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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!

Reinforcement Learning, Collusion, and the Folk Theorem

We explore the behaviour emerging from learning agents repeatedly interacting strategically for a wide range of learning dynamics that includes projected gradient, replicator and log-barrier dynamics....

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The article introduces new insights into the intersection of reinforcement learning and game theory, particularly focusing on how learning dynamics can influence strategic interactions and the emergence of collusion. The exploration of diverse dynamics goes beyond traditional game models, showcasing methodological rigor and novelty. Its implications for algorithmic collusion are particularly timely given the increasing relevance of AI systems in competitive environments.

RR Lyrae Stars in Intermediate-age Magellanic Clusters: Membership Probabilities and Delay Time Distribution

Recent works have challenged our canonical view of RR Lyrae (RRL) stars as tracers of exclusively old populations ( $\gtrsim10$ ~Gyr) by proposing a fraction of these stars to be of intermediate...

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This study introduces a significant reevaluation of the age distribution of RR Lyrae stars, challenging traditional views and providing novel insights into stellar populations. The methodology employed is robust, leveraging large datasets (Gaia DR3 and OGLE IV), which enhances the reliability of the findings. This work could lead to deeper understanding of stellar evolution and serve as a basis for future studies in this area.

Heuristic-Free Multi-Teacher Learning

We introduce Teacher2Task, a novel framework for multi-teacher learning that eliminates the need for manual aggregation heuristics. Existing multi-teacher methods typically rely on such heuristics to ...

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The proposed Teacher2Task framework presents a significant innovation by eliminating manual aggregation heuristics, which are commonly seen as a major hurdle in multi-teacher learning. This novel approach not only improves methodological rigor but also emphasizes the importance of individual teacher contributions, highlighting potential gains in model performance. The strong empirical results across diverse tasks and architectures enhance the framework's applicability and relevance.

Homogeneous planet masses I: Reanalysis of archival HARPS radial velocities

Empirical exoplanet mass-radius relations have been used to study the demographics and compositions of small exoplanets for many years. However, the heterogeneous nature of these measurements hinders ...

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The article addresses critical issues in the study of exoplanet demographics by reanalyzing archival data in a systematic way. Its methodological rigor, through the consistency of models and the innovative use of Gaussian Processes, enhances the quality and reliability of the findings. The availability of RV amplitudes facilitates further research and population studies, contributing significant value to the field.

An AI-Enabled Side Channel Power Analysis Based Hardware Trojan Detection Method for Securing the Integrated Circuits in Cyber-Physical Systems

Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). ICs are largely susceptible to various hardware attacks with malicious intents. One of t...

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This article addresses a critical and timely issue in the security of integrated circuits, particularly in the context of cyber-physical systems. The use of AI for detecting hardware trojans is innovative, combining advanced signal processing with machine learning, which enhances the novelty and applicability of the research. The methodological rigor is strong, as it includes a comparative analysis with baseline methods, showcasing a notable improvement in detection accuracy. This research has significant implications for both cybersecurity and hardware engineering.

Scaling laws for nonlinear dynamical models of speech

The addition of a nonlinear restoring force to dynamical models of the speech gesture significantly improves the empirical accuracy of model predictions, but nonlinearity introduces challenges in sele...

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This article introduces novel scaling laws addressing the complex integration of nonlinearity in dynamical models of speech production, which is essential in advancing speech science and related computational modeling. The methodological rigor in providing numerical methods for parameterization enhances the model's applicability, making it a significant contribution for researchers interested in both theoretical and practical applications in speech dynamics.

Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation

Despite rapid advancements in TTS models, a consistent and robust human evaluation framework is still lacking. For example, MOS tests fail to differentiate between similar models, and CMOS's pairw...

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This article addresses critical limitations in the current Text-to-Speech (TTS) evaluation frameworks, particularly the MUSHRA test. Its focus on refining evaluation methods is both novel and highly relevant given the advancements in TTS technology. The comprehensive assessment involving a large participant pool enhances its methodological rigor, while the new dataset (MANGO) represents a significant contribution to the field, particularly for Indian languages. The proposed improvements to MUSHRA can lead to more accurate evaluations, driving future developments in TTS systems.

Improvements on Permutation Reconstruction from Minors

We study the reconstruction problem of permutation sequences from their $k$ -minors, which are subsequences of length $k$ with entries renumbered by $1,2,\ldots,k$ preserving or...

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This article presents novel results regarding the reconstruction of permutations from their minors, providing asymptotic bounds that significantly improve upon previous research. The methodological rigor and the implications for a widely studied parameter in combinatorial theory bolster its relevance. The findings could lead to further exploration in computational combinatorics and related fields.

Excursion theory for Markov processes indexed by Levy trees

We develop an excursion theory that describes the evolution of a Markov process indexed by a Levy tree away from a regular and instantaneous point $x$ of the state space. The theory builds upo...

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This article introduces an innovative excursion theory for Markov processes indexed by Levy trees, which is a novel framework in the intersection of stochastic processes and probability theory. The clear connections made to classical excursion theory indicate that the results can bridge long-standing problems in probabilistic models. The methodological rigor and the ability to recover previous results within a different context enhance the credibility and applicability of the findings, suggesting significant potential to influence future work in related fields.

Starspots as an Explanation for the Mysterious IYJ Continuum Excess Emission in Classical T Tauri Stars

An accurate estimation of the continuum excess emission from accretion spots and inner circumstellar disk regions is crucial for a proper derivation of fundamental stellar parameters in accreting syst...

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This article presents a novel approach to understanding the complexities of spectral emissions in Classical T Tauri Stars, addressing a crucial challenge in the field. Its methodology, which incorporates both starspots and accretion emissions through new spectral models, enhances the accuracy and completeness of the analysis and contributes to the fundamental parameters derivation of these stars. This increases its applicability to observational astronomy and theoretical astrophysics.

Random Dehn function of groups

In this note, we study the notion of random Dehn function and compute an asymptotic upper bound for finitely presented acylindrically hyperbolic groups whose Dehn function is at most polynomial with i...

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The study of random Dehn functions in the context of acylindrically hyperbolic groups is a novel approach that adds depth to understanding the geometric and algebraic properties of these groups. The confirmation of Gromov's intuition through the introduction of asymptotic upper bounds presents a significant advancement in the field, likely influencing future research in geometric group theory.

Scoring and Favoritism in Optimal Procurement Design

We study buyer-optimal procurement mechanisms when quality is contractible. When some costs are borne by every participant of a procurement auction regardless of winning, the classic analysis should b...

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This article presents a novel approach to procurement design by addressing the impact of all-pay costs and information asymmetry, which adds significant depth to the literature. Its exploration of scoring auctions, favoritism, and asymmetry in treatment of firms introduces new paradigms for optimal procurement mechanisms, making it both relevant and innovative. The methodological rigor, particularly in distinguishing various auction formats and their implications, supports a strong applicability to real-world auction settings.

CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs

Large Vision-Language Model (LVLM) systems have demonstrated impressive vision-language reasoning capabilities but suffer from pervasive and severe hallucination issues, posing significant risks in cr...

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The article presents a novel approach, CATCH, to address critical issues of hallucinations in LVLMs, which is a pressing challenge in AI and machine learning ecosystems. The methodology is grounded in established theories like the Information Bottleneck, indicating strong theoretical underpinning and potential for high impact on future research addressing hallucinations. Furthermore, its applicability across various tasks without specific training shows exceptional versatility. However, more detailed analysis of experimental results could enhance the methodological rigor assessment.

Enhancing Multi-Class Disease Classification: Neoplasms, Cardiovascular, Nervous System, and Digestive Disorders Using Advanced LLMs

In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We exc...

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This article showcases a significant advancement in the application of advanced language models (LLMs) for medical text classification, specifically targeting multi-class diseases. The focus on specialized models, as well as a novel base model, highlights both rigor in methodology and innovation. The high accuracy rates demonstrate a practical contribution to medical data analysis, indicative of future applicability in clinical settings. However, the exclusion of non-cancer conditions somewhat narrows the impact.

UBSoft: A Simulation Platform for Robotic Skill Learning in Unbounded Soft Environments

It is desired to equip robots with the capability of interacting with various soft materials as they are ubiquitous in the real world. While physics simulations are one of the predominant methods for ...

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The article presents a novel simulation platform (UBSoft) that significantly addresses a critical gap in the field of robotic skill learning involving soft materials – a challenge that has been limited by previous simulation frameworks. The innovative approach of using spatially adaptive resolution provides potential for broader and more realistic robotic training scenarios, enhancing the methodological rigor of future research. By establishing benchmarks and validating the platform through experimental outcomes with both simulation and real-world applications, the findings hold substantial promise for impacting future studies and practical implementations of robotics. The reported improvements in trajectory optimization techniques further underscore the contribution to fundamental areas in robotics.

Travel Time Based Task Mapping for NoC-Based DNN Accelerator

Network-on-Chip (NoC) based architectures are recently proposed to accelerate deep neural networks in specialized hardware. Given that the hardware configuration is fixed post-manufacture, proper task...

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The article presents a novel approach to task mapping in NoC-based architectures for DNN accelerators, which is a crucial area given the increasing complexity of deep learning models. The proposed method demonstrates substantial improvements over traditional mapping techniques, indicating methodological rigor and applicability in real-world implementations where efficiency is critical. Furthermore, the integration of travel time data adds an innovative aspect, enhancing both performance and resource allocation. However, while the improvements are statistically significant, further research could expand on scaling and generalizing the method across diverse applications.

Dimensions of Generative AI Evaluation Design

There are few principles or guidelines to ensure evaluations of generative AI (GenAI) models and systems are effective. To help address this gap, we propose a set of general dimensions that capture cr...

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The article addresses a significant gap in the evaluation of generative AI systems by proposing a structured set of dimensions for effective evaluation design. The novelty lies in the systematic approach to this underexplored area, potentially influencing standardized practices in the field. Moreover, the discussion of practical illustrations enhances the applicability and rigor of the proposed framework.

The pale blue dot: using the Planetary Spectrum Generator to simulate signals from hyper realistic exo-Earths

The atmospheres and surfaces of planets show tremendous amount of spatial variation, which has a direct effect on the spectrum of the object, even if this may not be spatially resolved. Here, we apply...

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This article presents a significant advancement in simulating atmospheres of exoplanets, utilizing rigorous modeling techniques that are backed by observational validation. Its novelty lies in the detailed study of spatial variations and the implications for future observatory designs. Additionally, the publicly available tools enhance its impact by providing resources for further research in the field.

Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data

Imaging-based deep learning has transformed healthcare research, yet its clinical adoption remains limited due to challenges in comparing imaging models with traditional non-imaging and tabular data. ...

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Barttender addresses a significant gap in healthcare research by facilitating the comparison of imaging and non-imaging data within a single framework, enhancing interpretability and usability of deep learning models. The introduction of gIoU as a novel measure adds to its methodological rigor and applicability in clinical settings. The article shows promise in improving the adoption of deep learning in healthcare, a field that greatly benefits from interpretable AI methods.

A coronal mass ejection encountered by four spacecraft within 1 au from the Sun: Ensemble modelling of propagation and magnetic structure

Understanding and predicting the structure and evolution of coronal mass ejections (CMEs) in the heliosphere remains one of the most sought-after goals in heliophysics and space weather research. A po...

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This article presents a significant advancement in the understanding of coronal mass ejections (CMEs) and their impacts through the use of ensemble modeling across multiple spacecraft. Its methodological rigor is apparent in the analysis of a real CME event, providing new insights and validation opportunities for existing models. The findings not only enhance current predictions of CME behavior but also offer critical implications for space weather forecasting, which can have substantial societal impacts. The article’s interdisciplinary approach, integrating observational data and theoretical modeling, adds to its relevance.