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An accurate solar axions ray-tracing response of BabyIAXO

BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axion...

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The article presents a technologically advanced approach to solar axion detection, which is an important area in particle physics and astrophysics. Its thorough development of software tools for modeling helioscope components demonstrates a strong methodological rigor and offers significant potential for enhancing experimental sensitivity. The capacity for modeling various production mechanisms adds novelty and flexibility, which could inspire future research in related experiments.

ICODE: Modeling Dynamical Systems with Extrinsic Input Information

Learning models of dynamical systems with external inputs, that may be, for example, nonsmooth or piecewise, is crucial for studying complex phenomena and predicting future state evolution, which is e...

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The article presents a novel approach with ICODEs, which advances the modeling of dynamical systems by explicitly integrating external inputs. The methodological rigor is strong, backed by sufficient conditions for system properties and extensive experimental validations. This could influence future research by providing a foundational model for various complex systems, enhancing predictability and robustness in predictions.

Generalizing subdiffusive Black-Scholes model by variable exponent: Model transformation and numerical approximation

This work generalizes the subdiffusive Black-Scholes model by introducing the variable exponent in order to provide adequate descriptions for the option pricing, where the variable exponent may accoun...

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This article presents a novel approach to modifying the Black-Scholes model, enhancing its applicability in financial mathematics. The introduction of variable exponents provides significant potential for more accurately modeling market behaviors with memory effects, which is a developing area of study. The methodical transformations and rigorous numerical analysis contribute to its methodological strength, facilitating future practical applications.

Einstein manifolds of negative lower bounds on curvature operator of the second Kind

We demonstrate that $n$ -dimension closed Einstein manifolds, whose smallest eigenvalue of the curvature operator of the second kind of $\mathring{R}$ satisfies $λ_1 \ge -θ(n) \barλ...

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This article presents significant advancements in the understanding of Einstein manifolds, particularly in relation to the curvature operator of the second kind. The improvement over a previously established theorem highlights its novelty and relevance. The mathematical rigor in handling eigenvalues adds to the robustness of the findings, making it potentially impactful for further exploration in differential geometry and related fields.

Analytic torsion for irreducible holomorphic symplectic fourfolds with involution, II: the singularity of the invariant (with an Appendix by Ken-Ichi Yoshikawa)

We study the boundary behavior of the invariant of $K3^{[2]}$ -type manifolds with antisymplectic involution, which we obtained using equivariant analytic torsion. We show the algebraicity of t...

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The paper presents novel insights into the boundary behavior of invariants in the context of K3-type manifolds, utilizing equivariant analytic torsion. The results provide a deep connection between different invariants and could significantly influence further research on modular forms and symplectic geometry. Its well-defined methodology and proof of algebraicity enhance its robustness, and the findings have implications for both theoretical mathematics and applications in string theory.

Quantum gravity corrections to the spontaneous excitation of an accelerated atom interacting with a quantum scalar field

The Generalized Uncertainty Principle (GUP) extends the Heisenberg Uncertainty Principle by suggesting a minimum observable scale that includes the effects of quantum gravity, which is supposed to pot...

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This article presents a novel exploration of quantum gravity effects using the Generalized Uncertainty Principle (GUP), offering new insights into spontaneous emission in quantum systems. It addresses fundamental questions in quantum mechanics and utilizes rigorous methodologies. The potential for observable effects below the Planck energy scale adds considerable relevance to experimental physics, particularly in low-energy quantum systems.

Hybrid Physics-ML Modeling for Marine Vehicle Maneuvering Motions in the Presence of Environmental Disturbances

A hybrid physics-machine learning modeling framework is proposed for the surface vehicles' maneuvering motions to address the modeling capability and stability in the presence of environmental dis...

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The article presents a novel hybrid modeling framework that integrates physics with machine learning, which is rare and adds significant value to the field of marine vehicle dynamics. The use of real navigational data to validate the model enhances its applicability and demonstrates methodological rigor. Additionally, the approach addresses a relevant challenge (environmental disturbances), making it highly applicable to both theoretical and practical aspects of marine engineering. Its potential for extension to high-fidelity simulation systems suggests future research developments might benefit from it.

Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization

Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preser...

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The article presents a novel Heterogeneous Split Federated Learning framework that addresses significant limitations in current algorithms related to training efficiency and latency in heterogeneous environments. The methodological rigor is strong, as it employs advanced optimization techniques, including genetic algorithms and Lagrangian relaxation, enhancing its applicability in real-world scenarios. The use of simulations to validate the proposed framework further supports its relevance and robustness.

Structure-preserving model reduction of Hamiltonian systems by learning a symplectic autoencoder

Evolutionary partial differential equations play a crucial role in many areas of science and engineering. Spatial discretization of these equations leads to a system of ordinary differential equations...

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The article presents a novel approach to model reduction in Hamiltonian systems using symplectic autoencoders, integrating advanced machine learning techniques with rigorous mathematical frameworks. This strong intersection of fields enhances its relevance. The explicit focus on preserving symplectic structure is innovative and adds robustness to the model, likely influencing future research in this domain. The methodological rigor and practical validation further support its impact.

The transfer of Bell nonlocality between two and three-qubit dissipative systems with counter-rotating-wave terms

We investigate the effect of counter-rotating-wave terms on bell nonlocality (BN) and entanglement for three qubits coupled with a Lorentz-broadened cavity mode at zero temperature for strong and ultr...

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The study introduces a novel approach to understanding the role of counter-rotating-wave terms in two and three-qubit systems, addressing a significant gap in our knowledge about nonlocality and decoherence. The use of rigorous numerical methods adds to the methodological rigor, and the results have potential implications for quantum information science and technologies. However, the specific focus on certain conditions may limit broader applications.

Towards Full Delegation: Designing Ideal Agentic Behaviors for Travel Planning

How are LLM-based agents used in the future? While many of the existing work on agents has focused on improving the performance of a specific family of objective and challenging tasks, in this work, w...

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This article presents a novel perspective by focusing on the conceptual design of agentic behaviors for travel planning, particularly how these agents can take on routine decision-making for users. The introduction of the APEC Agent Constitution is a significant contribution that emphasizes both outcome and process evaluation of agents, enhancing trust and utility in automated systems. The methodological rigor is evident through the development and testing of APEC-Travel, which demonstrates practical applicability and robust performance improvements over baseline models, making it relevant for future developments in AI-driven decision-support systems.

AmpliNetECG12: A lightweight SoftMax-based relativistic amplitude amplification architecture for 12 lead ECG classification

The urgent need to promptly detect cardiac disorders from 12-lead Electrocardiograms using limited computations is motivated by the heart's fast and complex electrical activity and restricted comp...

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The article presents a significant advancement in the classification of 12-lead ECGs using a new activation function and a lightweight architecture. The novelty of the aSoftMax function, its application in a convolutional neural network, and its effectiveness in enhancing accuracy and interpretability make this research highly impactful. Additionally, the focus on resource efficiency aligns well with the growing demand for portable diagnostic tools in healthcare.

PIORS: Personalized Intelligent Outpatient Reception based on Large Language Model with Multi-Agents Medical Scenario Simulation

In China, receptionist nurses face overwhelming workloads in outpatient settings, limiting their time and attention for each patient and ultimately reducing service quality. In this paper, we present ...

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The paper presents a highly relevant and innovative application of Large Language Models (LLMs) in the healthcare domain, specifically targeting outpatient reception, which directly addresses a significant challenge in healthcare service quality. The integration of a simulation framework to enhance LLM performance showcases methodological rigor and novelty. The clear quantification of performance through both automatic and human assessments adds to the article's robustness, indicating both practical applicability and potential for scalability in real-world healthcare processes.

Dressing the Imagination: A Dataset for AI-Powered Translation of Text into Fashion Outfits and A Novel KAN Adapter for Enhanced Feature Adaptation

Specialized datasets that capture the fashion industry's rich language and styling elements can boost progress in AI-driven fashion design. We present FLORA (Fashion Language Outfit Representation...

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The article presents a novel dataset (FLORA) specifically designed to enhance AI-generated fashion design, which is a significant advancement in the field. The use of industry-specific language and detailed descriptions adds depth and applicability. The introduction of KAN Adapters provides a methodological innovation that may influence future model enhancements. This combination of dataset and methodological rigor suggests high potential for impact in fashion technology and AI applications.

Dissecting Conditional Branch Predictors of Apple Firestorm and Qualcomm Oryon for Software Optimization and Architectural Analysis

Branch predictor (BP) is a critical component of modern processors, and its accurate modeling is essential for compilers and applications. However, processor vendors have disclosed limited details abo...

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The paper addresses a significant gap in processor architecture research by providing a detailed reverse engineering approach to branch predictors, particularly for leading-edge processors from Apple and Qualcomm. Its novel methodology and the revelation of previously undisclosed predictor deficiencies not only contribute to theoretical knowledge but offer practical implications for software optimization and architectural enhancements. The comprehensive evaluation and proposed solutions enhance its applicability.

Schemato -- An LLM for Netlist-to-Schematic Conversion

Machine learning models are advancing circuit design, particularly in analog circuits. They typically generate netlists that lack human interpretability. This is a problem as human designers heavily r...

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The article presents a novel application of machine learning in the circuit design field, specifically offering a solution to the interpretability problem associated with netlists. Schemato's significant improvements in conversion rates compared to existing models highlight its potential impact on circuit design workflows and its applicability in practical scenarios. Its dual output format (LTSpice and CircuiTikz) enhances its utility for different user bases. The robust experimental validation and high performance metrics reinforce the paper's influence and applicability within the field.

Discovery of an Antiferromagnetic Topological Nodal-line Kondo Semimetal

The symbiosis of strong interactions, flat bands, topology and symmetry has led to the discovery of exotic phases of matter, including fractional Chern insulators, correlated moiré topological superco...

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The article presents a novel discovery of an antiferromagnetic topological nodal-line Kondo semimetal, which is significant due to its unique interaction between magnetic order and the Kondo effect. This combination of phenomena is relatively unexplored, showcasing high novelty and potential for impacting our understanding of topological materials and correlated electron systems. The rigorous exploration of both theoretical and experimental aspects strengthens its methodological robustness, promising applicability in future research on quantum materials.

Observing the eye of the storm I: testing regular black holes with LVK and EHT observations

According to the celebrated singularity theorems, space-time singularities in general relativity are inevitable. However, it is generally believed that singularities do not exist in nature, and their ...

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This article presents a novel approach to studying regular black holes by utilizing empirical data from LIGO, Virgo, and EHT. Its focus on a singularity-free black hole model represents a significant departure from traditional theories, potentially illuminating fundamental aspects of gravity and black hole physics. The methodological rigor is underscored by the use of multiple observational datasets to constrain parameters, making the findings relevant for both theoretical and observational astrophysics.

A blow up solution of the Navier-Stokes equations with a critical force

A forced solution $v$ of the Navier-Stokes equation in any open domain with no slip boundary condition is constructed. The scaling factor of the forcing term is the critical order $-2$ ...

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This article presents a novel solution to the Navier-Stokes equations, a fundamental problem in fluid dynamics, particularly through the introduction of critical force scaling. The construction of a blow-up solution under critical conditions demonstrates significant implications for the understanding of singularities in fluid flows influenced by common physical forces. The methodological rigor is evident in the detailed handling of the mathematical properties of the equations. This article likely opens avenues for further theoretical research as well as potential applications in various fields that encounter fluid dynamics under critical conditions.

Neutrino mass genesis in Scoto-Inverse Seesaw with Modular $A_4$

We propose a hybrid scotogenic inverse seesaw framework in which the Majorana mass term is generated at the one-loop level through the inclusion of a singlet fermion. This singlet Majorana fermion als...

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This article presents a novel hybrid approach combining scotogenic mechanics with modular symmetry for neutrino mass generation. Its methodological rigor and the dual implications for both neutrino physics and dark matter add significant value. The applicability of the model to various experimental tests makes it relevant for future research and experimental verifications.