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We report the measurement, using angle-resolved photoemission spectroscopy, of the metallic electronic structure of the hole-doped thermoelectric oxide CuRh$_{0.9}$Mg$_{0.1}$O$_2$. The material is found to have a ``pudding mold'' type band structure, with a nearly flat band edge located near the Fermi level, which is thought to be the origin of the thermoelectric behavior of this material. The experimental data match the density functional theory of the undoped parent compound, simply corrected by a rigid shift of the bands. Transport calculations based on the observed band structure yield a Seebeck coefficient of $\sim 200 \,\mu$V/K for the undoped parent material, consistent with experimental measurements. Our results show that CuRhO$_2$ is a textbook example of how pure band-structural effects can result in a large thermoelectric figure of merit, demonstrating that flat band edges in oxides are a realistic route for the efficient conversion of thermal energy., Comment: Accepted to Physical Review Materials

We investigate the properties of neutron stars with antikaon condensation in the framework of the Relativistic Mean-Field (RMF) model with a $\sigma$-cut potential. The well-known RMF models, TM1 and TM1e, are used to analyze the structure and composition of neutron stars. The antikaon condensation part of the equation of state (EoS) is constrained from the experimental data of K$^{-}$ atomic and kaon-nucleon scattering. The $\sigma$-cut potential, which is known to make the EoS stiffer at high densities, is modulated by a free parameter $f_{s}$. Our present analysis suggests that one can obtain neutron star configurations heavier than 2$M_{\odot}$ with antikaon condensates in most cases for $f_{s}$ = 0.6. The antikaon phase transition is a second-order for $f_{s}$ = 0.6 for both TM1 and TM1e parameter sets. The calculated global properties of neutron stars with antikaon condensates i.e., mass and radius seem to be in resonable agreement with other theoretical and observational data., Comment: 10 pages, 10 figures Accepted in Physical Review C (PRC) on 25-02-2025

With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communication turns out to be a critical bottleneck in large-scale distributed and parallel processing. The large message size in MPI collectives is particularly concerning because it can significantly degrade overall parallel performance. To address this issue, prior research simply applies off-the-shelf fixed-rate lossy compressors in the MPI collectives, leading to suboptimal performance, limited generalizability, and unbounded errors. In this paper, we propose a novel solution, called ZCCL, which leverages error-bounded lossy compression to significantly reduce the message size, resulting in a substantial reduction in communication costs. The key contributions are three-fold. (1) We develop two general, optimized lossy-compression-based frameworks for both types of MPI collectives (collective data movement as well as collective computation), based on their particular characteristics. Our framework not only reduces communication costs but also preserves data accuracy. (2) We customize fZ-light, an ultra-fast error-bounded lossy compressor, to meet the specific needs of collective communication. (3) We integrate ZCCL into multiple collectives, such as Allgather, Allreduce, Scatter, and Broadcast, and perform a comprehensive evaluation based on real-world scientific application datasets. Experiments show that our solution outperforms the original MPI collectives as well as multiple baselines by 1.9--8.9X.

This thesis investigates the impact of dark matter on neutron star properties, focusing on mass, radius, and tidal deformability. Using two-fluid and single-fluid models, dark matter is incorporated into the equation of state (EOS) via a Relativistic Mean Field (RMF) approach. The study finds that increasing dark matter content reduces the maximum mass, radius, and tidal deformability. Bayesian inference, supported by LIGO-Virgo gravitational wave data and NICER mass-radius measurements, refines these models. Despite dark matter's influence, the semi-universal C-Love relation remains valid. Machine learning techniques effectively classify dark matter-admixed neutron stars. The thesis also explores a sigma-cut potential in the EOS, which stiffens the EOS at high densities, favoring larger radii and lower f-mode frequencies. The study of non-radial oscillations, particularly f- and p-modes, highlights their sensitivity to neutron star composition and EOS. These findings enhance our understanding of neutron star interiors and dark matter's role, emphasizing the need for further observational and theoretical advancements., Comment: 192 pages Thesis Examiner: Prof. Ritam Mallick, IISER Bhopal

Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale, community-driven expansion of MTEB, covering over 500 quality-controlled evaluation tasks across 250+ languages. MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date. Using this collection, we develop several highly multilingual benchmarks, which we use to evaluate a representative set of models. We find that while large language models (LLMs) with billions of parameters can achieve state-of-the-art performance on certain language subsets and task categories, the best-performing publicly available model is multilingual-e5-large-instruct with only 560 million parameters. To facilitate accessibility and reduce computational cost, we introduce a novel downsampling method based on inter-task correlation, ensuring a diverse selection while preserving relative model rankings. Furthermore, we optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks that drastically reduce computational demands. For instance, our newly introduced zero-shot English benchmark maintains a ranking order similar to the full-scale version but at a fraction of the computational cost., Comment: Accepted for ICLR: https://openreview.net/forum?id=zl3pfz4VCV

Chaotic dynamical systems are characterized by the sensitive dependence of trajectories on initial conditions. Conventional sensitivity analysis of time-averaged functionals yields unbounded sensitivities when the simulation is chaotic. The least squares shadowing (LSS) is a popular approach to computing bounded sensitivities in the presence of chaotic dynamical systems. The current paper proves the existence, uniqueness, and boundedness of the adjoint of the LSS equations. In particular, the analysis yields a sharper bound on the condition number of the LSS equations than currently demonstrated in existing literature and shows that the condition number is bounded for large integration times. The derived bound on condition number also shows a relation between the conditioning of the LSS and the time dilation factor which is consistent with the trend numerically observed in the previous LSS literature. Furthermore, using the boundedness of the condition number for large integration times, we provide an alternate proof to (Chater et al., 2017) of the convergence of the LSS sensitivity to the true sensitivity at the rate of $\mathcal{O}\left(\frac{1}{\sqrt{T}}\right)$ regardless of the boundary conditions imposed on the adjoint, as long as the adjoint boundary conditions are bounded. Existence and uniqueness of the solution to the continuous-in-time adjoint LSS equation ensure that the LSS equation can be discretized independently of the primal equation and that the true LSS adjoint solution is recovered as the time step is refined. This allows for the adjoint LSS equation to be discretized on a coarser time domain than that of the primal governing equation to reduce the cost of solving the linear space-time system.

High-impedance arc faults in AC power systems have the potential to lead to catastrophic accidents. However, significant challenges exist in identifying these faults because of the much weaker characteristics and variety when grounded with different surfaces. Addressing a noteworthy gap in prior research, which largely focused on arc fault detection in low-voltage systems. A novel approach has been applied that offers rapid arc fault detection for medium voltage distribution lines. In contrast to existing black-box feature-based approaches, Hankel alternative view of the Koopman (HAVOK) analysis developed from nonlinear dynamics has been applied which offers not only interpretable features but also opens up new application options in the area of arc fault detection. The method displays a much faster detection speed within 0.45 ms making it appropriate for real-time applications. It demonstrates the ability to detect arc faults across various scenarios, boosting its practical importance for stakeholders in safety-critical industries.

Neural networks have shown substantial promise at automatic theorem-proving in interactive proof assistants (ITPs) like Lean and Coq. However, most neural theorem-proving models are restricted to specific ITPs, leaving out opportunities for cross-lingual $\textit{transfer}$ between ITPs. We address this weakness with a multilingual proof framework, ${\rm P{\small ROOF}W{\small ALA}}$, that allows a standardized form of interaction between neural theorem-provers and two established ITPs (Coq and Lean). It enables the collection of multilingual proof step data -- data recording the result of proof actions on ITP states -- for training neural provers. ${\rm P{\small ROOF}W{\small ALA}}$ allows the systematic evaluation of a model's performance across different ITPs and problem domains via efficient parallel proof search algorithms. We show that multilingual training enabled by ${\rm P{\small ROOF}W{\small ALA}}$ can lead to successful transfer across ITPs. Specifically, a model trained on a mix of ${\rm P{\small ROOF}W{\small ALA}}$-generated Coq and Lean data outperforms Lean-only and Coq-only models on the standard prove-at-$k$ metric. We open source all code including code for the ${\rm P{\small ROOF}W{\small ALA}}$ Framework (https://github.com/trishullab/proof-wala), and the Multilingual ITP interaction framework (https://github.com/trishullab/itp-interface).

An active chiral rotor is a spherical object that can generate chiral flows in a fluid by rotating about an axis. For example, if the flow around the upper hemisphere of the chiral rotor is in a clockwise direction, then the flow in the lower hemisphere is in the anti-clockwise direction, and vice versa. In this paper, we aim to study the combined behaviour of hydrodynamically interacting chiral rotors in the presence of an external chemical gradient. While a single isolated rotor is stationary in a fluid, a pair of rotors can move in linear or circular paths as they hydrodynamically interact with each other. It is observed that the emergent linear or circular trajectories depend on the type of rotors and the orientation of their rotation axes. The dynamics of the rotors are altered in a more complex environment, such as in an external chemical field. Interestingly, we observe two types of motion: chemotaxis and anti-chemotaxis. While in the anti-chemotaxis case, both rotors are driving away from the target, in the chemotaxis case, one of the rotors successfully reaches the chemical target. This study helps to understand the collective behavior of self-propelled microorganisms and artificial swimmers.

In the presence of gravity, the micron-sized charged dust particles in a complex (dusty) plasma are compressed into thin layers. However, under the microgravity conditions of the Plasma Kristall-4 (PK-4) experiment on the International Space Station (ISS), the particles fill the plasma, allowing us to investigate the properties of a three-dimensional (3D) multi-particle system. This paper examines the change in the spatial ordering and thermal state of the particle system created when dust particles are stopped by periodic oscillations of the electric field, known as polarity switching, in a dc glow discharge plasma. Data from the ISS is compared against experiments performed using a ground-based reference version of PK-4 and numerical simulations. Initial results show substantive differences in the velocity distribution functions between experiments on the ground and in microgravity. There are also differences in the motion of the dust cloud, in microgravity there is an expansion of the dust cloud at the application of polarity switching which is not seen in the ground-based experiments. It is proposed that the dust cloud in microgravity gains thermal energy at the application of polarity switching due to this expansion. Simulation results suggest that this may be due to a modification in the effective screening length of the dust at the onset of polarity switching, which arises from a configuration energy between the charged particles. Experimental measurements and simulations show that an extended time (much greater than the Epstein drag decay) is required to dissipate this energy.

In this work, we report the study of electronic structure, magnetism, and the existence of Weyl nodes in a pristine bulk FeNi$_{3}$, a member of Fe-Ni inver alloy compounds, known as good metal catalysts with high activity and stability for water splitting for a very long time. Our observation of Weyl points in the bulk FeNi$_{3}$ may lead to a new technology to design highly efficient topological catalysts. While the previous literature \cite{PhysRev.97.304} mainly focused on the thermal and catalytic properties of FeNi$_{3}$ we report the interplay of Fe $d$-Ni $d$ hybridization and spin-orbit coupling give rise to the ferromagnetic Weyl nodes in the bulk FeNi$_{3}$. Our study shows that the ground state of the bulk FeNi$_{3}$ is a Weyl metal with a large number of Weyl nodes at the Fermi energy away from high-symmetry $k$-points. Furthermore, we predict a large intrinsic anomalous Hall conductivity of about $10000~S/m$ at the ground state. In addition, we show the existence of Weyl nodes along the high symmetry $k$-points $~0.2eV$ above and $~0.05eV$ below self-consistent Fermi level that may be achieved either by the electron or hole doping, or by external perturbation. In this article, FeNi$_{3}$ has been studied to explore this scenario using first-principles density functional theory and subsequent Wannier90-based tight-binding method. Furthermore, we report the existence of two types of Weyl cones, type-I and type-II, $~0.2eV$ above the Fermi level. Our report provides a realistic material to further explore the intrinsic properties related to Weyl cones, and the spintronic applications., Comment: 5 pages, 5 figures

This study investigates the thermodynamic properties of face-centered cubic lead (fcc-Pb) using ab-initio methods within the quasi-harmonic approximation (QHA), examining the influence of spin-orbit coupling (SOC) and the exchange-correlation functionals. Two types of ultrasoft pseudopotential (US-PP) are considered: one that excludes (scalar relativistic PP) and one that includes the SOC effects (fully relativistic PP). Further, for each PP, we test the performance of three popular exchange-correlation functionals: Perdew-Burke-Ernzerhof generalized gradient approximation (PBE) (Perdew et al. Phys. Rev. Lett. 77, 3865 (1996)), PBE modified for dense solids (PBEsol) (Perdew et al. Phys. Rev. Lett. 100, 136406 (2008)), and local density approximation (LDA) (Perdew et al. Phys. Rev. B 23, 5048 (1981)). We calculate the Helmholtz free energy, incorporating lattice vibrations (phonons) and electronic excitation contributions. The estimated equation of state (at 4 K and 301 K), phonon dispersions (at 100 K and 300 K), mode-Gr\"uneisen parameters ({\gamma}q{\eta}) (at 100 K), volume thermal expansion coefficient (\b{eta}), isobaric heat capacity (CP), bulk modulus (BS), and thermodynamic average Gr\"uneisen parameter ({\gamma}) are compared with the available experimental and theoretical studies. Moreover, the 0 K pressure-dependent elastic constant-coefficient (Cij) of fcc lead and Pugh ratio, Debye temperature, and longitudinal and transverse sound velocities for polycrystalline lead are presented. The contributions of electronic excitations in all the thermodynamic properties are found to be negligible. With increasing pressure, the role of spin-orbit effects decreases but does not vanish. Our findings demonstrate that SOC leads to results distinct from the SR approach, but agreement with the experiment is not consistently improved by including SOC.

Implementing accurate models of the retina is a challenging task, particularly in the context of creating visual prosthetics and devices. Notwithstanding the presence of diverse artificial renditions of the retina, the imperative task persists to pursue a more realistic model. In this work, we are emulating a neuromorphic retina model on an FPGA. The key feature of this model is its powerful adaptation to luminance and contrast, which allows it to accurately emulate the sensitivity of the biological retina to changes in light levels. Phasic and tonic cells are realizable in the retina in the simplest way possible. Our FPGA implementation of the proposed biologically inspired digital retina, incorporating a receptive field with a center-surround structure, is reconfigurable and can support 128*128 pixel images at a frame rate of 200fps. It consumes 1720 slices, approximately 3.7k Look-Up Tables (LUTs), and Flip-Flops (FFs) on the FPGA. This implementation provides a high-performance, low-power, and small-area solution and could be a significant step forward in the development of biologically plausible retinal prostheses with enhanced information processing capabilities

Source-free domain adaptation (SFDA) utilizes a pre-trained source model with unlabeled target data. Self-supervised SFDA techniques generate pseudolabels from the pre-trained source model, but these pseudolabels often contain noise due to domain discrepancies between the source and target domains. Traditional self-supervised SFDA techniques rely on deterministic model predictions using the softmax function, leading to unreliable pseudolabels. In this work, we propose to introduce predictive uncertainty and softmax calibration for pseudolabel refinement using evidential deep learning. The Dirichlet prior is placed over the output of the target network to capture uncertainty using evidence with a single forward pass. Furthermore, softmax calibration solves the translation invariance problem to assist in learning with noisy labels. We incorporate a combination of evidential deep learning loss and information maximization loss with calibrated softmax in both prior and non-prior target knowledge SFDA settings. Extensive experimental analysis shows that our method outperforms other state-of-the-art methods on benchmark datasets., Comment: Accepted in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025

The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include {\gamma}-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy {\gamma}-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy., Comment: 67 pages, 24 figures, accepted for publication in SCIENCE CHINA Physics, Mechanics & Astronomy

With the rising threat of ballistic impacts, it is critical to devise a solution that is both efficient and economical. Recently, Polymer Matrix Sand Composites (PMSCs) have emerged as a viable cost-effective option. This ongoing research focuses on providing stronger protection against diverse ballistic impacts. The study examines the enhancement of ballistic resistance in PMSCs through the graded incorporation of sand. Variable properties are achieved along the thickness by altering the sand particle size and weight fraction in the polymer matrix. The gradation creates a stepwise structure, starting with a dense base impact zone containing abrasive sand particles with a typical size range. This layer is brittle and hard, effectively eroding incoming projectiles. Subsequent layers are less dense, offering tensile strength that reflects stress waves and reduces impact energy while supporting the frontal brittle zone. The minimally dense or neat matrix regions prevent backlash and provide a cushioning effect. The addition of sand particles increases the composite's surface area, enhancing adhesion between inclusions and the matrix. This improved adhesion ensures efficient load transfer, increasing overall hardness. PMSCs were fabricated to analyze the effects of two matrix compositions, and refining manufacturing methods. These composites were subjected to tensile testing, Izod impact testing, Shore-D hardness testing, and other evaluations to assess their mechanical properties. The findings show that varying the sand content significantly impacts mechanical properties up to an optimal weight fraction for a given size range of inclusions. Experimental and simulation studies were employed to extract properties and evaluate material behavior under ballistic threats. The research underscores the potential of PMSCs as affordable and sustainable composites for advanced ballistic protection.

Magnesium hydride (MgH2) is a promising material for hydrogen storage because of its abundance and beneficial properties, such as high storage capacity and cost-effectiveness under mild conditions. Despite of these benefits, MgH2 unfavorable thermodynamics and kinetics make it difficult to use in real applications. In this work, the hydrogen storage properties of MgH2have been improved using Al-Cu-Fe-Ni-Cr high entropy alloy (HEA) based catalysts, which has been synthesized via mechanical alloying. The experimental findings show that the beginning desorption temperature of MgH2significantly lowered from 425{\deg}C to 180{\deg}C by adding 5 wt. % Al-Cu-Fe-Ni-Cr HEA in MgH2. Moreover, the catalyst shows enhanced kinetics, attaining 7.3 wt. % hydrogen absorption in 3 minutes at 320{\deg}C with 15 atm hydrogen pressure, and ~5 wt. % desorption in 6 minutes at 320{\deg}C. These results highlight, how much lower its desorption temperature is than those of other well-known catalysts. Over a span of 25 cycles, MgH2 catalyzed by Al-Cu-Fe-Ni-Cr HEA exhibits remarkable cyclic stability with negligible fluctuations (~ 0.05 wt. %).After a thorough characterization of the materials, a workable catalytic mechanism for HEA was proposed in light of the results., Comment: 39 pages, 10 Figures

Per- and polyfluoroalkyl substances (PFAS), often referred as "forever chemicals," are pervasive environmental pollutants due to their resistance to degradation. Among these, perfluorooctanoic acid (PFOA) poses significant threats to human health, contaminating water sources globally. Here, we have demonstrated the potential of a novel electrochemical sensor based on two-dimensional (2D) aluminium-based multicomponent quasicrystals (2D-Al QC) for the ultrasensitive sub-picomolar level detection of PFOA. The 2D-Al QC-inked electrode was employed here to detect PFOA by differential pulse voltammetry (DPV). The limit of detection (LoD) achieved is 0.59 +/- 0.05 pM. The sensor was evaluated for selectivity with other interfering compounds, repeatability of cycles, and reproducibility for five similar electrodes with a deviation of 0.8 %. The stability of the sensor has also been analysed after ninety days ,which shows a minimal variation of 15%. Spectroscopic techniques and theoretical calculations were further utilized to understand the interaction between the 2D-Al QC and PFOA. The results demonstrate that the 2D-Al QC offers a promising platform for the rapid and sensitive detection of PFOA, potentially addressing current environmental monitoring challenges.

Semi-supervised (SS) semantic segmentation exploits both labeled and unlabeled images to overcome tedious and costly pixel-level annotation problems. Pseudolabel supervision is one of the core approaches of training networks with both pseudo labels and ground-truth labels. This work uses aleatoric or data uncertainty and energy based modeling in intersection-union pseudo supervised network.The aleatoric uncertainty is modeling the inherent noise variations of the data in a network with two predictive branches. The per-pixel variance parameter obtained from the network gives a quantitative idea about the data uncertainty. Moreover, energy-based loss realizes the potential of generative modeling on the downstream SS segmentation task. The aleatoric and energy loss are applied in conjunction with pseudo-intersection labels, pseudo-union labels, and ground-truth on the respective network branch. The comparative analysis with state-of-the-art methods has shown improvement in performance metrics., Comment: Accepted in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025

No-resource languages - those with minimal or no digital representation - pose unique challenges for machine translation (MT). Unlike low-resource languages, which rely on limited but existent corpora, no-resource languages often have fewer than 100 sentences available for training. This work explores the problem of no-resource translation through three distinct workflows: fine-tuning of translation-specific models, in-context learning with large language models (LLMs) using chain-of-reasoning prompting, and direct prompting without reasoning. Using Owens Valley Paiute as a case study, we demonstrate that no-resource translation demands fundamentally different approaches from low-resource scenarios, as traditional approaches to machine translation, such as those that work for low-resource languages, fail. Empirical results reveal that, although traditional approaches fail, the in-context learning capabilities of general-purpose large language models enable no-resource language translation that outperforms low-resource translation approaches and rivals human translations (BLEU 0.45-0.6); specifically, chain-of-reasoning prompting outperforms other methods for larger corpora, while direct prompting exhibits advantages in smaller datasets. As these approaches are language-agnostic, they have potential to be generalized to translation tasks from a wide variety of no-resource languages without expert input. These findings establish no-resource translation as a distinct paradigm requiring innovative solutions, providing practical and theoretical insights for language preservation.

In the present investigation, the chemical leaching of the poly-grain Al65Cu15Co20 and Al65Cu20Co15 decagonal quasicrystalline alloy have been studied. The polished surfaces of as-cast alloys were leached with 10 mole NaOH solution for 0.5- 8 hours. The x-ray diffraction, scanning electron microscopy and transmission electron microscopy techniques have been used for structural and microstructural characterization. Energy dispersive x-ray analysis has been carried out for chemical composition analysis. Chemical leaching exclusively removes the Al from the surfaces of the Al65Cu15Co20 and Al65Cu20Co15 decagonal quasicrystalline alloys, consequently by the formation of porous structure containing nano size particles of Cu, Co and their oxides have been observed. Al65Cu15Co20 exhibits high porosity in comparison to Al65Cu20Co15 alloy, however the size of the precipitated nano-particles i.e. Cu, Co, Cu2O and CuO were smaller in the case of Al65Cu20Co15 alloy. The energy dispersive X-ray analysis mapping suggests homogeneous distribution of Cu and Co found on the leached surface and the presence of oxygen was also detected., Comment: 14 pages, 10 figures

Long COVID continues to challenge public health by affecting a significant segment of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVID. This paper examines recent works focusing on mining, analyzing, and interpreting user-generated content on social media platforms such as Twitter, Reddit, Facebook, and YouTube to capture the broader discourse on persistent post-COVID conditions. A novel transformer-based zero-shot learning approach serves as the foundation for classifying research papers in this area into four primary categories: Clinical or Symptom Characterization, Advanced NLP or Computational Methods, Policy, Advocacy, or Public Health Communication, and Online Communities and Social Support. This methodology showcases the adaptability of advanced language models in categorizing research papers without predefined training labels, thus enabling a more rapid and scalable assessment of existing literature. This review highlights the multifaceted nature of Long COVID research, where computational techniques applied to social media data reveal insights into narratives of individuals suffering from Long COVID. This review also demonstrates the capacity of social media analytics to inform clinical practice and contribute to policy-making related to Long COVID.

Emojis are widely used across social media platforms but are often lost in noisy or garbled text, posing challenges for data analysis and machine learning. Conventional preprocessing approaches recommend removing such text, risking the loss of emojis and their contextual meaning. This paper proposes a three-step reverse-engineering methodology to retrieve emojis from garbled text in social media posts. The methodology also identifies reasons for the generation of such text during social media data mining. To evaluate its effectiveness, the approach was applied to 509,248 Tweets about the Mpox outbreak, a dataset referenced in about 30 prior works that failed to retrieve emojis from garbled text. Our method retrieved 157,748 emojis from 76,914 Tweets. Improvements in text readability and coherence were demonstrated through metrics such as Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, Automated Readability Index, Dale-Chall Readability Score, Text Standard, and Reading Time. Additionally, the frequency of individual emojis and their patterns of usage in these Tweets were analyzed, and the results are presented.

For the first time, we use relativistic mean-field (RMF) approximation with density-dependent couplings, adjusted by the DDME2 parameterization, to investigate the effects of dark matter on supernova remnants. We calculate the nuclear equation of state for nuclear and dark matter separately, under the thermodynamic conditions related to the evolution of supernova remnants. A mirrored model is adopted for dark matter, and its effect on remnant matter is studied using a two-fluid scenario. At each stage of the remnant evolution, we assume that dark and ordinary matter have the same entropy and lepton fraction, and a fixed proportion of dark matter mass fraction is added to the stellar matter to observe its effects on some microscopic and macroscopic properties of the star. We observe that dark matter in the remnant core reduces the remnant's maximum mass, radius, and tidal deformability. Moreover, dark matter heats the remnant matter and alters particle distributions, thereby decreasing its isospin asymmetry and increasing the sound speed through the matter., Comment: 15 pages, 6 figures

This study presents a data-driven analysis of COVID-19 discourse on YouTube, examining the sentiment, toxicity, and thematic patterns of video content published between January 2023 and October 2024. The analysis involved applying advanced natural language processing (NLP) techniques: sentiment analysis with VADER, toxicity detection with Detoxify, and topic modeling using Latent Dirichlet Allocation (LDA). The sentiment analysis revealed that 49.32% of video descriptions were positive, 36.63% were neutral, and 14.05% were negative, indicating a generally informative and supportive tone in pandemic-related content. Toxicity analysis identified only 0.91% of content as toxic, suggesting minimal exposure to toxic content. Topic modeling revealed two main themes, with 66.74% of the videos covering general health information and pandemic-related impacts and 33.26% focused on news and real-time updates, highlighting the dual informational role of YouTube. A recommendation system was also developed using TF-IDF vectorization and cosine similarity, refined by sentiment, toxicity, and topic filters to ensure relevant and context-aligned video recommendations. This system achieved 69% aggregate coverage, with monthly coverage rates consistently above 85%, demonstrating robust performance and adaptability over time. Evaluation across recommendation sizes showed coverage reaching 69% for five video recommendations and 79% for ten video recommendations per video. In summary, this work presents a framework for understanding COVID-19 discourse on YouTube and a recommendation system that supports user engagement while promoting responsible and relevant content related to COVID-19.

This study introduces the Community Sentiment and Engagement Index (CSEI), developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily post count, compound sentiment, fine-grain sentiments (fear, surprise, joy, sadness, anger, disgust, and neutral), readability, offensiveness, and domain diversity. Each component is systematically weighted through a multi-step Principal Component Analysis (PCA)-based framework, prioritizing features according to their variance contributions across temporal sentiment shifts. This approach dynamically adjusts component importance, enabling CSEI to precisely capture high-sensitivity shifts in public sentiment. The development of CSEI showed statistically significant correlations with its constituent features, underscoring internal consistency and sensitivity to specific sentiment dimensions. CSEI's responsiveness was validated using a dataset of 4,510,178 Reddit posts about COVID-19. The analysis focused on 15 major events, including the WHO's declaration of COVID-19 as a pandemic, the first reported cases of COVID-19 across different countries, national lockdowns, vaccine developments, and crucial public health measures. Cumulative changes in CSEI revealed prominent peaks and valleys aligned with these events, indicating significant patterns in public sentiment across different phases of the pandemic. Pearson correlation analysis further confirmed a statistically significant relationship between CSEI daily fluctuations and these events (p = 0.0428), highlighting the capacity of CSEI to infer and interpret shifts in public sentiment and engagement in response to major events related to COVID-19.

We study the effects of hyperons, delta baryons, and quark matter phase transitions on f-mode oscillations in neutron stars. Using the density-dependent relativistic mean-field model (DDME2) for the hadronic phase and the density-dependent quark mass (DDQM) model for the quark phase, we construct hadronic and hybrid equations of state (EoSs) consistent with astrophysical constraints. Including hyperons and delta baryons soften the EoS, reducing maximum masse, while phase transition to the quark matter further softens the EoS, decreasing the speed of sound and hence the maximum mass. f-mode frequencies, calculated using both the Cowling approximation and the general relativistic (GR) frameworks, reveal a significant overestimation by the Cowling method of about 10-30%, with discrepancies decreasing for more massive stars. We derive universal relations connecting the frequencies of the f-mode to the average density, compactness, and tidal deformability, finding significant deviations due to hyperons and delta baryons. Empirical relations for mass-scaled and radius-scaled frequencies are also provided, highlighting the importance of GR calculations for accurate modeling. These findings highlight the potential of gravitational wave asteroseismology to constrain neutron star EoSs and internal structure., Comment: 18 pages, 13 figures, 5 tables

This paper proposes a novel federated algorithm that leverages momentum-based variance reduction with adaptive learning to address non-convex settings across heterogeneous data. We intend to minimize communication and computation overhead, thereby fostering a sustainable federated learning system. We aim to overcome challenges related to gradient variance, which hinders the model's efficiency, and the slow convergence resulting from learning rate adjustments with heterogeneous data. The experimental results on the image classification tasks with heterogeneous data reveal the effectiveness of our suggested algorithms in non-convex settings with an improved communication complexity of $\mathcal{O}(\epsilon^{-1})$ to converge to an $\epsilon$-stationary point - compared to the existing communication complexity $\mathcal{O}(\epsilon^{-2})$ of most prior works. The proposed federated version maintains the trade-off between the convergence rate, number of communication rounds, and test accuracy while mitigating the client drift in heterogeneous settings. The experimental results demonstrate the efficiency of our algorithms in image classification tasks (MNIST, CIFAR-10) with heterogeneous data., Comment: FLUID Workshop@AAAI 2025

Quadrature Hybrid Couplers (QHDC) are critical components in RF, mm-wave, and sub-mm wave astronomical instrumentation, where wideband performance with minimal passband ripple is essential. Traditional designs have been limited to 5-sections at most due to computational limitations. In this work, we introduce a new analytical technique to design couplers with larger sections and improved performance. We do this by employing a Markov Chain Monte Carlo (MCMC) based solver. By defining a likelihood function based on S-parameter equations and incorporating physical priors, we derive optimized impedance values that enhance bandwidth beyond what is reported in the literature. Our flexible pipeline allows efficient tuning of the coupler design. The results demonstrate fractional bandwidths that reach 1.0 for a 9-section coupler, substantially outperforming previous designs. Statistical analysis and convergence tests confirm the robustness of our approach., Comment: 9 pages, 13 figures

Celestial holography is the conjecture that scattering amplitudes in $(d+2)$-dimensional asymptotically Minkowski spacetimes are dual to correlators of a $d$-dimensional conformal field theory (CFT) on the celestial sphere, called the celestial CFT (CCFT). In a CFT, we can calculate sub-region entanglement R\'{e}nyi entropies (EREs), including entanglement entropy (EE), from correlators of twist operators, via the replica trick. We argue that CCFT twist operators are holographically dual to cosmic branes in the $(d+2)$-dimensional spacetime, and that their correlators are holographically dual to the $(d+2)$-dimensional partition function (the vacuum-to-vacuum scattering amplitude) in the presence of these cosmic branes. We hence compute the EREs of a spherical sub-region of the CCFT's conformal vacuum, finding the form dictated by conformal symmetry, including a universal contribution determined by the CCFT's sphere partition function (odd $d$) or Weyl anomaly (even $d$). We find that this universal contribution vanishes when $d=4$ mod $4$, and otherwise is proportional to $i$ times the $d^{\textrm{th}}$ power of the $(d+2)$-dimensional long-distance cutoff in Planck units., Comment: 42+16 pages, 6 figures

SHAPE (Spectro-polarimetry of HAbitable Planet Earth) is an experiment onboard the Chandrayaan-3 Mission, designed to study the spectro-polarimetric signatures of the habitable planet Earth in the near-infrared (NIR) wavelength range (1.0 - 1.7 $\mu$m). The spectro-polarimeter is the only scientific payload (experimental in nature) on the Propulsion Module (PM) of the Chandrayaan-3 mission. The instrument is a compact and lightweight spectro-polarimeter with an Acousto-Optic Tunable Filter (AOTF) at its core. The AOTF operates in the frequency range of 80 MHz to 135 MHz with a power of 0.5 - 2.0 Watts. The two output beams (e-beam and o-beam) from the AOTF are focused onto two InGaAs detectors (pixelated, 1D linear array) with the help of focusing optics. The primary (aperture) optics, with a diameter of $\sim$2 mm, collects the NIR light for input to the AOTF, defining the field of view (FOV) of 2.6$^\circ$. The payload has a mass of 4.8 kg and operates at a power of 25 Watts. This manuscript highlights some of the ground-based results, including the post-launch initial performance of the payload while orbiting around the Moon to observe Earth., Comment: Accepted for publication in Journal of Aerospace Sciences and Technologies

Facial recognition technology has made significant advances, yet its effectiveness across diverse ethnic backgrounds, particularly in specific Indian demographics, is less explored. This paper presents a detailed evaluation of both traditional and deep learning-based facial recognition models using the established LFW dataset and our newly developed IITJ Faces of Academia Dataset (JFAD), which comprises images of students from IIT Jodhpur. This unique dataset is designed to reflect the ethnic diversity of India, providing a critical test bed for assessing model performance in a focused academic environment. We analyze models ranging from holistic approaches like Eigenfaces and SIFT to advanced hybrid models that integrate CNNs with Gabor filters, Laplacian transforms, and segmentation techniques. Our findings reveal significant insights into the models' ability to adapt to the ethnic variability within Indian demographics and suggest modifications to enhance accuracy and inclusivity in real-world applications. The JFAD not only serves as a valuable resource for further research but also highlights the need for developing facial recognition systems that perform equitably across diverse populations., Comment: Research Project - Computer Vision

Azo-containing liquid crystal elatomers are photomechanical materials which can be actuated by illumination. The photomechanical response is a result of the photoisomerization of the azo moiety, which produces bulk stresses in the material. These stresses arise via two distinct and competing mechanisms: order parameter change induced stress and direct contractile stress. We describe thermomechanical and photomechanical experiments aimed at assessing the relative contributions of these. we discuss our results and summarize our findings., Comment: 13 pages, 9 figures

Shared training approaches, such as multi-task learning (MTL) and gradient-based meta-learning, are widely used in various machine learning applications, but they often suffer from negative transfer, leading to performance degradation in specific tasks. While several optimisation techniques have been developed to mitigate this issue for pre-selected task cohorts, identifying optimal task combinations for joint learning - known as task grouping - remains underexplored and computationally challenging due to the exponential growth in task combinations and the need for extensive training and evaluation cycles. This paper introduces an efficient task grouping framework designed to reduce these overwhelming computational demands of the existing methods. The proposed framework infers pairwise task similarities through a sample-wise optimisation landscape analysis, eliminating the need for the shared model training required to infer task similarities in existing methods. With task similarities acquired, a graph-based clustering algorithm is employed to pinpoint near-optimal task groups, providing an approximate yet efficient and effective solution to the originally NP-hard problem. Empirical assessments conducted on 8 different datasets highlight the effectiveness of the proposed framework, revealing a five-fold speed enhancement compared to previous state-of-the-art methods. Moreover, the framework consistently demonstrates comparable performance, confirming its remarkable efficiency and effectiveness in task grouping., Comment: Under review at IEEE Transactions on Pattern Analysis and Machine Intelligence

Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primarily addressed how data can directly harm consumers and creators, such as through privacy breaches, copyright infringements, and bias and discrimination. Our work, instead, focuses on the comparatively neglected question of how data can enable new governance capacities for frontier AI models. This approach for "frontier data governance" opens up new avenues for monitoring and mitigating risks from advanced AI models, particularly as they scale and acquire specific dangerous capabilities. Still, frontier data governance faces challenges that stem from the fundamental properties of data itself: data is non-rival, often non-excludable, easily replicable, and increasingly synthesizable. Despite these inherent difficulties, we propose a set of policy mechanisms targeting key actors along the data supply chain, including data producers, aggregators, model developers, and data vendors. We provide a brief overview of 15 governance mechanisms, of which we centrally introduce five, underexplored policy recommendations. These include developing canary tokens to detect unauthorized use for producers; (automated) data filtering to remove malicious content for pre-training and post-training datasets; mandatory dataset reporting requirements for developers and vendors; improved security for datasets and data generation algorithms; and know-your-customer requirements for vendors. By considering data not just as a source of potential harm, but as a critical governance lever, this work aims to equip policymakers with a new tool for the governance and regulation of frontier AI models.

Dynamic taint tracking is the process of assigning label to variables in a program and then tracking the flow of the labels as the program executes. Dynamic taint tracking for java applications is achieved by instrumenting the application ie. adding parallel variable for each actual variable of the program and inserting additional bytecode instructions to track the flow of the parallel variables. In this paper we suggest partial instrumentation to achieve dynamic taint tracking with reasonable runtime overhead. Partial instrumentation involves instrumenting only parts of a java application, which are within the scope of a predefined source and sink set. Partial instrumentation is performed at the granularity level of a method. We use PetaBlox, a large-scale software analysis tool, which internally uses Datalog[3], to perform static analysis and infers all the methods within the scope of source and sink sets and a modified version of Phosphor[1] to achieve partial instrumentation. Test runs performed on some of the Dacapo benchmarks show a significant performance improvement over the version of Phosphor that performs complete instrumentation.

Lumped-element inductors are an integral component in the circuit QED toolbox. However, it is challenging to build inductors that are simultaneously compact, linear and low-loss with standard approaches that either rely on the geometric inductance of superconducting thin films or on the kinetic inductance of Josephson junctions arrays. In this work, we overcome this challenge by utilizing the high kinetic inductance offered by superconducting granular aluminum (grAl). We demonstrate lumped-element inductors with a few nH of inductance that are up to $100$ times more compact than inductors built from pure aluminum (Al). To characterize the properties of these linear inductors, we first report on the performance of lumped-element resonators built entirely out of grAl with sheet inductances varying from $30-320\,$pH/sq and self-Kerr non-linearities of $0.2-20\,\mathrm{Hz/photon}$. Further, we demonstrate ex-situ integration of these grAl inductors into hybrid resonators with Al or tantalum (Ta) capacitor electrodes without increasing total internal losses. Interestingly, the measured internal quality factors systematically decrease with increasing room-temperature resistivity of the grAl film for all devices, indicating a trade-off between compactness and internal loss. For our lowest resistivity grAl films, we measure quality factors reaching $3.5 \times 10^6$ for the all-grAl devices and $4.5 \times 10^6$ for the hybrid grAl/Ta devices, similar to state-of-the-art quantum circuits. Our loss analysis suggests that the surface loss factor of grAl is similar to that of pure Al for our lowest resistivity films, while the increasing losses with resistivity could be explained by increasing conductor loss in the grAl film., Comment: 10 pages, 7 figures (main text); 11 pages, 7 figures (supplementary)

Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (PEFT) strategies. To this end, we demonstrate the impact of two factors \textit{viz.}, client-specific layer importance score that selects the most important VLM layers for fine-tuning and inter-client layer diversity score that encourages diverse layer selection across clients for optimal VLM layer selection. We first theoretically motivate and leverage the principal eigenvalue magnitude of layerwise Neural Tangent Kernels and show its effectiveness as client-specific layer importance score. Next, we propose a novel layer updating strategy dubbed F$^3$OCUS that jointly optimizes the layer importance and diversity factors by employing a data-free, multi-objective, meta-heuristic optimization on the server. We explore 5 different meta-heuristic algorithms and compare their effectiveness for selecting model layers and adapter layers towards PEFT-FL. Furthermore, we release a new MedVQA-FL dataset involving overall 707,962 VQA triplets and 9 modality-specific clients and utilize it to train and evaluate our method. Overall, we conduct more than 10,000 client-level experiments on 6 Vision-Language FL task settings involving 58 medical image datasets and 4 different VLM architectures of varying sizes to demonstrate the effectiveness of the proposed method.

Millimeter-wave refracting telescopes targeting the degree-scale structure of the cosmic microwave background (CMB) have recently grown to diffraction-limited apertures of over 0.5 meters. These instruments are entirely housed in vacuum cryostats to support their sub-kelvin bolometric detectors and to minimize radiative loading from thermal emission due to absorption loss in their transmissive optical elements. The large vacuum window is the only optical element in the system at ambient temperature, and therefore minimizing loss in the window is crucial for maximizing detector sensitivity. This motivates the use of low-loss polymer materials and a window as thin as practicable. However, the window must simultaneously meet the requirement to keep sufficient vacuum, and therefore must limit gas permeation and remain mechanically robust against catastrophic failure under pressure. We report on the development of extremely thin composite polyethylene window technology that meets these goals. Two windows have been deployed for two full observing seasons on the BICEP3 and BA150 CMB telescopes at the South Pole. On BICEP3, the window has demonstrated a 6% improvement in detector sensitivity., Comment: 20 pages, 12 figures, 4 tables

This report provides an initial look at partial results from the TREC 2024 Retrieval-Augmented Generation (RAG) Track. We have identified RAG evaluation as a barrier to continued progress in information access (and more broadly, natural language processing and artificial intelligence), and it is our hope that we can contribute to tackling the many challenges in this space. The central hypothesis we explore in this work is that the nugget evaluation methodology, originally developed for the TREC Question Answering Track in 2003, provides a solid foundation for evaluating RAG systems. As such, our efforts have focused on "refactoring" this methodology, specifically applying large language models to both automatically create nuggets and to automatically assign nuggets to system answers. We call this the AutoNuggetizer framework. Within the TREC setup, we are able to calibrate our fully automatic process against a manual process whereby nuggets are created by human assessors semi-manually and then assigned manually to system answers. Based on initial results across 21 topics from 45 runs, we observe a strong correlation between scores derived from a fully automatic nugget evaluation and a (mostly) manual nugget evaluation by human assessors. This suggests that our fully automatic evaluation process can be used to guide future iterations of RAG systems.

The application of large language models to provide relevance assessments presents exciting opportunities to advance information retrieval, natural language processing, and beyond, but to date many unknowns remain. This paper reports on the results of a large-scale evaluation (the TREC 2024 RAG Track) where four different relevance assessment approaches were deployed in situ: the "standard" fully manual process that NIST has implemented for decades and three different alternatives that take advantage of LLMs to different extents using the open-source UMBRELA tool. This setup allows us to correlate system rankings induced by the different approaches to characterize tradeoffs between cost and quality. We find that in terms of nDCG@20, nDCG@100, and Recall@100, system rankings induced by automatically generated relevance assessments from UMBRELA correlate highly with those induced by fully manual assessments across a diverse set of 77 runs from 19 teams. Our results suggest that automatically generated UMBRELA judgments can replace fully manual judgments to accurately capture run-level effectiveness. Surprisingly, we find that LLM assistance does not appear to increase correlation with fully manual assessments, suggesting that costs associated with human-in-the-loop processes do not bring obvious tangible benefits. Overall, human assessors appear to be stricter than UMBRELA in applying relevance criteria. Our work validates the use of LLMs in academic TREC-style evaluations and provides the foundation for future studies.

Vehicle-to-Everything (V2X) communication, which includes Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Pedestrian (V2P) networks, is gaining significant attention due to the rise of connected and autonomous vehicles. V2X systems require diverse Quality of Service (QoS) provisions, with V2V communication demanding stricter latency and reliability compared to V2I. The 5G New Radio-V2X (NR-V2X) standard addresses these needs using multi-numerology Orthogonal Frequency Division Multiple Access (OFDMA), which allows for flexible allocation of radio resources. However, V2I and V2V users sharing the same radio resources leads to interference, necessitating efficient power and resource allocation. In this work, we propose a novel resource allocation and sharing algorithm for 5G-based V2X systems. Our approach first groups Resource Blocks (RBs) into Resource Chunks (RCs) and allocates them to V2I users using the Gale-Shapley stable matching algorithm. Power is then allocated to RCs to facilitate efficient resource sharing between V2I and V2V users through a bisection search method. Finally, the Gale-Shapley algorithm is used to pair V2I and V2V users, maintaining low computational complexity while ensuring high performance. Simulation results demonstrate that our proposed Gale-Shapley Resource Allocation with Gale-Shapley Sharing (GSRAGS) achieves competitive performance with lower complexity compared to existing works while effectively meeting the QoS demands of V2X communication systems., Comment: 8 pages, 10 figures

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

The use of parallel manipulators in aerospace engineering has gained significant attention due to their ability to provide improved stability and precision. This paper presents the design, control, and analysis of 'STEWIE', which is a three-degree-of-freedom (DoF) parallel manipulator robot developed by members of the thrustMIT rocketry team, as a payload stabilization mechanism for their sounding rocket, 'Altair'. The goal of the robot was to demonstrate the attitude control of the parallel plate against the continuous change in orientation experienced by the rocket during its flight, stabilizing the payloads. At the same time, the high gravitational forces (G-forces) and vibrations experienced by the sounding rocket are counteracted. A novel design of the mechanism, inspired by a standard Stewart platform, is proposed which was down-scaled to fit inside a 4U CubeSat within its space constraints. The robot uses three micro servo motors to actuate the links that control the alignment of the parallel plate. In addition to the actuation mechanism, a robust control system for its manipulation was developed for the robot. The robot represents a significant advancement in the field of space robotics in the aerospace industry by demonstrating the successful implementation of complex robotic mechanisms in small, confined spaces such as CubeSats, which are standard form factors for large payloads in the aerospace industry., Comment: For code and design files, refer to https://github.com/utkarshanand140/Stewie-Robot

The application of deep neural networks in geospatial data has become a trending research problem in the present day. A significant amount of statistical research has already been introduced, such as generalized least square optimization by incorporating spatial variance-covariance matrix, considering basis functions in the input nodes of the neural networks, and so on. However, for lattice data, there is no available literature about the utilization of asymptotic analysis of neural networks in regression for spatial data. This article proposes a consistent localized two-layer deep neural network-based regression for spatial data. We have proved the consistency of this deep neural network for bounded and unbounded spatial domains under a fixed sampling design of mixed-increasing spatial regions. We have proved that its asymptotic convergence rate is faster than that of \cite{zhan2024neural}'s neural network and an improved generalization of \cite{shen2023asymptotic}'s neural network structure. We empirically observe the rate of convergence of discrepancy measures between the empirical probability distribution of observed and predicted data, which will become faster for a less smooth spatial surface. We have applied our asymptotic analysis of deep neural networks to the estimation of the monthly average temperature of major cities in the USA from its satellite image. This application is an effective showcase of non-linear spatial regression. We demonstrate our methodology with simulated lattice data in various scenarios.

Federated learning (FL), with the growing IoT and edge computing, is seen as a promising solution for applications that are latency- and privacy-aware. However, due to the widespread dispersion of data across many clients, it is challenging to monitor client anomalies caused by malfunctioning devices or unexpected events. The majority of FL solutions now in use concentrate on the classification problem, ignoring situations in which anomaly detection may also necessitate privacy preservation and effectiveness. The system in federated learning is unable to manage the potentially flawed behavior of its clients completely. These behaviors include sharing arbitrary parameter values and causing a delay in convergence since clients are chosen at random without knowing the malfunctioning behavior of the client. Client selection is crucial in terms of the efficiency of the federated learning framework. The challenges such as client drift and handling slow clients with low computational capability are well-studied in FL. However, the detection of anomalous clients either for security or for overall performance in the FL frameworks is hardly studied in the literature. In this paper, we propose an anomaly client detection algorithm to overcome malicious client attacks and client drift in FL frameworks. Instead of random client selection, our proposed method utilizes anomaly client detection to remove clients from the FL framework, thereby enhancing the security and efficiency of the overall system. This proposed method improves the global model convergence in almost 50\% fewer communication rounds compared with widely used random client selection using the MNIST dataset.

Traditionally, digital hardware designs are written in the Verilog hardware description language (HDL) and debugged manually by engineers. This can be time-consuming and error-prone for complex designs. Large Language Models (LLMs) are emerging as a potential tool to help generate fully functioning HDL code, but most works have focused on generation in the single-shot capacity: i.e., run and evaluate, a process that does not leverage debugging and as such does not adequately reflect a realistic development process. In this work we evaluate the ability of LLMs to leverage feedback from electronic design automation (EDA) tools to fix mistakes in their own generated Verilog. To accomplish this we present an open-source, highly customizable framework, AutoChip, which combines conversational LLMs with the output from Verilog compilers and simulations to iteratively generate and repair Verilog. To determine the success of these LLMs we leverage the VerilogEval benchmark set. We evaluate four state-of-the-art conversational LLMs, focusing on readily accessible commercial models. EDA tool feedback proved to be consistently more effective than zero-shot prompting only with GPT-4o, the most computationally complex model we evaluated. In the best case we observed a 5.8% increase in the number of successful designs with a 34.2% decrease in cost over the best zero-shot results. Mixing smaller models with this larger model at the end of the feedback iterations resulted in equally as much success as with GPT-4o using feedback, but for an additional 41.9% less cost (overall decrease in cost over zero-shot of 89.6%).

We introduce a teleoperation system that integrates a 5 DOF actuated neck, designed to replicate natural human head movements and perception. By enabling behaviors like peeking or tilting, the system provides operators with a more intuitive and comprehensive view of the environment, improving task performance, reducing cognitive load, and facilitating complex whole-body manipulation. We demonstrate the benefits of natural perception across seven challenging teleoperation tasks, showing how the actuated neck enhances the scope and efficiency of remote operation. Furthermore, we investigate its role in training autonomous policies through imitation learning. In three distinct tasks, the actuated neck supports better spatial awareness, reduces distribution shift, and enables adaptive task-specific adjustments compared to a static wide-angle camera.