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Thesis Tide

Thesis Tide ranks papers based on their relevance to the fields, with the goal of making it easier to find the most relevant papers. It uses AI to analyze the content of papers and rank them!

Reliable Uncertainty Quantification for Fiber Orientation in Composite Molding Processes using Multilevel Polynomial Surrogates

Fiber orientation is decisive for the mechanical properties and thus for the performance of composite materials. During manufacturing, variations in material and process parameters can significantly i...

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The article presents a novel approach by employing multilevel polynomial surrogates for uncertainty quantification in composite molding processes, which is crucial for enhancing the mechanical properties of materials. The methodological rigor is evidenced by the derivation of error bounds and the utilization of established models for verification. This work not only addresses a significant challenge in the field but also introduces robust tools that could greatly influence manufacturing practices in composites and inspire future research into similar quantitative methods.

Heavy Tail Robust Estimation and Inference for Average Treatment Effects

We study the probability tail properties of Inverse Probability Weighting (IPW) estimators of the Average Treatment Effect (ATE) when there is limited overlap between the covariate distributions of th...

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The article offers a novel solution to a significant problem in causal inference by presenting a tail-trimmed IPW estimator that addresses limitations of existing methods when overlap is limited. Its rigorous theoretical underpinnings combined with empirical validation through Monte Carlo experiments enhance its robustness and applicability in real-world scenarios. The contributions made in bias reduction and performance improvements suggest high potential for impact, particularly in sensitive areas requiring accurate treatment effect estimation.

Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection

Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with symbolic reasoning. How...

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This article presents a novel approach (Abductive Reflection) to improve Neuro-Symbolic AI by addressing the issue of output inconsistencies with domain knowledge. Its high efficiency and positive experimental results suggest significant implications for the advancement of NeSy systems, integrating neural and symbolic methods effectively. The methodological rigor and innovative perspective on cognitive processes further enhance its relevance.

New stochastic comparisons based on tail values at risk

In this paper we provide a new criterion for the comparison of claims, when we have conditional claims arising in stop loss contracts or contracts with franchise deductible. These stochastic compariso...

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This article presents a novel approach to stochastic comparisons using Tail Value at Risk (TVaR) in the context of insurance claims. Its methodological rigor, along with the potential to influence practices in risk assessment and management within finance and insurance, indicates a strong relevance to advancing the field. The practical applications and provided examples further enhance its impact and utility for future research.

Existence of Special Types Primitive Pairs in Finite Fields Avoiding Affine Hyperplanes

Let \Fm be finite fields of order $q^m$ , where $m\geq 2$ and $q$ , a prime power. Given \F-affine hyperplanes $A_1,\ldots, A_m$ of \Fm in g...

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The paper addresses a relatively specialized topic within the field of finite fields and polynomial mappings, exploring the existence of certain primitive pairs that avoid affine hyperplanes. It offers novel insights and extends known results about primitive elements, which can have implications in algebraic structures and coding theory. However, the application might be limited to a specific niche within finite fields, reducing its broader impact and applicability.

Approximate Solutions in Linear Fractional Vector Optimization

This paper studies approximate solutions of a linear fractional vector optimization problem without requiring boundedness of the constraint set. We establish necessary and sufficient conditions for ap...

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The article addresses a niche yet significant area of optimization by examining approximate solutions in linear fractional vector optimization, which is relatively underexplored. Its contributions in establishing conditions for weakly efficient points provide a potentially strong theoretical foundation. The methodology appears rigorous, and the implications for linear vector optimization broaden its applicability. However, the impact may be limited to a specific audience in mathematical optimization rather than broader fields.

On best approximation by multivariate ridge functions with applications to generalized translation networks

We prove sharp upper and lower bounds for the approximation of Sobolev functions by sums of multivariate ridge functions, i.e., functions of the form $\mathbb{R}^d \ni x \mapsto \sum_{k=1}^n h_k(A...

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This article provides novel theoretical insights into the approximation of Sobolev functions, expanding prior univariate results to a multivariate context. The methodological rigor demonstrated through sharp upper and lower bounds enhances its relevance. Furthermore, the application of these findings to generalized translation networks and complex-valued neural networks indicates strong practical implications, which could inspire future innovational approaches in these areas.

Pion and kaon pair production in double gap events in ALICE Run 3

The ALICE detector at the LHC has undergone a major upgrade in the long shutdown 2019-2021 to be able to take data at much-increased rates in Runs 3 and 4. The upgrades of the detector systems used fo...

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This article discusses significant enhancements to the ALICE detector, which are crucial for improving high-energy particle collision data analysis. The focus on double gap events is particularly interesting as it adds a layer of complexity to the study of pion and kaon production, potentially influencing how researchers interpret heavy-ion collision data. While the upgrades are important, the impact will depend on the analysis of resulting data, which may not be fully elaborated in this abstract.

Intrinsic Thermo-Spintronic Response of Superconducting Quasiparticles: Berry Curvature Effects

We propose two thermo-spintronic responses of spin-orbit coupled superconductors - the intrinsic thermal Edelstein effect and intrinsic spin Nernst effect - stemming from phase-space and momentum-spac...

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This article introduces innovative concepts regarding thermo-spintronic responses influenced by Berry curvature effects in superconductors. Its novelty lies in linking spin-orbit coupling with superconducting properties through Bogoliubov quasiparticles, suggesting potential applications in spintronic materials. The methodological rigor appears robust, with a focus on fundamental phenomena that could drive future research into superconducting spintronics and other quantum materials.

Evidence for multiband gapless superconductivity in the topological superconductor candidate 4Hb-TaS2

We present the ultralow-temperature thermal conductivity measurements on single crystals of transition-metal dichalcogenide material 4Hb-TaS $_{2}$ , which has recently been proposed as a topolo...

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This article presents novel findings regarding multiband gapless superconductivity in a promising topological superconductor, which contributes to the understanding of superconducting mechanisms in new materials. The rigorous methodology of thermal conductivity measurements enhances reliability, making it potentially impactful for both theoretical and experimental research in superconductivity.

Calibrating the Tully-Fisher Relation to Measure the Hubble Constant

Boubel et al. 2024 (B24) recently used the Tully-Fisher (TF) relation to measure calibrated distances in the Hubble flow and found $H_0= 73.3 \pm 2.1 (stat) \pm 3.5 (sys)$ km/s/Mpc. The large ...

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This article presents a refined measurement of the Hubble constant (H_0) using the Tully-Fisher relation with reduced systematic errors, addressing significant discrepancies noted in previous calibrations. The methodological improvements and the reduction of systematic uncertainties are notable, which enhances the reliability of cosmological measurements. Additionally, the implications on the Hubble tension debate make this study highly relevant, potentially impacting future research in cosmology. Its direct engagement with prior works and clear presentation of the underlying issues further support its high relevance score.

Room Temperature Strong Orbital Moments in Perpendicularly Magnetized Magnetic Insulator

The balance between the orbital and spin magnetic moments in a magnetic system is the heart of many intriguing phenomena. Here, we show experimental evidence of a large orbital moment, which competes ...

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The article presents novel experimental evidence regarding the interplay between orbital and spin magnetic moments in a key ferrimagnetic insulator, which could have wide-reaching implications for the field of magnetism and spintronics. The use of advanced techniques like XMCD highlights the methodological rigor and potential for advancing our understanding of magnetic materials. The insights into g-factor variations and their relation to orbital contributions are particularly noteworthy, suggesting avenues for further research in orbitronics, a relatively nascent subfield.

Observational Features of Thin Accretion Disk Around Rotating Regular Black Hole

Rotating regular black hole, as a promising extension beyond general relativity, offer a phenomenological model that resolves spacetime singularities. In this study, we investigate the observational f...

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The article presents novel insights into the observational features of thin accretion disks around a specific class of rotating black holes that extend existing models beyond general relativity. The incorporation of a regular parameter and detailed ray-tracing methods increases the methodological rigor and adds depth to the analysis. This work has substantial implications for astrophysical observations and contributes significantly to theoretical models of black holes, making it highly relevant for future research in gravitational physics and astrophysics.

Pulsar Kick: Status and Perspective

The high speeds seen in rapidly rotating pulsars after supernova explosions present a longstanding puzzle in astrophysics. Numerous theories have been suggested over the years to explain this sudden &...

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This article provides a comprehensive review of the current understanding of pulsar kicks, highlighting various hypotheses and challenges that have emerged over time. The novelty lies in its synthesis of existing theories and its focus on unexplored avenues, which may inspire future research. Its relevance is underscored by its potential impact on both astrophysics and fundamental physics.

TapeAgents: a Holistic Framework for Agent Development and Optimization

We present TapeAgents, an agent framework built around a granular, structured log tape of the agent session that also plays the role of the session's resumable state. In TapeAgents we leverage tap...

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The article presents a novel and comprehensive framework called TapeAgents that addresses significant challenges in the development and optimization of LLM agents. Its focus on a tape-centered design for session management enhances usability and allows for better debugging, evaluation, and improvement of agents. The methodology appears robust, and its comprehensive application and demonstrated case study indicate high applicability in diverse scenarios, contributing greatly to its relevance in the field of AI agent development.

Toy Model Challenging Prevailing Definitions of Classicality

We analyze a toy model that obeys environmentally induced decoherence and quantum Darwinism and satisfies the decoherent histories criterion and Leggett-Garg inequalities with respect to the pointer b...

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The article presents a novel toy model that challenges established definitions of classicality within quantum mechanics, contributing new insights into the relationship between classical and quantum behavior. Its potential implications for understanding decoherence and non-Markovianity indicate a high relevance to both theoretical frameworks and practical applications in quantum technology.

POINTS1.5: Building a Vision-Language Model towards Real World Applications

Vision-language models have made significant strides recently, demonstrating superior performance across a range of tasks, e.g. optical character recognition and complex diagram analysis. Building on ...

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POINTS1.5 introduces several key innovations that enhance vision-language model capabilities, particularly in high-resolution image processing and bilingual functionality, addressing specific market needs in the Chinese language context. The methodological rigor in dataset filtering and evaluation further strengthens its potential for real-world applications, making this model highly relevant for both academic and practical implementations.

From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons

We examine the capability of Multimodal Large Language Models (MLLMs) to tackle diverse domains that extend beyond the traditional language and vision tasks these models are typically trained on. Spec...

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The article presents a novel approach to developing Generalist Embodied Agents (GEAs) by leveraging Multimodal Large Language Models (MLLMs) and incorporating insights from diverse domains such as AI and gaming. This interdisciplinary approach is backed by substantial methodological rigor, showcasing the importance of cross-domain data and reinforcement learning in training. The findings emphasize the potential of GEAs in real-world applications, making it highly relevant for future research and development in AI.

Dynamic Disentangled Fusion Network for RGBT Tracking

RGBT tracking usually suffers from various challenging factors of low resolution, similar appearance, extreme illumination, thermal crossover and occlusion, to name a few. Existing works often study c...

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The proposed Dynamic Disentangled Fusion Network (DDFNet) presents a novel approach to RGBT tracking by addressing challenges specific to varying conditions with tailored fusion models. The methodological rigor is evident in the design of six attribute-based fusion models and an adaptive aggregation module, ensuring robust performance without requiring extensive training data. The paper's innovative fusion strategies and the experimental validation on benchmark datasets highlight its potential to significantly advance RGBT tracking technology.

On partial stochastic comparisons based on tail values at risk

In risk theory, financial asset returns often follow heavy-tailed distributions. Investors and risk managers used to compare risk measures as the value at risk or tail value at risk in order over the ...

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This paper presents a novel approach to comparing tail values at risk in heavy-tailed distributions, which are critical in risk and financial management. The proposed stochastic orders and their interrelations with existing risk criteria enhance our understanding of risk measures focusing on severe losses, thus filling a gap in current methodologies. The inclusion of real datasets adds to its practical relevance.