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A solitonic model of the early universe is introduced by employing the Double-Sine-Gordon (DSG) potential. The model predicts the appropriate number of e-foldings ($N_e$) required for favored inflation and is an advantage for the model in addressing the flatness, horizon, and magnetic monopole problems. Compatibility of the model with observations, including the Planck $2018$ data \cite{Akrami et al. (2020)} and the Planck $2018$ data+BK$18$+BAO \cite{Ade et al. (2021)} paves the way to estimate the model's free parameters. The results generate acceptable and proper values for the spectral index ($n_s$) and the tensor-to-scalar ratio ($r$) in agreement with the Planck $2018$ data \cite{Akrami et al. (2020)} and the Planck $2018$ data+BK$18$+BAO \cite{Ade et al. (2021)}. Correspondingly, a consistent description of the reheating era is obtained, yielding positive reheating number of e-foldings ($N_{\mathrm{reh}}$) and reheating final temperature ($T_{\mathrm{reh}}$) from $10^{-2}$ GeV to $10^{16}$ GeV. Overall, the model seems viable at the inflationary and reheating eras.

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks., Comment: Code is available at: https://github.com/mohammad-gh009/Large-Language-Models-vs-Classical-Machine-learning and https://github.com/Sdamirsa/Tehran_COVID_Cohort. The datasets are available from the corresponding author on reasonable request (sdamirsa@ymail.com)

Histopathological image analysis is crucial for accurate cancer diagnosis and treatment planning. While deep learning models, especially convolutional neural networks, have advanced this field, their "black-box" nature raises concerns about interpretability and trustworthiness. Explainable Artificial Intelligence (XAI) techniques aim to address these concerns, but evaluating their effectiveness remains challenging. A significant issue with current occlusion-based XAI methods is that they often generate Out-of-Distribution (OoD) samples, leading to inaccurate evaluations. In this paper, we introduce Inpainting-Based Occlusion (IBO), a novel occlusion strategy that utilizes a Denoising Diffusion Probabilistic Model to inpaint occluded regions in histopathological images. By replacing cancerous areas with realistic, non-cancerous tissue, IBO minimizes OoD artifacts and preserves data integrity. We evaluate our method on the CAMELYON16 dataset through two phases: first, by assessing perceptual similarity using the Learned Perceptual Image Patch Similarity (LPIPS) metric, and second, by quantifying the impact on model predictions through Area Under the Curve (AUC) analysis. Our results demonstrate that IBO significantly improves perceptual fidelity, achieving nearly twice the improvement in LPIPS scores compared to the best existing occlusion strategy. Additionally, IBO increased the precision of XAI performance prediction from 42% to 71% compared to traditional methods. These results demonstrate IBO's potential to provide more reliable evaluations of XAI techniques, benefiting histopathology and other applications. The source code for this study is available at https://github.com/a-fsh-r/IBO., Comment: 19 pages, 6 figures

In this paper, we delve into the intricate thermodynamic topology of quantum-corrected Anti-de Sitter-Reissner-Nordstrm (AdS-RN) black hole within the framework of Kiselev spacetime. By employing the generalized off-shell Helmholtz free energy approach, we meticulously compute the thermodynamic topology of these selected black holes. Furthermore, we establish their topological classifications. Our findings reveal that quantum correction terms influence the topological charges of black holes in Kiselev spacetime, leading to novel insights into topological classifications. Our research findings elucidate that, in contrast to the scenario in which $\omega=0$ and $a=0.7$ with total topological charge $W=0$ and $\omega=-4/3$ with total topological charge $W=-1$, in other cases, the total topological charge for the black hole under consideration predominantly stabilizes at +1. This stabilization occurs with the significant influence of the parameters a, c, and $\omega$ on the number of topological charges. Specifically, when $\omega$ assumes the values of $\omega=-1/3$, $\omega=-2/3$ , $\omega=-1$, the total topological charge consistently be to W = +1., Comment: 17 pages, 5 figures. Accepted for publication in Chinese Physics C

The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical data pivotal in clinical contexts, into LLM paradigms has not been thoroughly explored. In this study, we examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record (EHR) data. We compare the performance of these embeddings with that of raw numerical EHR data when used as feature inputs to traditional machine learning (ML) algorithms that excel at tabular data learning, such as eXtreme Gradient Boosting. We focus on instruction-tuned LLMs in a zero-shot setting to represent abnormal physiological data and evaluating their utilities as feature extractors to enhance ML classifiers for predicting diagnoses, length of stay, and mortality. Furthermore, we examine prompt engineering techniques on zero-shot and few-shot LLM embeddings to measure their impact comprehensively. Although findings suggest the raw data features still prevails in medical ML tasks, zero-shot LLM embeddings demonstrate competitive results, suggesting a promising avenue for future research in medical applications., Comment: Under review

Combining multiple predictors obtained from distributed data sources to an accurate meta-learner is promising to achieve enhanced performance in lots of prediction problems. As the accuracy of each predictor is usually unknown, integrating the predictors to achieve better performance is challenging. Conventional ensemble learning methods assess the accuracy of predictors based on extensive labeled data. In practical applications, however, the acquisition of such labeled data can prove to be an arduous task. Furthermore, the predictors under consideration may exhibit high degrees of correlation, particularly when similar data sources or machine learning algorithms were employed during their model training. In response to these challenges, this paper introduces a novel structured unsupervised ensemble learning model (SUEL) to exploit the dependency between a set of predictors with continuous predictive scores, rank the predictors without labeled data and combine them to an ensembled score with weights. Two novel correlation-based decomposition algorithms are further proposed to estimate the SUEL model, constrained quadratic optimization (SUEL.CQO) and matrix-factorization-based (SUEL.MF) approaches. The efficacy of the proposed methods is rigorously assessed through both simulation studies and real-world application of risk genes discovery. The results compellingly demonstrate that the proposed methods can efficiently integrate the dependent predictors to an ensemble model without the need of ground truth data.

The performance of reinforcement learning (RL) algorithms is sensitive to the choice of hyperparameters, with the learning rate being particularly influential. RL algorithms fail to reach convergence or demand an extensive number of samples when the learning rate is not optimally set. In this work, we show that model selection can help to improve the failure modes of RL that are due to suboptimal choices of learning rate. We present a model selection framework for Learning Rate-Free Reinforcement Learning that employs model selection methods to select the optimal learning rate on the fly. This approach of adaptive learning rate tuning neither depends on the underlying RL algorithm nor the optimizer and solely uses the reward feedback to select the learning rate; hence, the framework can input any RL algorithm and produce a learning rate-free version of it. We conduct experiments for policy optimization methods and evaluate various model selection strategies within our framework. Our results indicate that data-driven model selection algorithms are better alternatives to standard bandit algorithms when the optimal choice of hyperparameter is time-dependent and non-stationary., Comment: RLC 2024 Workshop on Failure Modes of Sequential Decision-Making in Practice

In this paper, we delve into the study of thermodynamics and phase transition of charged Gauss-Bonnet black holes within the context of anti-de Sitter (AdS) space, with particular emphasis on the central charge's role within the dual conformal field theory (CFT). We employ a holographic methodology that interprets the cosmological constant and the Newton constant as thermodynamic variables, leading to the derivation of a modified first law of thermodynamics that incorporates the thermodynamic volume and pressure. Our findings reveal that the central charge of the CFT is intrinsically linked to the variation of these constants, and its stability can be ensured by simultaneous adjustment of these constants. We further explore the phase structures of the black holes, utilizing the free energy. Our research uncovers the existence of a critical value of the central charge, beyond which the phase diagram displays a first-order phase transition between small and large black holes. We also delve into the implications of our findings on the complexity of the CFT. Our conclusions underscore the significant role of the central charge in the holographic thermodynamics and phase transition of charged Gauss-Bonnet black holes. Furthermore, we conclude that while the central charge considered provides suitable and satisfactory solutions for this black hole in 4 and 5 dimensions, it becomes necessary to introduce a unique central charge for this structure of modified gravity. In essence, the central charge in holographic thermodynamics is not a universal value and requires modification in accordance with different modified gravities. Consequently, the physics of the problem will significantly deviate from the one discussed in this article, indicating a rich and complex landscape for future work., Comment: 26 pages, 4 figures, accepted for publication in General Relativity and Gravitation

Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks. Despite demonstrations of significant merits such as improved robustness and resilience to unseen or out-of-distribution inputs over their non- Bayesian counterparts, their practical use has faded to near insignificance. In this study, we introduce an innovative framework to mitigate the computational burden of Bayesian neural networks (BNNs). Our approach follows the principle of Bayesian techniques based on deep ensembles, but significantly reduces their cost via multiple low-rank perturbations of parameters arising from a pre-trained neural network. Both vanilla version of ensembles as well as more sophisticated schemes such as Bayesian learning with Stein Variational Gradient Descent (SVGD), previously deemed impractical for large models, can be seamlessly implemented within the proposed framework, called Bayesian Low-Rank LeArning (Bella). In a nutshell, i) Bella achieves a dramatic reduction in the number of trainable parameters required to approximate a Bayesian posterior; and ii) it not only maintains, but in some instances, surpasses the performance of conventional Bayesian learning methods and non-Bayesian baselines. Our results with large-scale tasks such as ImageNet, CAMELYON17, DomainNet, VQA with CLIP, LLaVA demonstrate the effectiveness and versatility of Bella in building highly scalable and practical Bayesian deep models for real-world applications., Comment: 17 pages, 14 figures, 11 tables

In this study, we investigate the dynamics and frame-by-frame phase transition of the first order in black hole thermodynamics. For our analysis, we will utilize the Kramers escape rate. Our focus is on charged anti-de Sitter (AdS) black holes influenced by Kaniadakis and Barrow statistics. The selection of these black holes aims to examine the effects of entropy variation on the dynamics of phase transition and to demonstrate that the Kramers escape rate, as an efficient tool, can effectively represent the dynamic transition from a small to a large black hole within the domain of first-order phase transitions. It is noteworthy that while the transition from small to large black holes should ostensibly dominate the entire process, our results indicate that the escape rate undergoes changes as it passes through the midpoint of the phase transition, leading to a reverse escape phenomenon. The findings suggest that the dynamic phase transition in charged AdS black holes affected by entropy change bears a significant resemblance to the outcomes of models influenced by Bekenstein-Hawking entropy\cite{23}. This similarity in results could serve as an additional motivation to further explore the potential capabilities of Kaniadakis and Barrow statistics in related cosmological fields. These capabilities could enhance our understanding of other cosmological properties, Comment: 20 pages, 9 figures

We propose a neural network for both forward and inverse continuous nonlinear Fourier transforms, NFT and INFT respectively. We demonstrate the network's capability to perform NFT and INFT for a random mix of NFDM-QAM signals. The network transformations (NFT and INFT) exhibit true characteristics of these transformations; they are significantly different for low and high-power input pulses. The network shows adequate accuracy with an RMSE of 5e-3 for forward and 3e-2 for inverse transforms. We further show that the trained network can be used to perform general nonlinear Fourier transforms on arbitrary pulses beyond the training pulse types.

We study the asymptotic dynamics of 3D gravity with Rindler boundary conditions both in flat and AdS spacetimes. We do this by using the angular quantization and Hamiltonian reduction of the action to the Wess-Zumino-Witten theory on the boundary. We then rewrite the boundary action as a functional of elements of the asymptotic symmetry group., Comment: 20 pages, v2: typos fixed; clarification and references added, v3: ref. added, v4: clarification and figures added, references updated, matching the published version

This paper proposes Bayesian Adaptive Trials (BAT) as both an efficient method to conduct trials and a unifying framework for evaluation social policy interventions, addressing limitations inherent in traditional methods such as Randomized Controlled Trials (RCT). Recognizing the crucial need for evidence-based approaches in public policy, the proposal aims to lower barriers to the adoption of evidence-based methods and align evaluation processes more closely with the dynamic nature of policy cycles. BATs, grounded in decision theory, offer a dynamic, ``learning as we go'' approach, enabling the integration of diverse information types and facilitating a continuous, iterative process of policy evaluation. BATs' adaptive nature is particularly advantageous in policy settings, allowing for more timely and context-sensitive decisions. Moreover, BATs' ability to value potential future information sources positions it as an optimal strategy for sequential data acquisition during policy implementation. While acknowledging the assumptions and models intrinsic to BATs, such as prior distributions and likelihood functions, the paper argues that these are advantageous for decision-makers in social policy, effectively merging the best features of various methodologies.

In this article, we anchor our analysis on the premise that the presence of a photon sphere is an intrinsic characteristic of any ultra-compact gravitational structure with spherical symmetry. Utilizing the concept of a topological photon sphere, we categorize the behaviors of diverse gravitational models based on the structure of their photon spheres. This innovative approach enables us to define boundaries for black hole parameters, which subsequently allows us to classify the model as either a black hole or a naked singularity. Indeed, we will demonstrate that the existence of this reciprocal relationship between the gravitational structure and the presence of a photon sphere offers a unique advantage that can be directly and inversely harnessed. Furthermore, we present a precise correlation between the behavior of the defined effective potential and the total topological charge for each model. Through an independent examination of the effective potential, we show that a gravitational model typically exhibits naked singularity behavior when at least one effective potential minimum (a stable photon sphere) protrudes beyond the event horizon. Finally, we will compare the effect of adding perfect fluid dark matter to the models under study and observe the impact of this quantity on the behavior of the photon sphere and the extent of their dominance., Comment: 38 pages, 16 figures, 7 Tables

Achieving optimal performance with frame-based vision sensors on aerial platforms poses a significant challenge due to the fundamental tradeoffs between bandwidth and latency. Event cameras, which draw inspiration from biological vision systems, present a promising alternative due to their exceptional temporal resolution, superior dynamic range, and minimal power requirements. Due to these properties, they are well-suited for processing and segmenting fast motions that require rapid reactions. However, previous methods for event-based motion segmentation encountered limitations, such as the need for per-scene parameter tuning or manual labelling to achieve satisfactory results. To overcome these issues, our proposed method leverages features from self-supervised transformers on both event data and optical flow information, eliminating the need for human annotations and reducing the parameter tuning problem. In this paper, we use an event camera with HD resolution onboard a highly dynamic aerial platform in an urban setting. We conduct extensive evaluations of our framework across multiple datasets, demonstrating state-of-the-art performance compared to existing works. Our method can effectively handle various types of motion and an arbitrary number of moving objects. Code and dataset are available at: \url{https://samiarja.github.io/evairborne/}, Comment: 31 pages, 11 figures, 8 tables

In this paper, we investigate the topological photon sphere from two distinct perspectives. In the first view, we examine the existence and characteristics of topological photon(anti-photon)spheres for black holes with different structures, such as Einstein-Young-Mills non-minimal, AdS black holes surrounded by Chaplygin-like dark fluid, and Bardeen-like black holes in Einstein-Gauss-Bonnet gravity. Furthermore, we delve into the deeper perspective of the necessity of photon spheres for super-compact gravitational structures such as black holes. By leveraging this necessity, we propose a classification of the parameter space of black hole models based on the existence and positioning of photon spheres. This approach enables the determination of parameter ranges that delineate whether a solution represents a black hole or a naked singularity. In essence, the paper illustrates the utility of the photon sphere as a notable test for establishing the permissible and non-permissible parameter ranges within specific theories of black hole solutions., Comment: 26 pages, 7 figures

Traditional static methods in phase transition studies, such as the swallowtail bifurcation diagram, provide good insights into the thermodynamics of black holes. However, they practically lose sight of the dynamic aspects and temporal sequence of events. The Kramers escape rate, central to our research, offers a somewhat dynamic approach to phase transition. We examine the free energy landscapes for black holes under the influence of ’dark’ and ’stringy+dark’ structures, assessing how additional parameters affect the escape rates and dynamics of the transition during the first-order phase transition from small to large black holes. In our analysis, we consider the escape rate as a function of the black hole radius and study its variations. We will observe that, on one hand, the escape rate well represents our assumption based on the movement from zero, increasing to a maximum point, and then decreasing back to zero as reactive structures become active during the phase transition interval. However, the critical point in this method is the encounter with a specific and distinct point. This is where the diagram of the direct process (escape rate from small to large black holes) intersects with the reverse process (large to small black holes), becoming equally probable (contact point). The point, which seems improbable at the onset of the phase transition or very negligible, gains more significance as the process progresses. This increase indicates the dominance of a region where the escape rate from larger black holes to smaller ones prevails. The predominance of the reverse process, which increases as we approach the end of the process and is necessarily accompanied by a variation in radius, may be considered as a natural reaction of the black hole against the ’phase change’ action. A reaction which attempting to prevent any uncontrolled radial growth that could jeopardize the stability of the black hole., Comment: 32 pages, 11 figures. The updated version of manuscript includes new calculations, discussions, and optimization of the results

In this paper, we explore the intriguing interplay between fundamental theoretical physics concepts within the context of charged black holes. Specifically, we focus on the consistency of the weak gravity conjecture (WGC) and weak cosmic censorship conjecture (WCCC) in the thermodynamics of conformal field theory (CFT), and restricted phase space (RPS) for AdS Reissner-Nordstr$\"o$m black holes with a perfect fluid dark matter (RN-PFDM). The WGC ensures that gravity remains the weakest force in the system. Meanwhile, the WCCC addresses the cosmic censorship problem by preventing the violation of fundamental physical laws near the black hole singularity. First, we analyze the RN black hole's free energy in both spaces, revealing a distinctive swallowtail pattern indicative of a first-order phase transition when certain free parameter conditions are met. We explore the WGC across different phase spaces, emphasizing the need for certain conditions in EPST, RPST, and CFT. We demonstrate that PFDM $( \gamma )$ and the radius of AdS ($l$) have a vital role in proving the satisfaction of the WGC. Also, these values have a linear relation with the range compatibility of WGC. The range of compatibility for WGC in RPST and EPST is the same while for CFT, this range is larger than EPST, and RPST. It means somehow the WGC and CFT are more consistent. The WCCC was examined at the critical juncture, confirming its validity with $( f(M_c, Q_c, r_c) = 0 )$. We conclude that the WGC is supported at the critical point of black holes, and the WCCC is also maintained, demonstrating the robustness of these conjectures within the critical ranges of black hole parameters., Comment: 29 pages, 7 figures

Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results show feasible applications in such a high-stakes domain., Comment: submitted to review

In this paper, we explore the Joule-Thomson expansion (JTE) process for the Einstein-Power-Young-Mills (EPYM) and the AdS Kerr Sen (AKS) black holes. We study the effect of free parameters on the Joule-Thomson coefficient (JTC), the inversion curve, and the $T_i^{min}/T_{c}$. The isenthalpic curves of the AKS black hole show cooling or heating behavior depending on the inversion curve, which is affected by the mass and the parameters $b$ and $a$ of the black hole. If we assume the parameter $b$ to be zero, the results reduce to the Kerr-AdS black holes[1]. In [2,3], for the Einstein-Power-Yang-Mills AdS black hole with $q>1$ and $n=2$, the $T_i^{min}/T_{c}$ is $1/2$. But in this paper, for the AdS-Maxwell-power-Yang-Mills black hole, when $q>1$, the $T_i^{min}/T_{c}$ is almost equal to $1/2$ for the increase of Maxwell's charge $C$, and when $q=1/2$, the $T_i^{min}/T_{c}$ is equal to $1/2$ for all values of $C$. Also, when $1/2

Investigating the existence of algebra and finding hidden symmetries in physical systems is one of the most important aspects for understanding their behavior and predicting their future. Expanding this unique method of study to cosmic structures and combining past knowledge with new data can be very interesting and lead to discovering new ways to analyze these systems. However, studying black hole symmetries always presents many complications and sometimes requires computational approximations. For example, checking the existence of Killing vectors and then calculating them is not always an easy task. It becomes much more difficult as the structure and geometry of the system become more complex. In this work, we will show that if the wave equations with a black hole background can be converted in the form of general Heun equation, based on its structure and coefficients, the algebra of the system can be easily studied, and computational and geometrical complications can be omitted. For this purpose, we selected two $AdS_5$ black holes: Reissner-Nordstrom (R-N) and Kerr, and analyzed the Klein-Gordon equation with the background of these black holes. Based on this concept, we observed that the radial part of the R-N black hole and both the radial and angular parts of the Kerr black hole could be transformed into the general form of the Heun equation. As a result, according to the algebraic structure that governs the Heun equation and its coefficients, one can easily achieve generalized $sl(2)$ algebra., Comment: 20 pages, Accepted for publication in International Journal of Modern Physics A