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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!

Enhancing Drug Discovery: Quantum Machine Learning for QSAR Prediction with Incomplete Data

Qualitative structure-activity relationship (QSAR) is important for drug discovery and offers valuable insights into the biological interactions of potential drug candidates. It has been demonstrated ...

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This article presents a novel application of quantum machine learning to a critical problem in drug discovery. By addressing the issue of incomplete data in QSAR predictions, it introduces innovative methodology combining quantum classifiers with intelligent feature selection techniques. Its potential for significant improvements in predictive accuracy under data constraints makes it highly relevant. The integration of quantum computing concepts into traditional machine learning in drug discovery offers a new perspective that could shape future research directions.

Concurrent Learning with Aggregated States via Randomized Least Squares Value Iteration

Designing learning agents that explore efficiently in a complex environment has been widely recognized as a fundamental challenge in reinforcement learning. While a number of works have demonstrated t...

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This article presents a novel theoretical framework for enhancing concurrent learning in multi-agent environments using randomized least-squares value iteration, which could significantly advance the understanding and implementation of efficient exploration strategies in reinforcement learning. The rigorous theoretical contributions combined with empirical validation increase its potential impact on both theoretical and applied aspects of the field.

Grover algorithm and absolute zeta functions

The Grover algorithm is one of the most famous quantum algorithms. On the other hand, the absolute zeta function can be regarded as a zeta function over $\mathbb{F}_{1}$ defined by a function ...

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This article presents a novel intersection between quantum computing and abstract algebra through the Grover algorithm and absolute zeta functions. The exploration of the relationship between these two topics could inspire future research in both quantum algorithms and number theory or algebra. The methodological rigor implied by the application of Kurokawa's theorem adds credibility to the study. However, the direct practical applicability may be limited to theoretical implications rather than immediate computational advancements, leading to a high but not perfect relevance score.

Time Series Embedding Methods for Classification Tasks: A Review

Time series analysis has become crucial in various fields, from engineering and finance to healthcare and social sciences. In this paper, we present a comprehensive review and evaluation of time serie...

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The article offers a comprehensive review and quantitative evaluation of time series embedding methods, demonstrating novelty by categorizing techniques and their application contexts. Its systematic approach and provision of an open-source code repository enhance applicability and encourage further research. The methodological rigor of testing across diverse datasets adds robustness to the findings.

Can Large Language Models Understand Preferences in Personalized Recommendation?

Large Language Models (LLMs) excel in various tasks, including personalized recommendations. Existing evaluation methods often focus on rating prediction, relying on regression errors between actual a...

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The study introduces a novel framework (PerRecBench) that challenges existing evaluation methods in personalized recommendations, highlighting significant issues with current approaches. The findings about the limitations of LLMs in recognizing user preferences, even when controlling for bias and quality, are critical. Additionally, the exploration of ranking methodologies offers valuable insights into improving models, thus pushing the boundaries of current research in the field.

Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear Bandits

We study lifelong learning in linear bandits, where a learner interacts with a sequence of linear bandit tasks whose parameters lie in an $m$ -dimensional subspace of $\mathbb{R}^d$ , th...

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This article addresses a significant gap in the field of linear bandit learning by challenging the common assumption of task diversity, which enhances its novelty and applicability. The development of an algorithm that effectively learns and transfers low-rank representations in a realistic setting has practical implications for real-world applications. The methodological rigor is evident in the comprehensive regret analysis and empirical validation, indicating robustness in the proposed approach. As such, it is likely to inspire further research in both theoretical developments and practical applications in related fields.

AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning

Robust training with noisy labels is a critical challenge in image classification, offering the potential to reduce reliance on costly clean-label datasets. Real-world datasets often contain a mix of ...

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The AEON approach presents a novel framework for addressing label noise in a unified manner, which is a significant advancement in robust learning. Its methodological rigor is evident through efficient one-stage learning and comprehensive benchmarking, which can enhance the generalizability of the model. This paper is highly relevant in the rapidly evolving field of machine learning, especially concerning applications in real-world datasets. The proposed solution's potential impact on reducing reliance on clean labels makes it a significant contribution.

Probe position determination with multichannel I-V measurements in a two-dimensional sheet $\colon$ Computational method and mathematical analysis

Atomically thin films and surfaces exhibit many distinctive two-dimensional electronic properties that are absent in bulk crystals. In situ microscale multi-probe measurements have been utilized as an...

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The article presents a novel computational method for determining probe positions in multi-probe measurements, which addresses a significant limitation in current experimental setups. The methodological rigor is notable, and the potential applications in various extreme conditions enhance its relevance. Furthermore, the implications for research on two-dimensional materials broaden the field's understanding of their electronic properties, marking this work as particularly influential and innovative.

From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training

We present a novel quality assessment method which can predict the perceptual quality of point clouds from new scenes without available annotations by leveraging the rich prior knowledge in images, ca...

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The proposed method is highly innovative as it introduces a cross-media approach for blind quality assessment of point clouds without annotated training, which is a significant gap in the current understanding and methodologies in this area. The use of domain adaptation techniques and the introduction of distortion-guided feature alignment showcases methodological rigor. The link to human perceptual quality further enhances its applicability and relevance in practical scenarios.

Classification of Homogeneous Local Representations of the Singular Braid Monoid

For a natural number $n$ , denote by $B_n$ the braid group on $n$ strings and by $SM_n$ the singular braid monoid on $n$ strings. $SM_n$ is one of the mo...

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The article builds on significant prior work in the field of braid groups and their representations, providing new classifications that expand existing knowledge. Its methodological rigor in extending the work of Mikhalchishina adds depth and relevance, especially for researchers focused on algebraic structures and their representations. The findings have potential implications for theoretical advancements and might inspire further studies on local representations in broader contexts.

Linear extrapolation for the graph of function of single variable based on walks

The quantum walk was introduced as a quantum counterpart of the random walk and has been intensively studied since around 2000. Its applications include topological insulators, radioactive waste reduc...

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The paper introduces a novel model based on quantum walks that enhances our understanding of function graphs, thereby adding to the existing body of research on quantum walks and their applications. The use of linear extrapolation signifies practical applicability, yet the abstract lacks detailed information on methodological rigor and potential experimental validations.

Probing new scalars with the lepton anomalous magnetic moment and the weak equivalence principle violation

A new scalar particle with generic couplings to the standard-model particles is a possible source for the lepton anomalous magnetic moment and the violation of the weak equivalence principle. Here, on...

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The article explores a novel theoretical framework involving new scalar particles that potentially address important phenomena in particle physics, specifically the lepton anomalous magnetic moment and violations of the weak equivalence principle. This is highly relevant as it builds upon existing experimental results and proposes constraints that can guide future experiments and theoretical work. The methodological approach, including one-loop calculations and consideration of multiple experimental outcomes, suggests a thorough investigation.

Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent

Low-rank tensor completion aims to recover a tensor from partially observed entries, and it is widely applicable in fields such as quantum computing and image processing. Due to the significant advant...

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The article presents a novel algorithm (PRGD) for low-rank tensor completion that demonstrates significant improvements in computational efficiency, which is crucial for applications across various fields. The strong theoretical backing of linear convergence along with robust experimental validation in both simulated and real datasets highlights its methodological rigor and applicability. The focus on important applications like hyperspectral imaging and quantum computing underscores the paper's relevance.

A Classifying Space for Phases of Matrix Product States

We construct a topological space $\mathcal{B}$ consisting of translation invariant injective matrix product states (MPS) of all physical and bond dimensions and show that it has the weak homot...

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This paper presents a novel construction of a topological space associated with matrix product states (MPS), offering important new insights into the classification of phases in many-body quantum systems. The approach has rigorous mathematical backing and addresses complex invariants that could inspire further research in both condensed matter physics and topology.

Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation

Advances in speech synthesis technologies, like text-to-speech (TTS) and voice conversion (VC), have made detecting deepfake speech increasingly challenging. Spoofing countermeasures often struggle to...

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The study addresses a significant challenge in the rapidly evolving domain of voice synthesis and deepfake detection. Its novel methodology combining Latent Space Refinement and Augmentation indicates a solid advancement over existing approaches, particularly in enhancing generalization to unseen attacks. The strong experimental validation across multiple datasets further supports its relevance and potential impact.

Efficient method for calculating magnon-phonon coupling from first principles

Linear magnon-phonon coupling hybridizes magnon and phonon bands at the same energy and momentum, resulting in an anticrossing signature.This hybrid quasiparticle benefits from a long phonon lifetime ...

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This paper introduces a novel and efficient first-principles approach for calculating magnon-phonon coupling, which is crucial for understanding hybrid quasiparticle behaviors in spintronics and quantum information science. It demonstrates strong methodological rigor by comparing results with established theories and experiments. The findings have significant implications for future experimental designs and applications in related fields.

Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding

We introduce Point-LN, a novel lightweight framework engineered for efficient 3D point cloud classification. Point-LN integrates essential non-parametric components-such as Farthest Point Sampling (FP...

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Point-LN presents a novel approach to 3D point cloud classification that balances efficiency with classification accuracy. Its integration of non-parametric components and a compact classifier is an innovative contribution that may facilitate applications in environments with limited computational resources, such as robotics and real-time processing. Additionally, the empirical evaluation against established datasets strengthens the validity of the claims, suggesting a significant impact on future research in this area.

Observation of Giant Orbital Hall Effect in Si

Controlling/storing information carriers, such as electron charge and spin, is key for modern information society, and significant efforts have been paid made to establish novel technologies at the na...

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The article presents a significant advancement in the understanding and application of the giant orbital Hall effect in silicon, which traditionally was thought incapable of such properties. This breakthrough demonstrates a novel integration of semiconductor technology with magnetism, which is crucial for future information storage and processing technologies. The innovative findings have the potential for wide applicability in developing energy-efficient magnetic memory devices, marking an important step forward in the field. Additionally, the economic and technological implications of enhancing memory devices make this work highly relevant.

STAR-RIS-Enabled Multi-Path Beam Routing with Passive Beam Splitting

Reconfigurable intelligent surfaces (RISs) can be densely deployed in the environment to create multi-reflection line-of-sight (LoS) links for signal coverage enhancement. However, conventional reflec...

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The article presents a novel approach to enhancing signal coverage through STAR-RISs, addressing the limitations of conventional schemes effectively. Its methodological rigor, including optimal solutions and the application of graph theory for complex multi-path problems, demonstrates significant innovation and practical applicability in wireless communication systems. The simulation results further validate the efficacy of the proposed system, making it a valuable contribution to the field.

A Data-driven Dynamic Temporal Correlation Modeling Framework for Renewable Energy Scenario Generation

Renewable energy power is influenced by the atmospheric system, which exhibits nonlinear and time-varying features. To address this, a dynamic temporal correlation modeling framework is proposed for r...

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The article presents a novel framework that combines dynamic temporal correlation modeling with a focus on renewable energy, addressing key challenges in uncertainty quantification and scenario generation. The methodological rigor is high, given the use of proper scoring rules and a decoupled mapping path that enhances interpretability. The implications of this work are significant for both theory and practice in renewable energy, driving future research towards more effective data-driven strategies in this area.