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We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

In the context of f(R, T) gravity theory for the flat Friedmann Lemaitre Robertson Walker (FLRW) model, the accelerating expansion of the universe is investigated using a specific form of the emergent Hubble parameter. Datasets from H(z), Type Ia supernovae (SNIa), and Baryon Acoustic Oscillations (BAO) are used to constrain the model and identify the ideal parameter values in order to evaluate the statistical significance of f(R, T) gravity. The best-fit parameters are derived by solving the modified Friedmann equations through a MCMC analysis. These parameters are used to compute the equation of state, statefinders, energy conditions, and the (w-w) plane. Furthermore, the evolution of kinematic cosmographic parameters is examined. The findings provide significant behavior and features of dark energy models. Our comprehension of the dynamics and evolution of the universe is improved by this study, which also advances our understanding of dark energy and how it shapes the universe., Comment: 12 pages, 8 figures

Developing a dark matter detector with wide mass tunability is an immensely desirable property, yet it is challenging due to maintaining strong sensitivity. Resonant cavities for dark matter detection have traditionally employed mechanical tuning, moving parts around to change electromagnetic boundary conditions. However, these cavities have proven challenging to operate in sub-Kelvin cryogenic environments due to differential thermal contraction, low heat capacities, and low thermal conductivities. Instead, we develop an electronically tunable cavity architecture by coupling a superconducting 3D microwave cavity with a DC flux tunable SQUID. With a flux delivery system engineered to maintain high coherence in the cavity, we perform a hidden-photon dark matter search below the quantum-limited threshold. A microwave photon counting technique is employed through repeated quantum non-demolition measurements using a transmon qubit. With this device, we perform a hidden-photon search with a dark count rate of around 64 counts/s and constrain the kinetic mixing angle to ${\varepsilon}< 4\times 10^{-13}$ in a tunable band from 5.672 GHz to 5.694 GHz. By coupling multimode tunable cavities to the transmon, wider hidden-photon searching ranges are possible.

A cyclic flat universe with quintom behaviour and future big rip has been presented in the framework of Rastall gravity, which is an extension of the standard $\Lambda$CDM model. The Hubble parameter oscillates periodically between positive and negative values from one cycle to the next. Cosmic transit has been simulated through an oscillating time-dependent deceleration parameter, and is expected to occur at approximately $ 8.7~~ \text{Gyr}$. The causality is satisfied all the time except near the initial singularity and the future Big Rip singularity.The apparent horizon, entropy and other thermodynamical quantities associated to the current model have been analyzed. Energy conditions have been investigated.

Quantum coherence in superconducting circuits has increased steadily over the last decades as a result of a growing understanding of the various loss mechanisms. Recently, tantalum (Ta) emerged as a promising material to address microscopic sources of loss found on niobium (Nb) or aluminum (Al) surfaces. However, the effects of film and interface microstructure on low-temperature microwave loss are still not well understood. Here we present a systematic study of the structural and electrical properties of Ta and Nb films sputtered on c-plane sapphire at varying growth temperatures. As growth temperature is increased, our results show that the onset of epitaxial growth of $\alpha$-phase Ta correlates with lower Ta surface roughness, higher critical temperature, and higher residual resistivity ratio, but surprisingly also correlates with a significant increase in loss at microwave frequency. We determine that the source of loss is located at the Ta/sapphire interface and show that it can be fully mitigated by either growing a thin, epitaxial Nb inter-layer between the Ta film and the substrate or by intentionally treating the sapphire surface with \textit{in-situ} argon plasma before Ta growth. In addition to elucidating this interfacial microwave loss, this work provides adequate process details that should allow for the reproducible growth of low-loss Ta film across fabrication facilities.

Construction robots operate in unstructured construction sites, where effective visual perception is crucial for ensuring safe and seamless operations. However, construction robots often handle large elements and perform tasks across expansive areas, resulting in occluded views from onboard cameras and necessitating the use of multiple environmental cameras to capture the large task space. This study proposes a multi-robot coordination framework in which a team of supervising robots equipped with cameras adaptively adjust their poses to visually perceive the operation of the primary construction robot and its surrounding environment. A viewpoint selection method is proposed to determine each supervising robot's camera viewpoint, optimizing visual coverage and proximity while considering the visibility of the upcoming construction robot operation. A case study on prefabricated wooden frame installation demonstrates the system's feasibility, and further experiments are conducted to validate the performance and robustness of the proposed viewpoint selection method across various settings. This research advances visual perception of robotic construction processes and paves the way for integrating computer vision techniques to enable real-time adaption and responsiveness. Such advancements contribute to the safe and efficient operation of construction robots in inherently unstructured construction sites.

This study explores the properties of quark stars (QS) formulated with an interacting quark matter equation of state (EoS) within the framework of Rastall gravity, a modified theory of gravity. We derive the mass-radius relationships and calculate the maximum gravitational masses and their corresponding radii, comparing these results under both Rastall gravity and general relativity. Our analysis incorporates recent observational data, including the GW190425 event, to constrain the model parameters ($\bar{\lambda}, \eta, B_{\rm eff}$). We also assess the stability of these quark stars by evaluating their static stability, adiabatic index, and sound velocity profiles, thus confirming their viability within the modified gravitational framework., Comment: 12 pages, 6 figures and accepted for publication in Classical and Quantum Gravity

This study delves into the cosmological implications of the $f(Q,C)$ modified gravity framework within the context of the FLRW spacetime which offers a dynamic alternative to the standard $\Lambda$CDM cosmology. Here, we define the transit form of Hubble's parameter to explain several geometrical and physical aspects. The chosen parametric form of the Hubble parameter represents a smooth transition from the decelerating early universe to the accelerating present and late-time evolution. Employing observational datasets such as the Hubble parameter, Type Ia supernovae, Baryon Acoustic Oscillations (BAO), and Standard Candles (SC), we constrain the model parameters using the Markov Chain Monte Carlo (MCMC) method. The isotropic pressure, energy density, equation of state parameter, and energy conditions were analyzed to explore the physical viability of the $f(Q,C)$ framework. The results highlight the model's ability to replicate key cosmological behaviors, including the accelerated expansion driven by dark energy., Comment: 12 pages, 7 figures

The proliferation of AI techniques for image generation, coupled with their increasing accessibility, has raised significant concerns about the potential misuse of these images to spread misinformation. Recent AI-generated image detection (AGID) methods include CNNDetection, NPR, DM Image Detection, Fake Image Detection, DIRE, LASTED, GAN Image Detection, AIDE, SSP, DRCT, RINE, OCC-CLIP, De-Fake, and Deep Fake Detection. However, we argue that the current state-of-the-art AGID techniques are inadequate for effectively detecting contemporary AI-generated images and advocate for a comprehensive reevaluation of these methods. We introduce the Visual Counter Turing Test (VCT^2), a benchmark comprising ~130K images generated by contemporary text-to-image models (Stable Diffusion 2.1, Stable Diffusion XL, Stable Diffusion 3, DALL-E 3, and Midjourney 6). VCT^2 includes two sets of prompts sourced from tweets by the New York Times Twitter account and captions from the MS COCO dataset. We also evaluate the performance of the aforementioned AGID techniques on the VCT$^2$ benchmark, highlighting their ineffectiveness in detecting AI-generated images. As image-generative AI models continue to evolve, the need for a quantifiable framework to evaluate these models becomes increasingly critical. To meet this need, we propose the Visual AI Index (V_AI), which assesses generated images from various visual perspectives, including texture complexity and object coherence, setting a new standard for evaluating image-generative AI models. To foster research in this domain, we make our https://huggingface.co/datasets/anonymous1233/COCO_AI and https://huggingface.co/datasets/anonymous1233/twitter_AI datasets publicly available., Comment: 13 pages, 9 figures

Subwavelength-scale light absorbers and reflectors have gained significant attention for their potential in photonic applications. These structures often utilize a metal-insulator-metal (MIM) architecture, similar to a Fabry-Perot nanocavity, using noble metals and dielectric or semiconductor spacers for narrow-band light absorption. In reflection mode, they function as band-stop filters, blocking specific wavelengths and reflecting others through Fabry-Perot resonance. Efficient color reflection requires asymmetric Fabry-Perot cavities, where metals with differing reflectivities and extinction coefficients enable substantial reflection for non-resonant wavelengths and near-perfect absorption at resonant ones. Unlike narrowband techniques, broadband absorption does not rely on a single resonance phenomenon. Recent developments show that integrating an asymmetric Fabry-Perot nanocavity with an anti-reflection coating achieves near-unity absorption across a broad wavelength range. This study introduces an asymmetric Fabry-Perot nanocavity with a dielectric-semiconductor-dielectric spacer, enabling near-unity color reflection. By incorporating silicon, the reflected color can be tuned with just a 5 nm thickness variation, while achieving broadband absorption over 70% in the 800-1600 nm range. The addition of an anti-reflection coating extends broadband absorption to near unity with minimal impact on reflected color. The planar, nanopattern-free design holds promise for display technologies with better color fidelity and applications in thermal photovoltaics.

We demonstrate that vision language models (VLMs) are capable of recognizing the content in audio recordings when given corresponding spectrogram images. Specifically, we instruct VLMs to perform audio classification tasks in a few-shot setting by prompting them to classify a spectrogram image given example spectrogram images of each class. By carefully designing the spectrogram image representation and selecting good few-shot examples, we show that GPT-4o can achieve 59.00% cross-validated accuracy on the ESC-10 environmental sound classification dataset. Moreover, we demonstrate that VLMs currently outperform the only available commercial audio language model with audio understanding capabilities (Gemini-1.5) on the equivalent audio classification task (59.00% vs. 49.62%), and even perform slightly better than human experts on visual spectrogram classification (73.75% vs. 72.50% on first fold). We envision two potential use cases for these findings: (1) combining the spectrogram and language understanding capabilities of VLMs for audio caption augmentation, and (2) posing visual spectrogram classification as a challenge task for VLMs.

Page 27 of Ramanujan's Lost Notebook contains a beautiful identity which not only gives, as a special case, a famous modular relation between the Rogers-Ramanujan functions $G(q)$ and $H(q)$ but also a relation between two fifth order mock theta functions and $G(q)$ and $H(q)$. We generalize Ramanujan's relation with the help of a parameter $s$ to get an infinite family of such identities. Our result shows that a theta function can always be ``dissected'' as a finite sum of products of generalized Rogers-Ramanujan functions. Several well-known results are shown to be consequences of our theorem, for example, a generalization of the Jacobi triple product identity and Andrews' relation between two of his generalized third order mock theta functions. We give enough evidence, through asymptotic analysis as well as by other means, to show that the identities we get from our main result for $s>2$ transcend the modular world and hence look difficult to be written in the form of a modular relation. Using asymptotic analysis, we also offer a clinching evidence that explains how Ramanujan may have arrived at his generalized modular relation., Comment: 23 pages, submitted for publication. Comments are welcome

The Automated Audio Captioning (AAC) task aims to describe an audio signal using natural language. To evaluate machine-generated captions, the metrics should take into account audio events, acoustic scenes, paralinguistics, signal characteristics, and other audio information. Traditional AAC evaluation relies on natural language generation metrics like ROUGE and BLEU, image captioning metrics such as SPICE and CIDEr, or Sentence-BERT embedding similarity. However, these metrics only compare generated captions to human references, overlooking the audio signal itself. In this work, we propose MACE (Multimodal Audio-Caption Evaluation), a novel metric that integrates both audio and reference captions for comprehensive audio caption evaluation. MACE incorporates audio information from audio as well as predicted and reference captions and weights it with a fluency penalty. Our experiments demonstrate MACE's superior performance in predicting human quality judgments compared to traditional metrics. Specifically, MACE achieves a 3.28% and 4.36% relative accuracy improvement over the FENSE metric on the AudioCaps-Eval and Clotho-Eval datasets respectively. Moreover, it significantly outperforms all the previous metrics on the audio captioning evaluation task. The metric is opensourced at https://github.com/satvik-dixit/mace

Spin gapless semiconductors based Heusler alloys are the special class of materials due to their unique band structure, high spin polarization and high Curie temperature. These materials exhibit a distinct electronic structure: a nonzero band gap in one spin channel while the other spin channel remains gapless, making them highly suitable for tunable spintronics. In this study, a comprehensive analysis of structural, magnetic, thermoelectric, and transport properties of the quaternary Heusler alloy CoFeMnSn is conducted. X-ray diffraction and Neutron diffraction analyses confirm a well ordered structure with partial antisite disorder between Co, Fe and Mn, Sn atoms. Magnetic studies show that the material exhibits room-temperature ferromagnetism, with a Curie temperature of around 660 K. Notably, we observe an anomalous Hall effect linked to intrinsic mechanisms driven by Berry curvature, underscoring the intricate relationship between structural disorder and electronic behavior. Transport measurements also highlight the impact of antisite disorder on the systems, with resistivity decreasing as temperature increases. These insights position CoFeMnSn as a promising material for future spintronic devices and advanced technological applications., Comment: 9 pages, 9 Figures

Desert locusts are notorious agriculture pests prompting billions in losses and global food scarcity concerns. With billions of these locusts invading agrarian lands, this is no longer a thing of the past. This study taps into the existing doppler weather radar (DWR) infrastructure which was originally deployed for meteorological applications. This study demonstrates a systematic approach to distinctly identify and track concentrations of desert locust swarms in near real time using single polarization radars. Findings reveal the potential to establish early warning systems with lead times of around 7 hours and spatial coverage of approximately 100 kilometers. Embracing these technological advancements are crucial to safeguard agricultural landscapes and upload global food security., Comment: 18 pages, 5 figures

Magnesium sulfate (${ \rm MgSO_4 }$) is used as an additive to reduce capacity fading in rechargeable zinc-ion batteries. This study investigates the ion pairing and solvation structure of ${ \rm Zn^{2+}-SO_4^{2-} }$ and ${ \rm Mg^{2+}-SO_4^{2-} }$ in mixtures of ${ \rm [ZnSO_4]{2M} + [MgSO_4]{0M} }$, ${ \rm [ZnSO_4]{1M} + [MgSO_4]{1M} }$, and ${ \rm [ZnSO_4]{0M} + [MgSO_4]{2M} }$. Using molecular dynamics simulations, we evaluated dipole dynamics, spatial distributions, and coordination of ions through radial distribution functions, running coordination numbers, and potentials of mean force. The results reveal that increasing ${ \rm MgSO_4 }$ concentration disrupts ${ \rm Zn^{2+} }$ self-association, while ${ \rm Mg^{2+} }$ shows enhanced association. The analysis indicates different solvation structures, with ${ \rm Zn^{2+} }$ maintaining a more ordered coordination with ${ \rm SO_4^{2-} }$ than ${ \rm Mg^{2+} }$. Preferential binding analysis highlights stronger ${ \rm Zn^{2+} }$ affinity for ${ \rm SO_4^{2-} }$, shedding light on ion interaction dynamics in mixed electrolytes.

Many students struggle with math word problems (MWPs), often finding it difficult to identify key information and select the appropriate mathematical operations. Schema-based instruction (SBI) is an evidence-based strategy that helps students categorize problems based on their structure, improving problem-solving accuracy. Building on this, we propose a Schema-Based Instruction Retrieval-Augmented Generation (SBI-RAG) framework that incorporates a large language model (LLM). Our approach emphasizes step-by-step reasoning by leveraging schemas to guide solution generation. We evaluate its performance on the GSM8K dataset, comparing it with GPT-4 and GPT-3.5 Turbo, and introduce a "reasoning score" metric to assess solution quality. Our findings suggest that SBI-RAG enhances reasoning clarity and facilitates a more structured problem-solving process potentially providing educational benefits for students., Comment: Accepted to the 4th MATH-AI Workshop at NeurIPS'24

We investigate two asymmetric loss functions, namely LINEX loss and power divergence loss for optimal spatial prediction with area-level data. With our motivation arising from the real estate industry, namely in real estate valuation, we use the Zillow Home Value Index (ZHVI) for county-level values to show the change in prediction when the loss is different (asymmetric) from a traditional squared error loss (symmetric) function. Additionally, we discuss the importance of choosing the asymmetry parameter, and propose a solution to this choice for a general asymmetric loss function. Since the focus is on area-level data predictions, we propose the methodology in the context of conditionally autoregressive (CAR) models. We conclude that choice of the loss functions for spatial area-level predictions can play a crucial role, and is heavily driven by the choice of parameters in the respective loss.

The Axion Dark Matter eXperiment is sensitive to narrow axion flows, given axions compose a fraction of the dark matter with a non-negligible local density. Detecting these low-velocity dispersion flows requires a high spectral resolution and careful attention to the expected signal modulation due to Earth's motion. We report an exclusion on the local axion dark matter density in narrow flows of $\rho_a \gtrsim 0.03\,\mathrm{GeV/cm^3}$ and $\rho_a \gtrsim 0.004\,\mathrm{GeV/cm^3}$ for Dine-Fischler-Srednicki-Zhitnitski and Kim-Shifman-Vainshtein-Zakharov axion-photon couplings, respectively, over the mass range $3.3-4.2\,\mu\text{eV}$. Measurements were made at selected resolving powers to allow for a range of possible velocity dispersions., Comment: 7 pages, 3 figures

In this paper, we establish the existence of positive ground state solutions for a class of mixed Schr\"{o}dinger systems with concave-convex nonlinearities in $\mathbb{R}^2$, subject to $L^2$-norm constraints; that is, \[ \left\{ \begin{aligned} -\partial_{xx} u + (-\Delta)_y^s u + \lambda_1 u &= \mu_1 u^{p-1} + \beta r_1 u^{r_1-1} v^{r_2}, && -\partial_{xx} v + (-\Delta)_y^s v + \lambda_2 v &= \mu_2 v^{q-1} + \beta r_2 u^{r_1} v^{r_2-1}, && \end{aligned} \right. \] subject to the $L^2$-norm constraints: \[ \int_{\mathbb{R}^2} u^2 \,\mathrm{d}x\mathrm{d}y = a \quad \text{and} \quad \int_{\mathbb{R}^2} v^2 \,\mathrm{d}x\mathrm{d}y = b, \] where $(x,y)\in \mathbb{R}^2$, $u, v \geq 0$, $s \in \left(1/2, 1 \right)$, $\mu_1, \mu_2, \beta > 0$, $r_1, r_2 > 1$, the prescribed masses $a, b > 0$, and the parameters $\lambda_1, \lambda_2$ appear as Lagrange multipliers. Moreover, the exponents $p, q, r_1 + r_2$ satisfy: \[ \frac{2(1+3s)}{1+s} < p, q, r_1 + r_2 < 2_s, \] where $2_s = \frac{2(1+s)}{1-s}$. To obtain our main existence results, we employ variational techniques such as the Mountain Pass Theorem, the Pohozaev manifold, Steiner rearrangement, and others, consolidating the works of Louis Jeanjean et al. \cite{jeanjean2024normalized}.

Speaker verification systems have seen significant advancements with the introduction of Multi-scale Feature Aggregation (MFA) architectures, such as MFA-Conformer and ECAPA-TDNN. These models leverage information from various network depths by concatenating intermediate feature maps before the pooling and projection layers, demonstrating that even shallower feature maps encode valuable speaker-specific information. Building upon this foundation, we propose a Multi-scale Feature Contrastive (MFCon) loss that directly enhances the quality of these intermediate representations. Our MFCon loss applies contrastive learning to all feature maps within the network, encouraging the model to learn more discriminative representations at the intermediate stage itself. By enforcing better feature map learning, we show that the resulting speaker embeddings exhibit increased discriminative power. Our method achieves a 9.05% improvement in equal error rate (EER) compared to the standard MFA-Conformer on the VoxCeleb-1O test set.

We derive Vorono\"{\dotlessi} summation formulas for the Liouville function $\lambda(n)$, the M\"{o}bius function $\mu(n)$, and for $d^{2}(n)$, where $d(n)$ is the divisor function. The formula for $\lambda(n)$ requires explicit evaluation of certain infinite series for which the use of the Vinogradov-Korobov zero-free region of the Riemann zeta function is indispensable. Several results of independent interest are obtained as special cases of these formulas. For example, a special case of the one for $\mu(n)$ is a famous result of Ramanujan, Hardy, and Littlewood. Cohen type and Ramanujan-Guinand type identities are established for $\lambda(n)$ and $\sigma_a(n)\sigma_b(n)$, where $\sigma_s(n)$ is the generalized divisor function. As expected, infinite series over the non-trivial zeros of $\zeta(s)$ now form an essential part of all of these formulas. A series involving $\sigma_a(n)\sigma_b(n)$ and product of modified Bessel functions occurring in one of our identities has appeared in a recent work of Dorigoni and Treilis in string theory. Lastly, we obtain results on oscillations of Riesz sums associated to $\lambda(n), \mu(n)$ and of the error term of Riesz sum of $d^2(n)$ under the assumption of the Riemann Hypothesis, simplicity of the zeros of $\zeta(s)$, the Linear Independence conjecture, and a weaker form of the Gonek-Hejhal conjecture., Comment: 45 pages, submitted for publication

High-entropy alloys (HEAs) are metallic materials with solid solutions stabilized by high mixing entropy. Some exhibit excellent strength, often accompanied by additional properties such as magnetic, invar, corrosion, or cryogenic response. This has spurred efforts to discover new HEAs, but the vast compositional search space has made these efforts challenging. Here we present a framework to predict and optimize the yield strength of face-centered cubic (FCC) HEAs, using CoCrFeMnNi-based alloys as a case study due to abundant available data. Our novel Residual Hybrid Learning Model (RELM) integrates Random Forest and Gradient Boosting, enhanced by material attribute data, to handle sparse, skewed datasets for real-world alloys. A hybrid Generative Adversarial Network-Variational Autoencoder model explores new alloy compositions beyond existing datasets. By incorporating processing parameters, which determine the microstructure and thus strength, RELM achieves an R$^2$ score of 0.915, surpassing traditional models. SHapley Additive Explanations (SHAP) and Partial Dependencies enhance interpretability, revealing composition-processing-property relationships, as validated by experiments, including X-ray diffraction, SEM analysis, and tensile testing. The model discovered two novel Co$_{20}$Cr$_{16}$Fe$_{20}$Mn$_{16}$Ni$_{24}$Al$_4$ and Co$_{24}$Cr$_{12}$Fe$_{12}$Mn$_{16}$Ni$_{28}$Al$_4$Si$_4$ HEAs with a maximum possible yield strength of 842 and 937 MPa, significantly exceeding previously reported values for these alloy systems. This study pioneers interpretable machine learning in alloy design, providing a rigorous, data-driven approach to discovering, processing, and optimizing real-world materials. The findings highlight the critical role of both compositional and post-fabrication processing parameters in advancing the understanding of composition-processing-property relationships.

A comprehensive 3-D finite element formulation for the coupled thermoelastic system is proposed based on the Total Lagrangian framework to study the thermoelastic damping (TED) in small scale structures. The proposed formulation takes into account geometric nonlinearity because of large deformation and material nonlinearity where material parameters are functions of temperature and strain field. Using the proposed finite element formulation, the TED quality factor is obtained for 1-D rod undergoing longitudinal vibrations using the eigenvalue analysis. We first validate the accuracy of the finite element implementation with previously known theoretical and numerical results. Subsequently we demonstrate the utility of the proposed numerical framework to study the effect of geometric nonlinearity, temperature and strain dependent material nonlinearity on the thermoelastic damping.In addition, the effect of internal/ external heating and different thermal boundary conditions on TED is discussed

Pre-trained deep learning embeddings have consistently shown superior performance over handcrafted acoustic features in speech emotion recognition (SER). However, unlike acoustic features with clear physical meaning, these embeddings lack clear interpretability. Explaining these embeddings is crucial for building trust in healthcare and security applications and advancing the scientific understanding of the acoustic information that is encoded in them. This paper proposes a modified probing approach to explain deep learning embeddings in the SER space. We predict interpretable acoustic features (e.g., f0, loudness) from (i) the complete set of embeddings and (ii) a subset of the embedding dimensions identified as most important for predicting each emotion. If the subset of the most important dimensions better predicts a given emotion than all dimensions and also predicts specific acoustic features more accurately, we infer those acoustic features are important for the embedding model for the given task. We conducted experiments using the WavLM embeddings and eGeMAPS acoustic features as audio representations, applying our method to the RAVDESS and SAVEE emotional speech datasets. Based on this evaluation, we demonstrate that Energy, Frequency, Spectral, and Temporal categories of acoustic features provide diminishing information to SER in that order, demonstrating the utility of the probing classifier method to relate embeddings to interpretable acoustic features.

In this work, the cosmic solutions, particularly the well-known $\Lambda$CDM model, are investigated in the framework of the Gauss-Bonnet gravity, where the gravitational action incorporates the Gauss-Bonnet invariant function. We utilize a specialized formulation of the deceleration parameter in terms of the Hubble parameter $H$, given by $q = -1 - \frac{\dot{H}}{H^2}$, to solve the field equations. To identify the appropriate model parameters, we align them to the most recent observational datasets, which include 31 data points from the Cosmic Chronometers, Pantheon+, and BAO datasets. The physical characteristics of the cosmographic parameters, such as pressure and energy density, that correlate to the limited values of the model parameters, are examined. The evolution of the deceleration parameter suggests a transition from a decelerated to an accelerated phase of the universe. Additionally, we examine the stability of the assumed model and provide an explanation for late-time acceleration using the energy conditions. The behavior of the equation of state parameter has been analyzed through dynamical variables by constraining various parameters in light of the recent observational data. This study has resulted in a quintessence-like evolution., Comment: 15 pages, 12 figures

The integration of fine-scale multispectral imagery with deep learning models has revolutionized land use and land cover (LULC) classification. However, the atmospheric effects present in Top-of-Atmosphere sensor measured Digital Number values must be corrected to retrieve accurate Bottom-of-Atmosphere surface reflectance for reliable analysis. This study employs look-up-table-based radiative transfer simulations to estimate the atmospheric path reflectance and transmittance for atmospherically correcting high-resolution CARTOSAT-3 Multispectral (MX) imagery for several Indian cities. The corrected surface reflectance data were subsequently used in supervised and semi-supervised segmentation models, demonstrating stability in multi-class (buildings, roads, trees and water bodies) LULC segmentation accuracy, particularly in scenarios with sparsely labelled data.

Large Language Models (LLMs) are increasingly integrated into diverse applications. The rapid evolution of LLMs presents opportunities for developers to enhance applications continuously. However, this constant adaptation can also lead to performance regressions during model migrations. While several interactive tools have been proposed to streamline the complexity of prompt engineering, few address the specific requirements of regression testing for LLM Migrations. To bridge this gap, we introduce RETAIN (REgression Testing guided LLM migrAtIoN), a tool designed explicitly for regression testing in LLM Migrations. RETAIN comprises two key components: an interactive interface tailored to regression testing needs during LLM migrations, and an error discovery module that facilitates understanding of differences in model behaviors. The error discovery module generates textual descriptions of various errors or differences between model outputs, providing actionable insights for prompt refinement. Our automatic evaluation and empirical user studies demonstrate that RETAIN, when compared to manual evaluation, enabled participants to identify twice as many errors, facilitated experimentation with 75% more prompts, and achieves 12% higher metric scores in a given time frame., Comment: Preprint

The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Effective question classification is crucial for AI-driven educational tools, enabling adaptive learning systems to categorize questions by skill area, difficulty level, and competence. This classification not only supports educational diagnostics and analytics but also enhances complex tasks like information retrieval and question answering by associating questions with relevant categories. Traditional methods, often based on word embeddings and conventional classifiers, struggle to capture the nuanced relationships in natural language, leading to suboptimal performance. To address this, we propose a novel approach leveraging graph convolutional networks, named Phrase Question-Graph Convolutional Network (PQ-GCN) to better model the inherent structure of questions. By representing questions as graphs-where nodes signify words or phrases and edges denote syntactic or semantic relationships-our method allows the model to learn from the interconnected nature of language more effectively. Additionally, we explore the incorporation of phrase-based features to enhance classification performance on question datasets of various domains and characteristics. Our findings demonstrate that the proposed model, augmented with these features, offer a promising solution for more robust and context-aware question classification, bridging the gap between graph neural network research and practical educational applications of AI.

Despite decades of research on blood flow, an analogue of Navier-Stokes equations that accurately describe blood flow properties has not been established yet. The reason behind this is that the properties of blood flow seem \`a priori non universal as they depend on various factors such as global concentration of red blood cells (RBCs) and channel width. Here, we have discovered a universal law when the stress and strain rate are measured at a given local RBCs concentration. However, the local concentration must be determined in order to close the problem. We propose a non-local diffusion equation of RBCs concentration that agrees with the full simulation. The universal law is exemplified for both shear and pressure driven flows. While the theory is restricted to a simplistic geometry (straight channel) it provides a fundamental basis for future research on blood flow dynamics and could lead to the development of a new theory that accurately describes blood flow properties under various conditions, such as in complex vascular networks.

The jet cross-section and jet-substructure observables in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-$k_{t}$ algorithm with a jet radius $R=0.3$ for jets with transverse momentum within $8.0

The ability to manipulate electrons with the intense laser pulse enables an unprecedented control over the electronic motion on its intrinsic timescale. Present work explores the desired control of photocurrent generation in monolayer graphene on ultrafast timescale. The origin of photocurrent is attributed to the asymmetric residual electronic population in the conduction band after the end of the laser pulse, which also facilitates valley polarization. Present study offers a comprehensive analysis of the differences between these two observables, namely photocurrent and valley polarization. It is found that the corotating circularly polarized $\omega-2\omega$ laser pulses allow the generation of photocurrent but no valley polarization, whereas counterrotating circularly polarized $\omega-2\omega$ laser pulses yield significant valley polarization without any photocurrent in graphene. Different laser parameters, such as subcycle phase, wavelength, and intensity provide different knobs to control the generation of the photocurrent. In addition, threefold increase in the photocurrent's amplitude can be achieved by altering electronic properties of graphene via strain engineering. Our findings reveal intriguing underlying mechanisms into the interplay between the symmetries of the graphene's electronic structure and the driving laser pulses, shedding light on the potential for harnessing graphene's properties for novel applications in ultrafast photonics, optoelectronic devices, and quantum technologies., Comment: 19 pages, 6 figures

Bubble bursting and subsequent collapse of the open cavity at free surfaces of contaminated liquids can generate aerosol droplets, facilitating pathogen transport. After film rupture, capillary waves focus at the cavity base, potentially generating fast Worthington jets that are responsible for ejecting the droplets away from the source. While extensively studied for Newtonian fluids, the influence of non-Newtonian rheology on this process remains poorly understood. Here, we employ direct numerical simulations to investigate the bubble cavity collapse in viscoelastic media, such as polymeric liquids. We find that the jet and drop formation are dictated by two dimensionless parameters: the elastocapillary number $Ec$ (the ratio of the elastic modulus and the Laplace pressure) and the Deborah number $De$ (the ratio of the relaxation time and the inertio-capillary timescale). We show that for low values of $Ec$ and $De$, the viscoelastic liquid adopts a Newtonian-like behavior, where the dynamics are governed by the solvent Ohnesorge number $Oh_s$ (the ratio of visco-capillary and inertio-capillary timescales). In contrast, for large values $Ec$ and $De$, the enhanced elastic stresses completely suppress the formation of the jet. For some cases with intermediate values of $Ec$ and $De$, smaller droplets are produced compared to Newtonian fluids, potentially enhancing aerosol dispersal. By mapping the phase space spanned by $Ec$, $De$, and $Oh_s$, we reveal three distinct flow regimes: (i) jets forming droplets, (ii) jets without droplet formation, and (iii) absence of jet formation. Our results elucidate the mechanisms underlying aerosol suppression versus fine spray formation in polymeric liquids, with implications for pathogen transmission and industrial processes involving viscoelastic fluids.

Land Use Land Cover (LULC) mapping is a vital tool for urban and resource planning, playing a key role in the development of innovative and sustainable cities. This study introduces a semi-supervised segmentation model for LULC prediction using high-resolution satellite images with a vast diversity of data distributions in different areas of India. Our approach ensures a robust generalization across different types of buildings, roads, trees, and water bodies within these distinct areas. We propose a modified Cross Pseudo Supervision framework to train image segmentation models on sparsely labelled data. The proposed framework addresses the limitations of the famous 'Cross Pseudo Supervision' technique for semi-supervised learning, specifically tackling the challenges of training segmentation models on noisy satellite image data with sparse and inaccurate labels. This comprehensive approach significantly enhances the accuracy and utility of LULC mapping, providing valuable insights for urban and resource planning applications.

Let $\phi(n)$ be the Euler totient function and $\sigma(n)$ denote the sum of divisors of $n$. In this note, we obtain explicit upper bounds on the number of positive integers $n\leq x$ such that $\phi(\sigma(n)) > cn$ for any $c>0$. This is a refinement of a result of Alaoglu and Erd\H{o}s., Comment: 5 pages

Temporal reasoning over tabular data presents substantial challenges for large language models (LLMs), as evidenced by recent research. In this study, we conduct a comprehensive analysis of temporal datasets to pinpoint the specific limitations of LLMs. Our investigation leads to enhancements in TempTabQA, a dataset specifically designed for tabular temporal question answering. We provide critical insights for improving LLM performance in temporal reasoning tasks with tabular data. Furthermore, we introduce a novel approach, C.L.E.A.R to strengthen LLM capabilities in this domain. Our findings demonstrate that our method significantly improves evidence-based reasoning across various models. Additionally, our experimental results reveal that indirect supervision with auxiliary data substantially boosts model performance in these tasks. This work contributes to a deeper understanding of LLMs' temporal reasoning abilities over tabular data and promotes advancements in their application across diverse fields., Comment: Total Pages 18, Total Tables 6, Total figures 7

Convex programming plays a fundamental role in machine learning, data science, and engineering. Testing convexity structure in nonlinear programs relies on verifying the convexity of objectives and constraints. \citet{grant2006disciplined} introduced a framework, Disciplined Convex Programming (DCP), for automating this verification task for a wide range of convex functions that can be decomposed into basic convex functions (atoms) using convexity-preserving compositions and transformations (rules). However, the restriction to Euclidean convexity concepts can limit the applicability of the framework. For instance, many notable instances of statistical estimators and matrix-valued (sub)routines in machine learning applications are Euclidean non-convex, but exhibit geodesic convexity through a more general Riemannian lens. In this work, we extend disciplined programming to this setting by introducing Disciplined Geodesically Convex Programming (DGCP). We determine convexity-preserving compositions and transformations for geodesically convex functions on general Cartan-Hadamard manifolds, as well as for the special case of symmetric positive definite matrices, a common setting in matrix-valued optimization. For the latter, we also define a basic set of atoms. Our paper is accompanied by a Julia package SymbolicAnalysis.jl, which provides functionality for testing and certifying DGCP-compliant expressions. Our library interfaces with manifold optimization software, which allows for directly solving verified geodesically convex programs.

High-refresh rate displays have become very popular in recent years due to the need for superior visual quality in gaming, professional displays and specialized applications like medical imaging. However, high-refresh rate displays alone do not guarantee a superior visual experience; the GPU needs to render frames at a matching rate. Otherwise, we observe disconcerting visual artifacts such as screen tearing and stuttering. Temporal supersampling is an effective technique to increase frame rates by predicting new frames from other rendered frames. There are two methods in this space: interpolation and extrapolation. Interpolation-based methods provide good image quality at the cost of a higher latency because they also require the next rendered frame. On the other hand, extrapolation methods are much faster at the cost of quality. This paper introduces PatchEX, a novel frame extrapolation method that aims to provide the quality of interpolation at the speed of extrapolation. It smartly partitions the extrapolation task into sub-tasks and executes them in parallel to improve both quality and latency. It then uses a patch-based inpainting method and a custom shadow prediction approach to fuse the generated sub-frames. This approach significantly reduces the overall latency while maintaining the quality of the output. Our results demonstrate that PatchEX achieves a 65.29% and 48.46% improvement in PSNR over the latest extrapolation methods ExtraNet and ExtraSS, respectively, while being 6x and 2x faster, respectively.

The skew symmetric Laplace uniform distribution SSLUD({\mu}) is introduced in Lohot, R. K. and Dixit, V. U. (2024) using the skewing mechanism of Azzalini (1985). Here we derive the most powerful (MP) test for symmetry of the SSLUD({\mu}). Since the form of the test statistic is complicated and it is difficult to obtain its exact distribution, critical values and the power of MP test are obtained using simulation. Further, we construct a confidence interval (CI) for parameter {\mu} assuming asymptotic normality and empirical distribution of the maximum likelihood estimator of {\mu}. These two methods are compared based on the average length and coverage probability of the CI. Finally, the CI of the parameter {\mu} is constructed using data on the transformed daily percentage change in the price of NIFTY 50, an Indian stock market index given in Lohot, R. K. and Dixit, V. U. (2024)., Comment: 21 pages, 7 tables

Laplace distribution is popular in the field of economics and finance. Still, data sets often show a lack of symmetry and a tendency of being bounded from either side of their support. In view of this, we introduce a new family of skew distribution using the skewing mechanism of Azzalini (1985), namely, skew-symmetric-Laplace-uniform distribution (SSLUD). Here uniform distribution is used not only to introduce skewness in Laplace distribution but also to restrict distribution support on one side of the real line. This paper provides a comprehensive description of the essential distributional properties of SSLUD. Estimators of the parameter are obtained using the method of moments and the method of maximum likelihood. The finite sample and asymptotic properties of these estimators are studied using simulation. It is observed that the maximum likelihood estimator is better than the moment estimator through a simulation study. Finally, an application of SSLUD to real-life data on the daily percentage change in the price of NIFTY 50, an Indian stock market index, is presented.

Spread complexity measures the minimized spread of quantum states over all choices of basis. It generalizes Krylov operator complexity to quantum states under continuous Hamiltonian evolution. In this paper, we study spread complexity in the context of high-energy astrophysical neutrinos and propose a new flavor ratio based on complexity. Our findings indicate that our proposal might favor an initial ratio of fluxes as $\phi_{\nu_e}^0: \phi_{\nu_\mu}^0: \phi_{\nu_\tau}^0 = 1:0:0$ over a more generally expected ratio of $1:2:0$, when the IceCube neutrino observatory achieves its projected sensitivity to discriminate between flavors. Additionally, complexity-based definitions of flavor ratios exhibit a slight but nonzero sensitivity to the neutrino mass ordering, which traditional flavor ratios cannot capture., Comment: 11 pages, 3 figures, Version accepted for publication in International Journal of Theoretical Physics