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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!

Bayesian Optimization via Continual Variational Last Layer Training

Gaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on tasks where correlat...

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The article introduces a novel approach that enhances Bayesian optimization, a critical area in machine learning and artificial intelligence. The emphasis on overcoming the limitations of traditional Gaussian Processes and Bayesian Neural Networks significantly contributes to the field. The proposed training method shows strong empirical performance across various tasks, suggesting both robustness and practical applicability. The methodological rigor and innovation make it a valuable contribution that could inspire further research in surrogate modeling and optimization techniques.

SIRI-2 Detection of the Gamma-ray Burst 221009A

SIRI-2 is a collection of Strontium Iodide gamma-ray detectors sensitive at approximately 400 keV to 10 MeV, launched on the Department of Defense's STPSat-6 to geosynchronous orbit. SIRI-2 detect...

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The research introduces the SIRI-2 detection system, revealing innovative capabilities in addressing gamma-ray bursts and contributing valuable data to the field of astrophysics. It offers novel insights into the characteristics of GRB 221009A, and the comparative analysis with other detection systems positions it as a potential benchmark for future gamma-ray research.

New keypoint-based approach for recognising British Sign Language (BSL) from sequences

In this paper, we present a novel keypoint-based classification model designed to recognise British Sign Language (BSL) words within continuous signing sequences. Our model's performance is assess...

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This article presents a novel application of keypoint-based classification specifically for British Sign Language, showcasing advancements in computational efficiency and resource management. The originality of this approach represents a significant contribution to the field, especially as it addresses a specific and underexplored area of sign language recognition. However, the impact might be limited by the reliance on a particular dataset, which could restrict the generalizability of findings.

Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency servi...

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This paper presents a well-founded exploration of Content Delivery Network (CDN) optimization through a data-driven approach, showcasing novel algorithmic improvements that address critical real-world challenges, particularly in the context of the rising demands for fast and efficient delivery systems post-pandemic. The methodological rigor is supported by empirical experimentation across different setups, which enhances the robustness of the findings and their applicability in practical scenarios. Additionally, the focus on multi-metric evaluations indicates a comprehensive perspective on performance optimization.

Majorana neutrino dipole moments and masses at a muon collider

A future multi-TeV muon collider would provide an important probe for Majorana neutrinos. A muon collider with a collision energy of ∼\sim30 TeV would be sensitive to νe−νμν_e-ν_μ transi...

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This article presents a novel approach to probing Majorana neutrinos through a muon collider, an emerging technology in particle physics. The discussion of sensitivity to dipole moments and mass entries, as well as the emphasis on clean identification of lepton number and flavor violation, demonstrates both methodological rigor and significant potential impact on the field. The potential to complement existing observational data makes it relevant to current debates in neutrino physics.

A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis

Chronic Kidney Disease (CKD) represents a significant global health challenge, characterized by the progressive decline in renal function, leading to the accumulation of waste products and disruptions...

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The article introduces a novel ensemble-based deep learning model that significantly improves the diagnostic accuracy for CKD, a critical health issue. The use of state-of-the-art models and the high accuracy achieved (96%) indicate strong methodological rigor and a meaningful advancement in the field. Moreover, the incorporation of Explainable AI enhances the model's applicability in clinical settings, adding a layer of trust and interpretability to AI-based diagnoses. The robust validation using a comprehensive dataset underscores the work's applicability and relevance in real-world scenarios. Overall, its potential to inform clinical practices and guide future research in machine learning applications for healthcare is substantial.

On the moderate deviation principles in the sparse multi-type Erdős Rényi random graph

We study the sparse multi-type Erdős Rényi random graphs. Despite that the corresponding central limit results are unknown, we are able to prove the moderate deviation principles for the size of the l...

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This article introduces moderate deviation principles in the context of sparse multi-type Erdős Rényi random graphs, which is a significant extension to existing central limit results. The findings contribute novel insights into graph theory and stochastic processes, particularly concerning connected components. The rigorous mathematical approach utilizing a multi-dimensional compound Poisson process showcases methodological rigor, making the results useful for theoretical advancement in probability and graph theory.

Anharmonic long-range electron-phonon interaction: analytic formalism

Describing electron-phonon interactions in a solid requires knowledge of the electron-phonon matrix elements in the Hamiltonian. State-of-the-art first-principles calculations for the electron-phonon ...

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The article presents a novel analytical framework for addressing long-range electron-phonon interactions, particularly the unexplored 1-electron-2-phonon interactions. This innovation enhances the understanding of anharmonic materials, especially those relevant in contemporary applications like halide perovskites. The methodology is rigorous and provides a first-principles approach that could significantly impact future research. Its applicability to materials with significant electron-phonon coupling places it at the forefront of condensed matter physics.

Influence of Nb Alloying on Nb Recrystallization and the Upper Critical Field of Nb3Sn

Nb3Sn conductors are important candidates for high-field magnets for particle accelerators, and they continue to be widely used for many laboratory and NMR magnets. However, the critical current densi...

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The study provides significant insights into the enhancement of the upper critical field (Hc2) in Nb3Sn alloys, which is crucial for high-performance superconductors used in high-field magnets. The methodological rigor is evident in the thorough alloying experiments and measurements across various fields. The findings present a novel approach to improving superconducting properties through alloying, addressing an important challenge in the field. Furthermore, the implications for manufacturing more efficient superconducting wires add to its relevance.

Neural Network Symmetrisation in Concrete Settings

Cornish (2024) recently gave a general theory of neural network symmetrisation in the abstract context of Markov categories. We give a high-level overview of these results, and their concrete implicat...

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This article presents a significant extension of theoretical frameworks relating to neural networks through the lens of Markov categories, addressing both deterministic functions and Markov kernels. Its potential impact is bolstered by the relevance of neural networks in various modern applications. The novelty of linking these abstract concepts to concrete implications enhances its applicability, promising future research avenues in both theory and practical implementations.

STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading

In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. Recently, we have witnessed the rise of variational autoencoder-based latent factor mode...

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The article presents a novel approach to financial trading by incorporating spatio-temporal factors in a dual vector quantized variational autoencoder framework. The methodological rigor, demonstrated through extensive experiments and the clear benefits in terms of robustness and flexibility in trading scenarios, make this study particularly relevant. Its contributions address known limitations in current factor models, indicating a significant advancement in the field.

Audios Don't Lie: Multi-Frequency Channel Attention Mechanism for Audio Deepfake Detection

With the rapid development of artificial intelligence technology, the application of deepfake technology in the audio field has gradually increased, resulting in a wide range of security risks. Especi...

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This article introduces a novel approach to audio deepfake detection through the innovative application of a multi-frequency channel attention mechanism combined with advanced signal processing techniques. The methodological rigor is demonstrated by the use of MobileNet V2 and extensive experimental results showing significant improvements over traditional methods. Its implications for security in critical fields like finance and social integrity enhance its relevance and urgency. The potential for influencing future research directions in audio forensics and deepfake detection firmly supports a high score.

Distributional Reinforcement Learning based Integrated Decision Making and Control for Autonomous Surface Vehicles

With the growing demands for Autonomous Surface Vehicles (ASVs) in recent years, the number of ASVs being deployed for various maritime missions is expected to increase rapidly in the near future. How...

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The article presents a novel approach to navigating Autonomous Surface Vehicles (ASVs) using Distributional Reinforcement Learning, which addresses critical challenges in complex maritime environments. Its strong methodological framework, validated through extensive simulations, enhances its contribution to the field. The integration of safety and efficiency metrics offers significant potential for real-world application, alongside unique contributions to decision-making under uncertainty in autonomous systems.

OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs

Recent advances in diffusion and flow-based generative models have demonstrated remarkable success in image restoration tasks, achieving superior perceptual quality compared to traditional deep learni...

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The proposed OFTSR framework introduces a novel approach to image super-resolution with a focus on tunable fidelity-realism trade-offs, addressing significant limitations of existing methods. Its methodological rigor is evident in the extensive experimentation on challenging datasets, suggesting robustness and applicability. The ability to serve various applications through adjustable output fidelity enhances its utility and potential influence on future research directions.

Long-lived quasinormal modes and asymptotic tails of regular Schwarzschild-like black holes in the presence of a magnetic field

We analyze the evolution of perturbations of a (charged) massive scalar field near a regular Simpson-Visser black hole, allowing for a non-zero external magnetic field. We show that the damping rate o...

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The article presents a novel examination of perturbations in regular Schwarzschild-like black holes under the influence of a magnetic field. The analysis of long-lived quasinormal modes and their response to external magnetic fields adds significant insight to the field of gravitational physics. The findings indicate potential directions for future research, particularly regarding the stability of black holes and their astrophysical implications. The robust analytical approach used to determine the effects on quasinormal frequencies also enhances the article's methodological rigor.

Counting and building operators in theories with hidden symmetries and application to HEFT

Identifying a full basis of operators to a given order is key to the generality of Effective Field Theory (EFT) and is by now a problem of known solution in terms of the Hilbert series. The present wo...

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This article presents important advancements in identifying operators in Effective Field Theory (EFT), particularly in relation to Higgs Effective Field Theory (HEFT). The novelty lies in bridging existing methodologies and providing new counting formulas linked with hidden symmetries. The introduction of a Mathematica code makes the findings accessible, enhancing the practical applicability of the research. The methodological rigor is supported by addressing various formulations of perturbation theory, which is crucial for theoretical consistency in this field.

Near-single-photon atto-watt detection at mid-infrared wavelengths by a room-temperature balanced heterodyne set-up

Single photon detection is the underpinning technology for quantum communication and quantum sensing applications. At visible and near-infrared wavelengths, single-photon-detectors (SPDs) underwent a ...

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The article presents a significant advancement in single-photon detection technology, particularly at mid-infrared wavelengths, which is a relatively underexplored area. The innovative approach using room-temperature detection systems marks a substantial improvement over existing cryogenic technologies. The potential applications in quantum communication and sensing, particularly in noisy environments, enhance its relevance.

Constraints on Pre-Big-Bang Cosmology from Advanced LIGO and Advanced Virgo's First Three Observing Runs

We search for the stochastic gravitational-wave background (SGWB) predicted by pre-big-bang (PBB) cosmology using data from the first three observing runs of Advanced LIGO and Advanced Virgo. PBB cosm...

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The article presents a significant investigation into pre-big-bang cosmology using cutting-edge gravitational wave data, which is a relatively novel approach in cosmology. By applying a robust Bayesian analysis and providing explicit constraints on model parameters, the authors offer valuable insights that challenge existing theoretical frameworks. The outcome contributes to the discourse on cosmology by ruling out certain models while maintaining relevance for future theoretical advancements.

The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective

The use of copyrighted materials in training generative language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the im...

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This article presents a significant exploration of the intersection between copyright law and artificial intelligence, specifically within the context of Norwegian language models. Its empirical approach provides a foundational understanding of how different types of copyrighted materials impact the performance of LLMs, which is a novel contribution to the field. Additionally, the implication of a compensation scheme for authors adds a practical legal dimension that is both timely and relevant. However, its focus on Norwegian raises concerns about generalizability to other languages or jurisdictions, lightly tempering its overall impact.

Temperature-Resistant Order in 2+1 Dimensions

High temperatures are typically thought to increase disorder. Here we examine this idea in Quantum Field Theory in 2+1 dimensions. For this sake we explore a novel class of tractable models, consistin...

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The article presents a novel investigation into unexpected symmetry breaking behavior at high temperatures within a rigorous Quantum Field Theory framework. Its focus on local, unitary models expands existing theoretical boundaries and offers new insights into temperature-related phase transitions in quantum systems. The methodology appears robust, and findings may inspire further exploration in related models, indicating strong potential for impact.