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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!

Classification of the limit shape for 1+1-dimensional FPP

We introduce a simplified model of planar first passage percolation where weights along vertical edges are deterministic. We show that the limit shape has a flat edge in the vertical direction if and ...

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This article provides significant advancements in understanding 1+1-dimensional first passage percolation models, demonstrating a novel approach to the limit shape characterization and deterministic weights, which is crucial for future theoretical explorations. The rigorous bounding of time constants adds methodological depth, enhancing the paper's impact on the field.

Probably Approximately Precision and Recall Learning

Precision and Recall are foundational metrics in machine learning where both accurate predictions and comprehensive coverage are essential, such as in recommender systems and multi-label learning. In ...

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This article presents a novel framework for considering Precision and Recall metrics in machine learning, particularly in situations with one-sided feedback. The introduction of graph-based hypotheses is an innovative approach that appears to have a rigorous statistical foundation. The results could significantly impact recommender systems and other areas where labeled data is limited and could inspire further research into learning from incomplete information.

A Theory for Compressibility of Graph Transformers for Transductive Learning

Transductive tasks on graphs differ fundamentally from typical supervised machine learning tasks, as the independent and identically distributed (i.i.d.) assumption does not hold among samples. Instea...

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This article addresses a significant gap in the understanding of Graph Transformers by focusing on the hidden dimension's impact on compressibility, which is crucial for enhancing efficiency in transductive learning. The theoretical bounds proposed provide practical insights that could stimulate further research in optimizing these models. Its exploration of sparsity in attention patterns is particularly timely, given the current trends in machine learning towards more efficient models.

Machine Learning Domain Adaptation in Spin Models with Continuous Phase Transitions

The main question raised in the letter is the applicability of a neural network trained on a spin lattice model in one universality class to test a model in another universality class. The quantities ...

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The article presents a novel approach to domain adaptation in the context of spin models dealing with continuous phase transitions, which adds significant value to the field of computational physics and machine learning. The focus on understanding the transferability of neural networks across different universality classes is a timely and relevant topic, and the proposed method of using binding energy distributions is both innovative and methodologically rigorous. The implications for studying critical phenomena are substantial, making this research relevant for various interdisciplinary applications.

X as Supervision: Contending with Depth Ambiguity in Unsupervised Monocular 3D Pose Estimation

Recent unsupervised methods for monocular 3D pose estimation have endeavored to reduce dependence on limited annotated 3D data, but most are solely formulated in 2D space, overlooking the inherent dep...

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The article presents a novel approach to a significant challenge in monocular 3D pose estimation—depth ambiguity. Its methodological innovation combines unsupervised techniques with the multi-hypothesis framework and tailored pretext tasks, making substantial strides in addressing an critical gap in current literature. The rigorous evaluation across multiple datasets and the proposed regularization approach enhance the credibility and applicability of the findings. However, while the work is strong, further exploration into broader applications beyond the specified datasets and potential real-world complications could have been discussed more thoroughly.

ORID: Organ-Regional Information Driven Framework for Radiology Report Generation

The objective of Radiology Report Generation (RRG) is to automatically generate coherent textual analyses of diseases based on radiological images, thereby alleviating the workload of radiologists. Cu...

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The ORID framework presents a novel method for improving radiology report generation by integrating multi-modal information and reducing noise, which addresses a significant challenge in the field of medical imaging and AI. Its incorporation of Graph Neural Networks for analyzing organ interconnections adds methodological rigor. The extensive experiments showing superior performance further bolster its relevance and potential impact.

Prior-based Objective Inference Mining Potential Uncertainty for Facial Expression Recognition

Annotation ambiguity caused by the inherent subjectivity of visual judgment has always been a major challenge for Facial Expression Recognition (FER) tasks, particularly for largescale datasets from i...

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The article presents a novel approach to addressing the significant challenge of annotation ambiguity in Facial Expression Recognition (FER), showcasing methodological rigor through its proposed Prior-based Objective Inference (POI) network. The integration of both prior knowledge and dynamic knowledge transfer demonstrates innovation and practicality, potentially improving FER systems. Moreover, the introduction of an uncertainty estimation module adds robustness to the framework, paving the way for more reliable applications in varying real-world scenarios. This combination of novelty, methodological soundness, and practical applicability supports a high relevance score.

Enhancing Transportation Cyber-Physical Systems Security: A Shift to Post-Quantum Cryptography

The rise of quantum computing threatens traditional cryptographic algorithms that secure Transportation Cyber-Physical Systems (TCPS). Shor's algorithm poses a significant threat to RSA and ECC, w...

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The article addresses a pressing issue within the field of cybersecurity, particularly in the context of transportation systems, which are increasingly vulnerable to quantum computing threats. Its thorough analysis of traditional cryptographic vulnerabilities and the examination of post-quantum cryptography (PQC) applicability provides high methodological rigor and relevance. The case study adds practical insights, making it highly applicable and inspiring for future research. Additionally, discussions on the challenges of implementing PQC in safety-critical environments demonstrate its interdisciplinary nature and applicability to real-world issues.

Training Physics-Driven Deep Learning Reconstruction without Raw Data Access for Equitable Fast MRI

Physics-driven deep learning (PD-DL) approaches have become popular for improved reconstruction of fast magnetic resonance imaging (MRI) scans. Even though PD-DL offers higher acceleration rates compa...

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The proposed CUPID method addresses a significant barrier to the use of advanced PD-DL techniques in MRI—namely, the lack of access to raw data in clinical settings outside specialized centers. This focus on equitable access and its capacity to generalize across diverse populations is particularly noteworthy, aligning well with current needs in medical imaging. Its methodological rigor and demonstrated effectiveness compared to existing approaches suggest high applicability and potential impact on the field.

Chanel-Orderer: A Channel-Ordering Predictor for Tri-Channel Natural Images

This paper shows a proof-of-concept that, given a typical 3-channel images but in a randomly permuted channel order, a model (termed as Chanel-Orderer) with ad-hoc inductive biases in terms of both ar...

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The article presents a novel approach in image processing by applying a new model, Chanel-Orderer, for predicting and correcting channel order in images, which addresses a practical issue in computational vision. The incorporation of human visual semantics adds depth to its application. The proof-of-concept demonstrates systematic rigor in the model's architecture and performance, suggesting strong potential for further development and practical use.

AsymDex: Leveraging Asymmetry and Relative Motion in Learning Bimanual Dexterity

We present Asymmetric Dexterity (AsymDex), a novel reinforcement learning (RL) framework that can efficiently learn asymmetric bimanual skills for multi-fingered hands without relying on demonstration...

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AsymDex presents a novel approach to learning bimanual dexterity using reinforcement learning that effectively addresses the challenges of learning from demonstrations, a significant limitation in current methods. The focus on natural asymmetry in human manipulation and the structured roles of the hands (facilitating vs. dominant) is innovative and offers a strong basis for enhancing sample efficiency. The experimental results indicate robust performance against established baselines, highlighting the practical applicability and methodological rigor of the framework. This may catalyze advancements in robotic dexterity and human-robot interaction.

Open-World Amodal Appearance Completion

Understanding and reconstructing occluded objects is a challenging problem, especially in open-world scenarios where categories and contexts are diverse and unpredictable. Traditional methods, however...

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The proposed framework addresses a significant gap in current methodologies by enabling amodal completion in open-world scenarios without the limitations of predefined object categories. Its innovative combination of segmentation, occlusion analysis, and inpainting, along with the incorporation of flexible text queries, positions the research as both novel and applicable. The extensive evaluations suggest robustness, pointing towards strong implications for future applications in 3D reconstruction and beyond. The release of the code and datasets will enhance reproducibility and further research opportunities.

Comparison of Kikuchi Diffraction Geometries in Scanning Electron Microscope

Recent advances in scanning electron microscope (SEM) based Kikuchi diffraction have demonstrated the important potential for reflection and transmission methods, like transmission Kikuchi diffraction...

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The article explores novel and methodologically sound comparisons of various Kikuchi diffraction methods in SEM, which represents a significant advancement in the analytical capabilities of SEM techniques. The approach of utilizing direct electron detectors and remapping diffraction patterns contributes to a robust evaluation of these methodologies, showcasing both novelty and potential for further insights into electron scattering phenomena.

Breaking the Cycle of Recurring Failures: Applying Generative AI to Root Cause Analysis in Legacy Banking Systems

Traditional banks face significant challenges in digital transformation, primarily due to legacy system constraints and fragmented ownership. Recent incidents show that such fragmentation often result...

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The article presents a novel method that combines generative AI with established analytical frameworks, addressing a critical issue in legacy banking systems. Its focus on automating root cause analysis not only pushes current boundaries in incident management but also provides a practical application that demonstrates significant findings. The methodological rigor shown through the case study and the extensive data analysis enhances its applicability and relevance.

HiFAST: An HI Data Calibration and Imaging Pipeline for FAST III. Standing Wave Removal

The standing waves existed in radio telescope data are primarily due to reflections among the instruments, which significantly impact the spectrum quality of the Five-hundred-meter Aperture Spherical ...

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The development of a novel FFT filter method for eliminating standing waves in radio telescope data represents a significant advancement in the calibration and imaging of FAST data. The method showcases methodological rigor and presents a clear improvement over existing techniques, making it a valuable contribution to the field. Its efficiency in detecting harmonic RFI further enhances its applicability and impact.

Strong XOR Lemma for Information Complexity

For any $\{0,1\}$ -valued function $f$ , its \emph{ $n$ -folded XOR} is the function $f^{\oplus n}$ where $f^{\oplus n}(X_1, \ldots, X_n) = f(X_1) \oplus \cdots \oplus ...

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This article presents a strong XOR lemma that significantly enhances the understanding of information complexity in randomized communication models. The proof of an optimal and asymptotically tight bound contributes novel insights to the ongoing discourse in theoretical computer science and information theory. The methodologies employed are rigorous and the implications for communication complexity further enhance its practical relevance.

Scalable Deep Metric Learning on Attributed Graphs

We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based on extending deep m...

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The article presents a novel approach to deep metric learning specifically tailored for attributed graphs, which is a relevant and growing area in machine learning. The introduction of scalable algorithms for semi-supervised and unsupervised learning showcases innovation. The generalization guarantee and connection between tuplet loss and contrastive learning add theoretical depth, making this work impactful for future research. Extensive experimental results further support the utility of the proposed methods, though more comparison with bottom-up methods could enhance robustness.

Revisiting the hyperfine interval for the $2s2p$ $^3\!P_{J}$ state in $^9$ Be

Using relativistic multiconfiguration Dirac-Hartree-Fock method, we calculate the hyperfine-structure properties of the $2s2p$ $^3\!P_{J}$ state in $^9$ Be. The hyperfine-struct...

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This study employs a sophisticated relativistic multiconfiguration Dirac-Hartree-Fock method to provide updated hyperfine-structure constants for the $2s2p$ $^3P_{J}$ state in $^9$Be. Its thoroughness in addressing higher-order corrections is noteworthy, suggesting significant methodological rigor. The updated hyperfine-structure constants and electric quadrupole moments could have implications for nuclear and atomic physics, wherein precise constants are pivotal for theoretical and experimental investigations. This research revisits prior measurements and resolves discrepancies, offering a solid foundation for future work, particularly regarding few-body calculations.

A toy model for $p$ -form gauge symmetry

The abelian $(p+1)$ -form gauge field is inherently coupled to the $p$ -brane worldvolume. After quantization, the corresponding $p$ -form gauge transformation is associated with ...

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This article introduces a toy model that provides a novel perspective on $p$-form gauge symmetry, offering potential insights into its interaction with $p$-branes. The analytical simplifications with matrix representations could significantly ease complex calculations in higher-dimensional gauge theories. The non-abelian generalization further enhances its relevance, suggesting broad applicability in theoretical physics. Overall, it shows methodological rigor, originality, and potential for future explorations in fundamental physics.

The temporal and spatial variations of lithium abundance in the Galactic disc

This study investigates the temporal and spatial variations in lithium abundance within the Milky Way using a sample of 22,034 main-sequence turn-off (MSTO) stars and subgiants, characterised by preci...

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This article presents a detailed and novel exploration of lithium abundance in the Milky Way, leveraging a robust dataset and advanced methodologies (3D NLTE analysis) which enhances its credibility. The findings regarding the temporal dynamics and spatial distribution of lithium have significant implications for stellar and galactic evolution theories, potentially guiding future research in both stellar astrophysics and chemical evolution of galaxies.