Search

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

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

This paper deals with gravitational thermodynamics on the dynamical apparent horizon of an FLRW universe with dissipation. The dissipation is assumed to arise due to adiabatic gravitational particle creation. For the thermodynamic study, we consider the Bekenstein-Hawking formalism and also assume a nonzero curvature $\kappa$ for a general study. In particular, we study the unified first law, the generalized second law, and thermodynamic stability in our model. The specific heat capacities are taken into account for the study of thermodynamic stability. Our study reveals a fascinating result! The ratio of the specific heat capacity at constant pressure and that at constant volume in a flat FLRW universe with dissipation is nothing but the negative of the deceleration parameter. In classical thermodynamics, this ratio is known as the isentropic expansion factor or (for ideal gases) the adiabatic index. A more interesting fact that has come to light is that this relation holds true in the $\Lambda$CDM model also! So, this is actually a generic result and is independent of the cosmological model used. We discuss the implications of this result on the evolution of the Universe. Finally, we determine the constraints on the effective equation of state which guarantees thermodynamic stability in our model., Comment: 8 pages

Digital media platforms (e.g., social media, science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this challenge, we designed TranSlider, an AI-powered tool that generates personalized translations of scientific text based on individual user profiles (e.g., hobbies, location, and education). Our tool features an interactive slider that allows users to steer the degree of personalization from 0 (weakly relatable) to 100 (strongly relatable), leveraging LLMs to generate the translations with given degrees. Through an exploratory study with 15 participants, we investigated both the utility of these AI-personalized translations and how interactive reading features influenced users' understanding and reading experiences. We found that participants who preferred higher degrees of personalization appreciated the relatable and contextual translations, while those who preferred lower degrees valued concise translations with subtle contextualization. Furthermore, participants reported the compounding effect of multiple translations on their understanding of scientific content. Given these findings, we discuss several implications of AI-personalized translation tools in facilitating communication in collaborative contexts., Comment: 23 pages, 5 figures, 1 table

Self-assembly of nanoscale synthetic subunits is a promising bottom-up strategy for fabrication of functional materials. Here, we introduce a design principle for DNA origami nanoparticles of 50-nm size, exploiting modularity, to make a family of versatile subunits that can target an abundant variety of self-assembled structures. The subunits are based on a core module that remains constant among all the subunits. Variable bond modules and angle modules are added to the exterior of the core to control interaction specificity, strength and structural geometry. A series of subunits with designed bond/angle modules are demonstrated to self-assemble into a rich variety of structures with different Gaussian curvatures, exemplified by sheets, spherical shells, and tubes. The design features flexible joints implemented using single-stranded angle modules between adjacent subunits whose mechanical properties, such as bending elastic moduli, are inferred from cryo-EM. Our findings suggest that incorporating a judicious amount of flexibility in the bond provides error tolerances in design and fabrication while still guaranteeing target fidelity. Lastly, while increasing flexibility could introduce greater variability and potential errors in assembly, these effects can be counterbalanced by increasing the number of distinct bonds, thereby allowing for precise targeting of specific structural binding angles within a broad range of configurations., Comment: 43 pages, 4 main figures, 9 supplementary figures

Fainter standard stars are essential for the calibration of larger telescopes. This work adds to the CALSPEC (calibration spectra) database 19 faint white dwarfs (WDs) with all-sky coverage and V magnitudes between 16.5 and 18.7. Included for these stars is new UV (ultraviolet) HST (Hubble Space Telescope) STIS (Space Telescope Imaging Spectrometer) spectrophotometry between 1150 and 3000~\AA\ with a resolution of $\sim$500. Pure hydrogen WD models are fit to these UV spectra and to six-band HST/WFC3 (Wide Field Camera 3) photometry at 0.28 to 1.6~\micron\ to construct predicted model SEDs (spectral energy distributions) covering wavelengths from 900~\AA\ to the JWST (James Webb Space Telescope) limit of 30~\micron\ using well-established CALSPEC procedures for producing flux standards with the goal of 1\% accuracy.

In the last few decades planet search surveys have been focusing on solar type stars, and only recently the high-mass regimes. This is mostly due to challenges arising from the lack of instrumental precision, and more importantly, the inherent active nature of fast rotating massive stars. Here we report NGTS-33b (TOI-6442b), a super-Jupiter planet with mass, radius and orbital period of 3.6 $\pm$ 0.3 M$_{\rm jup}$, 1.64 $\pm$ 0.07 R$_{\rm jup}$ and $2.827972 \pm 0.000001$ days, respectively. The host is a fast rotating ($0.6654 \pm 0.0006$ day) and hot (T$_{\rm eff}$ = 7437 $\pm$ 72 K) A9V type star, with a mass and radius of 1.60 $\pm$ 0.11 M$_{\odot}$ and 1.47 $\pm$ 0.06 R$_{\odot}$, respectively. Planet structure and Gyrochronology models shows that NGTS-33 is also very young with age limits of 10-50 Myr. In addition, membership analysis points towards the star being part of the Vela OB2 association, which has an age of $\sim$ 20-35 Myr, thus providing further evidences about the young nature of NGTS-33. Its low bulk density of 0.19$\pm$0.03 g cm$^{-3}$ is 13$\%$ smaller than expected when compared to transiting hot Jupiters with similar masses. Such cannot be solely explained by its age, where an up to 15$\%$ inflated atmosphere is expected from planet structure models. Finally, we found that its emission spectroscopy metric is similar to JWST community targets, making the planet an interesting target for atmospheric follow-up. Therefore, NGTS-33b's discovery will not only add to the scarce population of young, massive and hot Jupiters, but will also help place further strong constraints on current formation and evolution models for such planetary systems., Comment: 19 pages, 17 figures, 7 tables, accepted for publication in MNRAS

The concentration-virial mass (c-M) relation is a fundamental scaling relation within the standard cold dark matter ($\Lambda$CDM) framework well established in numerical simulations. However, observational constraints of this relation are hampered by the difficulty of characterising the properties of dark matter haloes. Recent comparisons between simulations and observations have suggested a systematic difference of the c-M relation, with higher concentrations in the latter. In this work, we undertake detailed comparisons between simulated galaxies and observations of a sample of strong-lensing galaxies. We explore several factors of the comparison with strong gravitational lensing constraints, including the choice of the generic dark matter density profile, the effect of radial resolution, the reconstruction limits of observed versus simulated mass profiles, and the role of the initial mass function in the derivation of the dark matter parameters. Furthermore, we show the dependence of the c-M relation on reconstruction and model errors through a detailed comparison of real and simulated gravitational lensing systems. An effective reconciliation of simulated and observed c-M relations can be achieved if one considers less strict assumptions on the dark matter profile, for example, by changing the slope of a generic NFW profile or focusing on rather extreme combinations of stellar-to-dark matter distributions. A minor effect is inherent to the applied method: fits to the NFW profile on a less well-constrained inner mass profile yield slightly higher concentrations and lower virial masses., Comment: 9 pages, 9 figures, 2 tables. Accepted for publication in A&A

We introduce the Extract-Refine-Retrieve-Read (ERRR) framework, a novel approach designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RAG) systems through query optimization tailored to meet the specific knowledge requirements of Large Language Models (LLMs). Unlike conventional query optimization techniques used in RAG, the ERRR framework begins by extracting parametric knowledge from LLMs, followed by using a specialized query optimizer for refining these queries. This process ensures the retrieval of only the most pertinent information essential for generating accurate responses. Moreover, to enhance flexibility and reduce computational costs, we propose a trainable scheme for our pipeline that utilizes a smaller, tunable model as the query optimizer, which is refined through knowledge distillation from a larger teacher model. Our evaluations on various question-answering (QA) datasets and with different retrieval systems show that ERRR consistently outperforms existing baselines, proving to be a versatile and cost-effective module for improving the utility and accuracy of RAG systems.

As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies, significantly enhancing the quality of synthesized videos. Particularly in the realm of biomedicine, video generation technology has shown immense potential such as medical concept explanation, disease simulation, and biomedical data augmentation. In this article, we thoroughly examine the latest developments in video generation models and explore their applications, challenges, and future opportunities in the biomedical sector. We have conducted an extensive review and compiled a comprehensive list of datasets from various sources to facilitate the development and evaluation of video generative models in biomedicine. Given the rapid progress in this field, we have also created a github repository to regularly update the advances of biomedical video generation at: https://github.com/Lee728243228/Biomedical-Video-Generation

We explore some of the existing research in HCI around technology for older adults and examine the role of LLMs in enhancing it. We also discuss the digital divide and emphasize the need for inclusive technology design. At the same time, we also surface concerns regarding privacy, security, and the accuracy of information provided by LLMs, alongside the importance of user-centered design to make technology accessible and effective for the elderly. We show the transformative possibilities of LLM-supported interactions at the intersection of aging, technology, and human-computer interaction, advocating for further research and development in this area., Comment: This short paper was accepted at CHI 2024 Workshop on HCI and Aging: New Directions, New Principles

Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands residues, or tailings, stored in settle basins commonly known as oil sands tailing ponds. In order to determine the methane emitting potential of these tailing ponds and have future methane projections, we use real-time weather data, mechanistic models developed from laboratory controlled experiments, and industrial reports to train a physics constrained machine learning model. Our trained model can successfully identify the directions of active ponds and estimate their emission levels, which are generally hard to obtain due to data sampling restrictions. We found that each active oil sands tailing pond could emit between 950 to 1500 tonnes of methane per year, whose environmental impact is equivalent to carbon dioxide emissions from at least 6000 gasoline powered vehicles. Although abandoned ponds are often presumed to have insignificant emissions, our findings indicate that these ponds could become active over time and potentially emit up to 1000 tonnes of methane each year. Taking an average over all datasets that was used in model training, we estimate that emissions around major oil sands regions would need to be reduced by approximately 12% over a year, to reduce the average methane concentrations to 2005 levels., Comment: 19 pages, 8 figures, 2 tables

In the quest to unravel the mysteries of the strong force and the underlying properties of the quark-gluon plasma, the ALICE collaboration at CERN has carried out a comprehensive study focusing on the correlations between net-conserved quantities such as net-baryon, net-charge and net-strangeness. These correlations play a crucial role in the study of QCD phase structure as they are closely related to the ratios of thermodynamic susceptibilities in lattice QCD calculations. This work mainly focuses on the correlations between net-kaon and net-proton, and net-kaon and net-charge in Pb$-$Pb collisions at $\sqrt{s_\mathrm{NN}} = 5.02$ TeV using data recorded during LHC Run 2. The net-proton and net-kaon serve as proxies for net-baryon and net-strangeness, respectively, with measurements analyzed as a function of collision centrality. Theoretical predictions from the Thermal-FIST model are compared with experimental results, providing insights into the effects of resonance decays and charge conservation laws on the correlations., Comment: 4 pages, 2 figures; contribution to the 21st International Conference on Strangeness in Quark Matter (SQM-2024). arXiv admin note: text overlap with arXiv:2409.10939

The tree edit distance (TED) between two rooted ordered trees with $n$ nodes labeled from an alphabet $\Sigma$ is the minimum cost of transforming one tree into the other by a sequence of valid operations consisting of insertions, deletions and relabeling of nodes. The tree edit distance is a well-known generalization of string edit distance and has been studied since the 1970s. Years of steady improvements have led to an $O(n^3)$ algorithm [DMRW 2010]. Fine-grained complexity casts light onto the hardness of TED showing that a truly subcubic time algorithm for TED implies a truly subcubic time algorithm for All-Pairs Shortest Paths (APSP) [BGMW 2020]. Therefore, under the popular APSP hypothesis, a truly subcubic time algorithm for TED cannot exist. However, unlike many problems in fine-grained complexity for which conditional hardness based on APSP also comes with equivalence to APSP, whether TED can be reduced to APSP has remained unknown. In this paper, we resolve this. Not only we show that TED is fine-grained equivalent to APSP, our reduction is tight enough, so that combined with the fastest APSP algorithm to-date [Williams 2018] it gives the first ever subcubic time algorithm for TED running in $n^3/2^{\Omega(\sqrt{\log{n}})}$ time. We also consider the unweighted tree edit distance problem in which the cost of each edit is one. For unweighted TED, a truly subcubic algorithm is known due to Mao [Mao 2022], later improved slightly by D\"{u}rr [D\"{u}rr 2023] to run in $O(n^{2.9148})$. Their algorithm uses bounded monotone min-plus product as a crucial subroutine, and the best running time for this product is $\tilde{O}(n^{\frac{3+\omega}{2}})\leq O(n^{2.6857})$ (where $\omega$ is the exponent of fast matrix multiplication). In this work, we close this gap and give an algorithm for unweighted TED that runs in $\tilde{O}(n^{\frac{3+\omega}{2}})$ time., Comment: Abstract shortened to meet arXiv requirements

Recently a one dimensional Schwarzian type theory was proposed as an effective dual theory of pure gravity in (2+1) dimensional asymptotically flat spacetimes. This codimension two `celestial' dual captures the Bekenstein-Hawking entropy of bulk flat cosmologies in semiclassical limit. In this paper, we extend this analysis beyond semiclassical approximation and evaluate the one loop partition function of this dual celestial theory. Our analysis results in novel nontrivial logarithmic corrections appearing from the one-loop path integral., Comment: 19 pages

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

We show that the Ward identities of a Carrollian CFT stress tensor at null infinity reproduce the leading and subleading soft graviton theorems for massless scattering in the bulk. We deduce the expressions of the stress tensor components in terms of the bulk radiative modes, and these components turn out to be local at $\mathscr{I}$ in terms of the twistor potentials. This analysis makes the correspondence between the large-time limit of Carrollian amplitudes and the soft limit of momentum space amplitudes manifest. We then construct Carrollian CFT currents from the ascendants of the hard graviton operator, which satisfy the $Lw_{1+\infty}$ algebra. We show that the large-time limit of their Ward identities implies an infinite tower of projected soft graviton theorems in the bulk, while their finite-time OPEs encode the collinear limit of scattering amplitudes.

Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Networks (GNNs) emerge as an important innovation, propelling DL into the non-Euclidean domain. Naturally, GNNs can effectively tackle the challenges posed by diverse modalities, multiple sensors, and the heterogeneous nature of EO data. To introduce GNNs in the related domains, our review begins by offering fundamental knowledge on GNNs. Then, we summarize the generic problems in EO, to which GNNs can offer potential solutions. Following this, we explore a broad spectrum of GNNs' applications to scientific problems in Earth systems, covering areas such as weather and climate analysis, disaster management, air quality monitoring, agriculture, land cover classification, hydrological process modeling, and urban modeling. The rationale behind adopting GNNs in these fields is explained, alongside methodologies for organizing graphs and designing favorable architectures for various tasks. Furthermore, we highlight methodological challenges of implementing GNNs in these domains and possible solutions that could guide future research. While acknowledging that GNNs are not a universal solution, we conclude the paper by comparing them with other popular architectures like transformers and analyzing their potential synergies., Comment: Accepted for publication in Geoscience and Remote Sensing Magazine (GRSM)

The present study numerically investigates two-dimensional mixed convective flow of air past a square cylinder placed at an angle of incidence of $\alpha = 45^{\circ}$ to the free-stream. We perform direct numerical simulations (DNS) for a Reynolds number (Re) of 100 and a range of Richardson numbers (Ri) between 0.0 and 1.0 and a Prandtl number (Pr) of 0.7. The critical Richardson number at which the near-field becomes a steady flow from an unsteady one, using Stuart-Landau analysis, is found to be Ri $=0.68$, and simultaneously, the far-field unsteadiness emerges. There is no range of Ri for which the entire flow field is seen to be steady. At a relatively moderate Ri, the flow field reveals the presence of vorticity inversion through the momentum deficit/addition in the downstream region. We discuss the dual wake-plume nature of the flow beyond the cylinder. The wake exhibits characteristics similar to those of a buoyant jet in the far-field at increased buoyancy. We explore the cause of the far-field unsteadiness, and discuss the mechanism of the observed flow physics using instantaneous and time-averaged flow fields. The important flow quantities, such as force coefficients, vortex shedding frequency, and Nusselt number, are discussed at various Richardson numbers.

In this work we focus on the effect of the QCD coupling parameter ($\alpha_c$) on various physical properties of compact objects admitting the MIT bag model EoS in the framework of the Tolman IV potential in the presence of nonzero strange quark mass ($m_s$). The internal matter consisting of the deconfined phase of the $3$-flavour quark, is overall charge neutral due to the presence of electrons and is assumed to strongly interact. Interestingly, the coupling parameter $\alpha_c$ has an upper limit due to thermodynamic consistency, and affects the stability in terms of energy per baryon ($E_B$). Our model is suitable for stars with masses of approximately $\leq 2.00~M_{\odot}$. The predicted radius from our model is in agreement with the estimated values of the radius from the observations. Various stability checks of our model are carried out and it is found that the model is stable within the parameter space used here., Comment: 32 pages, 29 figures

We present a search for long-lived particles (LLPs) produced from kaon decay that decay to two muons inside the ICARUS neutrino detector. This channel would be a signal of hidden sector models that can address outstanding issues in particle physics such as the strong CP problem and the microphysical origin of dark matter. The search is performed with data collected in the Neutrinos at the Main Injector (NuMI) beam at Fermilab corresponding to $2.41\times 10^{20}$ protons-on-target. No new physics signal is observed, and we set world-leading limits on heavy QCD axions, as well as for the Higgs portal scalar among dedicated searches. Limits are also presented in a model-independent way applicable to any new physics model predicting the process $K\to \pi+S(\to\mu\mu)$, for a long-lived particle S. This result is the first search for new physics performed with the ICARUS detector at Fermilab. It paves the way for the future program of long-lived particle searches at ICARUS.

We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using streaming data. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point, thereby allowing it to adapt to changing environments in real-time. A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning rate technique called AdaRelation, which adjusts based on the historical sensitivity of the decoder to changes in the interaction graph. Additionally, a data augmentation method named Trajectory Mirror (TM) is introduced to improve generalization by exposing the model to varied trajectory patterns. Experimental results on both synthetic datasets and real-world data (CMU MoCap for human motion) demonstrate that ORI significantly improves the accuracy and adaptability of relational inference in dynamic settings compared to existing methods. This approach is model-agnostic, enabling seamless integration with various neural relational inference (NRI) architectures, and offers a robust solution for real-time applications in complex, evolving systems., Comment: Accepted at NeurIPS 2024

The proton capture cross-section of the least abundant proton-rich stable isotope of cadmium, $^{108}$Cd (abundance 0.89\%), has been measured near the Gamow window corresponding to a temperature range of 3-4 GK. The measurement of the $^{108}$Cd(p,$\gamma$)$^{109}$In reaction was carried out using the activation technique. The cross-section at the lowest energy point of 3T$_9$, E$_p$$^{lab}$= 2.28 MeV, has been reported for the first time. The astrophysical S-factor was measured in the energy range relevant to the astrophysical p-process, between E$_p$$^{cm}$= 2.29 and 6.79 MeV. The experimental results have been compared with theoretical predictions of Hauser-Feshbach statistical model calculations using TALYS-1.96. A calculated proton-optical potential was implemented to achieve better fitting, with different combinations of available nuclear level densities (NLDs) and $\gamma$-ray strength functions in TALYS-1.96. The calculations provided satisfactory agreement with the experimental results. The reaction rate was calculated using the calculated potential in TALYS-1.96 and compared with the values provided in the REACLIB database.

The structural and magnetic effects of non-magnetic vanadium (V) doping in helimagnetic SrFeO$_{3-\delta}$ (SFO) are investigated, focusing on up to 3% substitution at the Fe site. Structural analysis from X-ray diffraction (XRD) and Raman spectroscopy, supported by phonon mode calculations, reveals that pure SFO exists as a mixed tetragonal-orthorhombic phase, while V-doped samples exhibit an emerging cubic phase alongside tetragonal symmetry. Magnetic hysteresis (M-H) loops show notable ferromagnetic behavior within the antiferromagnetic matrix, persisting even at room temperature. Temperature-dependent magnetization measurements indicate a Neel temperature (TN ) shift from 70K to 55K, along with increased magnetization differences in field-cooled (FC) and zero field-cooled (ZFC) data, reflecting heightened magnetic frustration due to competing FM/AFM exchange interactions. X-ray photoelectron spectroscopy (XPS) and X-ray absorption near-edge structure (XANES) analyses reveal a rise in Fe$^{3+}$ and V$^{5+}$ states, affecting oxygen vacancy distributions and corresponding structural shifts seen in XRD and Raman results. The multivalent Fe$^{3+}$/Fe$^{4+}$ and V$^{4+}$/V$^{5+}$ states enhance double-exchange (DE) and super-exchange (SE) interactions (Fe$^{3+}$-O-Fe$^{4+}$ and Fe$^{3+}$-O-V$^{5+}$), promoting ferromagnetism. Frequency-dependent magnetization studies display a subtle susceptibility peak shift, indicating spin-glass-like behavior in V-doped samples.

Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable. Additionally, many methods require supplementary data such as facial landmarks and masks to guide the synthesis process. In contrast, our approach uses diffusion models with only a reconstruction loss, eliminating the need for facial landmarks or masks while still producing images with intricate, fine-grained details. We validated our results on two public benchmarks through both quantitative and qualitative evaluations. Our model achieves state-of-the-art performance in three key areas: identity anonymization, facial attribute preservation, and image quality. Beyond its primary function of anonymization, our model can also perform face swapping tasks by incorporating an additional facial image as input, demonstrating its versatility and potential for diverse applications. Our code and models are available at https://github.com/hanweikung/face_anon_simple .

Model Compression has drawn much attention within the deep learning community recently. Compressing a dense neural network offers many advantages including lower computation cost, deployability to devices of limited storage and memories, and resistance to adversarial attacks. This may be achieved via weight pruning or fully discarding certain input features. Here we demonstrate a novel strategy to emulate principles of Bayesian model selection in a deep learning setup. Given a fully connected Bayesian neural network with spike-and-slab priors trained via a variational algorithm, we obtain the posterior inclusion probability for every node that typically gets lost. We employ these probabilities for pruning and feature selection on a host of simulated and real-world benchmark data and find evidence of better generalizability of the pruned model in all our experiments.

This study introduces Variational Automatic Relevance Determination (VARD), a novel approach tailored for fitting sparse additive regression models in high-dimensional settings. VARD distinguishes itself by its ability to independently assess the smoothness of each feature while enabling precise determination of whether a feature's contribution to the response is zero, linear, or nonlinear. Further, an efficient coordinate descent algorithm is introduced to implement VARD. Empirical evaluations on simulated and real-world data underscore VARD's superiority over alternative variable selection methods for additive models.

Clinical diagnosis of breast malignancy (BM) is a challenging problem in the recent era. In particular, Deep learning (DL) models have continued to offer important solutions for early BM diagnosis but their performance experiences overfitting due to the limited volume of breast ultrasound (BUS) image data. Further, large BUS datasets are difficult to manage due to privacy and legal concerns. Hence, image augmentation is a necessary and challenging step to improve the performance of the DL models. However, the current DL-based augmentation models are inadequate and operate as a black box resulting lack of information and justifications about their suitability and efficacy. Additionally, pre and post-augmentation need high-performance computational resources and time to produce the augmented image and evaluate the model performance. Thus, this study aims to develop a novel efficient augmentation approach for BUS images with advanced neural style transfer (NST) and Explainable AI (XAI) harnessing GPU-based parallel infrastructure. We scale and distribute the training of the augmentation model across 8 GPUs using the Horovod framework on a DGX cluster, achieving a 5.09 speedup while maintaining the model's accuracy. The proposed model is evaluated on 800 (348 benign and 452 malignant) BUS images and its performance is analyzed with other progressive techniques, using different quantitative analyses. The result indicates that the proposed approach can successfully augment the BUS images with 92.47% accuracy.

Causality is an essential factor in determining the maximum mass of a neutron star. Previous works study causality for smooth equations of state. The density at the core of neutron stars can be a few times nuclear saturation density, where the occurrence of first-order phase transition has not been ruled out. The causality condition for first-order phase transition is characteristically different from that of smooth EOS, which becomes evident in the mass-radius relation. In this letter, we find that equations of state having first-order phase transition, the causality line deviates considerably from the smooth equation of state. Depending on the onset density of phase transition, there is a narrow band in the mass-radius plot, which is available only to the stars having a smooth equation of state. This can have significant consequences in the sense that if some pulsars are to lie in this band, one can rule out the equation of state having first-order phase transition occurring at that particular onset density., Comment: 6 pages, 7 figures

Graph clustering is an essential aspect of network analysis that involves grouping nodes into separate clusters. Recent developments in deep learning have resulted in advanced deep graph clustering techniques, which have proven effective in many applications. Nonetheless, these methods often encounter difficulties when dealing with the complexities of real-world graphs, particularly in the presence of noisy edges. Additionally, many denoising graph clustering strategies tend to suffer from lower performance compared to their non-denoised counterparts, training instability, and challenges in scaling to large datasets. To tackle these issues, we introduce a new framework called the Dual Adaptive Assignment Approach for Robust Graph-Based Clustering (RDSA). RDSA consists of three key components: (i) a node embedding module that effectively integrates the graph's topological features and node attributes; (ii) a structure-based soft assignment module that improves graph modularity by utilizing an affinity matrix for node assignments; and (iii) a node-based soft assignment module that identifies community landmarks and refines node assignments to enhance the model's robustness. We assess RDSA on various real-world datasets, demonstrating its superior performance relative to existing state-of-the-art methods. Our findings indicate that RDSA provides robust clustering across different graph types, excelling in clustering effectiveness and robustness, including adaptability to noise, stability, and scalability.

Materials with magnetoelectric coupling (MEC) between ferroic orders at room temperature are extremely important in modern technology and physics. BaTiO$_3$ is a robust ferroelectric in which several doping have led to MEC. However, often MEC depends on the amount of substitution. In a series of (1-x)BaTiO$_{3.}$(x)CaMnO$_3$ (x=0,0.03, 0.06, 0.09), MEC was only observed in (0.97)BaTiO$_{3.}$(0.03)CaMnO$_3$. The structural modifications with changing substitution reveal a reduced Ti-O-Ti angle for this sample which is the most ferromagnetic in nature. A mixed phase of tetragonal P4mm and hexagonal P6$_3$/mmc space groups of BaTiO$_3$ is observed in the substituted samples, with nominal contribution of the hexagonal phase for x=0.03. A valence state study using XPS and XANES reveals the presence of enhanced proportion of Mn3+ ions in the sample which support a Jahn-Teller distortion, thereby supporting the ferroelectricity for x=0.03. Indirect and direct evidences of MEC was obtained from magnetodielectric and magnetoelectric measurements. A magnetocapacitance of 0.4% was observed with a dc magnetic field of 4kOe at 10kHz, while magnetoelectric coupling coefficient, $\alpha_{\rm ME} \sim 88$ mVcm$^{-1}$Oe$^{-1}$ was obtained for dc magnetic field of 600 Oe and a 10Hz ac field of 40 Oe. Such MEC was not observed for higher substitution which emphasizes the sensitivity of the structural properties on substitution.

In this article, we propose a new way to handle interactions between two scalar fields in the cosmological backdrop where one scalar field oscillates rapidly in the cosmological time scale while the other does not show any periodic behavior in the same time scale. We have interpreted the rapidly oscillating scalar field as the dark matter candidate while the other scalar field is supposed to be the canonical quintessence field or the non-canonical phantom field. A model of a generalized top-hat-like collapse is developed where the dark sector is composed of the aforementioned scalar fields. We show how the non-minimal coupling in the dark sector affects the gravitational collapse of a slightly overdense spherical patch of the universe. The results show that one can have both unclustered and clustered dark energy in such collapses, the result depends upon the magnitude of the non-minimal coupling strength., Comment: 17 pages, 8 figures

Idling vehicle detection (IVD) can be helpful in monitoring and reducing unnecessary idling and can be integrated into real-time systems to address the resulting pollution and harmful products. The previous approach [13], a non-end-to-end model, requires extra user clicks to specify a part of the input, making system deployment more error-prone or even not feasible. In contrast, we introduce an end-to-end joint audio-visual IVD task designed to detect vehicles visually under three states: moving, idling and engine off. Unlike feature co-occurrence task such as audio-visual vehicle tracking, our IVD task addresses complementary features, where labels cannot be determined by a single modality alone. To this end, we propose AVIVD-Net, a novel network that integrates audio and visual features through a bidirectional attention mechanism. AVIVD-Net streamlines the input process by learning a joint feature space, reducing the deployment complexity of previous methods. Additionally, we introduce the AVIVD dataset, which is seven times larger than previous datasets, offering significantly more annotated samples to study the IVD problem. Our model achieves performance comparable to prior approaches, making it suitable for automated deployment. Furthermore, by evaluating AVIVDNet on the feature co-occurrence public dataset MAVD [23], we demonstrate its potential for extension to self-driving vehicle video-camera setups.

In $D$ dimensional de Sitter space, a scalar field has an infinite tower of special tachyonic mass values at which enhanced shift symmetries appear. After modding out by these shift symmetries, these fields correspond to the unitary irreducible representations of the de Sitter group known as the discrete series. We show that in $D=2$ these theories have global conformal symmetry. In all but the massless case, these theories have no stress tensor and the conformal symmetry does not act in the usual way on the scalar field. We find the conformal symmetry by explicitly computing the correlators of the shift invariant local operators and showing that they take conformally invariant forms. We also demonstrate how these fields are self dual in $D=2$, and dual to the shift invariant massive vector fields, which are therefore also conformally invariant., Comment: 30 pages

The integration of non-terrestrial networks (NTN) into 5G new radio (NR) has opened up the possibility of developing a new positioning infrastructure using NR signals from Low-Earth Orbit (LEO) satellites. LEO-based cellular positioning offers several advantages, such as a superior link budget, higher operating bandwidth, and large forthcoming constellations. Due to these factors, LEO-based positioning, navigation, and timing (PNT) is a potential enhancement for NTN in 6G cellular networks. However, extending the existing terrestrial cellular positioning methods to LEO-based NTN positioning requires considering key fundamental enhancements. These include creating broad positioning beams orthogonal to conventional communication beams, time-domain processing at the user equipment (UE) to resolve large delay and Doppler uncertainties, and efficiently accommodating positioning reference signals (PRS) from multiple satellites within the communication resource grid. In this paper, we present the first set of design insights by incorporating these enhancements and thoroughly evaluating LEO-based positioning, considering the constraints and capabilities of the NR-NTN physical layer. To evaluate the performance of LEO-based NTN positioning, we develop a comprehensive NR-compliant simulation framework, including LEO orbit simulation, transmission (Tx) and receiver (Rx) architectures, and a positioning engine incorporating the necessary enhancements. Our findings suggest that LEO-based NTN positioning could serve as a complementary infrastructure to existing Global Navigation Satellite Systems (GNSS) and, with appropriate enhancements, may also offer a viable alternative., Comment: 7 pages, 6 figures, submitted to IEEE Communications Magazine

Simulating quantum circuits is a computationally intensive task that relies heavily on tensor products and matrix multiplications, which can be inefficient. Recent advancements, eliminate the need for tensor products and matrix multiplications, offering significant improvements in efficiency and parallelization. Extending these optimizations, we adopt a block-simulation methodology applicable to qubit-qudit hybrid systems. This method interprets the statevector as a collection of blocks and applies gates without computing the entire circuit unitary. Our method, a spiritual successor of the simulator QuDiet \cite{Chatterjee_2023}, utilizes this block-simulation method, thereby gaining major improvements over the simulation methods used by its predecessor. We exhibit that the proposed method is approximately 10$\times$ to 1000$\times$ faster than the state-of-the-art simulator for simulating multi-level quantum systems with various benchmark circuits., Comment: 12 pages, 10 figures

The Zirconium (Z = 40) isotopic chain has attracted interest for more than four decades. The abrupt lowering of the energy of the first $2^+$ state and the increase in the transition strength B(E2; $2_1^\rightarrow 0_1^+$ going from $^{98}$Zr to $^{100}$Zr has been the first example of "quantum phase transition" in nuclear shapes, which has few equivalents in the nuclear chart. Although a multitude of experiments have been performed to measure nuclear properties related to nuclear shapes and collectivity in the region, none of the measured lifetimes were obtained using the Recoil Distance Doppler Shift method in the $\gamma\gamma$-coincidence mode where a gate on the direct feeding transition of the state of interest allows a strict control of systematical errors. This work reports the results of lifetime measurements for the first yrast excited states in $^{98-104}$Zr carried out to extract reduced transition probabilities. The new lifetime values in $\gamma\gamma$-coincidence and $\gamma$-single mode are compared with the results of former experiments. Recent predictions of the Interacting Boson Model with Configuration Mixing, the Symmetry Conserving Configuration Mixing model based on the Hartree-Fock-Bogoliubov approach and the Monte Carlo Shell Model are presented and compared with the experimental data.

Open-domain long-form text generation requires generating coherent, comprehensive responses that address complex queries with both breadth and depth. This task is challenging due to the need to accurately capture diverse facets of input queries. Existing iterative retrieval-augmented generation (RAG) approaches often struggle to delve deeply into each facet of complex queries and integrate knowledge from various sources effectively. This paper introduces ConTReGen, a novel framework that employs a context-driven, tree-structured retrieval approach to enhance the depth and relevance of retrieved content. ConTReGen integrates a hierarchical, top-down in-depth exploration of query facets with a systematic bottom-up synthesis, ensuring comprehensive coverage and coherent integration of multifaceted information. Extensive experiments on multiple datasets, including LFQA and ODSUM, alongside a newly introduced dataset, ODSUM-WikiHow, demonstrate that ConTReGen outperforms existing state-of-the-art RAG models., Comment: Accepted at EMNLP'24 Findings

For specialized domains, there is often not a wealth of data with which to train large machine learning models. In such limited data / compute settings, various methods exist aiming to $\textit{do more with less}$, such as finetuning from a pretrained model, modulating difficulty levels as data are presented to a model (curriculum learning), and considering the role of model type / size. Approaches to efficient $\textit{machine}$ learning also take inspiration from $\textit{human}$ learning by considering use cases where machine learning systems have access to approximately the same number of words experienced by a 13 year old child (100M words). We investigate the role of 3 primary variables in a limited data regime as part of the multimodal track of the BabyLM challenge. We contrast: (i) curriculum learning, (ii), pretraining (with text-only data), (iii) model type. We modulate these variables and assess them on two types of tasks: (a) multimodal (text+image), and (b) unimodal (text-only) tasks. We find that curriculum learning benefits multimodal evaluations over non-curriclum learning models, particularly when combining text-only pretraining. On text-only tasks, curriculum learning appears to help models with smaller trainable parameter counts. We suggest possible reasons based on architectural differences and training designs as to why one might observe such results., Comment: CoNLL BabyLM Challenge 2024 camera ready

Client-Service Representatives (CSRs) are vital to organizations. Frequent interactions with disgruntled clients, however, disrupt their mental well-being. To help CSRs regulate their emotions while interacting with uncivil clients, we designed Pro-Pilot, an LLM-powered assistant, and evaluated its efficacy, perception, and use. Our comparative analyses between 665 human and Pro-Pilot-generated support messages demonstrate Pro-Pilot's ability to adapt to and demonstrate empathy in various incivility incidents. Additionally, 143 CSRs assessed Pro-Pilot's empathy as more sincere and actionable than human messages. Finally, we interviewed 20 CSRs who interacted with Pro-Pilot in a simulation exercise. They reported that Pro-Pilot helped them avoid negative thinking, recenter thoughts, and humanize clients; showing potential for bridging gaps in coworker support. Yet, they also noted deployment challenges and emphasized the irreplaceability of shared experiences. We discuss future designs and societal implications of AI-mediated emotional labor, underscoring empathy as a critical function for AI assistants in front-office roles.

Owing to the mass scale associated with the scalar longitudinal mode signal of gravitational wave predicted by modified theories of gravity, it should propagate at a subluminal speed and with a different frequency compared to the massless tensor mode signals which moves at the speed of light and are present in both standard general relativity and modified theories. This is ensured by the massless and massive dispersion relations obeyed respectively by the tensor and scalar modes of gravitational wave coming from a given source and thus having the same propagation vector. We show that because of its wider operational frequency band the recently designed levitated sensor detector \cite{Aggarwal} of gravitational wave has a better chance of detecting both the scalar and tensor modes at these different frequencies and thus can provide observational evidence in favour of modified theories of gravity over general relativity. This detector works on the principle of optical trapping \cite{Ashkin_1970} of a dielectric nanosphere sensor\cite{Geraci}. By adjusting the intensity of the optical beam the frequency of the harmonic potential trap can be varied widely so that the nanosphere sensor can undergo distinct resonant transitions induced by the tensor and scalar modes. We demonstrate that the dynamics of the sensor mass obeys a geodesic deviation equation in the proper detector frame and construct a quantum mechanical description of this system in modified gravity framework to compute the probabilities of resonant transitions in response to incoming gravitational wave signals of both periodic and aperiodic kind., Comment: 13 pages LaTex, no figure

Medical task-oriented dialogue systems can assist doctors by collecting patient medical history, aiding in diagnosis, or guiding treatment selection, thereby reducing doctor burnout and expanding access to medical services. However, doctor-patient dialogue datasets are not readily available, primarily due to privacy regulations. Moreover, existing datasets lack comprehensive annotations involving medical slots and their different attributes, such as symptoms and their onset, progression, and severity. These comprehensive annotations are crucial for accurate diagnosis. Finally, most existing datasets are non-English, limiting their utility for the larger research community. In response, we introduce MediTOD, a new dataset of doctor-patient dialogues in English for the medical history-taking task. Collaborating with doctors, we devise a questionnaire-based labeling scheme tailored to the medical domain. Then, medical professionals create the dataset with high-quality comprehensive annotations, capturing medical slots and their attributes. We establish benchmarks in supervised and few-shot settings on MediTOD for natural language understanding, policy learning, and natural language generation subtasks, evaluating models from both TOD and biomedical domains. We make MediTOD publicly available for future research., Comment: EMNLP2024 Camera Ready Version

Time series forecasting aids decision-making, especially for stakeholders who rely on accurate predictions, making it very important to understand and explain these models to ensure informed decisions. Traditional explainable AI (XAI) methods, which underline feature or temporal importance, often require expert knowledge. In contrast, natural language explanations (NLEs) are more accessible to laypeople. However, evaluating forecast NLEs is difficult due to the complex causal relationships in time series data. To address this, we introduce two new performance metrics based on simulatability, assessing how well a human surrogate can predict model forecasts using the explanations. Experiments show these metrics differentiate good from poor explanations and align with human judgments. Utilizing these metrics, we further evaluate the ability of state-of-the-art large language models (LLMs) to generate explanations for time series data, finding that numerical reasoning, rather than model size, is the main factor influencing explanation quality.

Over the past decade, predictive modeling of neural responses in the primate visual system has advanced significantly, largely driven by various DNN approaches. These include models optimized directly for visual recognition, cross-modal alignment through contrastive objectives, neural response prediction from scratch, and large language model embeddings.Likewise, different readout mechanisms, ranging from fully linear to spatial-feature factorized methods have been explored for mapping network activations to neural responses. Despite the diversity of these approaches, it remains unclear which method performs best across different visual regions. In this study, we systematically compare these approaches for modeling the human visual system and investigate alternative strategies to improve response predictions. Our findings reveal that for early to mid-level visual areas, response-optimized models with visual inputs offer superior prediction accuracy, while for higher visual regions, embeddings from LLMs based on detailed contextual descriptions of images and task-optimized models pretrained on large vision datasets provide the best fit. Through comparative analysis of these modeling approaches, we identified three distinct regions in the visual cortex: one sensitive primarily to perceptual features of the input that are not captured by linguistic descriptions, another attuned to fine-grained visual details representing semantic information, and a third responsive to abstract, global meanings aligned with linguistic content. We also highlight the critical role of readout mechanisms, proposing a novel scheme that modulates receptive fields and feature maps based on semantic content, resulting in an accuracy boost of 3-23% over existing SOTAs for all models and brain regions. Together, these findings offer key insights into building more precise models of the visual system.

The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, while emphasizing the need for robust performance in real-life applications. In this work, we propose a hybrid approach to enhance the state-of-the-art (SOTA) ULCNet model by integrating time alignment and parallel encoder blocks for the model inputs, resulting in better echo reduction and comparable noise reduction performance to existing SOTA methods. We also propose a channel-wise sampling-based feature reorientation method, ensuring robust performance across many challenging scenarios, while maintaining overall low computational and memory requirements., Comment: 5 pages, 4 figures

Enhancing speech quality under adverse SNR conditions remains a significant challenge for discriminative deep neural network (DNN)-based approaches. In this work, we propose DisCoGAN, which is a time-frequency-domain generative adversarial network (GAN) conditioned by the latent features of a discriminative model pre-trained for speech enhancement in low SNR scenarios. Our proposed method achieves superior performance compared to state-of-the-arts discriminative methods and also surpasses end-to-end (E2E) trained GAN models. We also investigate the impact of various configurations for conditioning the proposed GAN model with the discriminative model and assess their influence on enhancing speech quality, Comment: 5 pages, 2 figures

The conditional autoregressive (CAR) model, simultaneous autoregressive (SAR) model, and its variants have become the predominant strategies for modeling regional or areal-referenced spatial data. The overwhelming wide-use of the CAR/SAR model motivates the need for new classes of models for areal-referenced data. Thus, we develop a novel class of Markov random fields based on truncating the full-conditional distribution. We define this truncation in two ways leading to versions of what we call the truncated autoregressive (TAR) model. First, we truncate the full conditional distribution so that a response at one location is close to the average of its neighbors. This strategy establishes relationships between TAR and CAR. Second, we truncate on the joint distribution of the data process in a similar way. This specification leads to connection between TAR and SAR model. Our Bayesian implementation does not use Markov chain Monte Carlo (MCMC) for Bayesian computation, and generates samples directly from the posterior distribution. Moreover, TAR does not have a range parameter that arises in the CAR/SAR models, which can be difficult to learn. We present the results of the proposed truncated autoregressive model on several simulated datasets and on a dataset of average property prices., Comment: 41 pages, 10 figures

Large language models (LLMs) have shown promise in many natural language understanding tasks, including content moderation. However, these models can be expensive to query in real-time and do not allow for a community-specific approach to content moderation. To address these challenges, we explore the use of open-source small language models (SLMs) for community-specific content moderation tasks. We fine-tune and evaluate SLMs (less than 15B parameters) by comparing their performance against much larger open- and closed-sourced models. Using 150K comments from 15 popular Reddit communities, we find that SLMs outperform LLMs at content moderation -- 11.5% higher accuracy and 25.7% higher recall on average across all communities. We further show the promise of cross-community content moderation, which has implications for new communities and the development of cross-platform moderation techniques. Finally, we outline directions for future work on language model based content moderation. Code and links to HuggingFace models can be found at https://github.com/AGoyal0512/SLM-Mod., Comment: Preprint: 15 pages, 8 figures, 8 pages