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Autonomous agent applications powered by large language models (LLMs) have recently risen to prominence as effective tools for addressing complex real-world tasks. At their core, agentic workflows rely on LLMs to plan and execute the use of tools and external Application Programming Interfaces (APIs) in sequence to arrive at the answer to a user's request. Various benchmarks and leaderboards have emerged to evaluate an LLM's capabilities for tool and API use; however, most of these evaluations only track single or multiple isolated API calling capabilities. In this paper, we present NESTFUL, a benchmark to evaluate LLMs on nested sequences of API calls, i.e., sequences where the output of one API call is passed as input to a subsequent call. NESTFUL has a total of 300 human annotated samples divided into two types - executable and non-executable. The executable samples are curated manually by crawling Rapid-APIs whereas the non-executable samples are hand picked by human annotators from data synthetically generated using an LLM. We evaluate state-of-the-art LLMs with function calling abilities on NESTFUL. Our results show that most models do not perform well on nested APIs in NESTFUL as compared to their performance on the simpler problem settings available in existing benchmarks.

This study explores global citation diversity,examining its various patterns across countries and academic disciplines.We analyzed citation distributions in top institutes worldwide,revealing that the higher citation end of the distribution follow Power law or Pareto law pattern and the Pareto law's scaling exponent changes with the number of institutes considered.An entropy based novel citation inequality measure has been introduced, enhancing the precision of our analysis. Our findings show that countries with small and large economies often group similarly based on citation diversity, with shifting the groupings as the number of institutes considered changes.Moreover,we analyzed citation diversity among award-winning scientists across six scientific disciplines,finding significant variations.We also explored the evolution of citation diversity over the past century across multiple fields.A gender-based study in various disciplines highlights citation inequalities among male and female scientists.Our innovative citation diversity measure stands out as a vital tool for evaluating citation inequality,providing insights beyond what traditional citation counts can offer.This thorough analysis deepens our understanding of global scientific contributions and promotes a more equitable view of academic accomplishments., Comment: 21 pages, 18 figures, 13 tables

This work introduces ECOLIFE, the first carbon-aware serverless function scheduler to co-optimize carbon footprint and performance. ECOLIFE builds on the key insight of intelligently exploiting multi-generation hardware to achieve high performance and lower carbon footprint. ECOLIFE designs multiple novel extensions to Particle Swarm Optimization (PSO) in the context of serverless execution environment to achieve high performance while effectively reducing the carbon footprint.

Measurements of B-mode polarization in the CMB sourced from primordial gravitational waves would provide information on the energy scale of inflation and its potential form. To achieve these goals, one must carefully characterize the Galactic foregrounds, which can be distinguished from the CMB by conducting measurements at multiple frequencies. BICEP Array is the latest-generation multi-frequency instrument of the BICEP/Keck program, which specifically targets degree-scale primordial B-modes in the CMB. In its final configuration, this telescope will consist of four small-aperture receivers, spanning frequency bands from 30 to 270 GHz. The 220/270 GHz receiver designed to characterize Galactic dust is currently undergoing commissioning at Stanford University and is scheduled to deploy to the South Pole during the 2024--2025 austral summer. Here, we will provide an overview of this high-frequency receiver and discuss the integration status and test results as it is being commissioned.

In this paper, we deal with non-Baire rare sets in category bases which forms $\aleph_0$-independent family, where a rare set is a common generalization of both Luzin and Sierpinski set.

Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning has excelled in automating this task, a major hurdle is the need for numerous annotated segmentation masks, which are resource-intensive to produce due to the required expertise and time. This scenario often leads to ultra low-data regimes, where annotated images are extremely limited, posing significant challenges for the generalization of conventional deep learning methods on test images. To address this, we introduce a generative deep learning framework, which uniquely generates high-quality paired segmentation masks and medical images, serving as auxiliary data for training robust models in data-scarce environments. Unlike traditional generative models that treat data generation and segmentation model training as separate processes, our method employs multi-level optimization for end-to-end data generation. This approach allows segmentation performance to directly influence the data generation process, ensuring that the generated data is specifically tailored to enhance the performance of the segmentation model. Our method demonstrated strong generalization performance across 9 diverse medical image segmentation tasks and on 16 datasets, in ultra-low data regimes, spanning various diseases, organs, and imaging modalities. When applied to various segmentation models, it achieved performance improvements of 10-20\% (absolute), in both same-domain and out-of-domain scenarios. Notably, it requires 8 to 20 times less training data than existing methods to achieve comparable results. This advancement significantly improves the feasibility and cost-effectiveness of applying deep learning in medical imaging, particularly in scenarios with limited data availability.

The variable inclusion companions of logics have lately been thoroughly studied by multiple authors. There are broadly two types of these companions: the left and the right variable inclusion companions. Another type of companions of logics induced by Hilbert-style presentations (Hilbert-style logics) were introduced in a recent paper. A sufficient condition for the restricted rules companion of a Hilbert-style logic to coincide with its left variable inclusion companion was proved there, while a necessary condition remained elusive. The present article has two parts. In the first part, we give a necessary and sufficient condition for the left variable inclusion and the restricted rules companions of a Hilbert-style logic to coincide. In the rest of the paper, we recognize that the variable inclusion restrictions used to define variable inclusion companions of a logic $\langle\mathcal{L},\vdash\rangle$ are relations from $\mathcal{P}(\mathcal{L})$ to $\mathcal{L}$. This leads to a more general idea of a relational companion of a logical structure, a framework that we borrow from the field of universal logic. We end by showing that even Hilbert-style logics and the restricted rules companions of these can be brought under the umbrella of the general notions of logical structures and their relational companions that are discussed here., Comment: A version of the article has been submitted to the Indian Conference on Logic and its Applications 2025

Emulsion fuels have the potential to reduce both particulate matter and NOx emissions and can potentially improve the efficiency of combustion engines. However, their limited stability remains a critical barrier to practical use as an alternative fuel. In this study, we explore the evaporation behavior of thermodynamically stable water-in-oil microemulsions. The water-in-oil microemulsion droplets prepared from different types of oil were acoustically levitated and heated using a continuous laser at different irradiation intensities. We show that the evaporation characteristics of these microemulsions can be controlled by varying water-to-surfactant molar ratio ({\omega}) and volume fraction of the dispersed phase ({\phi}). The emulsion droplets undergo three distinct stages of evaporation, namely pre-heating, steady evaporation, and unsteady evaporation. During the steady evaporation phase, increasing {\phi} reduces the evaporation rate for a fixed {\omega}. It is observed that the evaporation of microemulsion is governed by the complex interplay between its constituents and their properties. We propose a parameter ({\eta}) denoting the volume fraction ratio between volatile and non-volatile components, which indicates the cumulative influence of various factors affecting the evaporation process. The evaporation of microemulsions eventually leads to the formation of solid spherical shells, which may undergo buckling. The distinction in the morphology of these shells is explored in detail using SEM imaging., Comment: 42 pages, 11 figures

Modern ML systems increasingly augment input instances with additional relevant information to enhance final prediction. Despite growing interest in such retrieval-augmented models, their fundamental properties and training are not well understood. We propose a statistical framework to study such models with two components: 1) a {\em retriever} to identify the relevant information out of a large corpus via a data-dependent metric; and 2) a {\em predictor} that consumes the input instances along with the retrieved information to make the final predictions. We present a principled method for end-to-end training of both components and draw connections with various training approaches in the literature. Furthermore, we establish excess risk bounds for retrieval-augmented models while delineating the contributions of both retriever and predictor towards the model performance. We validate the utility of our proposed training methods along with the key takeaways from our statistical analysis on open domain question answering task where retrieval augmentation is important.

New tridimensional plasma structures, that are oscillatory and classified as non-separable ballooning modes, can emerge in inhomogeneous plasmas and undergo resonant mode-particle interactions, e.g., with a minority population, that can lead them to modify their spatial profiles. Thus, unlike the case of previously known ballooning modes their amplitudes are not separable functions of time and space. The relevant resonance conditions are intrinsically different from those of the well-known Landau conditions for (ordinary) plasma waves: they involve the mode geometry and affect different regions of the distribution in momentum space at different positions in configuration space. A process for a transfer of energy among different particle populations is envisioned, Comment: 16 pages, 1 figure

State-of-the-art semantic segmentation models are typically optimized in a data-driven fashion, minimizing solely per-pixel classification objectives on their training data. This purely data-driven paradigm often leads to absurd segmentations, especially when the domain of input images is shifted from the one encountered during training. For instance, state-of-the-art models may assign the label ``road'' to a segment which is located above a segment that is respectively labeled as ``sky'', although our knowledge of the physical world dictates that such a configuration is not feasible for images captured by forward-facing upright cameras. Our method, Physically Feasible Semantic Segmentation (PhyFea), extracts explicit physical constraints that govern spatial class relations from the training sets of semantic segmentation datasets and enforces a differentiable loss function that penalizes violations of these constraints to promote prediction feasibility. PhyFea yields significant performance improvements in mIoU over each state-of-the-art network we use as baseline across ADE20K, Cityscapes and ACDC, notably a $1.5\%$ improvement on ADE20K and a $2.1\%$ improvement on ACDC.

We theoretically propose the occurrence of a quantum spin Hall (QSH) and a second order topological phase transition (TPT) driven by electron-phonon (e-p) coupling in a pseudospin-$1$ fermionic system on an $\alpha$-$T_3$ lattice. Our model is formulated in the spirit of the Kane-Mele model modified by the Holstein Hamiltonian. The Lang-Firsov approach is employed to describe polarons reasonably well in the anti-adiabatic (high frequency) limit and to obtain an effective electronic Hamiltonian. It is shown that the system possesses topologically nontrivial phases up to a critical e-p coupling, $\lambda_c$ and are characterized by the helical QSH edge states along with a non-zero $\mathbb{Z}_2$ invariant for a certain range of $\alpha$. The topological phase vanishes beyond $\lambda_c$ and is accompanied by a bulk gap closing transition at $\lambda_c$, manifesting a TPT. We observe a more intriguing phenomenon for higher values of $\alpha$, where the system exhibits TPTs supported by two distinct gap closing transitions at $\lambda_{c_1}$ and $\lambda_{c_2}$, while a slim region at slightly lower values hosts a semi-metallic signature below $\lambda_{c_1}$. Subsequently, to explore more intricate features, we introduce a time reversal symmetry breaking magnetic field to trigger the formation of a second order topological phase. The magnetic field, by construction causes a boundary dependent gapping out of the edge states, consequently giving rise to robust corner modes in a tailored open boundary conditions. We justify the formation of the higher order phase by employing an appropriate invariant, namely the projected spin Chern number. Finally, we show that the e-p coupling significantly influences the corner modes (and also the real space energy bandstructure), corroborating a higher order TPT as we tune $\lambda$ beyond a critical value for a given value of $\alpha$., Comment: 18 pages, 18 figures. Comments are welcome

In this article, we try to formulate a definition of ''many-valued logical structure''. For this, we embark on a deeper study of Suszko's Thesis ($\mathbf{ST}$) and show that the truth or falsity of $\mathbf{ST}$ depends, at least, on the precise notion of semantics. We propose two different notions of semantics and three different notions of entailment. The first one helps us formulate a precise definition of inferentially many-valued logical structures. The second and the third help us to generalise Suszko Reduction and provide adequate bivalent semantics for monotonic and a couple of nonmonotonic logical structures. All these lead us to a closer examination of the played by language/metalanguage hierarchy vis-\'a-vis $\mathbf{ST}$. We conclude that many-valued logical structures can be obtained if the bivalence of all the higher-order metalogics of the logic under consideration is discarded, building formal bridges between the theory of graded consequence and the theory of many-valued logical structures, culminating in generalisations of Suszko's Thesis., Comment: 46 pages, no figure

Magnetic field configurations extending over macroscopic scale distances are shown to be generated in rarefied collisionless plasmas when non-thermal and spatially inhomogeneous electron distributions in phase space emerge. The analyzed representative case is where, in the presence of a significant spatial gradient of the electron pressure and of a sheared magnetic field configuration, an alternating magnetic field reconnection process can develop associated with the anisotropy of the fluctuating electron temperature. The relevant process is intrinsically different from that known as the Biermann Battery which does not involve magnetic reconnection and requires that the unperturbed spatial gradient of the (isotropic) electron temperature be misaligned relative to that of the density gradient., Comment: 17 pages

This study explores the concept of cross-disease transferability (XDT) in medical imaging, focusing on the potential of binary classifiers trained on one disease to perform zero-shot classification on another disease affecting the same organ. Utilizing chest X-rays (CXR) as the primary modality, we investigate whether a model trained on one pulmonary disease can make predictions about another novel pulmonary disease, a scenario with significant implications for medical settings with limited data on emerging diseases. The XDT framework leverages the embedding space of a vision encoder, which, through kernel transformation, aids in distinguishing between diseased and non-diseased classes in the latent space. This capability is especially beneficial in resource-limited environments or in regions with low prevalence of certain diseases, where conventional diagnostic practices may fail. However, the XDT framework is currently limited to binary classification, determining only the presence or absence of a disease rather than differentiating among multiple diseases. This limitation underscores the supplementary role of XDT to traditional diagnostic tests in clinical settings. Furthermore, results show that XDT-CXR as a framework is able to make better predictions compared to other zero-shot learning (ZSL) baselines., Comment: Accepted in Machine Learning for Healthcare Conference MLHC 2024

The polarization position angles (PPA) of time samples with high linear polarization often show two parallel tracks across the pulsar profile that follow the rotating vector model (RVM). This feature support coherent curvature radiation (CCR) as the underlying mechanism of radio emission from pulsars, where the parallel tracks of the PPA represent the orthogonal extraordinary X and ordinary O eigen modes of strongly magnetized pair plasma. However, the frequency evolution of these high linearly polarized signals remains unexplored. In this work we explore the flux density spectral nature of high linearly polarized signals by studying the emission from PSR J0332+5434 over a frequency range between 300 MHz and 750 MHz, using the Giant Metrewave Radio Telescope. The pulsar average profile comprises of a central core and a pair of conal components. We find the high linearly polarized time samples to be broadband in nature and in many cases they resemble a narrow spiky feature in the conal regions. These spiky features are localised within a narrow pulse longitude, over the entire frequency range, and their spectral shapes sometimes resemble an inverted parabolic shape. In all such cases the PPA are exclusively along one of the orthogonal RVM tracks, likely corresponding to the X-mode. The inverted spectral shape can in principle be explained if the high linearly polarized emission in these time samples are formed due to incoherent addition of CCR from a large number of charged solitons (charge bunches) exciting the X-mode., Comment: Accepted for publication in ApJ

Quantum few-body systems are deceptively simple. Indeed, with the notable exception of a few special cases, their associated Schrodinger equation cannot be solved analytically for more than two particles. One has to resort to approximation methods to tackle quantum few-body problems. In particular, variational methods have been proposed to ease numerical calculations and obtain precise solutions. One such method is the Stochastic Variational Method, which employs a stochastic search to determine the number and parameters of correlated Gaussian basis functions used to construct an ansatz of the wave function. Stochastic methods, however, face numerical and optimization challenges as the number of particles increases. We introduce a family of gradient variational methods that replace stochastic search with gradient optimization. We comparatively and empirically evaluate the performance of the baseline Stochastic Variational Method, several instances of the gradient variational method family, and some hybrid methods for selected few-body problems. We show that gradient and hybrid methods can be more efficient and effective than the Stochastic Variational Method. We discuss the role of singularities, oscillations, and gradient optimization strategies in the performance of the respective methods.

The solar-type subgiant $\beta$ Hyi has long been studied as an old analog of the Sun. Although the rotation period has never been measured directly, it was estimated to be near 27 days. As a southern hemisphere target it was not monitored by long-term stellar activity surveys, but archival International Ultraviolet Explorer data revealed a 12 year activity cycle. Previous ground-based asteroseismology suggested that the star is slightly more massive and substantially larger and older than the Sun, so the similarity of both the rotation rate and the activity cycle period to solar values is perplexing. We use two months of precise time-series photometry from the Transiting Exoplanet Survey Satellite (TESS) to detect solar-like oscillations in $\beta$ Hyi and determine the fundamental stellar properties from asteroseismic modeling. We also obtain a direct measurement of the rotation period, which was previously estimated from an ultraviolet activity-rotation relation. We then use rotational evolution modeling to predict the rotation period expected from either standard spin-down or weakened magnetic braking (WMB). We conclude that the rotation period of $\beta$ Hyi is consistent with WMB, and that changes in stellar structure on the subgiant branch can reinvigorate the large-scale dynamo and briefly sustain magnetic activity cycles. Our results support the existence of a "born-again" dynamo in evolved subgiants -- previously suggested to explain the cycle in 94 Aqr Aa -- which can best be understood within the WMB scenario., Comment: ApJ accepted. 8 pages including 6 figures and 2 tables

In this work, we characterise a general set of quantum operations which possess a singular fixed point. These channels are dubbed as quantum ergodic channels. We have constructed Lindblad-type master equations for such channels for arbitrary dimensions. We also characterise the aspect of non-Markovianity for such a general class of channels. Interestingly, if the fixed point is considered a passive state, it turns out to be an ergotropy-decreasing channel at a given instant. The formalism of ergotropy is widely used in quantum batteries, where it quantifies how effectively a quantum battery can be used as an energy source under unitary evolution only. Our investigation explores how the usefulness of a quantum battery, or the ergotropy of a system, varies under ergodic dynamics. We observed that, while ergotropy always decreases in a Markovian process, it fluctuates under non-Markovian ergodic dynamics. This ergotropy backflow in the non-Markovian process makes it a potential candidate for measuring non-Markovianity. Consequently, this study enhances our understanding of quantum battery evolution in open dynamics and paves the way for more efficient quantum technology., Comment: 11 pages , 3 figures

Submodular optimization finds applications in machine learning and data mining. In this paper, we study the problem of maximizing functions of the form $h = f-c$, where $f$ is a monotone, non-negative, weakly submodular set function and $c$ is a modular function. We design a deterministic approximation algorithm that runs with ${{O}}(\frac{n}{\epsilon}\log \frac{n}{\gamma \epsilon})$ oracle calls to function $h$, and outputs a set ${S}$ such that $h({S}) \geq \gamma(1-\epsilon)f(OPT)-c(OPT)-\frac{c(OPT)}{\gamma(1-\epsilon)}\log\frac{f(OPT)}{c(OPT)}$, where $\gamma$ is the submodularity ratio of $f$. Existing algorithms for this problem either admit a worse approximation ratio or have quadratic runtime. We also present an approximation ratio of our algorithm for this problem with an approximate oracle of $f$. We validate our theoretical results through extensive empirical evaluations on real-world applications, including vertex cover and influence diffusion problems for submodular utility function $f$, and Bayesian A-Optimal design for weakly submodular $f$. Our experimental results demonstrate that our algorithms efficiently achieve high-quality solutions., Comment: To appear in CIKM'24. Full paper including omitted proofs

The morphology and the characteristic scale of polarized structures provide crucial insights into the mechanisms that drives turbulence and maintains magnetic fields in magneto-ionic plasma. We aim to establish the efficacy of Minkowski functionals as quantitative statistical probes of filamentary morphology of polarized synchrotron emission resulting from fluctuation dynamo action. Using synthetic observations generated from magnetohydrodynamic simulations of fluctuation dynamos with varying driving scales ($\ell_{\rm f}$) of turbulence in isothermal, incompressible, and subsonic media, we study the relation between different morphological measures, and their connection to fractional polarization ($p_{\rm f}$). We find that Faraday depolarization at low frequencies give rise to small-scale polarized structures that have higher filamentarity as compared to the intrinsic structures that are comparable to $\ell_{\rm f}$. Above $\sim3\,{\rm GHz}$, the number of connected polarized structures per unit area ($N_{\rm CC, peak}$) is related to the mean $p_{\rm f}$ ($\langle p_{\rm f} \rangle$) of the emitting region as $\langle p_{\rm f} \rangle \propto N_{\rm CC, peak}^{-1/4}$, provided the scale of the detectable emitting region is larger than $\ell_{\rm f}$. This implies that $N_{\rm CC,peak}$ represents the number of turbulent cells projected on the plane of the sky and can be directly used to infer $\ell_{\rm f}$ via the relation $\ell_{\rm f} \propto N_{\rm CC,peak}^{-1/2}$. An estimate on $\ell_{\rm f}$ thus directly allows for pinning down the turbulence driving mechanism in astrophysical systems. While the simulated conditions are mostly prevalent in the intracluster medium of galaxy clusters, the qualitative morphological features are also applicable in the context of interstellar medium in galaxies., Comment: Version resubmitted to ApJ after addressing comments of the referee

A phenomenological mass-energy dependent proton optical model potential has been computed for p-nuclei. The parameters of the Wood-Saxon optical potential are found to be a good fit for proton elastic scattering data involving p-nuclei and elements with mass numbers near p-nuclei (within the range of 74 < A < 148) at energies around the Coulomb barrier of the system. The elastic scattering data were meticulously fitted using the SFRESCO code, allowing for the calculation of the real and imaginary parts of the Wood Saxon optical potential. To validate the model, experimental proton capture cross-sections for 106Cd and 113In near the Coulomb barrier were compared with results obtained using the TALYS-1.96 code, showing better agreement than the available global proton optical model potential.

Distribution learning finds probability density functions from a set of data samples, whereas clustering aims to group similar data points to form clusters. Although there are deep clustering methods that employ distribution learning methods, past work still lacks theoretical analysis regarding the relationship between clustering and distribution learning. Thus, in this work, we provide a theoretical analysis to guide the optimization of clustering via distribution learning. To achieve better results, we embed deep clustering guided by a theoretical analysis. Furthermore, the distribution learning method cannot always be directly applied to data. To overcome this issue, we introduce a clustering-oriented distribution learning method called Monte-Carlo Marginalization for Clustering. We integrate Monte-Carlo Marginalization for Clustering into Deep Clustering, resulting in Deep Clustering via Distribution Learning (DCDL). Eventually, the proposed DCDL achieves promising results compared to state-of-the-art methods on popular datasets. Considering a clustering task, the new distribution learning method outperforms previous methods as well.

In recent years, talking head generation has become a focal point for researchers. Considerable effort is being made to refine lip-sync motion, capture expressive facial expressions, generate natural head poses, and achieve high video quality. However, no single model has yet achieved equivalence across all these metrics. This paper aims to animate a 3D face using Jamba, a hybrid Transformers-Mamba model. Mamba, a pioneering Structured State Space Model (SSM) architecture, was designed to address the constraints of the conventional Transformer architecture. Nevertheless, it has several drawbacks. Jamba merges the advantages of both Transformer and Mamba approaches, providing a holistic solution. Based on the foundational Jamba block, we present JambaTalk to enhance motion variety and speed through multimodal integration. Extensive experiments reveal that our method achieves performance comparable or superior to state-of-the-art models., Comment: 12 pages with 3 figures

We address how the interplay between the finite availability and carrying capacity of particles at different parts of a spatially extended system can control the steady state currents and density profiles in the one-dimensional current-carrying lanes connecting the different parts of the system. To study this, we set up a minimal model consisting of two particle reservoirs of the same finite carrying capacity connected by two equally sized anti-parallel asymmetric exclusion processes (TASEP). We focus on the steady-state currents and particle density profiles in the two TASEP lanes. The ensuing phases and the phase diagrams, which can be remarkably complex, are parametrized by the model parameters defining particle exchange between the TASEP lanes and the reservoirs and the filling fraction of the particles that determine the total resources available. These parameters may be tuned to make the densities of the two TASEP lanes globally uniform or piece-wise continuous in the form of a combination of a single localized domain wall and a spatially constant density or a pair of delocalized domain walls. Our model reveals that the two reservoirs can be preferentially populated or depopulated in the steady states., Comment: 18 pages, 4 figures

The universal relationships for compact stars have been investigated employing perturbative approach using canonical (APR) and Brussels-Montreal Skyrme (BSk22, BSk24, BSk26) equations of state describing hadronic matter of neutron stars. The neutron star matter has been considered to be $\beta$-equilibrated neutron-proton-electron-muon matter at the core with a rigid crust. The multipole moments of a slowly rotating neutron star characterize its external gravitational field. These variables are dependent on the interior structure of the neutron star described by the equation of state of the neutron star matter. The properties of neutron stars, such as the mass, the radius, the dimensionless moment of inertia, the compactness, the Love number, the dimensionless tidal deformability and the dimensionless quadrupole moment have been calculated and relations among these quantities have been explored. It is found that most of these relations do not depend sensitively on the details of the internal structure of neutron stars. Such universality implies that the measurement of a single quantity appearing in a universal relation would automatically provide information about the others, notwithstanding the fact that those may not be accessible observationally. These can be used to estimate deformability of compact stars through moment of inertia measurements, to quantify spin in binary inspirals by breaking degeneracies in the detection of gravitational waves and test General Relativity in a way that is independent of nuclear structure., Comment: 12 pages including 21 figures and 1 table

This study introduces OTCliM (Optical Turbulence Climatology using Machine learning), a novel approach for deriving comprehensive climatologies of atmospheric optical turbulence strength ($C_n^2$) using gradient boosting machines. OTCliM addresses the challenge of efficiently obtaining reliable site-specific $C_n^2$ climatologies, crucial for ground-based astronomy and free-space optical communication. Using gradient boosting machines and global reanalysis data, OTCliM extrapolates one year of measured $C_n^2$ into a multi-year time series. We assess OTCliM's performance using $C_n^2$ data from 17 diverse stations in New York State, evaluating temporal extrapolation capabilities and geographical generalization. Our results demonstrate accurate predictions of four held-out years of $C_n^2$ across various sites, including complex urban environments, outperforming traditional analytical models. Non-urban models also show good geographical generalization compared to urban models, which captured non-general site-specific dependencies. A feature importance analysis confirms the physical consistency of the trained models. It also indicates the potential to uncover new insights into the physical processes governing $C_n^2$ from data. OTCliM's ability to derive reliable $C_n^2$ climatologies from just one year of observations can potentially reduce resources required for future site surveys or enable studies for additional sites with the same resources.

Asteroseismology of dwarf stars cooler than the Sun is very challenging due to the low amplitudes and rapid timescales of oscillations. Here, we present the asteroseismic detection of solar-like oscillations at 4-minute timescales ($\nu_{\mathrm{max}}\sim4300\mu$Hz) in the nearby K-dwarf $\sigma$ Draconis using extreme precision Doppler velocity observations from the Keck Planet Finder and 20-second cadence photometry from NASA's Transiting Exoplanet Survey Satellite. The star is the coolest dwarf star to date with both velocity and luminosity observations of solar-like oscillations, having amplitudes of $5.9\pm0.8\,$cm$\,\text{s}^{-1}$ and $0.8\pm0.2$ ppm, respectively. These measured values are in excellent agreement with established luminosity-velocity amplitude relations for oscillations and provide further evidence that mode amplitudes for stars with $T_{\mathrm{eff}}<\,5500\,$K diminish in scale following a $(L/M)^{1.5}$ relation. By modeling the star's oscillation frequencies from photometric data, we measure an asteroseismic age of $4.5\pm0.9\,\rm{(ran)} \pm 1.2\,\rm{(sys)}$ Gyr. The observations demonstrate the capability of next-generation spectrographs and precise space-based photometry to extend observational asteroseismology to nearby cool dwarfs, which are benchmarks for stellar astrophysics and prime targets for directly imaging planets using future space-based telescopes., Comment: Accepted for publication in The Astrophysical Journal

The development of large language models (LLMs), such as GPT, has enabled the construction of several socialbots, like ChatGPT, that are receiving a lot of attention for their ability to simulate a human conversation. However, the conversation is not guided by a goal and is hard to control. In addition, because LLMs rely more on pattern recognition than deductive reasoning, they can give confusing answers and have difficulty integrating multiple topics into a cohesive response. These limitations often lead the LLM to deviate from the main topic to keep the conversation interesting. We propose AutoCompanion, a socialbot that uses an LLM model to translate natural language into predicates (and vice versa) and employs commonsense reasoning based on Answer Set Programming (ASP) to hold a social conversation with a human. In particular, we rely on s(CASP), a goal-directed implementation of ASP as the backend. This paper presents the framework design and how an LLM is used to parse user messages and generate a response from the s(CASP) engine output. To validate our proposal, we describe (real) conversations in which the chatbot's goal is to keep the user entertained by talking about movies and books, and s(CASP) ensures (i) correctness of answers, (ii) coherence (and precision) during the conversation, which it dynamically regulates to achieve its specific purpose, and (iii) no deviation from the main topic.

Fast radio bursts (FRB) will become important cosmological tools, as the number of observed FRBs is increasing rapidly with more surveys being carried out. A large sample of FRBs with dispersion measures (DM) and rotation measures (RM) can be used to study the intergalactic magnetic field. However, the observed DM and RM of FRBs have multiple contributors which must be quantified to obtain the intergalactic medium's (IGM) DM and RM. In this paper, we estimate one such contribution to DM and RM: that of FRB host galaxies. We show how it changes with redshift, galaxy type, and the stellar mass of the galaxies, inclination, and FRB's projected offset. Using the IllustrisTNG50 simulations, we selected 16500 galaxies at redshifts of 0<=z<=2, with stellar masses in the range 9<=log(M*/Msun)<=12. In each galaxy, we calculate the DM and RM contributions of 1000 sightlines, and construct DM and RM probability density functions. We find that the rest frame DM distributions of all galaxies at a given redshift can be fitted by a lognormal function, and the rest frame RM distribution is symmetric around 0 rad m$^{-2}$, and can be fitted by the combination of a Lorentzian and two Gaussian functions. The parameters of these functions change for different subsets of galaxies with different redshift, stellar mass, inclination, and FRB offset. These changes are due to an increasing $n_e$ with redshift, SFR, and stellar mass, and we find a more ordered B field at lower z compared to higher z, suggested by more galaxies with B field reversals and B fields dominated by random B field at higher z. We estimate the FRB host DM and RM contributions, which can be used in the future to isolate the IGM's contribution from the observed DM and RM of FRBs. We predict that to constrain an $\sigma_{\rm RM,IGM}$ of 2 rad m$^{-2}$ to 95% confidence level we need to observe 95000 FRBs at z=0.5, but only 9500 FRBs at z=2., Comment: 24 pages, 15 figures Accepted for publication in A&A

The contribution of the magnetic field to the formation of high-mass stars is poorly understood. We report the high-angular resolution ($\sim0.3^{\prime\prime}$, 870 au) map of the magnetic field projected on the plane of the sky (B$_\mathrm{POS}$) towards the high-mass star forming region G333.46$-$0.16 (G333), obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) at 1.2 mm as part of the Magnetic Fields in Massive Star-forming Regions (MagMaR) survey. The B$_\mathrm{POS}$ morphology found in this region is consistent with a canonical ``hourglass'' which suggest a dynamically important field. This region is fragmented into two protostars separated by $\sim1740$ au. Interestingly, by analysing H$^{13}$CO$^{+}$ ($J=3-2$) line emission, we find no velocity gradient over the extend of the continuum which is consistent with a strong field. We model the B$_\mathrm{POS}$, obtaining a marginally supercritical mass-to-flux ratio of 1.43, suggesting an initially strongly magnetized environment. Based on the Davis-Chandrasekhar-Fermi method, the magnetic field strength towards G333 is estimated to be 5.7 mG. The absence of strong rotation and outflows towards the central region of G333 suggests strong magnetic braking, consistent with a highly magnetized environment. Our study shows that despite being a strong regulator, the magnetic energy fails to prevent the process of fragmentation, as revealed by the formation of the two protostars in the central region.

We investigate the overdamped dynamics of a `passive' particle driven by nonreciprocal interaction with a `driver' Brownian particle. When the interaction between them is short-ranged, the long-time behavior of the driven particle is remarkably universal -- the mean-squared displacement (MSD) and the typical position of the driven particle exhibits the same qualitative behaviors independent of the specific form of the potential. In particular, the MSD grows as $t^{1/2}$ in one dimension and $\log t$ in two spatial dimensions. We compute the exact scaling functions for the position distribution in $d=1$ and $d=2$. These functions are universal when the interaction is short-ranged. For long-ranged interactions, the MSD of the driven particle grows as $t^{\phi}$ with exponent $\phi$ depending on the tail of the potential., Comment: 19 pages, 10 figures

Graphs are a fundamental data structure used to represent relationships in domains as diverse as the social sciences, bioinformatics, cybersecurity, the Internet, and more. One of the central observations in network science is that real-world graphs are globally sparse, yet contains numerous "pockets" of high edge density. A fundamental task in graph mining is to discover these dense subgraphs. Most common formulations of the problem involve finding a single (or a few) "optimally" dense subsets. But in most real applications, one does not care for the optimality. Instead, we want to find a large collection of dense subsets that covers a significant fraction of the input graph. We give a mathematical formulation of this problem, using a new definition of regularly triangle-rich (RTR) families. These families capture the notion of dense subgraphs that contain many triangles and have degrees comparable to the subgraph size. We design a provable algorithm, RTRExtractor, that can discover RTR families that approximately cover any RTR set. The algorithm is efficient and is inspired by recent results that use triangle counts for community testing and clustering. We show that RTRExtractor has excellent behavior on a large variety of real-world datasets. It is able to process graphs with hundreds of millions of edges within minutes. Across many datasets, RTRExtractor achieves high coverage using high edge density datasets. For example, the output covers a quarter of the vertices with subgraphs of edge density more than (say) $0.5$, for datasets with 10M+ edges. We show an example of how the output of RTRExtractor correlates with meaningful sets of similar vertices in a citation network, demonstrating the utility of RTRExtractor for unsupervised graph discovery tasks.

In the context of satellite monitoring of the earth, we can assume that the surface of the earth is divided into a set of regions. We assume that the impact of a big social/environmental event spills into neighboring regions. Using Identifying Code Sets (ICSes), we can deploy sensors in such a way that the region in which an event takes place can be uniquely identified, even with fewer sensors than regions. As Earth is almost a sphere, we use a soccer ball as a model. We construct a Soccer Ball Graph (SBG), and provide human-oriented, analytical proofs that 1) the SBG has at least 26 ICSes of cardinality ten, implying that there are at least 26 different ways to deploy ten satellites to monitor the Earth and 2) that the cardinality of the minimum Identifying Code Set (MICS) for the SBG is at least nine. We then provide a machine-oriented formal proof that the cardinality of the MICS for the SBG is in fact ten, meaning that one must deploy at least ten satellites to monitor the Earth in the SBG model. We also provide machine-oriented proof that there are exactly 26 ICSes of cardinality ten for the SBG., Comment: 22 pages, 5 figures, preprint

Let $\mathrm{R}$ be a real closed field, $S \subset \mathrm{R}^n$ a closed and bounded semi-algebraic set and $\mathbf{f} = (f_1,\ldots,f_p):S \rightarrow \mathrm{R}^p$ a continuous semi-algebraic map. We study the poset module structure in homology induced by the simultaneous filtrations of $S$ by the sub-level sets of the functions $f_i$ from an algorithmic and quantitative point of view. For fixed dimensional homology we prove a singly exponential upper bound on the complexity of these modules which are encoded as certain semi-algebraically constructible functions on $\mathrm{R}^p \times \mathrm{R}^p$. We also deduce for semi-algebraic filtrations of bounded complexity, upper bounds on the number of equivalence classes of finite poset modules that such a filtration induces -- establishing a tight analogy with a well-known graph theoretical result on the "speed'' of algebraically defined graphs., Comment: 35 pages, 1 figure. Comments welcome

We introduce a novel family of translationally-invariant su$(m|n)$ supersymmetric spin chains with long-range interaction not directly associated to a root system. We study the symmetries of these models, establishing in particular the existence of a boson-fermion duality characteristic of this type of systems. Taking advantage of the relation of the new chains with an associated many-body supersymmetric spin dynamical model, we are able to compute their partition function in closed form for all values of $m$ and $n$ and for an arbitrary number of spins. When both $m$ and $n$ are even, we show that the partition function factorizes as the product of the partition functions of two supersymmetric Haldane-Shastry spin chains, which in turn leads to a simple expression for the thermodynamic free energy per spin in terms of the Perron eigenvalue of a suitable transfer matrix. We use this expression to study the thermodynamics of a large class of these chains, showing in particular that the specific heat presents a single Schottky peak at approximately the same temperature as a suitable $k$-level model. We also analyze the critical behavior of the new chains, and in particular the ground state degeneracy and the existence of low energy excitations with a linear energy-momentum dispersion relation. In this way we are able to show that the only possible critical chains are the ones with $m=0,1,2$. In addition, using the explicit formula for the partition function we are able to establish the criticality of the su$(0|n)$ and su$(2|n)$ chains with even $n$, and to evaluate the central charge of their associated conformal field theory., Comment: 40 pages, 6 figures, typeset with LaTeX

We study CP violation (CPV) in the sneutrino sector within the B-L extension of the Minimal Supersymmetric Standard Model (BLSSM), wherein an inverse seesaw mechanism has been implemented. CPV arises from the new superpotential couplings in the (s)neutrino sector, which can be complex and the mixing of CP-eigenstates induced by those couplings. CPV leads to asymmetries in so called T-odd observables, but we argue that such asymmetries also lead to a wider distribution of those observables. We look at a final state where a sneutrino decays to a lepton, two jets and missing transverse momentum at the Future Circular Collider operating in hadron-hadron mode at $100$ TeV and with a luminosity of 3~ab$^{-1}$. In order to exclude the CP conserving scenario we need to improve traditional analysis by introducing boosted decision trees using both standard kinematic variables and T-odd observables and we need $Z^{\prime}$ boson not too much above current bounds as a portal to produce sneutrinos efficiently., Comment: 27 pages, 21 figures, 10 tables

The development of quantum computers has reached a great milestone, in spite of restrictions on important quantum resources: the number of qubits being entangled at a single-location quantum computer. Recently, there has been some work to combine single-location quantum computing and quantum networking techniques to develop distributed quantum systems such that large entangled qubit groups can be established through remote processors, and quantum algorithms can be executed distributively. We present DisQ as a framework to facilitate the rewrites of quantum algorithms to their distributed versions. The core of DisQ is a distributed quantum programming language that combines the concepts of Chemical Abstract Machine (CHAM) and Markov Decision Processes (MDP) with the objective of providing a clearly distinguishing quantum concurrent and distributed behaviors. Based on the DisQ language, we develop a simulation relation for verifying the equivalence of a quantum algorithm and its distributed versions. We present several case studies, such as quantum addition and Shor's algorithm, to demonstrate their equivalent rewrites to distributed versions., Comment: Version 1

Non-volatile Memory (NVM) could bridge the gap between memory and storage. However, NVMs are susceptible to data remanence attacks. Thus, multiple security metadata must persist along with the data to protect the confidentiality and integrity of NVM-resident data. Persisting Bonsai Merkel Tree (BMT) nodes, critical for data integrity, can add significant overheads due to need to write large amounts of metadata off-chip to the bandwidth-constrained NVMs. We propose iMIV for low-overhead, fine-grained integrity verification through in-memory computing. We argue that memory-intensive integrity verification operations (BMT updates and verification) should be employed close to the NVM to limit off-chip data movement. We design iMIV based on typical NVDIMM designs that have an onboard logic chip with a trusted encryption engine, separate from the untrusted storage media. iMIV reduces the performance overheads from 205% to 55% when integrity verification operations are offloaded to NVM compared to when all the security operations are employed at the memory controller.

In this paper, we first study the consequence of spacetime translations and Lorentz transformations on Celestial CFT OPEs. Working with the light transforms of the operators belonging to the modified Mellin basis, we found that the leading order singularity in the OPE of such operators could be fixed purely using Poincar\'e symmetries owing to the non-trivial action of the translations on these operators. The OPE coefficient is then fixed using the soft limit of the correlation functions. We check that this singular structure obtained from symmetries is consistent with the OPE limit of three-point functions. This approach could potentially be useful for studying Celestial CFT without adverting to bulk physics. As another goal, we explore the significance of light transformation in Carrollian CFTs. In the special cases we considered, we show that light transformation equips us with a map between two branches of Carroll CFT in $d=3$ dimension at the level of correlation functions in the near coincident limit., Comment: 31 pages, new references added in the current version

For any Tychonoff space X we have introduced the zero-set in-tersection graph on {\Gamma}(C+(X)) and studied the graph properties in connection with the algebraic properties of the semiring C+(X). We have shown that for any two realcompact spaces X and Y the graph isomorphism between {\Gamma}(C+(X)) and {\Gamma}(C+(Y )), the semiring isomorphism between C+(X) and C+(Y ), the topological homeomorphism between X and Y, the ring isomorphism between C(X) and C(Y ) and the graph isomorphism between {\Gamma}(C(X)) and {\Gamma}(C(Y )) are equivalent., Comment: 14 pages, 1 figure

We investigate the effect of a waveguide on an emitter spontaneous emission in its vicinity. The impact of various possible orientations of an emitter with respect to the waveguide surface is studied through simulations and compared with experimental demonstration. Quantum emitters are dip coated on waveguides and Purcell enhancement and a decrease in the lifetime of the emitter are observed. This study serves as a proof of concept for the Purcell effect offered by waveguides and helps ineffectively estimate the efficiency of waveguide-based evanescent sensors and quantum photonic applications, Comment: 5 pages

Deterministic policies are often preferred over stochastic ones when implemented on physical systems. They can prevent erratic and harmful behaviors while being easier to implement and interpret. However, in practice, exploration is largely performed by stochastic policies. First-order Bayesian Optimization (BO) methods offer a principled way of performing exploration using deterministic policies. This is done through a learned probabilistic model of the objective function and its gradient. Nonetheless, such approaches treat policy search as a black-box problem, and thus, neglect the reinforcement learning nature of the problem. In this work, we leverage the performance difference lemma to introduce a novel mean function for the probabilistic model. This results in augmenting BO methods with the action-value function. Hence, we call our method Augmented Bayesian Search~(ABS). Interestingly, this new mean function enhances the posterior gradient with the deterministic policy gradient, effectively bridging the gap between BO and policy gradient methods. The resulting algorithm combines the convenience of the direct policy search with the scalability of reinforcement learning. We validate ABS on high-dimensional locomotion problems and demonstrate competitive performance compared to existing direct policy search schemes., Comment: Accepted to the International Conference on Learning Representations (ICLR) 2024

The Rossby mode (r-mode) perturbation in pulsars as a steady gravitational wave (GW) source has been explored. The effect of a rigid crust on viscous damping and dissipation rate in the boundary layer between fluid core and crust has been studied. The time evolution and intensity of the emitted GWs in terms of the strain tensor amplitude have been estimated with the approximation of slow rotation using equation of state derived from the APR and Skyrme effective interactions with Brussels-Montreal parameter sets. The core of the neutron star has been considered to be $\beta$-equilibrated nuclear matter containing neutrons, protons, electrons and muons, surrounded by a solid crust. Calculations have been made for critical frequencies, their time evolution and rate of change of frequencies across a broad spectrum of pulsar masses., Comment: 8 pages including 9 figures. arXiv admin note: substantial text overlap with arXiv:2304.14644

This article exploits group-IV germanium (Ge) quantum dots (QDs) on Silicon-on-Insulator (SOI) grown by molecular beam epitaxy (MBE) in order to explore its optical behaviour in the Terahertz (THz) regime. In this work, Ge QDs, pumped by an above bandgap near infrared wavelength, exhibit THz amplitude modulation in the frequency range of 0.1-1.0 THz. The epitaxial Ge QDs outperform reference SOI substrate in THz amplitude modulation owing to higher carrier generation in weakly confined dots compared to its bulk counterpart. This is further corroborated using theoretical model based on the non-equilibrium Green's function (NEGF) method. This model enables the calculation of photo carriers generated (PCG) and their confinement in the Ge QD region. Our model also reroutes the calculation from PCG to corresponding plasma frequency and hence to refractive index and THz photo-conductivity. Moreover, the photo-generated confined holes accumulation at the Ge QDs-Si interface is elevated after optical illumination, leading to a decreased THz photo-conductivity. This augmentation in THz photo-conductivity contributes to a significant enhancement of THz modulation depth ~77% at Ge QDs-Si interfaces compared to bare SOI at 0.1 THz., Comment: 15 pages, 5 figures

Active Galactic Nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multi-messenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and ten years of neutrino data from IceCube. We also use a time-dependent approach which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are $\sim3\%$ and $\sim9\%$ for the time-averaged and time-dependent case, respectively., Comment: 14 Pages 7 Figures

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. To be submitted to Physical Review D

A/B testers conducting large-scale tests prioritize lifts and want to be able to control false rejections of the null. This work develops a decision-theoretic framework for maximizing profits subject to false discovery rate (FDR) control. We build an empirical Bayes solution for the problem via the greedy knapsack approach. We derive an oracle rule based on ranking the ratio of expected lifts and the cost of wrong rejections using the local false discovery rate (lfdr) statistic. Our oracle decision rule is valid and optimal for large-scale tests. Further, we establish asymptotic validity for the data-driven procedure and demonstrate finite-sample validity in experimental studies. We also demonstrate the merit of the proposed method over other FDR control methods. Finally, we discuss an application to actual Optimizely experiments.