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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!

Weighted estimates for Schrödinger--Calderón--Zygmund operators with exponential decay

In this work we obtain weighted boundedness results for singular integral operators with kernels exhibiting exponential decay. We also show that the classes of weights are characterized by a suitable ...

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The article presents significant advancements in the theory of singular integral operators, particularly emphasizing the role of weights and exponential decay in their boundedness. The methodology appears rigorous, focusing on mathematical aspects that could enhance our understanding of operator theory. The clear identification of weighted boundedness results marks a novel contribution that could influence future studies in this area, especially concerning generalized Schrödinger operators.

GenPlan: Generative sequence models as adaptive planners

Offline reinforcement learning has shown tremendous success in behavioral planning by learning from previously collected demonstrations. However, decision-making in multitask missions still presents s...

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The article presents a highly innovative approach (GenPlan) for adaptive planning in reinforcement learning, addressing key limitations of existing methods related to generalization and task adaptation. Its reliance on generative sequence modeling signifies a novel intersection of generative models and planning in robotics and AI, making it highly relevant for both theoretical and practical applications. The methodological rigor, evidenced by substantial performance improvements in simulations, bolsters its impact.

Can We Generate Visual Programs Without Prompting LLMs?

Visual programming prompts LLMs (large language mod-els) to generate executable code for visual tasks like visual question answering (VQA). Prompt-based methods are difficult to improve while also bei...

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This article presents a novel approach to visual programming by eliminating the reliance on prompt-based LLMs, addressing significant inefficiencies in current methodologies. The innovative use of synthetic data augmentation and the concept of decoupling programs into templates and arguments suggest a robust theoretical framework. Moreover, the results indicating competitive performance with smaller models present strong implications for resource efficiency in AI development.

Physics Based Differentiable Rendering for Inverse Problems and Beyond

Physics-based differentiable rendering (PBDR) has become an efficient method in computer vision, graphics, and machine learning for addressing an array of inverse problems. PBDR allows patterns to be ...

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The article offers a comprehensive overview of a novel approach (PBDR) that effectively combines physics with rendering techniques, which is expected to have a substantial impact on multiple fields such as computer vision and graphics. Its thorough examination of modern techniques and applications enhances its utility for future research. The author addresses current limitations, which may inspire further innovation, highlighting the article's potential significance.

An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios

Collaborative navigation becomes essential in situations of occluded scenarios in autonomous driving where independent driving policies are likely to lead to collisions. One promising approach to addr...

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The article introduces a novel approach for collaborative navigation among connected autonomous vehicles (CAVs) in occluded scenarios, addressing a significant safety concern in autonomous driving. It demonstrates methodological rigor through the use of Proximal Policy Optimisation for learning multi-agent policies without reliance on expert data, which adds a layer of originality and practicality to the methodology. The experimental setup using a CARLA simulator further substantiates the findings. The collaborative control method has strong potential applications in real-world autonomous navigation, reinforcing its relevance and impact.

An Enhanced Levenberg--Marquardt Method via Gram Reduction

This paper studied the problem of solving the system of nonlinear equations ${\bf F}({\bf x})={\bf 0}$ , where ${\bf F}:{\mathbb R}^{d}\to{\mathbb R}^d$ . We propose Gram-Reduced Levenbe...

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The paper presents a novel enhancement to the well-established Levenberg-Marquardt method, introducing a Gram reduction strategy that improves convergence rates and computational efficiency. The global convergence guarantee and the avoidance of line-search procedures represent significant methodological advancements. The validation through experiments in practical applications enhances its applicability, indicating potential for real-world impact.

Measure equivalence classification of right-angled Artin groups: the finite $\mathrm{Out}$ classes

Given a right-angled Artin group $G$ with finite outer automorphism group, we determine which right-angled Artin groups are measure equivalent (or orbit equivalent) to $G$ .

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This article explores the intricate relationship between right-angled Artin groups and their measure equivalence, which is a significant topic in geometric group theory. The focus on finite outer automorphism groups adds novelty and depth to the research. The methodology appears rigorous and offers clear implications for further exploration in the area of group theory.

Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning

Large Language Models are trained on extensive datasets that often contain sensitive, human-generated information, raising significant concerns about privacy breaches. While certified unlearning appro...

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This article addresses a critical and often overlooked issue of privacy risks specifically affecting minority populations in the context of large language models (LLMs). The novelty lies in its approach to leverage minority data to critique existing evaluation methods. By advocating for a more rigorous framework for LLM unlearning, the study not only brings important ethical considerations to the forefront but also proposes a practical solution for future research. Its methodological rigor, substantiated findings, and strong implications for equitable data practices enhance its relevance and potential impact.

Low-dimensional indecomposable representations of the braid group $B_3$

In this note we give a complete classification of all indecomposable yet reducible representations of $B_3$ for dimensions $2$ and $3$ over an algebraically closed field $K...

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This paper contributes significantly to the understanding of representations of braid groups, a topic that holds considerable importance in both algebra and topology. The focus on low-dimensional representations and the classification of indecomposable yet reducible cases adds novelty and depth to the field. The methodological rigor demonstrated by classifying these representations up to equivalence provides a solid foundation for future studies. Overall, the implications of these findings may inspire further exploration into related representations and their applications.

CHEOPS observations confirm nodal precession in the WASP-33 system

Aims: We aim to observe the transits and occultations of WASP-33b, which orbits a rapidly-rotating $δ$ Scuti pulsator, with the goal of measuring the orbital obliquity via the gravity-darkenin...

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This article provides valuable observations and measurements that enhance our understanding of planetary orbital characteristics, especially in systems with pulsating stars. The detailed analysis of obliquity and nodal precession introduces important insights that align well with theoretical models, showcasing methodological rigor and significant findings. It has implications for both exoplanet studies and stellar physics, offering a nuanced view of orbital dynamics.

Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks

End-to-end training with global optimization have popularized graph neural networks (GNNs) for node classification, yet inadvertently introduced vulnerabilities to adversarial edge-perturbing attacks....

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The paper presents a novel approach to enhance the robustness of graph neural networks against adversarial attacks, specifically targeting vulnerabilities resulting from global optimization in GNNs. Its methodological rigor, evidenced by theoretical proofs and experimental validation, is a strong indicator of its potential impact in the field. The introduction of the Graph Agent Network (GAgN) concept is innovative and addresses a growing concern in the area of graph-based machine learning, which enhances its relevance. However, the applicability of the approach may be limited to specific types of GNNs, and further validation across diverse datasets and attack scenarios is warranted.

Coherent frequency combs from electrons colliding with a laser pulse

Highly coherent and powerful light sources capable of generating frequency combs in the soft x-ray domain are essential for advancing high precision measurements and conducting rigorous tests of funda...

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The article presents a significant advancement in the generation of coherent frequency combs in the soft x-ray domain, which has profound implications for high-precision measurements and fundamental physics testing. The novelty lies in the analytical conditions derived for electron-laser interactions, and the use of numerical simulations adds rigor to the findings. Its applicability to different frequency ranges further enhances its impact.

A numerical method to simulate the stochastic linear-quadratic optimal control problem with control constraint in higher dimensions

We propose an {\em implementable} numerical scheme for the discretization of linear-quadratic optimal control problems involving SDEs in higher dimensions with {\em control constraint}. For time discr...

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The article presents a novel numerical scheme for a significant problem in control theory, specifically for stochastic linear-quadratic problems with constraints in higher dimensions. Its methodological rigor is underpinned by the use of implicit Euler schemes and a clear error analysis, ensuring that it contributes valuable insights and practical solutions. The focus on an implementable method adds to its relevance, enabling real-world applications. The demonstration through numerical examples highlights its potential effectiveness, making it a robust addition to the literature.

Integral representation and functional inequalities involving generalized polylogarithm

This paper focuses on the generalized polylogarithm $Φ_{p, q}(a, b; z)$ , which extends the notion of classical polylogarithm. A new integral representation for $Φ_{p, q}(a, b; z)$ is d...

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The paper presents significant advancements in the mathematical understanding of generalized polylogarithms, including new integral representations and functional inequalities that enhance existing theories. The derivation of Turán-type inequalities and the discussion on complete monotonicity suggests a robust methodological framework. Furthermore, the application of these findings may influence future research in related areas, enhancing both theoretical and applied mathematics.

pp-Waves and the Hidden Symmetries of Black Hole Quasinormal Modes

There are two interesting classes of trapped null geodesics in any black hole spacetime: those that lie on the photon ring and those that generate the horizon. Recent work introduced a "near-ring...

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The article explores emergent symmetries in black hole quasinormal modes (QNMs) through novel geometric approaches, presenting valuable implications for understanding black hole physics. Its focus on trapped null geodesics and the introduction of near-ring scaling limits adds significant novelty to existing research, particularly as it ties symmetries to observable features of QNM spectra. Methodologically sound, the work stands to inspire further investigations into black hole thermodynamics and gravitational wave astrophysics.

Sketch2Sound: Controllable Audio Generation via Time-Varying Signals and Sonic Imitations

We present Sketch2Sound, a generative audio model capable of creating high-quality sounds from a set of interpretable time-varying control signals: loudness, brightness, and pitch, as well as text pro...

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Sketch2Sound is a significant advancement in generative audio modeling, introducing innovative methods for controllable sound generation. Its combination of text prompts and time-varying control signals enhances creative expression while maintaining high audio quality, indicating both novelty and practical applicability. The lightweight implementation on existing transformer models also contributes to its potential for widespread adoption in various domains.

Bilevel Joint Unsupervised and Supervised Training for Automatic Speech Recognition

In this paper, we propose a bilevel joint unsupervised and supervised training (BL-JUST) framework for automatic speech recognition. Compared to the conventional pre-training and fine-tuning strategy ...

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The paper introduces a novel approach (BL-JUST) that innovatively combines unsupervised and supervised training for automatic speech recognition (ASR), which is crucial for improving model performance. The evaluation against existing methods provides strong empirical support for its effectiveness. The methodological rigor in solving the optimization problem enhances its credibility, and the potential to significantly improve ASR systems underscores its relevance to the field.

The Non-Cooperative Rational Synthesis Problem for Subgame Perfect Equilibria and omega-regular Objectives

This paper studies the rational synthesis problem for multi-player games played on graphs when rational players are following subgame perfect equilibria. In these games, one player, the system, declar...

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The paper provides a novel approach to the rational synthesis problem in multi-player games with rigorous complexity analysis, addressing fundamental aspects of game theory. The focus on subgame perfect equilibria and ω-regular objectives makes it relevant for advancing theoretical understanding and practical applications, particularly in system design and automated reasoning. The methodological rigor is strong, and the findings can inspire future research in both game theory and algorithm design.

Improving Satellite Imagery Masking using Multi-task and Transfer Learning

Many remote sensing applications employ masking of pixels in satellite imagery for subsequent measurements. For example, estimating water quality variables, such as Suspended Sediment Concentration (S...

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This article presents a robust multi-task learning framework to enhance satellite imagery masking, addressing significant challenges in precision and computational efficiency. The extensive evaluation against state-of-the-art methods and the focus on practical applications (e.g., water quality estimation) underscore its high impact on remote sensing methodologies.

Training Data Reconstruction: Privacy due to Uncertainty?

Being able to reconstruct training data from the parameters of a neural network is a major privacy concern. Previous works have shown that reconstructing training data, under certain circumstances, is...

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The article addresses a crucial issue in the intersection of machine learning and privacy, specifically focusing on the reconstruction of training data from neural network parameters. The novelty of proposing a new formulation to a bilevel optimization problem, along with empirical analysis, contributes significantly to understanding privacy concerns associated with neural networks. The emphasis on initialization effects adds a practical dimension that could influence how practitioners approach model training and privacy. The findings are highly relevant given the increasing attention to data privacy and security in AI deployment.