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ALMA chemical survey of disk-outflow sources in Taurus (ALMA-DOT) VII: the layered molecular outflow from HL Tau and its relationship with the ringed disk

The ringed disk around HL Tau stands out as the iconic signature of planet formation, but the origin of the substructures is still debated. The HL Tau system also drives a powerful bipolar wind, and w...

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This article presents significant findings regarding the interplay between a molecular outflow and its associated disk structure, utilizing advanced ALMA observations. The exploration of nested shell configurations and their implications for magnetohydrodynamic models introduces a novel perspective on wind dynamics in protoplanetary disks, potentially influencing both theoretical modeling and observational strategies in related fields.

On generalized Mittag-Leffler-type functions of two variables

We aim to study Mittag-Leffler type functions of two variables ${{D}_{1}}\left( x,y \right),...,{{D}_{5}}\left( x,y \right)$ by analogy with the Appell hypergeometric functions of two variable...

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The article presents a significant advancement in the understanding of Mittag-Leffler-type functions in two variables, expanding the existing knowledge by connecting these functions with hypergeometric functions. It employs rigorous mathematical techniques and provides new representations and properties, including integral representations and the construction of partial differential equations, which are likely to inspire further research in this area and related fields.

Optical absorption and luminescence of $α$ -LiV $_2$ O $_5$ from the Bethe Salpeter Equation

$α$ -Li $_x$ V $_2$ O $_5$ is obtained by intercalating Li between the layers of V $_2$ O $_5$ . The partial filling of the split-off conduction band by electron ...

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The article presents a detailed study of the electronic band structure of α-LiV₂O₅, utilizing advanced computational methods (QS GW and Bethe Salpeter equation) to explore its optical properties. This approach is rigorous and offers novel insights into the relationship between lithium intercalation and optical characteristics, which have implications for both fundamental research and practical applications, such as in optoelectronic materials.

Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback

The scientific research paradigm is undergoing a profound transformation owing to the development of Artificial Intelligence (AI). Recent works demonstrate that various AI-assisted research methods ca...

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The Dolphin framework represents a significant advancement in automating the scientific research process through an innovative closed-loop feedback mechanism. The integration of AI into research methodology not only enhances efficiency but also enables continuous improvement of research ideas. Its novelty lies in the combination of idea generation, automatic coding, and iterative feedback within a single framework, which could transform the landscape of automated research.

Self-Normalized Moderate Deviations for Degenerate U-Statistics

In this paper, we study self-normalized moderate deviations for degenerate { $U$ }-statistics of order $2$ . Let $\{X_i, i \geq 1\}$ be i.i.d. random variables and consider symme...

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This paper presents a novel approach to understanding moderate deviations in the context of degenerate U-statistics, contributing to an area of probability theory that has significant implications for statistical theory and practice. The focus on self-normalization and the derivation of limit laws enrich the theoretical framework, suggesting potential applications in asymptotic statistics. The methodological rigor applied in establishing conditions under which the results hold is commendable and adds valuable depth to the field.

Active Learning Techniques for Pomset Recognizers

Pomsets are a promising formalism for concurrent programs based on partially ordered sets. Among this class, series-parallel pomsets admit a convenient linear representation and can be recognized by s...

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The article presents a significant advance in the active learning paradigm for pomset recognizers. The novel approaches to counter-example analysis and the adaptation of existing algorithms demonstrate both methodological rigor and practical applicability, increasing efficiency and reducing redundancy. These improvements are likely to influence further research in learning algorithms for concurrent systems, making the article very relevant to the field.

Magnetron Stabilization using Frequency Modulation

An unstable magnetron RF source for driving an accelerator cavity at high power is stabilized using ferroelectric fast reactive tuning. The magnetron output is converted to a selected reference freque...

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The article presents a novel approach to stabilizing a magnetron RF source, which is critical in applications involving high power accelerators. The use of frequency modulation for stabilization and the ability to control amplitude and phase are significant advancements that can improve the performance of RF sources in various applications. The methodological rigor appears strong, which enhances the article's potential impact.

Pulsar timing in the Galactic Center

We propose a novel approach which implements the relativistic calculations of the photon travel time into a robust timing model for pulsars orbiting supermassive black holes. We demonstrate the inabil...

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This article presents a novel approach that addresses a significant limitation in current pulsar timing methodologies, offering improvements in accuracy by incorporating relativistic effects. The methodological rigor, especially in demonstrating the inadequacies of existing codes, and the implications for observational astronomy—especially for pulsars around supermassive black holes—enhance its potential relevance and impact on future research.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models

This paper broaches the peridynamic inverse problem of determining the horizon size of the kernel function in a one-dimensional model of a linear microelastic material. We explore different kernel fun...

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This article presents a novel application of Physics Informed Neural Networks (PINNs) in addressing the peridynamic inverse problem, which is a critical challenge in material science and engineering. Its focus on different kernel functions and the exploration of convergence behaviors offers substantial insights into the robustness of the proposed methods. The combination of numerical experiments and theoretical proofs enhances methodological rigor, making it suitable for advancing both the field of computational materials science and neural network applications.

HYB-VITON: A Hybrid Approach to Virtual Try-On Combining Explicit and Implicit Warping

Virtual try-on systems have significant potential in e-commerce, allowing customers to visualize garments on themselves. Existing image-based methods fall into two categories: those that directly warp...

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HYB-VITON presents a novel hybrid approach that successfully combines two established techniques in virtual try-on systems, addressing the limitations of each while demonstrating improved performance. The methodological rigor, as evidenced by a series of experiments yielding promising results, enhances its credibility. The applicability of this research to e-commerce and online fashion retail underscores its potential impact on the industry and future technological advancements in virtual reality.

General spherically symmetric black bounces within non-linear electrodynamics

Over the last years, the search of new regular black bounce solutions has drawn a lot of attentional over the international community working in gravitation. Indeed, in the era of gravitational waves ...

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This article presents innovative research in the field of black hole physics by exploring new types of solutions (black bounces) which have potential implications for our understanding of gravitation and its manifestations in astrophysical contexts. The incorporation of non-linear electrodynamics adds a significant layer of complexity and novelty, enhancing its relevance. The methodical approach to reconstruct black bounce solutions provides a robust foundation for further exploration and could lead to new insights in both theoretical and observational astrophysics.

Steady and transient thermal stress analysis using a polygonal finite element method

While the finite element method (FEM) has been widely used for thermal stress problems, it faces challenges in handling complex geometries, non-matching meshes, and achieving computational efficiency....

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The article introduces a novel polygonal finite element method (NS-FEM) that addresses significant challenges in the conventional finite element method for thermal stress analysis, suggesting strong potential for practical applications and future research. The methodological advancements, particularly in mesh adaptability and precision, indicate substantial improvements over existing techniques, which is critical for engineering problems with complex geometries. The use of numerical examples to demonstrate efficacy adds credibility to the findings.

Implicit Coordination using Active Epistemic Inference

A Multi-robot system (MRS) provides significant advantages for intricate tasks such as environmental monitoring, underwater inspections, and space missions. However, addressing potential communication...

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The article proposes a novel framework using Theory of Mind to enhance reasoning in multi-robot systems during communication failures, showcasing significant advancements over existing methods. Its focus on higher-order reasoning and effective epistemic planning addresses critical gaps in MRS research, making it applicable in key real-world scenarios like environmental monitoring and missions where traditional communication may fail. The methodological rigor is supported by simulations and experiments, indicating practical applicability.

A regularized transportation cost stemming from entropic approximation

We study the entropic regularizations of optimal transport problems under suitable summability assumptions on the point-wise transport cost. These summability assumptions already appear in the literat...

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This article presents a novel analysis of entropic regularizations in optimal transport, extending existing literature by identifying weak compactness conditions sufficient for convergence. This could significantly influence future research in optimal transport by refining methods and potentially broadening applicability, especially in higher-dimensional cases with more than two marginals.

mFabric: An Efficient and Scalable Fabric for Mixture-of-Experts Training

Mixture-of-Expert (MoE) models outperform conventional models by selectively activating different subnets, named \emph{experts}, on a per-token basis. This gated computation generates dynamic communic...

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The paper introduces a novel system (mFabric) specifically designed to enhance the training efficiency of Mixture-of-Expert models, addressing a critical challenge in distributed machine learning. The methodological rigor is apparent through experimental validation, including both prototype implementation and large-scale simulations. The ability to dynamically reconfigure network topology during training is a significant advancement that may inspire further research in deep learning infrastructure and optimization. Its clear relevance to current challenges in machine learning infrastructure marks it as a high-impact study.

Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study

The integration of large language models (LLMs) into public transit systems presents a transformative opportunity to enhance urban mobility. This study explores the potential of LLMs to revolutionize ...

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The study addresses a novel application of large language models, integrating advanced AI into public transportation, which has the potential to significantly reshape how transit systems operate. It employs a case study approach, providing concrete insights and practical implications for transit management. The methodological rigor, particularly the comparative analysis of different models, strengthens its contributions. This research not only addresses current issues in urban mobility but also lays a foundation for scalable applications in other cities, suggesting a wide-ranging impact on future research and development.

Explainable Reinforcement Learning via Temporal Policy Decomposition

We investigate the explainability of Reinforcement Learning (RL) policies from a temporal perspective, focusing on the sequence of future outcomes associated with individual actions. In RL, value func...

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The article introduces a novel approach (Temporal Policy Decomposition) to enhancing the explainability of RL, an area that is increasingly crucial as RL applications expand to complex systems. It focuses on temporal aspects that traditional methods often overlook, providing a significant contribution to both interpretability and practical applications. Its methodology appears rigorous as it leverages established learning techniques, and the implications for future research in aligning RL outputs with human expectations are substantial.

Grain boundaries amplify local chemical ordering in CrCoNi

Local chemical ordering plays an important role in the behavior of complex concentrated alloys, yet its characterization remains challenging due to the nanoscale dimensions and scattered spatial distr...

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This article presents a novel approach to understanding local chemical ordering in complex concentrated alloys (CCAs) by focusing on the role of grain boundaries. The integration of hybrid simulations provides a rigorous methodological framework, and the discovery of compositional waves at the nanoscale has significant implications for material design and characterization. Its potential to influence experimental approaches and deepen knowledge in alloy behavior underscores its high relevance.

Realising VCD for untwisted automorphism groups of RAAGs

The virtual cohomological dimension of $\operatorname{Out}(F_n)$ is given precisely by the dimension of the spine of Culler--Vogtmann Outer space. However, the dimension of the spine of untwis...

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The article addresses a significant issue in the study of outer automorphism groups of right-angled Artin groups (RAAGs), presenting novel contributions through graph-theoretic conditions and the introduction of a new contractible cube complex. Its potential to unify existing theories through the lens of virtual cohomological dimensions marks it as a meaningful advancement in the field. The methodological rigor and the implications for understanding the topology of RAAGs significantly enhance its relevance.

Origin of ion bombardment induced Tb oxidation in Tb/Co multilayers

Ion bombardment is currently an active area of research for patterning rare earth/transition metal ferrimagnetic thin films because the magnetic properties are extremely sensitive to changes in the co...

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The article explores an innovative approach to manipulating the magnetic properties of rare earth/transition metal ferrimagnets through ion bombardment-induced oxidation. The novelty lies in elucidating the mechanism of oxidation and its impact on the magnetic behavior of these materials, which is crucial for applications in spintronics and magnetic sensors. The study employs rigorous experimental methods to analyze the effects of different ion species, enhancing its methodological rigor. This has important implications for the design of advanced magnetic materials. However, the scope may be somewhat limited to specific multilayer systems and may not address broader applications immediately.