Thesis Tide - Find relevant and interesting papers

Testing Born's rule via photoionization of helium

It is shown how state-of-the-art attosecond photoionization experiments can test Born's rule -- a postulate of quantum mechanics -- via the so-called Sorkin test. A simulation of the Sorkin test u...

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This article presents a novel experimental approach to testing a fundamental postulate of quantum mechanics, which is significant for both theoretical and practical advancements in the field. The use of state-of-the-art attosecond photoionization techniques demonstrates methodological rigor and potential for high precision, elevating the discussion around Born's rule. Its implications for experimental quantum mechanics and foundational studies indicate a strong relevance for ongoing research.

$L$ -smooth factorization for Noetherian $F$ -finite rings

We show that any homomorphism between Noetherian $F$ -finite rings can be factored into a regular morphism between Noetherian $F$ -finite rings followed by a surjection. This result esta...

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This article presents significant advances in the understanding of morphisms between Noetherian $F$-finite rings, which has implications for the classification of ring maps and structures. The equivalence of regularity and formal smoothness with $L$-smoothness introduces a novel perspective in commutative algebra and algebraic geometry. The rigorous mathematical framework and connection to existing theorems enhance its impact and applicability, particularly for those studying algebraic structures and homomorphisms.

Scaling up self-supervised learning for improved surgical foundation models

Foundation models have revolutionized computer vision by achieving vastly superior performance across diverse tasks through large-scale pretraining on extensive datasets. However, their application in...

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The article presents a significant advancement in surgical computer vision through the introduction of a novel foundation model, SurgeNetXL. Its extensive training on the largest surgical dataset to date underscores its methodological rigor. The performance improvements reported are notable, as is the open-access availability of models and data, which enhances reproducibility and community engagement. The insights provided for further research also suggest strong applicability in future studies, positioning this work as a potential benchmark in the field.

A cluster theory approach from mutation invariants to Diophantine equations

In this paper, we define and classify the sign-equivalent exchange matrices. We give a Diophantine explanation for the differences between rank 2 cluster algebras of finite type and affine type based ...

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This article provides a novel perspective by connecting mutation invariants to Diophantine equations through cluster algebra theory. The approach showcases methodological rigor in classification tasks and provides significant insights into both finite and affine types of cluster algebras. The application to Diophantine equations further enhances its importance, potentially opening new avenues for research in algebra and number theory.

Agile System Development Lifecycle for AI Systems: Decision Architecture

Agile system development life cycle (SDLC) focuses on typical functional and non-functional system requirements for developing traditional software systems. However, Artificial Intelligent (AI) system...

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The article presents a significant advancement by addressing the gap in agile system development for AI systems, particularly focusing on decision architecture—a crucial aspect in AI development. Its innovative approach emphasizes the necessity of adapting traditional methodologies to accommodate the unique characteristics of AI, thus potentially influencing future methodologies in the field. The practical application in insurance claims processing enhances its relevance, demonstrating real-world applicability.

CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation

The synthesis of high-quality 3D assets from textual or visual inputs has become a central objective in modern generative modeling. Despite the proliferation of 3D generation algorithms, they frequent...

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The article presents a novel approach (CaPa) to 3D mesh generation, addressing key challenges in the field, such as multi-view consistency and texture quality. Its methodological rigor, including the decoupling of geometry and texture generation and the introduction of Spatially Decoupled Attention, showcases innovative advancements. The practical implications and efficient generation times further enhance its relevance, suggesting significant utility in commercial applications and potential to inspire future research in generative modeling.

On a Gelfand-Tsetlin representation of $\mathfrak{sl}_3$ in the space of sections of a local system with two monodromy parameters

We construct a Gelfand-Tsetlin representation of $\mathfrak{sl}_3$ in the space of sections of a local system. The local system lives on an open part of the flag variety given by the intersect...

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This article presents a significant advancement in the representation theory of Lie algebras, specifically $ ext{sl}_3$, through the construction of a Gelfand-Tsetlin representation. This is particularly relevant due to the introduction of two monodromy parameters and the application to sections of a local system, suggesting potential robustness in applicability. The rigorous analysis of the representation structure adds methodological strength, which can influence future research in both representation theory and related algebraic geometry fields.

A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy

While large language models (LLMs) present significant potential for supporting numerous real-world applications and delivering positive social impacts, they still face significant challenges in terms...

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The article offers a thorough survey that addresses critical risks associated with large language models (LLMs) and provides a unified framework for their responsible development and deployment. The novelty lies in its comprehensive approach that integrates various mitigation strategies across all phases of LLM usage, making it a vital resource for both researchers and practitioners. Its methodological rigor and focus on real-world applications also enhance its relevance for future research in the field.

HpC: A Calculus for Hybrid and Mobile Systems -- Full Version

Networked cybernetic and physical systems of the Internet of Things (IoT) immerse civilian and industrial infrastructures into an interconnected and dynamic web of hybrid and mobile devices. The key f...

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The article introduces a new calculus (HpC) specifically designed for hybrid and mobile systems, which is increasingly relevant in the context of growing IoT applications. Its conservative extension approach to the π-calculus ensures it retains established theoretical benefits while expanding applicability to dynamic environments. This research is methodologically robust, as it uses formal frameworks to ensure correctness and reliability, critical in fast-evolving technological fields. The showcase of a practical handover protocol example demonstrates real-world relevance.

ADAGE: A generic two-layer framework for adaptive agent based modelling

Agent-based models (ABMs) are valuable for modelling complex, potentially out-of-equilibria scenarios. However, ABMs have long suffered from the Lucas critique, stating that agent behaviour should ada...

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This article presents a significant advancement in the field of agent-based modeling by addressing two critical issues: the adaptation of agent behaviors to environmental changes and the simultaneous adaptation of the environment itself. The introduction of a two-layer framework formalized as a Stackelberg game represents a novel and systematic solution to a complex problem that has not been fully addressed in existing literature. The method's generality, along with its applicability to different tasks within ABMs, suggests high versatility and impact. The rigorous methodology and strong grounding in economic and financial applications further enhance its relevance.

Normal-NeRF: Ambiguity-Robust Normal Estimation for Highly Reflective Scenes

Neural Radiance Fields (NeRF) often struggle with reconstructing and rendering highly reflective scenes. Recent advancements have developed various reflection-aware appearance models to enhance NeRF&#...

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The article presents a novel approach to address a significant challenge in NeRF technology—accurate normal estimation in highly reflective environments. The introduction of a transmittance-gradient-based technique, alongside a dual activated densities module, reflects a strong methodological innovation. Its extensive experimental validation showcasing superior performance compared to existing methods highlights its potential impact on both theoretical and practical aspects of rendering reflective scenes.

Edge-Dependent Step-Flow Growth Mechanism in $β$ -Ga $_{2}$ O $_{3}$ (100) Facet at the Atomic Level

Homoepitaxial step-flow growth of high-quality $β$ -Ga $_{2}$ O $_{3}$ thin films is essential for the advancement of high-performance Ga $_{2}$ O $_{3}$ -based devices...

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The article presents a novel understanding of the step-flow growth mechanism specific to β-Ga₂O₃ at the atomic level using advanced computational techniques. The use of machine-learning molecular dynamics and density functional theory adds rigor and depth to the research, enhancing its reliability and applicability. Its findings have significant implications for high-performance device development, making it quite relevant in the field of materials science and semiconductor physics, particularly concerning homoepitaxy techniques.

AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling

Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automated counseling offers a potential solution for those h...

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The article presents a novel approach to enhancing online psychological counseling through an autonomous multi-agent framework for Cognitive Behavioral Therapy (CBT). Its integration of large language models and real psychological counseling techniques shows significant methodological rigor and innovation. The ability to improve response quality in automated settings can positively impact access to mental health resources, addressing a critical gap in the field. This could lead to further developments in AI-assisted psychology and mental health treatments.

Vision-Language Models Do Not Understand Negation

Many practical vision-language applications require models that understand negation, e.g., when using natural language to retrieve images which contain certain objects but not others. Despite advancem...

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The article presents a novel benchmark, NegBench, which specifically addresses a significant gap in the understanding of negation by vision-language models, suggesting that current advancements have limitations. The introduction of a data-centric finetuning approach adds methodological rigor, demonstrating clear improvements in model performance. This unique angle of assessing negation opens avenues for further research on model training in multimodal contexts, making it highly impactful.

From tomography of conditional states to tomography of states that never existed

Microscopic Schr{ö}dinger cat states with photon numbers of the order of one are produced from stationary quantum-correlated fields exploiting a probabilistic heralding photon-subtraction event. The s...

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The article presents a novel method for achieving quantum state tomography that enhances the study of non-classical states, which is a critical area in quantum optics and quantum information. The probabilistic approach shows methodological rigor and presents significant implications for future research on quantum technologies and measurement techniques.

Double-Scaled SYK, QCD, and the Flat Space Limit of de Sitter Space

A surprising connection exists between double-scaled SYK at infinite temperature, and large N QCD. The large N expansions of the two theories have the same form; the 't Hooft limit of QCD parallel...

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The paper presents a novel connection between double-scaled SYK and QCD, which is significant for advancing our understanding of large N quantum field theories. The findings about the flat space limit in the context of AdS/CFT are particularly intriguing, as they open pathways to explore less understood aspects of gauge theories. The methodological rigor, including both perturbative and non-perturbative approaches, enhances its robustness and applicability in theoretical physics.

Role of $a_0( 1710)$ in the $D_s^+\toρ^+φ,~~ρ^+ω$ decays

We look into the $D_s^+ \to ρ^+ φ$ and $D_s^+ \to ρ^+ ω$ weak decays recently measured by the BESIII collaboration, which proceed very differently in a first step of the weak decay. Wh...

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The article presents a thorough investigation into specific weak decay processes, addressing discrepancies in theoretical explanations compared to experimental observations. Its focus on the role of the $a_0(1710)$ resonance adds a novel angle to understanding these decay processes. The integration of final state interactions enhances its methodological rigor and provides a potentially significant impact on future research in particle physics, particularly in weak interactions and resonance studies.

Uncertainty in Elastic Turbulence

Elastic turbulence can lead to to increased flow resistance, mixing and heat transfer. Its control -- either suppression or promotion -- has significant potential, and there is a concerted ongoing eff...

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This article provides a novel insight into the concept of elastic turbulence, particularly by addressing the dynamics of uncertainty and its implications for flow behavior. The balance of various contributing factors like advective and polymeric effects is an innovative approach that could lead to practical applications in fluid dynamics. Its detailed exploration of numerical experiments adds methodological rigor, making the findings highly relevant for the field. Additionally, the potential to inform future studies on flow control mechanisms and instabilities in viscoelastic fluids enhances its significance.

Dynamic Neural Style Transfer for Artistic Image Generation using VGG19

Throughout history, humans have created remarkable works of art, but artificial intelligence has only recently started to make strides in generating visually compelling art. Breakthroughs in the past ...

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This paper presents a significant advancement in the field of neural style transfer by addressing various limitations seen in previous models, such as processing time and flexibility in style manipulation. The use of the VGG19 model for feature extraction suggests methodological rigor and adherence to established convolutional neural network practices. The proposed improvements could have substantial implications for both artistic applications and technical developments in AI-driven creativity, enhancing usability in real-time applications.

Speckle Tweezers in Action: Manipulating the Run-and-Tumble of Bacteria

This study explores the use of optical speckle tweezers (ST) to manipulate the motility of Escherichia coli bacteria. By employing the generated speckle patterns, we demonstrate the ability to control...

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The study presents a novel application of optical speckle tweezers in controlling bacterial motility, a significant advancement in the field of active matter and biophysics. The methodological rigor is showcased through experimental validation and highlights the innovative potential of ST technology for future research. Importantly, the non-invasive nature of the technique expands its applicability in microbiology and other related fields, although it may have limitations in scalability and practical implementations outside laboratory settings.