parameter $\alpha$. For Sgr A*, the angular position $\theta_\infty$ in $\in~(14.8-26.3)~\mu as$, while for M87* $\in~(11.12-19.78)~\mu as$. The angular separation $s$, for supermassive black holes (SMBHs) SgrA* and M87*, differs significantly, with values ranging $\in~(0.033-0.79)~\mu as$ for Sgr A* and $\in~(0.033-0.59)~\mu as$ for M87*. The deviations of the lensing observables $|\Delta\theta_\infty|$ and $|\Delta s|$ for RQCBH ($a=0.8,\alpha=0.4$) from Kerr black holes can reach up to $1.6~\mu as$ and $0.41~\mu as$ for Sgr A*, and $1.2~\mu as$ and $0.31~\mu as$ for M87*. The relative magnitude $r_{mag}$ $\in~(1.81-6.82)~\mu as$. We also compared the time delays between the relativistic images in the 22 SMBHs at the center of various galaxies. We found that RQCBH can be quantitatively distinguished from Kerr black holes. Interestingly, the time delay for Sgr A* and M87* can reach approximately 24.95 min and 308.15 hrs, respectively. Our analysis concludes that, within the 1$\sigma$ region, a significant portion of the parameter space agrees with the EHT results of M87* and Sgr A*., Comment: 15 pages, 10 figures, 3 tables

GRB170817A (also GW170817) became the first binary neutron star (BNS) merger event detected via gravitational waves and electromagnetic signals. Over the next 4 years, various multiband observations have led to re-imagine the various short Gamma Ray Burts (sGRB) and interstellar medium interaction models. While these models successfully explain the observed afterglow until ~900 days, a re-brightening or excess flux was observed in the 1keV X-Ray band after ~1000 days. In this study, we re-evaluate the jet parameters using the new observations (until ~1234 days) with a boosted fireball jet model. We study the observable effects of the counter-jet for GRB170817A, using our new afterglow code, Firefly. Our results show that it is indeed possible for the observed excess to coincide with the emissions from the counter-jet (~800 days). We also computed an empirical scaling law between the jet and counter-jet peak emission timescales and the observer angle. The Firefly code can also track the simulated object through the observers' sky and numerically model the apparent motion. The calculated apparent motion (~2.6c) does not match the observed apparent motion (7.5c to 5.2c). Hence we conclude, the excess flux of GRB170817A may not be associated with the counter jet; however, it is not enough to reject this hypothesis from the traditional counter jet visibility time scales, which predicts > 5000 days. The apparent motion, combined with the multi-band lightcurves, is needed to break degeneracy between geometrical parameters and the microphysical parameters of the afterglow., Comment: 13 pages, 9 figures. This manuscript has been accepted for publication in The Astrophysical Journal

In August of 2024, 495 hackers generated evaluations in an open-ended bug bounty targeting the Open Language Model (OLMo) from The Allen Institute for AI. A vendor panel staffed by representatives of OLMo's safety program adjudicated changes to OLMo's documentation and awarded cash bounties to participants who successfully demonstrated a need for public disclosure clarifying the intent, capacities, and hazards of model deployment. This paper presents a collection of lessons learned, illustrative of flaw reporting best practices intended to reduce the likelihood of incidents and produce safer large language models (LLMs). These include best practices for safety reporting processes, their artifacts, and safety program staffing., Comment: 8 pages, 5 figures

Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper introduces MLPerf Power, a comprehensive benchmarking methodology with capabilities to evaluate the energy efficiency of ML systems at power levels ranging from microwatts to megawatts. Developed by a consortium of industry professionals from more than 20 organizations, MLPerf Power establishes rules and best practices to ensure comparability across diverse architectures. We use representative workloads from the MLPerf benchmark suite to collect 1,841 reproducible measurements from 60 systems across the entire range of ML deployment scales. Our analysis reveals trade-offs between performance, complexity, and energy efficiency across this wide range of systems, providing actionable insights for designing optimized ML solutions from the smallest edge devices to the largest cloud infrastructures. This work emphasizes the importance of energy efficiency as a key metric in the evaluation and comparison of the ML system, laying the foundation for future research in this critical area. We discuss the implications for developing sustainable AI solutions and standardizing energy efficiency benchmarking for ML systems., Comment: 14 pages, 11 figures, 1 table

We investigate the universe's evolution within the framework of Rastall gravity, which is an extension of the standard $\Lambda$CDM model. Utilizing a linear parametrization of the Equation of State (EoS) in a Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) background, we constrain the model parameters through analysis of cosmic chronometers (CC), Pantheon, Gold, Gamma Ray Burst (GRB), and Baryon Acoustic Oscillations (BAO) datasets, as well as their joint analysis, under $1\sigma$ and $2\sigma$ confidence levels, considering the Rastall parameter $\lambda$. The constrained parameters are then used to compare our model with the standard $\Lambda$CDM model. Our findings include a detailed examination of the model's physical interpretations and demonstrate the potential for an accelerating universe expansion in later times, aligning with the observed behavior of dark energy., Comment: 21 pages, 19 figures, accepted for publication in Physica Scripta

Heterogeneous graphs are present in various domains, such as social networks, recommendation systems, and biological networks. Unlike homogeneous graphs, heterogeneous graphs consist of multiple types of nodes and edges, each representing different entities and relationships. Generating realistic heterogeneous graphs that capture the complex interactions among diverse entities is a difficult task due to several reasons. The generator has to model both the node type distribution along with the feature distribution for each node type. In this paper, we look into solving challenges in heterogeneous graph generation, by employing a two phase hierarchical structure, wherein the first phase creates a skeleton graph with node types using a prior diffusion based model and in the second phase, we use an encoder and a sampler structure as generator to assign node type specific features to the nodes. A discriminator is used to guide training of the generator and feature vectors are sampled from a node feature pool. We conduct extensive experiments with subsets of IMDB and DBLP datasets to show the effectiveness of our method and also the need for various architecture components.

Gathering is a fundamental coordination problem in swarm robotics, where the objective is to bring robots together at a point not known to them at the beginning. While most research focuses on continuous domains, some studies also examine the discrete domain. This paper addresses the optimal gathering problem on an infinite grid, aiming to improve the energy efficiency by minimizing the maximum distance any robot must travel. The robots are autonomous, anonymous, homogeneous, identical, and oblivious. We identify all initial configurations where the optimal gathering problem is unsolvable. For the remaining configurations, we introduce a deterministic distributed algorithm that effectively gathers $n$ robots ($n\ge 9$). The algorithm ensures that the robots gathers at one of the designated min-max nodes in the grid. Additionally, we provide a comprehensive characterization of the subgraph formed by the min-max nodes in this infinite grid model.

Multilingual Large Language Models (LLMs) have demonstrated exceptional performance in Machine Translation (MT) tasks. However, their MT abilities in the context of code-switching (the practice of mixing two or more languages in an utterance) remain under-explored. In this paper, we introduce Rule-Based Prompting, a novel prompting technique to generate code-mixed sentences. We measure and compare the code-mixed MT abilities of 3 popular multilingual LLMs: GPT-3.5-turbo, GPT-4, and Gemini Pro across five language pairs: English-{Hindi, Bengali, Gujarati, French, Spanish} using $k$-shot prompting ($k\in\{0, 1, 10, 20\}$) and Rule-Based Prompting. Our findings suggest that though $k$-shot prompting often leads to the best results, Rule-Based prompting shows promise in generating unique code-mixed sentences that vary in their style of code-mixing. We also use $k$-shot prompting to gauge the code-mixed to English translation abilities of multilingual LLMs. For this purpose, we create a gold-standard code-mixed dataset spanning five language pairs: English-{Hindi, Bengali, Gujarati, French, Spanish}. As a real-world application of our work, we create a code-mixed chatbot., Comment: Manuscript submitted to COLING 2025

Efficient multi-messenger observations of gravitational-wave candidates from compact binary coalescence candidate events rely on data products reported in low-latency by the International Gravitational-wave Network (IGWN). While data products such as $\texttt{HasNS}$, the probability of at least one neutron star, and $\texttt{HasRemnant}$, the probability of remnant matter forming after merger, exist, these are not direct observables for a potential kilonova. Here, we present new kilonova light curve and ejecta mass data products derived from merger quantities measured in low latency, by marginalizing over our uncertainty in our understanding of the neutron star equation of state and using measurements of the source properties of the merger, including masses and spins. Two additional types of data products are proposed. The first is the probability of a candidate event having mass ejecta ($m_{\mathrm{ej}}$) greater than $10^{-3} M_\odot$, which we denote as $\texttt{HasEjecta}$. The second are $m_{\mathrm{ej}}$ estimates and accompanying $\texttt{ugrizy}$ and $\texttt{HJK}$ kilonova light curves predictions produced from a surrogate model trained on a grid of kilonova light curves from $\texttt{POSSIS}$, a time-dependent, three-dimensional Monte Carlo radiative transfer code. We are developing these data products in the context of the IGWN low-latency alert infrastructure, and will be advocating for their use and release for future detections.

Code-mixing, the practice of alternating between two or more languages in an utterance, is a common phenomenon in multilingual communities. Due to the colloquial nature of code-mixing, there is no singular correct way to translate an English sentence into a code-mixed sentence. For this reason, standard n-gram-based MT evaluation metrics such as the BLEU score are not appropriate for code-mixed evaluation. To demonstrate this, we propose a novel method for code-mixed text generation: Controlled Generation, which parameterizes the code-mixing degree (CMD) and enables the generation of multiple semantically equivalent code-mixed sentences from a given English sentence. We introduce a robust new evaluation metric: GAME: A Gold-Standard Agnostic Measure for Evaluation of Code-Mixed Sentences. GAME is both language-agnostic and gold-standard-agnostic, i.e. unlike other metrics, GAME does not require gold-standard code-mixed sentences for evaluation, thus eliminating the need for human annotators in the code-mixed evaluation process. When used to evaluate semantically equivalent code-mixed sentences, we find that GAME scores have a lower standard deviation than BLEU scores. Further, we create and release a dataset containing gold-standard code-mixed sentences across 4 language pairs: English-{Hindi, Bengali, French, Spanish} to encourage more computational research on code-mixing., Comment: Manuscript submitted to COLING 2025

Businesses can benefit from customer feedback in different modalities, such as text and images, to enhance their products and services. However, it is difficult to extract actionable and relevant pairs of text segments and images from customer feedback in a single pass. In this paper, we propose a novel multi-modal method that fuses image and text information in a latent space and decodes it to extract the relevant feedback segments using an image-text grounded text decoder. We also introduce a weakly-supervised data generation technique that produces training data for this task. We evaluate our model on unseen data and demonstrate that it can effectively mine actionable insights from multi-modal customer feedback, outperforming the existing baselines by $14$ points in F1 score., Comment: NAACL 2024

We prove a sharp upper bound on the Hausdorff dimension of weighted singular vectors in $\mathbb{R}^m$ using dynamics on homogeneous spaces, specifically the method of integral inequalities. Together with the lower bound proved recently by Kim and Park \cite{KimPark2024}, this determines the Hausdorff dimension of weighted singular vectors, thereby generalizing to arbitrary dimension, the work of Liao, Shi, Solan, and Tamam \cite{LSST}, who determined the Hausdorff dimension of weighted singular vectors in two dimensions. We also provide the first known bounds for the Hausdorff dimension of weighted singular vectors restricted to fractal subsets., Comment: 19 Pages, Comments welcome

We present \llinstruct: An 8B instruction-tuned model that is designed to generate content for English Language Proficiency Assessments (ELPA) and related applications. Our work involves creating a new dataset of 70K instructions and explanations in the ELPA domain and using these to fine-tune Llama-3 8B models (SFT) of different sizes (e.g., SFT-17K, SFT-50K and SFT-70K). Human evaluations are conducted over unseen instructions to compare these SFT models against SOTA models (e.g., Dolly-2, Mistral, Llama-3 base version, and GPT-3.5). The findings show although all three SFT models perform comparably, the model trained on largest instruction dataset -- SFT-70K - leads to the most valid outputs ready for assessments. However, although the SFT models perform better than larger model, e.g., GPT 3.5 on the aspect of explanations of outputs, many outputs still need human interventions to make them actual ready for real world assessments.

The scarcity of quantum gravity (QG) inspired rotating black holes limits the progress of testing QG through observational data, such as that from the Event Horizon Telescope (EHT). Recently, the EHT imaged the supermassive black hole Sgr A*, revealing an angular shadow diameter of $d_{sh} = 48.7 \pm 7 \mu$as with a black hole mass of $M = 4.0_{-0.6}^{+1.1} \times 10^6 M\odot$. For M87*, with a mass of $M = (6.5 \pm 0.7) \times 10^9 M_\odot$, the EHT measured an angular diameter of $\theta_d = 42 \pm 3 \mu$as. These observations, consistent with Kerr black hole shadows in general relativity, provide opportunities to test gravity and quantum corrections. In this work, we present rotating quantum-corrected black hole (RQCBH) spacetimes with an additional QC parameter $\alpha$, examine its effect on the shadow properties, and constrain it by EHT observations of M87* and Sgr A*. The angular shadow diameter ($d_{sh}$ ) for Sgr A* at $\theta_o = 50^0$, the limits are $0.0 \leq \alpha \leq 1.443 M^2$ and $a \in (0, 0.8066 M)$. For $\theta_o = 90^0$, the limits are $0.0 \leq \alpha \leq 1.447 M^2$ and $a \in (0, 0.894 M)$. While for M87* at inclination $\theta_o = 17^0$, the bounds are $0.0 \leq \alpha \leq 0.8985 M^2$ and $a \in (0.6157M, 0.8511 M)$. For $\theta_o = 90^0$, the limits are $0.0 \leq \alpha \leq 0.4141 M^2$ and $a \in (0.8262M, 0.9799 M)$. These results demonstrate that $\alpha$ enormously impacts the shadow's size and shape, providing essential constraints on quantum gravity models. In conjunction with the EHT constraints on the Sgr A* and M87* observables, the RQCBHs and Kerr black holes are indiscernible in the substantial regions of the EHT-constrained parameter space., Comment: 22 Pages, 13 Figures and 9 Tables

We present a measurement of the branching fraction and time-dependent charge-parity ($CP$) decay-rate asymmetries in $B^0 \to J/\psi \pi^0$ decays. The data sample was collected with the Belle~II detector at the SuperKEKB asymmetric $e^+e^-$ collider in 2019-2022 and contains $(387\pm 6)\times 10^6$ $B\overline{B}$ meson pairs from $\Upsilon(4S)$ decays. We reconstruct $392\pm 24$ signal decays and fit the $CP$ parameters from the distribution of the proper-decay-time difference of the two $B$ mesons. We measure the branching fraction to be $B(B^0 \to J/\psi \pi^0)=(2.02 \pm 0.12 \pm 0.10)\times 10^{-5}$ and the direct and mixing-induced $CP$ asymmetries to be $C_{CP}=0.13 \pm 0.12 \pm 0.03$ and $S_{CP}=-0.88 \pm 0.17 \pm 0.03$, respectively, where the first uncertainties are statistical and the second are systematic. We observe mixing-induced $CP$ violation with a significance of $5.0$ standard deviations for the first time in this mode.

Machine learning (ML) models trained using Empirical Risk Minimization (ERM) often exhibit systematic errors on specific subpopulations of tabular data, known as error slices. Learning robust representation in presence of error slices is challenging, especially in self-supervised settings during the feature reconstruction phase, due to high cardinality features and the complexity of constructing error sets. Traditional robust representation learning methods are largely focused on improving worst group performance in supervised setting in computer vision, leaving a gap in approaches tailored for tabular data. We address this gap by developing a framework to learn robust representation in tabular data during self-supervised pre-training. Our approach utilizes an encoder-decoder model trained with Masked Language Modeling (MLM) loss to learn robust latent representations. This paper applies the Just Train Twice (JTT) and Deep Feature Reweighting (DFR) methods during the pre-training phase for tabular data. These methods fine-tune the ERM pre-trained model by up-weighting error-prone samples or creating balanced datasets for specific categorical features. This results in specialized models for each feature, which are then used in an ensemble approach to enhance downstream classification performance. This methodology improves robustness across slices, thus enhancing overall generalization performance. Extensive experiments across various datasets demonstrate the efficacy of our approach., Comment: TRL Workshop@NeurIPS2024

We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of sampled compositions and descriptors required to accurately predict fields like the electron density increases rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries and maintain approximately the same descriptor-vector size as alloy elements increase. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.

Hamiltonian dynamics describing conservative systems naturally preserves the standard notion of phase-space volume, a result known as the Liouville's theorem which is central to the formulation of classical statistical mechanics. In this paper, we obtain explicit expressions for invariant phase-space measures for certain dissipative systems, namely, systems described by conformal vector fields on symplectic manifolds that are cotangent bundles, contact Hamiltonian systems, and systems of the Li\'enard class with position-dependent damping. The latter class of systems can be described by certain generalized conformal vector fields on the cotangent bundle of the configuration space. The computation of the invariant measures is achieved by calculating the Jacobi last multiplier for the above-mentioned dissipative systems., Comment: v1: Preliminary version

With the pervasive deployment of Machine Learning (ML) models in real-world applications, verifying and auditing properties of ML models have become a central concern. In this work, we focus on three properties: robustness, individual fairness, and group fairness. We discuss two approaches for auditing ML model properties: estimation with and without reconstruction of the target model under audit. Though the first approach is studied in the literature, the second approach remains unexplored. For this purpose, we develop a new framework that quantifies different properties in terms of the Fourier coefficients of the ML model under audit but does not parametrically reconstruct it. We propose the Active Fourier Auditor (AFA), which queries sample points according to the Fourier coefficients of the ML model, and further estimates the properties. We derive high probability error bounds on AFA's estimates, along with the worst-case lower bounds on the sample complexity to audit them. Numerically we demonstrate on multiple datasets and models that AFA is more accurate and sample-efficient to estimate the properties of interest than the baselines.

We study symmetric bimatrix games that also have the common-payoff property, i.e., the two players receive the same payoff at any outcome of the game. Due to the symmetry property, these games are guaranteed to have symmetric Nash equilibria, where the two players play the same (mixed) strategy. While the problem of computing such symmetric equilibria in general symmetric bimatrix games is known to be intractable, namely PPAD-complete, this result does not extend to our setting. Indeed, due to the common-payoff property, the problem lies in the lower class CLS, ruling out PPAD-hardness. In this paper, we show that the problem remains intractable, namely it is CLS-complete. On the way to proving this result, as our main technical contribution, we show that computing a Karush-Kuhn-Tucker (KKT) point of a quadratic program remains CLS-hard, even when the feasible domain is a simplex., Comment: 13 pages, WINE'24

Along with the partition of a planar bounded domain $\Omega$ by the nodal set of a fixed eigenfunction of the Laplace operator in $\Omega$, one can consider another natural partition of $\Omega$ by, roughly speaking, gradient flow lines of a special type (separatrices) of this eigenfunction. Elements of such partition are called Neumann domains and their boundaries are Neumann lines. When the eigenfunction is a Morse function, this partition corresponds to the Morse--Smale complex and its fundamental properties have been systematically investigated by Band & Fajman (2016). Although, in the case of general position, eigenfunctions are always of the Morse type, particular eigenfunctions can possess degenerate critical points. In the present work, we propose a way to characterize Neumann domains and lines of an arbitrary eigenfunction. Instead of requiring the nondegeneracy of critical points of the eigenfunction, its real analyticity is principally used. The analyticity allows for the presence of degenerate critical points but significantly limits their possible diversity. Even so, the eigenfunction can possess curves of critical points, which have to belong naturally to the Neumann lines set, as well as critical points of a saddle-node type. We overview all possible types of degenerate critical points in the eigenfunction's critical set and provide a numerically based evidence that each of them can be observed for particular eigenfunctions. Alongside with [Band & Fajman, 2016], our approach is inspired by a little-known note of Weinberger that appeared back in 1963, where a part of the Neumann line set, under the name of "effectless cut", was explicitly introduced and studied for the first eigenfunctions in domains with nontrivial topology. In addition, we provide an asymptotic counting of Neumann domains for a disk and rectangles in analogy with the Pleijel constant., Comment: 46 pages, 11 figures

Decoupling charge and heat transport is essential for optimizing thermoelectric materials. Strategies to inhibit lattice-driven heat transport, however, also compromise carrier mobility, limiting the performance of most thermoelectrics, including Fe$_2$VAl Heusler compounds. Here, we demonstrate an innovative approach, which bypasses this tradeoff: via liquid-phase sintering, we incorporate the archetypal topological insulator Bi$_{1-x}$Sb$_{x}$ between Fe$_2$V$_{0.95}$Ta$_{0.1}$Al$_{0.95}$ grains. Structural investigations alongside extensive thermoelectric and magneto-transport measurements reveal distinct modifications in the microstructure, and a reduced lattice thermal conductivity and enhanced carrier mobility are simultaneously found. This yields a huge performance boost $-$ far beyond the effective-medium limit $-$ and results in one of the highest figure of merits among both half- and full-Heusler compounds, $z\approx 1.6\times 10^{-3}\,$K$^{-1}$ ($zT\approx 0.5$) at 295 K. Our findings highlight the potential of secondary phases to decouple charge and heat transport and call for more advanced theoretical studies of multiphase composites.

Event cameras are novel bio-inspired vision sensors that measure pixel-wise brightness changes asynchronously instead of images at a given frame rate. They offer promising advantages, namely a high dynamic range, low latency, and minimal motion blur. Modern computer vision algorithms often rely on artificial neural network approaches, which require image-like representations of the data and cannot fully exploit the characteristics of event data. We propose approaches to action recognition based on the Fourier Transform. The approaches are intended to recognize oscillating motion patterns commonly present in nature. In particular, we apply our approaches to a recent dataset of breeding penguins annotated for "ecstatic display", a behavior where the observed penguins flap their wings at a certain frequency. We find that our approaches are both simple and effective, producing slightly lower results than a deep neural network (DNN) while relying just on a tiny fraction of the parameters compared to the DNN (five orders of magnitude fewer parameters). They work well despite the uncontrolled, diverse data present in the dataset. We hope this work opens a new perspective on event-based processing and action recognition., Comment: 11 pages, 10 figures, 7 tables

We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance on various multimodal benchmarks, surpassing a number of larger models. Unlike many open-source models, Pixtral is also a cutting-edge text model for its size, and does not compromise on natural language performance to excel in multimodal tasks. Pixtral uses a new vision encoder trained from scratch, which allows it to ingest images at their natural resolution and aspect ratio. This gives users flexibility on the number of tokens used to process an image. Pixtral is also able to process any number of images in its long context window of 128K tokens. Pixtral 12B substanially outperforms other open models of similar sizes (Llama-3.2 11B \& Qwen-2-VL 7B). It also outperforms much larger open models like Llama-3.2 90B while being 7x smaller. We further contribute an open-source benchmark, MM-MT-Bench, for evaluating vision-language models in practical scenarios, and provide detailed analysis and code for standardized evaluation protocols for multimodal LLMs. Pixtral-12B is released under Apache 2.0 license.

In this article, we study the properties of charged gravastars in torsion-based $f(\mathbb{T})$ gravity in the presence of noncommutative geometry. We have taken the interior from noncommutative motivated space-time, noting that why, from a physical point of view, such a choice is justified, then we have taken the thin shell as stiff matter and taken three different exterior metrics (Reissner-Nordstrom (R-N), Bardeen and Ayon-Beato-Garcia (ABG) metric) to construct the gravastar model. We have studied the physical properties like proper length, entropy, energy, and EoS for these models, and we have also used Israel junction conditions to study the effective pressure, energy density, and potential of the thin shell. Finally, we comment on the stability of such a thin shell and the deflection angle caused by such a thin shell, which could, in principle, be tested by future radio telescopes like the Event Horizon Telescope (EHT)., Comment: Physics of the Dark Universe accepted version

The rapid expansion of records creates significant challenges in management, including retention and disposition, appraisal, and organization. Our study underscores the benefits of integrating artificial intelligence (AI) within the broad realm of archival science. In this work, we start by performing a thorough analysis to understand the current use of AI in this area and identify the techniques employed to address challenges. Subsequently, we document the results of our review according to specific criteria. Our findings highlight key AI driven strategies that promise to streamline record-keeping processes and enhance data retrieval efficiency. We also demonstrate our review process to ensure transparency regarding our methodology. Furthermore, this review not only outlines the current state of AI in archival science and records management but also lays the groundwork for integrating new techniques to transform archival practices. Our research emphasizes the necessity for enhanced collaboration between the disciplines of artificial intelligence and archival science.

Testing whether a sample survey is a credible representation of the population is an important question to ensure the validity of any downstream research. While this problem, in general, does not have an efficient solution, one might take a task-based approach and aim to understand whether a certain data analysis tool, like linear regression, would yield similar answers both on the population and the sample survey. In this paper, we design an algorithm to test the credibility of a sample survey in terms of linear regression. In other words, we design an algorithm that can certify if a sample survey is good enough to guarantee the correctness of data analysis done using linear regression tools. Nowadays, one is naturally concerned about data privacy in surveys. Thus, we further test the credibility of surveys published in a differentially private manner. Specifically, we focus on Local Differential Privacy (LDP), which is a standard technique to ensure privacy in surveys where the survey participants might not trust the aggregator. We extend our algorithm to work even when the data analysis has been done using surveys with LDP. In the process, we also propose an algorithm that learns with high probability the guarantees a linear regression model on a survey published with LDP. Our algorithm also serves as a mechanism to learn linear regression models from data corrupted with noise coming from any subexponential distribution. We prove that it achieves the optimal estimation error bound for $\ell_1$ linear regression, which might be of broader interest. We prove the theoretical correctness of our algorithms while trying to reduce the sample complexity for both public and private surveys. We also numerically demonstrate the performance of our algorithms on real and synthetic datasets.

The EHT observation revealed event horizon-scale images of the supermassive black holes Sgr A* and M87* and these results are consistent with the shadow of a Kerr black hole as predicted by general relativity. However, Kerr-like rotating black holes in modified gravity theories can not ruled out, as they provide a crucial testing ground for these theories through EHT observations. It motivates us to investigate the Bumblebee theory, a vector-tensor extension of the Einstein-Maxwell theory that permits spontaneous symmetry breaking, resulting in the field acquiring a vacuum expectation value and introducing Lorentz violation. We present rotating black holes within this bumblebee gravity model, which includes an additional parameter $\ell$ alongside the mass $M$ and spin parameter $a$ - namely RBHBG. Unlike the Kerr black hole, an extremal RBHBG, for $\ell<0$, refers to a black hole with angular momentum $a>M$. We derive an analytical formula necessary for the shadow of our rotating black holes, then visualize them with varying parameters $a$ and $\ell$, and also estimate the black hole parameters using shadow observables viz. shadow radius $R_s$, distortion $\delta_s$, shadow area $A$ and oblateness $D$ using two well-known techniques. We find that $\ell$ incrementally increases the shadow size and causes more significant deformation while decreasing the event horizon area. Remarkably, an increase in $\ell$ enlarges the shadow radius irrespective of spin or inclination angle $\theta_0$., Comment: 17 Pages, 13 Figures and 6 Tables

We describe a symplectic approach to thermodynamics in which thermodynamic transformations are described by Hamiltonian dynamics on thermodynamic spaces. By identifying the spaces of equilibrium states with Lagrangian submanifolds of a symplectic manifold, we construct a Hamiltonian description of thermodynamic processes where the space of equilibrium states of a system in a certain ensemble is the level set on which the Hamiltonian takes a constant value. In particular, we work out two explicit examples involving the ideal gas. Finally, we describe a Hamiltonian approach towards constructing maps between related thermodynamic systems, e.g., the ideal (non-interacting) gas and interacting gases., Comment: v1: Preliminary version

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

We introduce a construction for entanglement-assisted quantum error-correcting codes (EAQECCs) that saturates the classical Singleton bound with less shared entanglement than any known method for code rates below $ \frac{k}{n} = \frac{1}{3} $. For higher rates, our EAQECC also meets the Singleton bound, although with increased entanglement requirements. Additionally, we demonstrate that any classical $[n,k,d]_q$ code can be transformed into an EAQECC with parameters $[[n,k,d;2k]]_q$ using $2k$ pre-shared maximally entangled pairs. The complexity of our encoding protocol for $k$-qudits with $q$ levels is $\mathcal{O}(k \log_{\frac{q}{q-1}}(k))$, excluding the complexity of encoding and decoding the classical MDS code. While this complexity remains linear in $k$ for systems of reasonable size, it increases significantly for larger-levelled systems, highlighting the need for further research into complexity reduction., Comment: There was an error in the paper, namely, the application of the non-unitary quantum instrument breaks the entanglement upon measurement failure, there the protocol is wrong. The corrected version will be updated once it is finished

Compound Finance is a decentralized lending protocol that enables the secure and efficient borrowing and lending of cryptocurrencies, utilizing smart contracts and dynamic interest rates based on supply and demand to facilitate transactions. The protocol enables users to supply different crypto assets and accrue interest, while borrowers can avail themselves of loans secured by collateralized assets. Our collaboration with Compound Finance focuses on harnessing the power of the Chainrisk simulation engine to optimize risk parameters of the Compound V3 (Comet) protocol. This report delineates a comprehensive methodology aimed at calculating key risk metrics of the protocol. This optimization framework is pivotal for mitigating systemic risks and enhancing the overall stability of the protocol. By leveraging Chainrisk's Cloud Platform, we conduct millions of simulations to evaluate the protocol's Value at Risk (VaR) and Liquidations at Risk (LaR), ultimately providing recommendations for parameter adjustments.