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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!

Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series

Recently, leveraging pre-trained Large Language Models (LLMs) for time series (TS) tasks has gained increasing attention, which involves activating and enhancing LLMs' capabilities. Many methods a...

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The article proposes a novel framework, Context-Alignment, which integrates linguistic elements into time series analysis using large language models (LLMs). This innovative approach not only highlights the potential of LLMs beyond traditional applications but also addresses a gap in existing methodologies by emphasizing structural and logical alignments. The rigorous experimental validation enhances its credibility and suggests significant implications for various applications in time series forecasting and natural language processing. Its interdisciplinary nature, engaging both computing and linguistic aspects, adds to its relevance and potential impact.

A Multimodal Lightweight Approach to Fault Diagnosis of Induction Motors in High-Dimensional Dataset

An accurate AI-based diagnostic system for induction motors (IMs) holds the potential to enhance proactive maintenance, mitigating unplanned downtime and curbing overall maintenance costs within an in...

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The article presents a novel application of a lightweight deep learning model, ShuffleNetV2, for fault diagnosis in induction motors, specifically addressing the challenge of overfitting due to small datasets in prior studies. The utilization of a significantly large dataset of 57,500 images and the combination of advanced signal processing techniques enhances the robustness and applicability of the research. The performance metrics demonstrate high classification accuracy, indicating potential for real-world industrial implementation.

Strong Gravitational Lensing by Compact Object without Cauchy Horizons in Effective Quantum Gravity

In this work, we study strong gravitational lensing effects of a static, spherically symmetric solution in the context of effective quantum gravity (EQG). Among the three types of solutions proposed i...

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This article presents a novel approach to gravitational lensing effects in the framework of effective quantum gravity, exploring a unique class of solutions not characterized by Cauchy horizons. The methodological rigor demonstrated in analyzing lensing features in black and wormhole solutions enhances its scientific contribution. Furthermore, it effectively connects theoretical predictions to observational data, particularly relevant for future astronomical studies. This intersection between quantum gravity and observational astrophysics marks it as a valuable piece of research for the advancement of these fields.

Communication-reduced Conjugate Gradient Variants for GPU-accelerated Clusters

Linear solvers are key components in any software platform for scientific and engineering computing. The solution of large and sparse linear systems lies at the core of physics-driven numerical simula...

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This article presents a novel and efficient implementation of an established numerical method tailored for modern computing architectures, specifically Nvidia GPUs. Its focus on reducing communication overhead in parallel computations addresses a significant bottleneck in scientific computing, which could lead to performance improvements in various applications. The methodology is robust and well-supported by experimental validation, making it highly relevant for future research in high-performance computing and numerical simulation fields.

Computing accurate eigenvalues using a mixed-precision Jacobi algorithm

We provide a rounding error analysis of a mixed-precision preconditioned Jacobi algorithm, which uses low precision to compute the preconditioner, applies it in high precision (amounting to two matrix...

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The article introduces a novel mixed-precision approach to the Jacobi algorithm, significantly improving accuracy in computing eigenvalues by reducing rounding errors. The methodological rigor is evidenced by both theoretical analysis and experimental validation, making this work highly relevant for practitioners and researchers in numerical linear algebra. Its potential for influencing future research into precision and computational efficiency in eigenvalue problems further enhances its impact.

Neural encoding with affine feature response transforms

Current linearizing encoding models that predict neural responses to sensory input typically neglect neuroscience-inspired constraints that could enhance model efficiency and interpretability. To addr...

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The proposed affine feature response transform (AFRT) introduces a novel approach to encoding neural responses by integrating neuroscience-inspired constraints, enhancing interpretability and efficiency. Its application to multiple areas of the macaque brain demonstrates methodological rigor and significant advancements over existing models. The reduction of parameters while maintaining performance is a noteworthy contribution that addresses a critical challenge in the field, making it highly impactful for future research into neural encoding and related models.

Pseudo Strong Labels from Frame-Level Predictions for Weakly Supervised Sound Event Detection

Weakly Supervised Sound Event Detection (WSSED), which relies on audio tags without precise onset and offset times, has become prevalent due to the scarcity of strongly labeled data that includes exac...

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This article presents a novel approach (Frame-level Pseudo Strong Labeling) that effectively leverages weakly labeled data to improve sound event detection, addressing a key challenge in the field. The method shows promising results across multiple benchmark datasets, indicating methodological rigor and applicability. The research has substantial implications for improving machine learning models in sound analysis, an area with practical applications in various domains.

Boundedness of complements for generalized pairs

We prove the boundedness of complements for generalized pairs (for arbitrary coefficients) after Shokurov.

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The article addresses a significant aspect of algebraic geometry concerning the boundedness of complements for generalized pairs, which can impact the understanding of varieties and their properties. The approach taken appears to build on established work by Shokurov, suggesting a solid methodological framework. However, the abstract lacks detail on the implications of the findings, which slightly reduces the overall impact and novelty despite its technical relevance.

Exploring membership and variability in NGC 7419: An open cluster rich in super giants and Be type stars

NGC 7419 is a young open cluster notable for hosting five Red Supergiants and a much higher abundance of Classical Be stars (CBe) than typical open clusters. We perform a membership analysis using GAI...

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The study addresses a unique open cluster with an unusual abundance of CBe stars and provides a detailed membership analysis using advanced machine learning techniques, which enhances its methodological rigor. The investigation of variability in the identified stars, coupled with the novel findings about different mechanisms of variability, adds substantial depth and relevance to the existing literature on stellar populations. Furthermore, the application of GAIA DR3 data and various statistical analyses indicates strong potential for broader applicability in astrophysics research, especially in open cluster studies.

Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction

Magnetic Resonance Imaging (MRI) is widely used in clinical practice, but suffered from prolonged acquisition time. Although deep learning methods have been proposed to accelerate acquisition and demo...

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The article presents a novel approach to MRI reconstruction that addresses significant limitations of existing methods, particularly in the use of under-sampled data. Its methodological rigor is evident in the integration of image priors and a well-defined architecture, which could set a new standard in the field of MRI imaging. The experiments also suggest strong empirical support for the proposed method, showcasing potential for practical application in clinical settings.

An Effective Docker Image Slimming Approach Based on Source Code Data Dependency Analysis

Containerization is the mainstream of current software development, which enables software to be used across platforms without additional configuration of running environment. However, many images cre...

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The article introduces a novel approach to Docker image slimming that effectively addresses a significant issue in containerization, which is the reduction of image size while maintaining functionality. The use of static data dependency analysis is innovative, enhancing the methodological rigor. The experimental evaluation on real-world projects demonstrates practical applicability and potential for wide adoption in software development. However, more extensive validation across diverse environments could strengthen its impact.

Transport in integrable and perturbed easy-axis Heisenberg chain: Thouless approach

We study transport in the spin chains by employing the Thouless approach based on the level sensitivity to the boundary conditions, $R$ . Although spin transport in the integrable easy-axis XXZ...

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The article presents a novel approach to understanding transport phenomena in spin chains, applying the Thouless approach to discern the impacts of integrability and perturbations, which is a significant contribution to theoretical physics. It combines rigorous analytical justification with numerical calculations, demonstrating methodological robustness. The implications for spin transport mechanisms underpinning quantum systems enhance its relevance for future research.

Reinvestigating the semileptonic $B\to D^{(\ast)}τ\barν_τ$ decays in the model independent scenarios and leptoquark models

In this work, we revisit the possible new physics (NP) solutions by analyzing the observables associated with $B\to D^{(\ast)}τ\barν_τ$ decays. To explore the structure of new physics, the for...

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This article provides a comprehensive reevaluation of $B o D^{( ext{*})}τarν_τ$ decays, applying advanced theoretical frameworks and precise new physics (NP) scenarios. The methodological rigor stems from the integration of Belle data and lattice QCD results for form factors, enhancing the reliability of the predictions made. Furthermore, the focus on predicting observables that are crucial for testing lepton flavor universality and potential NP indicates its relevance in current experimental contexts. The combination of a systematic approach and pertinent theoretical implications solidifies its position as a significant contribution to the field.

Positive self-commutators of positive operators

We consider a positive operator $A$ on a Hilbert lattice such that its self-commutator $C = A^* A - A A^*$ is positive. If $A$ is also idempotent, then it is an orthogonal proj...

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The article presents novel results in the study of positive operators and their self-commutators in the context of Hilbert lattices. The findings extend existing knowledge by establishing conditions under which operators can be expressed as self-commutators, and by proving that positive central operators can be decomposed accordingly. This could inspire further research into the structure of operators and their relationships with other mathematical constructs, making it a significant contribution to the field. The methodological rigor is evident in the theoretical proofs provided, which add to the scholarly understanding of operator theory.

Goodness-of-fit tests for spatial point processes: A review

In this review, the state-of-the-art for goodness-of-fit testing for spatial point processes is summarized. Test statistics based on classical functional summary statistics and recent contributions fr...

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This article provides a comprehensive overview of goodness-of-fit tests specifically designed for spatial point processes, which is essential for the advancement of statistical methods in this area. The integration of classical statistics with contemporary techniques such as topological data analysis enhances its novelty and utility. Additionally, the categorization of various approaches creates a solid framework for future research, making it highly applicable for practitioners and researchers alike.

Bayesian EM Digital Twins Channel Estimation

This letter proposes a Bayesian channel estimation method that leverages on the a priori information provided by the Electromagnetic Digital Twin's (EM-DT) representation of the environment. The p...

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The proposed Bayesian channel estimation method stands out due to its innovative use of Electromagnetic Digital Twin representations, which is a novel application in the field. The improvement in NMSE and spectral efficiency is quantitatively significant, demonstrating methodological rigor. The practical implications of reduced pilot requirements at low SNR make it highly applicable, indicating potential for real-world adoption and further research developments in channel estimation techniques.

Dichotomy of electron-phonon interactions in the delafossite PdCoO $_2$ : From weak bulk to polaronic surface coupling

The metallic delafossites host ultra-high mobility carriers in the bulk, while at their polar surfaces, intrinsic electronic reconstructions stabilise markedly distinct electronic phases, from charge-...

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This article presents significant advancements in understanding electron-phonon interactions within the delafossite PdCoO$_2$, particularly by employing innovative microscopy techniques to resolve complex surface interactions. The study's focus on both bulk and surface phenomena showcases clear novelty and methodological rigor, addressing gaps in previous research. Moreover, the implications of the findings for future studies on polaronic behavior and electronic structure could guide new research avenues in related materials, enhancing applicability and potential impact.

Realistic Test-Time Adaptation of Vision-Language Models

The zero-shot capabilities of Vision-Language Models (VLMs) have been widely leveraged to improve predictive performance. However, previous works on transductive or test-time adaptation (TTA) often ma...

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The work addresses a significant gap in the evaluation and application of Vision-Language Models, specifically focusing on real-world deployment challenges. The introduction of a framework that accommodates realistic test conditions, along with the StatA method, demonstrates novelty and potential for impact in the field. Comprehensive evaluations and comparisons add to the methodological rigor, while the availability of code promotes reproducibility and adoption.

Electrochemically induced hyperfluorescence based on the formation of charge-transfer excimers

Despite the extensive use of electrochemiluminescence in sensing applications, its potential in lighting and display technology has been constrained by the low luminance and short operational lifetime...

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The article presents a significant advancement in electrochemiluminescence devices by introducing a novel mechanism (ECiHF) that greatly enhances luminance and longevity, addressing key limitations in the field. Its systematic analysis of energy levels and excimer interactions adds to its methodological rigor, while the potential applications in commercial lighting underscore its practical relevance. The findings are both innovative and applicable, positioning the research well for influence in relevant sectors.

Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives

Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Speech analysis offers a non-intrusive and scalable screening method, particularly through...

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The article addresses early detection of neurocognitive disorders using a novel methodology that integrates dynamic topic modeling with visual stimuli analysis. This approach is methodologically rigorous and innovative, showing a clear advancement over traditional narrative analyses. The empirical validation of the proposed methods with a significant dataset (CU-MARVEL Rabbit Story corpus) strengthens the findings' reliability and potential for real-world application. Its focus on macrostructures opens new avenues for understanding cognitive deficits in narrative communication, which may inspire further interdisciplinary research.