Search

In the rapidly evolving field of data science, efficiently navigating the expansive body of academic literature is crucial for informed decision-making and innovation. This paper presents an enhanced Retrieval-Augmented Generation (RAG) application, an artificial intelligence (AI)-based system designed to assist data scientists in accessing precise and contextually relevant academic resources. The AI-powered application integrates advanced techniques, including the GeneRation Of BIbliographic Data (GROBID) technique for extracting bibliographic information, fine-tuned embedding models, semantic chunking, and an abstract-first retrieval method, to significantly improve the relevance and accuracy of the retrieved information. This implementation of AI specifically addresses the challenge of academic literature navigation. A comprehensive evaluation using the Retrieval-Augmented Generation Assessment System (RAGAS) framework demonstrates substantial improvements in key metrics, particularly Context Relevance, underscoring the system's effectiveness in reducing information overload and enhancing decision-making processes. Our findings highlight the potential of this enhanced Retrieval-Augmented Generation system to transform academic exploration within data science, ultimately advancing the workflow of research and innovation in the field.

We present the first $\textit{NuSTAR}$ X-ray observation of EF Eri, a well-known polar system. The $\textit{NuSTAR}$ observation was conducted in conjunction with $\textit{NICER}$, shortly after EF Eri entered a high accretion state following an unprecedented period of low activity lasting 26 years since 1997. $\textit{NuSTAR}$ detected hard X-ray emission up to 50 keV with an X-ray flux of $1.2\times10^{-10}$ ergs s$^{-1}$ cm$^{-2}$ ($3\rm{-}50 keV$). Folded X-ray lightcurves exhibit a single peak with $\sim65\%$ spin modulation throughout the $3\rm{-}50$ keV band. We found no evidence of QPO signals at $\nu = 0.1\rm{-}100$ Hz with an upper limit on the QPO amplitude below $5\%$ ($90\%$ CL) at $\nu \sim 0.5$ Hz where the optical QPO was previously detected. Our 1-D accretion column model, called ${\tt MCVSPEC}$, was fitted to the $\textit{NuSTAR}$ spectral data, yielding an accurate WD mass measurement of $M = (0.55\rm{-}0.58) M_\odot$. $\texttt{MCVSPEC}$ accounts for radiative cooling by thermal bremsstrahlung and cyclotron emission, X-ray reflection off the WD surface, and a previously constrained range of the accretion column area. The derived WD mass range is in excellent agreement with the previous measurement of $M = (0.55\rm{-}0.60) M_\odot$ in the optical band. This demonstrates a combination of broadband X-ray spectral analysis and the ${\tt MCVSPEC}$ model that can be employed in our ongoing $\textit{NuSTAR}$ observation campaign of other polars to determine their WD masses accurately., Comment: 17 pages, 10 figures, 3 tables. Submitted to ApJ

A recent report on the detection of very-high-energy gamma rays from V4641 Sagittarii (V4641 Sgr) up to ~0.8 peta-electronvolt has made it the second confirmed "PeVatron" microquasar. Here we report on the observation of V4641 Sgr with X-Ray Imaging and Spectroscopy Mission (XRISM) in September 2024. Thanks to the large field of view and low background, the CCD imager Xtend successfully detected for the first time X-ray extended emission around V4641 Sgr with a significance of > 4.5 sigma and > 10 sigma based on our imaging and spectral analysis, respectively. The spatial extent is estimated to have a radius of $7 \pm 3$ arcmin ($13 \pm 5$ pc at a distance of 6.2 kpc) assuming a Gaussian-like radial distribution, which suggests that the particle acceleration site is within ~10 pc of the microquasar. If the X-ray morphology traces the diffusion of accelerated electrons, this spatial extent can be explained by either an enhanced magnetic field (~80 uG) or a suppressed diffusion coefficient (~$10^{27}$ cm$^2$ s$^{-1}$ at 100 TeV). The integrated X-ray flux, (4-6)$\times 10^{-12}$ erg s$^{-1}$ cm$^{-2}$ (2-10 keV), would require a magnetic field strength higher than the galactic mean (> 8 uG) if the diffuse X-ray emission originates from synchrotron radiation and the gamma-ray emission is predominantly hadronic. If the X-rays are of thermal origin, the measured extension, temperature, and plasma density can be explained by a jet with a luminosity of ~$2\times 10^{39}$ erg s$^{-1}$, which is comparable to the Eddington luminosity of this system., Comment: 9 pages, 5 figures, accepted for publication in ApJL

We present a spectrally accurate bounce-averaging operator implemented as a part of the automatically differentiable DESC stellarator optimization suite. Using this operator, we calculate the proxy for neoclassical transport coefficient $\epsilon_{\mathrm{eff}}^{3/2}$ in the $1/\nu$ regime and benchmark it against the NEO code. Ultimately, by employing this differentiable approximation, for the first time, we directly optimize a finite-$\beta$ stellarator to enhance neoclassical transport using reverse-mode differentiation. This ensures that the computational cost of determining the gradients does not depend on the number of input parameters.

The corrosion of metallic surfaces poses significant challenges across industries such as petroleum, energy, and biomedical sectors, leading to structural degradation, safety risks, and substantial maintenance costs. Traditional organic and metallic coatings provide some protection, but their limited durability and susceptibility to harsh environmental conditions necessitate the development of more advanced and efficient solutions. This has driven significant interest in two-dimensional (2D) materials, with graphene extensively studied for its exceptional mechanical strength and impermeability to gases and ions. However, while graphene offers short-term corrosion protection, its high electrical conductivity presents a long-term issue by promoting galvanic corrosion on metal surfaces. In contrast, hexagonal boron nitride (h-BN) has emerged as a promising alternative for anticorrosion coatings. h-BN combines exceptional chemical stability, impermeability, and electrical insulation, making it particularly suited for long-term protection in highly corrosive or high-temperature environments. While h-BN holds promise as anticorrosion material, challenges such as structural defects, agglomeration of nanosheets, and poor dispersion within coatings limit its performance. This review provides a comprehensive analysis of recent advancements in addressing these challenges, including novel functionalization strategies, scalable synthesis methods, and hybrid systems that integrate h-BN with complementary materials. By bridging the gap between fundamental research and industrial applications, this review outlines the potential for h-BN to revolutionize anticorrosion technologies. These obstacles necessitate advanced strategies such as surface functionalization to improve compatibility with polymer matrices and dispersion optimization to minimize agglomeration., Comment: 28 pages, 3 figures

Spacelike intrinsic rotational surfaces with constant mean curvature in the Lorentz-Minkowski space $\E_1^3$ have been recently investigated by Brander et al., extending the known Smyth's surfaces in Euclidean space. Assuming that the surface is intrinsic rotational with coordinates $(u,v)$ and conformal factor $\rho(u)^2$, we replace the constancy of the mean curvature with the property that the Weingarten endomorphism $A$ can be expressed as $\Phi_{-\alpha(v)}\left(\begin{array}{ll}\lambda_1(u)&0\\ 0&\lambda_2(u)\end{array}\right)\Phi_{\alpha(v)}$, where $\Phi_{\alpha(v)}$ is the (Euclidean or hyperbolic) rotation of angle $\alpha(v)$ at each tangent plane and $\lambda_i$ are the principal curvatures. Under these conditions, it is proved that the mean curvature is constant and $\alpha$ is a linear function. This result also covers the case that the surface is timelike. If the mean curvature is zero, we determine all spacelike and timelike intrinsic rotational surfaces with rotational angle $\alpha$. This family of surfaces includes the spacelike and timelike Enneper surfaces., Comment: 28 pages, 3 figures. Accepted to publish in Hacettepe Journal of Mathematics and Statistics

In multi-state models based on high-dimensional data, effective modeling strategies are required to determine an optimal, ideally parsimonious model. In particular, linking covariate effects across transitions is needed to conduct joint variable selection. A useful technique to reduce model complexity is to address homogeneous covariate effects for distinct transitions. We integrate this approach to data-driven variable selection by extended regularization methods within multi-state model building. We propose the fused sparse-group lasso (FSGL) penalized Cox-type regression in the framework of multi-state models combining the penalization concepts of pairwise differences of covariate effects along with transition grouping. For optimization, we adapt the alternating direction method of multipliers (ADMM) algorithm to transition-specific hazards regression in the multi-state setting. In a simulation study and application to acute myeloid leukemia (AML) data, we evaluate the algorithm's ability to select a sparse model incorporating relevant transition-specific effects and similar cross-transition effects. We investigate settings in which the combined penalty is beneficial compared to global lasso regularization.

Recently, a class of long-period radio transients (LPTs) has been discovered, exhibiting emission on timescales thousands of times longer than radio pulsars. Several models had been proposed implicating either a strong magnetic field neutron star, isolated white dwarf pulsar, or a white dwarf binary system with a low-mass companion. While several models for LPTs also predict X-ray emission, no LPTs have been detected in X-rays despite extensive searches. Here we report the discovery of an extremely bright LPT (10-20 Jy in radio), ASKAP J1832-0911, which has coincident radio and X-ray emission, both with a 44.2-minute period. The X-ray and radio luminosities are correlated and vary by several orders of magnitude. These properties are unique amongst known Galactic objects and require a new explanation. We consider a $\gtrsim0.5$ Myr old magnetar with a $\gtrsim 10^{13}$ G crustal field, or an extremely magnetised white dwarf in a binary system with a dwarf companion, to be plausible explanations for ASKAP J1832-0911, although both explanations pose significant challenges to formation and emission theories. The X-ray detection also establishes a new class of hour-scale periodic X-ray transients of luminosity $\sim10^{33}$ erg/s associated with exceptionally bright coherent radio emission., Comment: 52 pages, 17 figures, 3 tables

We present a detailed X-ray investigation of a region (S1) exhibiting non-thermal X-ray emission within the supernova remnant (SNR) CTB 37B hosting the magnetar CXOU J171405.7$-$381031. Previous analyses modeled this emission with a power law (PL), inferring various values for the photon index ($\Gamma$) and absorbing column density ($N_{\rm H}$). Based on these, S1 was suggested to be the SNR shell, a background pulsar wind nebula (PWN), or an interaction region between the SNR and a molecular cloud. Our analysis of a larger dataset favors a steepening (broken or curved PL) spectrum over a straight PL, with the best-fit broken power-law (BPL) parameters of $\Gamma=1.23\pm0.23$ and $2.24\pm0.16$ below and above a break at $5.57\pm0.52$ keV, respectively. However, a simple PL or srcut model cannot be definitively ruled out. For the BPL model, the inferred $N_{\rm H}=(4.08\pm0.72)\times 10^{22}\rm \ cm^{-2}$ towards S1 is consistent with that of the SNR, suggesting a physical association. The BPL-inferred spectral break $\Delta \Gamma \approx 1$ and hard $\Gamma$ can be naturally explained by a non-thermal bremsstrahlung (NTB) model. We present an evolutionary NTB model that reproduces the observed spectrum, which indicates the presence of sub-relativistic electrons within S1. However, alternate explanations for S1, an unrelated PWN or the SNR shock with unusually efficient acceleration, cannot be ruled out. We discuss these explanations and their implications for gamma-ray emission from CTB 37B, and describe future observations that could settle the origin of S1., Comment: 11 pages. 4 figures, Accepted for publication in Apj

We develop a new theoretical scheme for charge transport and thermoelectric response in two-dimensional disordered systems exhibiting crossover from weak localization (WL) to strong localization (SL). The scheme is based on the scaling theory for Anderson localization combined with the Kubo-Luttinger theory. Key aspects of the scheme include introducing a unified $\beta$ function that seamlessly connects the WL and SL regimes, as well as describing the temperature ($T$) dependence of the conductance from high to low $T$ regions on the basis of the dephasing length. We found that the Seebeck coefficient, $S$, behaves as $S\propto T$ in the WL limit and as $S\propto T^{1-p}$ ($p < 1$) in the SL limit, both with possible logarithmic corrections. The scheme is applied to analyze experimental data for thin films of the p-type organic semiconductor poly[2,5-bis(3-alkylthiophen-2-yl)thieno(3,2-b)thiophene] (PBTTT)., Comment: 6 pages, 2 figures

Young supernova remnants (SNRs) are believed to be the origin of energetic cosmic rays (CRs) below the "knee" of their spectrum at $\sim3$ petaelectronvolt (PeV, $10^{15}$ eV). Nevertheless, the precise location, duration, and operation of CR acceleration in young SNRs are open questions. Here, we report on multi-epoch X-ray observations of Cassiopeia A (Cas A), a 350-year-old SNR, in the 15-50 keV band that probes the most energetic CR electrons. The observed X-ray flux decrease $(15\pm1\%)$, contrary to the expected $>$90\% decrease based on previous radio, X-ray, and gamma-ray observations, provides unambiguous evidence for CR electron acceleration operating in Cas A. A temporal model for the radio and X-ray data accounting for electron cooling and continuous injection finds that the freshly injected electron spectrum is significantly harder (exponential cutoff power law index $q=2.15$), and its cutoff energy is much higher ($E_{cut}=36$ TeV) than the relic electron spectrum ($q=2.44\pm0.03$, $E_{cut}=4\pm1$ TeV). Both electron spectra are naturally explained by the recently developed modified nonlinear diffusive shock acceleration (mNLDSA) mechanism. The CR protons producing the observed gamma rays are likely accelerated at the same location by the same mechanism as those for the injected electron. The Cas A observations and spectral modeling represent the first time radio, X-ray, gamma ray and CR spectra have been self-consistently tied to a specific acceleration mechanism -- mNLDSA -- in a young SNR., Comment: Accepted for publication in ApJ. 14 pages, 4 figures, 2 tables

Spanning Centrality is a measure used in network analysis to determine the importance of an edge in a graph based on its contribution to the connectivity of the entire network. Specifically, it quantifies how critical an edge is in terms of the number of spanning trees that include that edge. The current state-of-the-art for All Edges Spanning Centrality~(AESC), which computes the exact centrality values for all the edges, has a time complexity of $\mathcal{O}(mn^{3/2})$ for $n$ vertices and $m$ edges. This makes the computation infeasible even for moderately sized graphs. Instead, there exist approximation algorithms which process a large number of random walks to estimate edge centralities. However, even the approximation algorithms can be computationally overwhelming, especially if the approximation error bound is small. In this work, we propose a novel, hash-based sampling method and a vectorized algorithm which greatly improves the execution time by clustering random walks into {\it Bouquets}. On synthetic random walk benchmarks, {\it Bouquets} performs $7.8\times$ faster compared to naive, traditional random-walk generation. We also show that the proposed technique is scalable by employing it within a state-of-the-art AESC approximation algorithm, {\sc TGT+}. The experiments show that using Bouquets yields more than $100\times$ speed-up via parallelization with 16 threads.

Influence Maximization (IM) aims to find a given number of "seed" vertices that can effectively maximize the expected spread under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, approximation algorithms are often used for IM. However, these algorithms require a large number of simulations to find good seed sets. In this work, we propose DiFuseR, a blazing-fast, high-quality IM algorithm that can run on multiple GPUs in a distributed setting. DiFuseR is designed to increase GPU utilization, reduce inter-node communication, and minimize overlapping data/computation among the nodes. Based on the experiments with various graphs, containing some of the largest networks available, and diffusion settings, the proposed approach is found to be 3.2x and 12x faster on average on a single GPU and 8 GPUs, respectively. It can achieve up to 8x and 233.7x speedup on the same hardware settings. Furthermore, thanks to its smart load-balancing mechanism, on 8 GPUs, it is on average 5.6x faster compared to its single-GPU performance.

To build an economically viable stellarator, it is essential to find a configuration that satisfies a set of favorable properties to achieve efficient steady-state nuclear fusion. One such property is omnigenity, which ensures confinement of trapped particles. After creating an omnigenous equilibrium, one must also ensure reduced transport resulting from kinetic and magnetohydrodynamic (MHD) instabilities. This study introduces and leverages the GPU-accelerated DESC optimization suite, which is used to design stable high-$\beta$ omnigenous equilibria, achieving Mercier, ideal ballooning, and enhanced kinetic ballooning stability. We explain the link between ideal and kinetic ballooning modes and discover stellarators with second stability, a regime of large pressure gradient where an equilibria becomes ideal ballooning stable., Comment: Supplementary material appended after the appendix. 33 pages + 10 pages supplementary material, 13 figures, 4 tables

The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI research since its inception. With the advent of large language models (LLMs), there has been considerable interest in the question of whether or not they possess such planning abilities, but -- despite the slew of new private and open source LLMs since GPT3 -- progress has remained slow. OpenAI claims that their recent o1 (Strawberry) model has been specifically constructed and trained to escape the normal limitations of autoregressive LLMs -- making it a new kind of model: a Large Reasoning Model (LRM). In this paper, we evaluate the planning capabilities of two LRMs (o1-preview and o1-mini) on both planning and scheduling benchmarks. We see that while o1 does seem to offer significant improvements over autoregressive LLMs, this comes at a steep inference cost, while still failing to provide any guarantees over what it generates. We also show that combining o1 models with external verifiers -- in a so-called LRM-Modulo system -- guarantees the correctness of the combined system's output while further improving performance., Comment: arXiv admin note: text overlap with arXiv:2409.13373

It is well-known that RANDAO manipulation is possible in Ethereum if an adversary controls the proposers assigned to the last slots in an epoch. We provide a methodology to compute, for any fraction $\alpha$ of stake owned by an adversary, the maximum fraction $f(\alpha)$ of rounds that a strategic adversary can propose. We further implement our methodology and compute $f(\cdot)$ for all $\alpha$. For example, we conclude that an optimal strategic participant with $5\%$ of the stake can propose a $5.048\%$ fraction of rounds, $10\%$ of the stake can propose a $10.19\%$ fraction of rounds, and $20\%$ of the stake can propose a $20.68\%$ fraction of rounds., Comment: 28 pages, 7 figures, full version of the AFT 2024 paper

We report on XMM-Newton, NuSTAR, and NICER X-ray observations of CTCV J2056-3014, a cataclysmic variable (CV) with one of the fastest-spinning white dwarfs (WDs) at P = 29.6 s. While previously classified as an intermediate polar (IP), CJ2056 also exhibits the properties of WZ-Sge-type CVs, such as dwarf novae and superoutbursts. With XMM-Newton and NICER, we detected the spin period up to approximately 2 keV with 7-$\sigma$ significance. We constrained its derivative to |$\dot{P}$| < 1.8e-12 s/s after correcting for binary orbital motion. The pulsed profile is characterized by a single broad peak with approximately 25% modulation. NuSTAR detected a four-fold increase in unabsorbed X-ray flux coincident with an optical flare in November 2022. The XMM-Newton and NICER X-ray spectra in 0.3-10 keV are best characterized by an absorbed optically-thin three-temperature thermal plasma model (kT = 0.3, 1.0, and 4.9 keV), while the NuSTAR spectra in 3-30 keV are best fit by a single-temperature thermal plasma model (kT = 8.4 keV), both with Fe abundance $Z_{Fe}/Z_\odot$ = 0.3. CJ2056 exhibits similarities to other fast-spinning CVs, such as low plasma temperatures, and no significant X-ray absorption at low energies. As the WD's magnetic field strength is unknown, we applied both non-magnetic and magnetic CV spectral models (MKCFLOW and MCVSPEC) to determine the WD mass. The derived WD mass range (M = 0.7-1.0 $M_\odot$) is above the centrifugal break-up mass limit of 0.56 $M_\odot$ and consistent with the mean WD mass of local CVs (M $\approx$ 0.8-0.9 $M_\odot$)., Comment: 22 pages, 12 figures, accepted to ApJ

There have been rapid advancements in the capabilities of large language models (LLMs) in recent years, greatly revolutionizing the field of natural language processing (NLP) and artificial intelligence (AI) to understand and interact with human language. Therefore, in this work, we conduct a systematic investigation of the literature to identify the prominent themes and directions of LLM developments, impacts, and limitations. Our findings illustrate the aims, methodologies, limitations, and future directions of LLM research. It includes responsible development considerations, algorithmic improvements, ethical challenges, and societal implications of LLM development. Overall, this paper provides a rigorous and comprehensive overview of current research in LLM and identifies potential directions for future development. The article highlights the application areas that could have a positive impact on society along with the ethical considerations., Comment: 28 pages, 5 figures, preprint submitted to journal

The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI research since its inception. With the advent of large language models (LLMs), there has been considerable interest in the question of whether or not they possess such planning abilities. PlanBench, an extensible benchmark we developed in 2022, soon after the release of GPT3, has remained an important tool for evaluating the planning abilities of LLMs. Despite the slew of new private and open source LLMs since GPT3, progress on this benchmark has been surprisingly slow. OpenAI claims that their recent o1 (Strawberry) model has been specifically constructed and trained to escape the normal limitations of autoregressive LLMs--making it a new kind of model: a Large Reasoning Model (LRM). Using this development as a catalyst, this paper takes a comprehensive look at how well current LLMs and new LRMs do on PlanBench. As we shall see, while o1's performance is a quantum improvement on the benchmark, outpacing the competition, it is still far from saturating it. This improvement also brings to the fore questions about accuracy, efficiency, and guarantees which must be considered before deploying such systems.

We study the problem of finding curves of minimum pointwise-maximum arc-length derivative of curvature, here simply called curves of minimax spirality, among planar curves of fixed length with prescribed endpoints and tangents at the endpoints. We consider the case when simple bounds (constraints) are also imposed on the curvature along the curve. The curvature at the endpoints may or may not be specified. We prove via optimal control theory that the optimal curve is some concatenation of Euler spiral arcs, circular arcs, and straight line segments. When the curvature is not constrained (or when the curvature constraint does not become active), an optimal curve is only made up of a concatenation of Euler spiral arcs, unless the oriented endpoints lie in a line segment or a circular arc of the prescribed length, in which case the whole curve is either a straight line segment or a circular arc segment, respectively. We propose numerical methods and illustrate these methods and the results by means of three example problems of finding such curves., Comment: 23 pages

We study Babai numbers and Babai $k$-spectra of paths and cycles. We completely determine the Babai numbers of paths $P_n$ for $n>1$ and $1 \leq k \leq n-1$, and the Babai $k$-spectra for $P_n$ when $1 \leq k \leq n/2$. We also completely determine Babai numbers and Babai $k$-spectra of all cycles $C_n$ for $k \in \{1,2\}$ and $n \geq 3$ if $k=1$ and $n > 3$ if $k=2$., Comment: 21 pages, 1 figure, 7 references

Node-based programming languages are increasingly popular in media arts coding domains. These languages are designed to be accessible to users with limited coding experience, allowing them to achieve creative output without an extensive programming background. Using LLM-based code generation to further lower the barrier to creative output is an exciting opportunity. However, the best strategy for code generation for visual node-based programming languages is still an open question. In particular, such languages have multiple levels of representation in text, each of which may be used for code generation. In this work, we explore the performance of LLM code generation in audio programming tasks in visual programming languages at multiple levels of representation. We explore code generation through metaprogramming code representations for these languages (i.e., coding the language using a different high-level text-based programming language), as well as through direct node generation with JSON. We evaluate code generated in this way for two visual languages for audio programming on a benchmark set of coding problems. We measure both correctness and complexity of the generated code. We find that metaprogramming results in more semantically correct generated code, given that the code is well-formed (i.e., is syntactically correct and runs). We also find that prompting for richer metaprogramming using randomness and loops led to more complex code.

Understanding electrical characteristics and corresponding transport models at single molecular junctions is crucial. There have been many reports on organic compounds-based single molecular junctions. However, organometallic compounds-based single molecular junctions have not been explored yet. Re(I) organometallic compounds are known to exhibit intriguing photophysical properties scrutinized for photocatalysis, and light-emitting diodes but have not been explored in molecular electronics. In this work, a theoretical model study on the I-V characteristics of two Re(I)-carbonyl complexes bearing Re-P and Re-N-N linkage has been meticulously chosen. Tunneling and hopping transport in Au/Re(I)-complex/Au single-molecule junctions are governed by Landauer-formalism and the Marcus theory, respectively. Interestingly, variations in molecular architecture culminate in notable variations in junction functionality and mechanism of charge conduction. Physical parameters influencing the device characteristics such as dipole moment, molecule-electrode coupling strength, voltage division factor, and temperature have been extensively studied which offers modulation of the characteristics and device design. The dominant hopping current in Re complex bearing bipyridine linkage was found to be responsible for the observed asymmetric electrical (I-V) behavior. Our work paves the way for constructing various organometallic compounds-based molecular junctions to understand electronic functions and the underlying transport mechanisms., Comment: 17 pages, 8 figures

The structure of automorphism groups of $\kappa$-existentially closed groups are studied by Kaya-Kuzucuo\u{g}lu in 2022. It was proved that Aut(G) is the union of subgroups of level preserving automorphisms and $|Aut(G)|=2^\kappa$ whenever $\kappa$ is an inaccessible cardinal and $G$ is the unique $\kappa$-existentially closed group of cardinality $\kappa$. The cardinality of the automorphism group of a $\kappa$-existentially closed group of cardinality $\lambda>\kappa$ is asked in Kourovka Notebook Question 20.40. Here we answer positively the promised case $\kappa=\lambda$ namely: If $G$ is a $\kappa$-existentially closed group of cardinality $\kappa$, then $|Aut(G)|=2^{\kappa}$. We also answer Kegel's question on universal groups, namely: For any uncountable cardinal $\kappa$, there exist universal groups of cardinality $\kappa$.

We present volume-limited samples of cataclysmic variables (CVs) and AM CVn binaries jointly selected from SRG/eROSITA eRASS1 and \textit{Gaia} DR3 using an X-ray + optical color-color diagram (the ``X-ray Main Sequence"). This tool identifies all CV subtypes, including magnetic and low-accretion rate systems, in contrast to most previous surveys. We find 23 CVs, 3 of which are AM CVns, out to 150 pc in the Western Galactic Hemisphere. Our 150 pc sample is spectroscopically verified and complete down to $L_X = 1.3\times 10^{29} \;\textrm{erg s}^{-1}$ in the 0.2--2.3 keV band, and we also present CV candidates out to 300 pc and 1000 pc. We discovered two previously unknown systems in our 150 pc sample: the third nearest AM CVn and a magnetic period bouncer. We find the mean $L_X$ of CVs to be $\langle L_X \rangle \approx 4.6\times 10^{30} \;\textrm{erg s}^{-1}$, in contrast to previous surveys which yielded $\langle L_X \rangle \sim 10^{31}-10^{32} \;\textrm{erg s}^{-1}$. We construct X-ray luminosity functions that, for the first time, flatten out at $L_X\sim 10^{30} \; \textrm{erg s}^{-1}$. We find average number, mass, and luminosity densities of $\rho_\textrm{N, CV} = (3.7 \pm 0.7) \times 10^{-6} \textrm{pc}^{-3}$, $\rho_M = (5.0 \pm 1.0) \times 10^{-5} M_\odot^{-1}$, and $\rho_{L_X} = (2.3 \pm 0.4) \times 10^{26} \textrm{erg s}^{-1}M_\odot^{-1}$, respectively, in the solar neighborhood. Our uniform selection method also allows us to place meaningful estimates on the space density of AM CVns, $\rho_\textrm{N, AM CVn} = (5.5 \pm 3.7) \times 10^{-7} \textrm{pc}^{-3}$. Magnetic CVs and period bouncers make up $35\%$ and $25\%$ of our sample, respectively. This work, through a novel discovery technique, shows that the observed number densities of CVs and AM CVns, as well as the fraction of period bouncers, are still in tension with population synthesis estimates., Comment: Submitted to PASP, comments welcome

In a conventional Federated Learning framework, client selection for training typically involves the random sampling of a subset of clients in each iteration. However, this random selection often leads to disparate performance among clients, raising concerns regarding fairness, particularly in applications where equitable outcomes are crucial, such as in medical or financial machine learning tasks. This disparity typically becomes more pronounced with the advent of performance-centric client sampling techniques. This paper introduces two novel methods, namely SUBTRUNC and UNIONFL, designed to address the limitations of random client selection. Both approaches utilize submodular function maximization to achieve more balanced models. By modifying the facility location problem, they aim to mitigate the fairness concerns associated with random selection. SUBTRUNC leverages client loss information to diversify solutions, while UNIONFL relies on historical client selection data to ensure a more equitable performance of the final model. Moreover, these algorithms are accompanied by robust theoretical guarantees regarding convergence under reasonable assumptions. The efficacy of these methods is demonstrated through extensive evaluations across heterogeneous scenarios, revealing significant improvements in fairness as measured by a client dissimilarity metric., Comment: 13 pages

Even a relatively weak drive force is enough to push a typical nanomechanical resonator into the nonlinear regime. Consequently, nonlinearities are widespread in nanomechanics and determine the critical characteristics of nanoelectromechanical systems (NEMS) resonators. A thorough understanding of the nonlinear dynamics of higher eigenmodes of NEMS resonators would be beneficial for progress, given their use in applications and fundamental studies. Here, we characterize the nonlinearity and the linear dynamic range (LDR) of each eigenmode of two nanomechanical beam resonators with different intrinsic tension values up to eigenmode $n=11$. We find that the modal Duffing constant increases as $n^4$, while the critical amplitude for the onset of nonlinearity decreases as $1/n$. The LDR, determined from the ratio of the critical amplitude to the thermal noise amplitude, increases weakly with $n$. Our findings are consistent with our theory treating the beam as a string, with the nonlinearity emerging from stretching at high amplitudes. These scaling laws, observed in experiments and validated theoretically, can be leveraged for pushing the limits of NEMS-based sensing even further., Comment: 7 pages, 3 figures

The increasing wireless communication capabilities of vehicles creates opportunities for more efficient intersection management strategies. One promising approach is the replacement of traffic lights with a system wherein vehicles run protocols among themselves to determine right of way. In this paper, we define the intersection problem to model this scenario abstractly, without any assumptions on the specific structure of the intersection or a bound on the number of vehicles. Protocols solving the intersection problem must guarantee safety (no collisions) and liveness (every vehicle eventually goes through). In addition, we would like these protocols to satisfy various optimality criteria, some of which turn out to be achievable only in a subset of the contexts. In particular, we show a partial equivalence between eliminating unnecessary waiting, a criterion of interest in the distributed mutual-exclusion literature, and a notion of optimality that we define called lexicographical optimality. We then introduce a framework to design protocols for the intersection problem by converting an intersection policy, which is based on a global view of the intersection, to a protocol that can be run by the vehicles through the use of knowledge-based programs. Our protocols are shown to guarantee safety and liveness while also being optimal under sufficient conditions on the context. Finally, we investigate protocols in the presence of faulty vehicles that experience communication failures and older vehicles with limited communication capabilities. We show that intersection protocols can be made safe, live and optimal even in the presence of faulty behavior., Comment: The full version of the DISC 2024 paper

Road traffic congestion prediction is a crucial component of intelligent transportation systems, since it enables proactive traffic management, enhances suburban experience, reduces environmental impact, and improves overall safety and efficiency. Although there are several public datasets, especially for metropolitan areas, these datasets may not be applicable to practical scenarios due to insufficiency in the scale of data (i.e. number of sensors and road links) and several external factors like different characteristics of the target area such as urban, highways and the data collection location. To address this, this paper introduces a novel IBB Traffic graph dataset as an alternative benchmark dataset to mitigate these limitations and enrich the literature with new geographical characteristics. IBB Traffic graph dataset covers the sensor data collected at 2451 distinct locations. Moreover, we propose a novel Road Traffic Prediction Model that strengthens temporal links through feature engineering, node embedding with GLEE to represent inter-related relationships within the traffic network, and traffic prediction with ExtraTrees. The results indicate that the proposed model consistently outperforms the baseline models, demonstrating an average accuracy improvement of 4%.

Creation of new annotated public datasets is crucial in helping advances in 3D computer vision and machine learning meet their full potential for automatic interpretation of 3D plant models. In this paper, we introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants. PLANesT-3D is composed of 34 point cloud models representing 34 real plants from three different plant species: \textit{Capsicum annuum}, \textit{Rosa kordana}, and \textit{Ribes rubrum}. Both semantic labels in terms of "leaf" and "stem", and organ instance labels were manually annotated for the full point clouds. As an additional contribution, SP-LSCnet, a novel semantic segmentation method that is a combination of unsupervised superpoint extraction and a 3D point-based deep learning approach is introduced and evaluated on the new dataset. Two existing deep neural network architectures, PointNet++ and RoseSegNet were also tested on the point clouds of PLANesT-3D for semantic segmentation.

Graphs are crucial for representing interrelated data and aiding predictive modeling by capturing complex relationships. Achieving high-quality graph representation is important for identifying linked patterns, leading to improvements in Graph Neural Networks (GNNs) to better capture data structures. However, challenges such as data scarcity, high collection costs, and ethical concerns limit progress. As a result, generative models and data augmentation have become more and more popular. This study explores using generated graphs for data augmentation, comparing the performance of combining generated graphs with real graphs, and examining the effect of different quantities of generated graphs on graph classification tasks. The experiments show that balancing scalability and quality requires different generators based on graph size. Our results introduce a new approach to graph data augmentation, ensuring consistent labels and enhancing classification performance.

Federated edge learning (FEEL) has emerged as a core paradigm for large-scale optimization. However, FEEL still suffers from a communication bottleneck due to the transmission of high-dimensional model updates from the clients to the federator. Over-the-air computation (AirComp) leverages the additive property of multiple-access channels by aggregating the clients' updates over the channel to save communication resources. While analog uncoded transmission can benefit from the increased signal-to-noise ratio (SNR) due to the simultaneous transmission of many clients, potential errors may severely harm the learning process for small SNRs. To alleviate this problem, channel coding approaches were recently proposed for AirComp in FEEL. However, their error-correction capability degrades with an increasing number of clients. We propose a digital lattice-based code construction with constant error-correction capabilities in the number of clients, and compare to nested-lattice codes, well-known for their optimal rate and power efficiency in the point-to-point AWGN channel.