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The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times, and difficulty selecting appropriate CPs. To address these challenges, we propose a novel end-to-end framework called Stable Matching EV Charging Assignment (SMEVCA) that efficiently assigns charge-seeking EVs to CPs with assistance from roadside units (RSUs). The proposed framework operates within a subscription-based model, ensuring that the subscribed EVs complete their charging within a predefined time limit enforced by a service level agreement (SLA). The framework SMEVCA employs a stable, fast, and efficient EV-CP assignment formulated as a one-to-many matching game with preferences. The matching process identifies the preferred coalition (a subset of EVs assigned to the CPs) using two strategies: (1) Preferred Coalition Greedy (PCG) that offers an efficient, locally optimal heuristic solution and (2) Preferred Coalition Dynamic (PCD) that is more computation-intensive but delivers a globally optimal coalition. Extensive simulations reveal that PCG and PCD achieve a gain of 14.6% and 20.8% over random elimination for in-network charge transferred with only 3% and 0.1% EVs unserved within the RSUs vicinity., Comment: This paper has been accepted for presentation at the 26th International Conference on Distributed Computing and Networking (ICDCN), 2025

We introduce the Coarse Payoff-Assessment Learning (CPAL) model, which captures reinforcement learning by boundedly rational decision-makers who focus on the aggregate outcomes of choosing among exogenously defined clusters of alternatives (similarity classes), rather than evaluating each alternative individually. Analyzing a smooth approximation of the model, we show that the learning dynamics exhibit steady-states corresponding to smooth Valuation Equilibria (Jehiel and Samet, 2007). We demonstrate the existence of multiple equilibria in decision trees with generic payoffs and establish the local asymptotic stability of pure equilibria when they occur. Conversely, when trivial choices featuring alternatives within the same similarity class yield sufficiently high payoffs, a unique mixed equilibrium emerges, characterized by indifferences between similarity classes, even under acute sensitivity to payoff differences. Finally, we prove that this unique mixed equilibrium is globally asymptotically stable under the CPAL dynamics., Comment: 31 pages + Appendix

The topological Hall effect (THE) is the result of spin-asymmetric deflection of charge carriers flowing through a non-collinear spin system. Effective manipulation of the topological Hall conductivity (THC) in skyrmions is currently a vigorous area of research with an eye towards potential spintronics application. Here, we show that the band topology and the THC in a skyrmion crystal can be tuned by changing the strength of the Rashba spin-orbit coupling (SOC), which can be accomplished via a perpendicular electric field. This results in the change of the subband Chern numbers and a transition between ordinary insulator and Chern insulator as the Rashba SOC is varied. For partially filled subbands, the Rashba SOC can tune the THC and reverse its sign, so that the direction of the Hall current is flipped. The critical Rashba strength for this depends on the skyrmion type and the carrier density. We extend our analysis to the cases of Dresselhaus and Weyl SOC as well, and show that they can be directly mapped to the Rashba SOC case and therefore lead to similar results. Our work suggests new avenues for the control of charge transport in skyrmion crystals., Comment: 17 pages, 12 figures, 1 table

Recent advancements in QA with LLM, like GPT-4, have shown limitations in handling complex multi-hop queries. We propose AT-RAG, a novel multistep RAG incorporating topic modeling for efficient document retrieval and reasoning. Using BERTopic, our model dynamically assigns topics to queries, improving retrieval accuracy and efficiency. We evaluated AT-RAG on multihop benchmark datasets QA and a medical case study QA. Results show significant improvements in correctness, completeness, and relevance compared to existing methods. AT-RAG reduces retrieval time while maintaining high precision, making it suitable for general tasks QA and complex domain-specific challenges such as medical QA. The integration of topic filtering and iterative reasoning enables our model to handle intricate queries efficiently, which makes it suitable for applications that require nuanced information retrieval and decision-making.

We demonstrate the formation of orbital and spin Hall effects (OHE/SHE) in the 1T phase of non-magnetic transition metal dichalcogenides. With the aid of density functional theory calculations and model Hamiltonian studies on MX$_2$ (M = Pt, Pd and X = S, Se, and Te), we show an intrinsic orbital Hall conductivity ($\sim 10^3 \hbar /e\ \Omega^{-1}cm^{-1}$) , which primarily emerges due to the orbital texture around the valleys in the momentum space. The robust spin-orbit coupling in these systems induces a sizable SHE out of OHE. Furthermore, to resemble the typical experimental setups, where the magnetic overlayers produce a proximity magnetic field, we examine the effect of magnetic field on OHE and SHE and showed that the latter can be doubled in these class of compounds. With a giant OHE and tunable SHE, the 1T-TMDs are promising candidates for spin and orbital driven quantum devices such as SOT-MRAM, spin nano-oscillators, spin logic devices etc., and to carry out spin-charge conversion experiments for fundamental research.

Self-Supervised Learning (SSL) has shown great promise in learning representations from unlabeled data. The power of learning representations without the need for human annotations has made SSL a widely used technique in real-world problems. However, SSL methods have recently been shown to be vulnerable to backdoor attacks, where the learned model can be exploited by adversaries to manipulate the learned representations, either through tampering the training data distribution, or via modifying the model itself. This work aims to address defending against backdoor attacks in SSL, where the adversary has access to a realistic fraction of the SSL training data, and no access to the model. We use novel methods that are computationally efficient as well as generalizable across different problem settings. We also investigate the adversarial robustness of SSL models when trained with our method, and show insights into increased robustness in SSL via frequency domain augmentations. We demonstrate the effectiveness of our method on a variety of SSL benchmarks, and show that our method is able to mitigate backdoor attacks while maintaining high performance on downstream tasks. Code for our work is available at github.com/Aryan-Satpathy/Backdoor, Comment: 8 pages, 2 figures

Carbonaceous chondrites are among the most important meteorite types and have played a vital role in deciphering the origin and evolution of our solar system. They have been linked to low-albedo C-type asteroids, but due to subdued absorption bands, definitive asteroid-meteorite linkages remain elusive. A majority of these existing linkages rely on fine-grained (typically < 45 micron) powders across a limited wavelength range in the visible to near-infrared (0.35-2.5 microns). While this is useful in interpreting the fine-grained regolith of larger main-belt objects like Ceres, recent spacecraft missions to smaller near-Earth asteroids (NEAs), such as Bennu and Ryugu, have shown that their surfaces are dominated by larger grain size material. To better interpret the surfaces of these smaller, carbonaceous NEAs, we obtained laboratory reflectance spectra of seven carbonaceous chondrite meteorite groups (CI, CM, CO, CV, CR, CK, C2-ungrouped) over the ultraviolet to mid-infrared range (0.2-14 microns). Each meteorite contained five grain size bins (45-1000 microns) to help constrain spectral grain size effects. We find a correlation between grain size and absolute reflectance, spectral slope, band depth, and the Christiansen feature band center. Principal component analysis of grain size variation illustrates a similar trend to lunar-style space weathering. We also show that the Bus-DeMeo asteroid taxonomic classification of our samples is affected by grain size, specifically shifting CM2 Aguas Zarcas from a Ch-type to B-type with increasing grain size. This has implications for the parent body of the OSIRIS-REx target, Bennu. With Aguas Zarcas, we present results from Hapke modeling., Comment: 40 pages, 15 figures, published in the Planetary Science Journal

Heterogeneity in the cell population of cancer tissues poses many challenges in cancer diagnosis and treatment. Studying the heterogeneity in cell populations from gene expression measurement data in the context of cancer research is a problem of paramount importance. In addition, reducing the computation time of the algorithms that deal with high volumes of data has its obvious merits. Parallelizable models using Markov chain Monte Carlo methods are typically slow. This paper shows a novel, computationally efficient, and parallelizable model to analyze heterogeneity in cancer tissues using GPUs. Because our model is parallelizable, the input data size does not affect the computation time much, provided the hardware resources are not exhausted. Our model uses qPCR (quantitative polymerase chain reaction) gene expression measurements to study heterogeneity in cancer tissue. We compute the cell proportion breakup by accelerating variational methods on a GPU. We test this model on synthetic and real-world gene expression data collected from fibroblasts and compare the performance of our algorithm with those of MCMC and Expectation Maximization. Our new model is computationally less complex and faster than existing Bayesian models for cancer tissue heterogeneity.

Unmanned Aerial Vehicles (UAVs) have become pivotal in domains spanning military, agriculture, surveillance, and logistics, revolutionizing data collection and environmental interaction. With the advancement in drone technology, there is a compelling need to develop a holistic methodology for designing UAVs. This research focuses on establishing a procedure encompassing conceptual design, use of composite materials, weight optimization, stability analysis, avionics integration, advanced manufacturing, and incorporation of autonomous payload delivery through object detection models tailored to satisfy specific applications while maintaining cost efficiency. The study conducts a comparative assessment of potential composite materials and various quadcopter frame configurations. The novel features include a payload-dropping mechanism, a unibody arm fixture, and the utilization of carbon-fibre-balsa composites. A quadcopter is designed and analyzed using the proposed methodology, followed by its fabrication using additive manufacturing and vacuum bagging techniques. A computer vision-based deep learning model enables precise delivery of payloads by autonomously detecting targets.

The Near-Earth Object (NEO) Surveyor mission is a NASA observatory designed to discover and characterize near-Earth asteroids and comets. The mission's primary objective is to find the majority of objects large enough to cause severe regional impact damage ($>$140 m in effective spherical diameter) within its five-year baseline survey. Operating at the Sun-Earth L1 Lagrange point, the mission will survey to within 45 degrees of the Sun in an effort to find the objects in the most Earth-like orbits. The survey cadence is optimized to provide observational arcs long enough to reliably distinguish near-Earth objects from more distant small bodies that cannot pose an impact hazard. Over the course of its survey, NEO Surveyor will discover $\sim$200,000 - 300,000 new NEOs down to sizes as small as $\sim$10 m and thousands of comets, significantly improving our understanding of the probability of an Earth impact over the next century., Comment: accepted to PSJ

Multi-label text classification (MLTC) tasks in the medical domain often face the long-tail label distribution problem. Prior works have explored hierarchical label structures to find relevant information for few-shot classes, but mostly neglected to incorporate external knowledge from medical guidelines. This paper presents DKEC, Domain Knowledge Enhanced Classification for diagnosis prediction with two innovations: (1) automated construction of heterogeneous knowledge graphs from external sources to capture semantic relations among diverse medical entities, (2) incorporating the heterogeneous knowledge graphs in few-shot classification using a label-wise attention mechanism. We construct DKEC using three online medical knowledge sources and evaluate it on a real-world Emergency Medical Services (EMS) dataset and a public electronic health record (EHR) dataset. Results show that DKEC outperforms the state-of-the-art label-wise attention networks and transformer models of different sizes, particularly for the few-shot classes. More importantly, it helps the smaller language models achieve comparable performance to large language models.

Recent discovery of superconductivity at a transition temperature of $73$K in the doped layered compound Ba$_{2}$CuO$_{3+x}$ for $x\sim 0.2$ has generated a lot of interest. Experiments in this alternately stacked oxygen octahedral and chain layered structure reveal that a compression of the octahedra causes the Cu- {$d_{z^{2}}$} orbital to lie above the Cu- {$d_{x^2-y^2}$} orbital unlike in the well-known cuprate superconducting materials. Our first-principle calculations and low-energy Hamiltonian studies on the $x$ = 0.25 system reveal that this energy ordering results in the formation of $d_{z^2}$ dominated electron pockets. The strong nesting in the Fermi pockets leads to an AFM spin fluctuation mediated $d_{xy}$ wave superconducting state dominated by pairing among the $d_{z^{2}}$ orbitals. This is in contrast to the cuprate superconductors (e.g., YBCO) where both electron and hole pockets exist and the superconducting state with B$_{1g}$ symmetry is formed by the $d_{x^2-y^2}$ orbital electrons. Unlike the earlier reports, we find that inter-layer hybridization has an important contribution to the low-energy band structure and formation of the unconventional superconducting state., Comment: 13 pages, 12 figures

Probing small main-belt asteroids provides insight into their formation and evolution through multiple dynamical and collisional processes. These asteroids also overlap in size with the potentially hazardous near-earth object population and supply the majority of these objects. The Lucy mission will provide an opportunity for study of a small main-belt asteroid, (152830) Dinkinesh. The spacecraft will perform a flyby of this object on November 1, 2023, in preparation for its mission to the Jupiter Trojan asteroids. We employed aperture photometry on stacked frames of Dinkinesh obtained by the Wide-field-Infrared Survey Explorer and performed thermal modeling on a detection at 12 $\mu$m to compute diameter and albedo values. Through this method, we determined Dinkinesh has an effective spherical diameter of $0.76^{+0.11}_{-0.21}$ km and a visual geometric albedo of $0.27^{+0.25}_{-0.06}$ at the 16th and 84th percentiles. This albedo is consistent with typical stony (S-type) asteroids., Comment: Submitted to Astrophysical Journal Letters