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Next-generation gravitational-wave observatories are expected to detect over a thousand compact binary coalescence signals daily, with some lasting from minutes to hours. Consequently, multiple signals will overlap in the time-frequency plane, generating a "foreground noise" that predominantly affects the low-frequency range, where binary neutron star inspiral evolution is gradual. This study investigates the impact of such foreground noise on parameter estimation for short-duration binary black hole signals, particularly those with high detector-frame masses and/or located at large redshifts. Our results show a reduction in detection sensitivity by approximately 25\% when the noise power spectrum deviates by up to 50\% from Gaussian noise due to foreground contamination. Despite this, using standard parameter estimation techniques without subtracting overlapping signals, we find that foreground noise has minimal impact, primarily affecting precision. These findings suggest that even in the presence of substantial foreground noise, global-fit techniques, and/or signal subtraction will not be necessary, as accurate recovery of system parameters is achievable with minimal loss in precision., Comment: 12 pages, 11 figures

The Milky Way hosts at least two modes in its present day distribution of Fe and alpha-elements. The exact cause of this bimodality is disputed, but one class of explanations involves the merger between the Milky Way and a relatively massive satellite (Gaia-Sausage-Enceladus) at z~2. However, reproducing this bimodality in simulations is not straightforward, with conflicting results on the prevalance, morphology, and mechanism behind multimodality. We present a case study of a galaxy in the Illustris TNG50 simulation which undergoes sequential phases of starburst, brief quiescence, and then rejuvenation. This scenario results in a pronounced abundance bimodality after a post-processing adjustment of the [alpha/Fe] of old stars designed to mimic a higher star formation efficiency in dense gas. The high- and low-alpha sequences are separated in time by the brief quiescent period, which is not associated with a merger but by the formation of a bar followed by AGN activity. This galaxy indicates a novel scenario in which the alpha-bimodality in the Milky Way is caused by the formation of the bar via AGN-induced quenching. In addition to a stellar age gap in the Milky Way, we predict that abundance bimodalities should be more common in barred as opposed to unbarred galaxies., Comment: 13+10 pages, 5+18 figures, to be submitted to ApJ; comments welcome

We consider algorithms and spectral bounds for sparsest cut and conductance in directed polymatrodal networks. This is motivated by recent work on submodular hypergraphs \cite{Yoshida19,LiM18,ChenOT23,Veldt23} and previous work on multicommodity flows and cuts in polymatrodial networks \cite{ChekuriKRV15}. We obtain three results. First, we obtain an $O(\sqrt{\log n})$-approximation for sparsest cut and point out how this generalizes the result in \cite{ChenOT23}. Second, we consider the symmetric version of conductance and obtain an $O(\sqrt{OPT \log r})$ approximation where $r$ is the maximum degree and we point out how this generalizes previous work on vertex expansion in graphs. Third, we prove a non-constructive Cheeger like inequality that generalizes previous work on hypergraphs. We provide a unified treatment via line-embeddings which were shown to be effective for submodular cuts in \cite{ChekuriKRV15}.

We consider the task of classifying trajectories of boat activities as a proxy for assessing maritime threats. Previous approaches have considered entropy-based metrics for clustering boat activity into three broad categories: random walk, following, and chasing. Here, we comprehensively assess the accuracy of neural network-based approaches as alternatives to entropy-based clustering. We train four neural network models and compare them to shallow learning using synthetic data. We also investigate the accuracy of models as time steps increase and with and without rotated data. To improve test-time robustness, we normalize trajectories and perform rotation-based data augmentation. Our results show that deep networks can achieve a test-set accuracy of up to 100% on a full trajectory, with graceful degradation as the number of time steps decreases, outperforming entropy-based clustering.

Adverse Drug Reactions (ADRs) from psychiatric medications are the leading cause of hospitalizations among mental health patients. With healthcare systems and online communities facing limitations in resolving ADR-related issues, Large Language Models (LLMs) have the potential to fill this gap. Despite the increasing capabilities of LLMs, past research has not explored their capabilities in detecting ADRs related to psychiatric medications or in providing effective harm reduction strategies. To address this, we introduce the Psych-ADR benchmark and the Adverse Drug Reaction Response Assessment (ADRA) framework to systematically evaluate LLM performance in detecting ADR expressions and delivering expert-aligned mitigation strategies. Our analyses show that LLMs struggle with understanding the nuances of ADRs and differentiating between types of ADRs. While LLMs align with experts in terms of expressed emotions and tone of the text, their responses are more complex, harder to read, and only 70.86% aligned with expert strategies. Furthermore, they provide less actionable advice by a margin of 12.32% on average. Our work provides a comprehensive benchmark and evaluation framework for assessing LLMs in strategy-driven tasks within high-risk domains., Comment: 30 pages, 8 figures, 16 tables

Fluorescence imaging allows for non-invasively visualizing and measuring key physiological parameters like pH and dissolved oxygen. In our work, we created two ratiometric fluorescent microsensors designed for accurately tracking dissolved oxygen levels in 3D cell cultures. We developed a simple and cost-effective method to produce hybrid core-shell silica microparticles that are biocompatible and versatile. These sensors incorporate oxygen-sensitive probes (Ru(dpp) or PtOEP) and reference dyes (RBITC or A647 NHS-Ester). SEM analysis confirmed efficient loading and distribution of the sensing dye on the outer shell. Fluorimetric and CLSM tests demonstrated the sensors' reversibility and high sensitivity to oxygen, even when integrated into 3D scaffolds. Aging and bleaching experiments validated the stability of our hybrid core-shell silica microsensors for 3D monitoring. The Ru(dpp)-RBITC microparticles showed the most promising performance, especially in a pancreatic cancer model using alginate microgels. By employing computational segmentation, we generated 3D oxygen maps during live cell imaging, revealing oxygen gradients in the extracellular matrix and indicating a significant decrease in oxygen levels characteristic of solid tumors. Notably, after 12 hours, the oxygen concentration dropped to a hypoxic level of PO2 2.7 +/- 0.1%.

The intersection of LLMs (Large Language Models) and UAV (Unoccupied Aerial Vehicles) technology represents a promising field of research with the potential to enhance UAV capabilities significantly. This study explores the application of LLMs in UAV control, focusing on the opportunities for integrating advanced natural language processing into autonomous aerial systems. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage, making them accessible to a broader user base and facilitating more intuitive human-machine interactions. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols. Through a comprehensive review of current developments and potential future directions, this study aims to highlight how LLMs can transform UAV operations, making them more adaptable, responsive, and efficient in complex environments. A template development framework for integrating LLMs in UAV control is also described. Proof of Concept results that integrate existing LLM models and popular robotic simulation platforms are demonstrated. The findings suggest that while there are substantial technical and ethical challenges to address, integrating LLMs into UAV control holds promising implications for advancing autonomous aerial systems.

Extending Large Language Models (LLMs) to advanced applications requires reliable structured output generation. Existing methods which often rely on rigid JSON schemas, can lead to unreliable outputs, diminished reasoning capabilities, and increased computational overhead, limiting LLMs' adaptability for complex tasks. We introduce Meaning Typed Prompting (MTP), a technique for efficient structured output generation that integrates types, meanings, and abstractions, such as variables and classes, into the prompting process. By utilizing expressive type definitions, MTP enhances output clarity and reduces dependence on complex abstractions, simplifying development, and improving implementation efficiency. This enables LLMs to understand relationships and generate structured data more effectively. Empirical evaluations on multiple benchmarks demonstrate that MTP outperforms existing frameworks in accuracy, reliability, consistency, and token efficiency. We present Semantix, a framework that implements MTP, providing practical insights into its application.

Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we propose LongVU, a spatiotemporal adaptive compression mechanism thats reduces the number of video tokens while preserving visual details of long videos. Our idea is based on leveraging cross-modal query and inter-frame dependencies to adaptively reduce temporal and spatial redundancy in videos. Specifically, we leverage DINOv2 features to remove redundant frames that exhibit high similarity. Then we utilize text-guided cross-modal query for selective frame feature reduction. Further, we perform spatial token reduction across frames based on their temporal dependencies. Our adaptive compression strategy effectively processes a large number of frames with little visual information loss within given context length. Our LongVU consistently surpass existing methods across a variety of video understanding benchmarks, especially on hour-long video understanding tasks such as VideoMME and MLVU. Given a light-weight LLM, our LongVU also scales effectively into a smaller size with state-of-the-art video understanding performance., Comment: Project page: https://vision-cair.github.io/LongVU

We consider Directed Steiner Forest (DSF), a fundamental problem in network design. The input to DSF is a directed edge-weighted graph $G = (V, E)$ and a collection of vertex pairs $\{(s_i, t_i)\}_{i \in [k]}$. The goal is to find a minimum cost subgraph $H$ of $G$ such that $H$ contains an $s_i$-$t_i$ path for each $i \in [k]$. DSF is NP-Hard and is known to be hard to approximate to a factor of $\Omega(2^{\log^{1 - \epsilon}(n)})$ for any fixed $\epsilon > 0$ [DK'99]. DSF admits approximation ratios of $O(k^{1/2 + \epsilon})$ [CEGS'11] and $O(n^{2/3 + \epsilon})$ [BBMRY'13]. In this work we show that in planar digraphs, an important and useful class of graphs in both theory and practice, DSF is much more tractable. We obtain an $O(\log^6 k)$-approximation algorithm via the junction tree technique. Our main technical contribution is to prove the existence of a low density junction tree in planar digraphs. To find an approximate junction tree we rely on recent results on rooted directed network design problems [FM'23, CJKZZ'24], in particular, on an LP-based algorithm for the Directed Steiner Tree problem [CJKZZ'24]. Our work and several other recent ones on algorithms for planar digraphs [FM'23, KS'21, CJKZZ'24] are built upon structural insights on planar graph reachability and shortest path separators [Thorup'04].

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

Social media is a great source of data for users reporting information and regarding their health and how various things have had an effect on them. This paper presents various approaches using Transformers and Large Language Models and their ensembles, their performance along with advantages and drawbacks for various tasks of SMM4H'24 - Classifying texts on impact of nature and outdoor spaces on the author's mental health (Task 3), Binary classification of tweets reporting their children's health disorders like Asthma, Autism, ADHD and Speech disorder (task 5), Binary classification of users self-reporting their age (task 6)., Comment: short paper , acl 2024

This paper significantly advances the application of Quantum Key Distribution (QKD) in Free- Space Optics (FSO) satellite-based quantum communication. We propose an innovative satellite quantum channel model and derive the secret quantum key distribution rate achievable through this channel. Unlike existing models that approximate the noise in quantum channels as merely Gaussian distributed, our model incorporates a hybrid noise analysis, accounting for both quantum Poissonian noise and classical Additive-White-Gaussian Noise (AWGN). This hybrid approach acknowledges the dual vulnerability of continuous variables (CV) Gaussian quantum channels to both quantum and classical noise, thereby offering a more realistic assessment of the quantum Secret Key Rate (SKR). This paper delves into the variation of SKR with the Signal-to-Noise Ratio (SNR) under various influencing parameters. We identify and analyze critical factors such as reconciliation efficiency, transmission coefficient, transmission efficiency, the quantum Poissonian noise parameter, and the satellite altitude. These parameters are pivotal in determining the SKR in FSO satellite quantum channels, highlighting the challenges of satellitebased quantum communication. Our work provides a comprehensive framework for understanding and optimizing SKR in satellite-based QKD systems, paving the way for more efficient and secure quantum communication networks.

Gallbladder cancer (GBC) is the most frequent cause of disease among biliary tract neoplasms. Identifying the molecular mechanisms and biomarkers linked to GBC progression has been a significant challenge in scientific research. Few recent studies have explored the roles of biomarkers in GBC. Our study aimed to identify biomarkers in GBC using machine learning (ML) and bioinformatics techniques. We compared GBC tumor samples with normal samples to identify differentially expressed genes (DEGs) from two microarray datasets (GSE100363, GSE139682) obtained from the NCBI GEO database. A total of 146 DEGs were found, with 39 up-regulated and 107 down-regulated genes. Functional enrichment analysis of these DEGs was performed using Gene Ontology (GO) terms and REACTOME pathways through DAVID. The protein-protein interaction network was constructed using the STRING database. To identify hub genes, we applied three ranking algorithms: Degree, MNC, and Closeness Centrality. The intersection of hub genes from these algorithms yielded 11 hub genes. Simultaneously, two feature selection methods (Pearson correlation and recursive feature elimination) were used to identify significant gene subsets. We then developed ML models using SVM and RF on the GSE100363 dataset, with validation on GSE139682, to determine the gene subset that best distinguishes GBC samples. The hub genes outperformed the other gene subsets. Finally, NTRK2, COL14A1, SCN4B, ATP1A2, SLC17A7, SLIT3, COL7A1, CLDN4, CLEC3B, ADCYAP1R1, and MFAP4 were identified as crucial genes, with SLIT3, COL7A1, and CLDN4 being strongly linked to GBC development and prediction.

How much does AI assistance impact developer productivity? To date, the software engineering literature has provided a range of answers, targeting a diversity of outcomes: from perceived productivity to speed on task and developer throughput. Our randomized controlled trial with 96 full-time Google software engineers contributes to this literature by sharing an estimate of the impact of three AI features on the time developers spent on a complex, enterprise-grade task. We found that AI significantly shortened the time developers spent on task. Our best estimate of the size of this effect, controlling for factors known to influence developer time on task, stands at about 21\%, although our confidence interval is large. We also found an interesting effect whereby developers who spend more hours on code-related activities per day were faster with AI. Product and future research considerations are discussed. In particular, we invite further research that explores the impact of AI at the ecosystem level and across multiple suites of AI-enhanced tools, since we cannot assume that the effect size obtained in our lab study will necessarily apply more broadly, or that the effect of AI found using internal Google tooling in the summer of 2024 will translate across tools and over time., Comment: 12 pages, 7 figures, 3 tables

Urgent applications like wildfire management and renewable energy generation require precise, localized weather forecasts near the Earth's surface. However, weather forecast products from machine learning or numerical weather models are currently generated on a global regular grid, on which a naive interpolation cannot accurately reflect fine-grained weather patterns close to the ground. In this work, we train a heterogeneous graph neural network (GNN) end-to-end to downscale gridded forecasts to off-grid locations of interest. This multi-modal GNN takes advantage of local historical weather observations (e.g., wind, temperature) to correct the gridded weather forecast at different lead times towards locally accurate forecasts. Each data modality is modeled as a different type of node in the graph. Using message passing, the node at the prediction location aggregates information from its heterogeneous neighbor nodes. Experiments using weather stations across the Northeastern United States show that our model outperforms a range of data-driven and non-data-driven off-grid forecasting methods. Our approach demonstrates how the gap between global large-scale weather models and locally accurate predictions can be bridged to inform localized decision-making.

Contemporary evaluation techniques are inadequate for agentic systems. These approaches either focus exclusively on final outcomes -- ignoring the step-by-step nature of agentic systems, or require excessive manual labour. To address this, we introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We apply the Agent-as-a-Judge to the task of code generation. To overcome issues with existing benchmarks and provide a proof-of-concept testbed for Agent-as-a-Judge, we present DevAI, a new benchmark of 55 realistic automated AI development tasks. It includes rich manual annotations, like a total of 365 hierarchical user requirements. We benchmark three of the popular agentic systems using Agent-as-a-Judge and find it dramatically outperforms LLM-as-a-Judge and is as reliable as our human evaluation baseline. Altogether, we believe that Agent-as-a-Judge marks a concrete step forward for modern agentic systems -- by providing rich and reliable reward signals necessary for dynamic and scalable self-improvement., Comment: The project can be found at https://github.com/metauto-ai/agent-as-a-judge. The dataset is released at https://huggingface.co/DEVAI-benchmark

India has 1369 languages of which 22 are official. About 13 different scripts are used to represent these languages. A Common Label Set (CLS) was developed based on phonetics to address the issue of large vocabulary of units required in the End to End (E2E) framework for multilingual synthesis. This reduced the footprint of the synthesizer and also enabled fast adaptation to new languages which had similar phonotactics, provided language scripts belonged to the same family. In this paper, we provide new insights into speech synthesis, where the script belongs to one family, while the phonotactics comes from another. Indian language text is first converted to CLS, and then a synthesizer that matches the phonotactics of the language is used. Quality akin to that of a native speaker is obtained for Sanskrit and Konkani with zero adaptation data, using Kannada and Marathi synthesizers respectively. Further, this approach also lends itself seamless code switching across 13 Indian languages and English in a given native speaker's voice., Comment: 5 Pages, 1 Figure, Submitted to ICASSP 2025

Video generation has been used to generate visual plans for controlling robotic systems. Given an image observation and a language instruction, previous work has generated video plans which are then converted to robot controls to be executed. However, a major bottleneck in leveraging video generation for control lies in the quality of the generated videos, which often suffer from hallucinatory content and unrealistic physics, resulting in low task success when control actions are extracted from the generated videos. While scaling up dataset and model size provides a partial solution, integrating external feedback is both natural and essential for grounding video generation in the real world. With this observation, we propose VideoAgent for self-improving generated video plans based on external feedback. Instead of directly executing the generated video plan, VideoAgent first refines the generated video plans using a novel procedure which we call self-conditioning consistency, utilizing feedback from a pretrained vision-language model (VLM). As the refined video plan is being executed, VideoAgent collects additional data from the environment to further improve video plan generation. Experiments in simulated robotic manipulation from MetaWorld and iTHOR show that VideoAgent drastically reduces hallucination, thereby boosting success rate of downstream manipulation tasks. We further illustrate that VideoAgent can effectively refine real-robot videos, providing an early indicator that robotics can be an effective tool in grounding video generation in the physical world.

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

The Large Magellanic Cloud (LMC) is a Milky Way (MW) satellite that is massive enough to gravitationally attract the MW disc and inner halo, causing significant motion of the inner MW with respect to the outer halo. In this work, we probe this interaction by constructing a sample of 9,866 blue horizontal branch (BHB) stars with radial velocities from the DESI spectroscopic survey out to 120 kpc from the Galactic centre. This is the largest spectroscopic set of BHB stars in the literature to date, and it contains four times more stars with Galactocentric distances beyond 50 kpc than previous BHB catalogues. Using the DESI BHB sample combined with SDSS BHBs, we measure the bulk radial velocity of stars in the outer halo and observe that the velocity in the Southern Galactic hemisphere is different by 3.7$\sigma$ from the North. Modelling the projected velocity field shows that its dipole component is directed at a point 22 degrees away from the LMC along its orbit, which we interpret as the travel direction of the inner MW. The velocity field includes a monopole term that is -24 km/s, which we refer to as compression velocity. This velocity is significantly larger than predicted by the current models of the MW and LMC interaction. This work uses DESI data from its first two years of observations, but we expect that with upcoming DESI data releases, the sample of BHB stars will increase and our ability to measure the MW-LMC interaction will improve significantly., Comment: 22 pages, 19 figures. Submitted to MNRAS

Designing an operational architecture for the Quantum Internet is a challenging task in light of both fundamental limitations imposed by the laws of physics and technological constraints. Here, we propose a method to abstract away most of the quantum-specific elements and formulate a best-effort quantum network architecture based on packet-switching, akin to that of the classical Internet. Such reframing provides an opportunity to exploit the many tools and protocols available and well-understood within the Internet. As an illustration, we tailor and adapt classical congestion control and active queue management protocols to quantum networks, comprising an architecture wherein quantum end- and intermediate nodes effectively regulate demand and resource utilization, respectively. Results show that these classical networking tools can be effectively used to combat quantum memory decoherence and keep end-to-end fidelity around a target value., Comment: 9 pages, 5 figures

In this work, we report an innovative pump-probe based experimental set up, to study the melting, subsequent evaporation, plasma formation and redeposition in a thin film coated on a glass substrate under different ambient conditions and laser fluences. The ambient conditions restrict the expansion of the plasma plume. At high ambient pressure, plume expansion stops closer to the substrate and get re-deposited at the site of the ablation. This helps in the identification of multiple processes and their temporal evolutions during the melting, expansion and re-deposition stages. The ambient conditions affect the plasma plume formed upon ablation, thus modulating the transmission of probe laser pulses, which provides information about the plume dynamics. Further, the study offers valuable insights into the laser-based ablation of thin film coatings, which will have implications in in situ cleaning of view ports on large experimental facilities such as tokamaks and other systems e.g. coating units, pulsed laser deposition, Laser induced forward transfer, Laser surface structuring, etc.

Glasses are known to have medium range order (MRO) but its link to any experimentally measurable quantity is still ambiguous. The first sharp diffraction peak (FSDP) in structure-factor S(q) obtained from diffraction experiments on glasses has been associated with this MRO but understanding the fundamental origin of this universal peak is still an open problem. We have addressed this issue for a complex glass i.e. iron phosphate glass (IPG) through atomistic models generated from a hybrid approach (our in-house developed MC code with MD simulation). We have performed a comparative study by generating glass models from different initial configurations and randomization techniques. The developed IPG models were first validated with existing data on short range order (SRO) and MRO, through study of pair correlation functions, bond angle distributions and coordination number (CN) for SRO and rings distribution, FSDP in structure factor and void size distribution for MRO. The study of coordination environment of oxygen is specifically shown to aide in understanding glass formation through topological constraint theory. Thereafter to understand the fundamental origin of FSDP in S(q), structure factors were calculated corresponding to individual ring sizes present in the model. The relative contribution of these individual S(q)s in the total experimental S(q) is estimated using an inverse fitting approach. The contributions thus obtained directly correlated with rings size percentages in the models for the considered q-range. Through this exercise we can connect the rings distribution of an atomistic glass model with an experimentally measurable quantity like FSDP in S(q) for a complex glass like IPG., Comment: 25 pages, 12 figures

Laser-induced breakdown spectroscopy (LIBS) is a well-established technique widely used in fundamental research and diverse practical fields. Polarization-resolved LIBS, a variant of this technique, aims to improve the sensitivity, which is a critical aspect in numerous scientific domains. In our recent work we demonstrated that the degree of polarization (DOP) in the emission depends on the spatial location and time in a nano second laser generated aluminium plasma1. Present study investigates the effect of polarized emission on the estimation of plasma parameters. The plasma parameters are estimated using the conventional spectroscopic methods such as Boltzmann plot and line intensity ratio for the estimation of electron temperature and Stark broadening for estimating the electron density. The estimated plasma temperature using Boltzmann plot method shows large errors in electron temperature for the locations where DOP is higher. However, the electron density estimated using the Stark width does not show such variation. The observed ambiguity in temperature estimation using the Boltzmann plot method appears to be a consequence of deviation from expected Maxwell Boltzmann distribution of population of the involved energy levels. These findings highlight the need of assessing the DOP of the plasma before selecting the polarization for PRLIBS or temperature estimation using Boltzmann plots in elemental analysis.

Release engineering has traditionally focused on continuously delivering features and bug fixes to users, but at a certain scale, it becomes impossible for a release engineering team to determine what should be released. At Meta's scale, the responsibility appropriately and necessarily falls back on the engineer writing and reviewing the code. To address this challenge, we developed models of diff risk scores (DRS) to determine how likely a diff is to cause a SEV, i.e., a severe fault that impacts end-users. Assuming that SEVs are only caused by diffs, a naive model could randomly gate X% of diffs from landing, which would automatically catch X% of SEVs on average. However, we aimed to build a model that can capture Y% of SEVs by gating X% of diffs, where Y >> X. By training the model on historical data on diffs that have caused SEVs in the past, we can predict the riskiness of an outgoing diff to cause a SEV. Diffs that are beyond a particular threshold of risk can then be gated. We have four types of gating: no gating (green), weekend gating (weekend), medium impact on end-users (yellow), and high impact on end-users (red). The input parameter for our models is the level of gating, and the outcome measure is the number of captured SEVs. Our research approaches include a logistic regression model, a BERT-based model, and generative LLMs. Our baseline regression model captures 18.7%, 27.9%, and 84.6% of SEVs while respectively gating the top 5% (weekend), 10% (yellow), and 50% (red) of risky diffs. The BERT-based model, StarBERT, only captures 0.61x, 0.85x, and 0.81x as many SEVs as the logistic regression for the weekend, yellow, and red gating zones, respectively. The generative LLMs, iCodeLlama-34B and iDiffLlama-13B, when risk-aligned, capture more SEVs than the logistic regression model in production: 1.40x, 1.52x, 1.05x, respectively.

We have realized a compact optical particle counter utilizing enhancement of light scattering within a high finesse Fabry-Perot optical cavity. In contrast to laser-based approaches such as cavity ringdown spectroscopy we use the light stream from both superluminescent and light-emitting diodes that have no longitudinal coherence. This eliminates the vibration sensitivity that is typical of laser-based cavity methods. The use of the transmission mode of detection allows us to reduce the cavity mirror separation to below 1 cm, with no obvious limit to miniaturization. Typical light scattering instruments are larger, in part due to their sensitivity to background signals from the light source. Our approach paves the way toward a new generation of compact and portable instruments. A simultaneous comparison of the scattering signals with a commercial particle counter shows that our cavity is also sensitive to ultrafine particles below 300 nm diameter that are typically not recorded in such counters., Comment: 13 pages, 6 figures, submitted to Applied Optics

Question-answering systems for Bengali have seen limited development, particularly in domain-specific applications. Leveraging advancements in natural language processing, this paper explores a fine-tuned BERT-Bangla model to address this gap. It presents the development of a question-answering system for Bengali using a fine-tuned BERT-Bangla model in a closed domain. The dataset was sourced from Khulna University of Engineering \& Technology's (KUET) website and other relevant texts. The system was trained and evaluated with 2500 question-answer pairs generated from curated data. Key metrics, including the Exact Match (EM) score and F1 score, were used for evaluation, achieving scores of 55.26\% and 74.21\%, respectively. The results demonstrate promising potential for domain-specific Bengali question-answering systems. Further refinements are needed to improve performance for more complex queries.

From a communications perspective, a frame defines the packaging of the language used in such a way as to encourage certain interpretations and to discourage others. For example, a news article can frame immigration as either a boost or a drain on the economy, and thus communicate very different interpretations of the same phenomenon. In this work, we argue that to explain framing devices we have to look at the way narratives are constructed. As a first step in this direction, we propose a framework that extracts events and their relations to other events, and groups them into high-level narratives that help explain frames in news articles. We show that our framework can be used to analyze framing in U.S. news for two different domains: immigration and gun control., Comment: Accepted to the 6th Workshop on Narrative Understanding, co-located with EMNLP 2024

The vision for 6G aims to enhance network capabilities with faster data rates, near-zero latency, and higher capacity, supporting more connected devices and seamless experiences within an intelligent digital ecosystem where artificial intelligence (AI) plays a crucial role in network management and data analysis. This advancement seeks to enable immersive mixed-reality experiences, holographic communications, and smart city infrastructures. However, the expansion of 6G raises critical security and privacy concerns, such as unauthorized access and data breaches. This is due to the increased integration of IoT devices, edge computing, and AI-driven analytics. This paper provides a comprehensive overview of 6G protocols, focusing on security and privacy, identifying risks, and presenting mitigation strategies. The survey examines current risk assessment frameworks and advocates for tailored 6G solutions. We further discuss industry visions, government projects, and standardization efforts to balance technological innovation with robust security and privacy measures.

Scaling laws in language modeling traditionally quantify training loss as a function of dataset size and model parameters, providing compute-optimal estimates but often neglecting the impact of data quality on model generalization. In this paper, we extend the conventional understanding of scaling law by offering a microscopic view of data quality within the original formulation -- effective training tokens -- which we posit to be a critical determinant of performance for parameter-constrained language models. Specifically, we formulate the proposed term of effective training tokens to be a combination of two readily-computed indicators of text: (i) text diversity and (ii) syntheticity as measured by a teacher model. We pretrained over $200$ models of 25M to 1.5B parameters on a diverse set of sampled, synthetic data, and estimated the constants that relate text quality, model size, training tokens, and eight reasoning task accuracy scores. We demonstrated the estimated constants yield +0.83 Pearson correlation with true accuracies, and analyzed it in scenarios involving widely-used data techniques such as data sampling and synthesis which aim to improve data quality., Comment: Accepted to EMNLP 2024 Industry Track, 18 pages, 9 figures, 4 tables

Dwarf galaxies hold the key to crucial frontiers of astrophysics, however, their faintness renders spectroscopy challenging. Here we present the JWST Cycle 2 survey, All the Little Things (ALT, PID 3516), which is designed to seek late-forming Pop III stars and the drivers of reionization at $z\sim6-7$. ALT has acquired the deepest NIRCam grism spectroscopy yet (7-27 hr), at JWST's most sensitive wavelengths (3-4 $\mu$m), covering the powerful lensing cluster Abell 2744. Over the same 30 arcmin$^2$, ALT's ultra-deep F070W+F090W imaging ($\sim$30 mag) enables selection of very faint sources at $z>6$. We demonstrate the success of ALT's novel ``butterfly" mosaic to solve spectral confusion and contamination, and introduce the ``Allegro" method for emission line identification. By collecting spectra for every source in the field of view, ALT has measured precise ($R\sim1600$) redshifts for 1630 sources at $z=0.2-8.5$. This includes one of the largest samples of distant dwarf galaxies: [1015, 475, 50] sources less massive than the SMC, Fornax, and Sculptor with $\log(M_{*}/M_{\odot})<$[8.5, 7.5, 6.5]. We showcase ALT's discovery space with: (i) spatially resolved spectra of lensed clumps in galaxies as faint as $M_{\rm{UV}}\sim-15$; (ii) large-scale clustering -- overdensities at $z$=[2.50, 2.58, 3.97, 4.30, 5.66, 5.77, 6.33] hosting massive galaxies with striking Balmer breaks; (iii) small-scale clustering -- a system of satellites around a Milky Way analog at $z\sim6$; (iv) spectroscopically confirmed multiple images that help constrain the lensing model underlying all science in this legacy field; (v) sensitive star-formation maps based on dust-insensitive tracers such as Pa$\alpha$; (vi) direct spectroscopic discovery of rare sources such as AGN with ionized outflows. These results provide a powerful proof of concept for how grism surveys maximize the potential of strong lensing fields., Comment: Submitted to the Open Journal of Astrophysics. Figs. 6 and 10 illustrate the quality of the spectra and imaging, while Fig. 12 summarizes the yield of the survey. Comments warmly welcomed and greatly appreciated. The ALT DR1 catalog is available at https://zenodo.org/records/13871850

This study conducts a numerical investigation into the three-dimensional film boiling of liquid under the influence of external magnetic fields. The numerical method incorporates a sharp phase-change model based on the volume-of-fluid approach to track the liquid-vapor interface. Additionally, a consistent and conservative scheme is employed to calculate the induced current densities and electromagnetic forces. We investigate the magnetohydrodynamic effects on film boiling, particularly examining the pattern transition of the vapor bubble and the evolution of heat transfer characteristics, exposed to either a vertical or horizontal magnetic field. In single-mode scenarios, film boiling under a vertical magnetic field displays an isotropic flow structure, forming a columnar vapor jet at higher magnetic field intensities. In contrast, horizontal magnetic fields result in anisotropic flow, creating a two-dimensional vapor sheet as the magnetic strength increases. In multi-mode scenarios, the patterns observed in single-mode film boiling persist, with the interaction of vapor bubbles introducing additional complexity to the magnetohydrodynamic flow. More importantly, our comprehensive analysis reveals how and why distinct boiling effects are generated by various orientations of magnetic fields, which induce directional electromagnetic forces to suppress flow vortices within the cross-sectional plane., Comment: 37 pages, 28 figures, Journal of Fluid Mechanics

This work explores non-prehensile manipulation (NPM) and whole-body interaction as strategies for enabling robotic manipulators to conduct manipulation tasks despite experiencing locked multi-joint (LMJ) failures. LMJs are critical system faults where two or more joints become inoperable; they impose constraints on the robot's configuration and control spaces, consequently limiting the capability and reach of a prehensile-only approach. This approach involves three components: i) modeling the failure-constrained workspace of the robot, ii) generating a kinodynamic map of NPM actions within this workspace, and iii) a manipulation action planner that uses a sim-in-the-loop approach to select the best actions to take from the kinodynamic map. The experimental evaluation shows that our approach can increase the failure-constrained reachable area in LMJ cases by 79%. Further, it demonstrates the ability to complete real-world manipulation with up to 88.9% success when the end-effector is unusable and up to 100% success when it is usable., Comment: To be published in the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems

Medical time series datasets feature missing values that need data imputation methods, however, conventional machine learning models fall short due to a lack of uncertainty quantification in predictions. Among these models, the CATSI (Context-Aware Time Series Imputation) stands out for its effectiveness by incorporating a context vector into the imputation process, capturing the global dependencies of each patient. In this paper, we propose a Bayesian Context-Aware Time Series Imputation (Bayes-CATSI) framework which leverages uncertainty quantification offered by variational inference. We consider the time series derived from electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (EKG). Variational Inference assumes the shape of the posterior distribution and through minimization of the Kullback-Leibler(KL) divergence it finds variational densities that are closest to the true posterior distribution. Thus , we integrate the variational Bayesian deep learning layers into the CATSI model. Our results show that Bayes-CATSI not only provides uncertainty quantification but also achieves superior imputation performance compared to the CATSI model. Specifically, an instance of Bayes-CATSI outperforms CATSI by 9.57 %. We provide an open-source code implementation for applying Bayes-CATSI to other medical data imputation problems.

The remarkable capabilities of large language models (LLMs) in language understanding and generation have not rendered them immune to hallucinations. LLMs can still generate plausible-sounding but factually incorrect or fabricated information. As LLM-empowered chatbots become popular, laypeople may frequently ask health-related queries and risk falling victim to these LLM hallucinations, resulting in various societal and healthcare implications. In this work, we conduct a pioneering study of hallucinations in LLM-generated responses to real-world healthcare queries from patients. We propose MedHalu, a carefully crafted first-of-its-kind medical hallucination dataset with a diverse range of health-related topics and the corresponding hallucinated responses from LLMs with labeled hallucination types and hallucinated text spans. We also introduce MedHaluDetect framework to evaluate capabilities of various LLMs in detecting hallucinations. We also employ three groups of evaluators -- medical experts, LLMs, and laypeople -- to study who are more vulnerable to these medical hallucinations. We find that LLMs are much worse than the experts. They also perform no better than laypeople and even worse in few cases in detecting hallucinations. To fill this gap, we propose expert-in-the-loop approach to improve hallucination detection through LLMs by infusing expert reasoning. We observe significant performance gains for all the LLMs with an average macro-F1 improvement of 6.3 percentage points for GPT-4., Comment: 14 pages

Federated learning is emerging as a promising machine learning technique in the medical field for analyzing medical images, as it is considered an effective method to safeguard sensitive patient data and comply with privacy regulations. However, recent studies have revealed that the default settings of federated learning may inadvertently expose private training data to privacy attacks. Thus, the intensity of such privacy risks and potential mitigation strategies in the medical domain remain unclear. In this paper, we make three original contributions to privacy risk analysis and mitigation in federated learning for medical data. First, we propose a holistic framework, MedPFL, for analyzing privacy risks in processing medical data in the federated learning environment and developing effective mitigation strategies for protecting privacy. Second, through our empirical analysis, we demonstrate the severe privacy risks in federated learning to process medical images, where adversaries can accurately reconstruct private medical images by performing privacy attacks. Third, we illustrate that the prevalent defense mechanism of adding random noises may not always be effective in protecting medical images against privacy attacks in federated learning, which poses unique and pressing challenges related to protecting the privacy of medical data. Furthermore, the paper discusses several unique research questions related to the privacy protection of medical data in the federated learning environment. We conduct extensive experiments on several benchmark medical image datasets to analyze and mitigate the privacy risks associated with federated learning for medical data.

Coherent radio emission with properties similar to planetary auroral signals has been reported from GJ 1151, a quiescent, slow-rotating mid-M star, by the LOFAR Two-metre (120-170 MHz) Sky Survey (LoTSS). The observed {LOFAR} emission is fairly bright at 0.89 mJy with 64% circular polarization, and the emission characteristics are consistent with the interaction between an Earth-sized planet with an orbital period of 1-5 days and the magnetic field of the host star. However, no short-period planet has been detected around GJ 1151. To confirm the reported radio emission caused by the putative planet around GJ 1151 and to investigate the nature of this emission, we carried out uGMRT observations of GJ 1151 at 150, 218, and 400 MHz over 33 hours across ten epochs. No emission was detected at any frequency. While at 150 MHz and 218 MHz, non-detection could be due to the low sensitivity of our observations, at 400 MHz, the rms sensitivities achieved were sufficient to detect the emission observed with LOFAR at $\sim$ 20$\sigma$ level. Our findings suggest that the radio emission is highly time-variable, likely influenced by the star-planet system's phase and the host star's magnetic field. Additional observations below 170 MHz, at more frequent epochs (as the periodicity of the emission is unknown), especially during periods of high stellar magnetic field strength, are needed to confirm the emission., Comment: Accepted at AJ, 8 pages, 2 figures

Excitons are fundamental excitations that govern the optical properties of semiconductors. Interacting excitons can lead to various emergent phases of matter and large nonlinear optical responses. In most semiconductors, excitons interact via exchange interaction or phase space filling. Correlated materials that host excitons coupled to other degrees of freedom offer hitherto unexplored pathways for controlling these interactions. Here, we demonstrate magnon-mediated excitonic interactions in CrSBr, an antiferromagnetic semiconductor. This interaction manifests as the dependence of exciton energy on exciton density via a magnonic adjustment of the spin canting angle. Our study demonstrates the emergence of quasiparticle-mediated interactions in correlated quantum materials, leading to large nonlinear optical responses and potential device concepts such as magnon-mediated quantum transducers., Comment: 33 pages, 14 figures

We present the first multi-frequency analysis of the candidate ultra-steep spectrum radio halo in the galaxy cluster PLCKESZ G171.94$-$40.65, using the upgraded Giant Metrewave Radio telescope (uGMRT; 400 MHz), and Karl G. Jansky Very Large Array (JVLA; 1-2 GHz) observations. Our radio data have been complemented with archival \textit{Chandra} X-ray observations to provide a crucial insight into the complex intracluster medium (ICM) physics, happening at large scales. We detect the radio halo emission to the extent of $\sim$ 1.5 Mpc at 400 MHz, significantly larger than previously reported, along with five tailed galaxies in the central region. We also report the discovery of an unknown diffuse source 'U', at the cluster periphery, with an extent of 300 kpc. Using the available observations, we have found that the radio spectrum of the halo is well-fitted with a single power law, having a spectral index of $-1.36 \pm 0.05$, indicating that it is not an ultra-steep spectrum radio halo. Our low-resolution (25$''$) resolved spectral map shows an overall uniform spectral index, with some patches of fluctuations. The X-ray and radio surface brightness are morphologically co-spatial, with a slight extension along the northwest-southeast direction, seen in both maps. The radio and X-ray surface brightness indicates strong positive correlations, with sub-linear correlation slopes ($\sim$ 0.71). Multiple tailed galaxies and the radio halo indicate a high dynamical activity at the cluster central region., Comment: 16 pages, 12 Figures, 6 Tables, Accepted for publication in ApJ

Mature radio galaxies such as M87 belong to a specific subclass of active galaxies (AGN) whose evolution in time endows them with five distinguishing characteristics, including (1) low excitation emission, (2) low star formation rates, (3) high bulge stellar-velocity dispersion, (4) bright stellar nuclei, and (5) weak or nonexistent merger signatures. We show how to understand these seemingly disparate characteristics as originating from the time evolution of powerful radio quasars and describe a new model prediction that tilted accretion disks in AGN are expected to occur in bright quasars but not in other subclasses of AGN. The picture we present should be understood as the most compelling evidence for counter-rotation as a key element in feedback from accreting black holes., Comment: 11 pages, 6 Figures, Published in the Universe journal

Quantum Machine Learning (QML) is an emerging field of research with potential applications to distributed collaborative learning, such as Split Learning (SL). SL allows resource-constrained clients to collaboratively train ML models with a server, reduce their computational overhead, and enable data privacy by avoiding raw data sharing. Although QML with SL has been studied, the problem remains open in resource-constrained environments where clients lack quantum computing capabilities. Additionally, data privacy leakage between client and server in SL poses risks of reconstruction attacks on the server side. To address these issues, we propose Hybrid Quantum Split Learning (HQSL), an application of Hybrid QML in SL. HQSL enables classical clients to train models with a hybrid quantum server and curtails reconstruction attacks. In addition, we introduce a novel qubit-efficient data-loading technique for designing a quantum layer in HQSL, minimizing both the number of qubits and circuit depth. Experiments on five datasets demonstrate HQSL's feasibility and ability to enhance classification performance compared to its classical models. Notably, HQSL achieves mean improvements of over 3% in both accuracy and F1-score for the Fashion-MNIST dataset, and over 1.5% in both metrics for the Speech Commands dataset. We expand these studies to include up to 100 clients, confirming HQSL's scalability. Moreover, we introduce a noise-based defense mechanism to tackle reconstruction attacks on the server side. Overall, HQSL enables classical clients to collaboratively train their models with a hybrid quantum server, leveraging quantum advantages while improving model performance and security against data privacy leakage-related reconstruction attacks., Comment: 47 pages

The use of cryptocurrencies has led to an increase in illicit activities such as money laundering, with traditional rule-based approaches becoming less effective in detecting and preventing such activities. In this paper, we propose a novel approach to tackling this problem by applying graph attention networks with residual network-like architecture (GAT-ResNet) to detect illicit transactions related to anti-money laundering/combating the financing of terrorism (AML/CFT) in blockchains. We train various models on the Elliptic Bitcoin Transaction dataset, implementing logistic regression, Random Forest, XGBoost, GCN, GAT, and our proposed GAT-ResNet model. Our results demonstrate that the GAT-ResNet model has a potential to outperform the existing graph network models in terms of accuracy, reliability and scalability. Our research sheds light on the potential of graph related machine learning models to improve efforts to combat financial crime and lays the foundation for further research in this area., Comment: 9 pages, 7 figures

Language model pretraining generally targets a broad range of use cases and incorporates data from diverse sources. However, there are instances where we desire a model that excels in specific areas without markedly compromising performance in other areas. A cost-effective and straightforward approach is sampling with low-dimensional data features, which allows to select large-scale pretraining data for domain-specific use cases. In this work, we revisit importance sampling with n-gram features consisting of multi-granular tokens, which strikes a good balance between sentence compression and representation capabilities. We observed the sampled data to have a high correlation with the target downstream task performance while preserving its effectiveness on other tasks. This leads to the proposed data sampling paradigm where language models can be pretrained more efficiently on selected documents. On eight benchmarks we demonstrate with $\sim$1% of the data, pretrained models perform on par with the full RefinedWeb data and outperform randomly selected samples for model sizes ranging from 125M to 1.5B., Comment: Accepted to EMNLP 2024 Main Conference, 9 pages, 6 figures, 3 tables

The Rydberg atomic quantum receivers (RAQR) are emerging quantum precision sensing platforms designed for receiving radio frequency (RF) signals. It relies on creation of Rydberg atoms from normal atoms by exciting one or more electrons to a very high energy level, thereby making the atom sensitive to RF signals. RAQRs realize RF-to-optical conversions based on light-atom interactions relying on the so called electromagnetically induced transparency (EIT) and Aulter-Townes splitting (ATS), so that the desired RF signal can be read out optically. The large dipole moments of Rydberg atoms associated with rich choices of Rydberg states and various modulation schemes facilitate an ultra-high sensitivity ($\sim$ nV/cm/$\sqrt{\text{Hz}}$) and an ultra-broadband tunability (direct-current to Terahertz). RAQRs also exhibit compelling scalability and lend themselves to the construction of innovative, compact receivers. Initial experimental studies have demonstrated their capabilities in classical wireless communications and sensing. To fully harness their potential in a wide variety of applications, we commence by outlining the underlying fundamentals of Rydberg atoms, followed by the principles and schemes of RAQRs. Then, we overview the state-of-the-art studies from both physics and communication societies. Furthermore, we conceive Rydberg atomic quantum single-input single-output (RAQ-SISO) and multiple-input multiple-output (RAQ-MIMO) schemes for facilitating the integration of RAQRs with classical wireless systems. Finally, we conclude with a set of potent research directions., Comment: 9 pages, 5 figures, 1 table

In the present paper, we investigate constrained transit cosmological models in the most recent proposed modified gravity theory, $f(R,L_{m},T)$-gravity. We obtain the modified field equations for a flat homogeneous and isotropic Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) spacetime metric. We constrain the equation of continuity by imposing the equation of state for the perfect fluid source $p=-\frac{1}{3}\rho+p_{0}$ so that we get energy conservation equation as $\dot{\rho}+3H(\rho+p)=0$, (because generally, energy conservation law is not satisfied in $f(R,L_{m},T)$-gravity). Using this constraint, we establish a relation between the energy density parameters $\Omega_{m0}$, $\Omega_{r0}$, and $\Omega_{f0}$ and the Hubble function. After that, we made observational constraints on $H(z)$ to obtain the best-fit present values of $\Omega_{m0}$, $\Omega_{r0}$, and $H_{0}$. Then, we use these best-fit values of energy parameters to investigate cosmological parameters such as the deceleration parameter, the effective equation of state $\omega_{eff}$, and the energy density parameters $\Omega_{m}$, $\Omega_{r}$, and $\Omega_{f}$ to learn more about the components and history of the expanding universe. We found an effective EoS parameter in the range $-1\le \omega_{eff}\le\frac{1}{3}$ with a deceleration-acceleration transition redshift value of $z_{t}=0.6377, 0.6424$ along two datasets cosmic chronometer (CC) and Pantheon SNIa, respectively., Comment: 21 pages, 6 figures

Perspective distortion (PD) leads to substantial alterations in the shape, size, orientation, angles, and spatial relationships of visual elements in images. Accurately determining camera intrinsic and extrinsic parameters is challenging, making it hard to synthesize perspective distortion effectively. The current distortion correction methods involve removing distortion and learning vision tasks, thus making it a multi-step process, often compromising performance. Recent work leverages the M\"obius transform for mitigating perspective distortions (MPD) to synthesize perspective distortions without estimating camera parameters. M\"obius transform requires tuning multiple interdependent and interrelated parameters and involving complex arithmetic operations, leading to substantial computational complexity. To address these challenges, we propose Log Conformal Maps (LCM), a method leveraging the logarithmic function to approximate perspective distortions with fewer parameters and reduced computational complexity. We provide a detailed foundation complemented with experiments to demonstrate that LCM with fewer parameters approximates the MPD. We show that LCM integrates well with supervised and self-supervised representation learning, outperform standard models, and matches the state-of-the-art performance in mitigating perspective distortion over multiple benchmarks, namely Imagenet-PD, Imagenet-E, and Imagenet-X. Further LCM demonstrate seamless integration with person re-identification and improved the performance. Source code is made publicly available at https://github.com/meenakshi23/Log-Conformal-Maps., Comment: Accepted to Asian Conference on Computer Vision (ACCV2024)

Multilingual large language models (LLMs) today may not necessarily provide culturally appropriate and relevant responses to its Filipino users. We introduce Kalahi, a cultural LLM evaluation suite collaboratively created by native Filipino speakers. It is composed of 150 high-quality, handcrafted and nuanced prompts that test LLMs for generations that are relevant to shared Filipino cultural knowledge and values. Strong LLM performance in Kalahi indicates a model's ability to generate responses similar to what an average Filipino would say or do in a given situation. We conducted experiments on LLMs with multilingual and Filipino language support. Results show that Kalahi, while trivial for Filipinos, is challenging for LLMs, with the best model answering only 46.0% of the questions correctly compared to native Filipino performance of 89.10%. Thus, Kalahi can be used to accurately and reliably evaluate Filipino cultural representation in LLMs., Comment: Accepted for presentation at Paclic 38, 2024

We present a time-domain inspiral-merger-ringdowm (IMR) waveform model ESIGMAHM constructed within a framework we named ESIGMA for coalescing binaries of spinning black holes on moderately eccentric orbits (Huerta et al. (2018) [Phys. Rev. D 97, 024031]). We now include the effect of black hole spins on the dynamics of eccentric binaries, as well as model sub-dominant waveform harmonics emitted by them. The inspiral evolution is described by a consistent combination of latest results from post-Newtonian theory, self-force, and black hole perturbation theory. We assume that these moderately eccentric binaries radiate away most of their orbital eccentricity before merger, and seamlessly connect the eccentric inspiral with a numerical relativity based surrogate waveform model for mergers of spinning binaries on quasi-circular orbits. We validate ESIGMAHM against eccentric Numerical Relativity simulations, and also against contemporary effective-one-body and phenomenological models in the quasi-circular limit. We find that ESIGMAHM achieves match values greater than $99\%$ for quasi-circular spin-aligned binaries with mass ratios up to $8$, and above $97\%$ for non-spinning and spinning eccentric systems with small or positively aligned spins. Using IMRESIGMA, we quantify the impact of orbital eccentricity on GW signals, showing that next-generation detectors can detect eccentric sources up to $10\%$ louder than quasi-circular ones. We also show that current templated LIGO-Virgo searches will lose more than $10\%$ of optimal SNR for about $20\%$ of all eccentric sources by using only quasi-circular waveform templates. The same will result in a $25\%$ loss in detection rate for eccentric sources with mass ratios $m_1/m_2\geq 4$. Our results highlight the need for including eccentricity and higher-order modes in GW source models and searches for asymmetric eccentric BBH signals.

Brain tumor segmentation plays a crucial role in computer-aided diagnosis. This study introduces a novel segmentation algorithm utilizing a modified nnU-Net architecture. Within the nnU-Net architecture's encoder section, we enhance conventional convolution layers by incorporating omni-dimensional dynamic convolution layers, resulting in improved feature representation. Simultaneously, we propose a multi-scale attention strategy that harnesses contemporary insights from various scales. Our model's efficacy is demonstrated on diverse datasets from the BraTS-2023 challenge. Integrating omni-dimensional dynamic convolution (ODConv) layers and multi-scale features yields substantial improvement in the nnU-Net architecture's performance across multiple tumor segmentation datasets. Remarkably, our proposed model attains good accuracy during validation for the BraTS Africa dataset. The ODconv source code along with full training code is available on GitHub., Comment: 9 pages, 3 figures. Accepted at MICCAI 2023, to be published in Springer LNCS. GitHub: https://github.com/i-sahajmistry/nnUNet_BraTS2023