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Artificial Intelligence has become a double edged sword in modern society being both a boon and a bane. While it empowers individuals it also enables malicious actors to perpetrate scams such as fraudulent phone calls and user impersonations. This growing threat necessitates a robust system to protect individuals In this paper we introduce a novel real time fraud detection mechanism using Retrieval Augmented Generation technology to address this challenge on two fronts. First our system incorporates a continuously updating policy checking feature that transcribes phone calls in real time and uses RAG based models to verify that the caller is not soliciting private information thus ensuring transparency and the authenticity of the conversation. Second we implement a real time user impersonation check with a two step verification process to confirm the callers identity ensuring accountability. A key innovation of our system is the ability to update policies without retraining the entire model enhancing its adaptability. We validated our RAG based approach using synthetic call recordings achieving an accuracy of 97.98 percent and an F1score of 97.44 percent with 100 calls outperforming state of the art methods. This robust and flexible fraud detection system is well suited for real world deployment.

The second law of thermodynamics governs that nonequilibrium systems evolve towards states of higher entropy over time. However, it does not specify the rate of this evolution and the role of fluctuations that impact the system's dynamics. Entropy production quantifies how far a system is driven away from equilibrium and provides a measure of irreversibility. In stochastic systems, entropy production becomes essential for understanding the approach to nonequilibrium states. While macroscopic observations provide valuable insights, they often overlook the local behaviors of the system, governed by fluctuations. In this study, we focus on measuring the lower bound of entropy production at short time scales for generalized stochastic systems by calculating the Kullback-Leibler divergence (KLD) between the probability density functions of forward and backward trajectories. By analysing the entropy production across sliding time scales, we uncover patterns that reveal distinctions between local, small-scale dynamics and the global, macroscopic behavior, offering deeper insights into the system's departure from equilibrium. We also analysed the effects of switching to different types of noise or fluctuations and found that the observations at larger time scales provide no distinction between the different forms of noise while at short time scales, the distinction is significant.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

The high-pressure growth technique is a vital approach that facilitates the stabilization of new phases and allows for meticulous control of structural parameters, which significantly impact electronic and magnetic properties. We present a short review of our ongoing investigations into various families of iron-based superconductors (IBS), employing the high-gas pressure and high-temperature synthesis (HP-HTS) method. This technique is capable of producing the gas pressures up to 1.8 GPa and a heating temperature of up to 1700 {\deg}C through a three-zone furnace within a cylindrical chamber. Different kinds of IBS samples are prepared using HPHTS and characterized through various measurements to reach the final conclusions. The results demonstrate that the high-pressure growth technique significantly enhances the properties of IBS, including the transition temperature, critical current density, and pinning force. In addition, the quality of the samples and their density are improved through the intergrain connections. Furthermore, the comprehensive evaluations and investigations prove that a growth pressure of 0.5 GPa is sufficient for producing high-quality IBS bulks under the optimized synthesis conditions., Comment: 21 pages, 4 figures, 1 table

Oceanographers rely on visual analysis to interpret model simulations, identify events and phenomena, and track dynamic ocean processes. The ever increasing resolution and complexity of ocean data due to its dynamic nature and multivariate relationships demands a scalable and adaptable visualization tool for interactive exploration. We introduce pyParaOcean, a scalable and interactive visualization system designed specifically for ocean data analysis. pyParaOcean offers specialized modules for common oceanographic analysis tasks, including eddy identification and salinity movement tracking. These modules seamlessly integrate with ParaView as filters, ensuring a user-friendly and easy-to-use system while leveraging the parallelization capabilities of ParaView and a plethora of inbuilt general-purpose visualization functionalities. The creation of an auxiliary dataset stored as a Cinema database helps address I/O and network bandwidth bottlenecks while supporting the generation of quick overview visualizations. We present a case study on the Bay of Bengal (BoB) to demonstrate the utility of the system and scaling studies to evaluate the efficiency of the system.

X-ray emissions due to the two-electron one-photon (TEOP) process in the neon projectile and aluminum target have been successfully observed for the beam energy window of 1.8-2.1 MeV. Experimental TEOP transition energies have been compared with theoretical predictions of flexible atomic structure code (FAC) and General-purpose Relativistic Atomic Structure (GRASP) package. Present results have been verified with reported theoretical and experimental values. Transition rates of the TEOP transitions have also been studied using the said codes. The observed lines have been assigned when the measured transition energies are in good agreement with the theoretical values. Such assignments have further been validated with the good agreements between the experimental and theoretical transition rates. Note that only the TEOP lines in projectile ions are seen with 1.8 MeV energy. In contrast, the TEOP lines in target ions are also observed well with 2.1 MeV energy. Thus, this study sheds useful light on the excitation mechanism of the TEOP processes in the low energy regimes., Comment: 6 pages, 2 figures, 3 tables. arXiv admin note: text overlap with arXiv:2201.02566

The Solar Ultraviolet Imaging Telescope (SUIT) is an instrument on the Aditya-L1 mission of the Indian Space Research Organization (ISRO) launched on September 02, 2023. SUIT continuously provides, near-simultaneous full-disk and region-of-interest images of the Sun, slicing through the photosphere and chromosphere and covering a field of view up to 1.5 solar radii. For this purpose, SUIT uses 11 filters tuned at different wavelengths in the 200{--}400~nm range, including the Mg~{\sc ii} h~and~k and Ca~{\sc ii}~H spectral lines. The observations made by SUIT help us understand the magnetic coupling of the lower and middle solar atmosphere. In addition, for the first time, it allows the measurements of spatially resolved solar broad-band radiation in the near and mid ultraviolet, which will help constrain the variability of the solar ultraviolet irradiance in a wavelength range that is central for the chemistry of the Earth's atmosphere. This paper discusses the details of the instrument and data products., Comment: 37 pages, Accepted for Publication in Solar Physics

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

Superconductivity and nontrivial topology are highly sought-after phenomena in quantum materials. While many topological crystalline materials have been found to exhibit superconductivity, their presence in quasicrystals - materials with a unique aperiodic yet ordered structure - has remained largely unexplored. In this work, we report the discovery of superconductivity in a monoclinic approximant to the decagonal quasicrystal Al$_{13}$Os$_{4}$, that exhibits a high superconducting transition temperature and a nontrivial electronic structure. The resistivity, magnetization, specific heat, and $\mu$SR measurements confirm superconductivity with a critical temperature of $\sim5.47$ K. Detailed electronic structure and symmetry analysis reveal nontrivial state with $\mathcal{Z}_{2}=1$ and spin-polarized conducting surface states. Importantly, we identify three-dimensional saddle point van Hove singularities with substantial flat energy dispersion at the Fermi level, which can enhance superconductivity. Our results highlight a rich interplay between superconductivity and nontrivial electronic states in Al$_{13}$Os$_{4}$, demonstrating it as a unique platform for exploring unconventional superconducting states in quasicrystalline materials., Comment: 17 pages, 3 figures

Objective: To develop a novel deep learning framework for the automated segmentation of colonic polyps in colonoscopy images, overcoming the limitations of current approaches in preserving precise polyp boundaries, incorporating multi-scale features, and modeling spatial dependencies that accurately reflect the intricate and diverse morphology of polyps. Methods: To address these limitations, we propose a novel Multiscale Network with Spatial-enhanced Attention (MNet-SAt) for polyp segmentation in colonoscopy images. This framework incorporates four key modules: Edge-Guided Feature Enrichment (EGFE) preserves edge information for improved boundary quality; Multi-Scale Feature Aggregator (MSFA) extracts and aggregates multi-scale features across channel spatial dimensions, focusing on salient regions; Spatial-Enhanced Attention (SEAt) captures spatial-aware global dependencies within the multi-scale aggregated features, emphasizing the region of interest; and Channel-Enhanced Atrous Spatial Pyramid Pooling (CE-ASPP) resamples and recalibrates attentive features across scales. Results: We evaluated MNet-SAt on the Kvasir-SEG and CVC-ClinicDB datasets, achieving Dice Similarity Coefficients of 96.61% and 98.60%, respectively. Conclusion: Both quantitative (DSC) and qualitative assessments highlight MNet-SAt's superior performance and generalization capabilities compared to existing methods. Significance: MNet-SAt's high accuracy in polyp segmentation holds promise for improving clinical workflows in early polyp detection and more effective treatment, contributing to reduced colorectal cancer mortality rates.

The current study presents the cross-section measurement of $^{181}$Ta(n,$\gamma$)$^{182}$Ta reaction at 1.37 $\pm$ 0.13, 2.06 $\pm$ 0.14, 2.56 $\pm$ 0.15, and 3.05 $\pm$ 0.17 MeV neutron energies utilizing offline $\gamma$-ray spectroscopy. The neutrons were generated through the $^{7}$Li(p,n)$^{7}$Be reaction. The $^{115}$In(n,n'$\gamma$)$^{115m}$In reaction served as a monitor reaction. The covariance analysis was used to quantify the uncertainties in the measured cross-sections for the first time for the $^{181}$Ta(n,$\gamma$)$^{182}$Ta reaction. The present study provides detailed information on the propagation of uncertainty in the overall result. The required corrections for low energy background neutron and $\gamma$-ray coincidence summing effect have been made in the present measurement. The output is compared with the pre-existing cross-section data from the EXFOR database, evaluated data libraries and theoretical model predictions.

In the present work, spin orientation and variation of the strength of coupling in [Co/Pt]ML/Fe multilayer have been investigated as a function of the thickness of the Fe layer. [Co/Pt]ML/Fe multilayer has orthogonal anisotropy with the Fe layer, and [Co/Pt]ML has in-plane magnetic anisotropy and perpendicular anisotropy, respectively. Measurements are performed using in-situ magneto-optical Kerr effect (MOKE) and isotope-sensitive depth-resolved nuclear resonance scattering (NRS) technique. Real-time in-situ MOKE measurement during Fe growth reveals that with an increase in thickness of the Fe layer, moments of Fe layer reorientation from out of the plane to in-plane direction. This is attributed to the decrease in the coupling between the [Co/Pt]ML and Fe layer. For the depth-dependent study, two [Co/Pt]ML/Fe multilayers having the same thickness but different positions of the Fe57 marker layer ([Co/Pt]MLFe/Fe57) and [Co/Pt]MLFe57/Fe ) were studied using the NRS technique. Films with varying external magnetic fields were also studied to investigate coupling strength. Measurements were performed under the x-ray standing wave conditions to enhance resonance yield. It is observed that for the 75 {\AA} Fe in [Co/Pt]ML/Fe multilayer, the coupling varies along the depth of the Fe layer. The coupling is strong at [Co/Pt]ML and Fe interface with spins of the Fe layer aligned in the out-of-plane direction, whereas moments away from the interface are weakly coupled and aligned in-plane along the magnetic easy axis. Due to this gradient in strength of coupling along the depth, a large magnetic field is required to reorient spins at the interface along the magnetic hard axis of the Fe layer; however, spins away from the interface can rotate freely even in the low magnetic field., Comment: 15 pages, 7 figures

Realizing universal fault-tolerant quantum computation is a key goal in quantum information science. By encoding quantum information into logical qubits utilizing quantum error correcting codes, physical errors can be detected and corrected, enabling substantial reduction in logical error rates. However, the set of logical operations that can be easily implemented on such encoded qubits is often constrained, necessitating the use of special resource states known as 'magic states' to implement universal, classically hard circuits. A key method to prepare high-fidelity magic states is to perform 'distillation', creating them from multiple lower fidelity inputs. Here we present the experimental realization of magic state distillation with logical qubits on a neutral-atom quantum computer. Our approach makes use of a dynamically reconfigurable architecture to encode and perform quantum operations on many logical qubits in parallel. We demonstrate the distillation of magic states encoded in d=3 and d=5 color codes, observing improvements of the logical fidelity of the output magic states compared to the input logical magic states. These experiments demonstrate a key building block of universal fault-tolerant quantum computation, and represent an important step towards large-scale logical quantum processors., Comment: 8+11 pages, 4+4 figures

This study investigates students' perceptions of Generative Artificial Intelligence (AI), with a focus on Higher Education institutions in Northern Ireland and India. We collect quantitative Likert ratings and qualitative comments from 1,211 students on their awareness and perceptions of AI and investigate variations in attitudes toward AI across institutions and subject areas, as well as interactions between these variables with demographic variables (focusing on gender). We find that: (a) while perceptions varied across institutions, responses for Computer Sciences students were similar; and (b) after controlling for institution and subject area, we observed no effect of gender. These results are consistent with previous studies, which find that students' perceptions are predicted by prior experience. We consider the implications of this relation and some considerations for the role of experience.

In this paper, we construct an isotropic cosmological model in the $ f(Q, T) $ theory of gravity in the frame of a flat FLRW spacetime being $ Q $ the non-metricity tensor and $ T $ the trace of the energy-momentum tensor. The gravity function is taken to be a quadratic equation, $ f(Q, T)=\zeta Q^2 + \gamma T $, where $ \zeta<0 $ and $ \gamma $ are the arbitrary constants. We constrain the model parameters $ \alpha $ and $ H_0 $ using the recent observational datasets: the Hubble dataset (OHD), the Pantheon dataset of $ 1048 $ points, and the joint dataset (OHD + Pantheon). The universe model transits from an early deceleration state to an acceleration in late times. This model also provides the ekpyrotic phase of the universe on the high redshift $ z>12.32 $. In this model, the Big Bang is described as a collision of branes, and thus, the Big Bang is not the beginning of time. Before the Big Bang, there is an ekpyrotic phase with the equation of state $ \omega >> 1 $. In late times, the undeviating Hubble measurements reduce the $ H_0 $ tension in the reconstructed $ f(Q, T) $ function. Additionally, we study various physical parameters of the model. Finally, our model describes a quintessence dark energy model at later times., Comment: 17 pages, 18 figures

The implementation of 5G and the future deployment of 6G necessitate the utilization of optical networks that possess substantial capacity and exhibit minimal latency. The dynamic arrival and departure of connection requests in optical networks result in particular central links experiencing more traffic and congestion than non-central links. The occurrence of congested links leads to service blocking despite the availability of resources within the network, restricting the efficient utilization of network resources. The available algorithms in the literature that aim to balance load among network links offer a trade-off between blocking performance and algorithmic complexity, thus increasing service provisioning time. This work proposes a dynamic routing-based congestion-aware routing, modulation, core, and spectrum assignment (RMCSA) algorithm for space division multiplexing elastic optical networks (SDM-EONs). The algorithm finds alternative candidate paths based on real-time link occupancy metrics to minimize blocking due to link congestion under dynamic traffic scenarios. As a result, the algorithm reduces the formation of congestion hotspots in the network owing to link-betweenness centrality. We have performed extensive simulations using two realistic network topologies to compare the performance of the proposed algorithm with relevant RMCSA algorithms available in the literature. The simulation results verify the superior performance of our proposed algorithm compared to the benchmark Yen's K-shortest paths and K-Disjoint shortest paths RMCSA algorithms in connection blocking ratio and spectrum utilization efficiency. To expedite the route-finding process, we present a novel caching strategy that allows the proposed algorithm to demonstrate a much-reduced service delay time compared to the recently developed adaptive link weight-based load-balancing RMCSA algorithm., Comment: The preliminary work was presented at ONDM 2023 conference. https://doi.org/10.23919/ONDM57372.2023.10144866

We conduct a numerical study of relativistic viscous fluid dynamics in the Density Frame for one-dimensional fluid flows. The Density Frame is a formulation of relativistic viscous hydrodynamics that is first-order in time, requires no auxiliary fields, and has no non-hydrodynamic modes. We compare our results to QCD kinetic theory simulations and find excellent agreement within the regime of applicability of hydrodynamics. Additionally, the Density Frame results remain well-behaved and robust near the boundary of applicability. We also compare our findings to the second-order-in-time hydrodynamic theory developed by Bemfica, Disconzi, Noronha, and Kovtun (BDNK) and a well-known M\"uller-Israel-Stewart-type hydrodynamics code, MUSIC, which is commonly used to simulate heavy-ion collisions., Comment: 28 pages, 11 figures

Plant breeding programs require assessments of days to maturity for accurate selection and placement of entries in appropriate tests. In the early stages of the breeding pipeline, soybean breeding programs assign relative maturity ratings to experimental varieties that indicate their suitable maturity zones. Traditionally, the estimation of maturity value for breeding varieties has involved breeders manually inspecting fields and assessing maturity value visually. This approach relies heavily on rater judgment, making it subjective and time-consuming. This study aimed to develop a machine-learning model for evaluating soybean maturity using UAV-based time-series imagery. Images were captured at three-day intervals, beginning as the earliest varieties started maturing and continuing until the last varieties fully matured. The data collected for this experiment consisted of 22,043 plots collected across three years (2021 to 2023) and represent relative maturity groups 1.6 - 3.9. We utilized contour plot images extracted from the time-series UAV RGB imagery as input for a neural network model. This contour plot approach encoded the temporal and spatial variation within each plot into a single image. A deep learning model was trained to utilize this contour plot to predict maturity ratings. This model significantly improves accuracy and robustness, achieving up to 85% accuracy. We also evaluate the model's accuracy as we reduce the number of time points, quantifying the trade-off between temporal resolution and maturity prediction. The predictive model offers a scalable, objective, and efficient means of assessing crop maturity, enabling phenomics and ML approaches to reduce the reliance on manual inspection and subjective assessment. This approach enables the automatic prediction of relative maturity ratings in a breeding program, saving time and resources.

Materials discovery and development are critical for addressing global challenges. Yet, the exponential growth in materials science literature comprising vast amounts of textual data has created significant bottlenecks in knowledge extraction, synthesis, and scientific reasoning. Large Language Models (LLMs) offer unprecedented opportunities to accelerate materials research through automated analysis and prediction. Still, their effective deployment requires domain-specific adaptation for understanding and solving domain-relevant tasks. Here, we present LLaMat, a family of foundational models for materials science developed through continued pretraining of LLaMA models on an extensive corpus of materials literature and crystallographic data. Through systematic evaluation, we demonstrate that LLaMat excels in materials-specific NLP and structured information extraction while maintaining general linguistic capabilities. The specialized LLaMat-CIF variant demonstrates unprecedented capabilities in crystal structure generation, predicting stable crystals with high coverage across the periodic table. Intriguingly, despite LLaMA-3's superior performance in comparison to LLaMA-2, we observe that LLaMat-2 demonstrates unexpectedly enhanced domain-specific performance across diverse materials science tasks, including structured information extraction from text and tables, more particularly in crystal structure generation, a potential adaptation rigidity in overtrained LLMs. Altogether, the present work demonstrates the effectiveness of domain adaptation towards developing practically deployable LLM copilots for materials research. Beyond materials science, our findings reveal important considerations for domain adaptation of LLMs, such as model selection, training methodology, and domain-specific performance, which may influence the development of specialized scientific AI systems.

Dzyaloshinskii-Moriya interaction (DMI) plays a crucial role to stabilize the exotic topologically stable skyrmion spin-textures in the noncentrosymmetric crystals. The recent discovery of biskyrmions and skyrmions in the globally centrosymmetric crystals has raised debate about the role of the DMI in causing the spin textures, since DMI vanishes in such crystal structures. Theoretical studies, on the other hand, suggest non-vanishing DMI even if there is local inversion symmetry breaking in an otherwise globally centrosymmetric crystal structure. Motivated by such theoretical predictions, we present here the results of a systematic crystal structure study of two skyrmion-hosting Ni2In-type centrosymmetric hexagonal compounds, MnNiGa and MnPtGa, using the atomic pair distribution function (PDF) technique. Our result provides information about structural correlations in the short-range (SR), medium-range (MR) and long-range (LR) regimes simultaneously. The analysis of the experimental PDFs, obtained from high flux, high energy and high-Q synchrotron x-ray powder diffraction patterns, reveal that the local SR structure of both MnNiGa and MnPtGa compounds corresponds to the noncentrosymmetric trigonal space group P3m1, while the structure in the MR+LR regimes remains hexagonal in the centrosymmetric P63/mmc space group. These findings are also supported by theoretical DFT calculations. Our results in conjunction with the previous theoretical predictions, provide a rationale for the genesis of skyrmions in centrosymmetric materials in terms of non-vanishing DMI due to local inversion symmetry breaking. We believe that our findings would encourage a systematic search of skyrmionic textures and other topological phenomena in a vast family of centrosymmetric materials.

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

In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among college students by applying various machine learning algorithms: Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and K-nearest Neighbors. The primary objective of this work is to leverage a research study to predict and mitigate stress and non-stress based on the collected questionnaire dataset. We conducted a workshop with the primary goal of studying the stress levels found among the students. This workshop was attended by Approximately 843 students aged between 18 to 21 years old. A questionnaire was given to the students validated under the guidance of the experts from the All India Institute of Medical Sciences (AIIMS) Raipur, Chhattisgarh, India, on which our dataset is based. The survey consists of 28 questions, aiming to comprehensively understand the multidimensional aspects of stress, including emotional well-being, physical health, academic performance, relationships, and leisure. This work finds that Support Vector Machines have a maximum accuracy for Stress, reaching 95\%. The study contributes to a deeper understanding of stress determinants. It aims to improve college student's overall quality of life and academic success, addressing the multifaceted nature of stress., Comment: This paper was presented at an IEEE conference and is 5 pages long with 5 figures. It discusses machine learning algorithms for detecting mental stress in college students

After decades of experimental efforts, the DAMA/LIBRA(DL) annual modulation (AM) analysis on the ${\chi}$N (WIMP Dark Matter interactions on nucleus) channel remains the only one which can be interpreted as positive signatures. This has been refuted by numerous time-integrated (TI) and AM analysis. It has been shown that ${\chi}$e (WIMP interactions with electrons) alone is not compatible with the DL AM data. We expand the investigations by performing an AM analysis with the addition of ${\chi}$e long-range and short-range interactions to ${\chi}$N, derived using the frozen-core approximation method. Two scenarios are considered, where the ${\chi}$N and ${\chi}$e processes are due to a single ${\chi}$ (${\Gamma}^{1\chi}_{tot}$) or two different ${\chi}$s (${\Gamma}^{2\chi}_{tot}$). The combined fits with ${\chi}$N and ${\chi}$e provide stronger significance to the DL AM data which are compatible with the presence of additional physical effects beyond \c{hi}N alone. This is the first analysis which explores how ${\chi}$e AM can play a role in DL AM. The revised allowed regions as well as the exclusion contours from the other null AM experiments are presented. All DL AM allowed parameter spaces in ${\chi}$N and ${\chi}$e channels under both ${\Gamma}^{1\chi}_{tot}$ and ${\Gamma}^{2\chi}_{tot}$ are excluded at the 90\% confidence level by the combined null AM results. It can be projected that DL-allowed parameter spaces from generic models with interactions induced by two-WIMPs are ruled out., Comment: 6 pages, 5 figures

We present a novel method for soybean (Glycine max (L.) Merr.) yield estimation leveraging high throughput seed counting via computer vision and deep learning techniques. Traditional methods for collecting yield data are labor-intensive, costly, prone to equipment failures at critical data collection times, and require transportation of equipment across field sites. Computer vision, the field of teaching computers to interpret visual data, allows us to extract detailed yield information directly from images. By treating it as a computer vision task, we report a more efficient alternative, employing a ground robot equipped with fisheye cameras to capture comprehensive videos of soybean plots from which images are extracted in a variety of development programs. These images are processed through the P2PNet-Yield model, a deep learning framework where we combined a Feature Extraction Module (the backbone of the P2PNet-Soy) and a Yield Regression Module to estimate seed yields of soybean plots. Our results are built on three years of yield testing plot data - 8500 in 2021, 2275 in 2022, and 650 in 2023. With these datasets, our approach incorporates several innovations to further improve the accuracy and generalizability of the seed counting and yield estimation architecture, such as the fisheye image correction and data augmentation with random sensor effects. The P2PNet-Yield model achieved a genotype ranking accuracy score of up to 83%. It demonstrates up to a 32% reduction in time to collect yield data as well as costs associated with traditional yield estimation, offering a scalable solution for breeding programs and agricultural productivity enhancement., Comment: 23 pages, 12 figures, 2 tables

With the growing demand for interpretable deep learning models, this paper introduces Integrative CAM, an advanced Class Activation Mapping (CAM) technique aimed at providing a holistic view of feature importance across Convolutional Neural Networks (CNNs). Traditional gradient-based CAM methods, such as Grad-CAM and Grad-CAM++, primarily use final layer activations to highlight regions of interest, often neglecting critical features derived from intermediate layers. Integrative CAM addresses this limitation by fusing insights across all network layers, leveraging both gradient and activation scores to adaptively weight layer contributions, thus yielding a comprehensive interpretation of the model's internal representation. Our approach includes a novel bias term in the saliency map calculation, a factor frequently omitted in existing CAM techniques, but essential for capturing a more complete feature importance landscape, as modern CNNs rely on both weighted activations and biases to make predictions. Additionally, we generalize the alpha term from Grad-CAM++ to apply to any smooth function, expanding CAM applicability across a wider range of models. Through extensive experiments on diverse and complex datasets, Integrative CAM demonstrates superior fidelity in feature importance mapping, effectively enhancing interpretability for intricate fusion scenarios and complex decision-making tasks. By advancing interpretability methods to capture multi-layered model insights, Integrative CAM provides a valuable tool for fusion-driven applications, promoting the trustworthy and insightful deployment of deep learning models.

Submillimeter galaxies (SMGs) constitute a key population of bright star-forming galaxies at high redshift. These galaxies challenge galaxy formation models, particularly in reproducing their observed number counts and redshift distributions. Furthermore, although SMGs contribute significantly to the cosmic star formation rate density (SFRD), their precise role remains uncertain. Upcoming surveys, such as the Ultra Deep Survey with the TolTEC camera, are expected to offer valuable insights into SMG properties and their broader impact. Robust modeling of SMGs in a cosmologically representative volume is necessary to investigate their nature in preparation for next-generation submillimeter surveys. We implement and test parametric relations derived from radiative transfer calculations across three cosmological simulations: EAGLE, IllustrisTNG, and FLAMINGO. Particular emphasis is placed on the FLAMINGO due to their large volume and robust statistical sampling of SMGs. Based on the model that best reproduces observations, we forecast submillimeter fluxes within the simulations, analyze the properties of SMGs, and evaluate their evolution over cosmic time. Our results show that the FLAMINGO reproduces the observed redshift distribution and source number counts of SMGs without requiring a top-heavy initial mass function. On the other hand, the EAGLE and IllustrisTNG show a deficit of bright SMGs. We find that SMGs with S850 > 1 mJy contribute up to 27% of the SFRD at z=2.6 in the FLAMINGO, consistent with recent observations. Flux density functions reveal a rise in SMG abundance from z = 6 to 2.5, followed by a sharp decline in the number of brighter SMGs from z = 2.5 to 0. Leveraging the SMG population in FLAMINGO, we forecast that the TolTEC UDS will detect 80,000 sources over 0.8 deg^2 at 1.1 mm (at the 4{\sigma} detection limit), capturing about 50% of the cosmic SFRD at z=2.5., Comment: 19 pages, 13 figures, submitted to Astronomy & Astrophysics Journal, abstract is reduced to meet Arxiv character limit

The modern theory of rare events is grounded in near equilibrium ideas, however many systems of modern interest are sufficiently far from equilibrium that traditional approaches do not apply. Using the recently developed variational path sampling methodology, we study systems evolving within nonequilibrium steady states to elucidate how reactive processes are altered away from equilibrium. Variational path sampling provides access to ensembles of reactive events, and a means of quantifying the relative importance of each dynamical degree of freedom in such processes. With it, we have studied the conformational change of a solute in an active bath. We illustrate how energy injection generically enhances the rates of rare events, even when energy is not directed into specific reactive modes. By studying the folding and unfolding transitions of a grafted polymer under shear, we illustrate how nonequilibrium reactive processes do not follow gradient paths due to the emergence of persistent currents. The breaking of detailed balance allows for the mechanisms of forward and backward reactions to be distinct, enabling novel pathways to be explored and designed, and states unstable in equilibrium to become stabilized kinetically away from it. The analysis presented in this work establishes some basic principles for nonequilibrium reactive events, and is made possible by the use of a numerical method that does not invoke proximity to equilibrium or requires strong prior assumptions about the mechanism of reaction., Comment: 24 pages, 17 figures, comments welcome

Coordination behavior in robot swarms is inherently multi-modal in nature. That is, there are numerous ways in which a swarm of robots can avoid inter-agent collisions and reach their respective goals. However, the problem of generating diverse and feasible swarm behaviors in a scalable manner remains largely unaddressed. In this paper, we fill this gap by combining generative models with a safety-filter (SF). Specifically, we sample diverse trajectories from a learned generative model which is subsequently projected onto the feasible set using the SF. We experiment with two choices for generative models, namely: Conditional Variational Autoencoder (CVAE) and Vector-Quantized Variational Autoencoder (VQ-VAE). We highlight the trade-offs these two models provide in terms of computation time and trajectory diversity. We develop a custom solver for our SF and equip it with a neural network that predicts context-specific initialization. Thecinitialization network is trained in a self-supervised manner, taking advantage of the differentiability of the SF solver. We provide two sets of empirical results. First, we demonstrate that we can generate a large set of multi-modal, feasible trajectories, simulating diverse swarm behaviors, within a few tens of milliseconds. Second, we show that our initialization network provides faster convergence of our SF solver vis-a-vis other alternative heuristics., Comment: Submitted to RAL

This paper addresses sampling-based trajectory optimization for risk-aware navigation under stochastic dynamics. Typically such approaches operate by computing $\tilde{N}$ perturbed rollouts around the nominal dynamics to estimate the collision risk associated with a sequence of control commands. We consider a setting where it is expensive to estimate risk using perturbed rollouts, for example, due to expensive collision-checks. We put forward two key contributions. First, we develop an algorithm that distills the statistical information from a larger set of rollouts to a reduced-set with sample size $N<<\tilde{N}$. Consequently, we estimate collision risk using just $N$ rollouts instead of $\tilde{N}$. Second, we formulate a novel surrogate for the collision risk that can leverage the distilled statistical information contained in the reduced-set. We formalize both algorithmic contributions using distribution embedding in Reproducing Kernel Hilbert Space (RKHS) and Maximum Mean Discrepancy (MMD). We perform extensive benchmarking to demonstrate that our MMD-based approach leads to safer trajectories at low sample regime than existing baselines using Conditional Value-at Risk (CVaR) based collision risk estimate., Comment: https://github.com/Basant1861/MPC-MMD

Edge detection is a cornerstone of image processing, yet existing methods often face critical limitations. Traditional deep learning edge detection methods require extensive training datasets and fine-tuning, while classical techniques often fail in complex or noisy scenarios, limiting their real-world applicability. To address these limitations, we propose a training-free, quantum-inspired edge detection model. Our approach integrates classical Sobel edge detection, the Schr\"odinger wave equation refinement, and a hybrid framework combining Canny and Laplacian operators. By eliminating the need for training, the model is lightweight and adaptable to diverse applications. The Schr\"odinger wave equation refines gradient-based edge maps through iterative diffusion, significantly enhancing edge precision. The hybrid framework further strengthens the model by synergistically combining local and global features, ensuring robustness even under challenging conditions. Extensive evaluations on datasets like BIPED, Multicue, and NYUD demonstrate superior performance of the proposed model, achieving state-of-the-art metrics, including ODS, OIS, AP, and F-measure. Noise robustness experiments highlight its reliability, showcasing its practicality for real-world scenarios. Due to its versatile and adaptable nature, our model is well-suited for applications such as medical imaging, autonomous systems, and environmental monitoring, setting a new benchmark for edge detection., Comment: 12 pages, 6 figure

We present observations of the Type IIP supernova (SN) 2024jlf, including spectroscopy beginning just 0.7 days ($\sim$17 hours) after first light. Rapid follow-up was enabled by the new $\texttt{BTSbot-nearby}$ program, which involves autonomously triggering target-of-opportunity requests for new transients in Zwicky Transient Facility data that are coincident with nearby ($D<60$ Mpc) galaxies and identified by the $\texttt{BTSbot}$ machine learning model. Early photometry and non-detections shortly prior to first light show that SN 2024jlf initially brightened by $>$4 mag/day, quicker than $\sim$90% of Type II SNe. Early spectra reveal weak flash ionization features: narrow, short-lived ($1.3 < \tau ~\mathrm{[d]} < 1.8$) emission lines of H$\alpha$, He II, and C IV. Assuming a wind velocity of $v_w=50$ km s$^{-1}$, these properties indicate that the red supergiant progenitor exhibited enhanced mass-loss in the last year before explosion. We constrain the mass-loss rate to $10^{-4} < \dot{M}~\mathrm{[M_\odot~yr^{-1}]} < 10^{-3}$ by matching observations to model grids from two independent radiative hydrodynamics codes. $\texttt{BTSbot-nearby}$ automation minimizes spectroscopic follow-up latency, enabling the observation of ephemeral early-time phenomena exhibited by transients., Comment: 23 pages, 9 figures

Consider a scenario in which a user searches for information, only to encounter texts flooded with misleading or non-relevant content. This scenario exemplifies a simple yet potent vulnerability in neural Information Retrieval (IR) pipelines: content injection attacks. We find that embedding models for retrieval, rerankers, and large language model (LLM) relevance judges are vulnerable to these attacks, in which adversaries insert misleading text into passages to manipulate model judgements. We identify two primary threats: (1) inserting unrelated or harmful content within passages that still appear deceptively "relevant", and (2) inserting entire queries or key query terms into passages to boost their perceived relevance. While the second tactic has been explored in prior research, we present, to our knowledge, the first empirical analysis of the first threat, demonstrating how state-of-the-art models can be easily misled. Our study systematically examines the factors that influence an attack's success, such as the placement of injected content and the balance between relevant and non-relevant material. Additionally, we explore various defense strategies, including adversarial passage classifiers, retriever fine-tuning to discount manipulated content, and prompting LLM judges to adopt a more cautious approach. However, we find that these countermeasures often involve trade-offs, sacrificing effectiveness for attack robustness and sometimes penalizing legitimate documents in the process. Our findings highlight the need for stronger defenses against these evolving adversarial strategies to maintain the trustworthiness of IR systems. We release our code and scripts to facilitate further research.

Tailoring the degrees of freedom (DoF) of light for a desired purpose, so-called structured light, has delivered numerous advances over the past decade, ranging from communications and quantum cryptography to optical trapping, and microscopy. The shaping toolkit has traditionally been linear in nature, only recently extended to the nonlinear regime, where input beams overlap in a nonlinear crystal to generate a structured output beam. Here we show how to enhance the fidelity of the structured output by aligning light with light. Using orbital angular momentum modes and difference frequency generation as an example, we demonstrate precise control of the spatial overlap in both the transverse and longitudinal directions using the structure of one mode as a virtual structured (in amplitude and phase) light-based aperture for the other. Our technique can easily be translated to other structured light fields as well as alternative nonlinear processes such as second harmonic generation and sum frequency generation, enabling advancements in communication, imaging, and spectroscopy., Comment: 11 pages, 6 figures

Diffusion-based motion planners are becoming popular due to their well-established performance improvements, stemming from sample diversity and the ease of incorporating new constraints directly during inference. However, a primary limitation of the diffusion process is the requirement for a substantial number of denoising steps, especially when the denoising process is coupled with gradient-based guidance. In this paper, we introduce, diffusion in the parametric space of trajectories, where the parameters are represented as Bernstein coefficients. We show that this representation greatly improves the effectiveness of the cost function guidance and the inference speed. We also introduce a novel stitching algorithm that leverages the diversity in diffusion-generated trajectories to produce collision-free trajectories with just a single cost function-guided model. We demonstrate that our approaches outperform current SOTA diffusion-based motion planners for manipulators and provide an ablation study on key components.

We present a rack-scale compute architecture for ML using multi-accelerator servers connected via chip-to-chip silicon photonic components. Our architecture achieves (1) multi-tenanted resource slicing without fragmentation, (2) 74% faster rack-scale collective communication, and (3) 1.7X speedup in end-to-end ML training throughput., Comment: Accepted at OFC 2025, https://www.ofcconference.org/en-us/home/program-speakers/symposia/advanced-packaging-and-integrated-optics/

This is a very brief overview of the ongoing research program of unification known as the octonionic theory. We highlight the quantum foundational origins for the theory, and the seven key ingredients which go into its making., Comment: 12 pages, 5 figures, based on a plenary talk given at the 28th International Conference on Integrable Systems and Quantum Symmetries (ISQS-28) 1-5 July 2024. Prague, Czechia

Large language models (LLMs) are vulnerable to universal jailbreaks-prompting strategies that systematically bypass model safeguards and enable users to carry out harmful processes that require many model interactions, like manufacturing illegal substances at scale. To defend against these attacks, we introduce Constitutional Classifiers: safeguards trained on synthetic data, generated by prompting LLMs with natural language rules (i.e., a constitution) specifying permitted and restricted content. In over 3,000 estimated hours of red teaming, no red teamer found a universal jailbreak that could extract information from an early classifier-guarded LLM at a similar level of detail to an unguarded model across most target queries. On automated evaluations, enhanced classifiers demonstrated robust defense against held-out domain-specific jailbreaks. These classifiers also maintain deployment viability, with an absolute 0.38% increase in production-traffic refusals and a 23.7% inference overhead. Our work demonstrates that defending against universal jailbreaks while maintaining practical deployment viability is tractable.

The intense magnetic fields present in neutron stars are closely linked to their observed temperature and spectral characteristics, timing properties, including spin period and its derivatives. Therefore, a comprehensive theoretical analysis of magnetic field evolution is essential for understanding how the strength of the magnetic field change over time. The decay rate of magnetic field in isolated, non-accreting neutron stars can be assessed by evaluating the second derivative of the spin frequency. Another method to estimate this rate involves monitoring an increase in thermal emission beyond what is expected from standard cooling processes, assuming no additional heating mechanisms are present. Our findings indicate that for X-ray emitting isolated neutron stars, the evolution rate of spin period derivative aligns with the dissipation rate of magnetic energy from the dipolar field, provided that a substantial portion of the released energy is emitted as X-rays. The time scale of magnetic field decay is found to be much shorter than typical age of radio pulsars., Comment: 6 pages including 1 figure and 1 table

Understanding the origin and evolution of supermassive black holes (SMBH) stands as one of the most important challenges in astrophysics and cosmology, with little current theoretical consensus. Improved observational constraints on the cosmological evolution of SMBH demographics are needed. Here we report results of a search via photometric variability for SMBHs appearing as active galactic nuclei (AGN) in the cosmological volume defined by the Hubble Ultra Deep Field (HUDF). This work includes particular focus on a new observation carried out in 2023 with the \textit{Hubble Space Telescope (HST)} using the WFC3/IR/F140W, which is compared directly to equivalent data taken 11 years earlier in 2012. Two earlier pairs of observations from 2009 to 2012 with WFC3/IR/F105W and WFC3/IR/F160W are also analysed. We identify 443, 149, and 78 AGN candidates as nuclear sources that exhibit photometric variability at a level of 2, 2.5 and 3~$\sigma$ in at least one filter. This sample includes 29, 14, and 9 AGN at redshifts $z>6$, when the Universe was $\lesssim900$~Myr old. After variability and luminosity function (down to $M_{\rm UV}=-17\:$mag) completeness corrections, we estimate the co-moving number density of SMBHs, $n_{\rm SMBH}(z)$. At $z = 6 - 9$, $n_{\rm SMBH}\gtrsim 10^{-2}\:{\rm cMpc^{-3}}$. At low-$z$ our observations are sensitive to AGN fainter than $M_{\rm UV}=-17 \:$mag, and we estimate $n_{\rm SMBH}\gtrsim 6\times 10^{-2}\:{\rm cMpc^{-3}}$. We discuss how these results place strong constraints on a variety of SMBH seeding theories., Comment: Submitted to ApJ. Comments welcome

This article explores charge diffusion in relativistic hydrodynamics using kinetic theory with a modified collision kernel that incorporates the momentum dependence of the particle relaxation time. Starting from the Boltzmann equation within the extended relaxation time approximation (ERTA), we derive second-order evolution equations for the dissipative charge current and calculate the associated transport coefficients. The sensitivity of transport coefficients to the particle momentum dependence of the collision time scale of the microscopic interactions in the hot QCD medium is analyzed. For a conformal charge-conserved system, we compare the ERTA-modified transport coefficients for particle diffusion with exact results derived from scalar field theory. With an appropriate parameterization of the relaxation time, we demonstrate the consistency of our analysis and assess the degree of agreement of the results with the exact solutions. The relaxation times for the shear and number diffusion modes are seen to be distinct in general when the momentum dependence of the relaxation time is taken into consideration., Comment: 14 pages, revtex4-2, 1 figure, 1 table

We present an optical circuit switch design for programmable integrated photonics (PIPs). Our solution finds the correct and optimal set of matchings that provides all-to-all network connectivity and demonstrates scalability to 32 ports.

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

We present a consistent continuum framework and a variable-coefficient acoustofluidic solver to analyze acoustic streaming. A perturbation approach is used to split the compressible Navier-Stokes equations into two sub-systems: a first-order harmonic system and a time-averaged second-order mean system. Prior acoustofluidic numerical studies have typically employed simplifying assumptions regarding the second-order mass balance equation and boundary conditions. These assumptions -- frequently left unjustified -- result in an incongruent problem statement where the boundary conditions are not consistent with the governing equations. To clarify these assumptions and mitigate the associated confusion, we systematically formulate the second-order mass balance equation into two analytically equivalent but numerically distinct forms by introducing the fluid's Lagrangian and mass transport velocity. We clarify the relation between these quantities to illustrate that a zero mass transport velocity does not necessarily imply a zero Lagrangian velocity. A second-order accurate finite difference/volume scheme is used to discretize the governing equations and boundary conditions expressed in Eulerian form. We solve the first-order coupled Helmholtz system via a sparse direct solver while the second-order system is solved via a Krylov (FGMRES) solver with a projection method-based preconditioner. To ascertain the spatial accuracy and consistency of numerical implementation of the split system of equations and boundary conditions, we demonstrate verification results and convergence rates using manufactured solutions. We provide benchmark test cases to demonstrate the impact of the choice of boundary conditions on second-order flow field. Lastly, we examine a system with spatially varying density to illustrate the difference in flow fields between constant and variable density systems.

We consider multi-agent systems with general information networks where an agent may only observe a subset of other agents. A system designer assigns local utility functions to the agents guiding their actions towards an outcome which determines the value of a given system objective. The aim is to design these local utility functions such that the Price of Anarchy (PoA), which equals the ratio of system objective at worst possible outcome to that at the optimal, is maximized. Towards this, we first develop a linear program (LP) that characterizes the PoA for any utility design and any information network. This leads to another LP that optimizes the PoA and derives the optimal utility design. Our work substantially generalizes existing approaches to the utility design problem. We also numerically show the robustness of proposed framework against unanticipated communication failures.

We report the discovery of a novel gapless quantum spin liquid in the S=1 honeycomb system Cu3LiRu2O6 with Ru4+ (4d4) where moments remain dynamic down to 50 mK. Heat capacity measurements show no sign of magnetic ordering down to 60 mK in spite of a Curie-Weiss temperature = -222 K indicating a strong antiferromagnetic interaction. In zero field, magnetic heat capacity shows a linear T-dependence with Sommerfeld coefficient = 107 mJ/mol K2 is much larger than that found in typical Fermi liquids. Our local probe 7Li nuclear magnetic resonance (NMR) measurements find a significant temperature-independent 7Li NMR shift (and hence a non-zero spin susceptibility) at low-T and a linear T-variation of the 7Li NMR spin-lattice relaxation rate 1/T1 at low-T reminiscent of fermionic excitations. Muon spin relaxation measurements detect neither long-range ordering nor spin freezing down to 50 mK and the temperature variation of the muon depolarization rate shows a gradual increase with decreasing temperature and a leveling off below about 1 K evincing a persistent spin dynamics common to several spin liquid candidates. Our results provide strong signatures of a quantum spin liquid in the titled honeycomb material., Comment: 5 pages and 6 figures in main, 8 pages and 16 figures in the supplement

In this work, we focus on Weakly Supervised Spatio-Temporal Video Grounding (WSTVG). It is a multimodal task aimed at localizing specific subjects spatio-temporally based on textual queries without bounding box supervision. Motivated by recent advancements in multi-modal foundation models for grounding tasks, we first explore the potential of state-of-the-art object detection models for WSTVG. Despite their robust zero-shot capabilities, our adaptation reveals significant limitations, including inconsistent temporal predictions, inadequate understanding of complex queries, and challenges in adapting to difficult scenarios. We propose CoSPaL (Contextual Self-Paced Learning), a novel approach which is designed to overcome these limitations. CoSPaL integrates three core components: (1) Tubelet Phrase Grounding (TPG), which introduces spatio-temporal prediction by linking textual queries to tubelets; (2) Contextual Referral Grounding (CRG), which improves comprehension of complex queries by extracting contextual information to refine object identification over time; and (3) Self-Paced Scene Understanding (SPS), a training paradigm that progressively increases task difficulty, enabling the model to adapt to complex scenarios by transitioning from coarse to fine-grained understanding., Comment: ICLR'25 Main Conference. Project Page: https://akash2907.github.io/cospal_webpage