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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!

A Novel Method of Estimating GRB Peak Energies Beyond the \emph{Swift}/BAT Limit

The \emph{Swift} Burst Alert Telescope (BAT), operating in the 15--150 keV energy band, struggles to detect the peak energy ( $E_{\rm p}$ ) of gamma-ray bursts (GRBs), as most GRBs have $E_{...

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The article presents a novel method for estimating gamma-ray burst (GRB) peak energies beyond the limitations posed by the Swift/BAT. This innovative approach contributes significantly to gamma-ray astronomy by addressing a critical gap in current observational capabilities, thus potentially advancing our understanding of GRB phenomena. The methodological rigor demonstrated by analyzing various GRB groups adds credibility to the findings. However, the study is limited by the sample size (only 17 GRBs), which may affect the generalizability of the results, and its focus primarily on one specific observational issue.

Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty

A popular strategy for active learning is to specifically target a reduction in epistemic uncertainty, since aleatoric uncertainty is often considered as being intrinsic to the system of interest and ...

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The article presents a novel approach to addressing the challenge of epistemic uncertainty in active learning, which is significant as it can enhance the performance and applicability of machine learning models in various fields. The incorporation of both probability and possibility theories to create new active learning strategies demonstrates methodological rigor and innovation. Furthermore, the empirical validation on both simulated and real datasets indicates the practical relevance of the proposed methods, offering a potential upgrade to existing techniques in the field.

Fast pick-freeze estimation of Sobol' sensitivity maps using basis expansions

Global sensitivity analysis (GSA) aims at quantifying the contribution of input variables over the variability of model outputs. In the frame of functional outputs, a common goal is to compute sensiti...

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The article introduces a novel approach to improve the computation efficiency of Sobol' sensitivity maps, which is a significant advancement in global sensitivity analysis. The use of basis expansions for dimension reduction in a general setting is innovative and addresses a previously under-explored area in the literature. Methodologically rigorous, the study's focus on statistical estimation and computational cost enhancement contributes valuable tools for researchers. Furthermore, its application to non-Newtonian hydraulics provides practical relevance and demonstrates real-world applicability, enhancing its impact potential.

Zeitgebers-Based User Time Perception Analysis and Data-Driven Modeling via Transformer in VR

Virtual Reality (VR) creates a highly realistic and controllable simulation environment that can manipulate users' sense of space and time. While the sensation of "losing track of time" ...

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The study addresses a novel intersection of time perception and user experience in VR, a topic that has been underexplored but has significant implications for both theoretical understanding and practical applications. It employs rigorous methodologies, including novel modeling techniques that enhance the understanding of user experience dynamics. The potential implications for VR applications in therapy and training make it particularly relevant for both users and developers.

Structured IB: Improving Information Bottleneck with Structured Feature Learning

The Information Bottleneck (IB) principle has emerged as a promising approach for enhancing the generalization, robustness, and interpretability of deep neural networks, demonstrating efficacy across ...

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The article presents a novel framework, Structured IB, that enhances the Information Bottleneck principle by incorporating auxiliary encoders for feature extraction. This innovation potentially addresses known limitations of IB approaches, offering a significant advancement in deep learning interpretability and performance. The experimental results, indicating improved accuracy and information preservation, suggest strong methodological rigor and applicability across various domains such as computer vision and data clustering.

Generate Any Scene: Evaluating and Improving Text-to-Vision Generation with Scene Graph Programming

DALL-E and Sora have gained attention by producing implausible images, such as "astronauts riding a horse in space." Despite the proliferation of text-to-vision models that have inundated th...

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The article introduces a novel framework, 'Generate Any Scene,' that addresses limitations in existing text-to-vision generation models through the use of scene graph programming. This approach is both innovative and methodologically rigorous, as it enhances evaluation metrics and processes in a rapidly evolving field. The insights gained from extensive evaluations across multiple model categories signify significant practical implications. Its applications in model performance improvement and content moderation reflect the interdisciplinary impact and utility of this work, suggesting strong potential for future research developments.

Point Source Identification in Subdiffusion from A Posteriori Internal Measurement

In this work we investigate an inverse problem of recovering point sources and their time-dependent strengths from {a posteriori} partial internal measurements in a subdiffusion model which involves a...

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This article presents a significant advancement in the field of inverse problems associated with subdiffusion models. The novelty lies in its exploration of a fractional derivative framework within a general elliptic operator context, enhancing our understanding of point source identification. The methodological rigor is demonstrated through well-posedness proofs and numerical experiments, which strengthen the applicability of the findings. The extension of current methodologies specifically for parabolic type problems adds substantial value, making potential applications across various scientific disciplines more robust.

Neural Operator Feedback for a First-Order PIDE with Spatially-Varying State Delay

A transport PDE with a spatial integral and recirculation with constant delay has been a benchmark for neural operator approximations of PDE backstepping controllers. Introducing a spatially-varying d...

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The article presents a novel approach to handling spatially-varying delays in transport PDEs, which is significant given the complexity of these problems in control theory. The introduction of a neural operator for a two-branch feedback law is innovative and addresses a subfield of control systems that is relatively underexplored. Moreover, proving the stability of the approximator adds methodological rigor that will be valuable for future research.

Maximal Clique Enumeration with Hybrid Branching and Early Termination

Maximal clique enumeration (MCE) is crucial for tasks like community detection and biological network analysis. Existing algorithms typically adopt the branch-and-bound framework with the vertex-orien...

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The article presents a novel hybrid approach to maximal clique enumeration that significantly improves upon existing methods. Its methodological rigor is exemplified by the introduction of a new branching strategy and an early termination technique that enhances efficiency. This research addresses a fundamental problem in various applications, putting it at the forefront of relevant computational advancements. The extensive experimental validation reinforces the claims of superior performance, indicating strong applicability in real-world scenarios.

A computational framework to predict weld integrity and microstructural heterogeneity: application to hydrogen transmission

We present a novel computational framework to assess the structural integrity of welds. In the first stage of the simulation framework, local fractions of microstructural constituents within weld regi...

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The article presents a novel computational framework combining microstructural analysis and structural integrity assessment, specifically applied to hydrogen transmission—a critical area given the global shift towards hydrogen as a clean energy source. The methodology is well-defined, showcasing robust simulation and validation against experimental data. The applicability of the research to future hydrogen infrastructure repurposing adds significant value in a relevant and urgent field.

Nascent water waves induced by the impulsive motion of a solid wall

In the present study, we investigated the generation phase of laboratory-scale water waves induced by the impulsive motion of a rigid piston, whose maximum velocity $U$ and total stroke $L...

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The study provides insightful experimental data on water wave generation, specifically the interactions between impulsive motion and fluid dynamics. The exploration of dimensionless parameters and their influence on wave behavior reveals significant novelty and depth. Moreover, the analytical models proposed enrich the theoretical framework of hydrodynamics, making this research potentially impactful for both practical applications and further theoretical investigations in fluid mechanics.

Branching Stochastic Processes for Feynman-Kac Representations of Drift-involving Non-linearities

Many drift-diffusion transport models rely on a coupling with a sub-model of the drift velocity. In this letter we extend Feynman-Kac's theory to provide probabilistic representations of such velo...

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The article presents a novel extension of Feynman-Kac theory to include drift-involving non-linearities, which fills a gap in the current understanding of drift-diffusion transport models. The utilization of a single embedded stochastic process is innovative and could significantly advance the modeling of complex physical systems, particularly in interpreting non-linear phenomena. The potential interdisciplinary applications in areas like fluid dynamics and biological systems suggest a strong applicability and impact, although the complexity of the topic may limit immediate accessibility for broader audiences.

Initial layer of the anti-cyclotomic $\mathbb{Z}_3$ -extension of $\mathbb{Q}(\sqrt{-m})$ and capitulation phenomenon

Let $k=\mathbb{Q}(\sqrt-m)$ be an imaginary quadratic field. We consider the properties of capitulation of the p-class group of k in the anti-cyclotomic $\mathbb{Z}_p$ -extension of ...

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The article addresses a specific and nuanced topic in number theory, namely the capitulation phenomena in anti-cyclotomic extensions, which is a relatively novel approach in this area. It introduces a new algorithm for determining certain field properties, along with conjectures that could influence future research directions. Its methodological rigor, including concrete numerical examples, enhances its applicability and relevance.

Enhancement of superconductivity in electron-hole coexisting Sr $_{1-x}$ Eu $_{x}$ CuO $_{2+y}$ films

We report transport measurements of infinite-layer cuprate Sr $_{1-x}$ Eu $_{x}$ CuO $_{2+y}$ films with controlled electron (by trivalent europium) and hole (by interstitial apical...

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The article presents a novel investigation into the enhancement of superconductivity through controlling both electron and hole carriers in cuprate films, which is an understudied area with potential implications for high-temperature superconductivity research. The combination of advanced growth techniques and careful measurements supports the robustness of the findings, while the two-dimensional nature of the superconductivity adds an important dimension to the understanding of these systems. The ability to manipulate conditions for superconductivity at the nanoscale can inform future experimental and theoretical work in the field.

Unveiling the Invisible: ALPs and Sterile Neutrinos at the LHC and HL-LHC

We investigate the potential of using the signature of mono-Higgs plus large missing energies to constrain on two new physics models, namely the model of an axion-like particle (ALP) and the model of ...

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The article presents a novel analysis of ALPs and sterile neutrinos utilizing existing LHC data, addressing significant aspects of theoretical particle physics. The methodological rigor of combining both experimental data with theoretical models demonstrates a robust framework for constraining new physics scenarios. Moreover, the focus on mono-Higgs signatures indicates applicability for future high-energy physics experiments, particularly at the HL-LHC, enhancing its potential impact. The findings could inspire further investigations into beyond the Standard Model physics, indicating the relevance of this research for ongoing studies.

Coarse-to-Fine: A Dual-Phase Channel-Adaptive Method for Wireless Image Transmission

Developing channel-adaptive deep joint source-channel coding (JSCC) systems is a critical challenge in wireless image transmission. While recent advancements have been made, most existing approaches a...

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The article presents a novel approach to a pressing issue in wireless image transmission by addressing the limitations of existing methods in dynamic channel environments. The introduction of reinforcement learning for channel quality indicators enhances its innovative value. The methodology appears rigorous, and the application of the coarse-to-fine framework adds to its practical relevance, potentially influencing future research in this area.

Unicorn: Unified Neural Image Compression with One Number Reconstruction

Prevalent lossy image compression schemes can be divided into: 1) explicit image compression (EIC), including traditional standards and neural end-to-end algorithms; 2) implicit image compression (IIC...

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The paper presents a novel approach to image compression that effectively combines aspects of explicit and implicit compression, addressing existing limitations in both categories. The introduction of the Unicorn paradigm demonstrates substantial improvements in compression efficiency and model simplicity, suggesting strong potential for real-world application. Its methodology appears rigorous and includes proof of concept, enhancing credibility. However, future research directions and broader applications could be further elaborated to maximize impact.

Time-optimal persistent homology representatives for univariate time series

Persistent homology (PH) is one of the main methods used in Topological Data Analysis. An active area of research in the field is the study of appropriate notions of PH representatives, which allow to...

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The article introduces a novel concept of time-optimal persistent homology representatives, addressing an important optimization problem in Topological Data Analysis (TDA). The focus on time-varying data and the application to synthetic and climate model time series adds practical relevance and illustrates methodological advancements. The computational aspect and its comparison to existing methodologies enhance its impact and applicability.

A variety of partially conservative sentences

We study the existence of a $Θ$ sentence which is simultaneously $Γ$ -conservative over consistent RE extensions $T$ and $U$ of Peano Arithmetic for various reasonable p...

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The article addresses fundamental questions in mathematical logic, particularly concerning the properties of sentences in the context of Peano Arithmetic. This focus on the conservativity of sentences is both novel and technically rigorous, potentially impacting ongoing discussions on the nature of formal theories. It's particularly relevant for its affirmative answer to a previously open question posed by Guaspari, demonstrating its significance in the field.

Measurement-based quantum convolutional neural network for deep learning

Recently, quantum convolutional neural networks (QCNNs) are proposed, harnessing the power of quantum computing for faster training compared to the classical counterparts. However, this framework for ...

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This article presents a novel approach to quantum convolutional neural networks by using cluster states, which enhances the feasibility of QCNNs amidst the complexities of quantum control. The proposed method seems to be timely given the current limitations in scaling quantum systems and introduces significant potential for practical applications in quantum deep learning. The rigorous numerical evidence supporting faster convergence is commendable, indicating a thorough evaluation of the method's effectiveness. However, further exploration into the robustness of the proposed method and its applicability in diverse quantum systems could have further strengthened its findings.