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SqlCompose is a tool that uses generative AI to assist with data analytics tasks, specifically SQL queries. It addresses the challenges of SQL being declarative, having formal table schemas, and often being written in a non-linear manner. The authors develop an internal SQL benchmark to test the performance of the Public Llama model and find that it performs well, with a BLEU score of 53% for single-line predictions and 24% for multi-line predictions. They then fine-tune the Llama model on their internal data and database schemas, resulting in a substantial improvement in performance. They also develop a fill-in-the-middle model, SqlComposeFIM, which is aware of the context before and after the line(s) that need to be completed, and this model outperforms the other two models by 35 percentage points. Additionally, they measure how often the models get the correct table names and find that SqlComposeFIM is able to do this 75% of the time, a major improvement over the other two models. The authors also roll out SqlComposeFIM at Meta and receive positive feedback from users, including completing tedious or repetitive SQL clauses, suggesting boilerplate coding, and help in eliminating the need to remember difficult SQL syntax. However, some users report table and column name hallucinations, which has been reduced with the release of SqlComposeFIM. Overall, the SqlCompose models consistently outperform public and internal LLMs despite their smaller size, providing early indications that smaller specialist models can outperform larger general purpose models., Comment: 11 pages

We experimentally investigate the morphology and breakup of a droplet as it descends freely from a height and encounters an airstream. The size distributions of the child droplets are analysed using high-speed shadowgraphy and in-line holography techniques. We found that a droplet falling from various heights exhibits shape oscillations due to the intricate interplay between inertia and surface tension forces, leading to significant variations in the radial deformation of the droplet, influencing the breakup dynamics under an identical airstream condition. Specifically, the droplet undergoes vibrational breakup when introduced at a location slightly above the air nozzle. In contrast, as the release height of the droplet increases, keeping the Weber number defined based on the velocity of the airstream fixed, a dynamic interplay between the inertia of the droplet and the aerodynamic flow field comes into play, resulting in a sequence of breakup modes transitioning from vibrational breakup to retracting bag breakup, bag breakup, bag-stamen, retracting bag-stamen breakup, and eventually returning to vibrational breakup. Our experiments also reveal that the size distribution resulting from retracting bag breakup primarily arises from rim and node fragmentation, leading to a bimodal distribution. In contrast, bag and bag-stamen breakups yield a tri-modal size distribution due to the combined contributions of bag, rim, and node breakup mechanisms. Furthermore, we utilize a theoretical model that incorporates the effective Weber number, considering different release heights. This model accurately predicts the size distribution of the child droplets resulting from the various breakup modes observed in our experiments., Comment: 23 pages, 17 figures

Assessing the quality of Natural Language Generation (NLG) outputs, such as those produced by large language models (LLMs), poses significant challenges. Traditional approaches involve either resource-intensive human evaluations or automatic metrics, which often exhibit a low correlation with human judgment. In this study, we propose Review-Feedback-Reason (ReFeR), a novel evaluation framework for NLG using LLM agents. We rigorously test ReFeR using two pre-existing benchmark datasets on diverse NLG tasks. The proposed framework not only enhances the accuracy of NLG evaluation, surpassing previous benchmarks by $\sim$20\%, but also generates constructive feedback and significantly improves collective reasoning. This feedback is then leveraged for the creation of instruction-tuning datasets, which, when used to fine-tune smaller models like Mistral-7B, makes them extremely good evaluators, yielding a better correlation with human evaluations and performance nearly on par with GPT-3.5. We highlight the effectiveness of our methodology through its application on three reasoning benchmarks, where it outperforms most of the state-of-the-art methods, and also outperforms the reasoning capabilities of models like GPT-3.5 Turbo by $\sim$11.67\% and GPT-4 by $\sim$1\% on an average., Comment: Paper Under Review

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

In this paper, we address the challenge of differential privacy in the context of graph cuts, specifically focusing on the minimum $k$-cut and multiway cut problems. We introduce edge-differentially private algorithms that achieve nearly optimal performance for these problems. For the multiway cut problem, we first provide a private algorithm with a multiplicative approximation ratio that matches the state-of-the-art non-private algorithm. We then present a tight information-theoretic lower bound on the additive error, demonstrating that our algorithm on weighted graphs is near-optimal for constant $k$. For the minimum $k$-cut problem, our algorithms leverage a known bound on the number of approximate $k$-cuts, resulting in a private algorithm with optimal additive error $O(k\log n)$ for fixed privacy parameter. We also establish a information-theoretic lower bound that matches this additive error. Additionally, we give an efficient private algorithm for $k$-cut even for non-constant $k$, including a polynomial-time 2-approximation with an additive error of $\widetilde{O}(k^{1.5})$., Comment: 38 pages

In different nonlinear mediums, the wave trains carry energy and expose many amazing features. To describe a nonlinear phenomenon, a soliton is one that preserves its shape and amplitude even after the collision. Breather is one kind of soliton structure, which is a localized wave that periodically oscillates in amplitude. This article uses the reductive perturbation technique (RPT) to get the GE from a plasma system with four parts: cold positrons that can move, hot positrons and hot electrons that are spread out in a kappa pattern, and positive ions that can't move. Then, using the Hirota bilinear method (HBM), it is possible to obtain the multi-soliton and breather structures of GE. Breathers are fluctuating regional wave packets and significantly participate in hydrodynamics as well as optics; besides, their interaction can alter the dynamical characteristics of the wave fields. We also incorporate a detailed numerical simulation study based on a newly designed code by two of the co-authors. It is found that in our plasma system, soliton solutions, especially breather solutions, exist. Although superthermal (kappa-distributed) electrons and positrons play an important role in soliton structures, This type of analysis can also apply to the propagation of finite-amplitude waves in natural phenomena like the atmosphere, ocean, optic fibres, signal processing, etc. It should also be useful to study different electrostatic disturbances in space and laboratory plasmas, where immobile positive ions, superthermal electrons, superthermal hot positrons, and mobile cold positrons are the major plasma species., Comment: 34 pages, 26 figures

Due to factors such as low population density and expansive geographical distances, network deployment falls behind in rural regions, leading to a broadband divide. Wireless spectrum serves as the blood and flesh of wireless communications. Shared white spaces such as those in the TVWS and CBRS spectrum bands offer opportunities to expand connectivity, innovate, and provide affordable access to high-speed Internet in under-served areas without additional cost to expensive licensed spectrum. However, the current methods to utilize these white spaces are inefficient due to very conservative models and spectrum policies, causing under-utilization of valuable spectrum resources. This hampers the full potential of innovative wireless technologies that could benefit farmers, small Internet Service Providers (ISPs) or Mobile Network Operators (MNOs) operating in rural regions. This study explores the challenges faced by farmers and service providers when using shared spectrum bands to deploy their networks while ensuring maximum system performance and minimizing interference with other users. Additionally, we discuss how spatiotemporal spectrum models, in conjunction with database-driven spectrum-sharing solutions, can enhance the allocation and management of spectrum resources, ultimately improving the efficiency and reliability of wireless networks operating in shared spectrum bands.

We introduce a simple and efficient text-controllable high-fidelity music generation and editing model. It operates on sequences of continuous latent representations from a low frame rate 48 kHz stereo variational auto encoder codec that eliminates the information loss drawback of discrete representations. Based on a diffusion transformer architecture trained on a flow-matching objective the model can generate and edit diverse high quality stereo samples of variable duration, with simple text descriptions. We also explore a new regularized latent inversion method for zero-shot test-time text-guided editing and demonstrate its superior performance over naive denoising diffusion implicit model (DDIM) inversion for variety of music editing prompts. Evaluations are conducted on both objective and subjective metrics and demonstrate that the proposed model is not only competitive to the evaluated baselines on a standard text-to-music benchmark - quality and efficiency-wise - but also outperforms previous state of the art for music editing when combined with our proposed latent inversion. Samples are available at https://melodyflow.github.io.

We performed an analysis of the extreme-ultraviolet (EUV) wave event on 2022 March 31. The event originated from active region (AR) 12975 located at N13W52 in the field of view of the Atmospheric imaging Assembly (AIA) and exactly at the west limb viewed by the EUV Imager (EUVI) of the Solar Terrestrial Relations Observatory-Ahead (STEREO-A) satellite. The EUV wave was associated with an M9.6 class flare. The event was also well observed by MLSO and COR1 coronagraphs. We revealed here evident coexistence of two components of EUV waves in AIA as well as in EUVI images i.e., a fast-mode wave and a nonwave, which was predicted by the EUV wave hybrid model. The speeds of the fast-mode and non wave EUV wave components in AIA varies from ~430 to 658 km/s and ~157 to 205 km/s, respectively. The computed speeds in STEREO-A for the fast-mode wave and nonwave components are ~520 and ~152 km/s, respectively. Another wave emanated from the source AR and interacted with ambient coronal loops, showing evident reflection in the EUV images above the solar limb. The speed of the reflected wave in the plane of the sky is ~175 km/s. With the precise alignments, we found that the fast-mode EUV wave is just ahead of the coronal mass ejection (CME) and the nonwave component is cospatial with the frontal loop of the accompanied CME. The event also showed stationary fronts., Comment: 6 figures, 16 pages

Hysteresis modeling is crucial to comprehend the behavior of magnetic devices, facilitating optimal designs. Hitherto, deep learning-based methods employed to model hysteresis, face challenges in generalizing to novel input magnetic fields. This paper addresses the generalization challenge by proposing neural operators for modeling constitutive laws that exhibit magnetic hysteresis by learning a mapping between magnetic fields. In particular, two prominent neural operators -- deep operator network and Fourier neural operator -- are employed to predict novel first-order reversal curves and minor loops, where novel means they are not used to train the model. In addition, a rate-independent Fourier neural operator is proposed to predict material responses at sampling rates different from those used during training to incorporate the rate-independent characteristics of magnetic hysteresis. The presented numerical experiments demonstrate that neural operators efficiently model magnetic hysteresis, outperforming the traditional neural recurrent methods on various metrics and generalizing to novel magnetic fields. The findings emphasize the advantages of using neural operators for modeling hysteresis under varying magnetic conditions, underscoring their importance in characterizing magnetic material based devices., Comment: 8 pages, 5 figures

This study focuses on solving the numerical challenges of imposing absorbing boundary conditions for dynamic simulations in the material point method (MPM). To attenuate elastic waves leaving the computational domain, the current work integrates the Perfectly Matched Layer (PML) theory into the implicit MPM framework. The proposed approach introduces absorbing particles surrounding the computational domain that efficiently absorb outgoing waves and reduce reflections, allowing for accurate modeling of wave propagation and its further impact on geotechnical slope stability analysis. The study also includes several benchmark tests to validate the effectiveness of the proposed method, such as several types of impulse loading and symmetric and asymmetric base shaking. The conducted numerical tests also demonstrate the ability to handle large deformation problems, including the failure of elasto-plastic soils under gravity and dynamic excitations. The findings extend the capability of MPM in simulating continuous analysis of earthquake-induced landslides, from shaking to failure., Comment: 29 pages, 19 figures

In the Directed Steiner Tree (DST) problem the input is a directed edge-weighted graph $G=(V,E)$, a root vertex $r$ and a set $S \subseteq V$ of $k$ terminals. The goal is to find a min-cost subgraph that connects $r$ to each of the terminals. DST admits an $O(\log^2 k/\log \log k)$-approximation in quasi-polynomial time, and an $O(k^{\epsilon})$-approximation for any fixed $\epsilon > 0$ in polynomial-time. Resolving the existence of a polynomial-time poly-logarithmic approximation is a major open problem in approximation algorithms. In a recent work, Friggstad and Mousavi [ICALP 2023] obtained a simple and elegant polynomial-time $O(\log k)$-approximation for DST in planar digraphs via Thorup's shortest path separator theorem. We build on their work and obtain several new results on DST and related problems. - We develop a tree embedding technique for rooted problems in planar digraphs via an interpretation of the recursion in Friggstad and Mousavi [ICALP 2023]. Using this we obtain polynomial-time poly-logarithmic approximations for Group Steiner Tree, Covering Steiner Tree, and the Polymatroid Steiner Tree problems in planar digraphs. All these problems are hard to approximate to within a factor of $\Omega(\log^2 n/\log \log n)$ even in trees. - We prove that the natural cut-based LP relaxation for DST has an integrality gap of $O(\log^2 k)$ in planar graphs. This is in contrast to general graphs where the integrality gap of this LP is known to be $\Omega(k)$ and $\Omega(n^{\delta})$ for some fixed $\delta > 0$. - We combine the preceding results with density based arguments to obtain poly-logarithmic approximations for the multi-rooted versions of the problems in planar digraphs. For DST our result improves the $O(R + \log k)$ approximation of Friggstad and Mousavi [ICALP 2023] when $R= \omega(\log^2 k)$.

We consider the control of discrete-time linear dynamical systems using sparse inputs where we limit the number of active actuators at every time step. We develop an algorithm for determining a sparse actuator schedule that ensures the existence of a sparse control input sequence, following the schedule, that takes the system from any given initial state to any desired final state. Since such an actuator schedule is not unique, we look for a schedule that minimizes the energy of sparse inputs. For this, we optimize the trace of the inverse of the resulting controllability Gramian, which is an approximate measure of the average energy of the inputs. We present a greedy algorithm along with its theoretical guarantees. Finally, we empirically show that our greedy algorithm ensures the controllability of the linear system with a small number of active actuators per time step without a significant average energy expenditure compared to the fully actuated system.

Deep learning-based partial differential equation(PDE) solvers have received much attention in the past few years. Methods of this category can solve a wide range of PDEs with high accuracy, typically by transforming the problems into highly nonlinear optimization problems of neural network parameters. This work reviews several deep learning solvers proposed a few years ago, including PINN, WAN, DRM, and VPINN. Numerical results are provided to make comparisons amongst them and address the importance of loss formulation and the optimization method. A rigorous error analysis for PINN is also presented. Finally, we discuss the current limitations and bottlenecks of these methods.

PSR B1259-63 is a gamma-ray binary system that hosts a pulsar in an eccentric orbit, with a 3.4 year period, around an O9.5Ve star. At orbital phases close to periastron passages, the system radiates bright and variable non-thermal emission. We report on an extensive VHE observation campaign conducted with the High Energy Stereoscopic System, comprised of ~100 hours of data taken from $t_p-24$ days to $t_p+127$ days around the system's 2021 periastron passage. We also present the timing and spectral analyses of the source. The VHE light curve in 2021 is consistent with the stacked light curve of all previous observations. Within the light curve, we report a VHE maximum at times coincident with the third X-ray peak first detected in the 2021 X-ray light curve. In the light curve -- although sparsely sampled in this time period -- we see no VHE enhancement during the second disc crossing. In addition, we see no correspondence to the 2021 GeV flare in the VHE light curve. The VHE spectrum obtained from the analysis of the 2021 dataset is best described by a power law of spectral index $\Gamma = 2.65 \pm 0.04_{\text{stat}}$ $\pm 0.04_{\text{sys}}$, a value consistent with the previous H.E.S.S. observations of the source. We report spectral variability with a difference of $\Delta \Gamma = 0.56 ~\pm~ 0.18_{\text{stat}}$ $~\pm~0.10_{\text{sys}}$ at 95% c.l., between sub-periods of the 2021 dataset. We also find a linear correlation between contemporaneous flux values of X-ray and TeV datasets, detected mainly after $t_p+25$ days, suggesting a change in the available energy for non-thermal radiation processes. We detect no significant correlation between GeV and TeV flux points, within the uncertainties of the measurements, from $\sim t_p-23$ days to $\sim t_p+126$ days. This suggests that the GeV and TeV emission originate from different electron populations., Comment: accepted to A&A

For the first time, in March 2024, the transient Galactic black hole candidate Swift J151857.0-572147 experienced an outburst. Using publicly available archived {\it Insight}-HXMT data, we analyze the timing and spectral features of this source. Through model fitting of the power density spectrum, we were able to extract the properties of quasi-periodic oscillations, and based on those properties, we have determined that the QPOs are of type C. We also conclude that the shock instabilities in the transonic advective accretion processes surrounding black holes may be the source of the QPOs. This shock instability could produce variabilities of flux up to 48 keV, as we checked from the QPO energy dependence. High-frequency QPO is not observed during this period. In the broad energy band of $2-100$ keV, simultaneous data from the three on-board instruments of \textit{Insight}-HXMT were used to perform the spectral analysis. A combination of models, including broken power-law, multi-color disk-blackbody continuum, interstellar absorption, and reflection in both neutral and ionized medium were needed for spectral fitting to obtain the best fit. We discovered that at the beginning of the analysis period, the source was in an intermediate state and was transitioning toward the softer states based on the spectral features. It has a hydrogen column density of $(4.3-6.9) \times 10^{22}$ cm$^{-2}$., Comment: 23 pages (1 page Appendix), 14 figures, 7 tables

In recent years, systems involving massive stars with large wind kinetic power have been considered as promising sites for investigating relativistic particle acceleration in low radio frequencies. With this aim, we observed two Wolf-Rayet systems, WR 114 and WR 142, using upgraded Giant Meterwave Radio Telescope observations in Band 4 (550-950 MHz) and Band 5 (1050-1450 MHz). None of the targets was detected at these frequencies. Based on the non-detection, we report 3$\sigma$ upper limits to the radio flux densities at 735 and 1260 MHz (123 and 66 $\mu$Jy for WR 114, and 111 and 96 $\mu$Jy for WR 142, respectively). The plausible scenarios to interpret this non-detection are presented., Comment: 11 pages, 2 figures, Published in the Proceedings of the 3rd BINA Workshop on the Scientific Potential of the Indo-Belgian Cooperation, held at the Graphic Era Hill University, Bhimtal (India)

Non-convex functional constrained optimization problems have gained substantial attention in machine learning and signal processing. This paper develops a new primal-dual algorithm for solving this class of problems. The algorithm is based on a novel form of the Lagrangian function, termed {\em Proximal-Perturbed Augmented Lagrangian}, which enables us to develop an efficient and simple first-order algorithm that converges to a stationary solution under mild conditions. Our method has several key features of differentiation over existing augmented Lagrangian-based methods: (i) it is a single-loop algorithm that does not require the continuous adjustment of the penalty parameter to infinity; (ii) it can achieves an improved iteration complexity of $\widetilde{\mathcal{O}}(1/\epsilon^2)$ or at least ${\mathcal{O}}(1/\epsilon^{2/q})$ with $q \in (2/3,1)$ for computing an $\epsilon$-approximate stationary solution, compared to the best-known complexity of $\mathcal{O}(1/\epsilon^3)$; and (iii) it effectively handles functional constraints for feasibility guarantees with fixed parameters, without imposing boundedness assumptions on the dual iterates and the penalty parameters. We validate the effectiveness of our method through numerical experiments on popular non-convex problems.

The recent years have witnessed great advances in video generation. However, the development of automatic video metrics is lagging significantly behind. None of the existing metric is able to provide reliable scores over generated videos. The main barrier is the lack of large-scale human-annotated dataset. In this paper, we release VideoFeedback, the first large-scale dataset containing human-provided multi-aspect score over 37.6K synthesized videos from 11 existing video generative models. We train VideoScore (initialized from Mantis) based on VideoFeedback to enable automatic video quality assessment. Experiments show that the Spearman correlation between VideoScore and humans can reach 77.1 on VideoFeedback-test, beating the prior best metrics by about 50 points. Further result on other held-out EvalCrafter, GenAI-Bench, and VBench show that VideoScore has consistently much higher correlation with human judges than other metrics. Due to these results, we believe VideoScore can serve as a great proxy for human raters to (1) rate different video models to track progress (2) simulate fine-grained human feedback in Reinforcement Learning with Human Feedback (RLHF) to improve current video generation models.

A vast majority of electrical devices have integrated magnetic units, which generate constant magnetic fields with noticeable vibrations. The majority of existing nanogenerators acquire energy through friction/mechanical forces and most of these instances overlook acoustic vibrations and magnetic fields. Magnetic two-dimensional (2D) tellurides present a wide range of possibilities for devising a potential flexible energy harvester. We have synthesized two-dimensional chromium telluride (2D CrTe3) which exhibits ferromagnetic (FM) nature with a Tc of 224 K. The structure exhibits stable high remnant magnetization, making 2D CrTe3 flakes a potential material for harvesting of magneto-acoustic waves at room temperature. A magneto-acoustic nanogenerator (MANG) was fabricated composing of 2D CrTe3 dispersed in a polymer matrix. Basic mechanical stability and sensitivity of the device with change in load conditions were tested. A high surface charge density of 2.919 mC m-2 was obtained for the device. The thermal strain created in the lattice structure was examined using in-situ Raman spectroscopic measurements. The magnetic anisotropy energy (MAE) responsible for long-range FM ordering was calculated with the help of theoretical modelling. The theoretical calculations also showed opening of electronic bandgap which enhances the flexoelectric effects. The MANG can be a potential energy harvester to synergistically tap into the magneto-acoustic vibrations generated from the frequency changes of a vibrating device such as loudspeakers.

In the Priority $k$-Supplier problem the input consists of a metric space $(F \cup C, d)$ over set of facilities $F$ and a set of clients $C$, an integer $k > 0$, and a non-negative radius $r_v$ for each client $v \in C$. The goal is to select $k$ facilities $S \subseteq F$ to minimize $\max_{v \in C} \frac{d(v,S)}{r_v}$ where $d(v,S)$ is the distance of $v$ to the closes facility in $S$. This problem generalizes the well-studied $k$-Center and $k$-Supplier problems, and admits a $3$-approximation [Plesn\'ik, 1987, Bajpai et al., 2022. In this paper we consider two outlier versions. The Priority $k$-Supplier with Outliers problem [Bajpai et al., 2022] allows a specified number of outliers to be uncovered, and the Priority Colorful $k$-Supplier problem is a further generalization where clients are partitioned into $c$ colors and each color class allows a specified number of outliers. These problems are partly motivated by recent interest in fairness in clustering and other optimization problems involving algorithmic decision making. We build upon the work of [Bajpai et al., 2022] and improve their $9$-approximation Priority $k$-Supplier with Outliers problem to a $1+3\sqrt{3}\approx 6.196$-approximation. For the Priority Colorful $k$-Supplier problem, we present the first set of approximation algorithms. For the general case with $c$ colors, we achieve a $17$-pseudo-approximation using $k+2c-1$ centers. For the setting of $c=2$, we obtain a $7$-approximation in random polynomial time, and a $2+\sqrt{5}\approx 4.236$-pseudo-approximation using $k+1$ centers.

Two-dimensional (2D) chromium telluride Cr2Te3 exhibits strong ferromagnetic ordering with high coercivity at low temperatures and paramagnetic behaviour when approaching room temperature. The spin states of monolayer Cr2Te3 show ferromagnetic ordering in the ground state, and in-situ Raman analysis shows reversible structure transformation and hence a ferromagnetic to paramagnetic transition during low-temperature heating cycles (0 - 25 {\deg}C). The magnetic phase transition near room temperature in the 2D Cr2Te3 prompted the exploration of these layered materials for energy application. We demonstrate that the low-temperature ferromagnetic behavior can be used to magnetically deice material surfaces using an external magnetic source, avoiding the use of harsh chemicals and high temperatures. The hydrophobic nature and dipole interactions of H2O molecules with the surface of the 2D Cr2Te3 coating aid in the condensation of ice droplets formed on the surface. First-principles calculations also confirm the observed crystal structure, surface interaction, and magnetic properties of 2D Cr2Te3.

2D topological insulators promise novel approaches towards electronic, spintronic, and quantum device applications. This is owing to unique features of their electronic band structure, in which bulk-boundary correspondences enforces the existence of 1D spin-momentum locked metallic edge states - both helical and chiral - surrounding an electrically insulating bulk. Forty years since the first discoveries of topological phases in condensed matter, the abstract concept of band topology has sprung into realization with several materials now available in which sizable bulk energy gaps - up to a few hundred meV - promise to enable topology for applications even at room-temperature. Further, the possibility of combining 2D TIs in heterostructures with functional materials such as multiferroics, ferromagnets, and superconductors, vastly extends the range of applicability beyond their intrinsic properties. While 2D TIs remain a unique testbed for questions of fundamental condensed matter physics, proposals seek to control the topologically protected bulk or boundary states electrically, or even induce topological phase transitions to engender switching functionality. Induction of superconducting pairing in 2D TIs strives to realize non-Abelian quasiparticles, promising avenues towards fault-tolerant topological quantum computing. This roadmap aims to present a status update of the field, reviewing recent advances and remaining challenges in theoretical understanding, materials synthesis, physical characterization and, ultimately, device perspectives.

Galactic cosmic rays (GCRs) constitute a significant part of the energy budget of our Galaxy, and the study of their accelerators is of high importance in modern astrophysics. Their main sources are likely supernova remnants (SNRs). These objects are capable to convert a part of their mechanical energy into accelerated charged particles. However, even though the mechanical energy reservoir of SNRs is promising, a conversion rate into particle energy of 10 to 20% is necessary to feed the population of GCRs. Such an efficiency is however not guaranteed. Complementary sources deserve thus to be investigated. This communication aims to address the question of the contribution to the acceleration of GCRs by pre-supernova massive stars in binary or higher multiplicity systems, Comment: 8 pages, 1 figure, Published in the Proceedings of the 3rd BINA Workshop on the Scientific Potential of the Indo-Belgian Cooperation

The kinematics of the stellar halo hold important clues to the assembly history and mass distribution of the Galaxy. In this study, we map the kinematics of stars across the Galactic halo with the H3 Survey. We find a complex distribution that breaks both azimuthal symmetry about the $Z$-axis and mirror symmetry about the Galactic plane. This asymmetry manifests as large variations in the radial velocity dispersion $\sigma_r$ from as ``cold'' as 70 $\text{km}\text{ s}^{-1}$ to as ``hot'' as 160 $\text{km}\text{ s}^{-1}$. We use stellar chemistry to distinguish accreted stars from in-situ stars in the halo, and find that the accreted population has higher $\sigma_r$ and radially biased orbits, while the in-situ population has lower $\sigma_r$ and isotropic orbits. As a result, the Galactic halo kinematics are highly heterogeneous and poorly approximated as being spherical or axisymmetric. We measure radial profiles of $\sigma_r$ and the anisotropy parameter $\beta$ over Galactocentric radii $10-80\text{ kpc}$, and find that discrepancies in the literature are due to the nonspherical geometry and heterogeneous nature of the halo. Investigating the effect of strongly asymmetric $\sigma_r$ and $\beta$ on equilibrium models is a path forward to accurately constraining the Galactic gravitational field, including its total mass., Comment: submitted to ApJ; comments welcome

Motivated by the recent discovery of dielectric relaxation by quantum critical magnons in Cs$_2$Cu$_2$Mo$_3$O$_{12}$, we conduct a detailed analysis of its dielectric response and compare it to that in the isostructural compound Rb$_2$Cu$_2$Mo$_3$O$_{12}$. Measurements in the vicinity of the field-induced magnon softening show that its description in terms of 3D Bose-Einstein condensation of magnons quantum critical point is unaltered by the presence of dielectric relaxation. We also demonstrate the existence of dielectric relaxation anomalies at 19 K in Rb$_2$Cu$_2$Mo$_3$O$_{12}$ and discuss the implications for the microscopic origin of dielectric activity in two compounds., Comment: 8 pages, 10 figures

Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a novel General-purpose Large Audio-Language Model (LALM) with Advanced Audio Understanding and Complex Reasoning Abilities. We build GAMA by integrating an LLM with multiple types of audio representations, including features from a custom Audio Q-Former, a multi-layer aggregator that aggregates features from multiple layers of an audio encoder. We fine-tune GAMA on a large-scale audio-language dataset, which augments it with audio understanding capabilities. Next, we propose CompA-R (Instruction-Tuning for Complex Audio Reasoning), a synthetically generated instruction-tuning (IT) dataset with instructions that require the model to perform complex reasoning on the input audio. We instruction-tune GAMA with CompA-R to endow it with complex reasoning abilities, where we further add a soft prompt as input with high-level semantic evidence by leveraging event tags of the input audio. Finally, we also propose CompA-R-test, a human-labeled evaluation dataset for evaluating the capabilities of LALMs on open-ended audio question-answering that requires complex reasoning. Through automated and expert human evaluations, we show that GAMA outperforms all other LALMs in literature on diverse audio understanding tasks by margins of 1%-84%. Further, GAMA IT-ed on CompA-R proves to be superior in its complex reasoning and instruction following capabilities., Comment: Project Website: https://sreyan88.github.io/gamaaudio/

Cultural variation exists between nations (e.g., the United States vs. China), but also within regions (e.g., California vs. Texas, Los Angeles vs. San Francisco). Measuring this regional cultural variation can illuminate how and why people think and behave differently. Historically, it has been difficult to computationally model cultural variation due to a lack of training data and scalability constraints. In this work, we introduce a new research problem for the NLP community: How do we measure variation in cultural constructs across regions using language? We then provide a scalable solution: building knowledge-guided lexica to model cultural variation, encouraging future work at the intersection of NLP and cultural understanding. We also highlight modern LLMs' failure to measure cultural variation or generate culturally varied language., Comment: Accepted at NAACL 2024

A site-specific radio channel representation considers the surroundings of the communication system through the environment geometry, such as buildings, vegetation, and mobile objects including their material and surface properties. In this article, we focus on communication technologies for 5G and beyond that are increasingly able to exploit the specific environment geometry for both communication and sensing. We present methods for a site-specific radio channel representation that is spatially consistent, such that mobile transmitter and receveiver cause a correlated time-varying channel impulse response. When modelled as random, this channel impulse response has non-stationary statistical properties, i.e., a time-variant Doppler spectrum, power delay profile, K-factor and spatial correlation. A site-specific radio channel representation will enable research into emerging 5G and beyond technologies such as distributed multiple-input multiple-output systems, reconfigurable intelligent surfaces, multi-band communication, and joint communication and sensing. These 5G and beyond technologies will be deployed for a wide range of environments, from dense urban areas to railways, road transportation, industrial automation, and unmanned aerial vehicles., Comment: 7 pages, 5 figures, submitted to the IEEE Communication Magazine

The Facial Action Coding System (FACS) for studying facial expressions is manual and requires significant effort and expertise. This paper explores the use of automated techniques to generate Action Units (AUs) for studying facial expressions. We propose an unsupervised approach based on Principal Component Analysis (PCA) and facial keypoint tracking to generate data-driven AUs called PCA AUs using the publicly available DISFA dataset. The PCA AUs comply with the direction of facial muscle movements and are capable of explaining over 92.83 percent of the variance in other public test datasets (BP4D-Spontaneous and CK+), indicating their capability to generalize facial expressions. The PCA AUs are also comparable to a keypoint-based equivalence of FACS AUs in terms of variance explained on the test datasets. In conclusion, our research demonstrates the potential of automated techniques to be an alternative to manual FACS labeling which could lead to efficient real-time analysis of facial expressions in psychology and related fields. To promote further research, we have made code repository publicly available., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in [LNCS,volume 14301], and is available online at https://doi.org/10.1007/978-3-031-45170-6_85

Let $G$ be a simple graph with associated diagonal matrix of vertex degrees $D(G)$, adjacency matrix $A(G)$, Laplacian matrix $L(G)$ and signless Laplacian matrix $Q(G)$. Recently, Nikiforov proposed the family of matrices $A_\alpha(G)$ defined for any real $\alpha\in [0,1]$ as $A_\alpha(G):=\alpha\,D(G)+(1-\alpha)\,A(G)$, and also mentioned that the matrices $A_\alpha(G)$ can underpin a unified theory of $A(G)$ and $Q(G)$. Inspired from the above definition, we introduce the $B_\alpha$-matrix of $G$, $B_\alpha(G):=\alpha A(G)+(1-\alpha)L(G)$ for $\alpha\in [0,1]$. Note that $ L(G)=B_0(G), D(G)=2B_{\frac{1}{2}}(G), Q(G)=3B_{\frac{2}{3}}(G), A(G)=B_1(G)$. In this article, we study several spectral properties of $ B_\alpha $-matrices to unify the theories of adjacency, Laplacian, and signless Laplacian matrices of graphs. In particular, we prove that each eigenvalue of $ B_\alpha(G) $ is continuous on $ \alpha $. Using this, we characterize positive semidefinite $ B_\alpha $-matrices in terms of $\alpha$. As a consequence, we provide an upper bound of the independence number of $ G $. Besides, we establish some bounds for the largest and the smallest eigenvalues of $B_\alpha(G)$. As a result, we obtain a bound for the chromatic number of $G$ and deduce several known results. In addition, we present a Sachs-type result for the characteristic polynomial of a $ B_\alpha $-matrix., Comment: The final version of the article to be appear in Ars Mathematica Contemporanea

Monolayer $\mathrm{MoS_2}$ under strain has many interesting properties and possible applications in technology. A recent experimental study examined the effect of strain on the bandgap of monolayer $\mathrm{MoS_2}$ on a mildly curved graphite surface, reporting that under biaxial strain with a Poisson's ratio of 0.44, the bandgap decreases at a rate of 400 meV/% strain. In this work, we performed density functional theory (DFT) calculations for a free-standing $\mathrm{MoS_2}$ monolayer, using the generalized gradient approximation (GGA) PBE, the hybrid functional HSE06, and many-body perturbation theory with the GW approximation using PBE wavefunctions (G0W0@PBE). We found that under biaxial strain with the experimental Poisson's ratio, the bandgap decreases at rates of 63 meV/% strain (PBE), 73 meV/% strain (HSE06), and 43 meV/% strain (G0W0@PBE), which are significantly smaller than the experimental rate. We also found that PBE predicts a similarly smaller rate (90 meV/% strain) for a different Poisson's ratio of 0.25. Spin-orbit correction (SOC) has little effect on the gap or its strain dependence. Additionally, we observed a semiconductor-to-metal transition under an equal tensile biaxial strain of 10% and a transition from a direct to an indirect bandgap, consistent with previous theoretical work.

While coreference resolution is a well-established research area in Natural Language Processing (NLP), research focusing on Thai language remains limited due to the lack of large annotated corpora. In this work, we introduce ThaiCoref, a dataset for Thai coreference resolution. Our dataset comprises 777,271 tokens, 44,082 mentions and 10,429 entities across four text genres: university essays, newspapers, speeches, and Wikipedia. Our annotation scheme is built upon the OntoNotes benchmark with adjustments to address Thai-specific phenomena. Utilizing ThaiCoref, we train models employing a multilingual encoder and cross-lingual transfer techniques, achieving a best F1 score of 67.88\% on the test set. Error analysis reveals challenges posed by Thai's unique linguistic features. To benefit the NLP community, we make the dataset and the model publicly available at http://www.github.com/nlp-chula/thai-coref .

The development of existing facial coding systems, such as the Facial Action Coding System (FACS), relied on manual examination of facial expression videos for defining Action Units (AUs). To overcome the labor-intensive nature of this process, we propose the unsupervised learning of an automated facial coding system by leveraging computer-vision-based facial keypoint tracking. In this novel facial coding system called the Data-driven Facial Expression Coding System (DFECS), the AUs are estimated by applying dimensionality reduction to facial keypoint movements from a neutral frame through a proposed Full Face Model (FFM). FFM employs a two-level decomposition using advanced dimensionality reduction techniques such as dictionary learning (DL) and non-negative matrix factorization (NMF). These techniques enhance the interpretability of AUs by introducing constraints such as sparsity and positivity to the encoding matrix. Results show that DFECS AUs estimated from the DISFA dataset can account for an average variance of up to 91.29 percent in test datasets (CK+ and BP4D-Spontaneous) and also surpass the variance explained by keypoint-based equivalents of FACS AUs in these datasets. Additionally, 87.5 percent of DFECS AUs are interpretable, i.e., align with the direction of facial muscle movements. In summary, advancements in automated facial coding systems can accelerate facial expression analysis across diverse fields such as security, healthcare, and entertainment. These advancements offer numerous benefits, including enhanced detection of abnormal behavior, improved pain analysis in healthcare settings, and enriched emotion-driven interactions. To facilitate further research, the code repository of DFECS has been made publicly accessible.

In this work, we present a heretofore unseen application of Ising machines to perform trust region-based optimisation with box constraints. This is done by considering a specific form of opto-electronic oscillator-based coherent Ising machines with clipped transfer functions, and proposing appropriate modifications to facilitate trust-region optimisation. The enhancements include the inclusion of non-symmetric coupling and linear terms, modulation of noise, and compatibility with convex-projections to improve its convergence. The convergence of the modified Ising machine has been shown under the reasonable assumptions of convexity or invexity. The mathematical structures of the modified Ising machine and trust-region methods have been exploited to design a new trust-region method to effectively solve unconstrained optimisation problems in many scenarios, such as machine learning and optimisation of parameters in variational quantum algorithms. Hence, the proposition is useful for both classical and quantum-classical hybrid scenarios. Finally, the convergence of the Ising machine-based trust-region method, has also been proven analytically, establishing the feasibility of the technique., Comment: This is a first draft; proofs of the lemmas, theorems, and corollaries herein will be included in the next version, along with experimental results. Reviews, comments, and discussions are welcome

Voice conversion (VC) research traditionally depends on scripted or acted speech, which lacks the natural spontaneity of real-life conversations. While natural speech data is limited for VC, our study focuses on filling in this gap. We introduce a novel data-sourcing pipeline that makes the release of a natural speech dataset for VC, named NaturalVoices. The pipeline extracts rich information in speech such as emotion and signal-to-noise ratio (SNR) from raw podcast data, utilizing recent deep learning methods and providing flexibility and ease of use. NaturalVoices marks a large-scale, spontaneous, expressive, and emotional speech dataset, comprising over 3,800 hours speech sourced from the original podcasts in the MSP-Podcast dataset. Objective and subjective evaluations demonstrate the effectiveness of using our pipeline for providing natural and expressive data for VC, suggesting the potential of NaturalVoices for broader speech generation tasks.

Community and core-periphery are two widely studied graph structures, with their coexistence observed in real-world graphs (Rombach, Porter, Fowler \& Mucha [SIAM J. App. Math. 2014, SIAM Review 2017]). However, the nature of this coexistence is not well understood and has been pointed out as an open problem (Yanchenko \& Sengupta [Statistics Surveys, 2023]). Especially, the impact of inferring the core-periphery structure of a graph on understanding its community structure is not well utilized. In this direction, we introduce a novel quantification for graphs with ground truth communities, where each community has a densely connected part (the core), and the rest is more sparse (the periphery), with inter-community edges more frequent between the peripheries. Built on this structure, we propose a new algorithmic concept that we call relative centrality to detect the cores. We observe that core-detection algorithms based on popular centrality measures such as PageRank and degree centrality can show some bias in their outcome by selecting very few vertices from some cores. We show that relative centrality solves this bias issue and provide theoretical and simulation support, as well as experiments on real-world graphs. Core detection is known to have important applications with respect to core-periphery structures. In our model, we show a new application: relative-centrality-based algorithms can select a subset of the vertices such that it contains sufficient vertices from all communities, and points in this subset are better separable into their respective communities. We apply the methods to 11 biological datasets, with our methods resulting in a more balanced selection of vertices from all communities such that clustering algorithms have better performance on this set.

$ $Large language models (LLMs) have become an increasingly important workload for cloud providers catering to both enterprise and consumer applications. LLM inference requests from these applications have end-to-end latency SLOs that must be adhered to in production settings. However, existing LLM serving systems focus on optimization objectives such as request serving throughput or request execution latency rather than the end-to-end latency SLOs. Achieving end-to-end SLOs for latency-sensitive requests is challenging due to head-of-line (HOL) blocking in the request queue, which results from bursty arrival rates and insufficient resources. To address the above challenge, we propose QLM, a multi-model queue management framework for LLM serving. QLM uses stochastic programming to orchestrate the actions of multiple LLM Serving Operations (LSOs) to reduce HOL blocking and maximize SLO attainment. Specifically, QLM uses the following LSOs: model swapping, request eviction, GPU-CPU state swapping, load balancing, and warm model start. Evaluation on heterogeneous GPU devices and models with real-world LLM serving dataset shows that QLM improves SLO attainment by 40-90% and throughput by 20-400% while maintaining or improving device utilization compared to other state-of-the-art LLM serving systems.

The morphology of the 21-cm signal emitted by the neutral hydrogen present in the intergalactic medium (IGM) during the Epoch of Reionization (EoR) depends both on the properties of the sources of ionizing radiation and on the underlying physical processes within the IGM. Variation in the morphology of the IGM 21-cm signal due to the different sources of the EoR is expected to have a significant impact on the 21-cm bispectrum, which is one of the crucial observable statistics that can evaluate the non-Gaussianity present in the signal and which can be estimated from radio interferometric observations of the EoR. Here we present the 21-cm bispectrum for different reionization scenarios assuming different simulated models for the sources of reionization. We also demonstrate how well the 21-cm bispectrum can distinguish between different IGM 21-cm signal morphologies, arising due to the differences in the reionization scenarios, which will help us shed light on the nature of the sources of ionizing photons. Our estimated large-scale bispectrum for all unique $k$-triangle shapes shows a significant difference in their magnitude and sign across different reionization scenarios. Additionally, our focused analysis of bispectrum for a few specific $k$-triangle shapes (e.g. squeezed-limit, linear, and shapes in the vicinity of the squeezed-limit) shows that the large scale 21-cm bispectrum can distinguish between reionization scenarios that show inside-out, outside-in and a combination of inside-out and outside-in morphologies. These results highlight the potential of using the 21-cm bispectrum for constraining different reionization scenarios., Comment: 28 pages, 7 figures, comments are welcome, prepared for submission to JCAP

Recent advances in style transfer text-to-speech (TTS) have improved the expressiveness of synthesized speech. Despite these advancements, encoding stylistic information from diverse and unseen reference speech remains challenging. This paper introduces StyleMoE, an approach that divides the embedding space, modeled by the style encoder, into tractable subsets handled by style experts. The proposed method replaces the style encoder in a TTS system with a Mixture of Experts (MoE) layer. By utilizing a gating network to route reference speeches to different style experts, each expert specializes in aspects of the style space during optimization. Our experiments objectively and subjectively demonstrate the effectiveness of our proposed method in increasing the coverage of the style space for diverse and unseen styles. This approach can enhance the performance of existing state-of-the-art style transfer TTS models, marking the first study of MoE in style transfer TTS to our knowledge.

Spatiotemporal data is prevalent in a wide range of edge devices, such as those used in personal communication and financial transactions. Recent advancements have sparked a growing interest in integrating spatiotemporal analysis with large-scale language models. However, spatiotemporal data often contains sensitive information, making it unsuitable for open third-party access. To address this challenge, we propose a Graph-GAN-based model for generating privacy-protected spatiotemporal data. Our approach incorporates spatial and temporal attention blocks in the discriminator and a spatiotemporal deconvolution structure in the generator. These enhancements enable efficient training under Gaussian noise to achieve differential privacy. Extensive experiments conducted on three real-world spatiotemporal datasets validate the efficacy of our model. Our method provides a privacy guarantee while maintaining the data utility. The prediction model trained on our generated data maintains a competitive performance compared to the model trained on the original data.

Modular data centers (MDCs) that can be placed right at the energy farms and powered mostly by renewable energy, are proven to be a flexible and effective approach to lowering the carbon footprint of data centers. However, the main challenge of using renewable energy is the high variability of power produced, which implies large volatility in powering computing resources at MDCs, and degraded application performance due to the task evictions and migrations. This causes challenges for platform operators to decide the MDC deployment. To this end, we present SkyBox, a framework that employs a holistic and learning-based approach for platform operators to explore the efficient use of renewable energy with MDC deployment across geographical regions. SkyBox is driven by the insights based on our study of real-world power traces from a variety of renewable energy farms -- the predictable production of renewable energy and the complementary nature of energy production patterns across different renewable energy sources and locations. With these insights, SkyBox first uses the coefficient of variation metric to select the qualified renewable farms, and proposes a subgraph identification algorithm to identify a set of farms with complementary energy production patterns. After that, SkyBox enables smart workload placement and migrations to further tolerate the power variability. Our experiments with real power traces and datacenter workloads show that SkyBox has the lowest carbon emissions in comparison with current MDC deployment approaches. SkyBox also minimizes the impact of the power variability on cloud virtual machines, enabling rMDCs a practical solution of efficiently using renewable energy.

We study the importance of precise modelling of the photometric redshift error distributions on the estimation of parameters from cross correlation measurements and present a working example of the scattering matrix formalism to correct for the redshift bin mismatch of objects in tomographic cross correlation analysis. We measured the angular galaxy auto-power spectrum and cross-power spectrum in four tomographic bins with redshift intervals $z = [0.0,0.3,0.45,0.6,0.8]$ from the cross correlation of Planck Cosmic Microwave Background lensing potential and photometric galaxy catalogue from the Dark Energy Spectroscopic Instrument Legacy Imaging Survey Data Release 8. We estimated galaxy linear bias and amplitude of cross correlation using maximum likelihood estimation to put constraints on the $\sigma_{8}$ parameter. We show that the modified Lorentzian function used to fit the photometric redshift error distribution performs well only near the peaks of the distribution. We adopt a sum of Gaussians model to capture the broad tails of the error distribution. Our sum of Gaussians model yields $\sim 2-5\,\sigma$ smaller values of cross correlation amplitude compared to the $\Lambda$CDM expectations. We compute the $\sigma_{8}$ parameter after correcting for the redshift bin mismatch of objects following the scattering matrix approach. The $\sigma_{8}$ parameter becomes consistent with $\Lambda$CDM model in the last tomographic bin but shows $\sim 1-3\,\sigma$ tension in other redshift bins., Comment: 21 pages, 17 figures, 4 tables, submitted to Astronomy and Astrophysics

During epithelial-mesenchymal transition (EMT), epithelial cells change their morphology, disperse, and gain mesenchymal-like characteristics. Usually, cells are categorized into discrete cell types or states based on gene expression and other cellular features. Subsequently, EMT is investigated as a dynamical process where cells jump from one discrete state to another. In the current work, we moved away from this idea of discrete state transition and investigated EMT dynamics in a continuous phenotypic space. We used morphology to define the phenotype of a cell. We used the data from quantitative image analysis of MDA-MB-468 cells undergoing EGF-induced EMT. We defined the morphological state space or 'morphospace' using the morphological features extracted through image analysis. During EMT, as the morphology changed, the distribution of cells in the morphospace also changed. However, this morphospace had a very high dimension. We reduced it to a 2-dimensional "reduced morphospace" and investigated the temporal change in the spatial distribution of cells in this reduced space. We used proper orthogonal decomposition to find dominant dynamical features of this spatio-temporal data. The modal analysis detected key features of EMT in this experimental system - reversible transition, distinct paths of phenotypic transition during induction and reversal of EMT, and enhanced diversity of cells during reversal of EMT. We also provide some intuitive physical meaning of the spatial modes and connect them to the key molecular event during EMT., Comment: 33 pages, 8 figures

We show with Gaia XP spectroscopy that extremely metal-rich stars in the Milky Way (EMR; $[M/H]_{XP} > 0.5$) - but only those - are largely confined to a tight "knot" at the center of the Galaxy. This EMR knot is round in projection, has a fairly abrupt edge near $\sim 1.5$kpc, and is a dynamically hot system. This central knot also contains very metal-rich (VMR; $+0.2\le [M/H]_{XP} \le +0.4$) stars. However, in contrast to EMR stars, the bulk of VMR stars form an extended, highly flattened distribution in the inner Galaxy ($R_{\mathrm{GC}}\lesssim 5$ kpc). We draw on TNG50 simulations of Milky Way analogs for context and find that compact, metal-rich knots confined to $<1.5$kpc are a universal feature. In typical simulated analogs, the top 5-10% most metal-rich stars are confined to a central knot; however, in our Milky Way data this fraction is only 0.1%. Dust-penetrating wide-area near-infrared spectroscopy, such as SDSS-V, will be needed for a rigorous estimate of the fraction of stars in the Galactic EMR knot. Why in our Milky Way only EMR giants are confined to such a central knot remains to be explained. Remarkably, the central few kiloparsecs of the Milky Way harbor both the highest concentration of metal-poor stars (the `poor old heart') and almost all EMR stars. This highlights the stellar population diversity at the bottom of galactic potential wells., Comment: 11 pages, 7 figures, submitted to ApJ

The infall of the Large Magellanic Cloud (LMC) is predicted to displace the inner Milky Way (MW), imprinting an apparent 'reflex motion' on the observed velocities of distant halo stars. We construct the largest all-sky spectroscopic dataset of luminous red giant stars from $50-160$ kpc, including a new survey of the southern celestial hemisphere. We fit the full 6D kinematics of our data to measure the amplitude and direction of the inner MW's motion towards the outer halo. The observed velocity grows with distance such that, relative to halo stars at $100$ kpc, the inner MW is lurching at $\approx 40$ km s$^{-1}$ towards a recent location along the LMC's past orbit. Our measurements align with N-body simulations of the halo's response to a $1.8 \times 10^{11} M_\odot$ LMC on first infall, suggesting that the LMC is at least 15% as massive as the MW. Our findings highlight the dramatic disequilibrium of the MW outskirts, and will enable more accurate measurements of the total mass of our Galaxy., Comment: 25 pages, 15 figures. Submitted to ApJ

In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse domains. However, as models continue to improve, their performance on these benchmarks has begun to plateau, making it increasingly difficult to discern differences in model capabilities. This paper introduces MMLU-Pro, an enhanced dataset designed to extend the mostly knowledge-driven MMLU benchmark by integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. Additionally, MMLU-Pro eliminates the trivial and noisy questions in MMLU. Our experimental results show that MMLU-Pro not only raises the challenge, causing a significant drop in accuracy by 16% to 33% compared to MMLU but also demonstrates greater stability under varying prompts. With 24 different prompt styles tested, the sensitivity of model scores to prompt variations decreased from 4-5% in MMLU to just 2% in MMLU-Pro. Additionally, we found that models utilizing Chain of Thought (CoT) reasoning achieved better performance on MMLU-Pro compared to direct answering, which is in stark contrast to the findings on the original MMLU, indicating that MMLU-Pro includes more complex reasoning questions. Our assessments confirm that MMLU-Pro is a more discriminative benchmark to better track progress in the field.

Today's era is the digitized era. Managing such generated big data is an important factor for data scientists. Day by day, it increases the demand for big data storage systems. Different organizations are involved in providing storage-related services. They follow the different architectures or storage models for storing big data. In this survey paper, our target is to highlight such storage architectures which provided by different renowned storage service providers. On an architectural basis, we divide the big data storage systems into five parts, Distributed file systems (DFS), Clustered File Systems (CFS), Cloud Storage, Archive Storage, and Object Storage Systems (OSS). Also, we reveal a detailed architectural view of the big data storage systems provided by the different organizations under these parts., Comment: 32 Pages

Approximation of solutions to partial differential equations (PDE) is an important problem in computational science and engineering. Using neural networks as an ansatz for the solution has proven a challenge in terms of training time and approximation accuracy. In this contribution, we discuss how sampling the hidden weights and biases of the ansatz network from data-agnostic and data-dependent probability distributions allows us to progress on both challenges. In most examples, the random sampling schemes outperform iterative, gradient-based optimization of physics-informed neural networks regarding training time and accuracy by several orders of magnitude. For time-dependent PDE, we construct neural basis functions only in the spatial domain and then solve the associated ordinary differential equation with classical methods from scientific computing over a long time horizon. This alleviates one of the greatest challenges for neural PDE solvers because it does not require us to parameterize the solution in time. For second-order elliptic PDE in Barron spaces, we prove the existence of sampled networks with $L^2$ convergence to the solution. We demonstrate our approach on several time-dependent and static PDEs. We also illustrate how sampled networks can effectively solve inverse problems in this setting. Benefits compared to common numerical schemes include spectral convergence and mesh-free construction of basis functions., Comment: 16 pages, 15 figures