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We study the fundamental problem of calibrating a linear binary classifier of the form $\sigma(\hat{w}^\top x)$, where the feature vector $x$ is Gaussian, $\sigma$ is a link function, and $\hat{w}$ is an estimator of the true linear weight $w^\star$. By interpolating with a noninformative $\textit{chance classifier}$, we construct a well-calibrated predictor whose interpolation weight depends on the angle $\angle(\hat{w}, w_\star)$ between the estimator $\hat{w}$ and the true linear weight $w_\star$. We establish that this angular calibration approach is provably well-calibrated in a high-dimensional regime where the number of samples and features both diverge, at a comparable rate. The angle $\angle(\hat{w}, w_\star)$ can be consistently estimated. Furthermore, the resulting predictor is uniquely $\textit{Bregman-optimal}$, minimizing the Bregman divergence to the true label distribution within a suitable class of calibrated predictors. Our work is the first to provide a calibration strategy that satisfies both calibration and optimality properties provably in high dimensions. Additionally, we identify conditions under which a classical Platt-scaling predictor converges to our Bregman-optimal calibrated solution. Thus, Platt-scaling also inherits these desirable properties provably in high dimensions.

In this study, we analyzed the optical, thermodynamic and electronic properties of 2D MoGe2P4 from the first principle calculation. 2D MoGe2P4 demonstrates superior optical absorption in the NIR-I biological window (750 nm ~ 1000 nm) with a peak near 808 nm and excellent thermal conductivity (63 Wm-1K-1). Finite-difference time-domain (FDTD) simulations and Heat simulations demonstrate that 2D MoGe2P4 possesses efficient photothermal conversion under low laser power (0.5 W/cm2) which is operated in 808nm. Theoretical investigation demonstrates, rapid temperature elevation ({\Delta}T = 24.8 {\deg}C) of the 2D MoGe2P4 within two minutes and photothermal stability over multiple laser cycles, achieving temperatures suitable for effective photothermal therapeutic applications. Photothermal therapy (PTT) is an emerging tumor treatment technique that utilizes photothermal agents (PTAs) to convert near-infrared (NIR) light into localized heat for tumor ablation. To enhance biocompatibility, we analyzed the PEGylation of 2D MoGe2P4 nanosheets through molecular dynamics simulation. PEGylation at human body temperature was stable which signifies 2D MoGe2P4's prospect in therapeutic applications. This research highlights the potential of 2D MoGe2P4 as an emerging material for PTA, establishing a foundation for experimental and clinical trials.

The use of monoids in the study of word languages recognized by finite-state automata has been quite fruitful. In this work, we look at the same idea of "recognizability by finite monoids" for other monoids. In particular, we attempt to characterize recognizable subsets of various additive and multiplicative monoids over integers, rationals, reals, and complex numbers. While these recognizable sets satisfy properties such as closure under Boolean operations and inverse morphisms, they do not enjoy many of the nice properties that recognizable word languages do., Comment: 12 pages, to be published in ICLA 2025

Aspect-based sentiment analysis (ASBA) is a refined approach to sentiment analysis that aims to extract and classify sentiments based on specific aspects or features of a product, service, or entity. Unlike traditional sentiment analysis, which assigns a general sentiment score to entire reviews or texts, ABSA focuses on breaking down the text into individual components or aspects (e.g., quality, price, service) and evaluating the sentiment towards each. This allows for a more granular level of understanding of customer opinions, enabling businesses to pinpoint specific areas of strength and improvement. The process involves several key steps, including aspect extraction, sentiment classification, and aspect-level sentiment aggregation for a review paragraph or any other form that the users have provided. ABSA has significant applications in areas such as product reviews, social media monitoring, customer feedback analysis, and market research. By leveraging techniques from natural language processing (NLP) and machine learning, ABSA facilitates the extraction of valuable insights, enabling companies to make data-driven decisions that enhance customer satisfaction and optimize offerings. As ABSA evolves, it holds the potential to greatly improve personalized customer experiences by providing a deeper understanding of sentiment across various product aspects. In this work, we have analyzed the strength of LLMs for a complete cross-domain aspect-based sentiment analysis with the aim of defining the framework for certain products and using it for other similar situations. We argue that it is possible to that at an effectiveness of 92\% accuracy for the Aspect Based Sentiment Analysis dataset of SemEval-2015 Task 12.

Hopf insulators represent an exceptional class of topological matter unanticipated by the periodic table of topological invariants. These systems point to the existence of previously unexplored states of matter with unconventional topology. In this work, we take a step toward exploring this direction by investigating correlation-driven instabilities of Hopf insulators. Organizing our analysis around the topological quantum critical point that separates the Hopf insulating phase from a trivial insulator, we demonstrate the emergence of unconventional Weyl semimetallic and topological insulating states. Notably, the Weyl semimetal supports non-reciprocal superconductivity and a Bogoliubov-Fermi surface, potentially providing a novel framework for realizing the superconducting diode effect. Finally, we highlight the interconnectedness of the effective descriptions of correlated Hopf insulators, two-dimensional quadratic band-touching semimetals, and Luttinger semimetals., Comment: 13 pages, 11 figures

With the recent realization that there likely are stably-stratified regions in the interiors of both Jupiter and Saturn, we construct new non-adiabatic, inhomogeneous evolutionary models with the same microphysics for each that result at the present time in respectable fits for all major bulk observables for both planets. These include the effective temperature, radius, atmospheric heavy-element and helium abundances (including helium rain), and the lower-order gravity moments J2 and J4. The models preserve from birth most of an extended "fuzzy" heavy-element core. Our predicted atmospheric helium mass fraction for Saturn is ~0.2, close to some measured estimates, but in disagreement with some published predictions. To preserve a fuzzy core from birth, the interiors of both planets must start out at lower entropies than would be used for traditional "hot start" adiabatic models, though the initial exterior mantle entropies can range from hot to warm start values. We do not see a helium ocean in Saturn's interior, and both models have inner envelopes with significant Brunt-Vaisala frequencies; this region for Saturn at the current epoch is more extended and in it, the Brunt is larger. The total heavy-element mass fraction in Jupiter and in Saturn is determined to be ~14% and ~26%, respectively, though there is some play in these determinations., Comment: 13 pages, 4 figures, 1 table, Accepted for publication in The Astrophysical Journal Letters

Sarcasm detection is a significant challenge in sentiment analysis due to the nuanced and context-dependent nature of verbiage. We introduce Pragmatic Metacognitive Prompting (PMP) to improve the performance of Large Language Models (LLMs) in sarcasm detection, which leverages principles from pragmatics and reflection helping LLMs interpret implied meanings, consider contextual cues, and reflect on discrepancies to identify sarcasm. Using state-of-the-art LLMs such as LLaMA-3-8B, GPT-4o, and Claude 3.5 Sonnet, PMP achieves state-of-the-art performance on GPT-4o on MUStARD and SemEval2018. This study demonstrates that integrating pragmatic reasoning and metacognitive strategies into prompting significantly enhances LLMs' ability to detect sarcasm, offering a promising direction for future research in sentiment analysis., Comment: Accepted at COLING 2024, CHum Workshop

We present a classical theory of gravity, which is singularity free at short distances and reduces to General Relativity at large distances. We discuss its implications., Comment: Figure and references added, typos corrected. Accepted for publication in Physical Review D

In topological materials, Berry curvature leads to intrinsic Hall responses. Focusing on time-reversal symmetric systems with broken inversion symmetry, a spontaneoous (zero magnetic field) Hall effect is expected to develop under an applied electric field. Motivated by recent developments in Weyl-Kondo semimetals, here we advance a fully nonequilibrium (FNE) Hall response due to the Berry curvature. In particular, we show that, while the spontaneous Hall current is quadratic in the previously described regime of weak electric field, due to the contribution from the dipole moment of the Berry curvature, the FNE Hall response for non-perturbative electric fields is not controlled by the Berry curvature dipole. Remarkably, the FNE Hall response resembles what happens in systems that break the microscopic time-reversal symmetry. We illustrate the universality of these results by comparing them with their counterparts in systems with any higher-multipole of the Berry curvature. The implications of our results for the understanding of strongly correlated topological semimetals are discussed., Comment: 6 + 8 pages, 3 + 4 figures. v2) fixed equation numbering in the appendix; some clarifications added to main text

Rare-earth magnets with parent cubic symmetry exhibit unique topological properties. However, the origin of these behaviors remains presently unclear. Here, we develop minimal models for Dirac semimetals (DSMs) with accidental band crossings and higher-order topology in cubic systems, incorporating candidate magnetic order to analyze bulk, surface, and hinge state characteristics. In certain cubic-symmetric DSMs, we identify an effective Z2 chiral symmetry which significantly impacts surface and hinge-localized states. Our results highlight distinct features in surface state dispersions, Fermi arcs, polarization dependence, and band splitting that correlate with photoemission data in rare-earth monopnictides. We also suggest candidate materials and experimental tests for further validation. These findings advance our understanding of surface states in rare-earth magnets with parent cubic symmetries and illuminate the role of DSM physics in these systems., Comment: 13 pages, 7 figures

We explore the distribution in space and time of a single-photon excitation shared by a network of dipole-dipole interacting atoms that are also coupled to a common photonic field mode. Time-averaged distributions reveal partial trapping of the excitation near the initially excited atom. This trapping is associated with resonances of the excitation at crossing points of the photon-dressed energy eigenvalues of the network. The predicted photon-induced many-atom trapped excitation (PIMATE) is sensitive to atomic position disorder which broadens the excitation resonances and transforms them to avoided crossings. PIMATE is shown to allow highly effective and accurate sensing of multi-atom networks and their disorder.

We show that a slowly varying Newton's constant, consistent with existing bounds, can potentially explain a host of observations pertaining to gravitational effects or phenomena across distances spanning from planetary to the cosmological, relying neither on the existence of Dark Matter or (and) Dark Energy, nor on any expected high proportions of either of them in the Universe. It may also have implications at very short distances or quantum gravity scales., Comment: 22 pages, 2 figures, 1 table; to appear in Eur. Phys. J. Plus

The integration of unmanned platforms equipped with advanced sensors promises to enhance situational awareness and mitigate the "fog of war" in military operations. However, managing the vast influx of data from these platforms poses a significant challenge for Command and Control (C2) systems. This study presents a novel multi-agent learning framework to address this challenge. Our method enables autonomous and secure communication between agents and humans, which in turn enables real-time formation of an interpretable Common Operational Picture (COP). Each agent encodes its perceptions and actions into compact vectors, which are then transmitted, received and decoded to form a COP encompassing the current state of all agents (friendly and enemy) on the battlefield. Using Deep Reinforcement Learning (DRL), we jointly train COP models and agent's action selection policies. We demonstrate resilience to degraded conditions such as denied GPS and disrupted communications. Experimental validation is performed in the Starcraft-2 simulation environment to evaluate the precision of the COPs and robustness of policies. We report less than 5% error in COPs and policies resilient to various adversarial conditions. In summary, our contributions include a method for autonomous COP formation, increased resilience through distributed prediction, and joint training of COP models and multi-agent RL policies. This research advances adaptive and resilient C2, facilitating effective control of heterogeneous unmanned platforms., Comment: 29th International Command and Control Research & Technology Symposium

We address the issue of which broad set of initial conditions for the planet Jupiter best matches the current presence of a ``fuzzy core" of heavy elements, while at the same time comporting with measured parameters such as its effective temperature, atmospheric helium abundance, radius, and atmospheric metallicity. Our focus is on the class of fuzzy cores that can survive convective mixing to the present day and on the unique challenges of an inhomogeneous Jupiter with stably-stratified regions now demanded by the \textit{Juno} gravity data. Hence, using the new code \texttt{APPLE}, we attempt to put a non-adiabatic Jupiter into an evolutionary context. This requires not only a mass density model, the major relevant byproduct of the \textit{Juno} data, but a thermal model that is subject to interior heat transport, a realistic atmospheric flux boundary, a helium rain algorithm, and the latest equation of state. The result is a good fit to most major thermal, compositional, and structural constraints that still preserve a fuzzy core and that should inform future more detailed models of the current Jupiter in the context of its evolution from birth., Comment: 23 pages, 11 figures, 3 tables, accepted to ApJ

Recently, superconductivity has been observed in twisted WSe$_2$ moir\'{e} structures (Xia et al., arXiv:2405.14784; Guo et al., arXiv:2406.03418). Its transition temperature is high, reaching a few percent of the Fermi temperature scale. Here, we advance a mechanism for superconductivity based on the notion that electronic topology enables quantum fluctuations in a suitable regime of intermediate correlations. In this regime, the Coulomb interaction requires that an active topological flat band and nearby wider bands are considered together. Compact molecular orbitals arise, which experience quantum fluctuations through topology-dictated hybridization with the other molecular orbitals. The hybridization competes with the active flat band' natural tendency towards static electronic ordering, thereby weakening the latter; we link this effect with certain salient observations by experiments. Furthermore, the competition yields a quantum critical regime where quasiparticles are lost. The corresponding quantum critical fluctuations drive superconductivity. Broader implications and new connections among correlated materials platforms are discussed., Comment: 8 pages, 5 figures

Recently, transformers have captured significant interest in the area of single-image super-resolution tasks, demonstrating substantial gains in performance. Current models heavily depend on the network's extensive ability to extract high-level semantic details from images while overlooking the effective utilization of multi-scale image details and intermediate information within the network. Furthermore, it has been observed that high-frequency areas in images present significant complexity for super-resolution compared to low-frequency areas. This work proposes a transformer-based super-resolution architecture called ML-CrAIST that addresses this gap by utilizing low-high frequency information in multiple scales. Unlike most of the previous work (either spatial or channel), we operate spatial and channel self-attention, which concurrently model pixel interaction from both spatial and channel dimensions, exploiting the inherent correlations across spatial and channel axis. Further, we devise a cross-attention block for super-resolution, which explores the correlations between low and high-frequency information. Quantitative and qualitative assessments indicate that our proposed ML-CrAIST surpasses state-of-the-art super-resolution methods (e.g., 0.15 dB gain @Manga109 $\times$4). Code is available on: https://github.com/Alik033/ML-CrAIST.

Underwater imagery is often compromised by factors such as color distortion and low contrast, posing challenges for high-level vision tasks. Recent underwater image restoration (UIR) methods either analyze the input image at full resolution, resulting in spatial richness but contextual weakness, or progressively from high to low resolution, yielding reliable semantic information but reduced spatial accuracy. Here, we propose a lightweight multi-stage network called Lit-Net that focuses on multi-resolution and multi-scale image analysis for restoring underwater images while retaining original resolution during the first stage, refining features in the second, and focusing on reconstruction in the final stage. Our novel encoder block utilizes parallel $1\times1$ convolution layers to capture local information and speed up operations. Further, we incorporate a modified weighted color channel-specific $l_1$ loss ($cl_1$) function to recover color and detail information. Extensive experimentations on publicly available datasets suggest our model's superiority over recent state-of-the-art methods, with significant improvement in qualitative and quantitative measures, such as $29.477$ dB PSNR ($1.92\%$ improvement) and $0.851$ SSIM ($2.87\%$ improvement) on the EUVP dataset. The contributions of Lit-Net offer a more robust approach to underwater image enhancement and super-resolution, which is of considerable importance for underwater autonomous vehicles and surveillance. The code is available at: https://github.com/Alik033/Lit-Net.

In most of charge density wave (CDW) systems of different material classes, ranging from traditional correlated systems in low-dimension to recent topological systems with Kagome lattice, superconductivity emerges when the system is driven toward the quantum critical point (QCP) of CDW via external parameters of doping and pressure. Despite this rather universal trend, the essential hinge between CDW and superconductivity has not been established yet. Here, the evidence of coupling between electron and CDW fluctuation is reported, based on a temperature- and intercalation-dependent kink in the angle-resolved photoemission spectra of 2H-PdxTaSe2. Kinks are observed only when the system is in the CDW phase, regardless of whether a long- or short-range order is established. Notably, the coupling strength is enhanced upon long-range CDW suppression, albeit the coupling energy scale is reduced. Interestingly, estimation of the superconducting critical temperature by incorporating the observed coupling characteristics into McMillan's equation yields result closely resembling the known values of the superconducting dome. Our results thus highlight a compelling possibility that this new coupling mediates Cooper pairs, which provides new insights on the competing relationship not only for CDW, but also for other competing orders., Comment: 20 pages, 4 figures for the main text. To be published in Advanced Science

The morphology and the characteristic scale of polarized structures provide crucial insights into the mechanisms that drive turbulence and maintain magnetic fields in magneto-ionic plasma. We aim to establish the efficacy of Minkowski functionals as quantitative statistical probes of filamentary morphology of polarized synchrotron emission resulting from fluctuation dynamo action. Using synthetic observations generated from magnetohydrodynamic simulations of fluctuation dynamos with varying driving scales ($\ell_{\rm f}$) of turbulence in isothermal, incompressible, and subsonic media, we study the relation between different morphological measures and their connection to fractional polarization ($p_{\rm f}$). We find that Faraday depolarization at low frequencies gives rise to small-scale polarized structures that have higher filamentarity as compared to the intrinsic structures that are comparable to $\ell_{\rm f}$. Above $\sim3\,{\rm GHz}$, the number of connected polarized structures per unit area ($N_{\rm CC, peak}$) is related to the mean $p_{\rm f}$ ($\langle p_{\rm f} \rangle$) of the emitting region as $\langle p_{\rm f}\rangle \propto N_{\rm CC, peak}^{-1/4}$, provided the scale of the detectable emitting region is larger than $\ell_{\rm f}$. This implies that $N_{\rm CC,peak}$ represents the number of turbulent cells projected on the plane of the sky and can be directly used to infer $\ell_{\rm f}$ via the relation $\ell_{\rm f} \propto N_{\rm CC,peak}^{-1/2}$. An estimate of $\ell_{\rm f}$ thus directly allows for pinning down the turbulence-driving mechanism in astrophysical systems. While the simulated conditions are mostly prevalent in the intracluster medium of galaxy clusters, the qualitative morphological features are also applicable in the context of interstellar medium in galaxies., Comment: ApJ (in press)

Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of a metallic materials. The analysis of EIS hinges on the selection of an appropriate equivalent circuit model (ECM) that accurately characterizes the system under study. In this work, we systematically examined the applicability of three commonly used ECMs across several typical material degradation scenarios. By applying Bayesian Inference to simulated corrosion EIS data, we assessed the suitability of these ECMs under different corrosion conditions and identified regions where the EIS data lacks sufficient information to statistically substantiate the ECM structure. Additionally, we posit that the traditional approach to EIS analysis, which often requires measurements to very low frequencies, might not be always necessary to correctly model the appropriate ECM. Our study assesses the impact of omitting data from low to medium-frequency ranges on inference results and reveals that a significant portion of low-frequency measurements can be excluded without substantially compromising the accuracy of extracting system parameters. Further, we propose simple checks to the posterior distributions of the ECM components and posterior predictions, which can be used to quantitatively evaluate the suitability of a particular ECM and the minimum frequency required to be measured. This framework points to a pathway for expediting EIS acquisition by intelligently reducing low-frequency data collection and permitting on-the-fly EIS measurements

Hand gesture recognition allows humans to interact with machines non-verbally, which has a huge application in underwater exploration using autonomous underwater vehicles. Recently, a new gesture-based language called CADDIAN has been devised for divers, and supervised learning methods have been applied to recognize the gestures with high accuracy. However, such methods fail when they encounter unseen gestures in real time. In this work, we advocate the need for zero-shot underwater gesture recognition (ZSUGR), where the objective is to train a model with visual samples of gestures from a few ``seen'' classes only and transfer the gained knowledge at test time to recognize semantically-similar unseen gesture classes as well. After discussing the problem and dataset-specific challenges, we propose new seen-unseen splits for gesture classes in CADDY dataset. Then, we present a two-stage framework, where a novel transformer learns strong visual gesture cues and feeds them to a conditional generative adversarial network that learns to mimic feature distribution. We use the trained generator as a feature synthesizer for unseen classes, enabling zero-shot learning. Extensive experiments demonstrate that our method outperforms the existing zero-shot techniques. We conclude by providing useful insights into our framework and suggesting directions for future research., Comment: Accepted to ICPR 2024. 15 pages, 6 figures. Project page: https://github.com/sandipan211/ZSUGR

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

Recent quantum technologies and quantum error-correcting codes emphasize the requirement for arranging interacting qubits in a nearest-neighbor (NN) configuration while mapping a quantum circuit onto a given hardware device, in order to avoid undesirable noise. It is equally important to minimize the wastage of qubits in a quantum hardware device with m qubits while running circuits of n qubits in total, with n < m. In order to prevent cross-talk between two circuits, a buffer distance between their layouts is needed. Furthermore, not all the qubits and all the two-qubit interactions are at the same noise-level. Scheduling multiple circuits on the same hardware may create a possibility that some circuits are executed on a noisier layout than the others. In this paper, we consider an optimization problem which schedules as many circuits as possible for execution in parallel on the hardware, while maintaining a pre-defined layout quality for each. An integer linear programming formulation to ensure maximum fidelity while preserving the nearest neighbor arrangement among interacting qubits is presented. Our assertion is supported by comprehensive investigations involving various well-known quantum circuit benchmarks. As this scheduling problem is shown to be NP Hard, we also propose a greedy heuristic method which provides 2x and 3x better utilization for 27-qubit and 127-qubit hardware devices respectively in terms of qubits and time., Comment: 18 pages, single column

This paper establishes the generalization error of pooled min-$\ell_2$-norm interpolation in transfer learning where data from diverse distributions are available. Min-norm interpolators emerge naturally as implicit regularized limits of modern machine learning algorithms. Previous work characterized their out-of-distribution risk when samples from the test distribution are unavailable during training. However, in many applications, a limited amount of test data may be available during training, yet properties of min-norm interpolation in this setting are not well-understood. We address this gap by characterizing the bias and variance of pooled min-$\ell_2$-norm interpolation under covariate and model shifts. The pooled interpolator captures both early fusion and a form of intermediate fusion. Our results have several implications: under model shift, for low signal-to-noise ratio (SNR), adding data always hurts. For higher SNR, transfer learning helps as long as the shift-to-signal (SSR) ratio lies below a threshold that we characterize explicitly. By consistently estimating these ratios, we provide a data-driven method to determine: (i) when the pooled interpolator outperforms the target-based interpolator, and (ii) the optimal number of target samples that minimizes the generalization error. Under covariate shift, if the source sample size is small relative to the dimension, heterogeneity between between domains improves the risk, and vice versa. We establish a novel anisotropic local law to achieve these characterizations, which may be of independent interest in random matrix theory. We supplement our theoretical characterizations with comprehensive simulations that demonstrate the finite-sample efficacy of our results., Comment: 53 pages, 2 figures

We report the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on germanium, measured at the Spallation Neutron Source at Oak Ridge National Laboratory. The Ge-Mini detector of the COHERENT collaboration employs large-mass, low-noise, high-purity germanium spectrometers, enabling excellent energy resolution, and an analysis threshold of 1.5 keV electron-equivalent ionization energy. We observe a on-beam excess of 20.6$_{+7.1}^{-6.3}$ counts with a total exposure of 10.22 GWhkg and we reject the no-CEvNS hypothesis with 3.9 sigma significance. The result agrees with the predicted standard model of particle physics signal rate within 2 sigma., Comment: 7 pages, 5 figures

Two key tasks in high-dimensional regularized regression are tuning the regularization strength for accurate predictions and estimating the out-of-sample risk. It is known that the standard approach -- $k$-fold cross-validation -- is inconsistent in modern high-dimensional settings. While leave-one-out and generalized cross-validation remain consistent in some high-dimensional cases, they become inconsistent when samples are dependent or contain heavy-tailed covariates. As a first step towards modeling structured sample dependence and heavy tails, we use right-rotationally invariant covariate distributions -- a crucial concept from compressed sensing. In the proportional asymptotics regime where the number of features and samples grow comparably, which is known to better reflect the empirical behavior in moderately sized datasets, we introduce a new framework, ROTI-GCV, for reliably performing cross-validation under these challenging conditions. Along the way, we propose new estimators for the signal-to-noise ratio and noise variance. We conduct experiments that demonstrate the accuracy of our approach in a variety of synthetic and semi-synthetic settings., Comment: 25 pages, 3 figures

Estimating heritability remains a significant challenge in statistical genetics. Diverse approaches have emerged over the years that are broadly categorized as either random effects or fixed effects heritability methods. In this work, we focus on the latter. We propose HEDE, an ensemble approach to estimate heritability or the signal-to-noise ratio in high-dimensional linear models where the sample size and the dimension grow proportionally. Our method ensembles post-processed versions of the debiased lasso and debiased ridge estimators, and incorporates a data-driven strategy for hyperparameter selection that significantly boosts estimation performance. We establish rigorous consistency guarantees that hold despite adaptive tuning. Extensive simulations demonstrate our method's superiority over existing state-of-the-art methods across various signal structures and genetic architectures, ranging from sparse to relatively dense and from evenly to unevenly distributed signals. Furthermore, we discuss the advantages of fixed effects heritability estimation compared to random effects estimation. Our theoretical guarantees hold for realistic genotype distributions observed in genetic studies, where genotypes typically take on discrete values and are often well-modeled by sub-Gaussian distributed random variables. We establish our theoretical results by deriving uniform bounds, built upon the convex Gaussian min-max theorem, and leveraging universality results. Finally, we showcase the efficacy of our approach in estimating height and BMI heritability using the UK Biobank., Comment: 58 pages, 7 figures

High-Level Synthesis (HLS) has transformed the development of complex Hardware IPs (HWIP) by offering abstraction and configurability through languages like SystemC/C++, particularly for Field Programmable Gate Array (FPGA) accelerators in high-performance and cloud computing contexts. These IPs can be synthesized for different FPGA boards in cloud, offering compact area requirements and enhanced flexibility. HLS enables designs to execute directly on ARM processors within modern FPGAs without the need for Register Transfer Level (RTL) synthesis, thereby conserving FPGA resources. While HLS offers flexibility and efficiency, it also introduces potential vulnerabilities such as the presence of hidden circuitry, including the possibility of hosting hardware trojans within designs. In cloud environments, these vulnerabilities pose significant security concerns such as leakage of sensitive data, IP functionality disruption and hardware damage, necessitating the development of robust testing frameworks. This research presents an advanced testing approach for HLS-developed cloud IPs, specifically targeting hidden malicious functionalities that may exist in rare conditions within the design. The proposed method leverages selective instrumentation, combining greybox fuzzing and concolic execution techniques to enhance test generation capabilities. Evaluation conducted on various HLS benchmarks, possessing characteristics of FPGA-based cloud IPs with embedded cloud related threats, demonstrates the effectiveness of our framework in detecting trojans and rare scenarios, showcasing improvements in coverage, time efficiency, memory usage, and testing costs compared to existing methods.

In medical imaging, segmentation and localization of spinal tumors in three-dimensional (3D) space pose significant computational challenges, primarily stemming from limited data availability. In response, this study introduces a novel data augmentation technique, aimed at automating spine tumor segmentation and localization through AI approaches. Leveraging a fusion of fuzzy c-means clustering and Random Forest algorithms, the proposed method achieves successful spine tumor segmentation based on predefined masks initially delineated by domain experts in medical imaging. Subsequently, a Convolutional Neural Network (CNN) architecture is employed for tumor classification. Moreover, 3D vertebral segmentation and labeling techniques are used to help pinpoint the exact location of the tumors in the lumbar spine. Results indicate a remarkable performance, with 99% accuracy for tumor segmentation, 98% accuracy for tumor classification, and 99% accuracy for tumor localization achieved with the proposed approach. These metrics surpass the efficacy of existing state-of-the-art techniques, as evidenced by superior Dice Score, Class Accuracy, and Intersection over Union (IOU) on class accuracy metrics. This innovative methodology holds promise for enhancing the diagnostic capabilities in detecting and characterizing spinal tumors, thereby facilitating more effective clinical decision-making., Comment: 9 pages, 12 figures

Quantum many-body systems serve as a suitable working medium for realizing quantum thermal machines (QTMs) by offering distinct advantages such as cooperative many-body effects, and performance boost at the quantum critical points. However, the bulk of the existing literature exploring the criticality of many-body systems in the context of QTMs involves models sans the electronic interactions, which are non-trivial to deal with and require sophisticated numerical techniques. Here we adopt the prototypical Hubbard model in two dimensions (2D) in the framework of the line graph Lieb-kagome lattice for the working medium of a multi-functional QTM. We resort to a non-perturbative, static path approximated (SPA) Monte Carlo technique to deal with the repulsive Hubbard model. We observe that in a Stirling cycle, in both the interacting and non-interacting limits, the heat engine function dominates and its performance gets better when the strain is induced from the kagome to the Lieb limit, while for the reverse the refrigeration action is preferred. Further, we show that the QTM performs better when the difference between the temperatures of the two baths is lower and the QTM reaches the Carnot limit in this regime. Further, we extensively study the performance of the QTM in the repulsive Hubbard interacting regime where the magnetic orders come into the picture. We explore the performance of the QTM along the quantum critical points and in the large interaction limit., Comment: Preliminary draft, comments welcomed

Segmentation and labeling of vertebrae in MRI images of the spine are critical for the diagnosis of illnesses and abnormalities. These steps are indispensable as MRI technology provides detailed information about the tissue structure of the spine. Both supervised and unsupervised segmentation methods exist, yet acquiring sufficient data remains challenging for achieving high accuracy. In this study, we propose an enhancing approach based on modified attention U-Net architecture for panoptic segmentation of 3D sliced MRI data of the lumbar spine. Our method achieves an impressive accuracy of 99.5\% by incorporating novel masking logic, thus significantly advancing the state-of-the-art in vertebral segmentation and labeling. This contributes to more precise and reliable diagnosis and treatment planning., Comment: 9 pages, 10 figures

The nature of charge carriers in strange metals has become a topic of intense current investigation. Recent shot noise measurements in the quantum critical heavy fermion metal YbRh$_2$Si$_2$ revealed a suppression of the Fano factor that cannot be understood from electron-phonon scattering or strong electron correlations in a Fermi liquid, indicating loss of quasiparticles. The experiment motivates the consideration of shot noise in a variety of theoretical models in which quasiparticles may be lost. Here we study shot noise in systems with co-existing itinerant electrons and dispersive bosons, going beyond the regime where the bosons are on their own in thermal equilibrium. We construct the Boltzmann-Langevin equations for the coupled system, and show that adequate electron-boson couplings restore the Fano factor to its Fermi liquid value. Our findings point to the beyond-Landau form of quantum criticality as underlying the suppressed shot noise of strange metals in heavy fermion metals and beyond., Comment: 13 pages, 4 figures

We introduce APPLE, a novel planetary evolution code designed specifically for the study of giant exoplanet and Jovian planet evolution in the era of Galileo, Juno, and Cassini. With APPLE, state-of-the-art equations of state for hydrogen, helium, ice, and rock are integrated with advanced features to treat ice/rock cores and metals in the gaseous envelope; models for helium rain and hydrogen/helium immiscibility; detailed atmosphere boundary tables that also provide self-consistent albedos and spectra; and options to address envelope metal gradients and stably-stratified regions. Our hope is that these purpose-built features of APPLE will help catalyze the development of the next generation of giant exoplanet and Jovian planet evolutionary models., Comment: Accepted for publication in ApJ