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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!

Accelerating Numerical Relativity with Code Generation: CUDA-enabled Hyperbolic Relaxation

Next-generation gravitational wave detectors such as Cosmic Explorer, the Einstein Telescope, and LISA, demand highly accurate and extensive gravitational wave (GW) catalogs to faithfully extract phys...

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This article presents a significant innovation in numerical relativity by introducing a CUDA-optimized framework, which is crucial for future gravitational wave detections that require extensive and accurate data. The methodological rigor demonstrated through performance comparisons on both consumer and high-performance computing setups provides strong evidence of its utility. Its contributions to speeding up simulations and optimizing code generation are likely to have a positive impact on future research in the field.

Distributed quantum error correction based on hyperbolic Floquet codes

Quantum computing offers significant speedups, but the large number of physical qubits required for quantum error correction introduces engineering challenges for a monolithic architecture. One soluti...

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This article presents a compelling advancement in the field of quantum error correction by showcasing the potential of hyperbolic Floquet codes. The novelty of applying these codes in a distributed quantum computing context is significant, aiming to overcome limitations of existing methods, like surface codes. The methodological rigor displayed through simulations and the direct implication for distributed architectures enhance the article's impact. Furthermore, the exploration of efficiency in error correction aligns well with future research directions in quantum computing scalability.

The observed phase space of mass-loss history from massive stars based on radio observations of a large supernova sample

In this work we study the circumstellar material (CSM) around massive stars, and the mass-loss rates depositing this CSM, using a large sample of radio observations of 325 core-collapse supernovae (CC...

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This article presents significant new insights into mass-loss rates from massive stars, utilizing a large dataset derived from radio observations. The methodological rigor, including a systematic approach to analyzing both archival and new data, enhances the validity of their findings. The exploration of discrepancies in mass-loss rate measurements adds depth to the current understanding and challenges existing models, suggesting adjustments in theoretical frameworks. The interdisciplinary implications for both stellar evolution studies and supernova research elevate its relevance.

Bell nonlocality in quantum networks with unreliable sources: Loophole-free postelection via self-testing

We discuss Bell nonlocality in quantum networks with unreliable sources. Our main result is a condition on the observed data which ensures that inconclusive events can be safely discarded, without int...

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This article presents novel results regarding Bell nonlocality in quantum networks, specifically addressing the challenges posed by unreliable sources. Its methodological rigor in defining fairness in sampling and its implications for source independence are significant contributions to the field. By ensuring no loopholes exist in the experiments conducted, this research provides strong foundational knowledge for future studies on quantum networking and randomness generation, making it highly relevant and impactful.

Luminous Mid-IR Selected Type-2 Quasars at Cosmic Noon in SDSS Stripe82 I: Selection, Composite Photometry, and Spectral Energy Distributions

We analyze 23 spectroscopically confirmed Type-2 quasars (QSOs) selected from the WISE 22 $\rm μ$ m band in the SDSS Stripe 82 region, focusing on their multi-band photometry and spectral energy...

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The study presents a novel approach to selecting Type-2 quasars using mid-infrared data, which addresses a significant gap in understanding high-redshift (z>2) quasars. The robust methodology, including multi-band photometry and SED fitting, enhances its credibility. Furthermore, the article proposes new insights into the nature of the IR emissions and the characteristics of Type-2 QSOs, which could inspire future research in related cosmology and astrophysics fields.

Cataclysmic Variables in Triples: Formation Models and New Discoveries

The formation of cataclysmic variables (CVs) has long been modeled as a product of common envelope evolution (CEE) in isolated binaries. However, a significant fraction of intermediate-mass stars -- t...

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This article introduces a novel framework for understanding the formation of cataclysmic variables (CVs) by incorporating the dynamics of triple star systems, which is a significant advancement over existing binary-only models. The utilization of Gaia astrometry and three-body simulations adds methodological rigor and robustness to the findings. Moreover, the implications for a broad spectrum of binary populations (CVs, ultracompact binaries, low-mass X-ray binaries) enhance its relevance. The discovery that a notable fraction of CVs exist in hierarchical triples could inspire new research directions in stellar formation and evolution.

Exact quantum many-body scars tunable from volume to area law

Teleportation of quantum information over long distances requires robust entanglement on the macroscopic scale. The construction of a manifold of highly energetic eigenstates with tunable long-range e...

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The article presents a novel approach to constructing quantum many-body states with tunable entanglement properties, which is foundational for advancements in quantum information and quantum computing. Its methodological rigor in demonstrating exact eigenstates in non-integrable models positions this work as a significant contribution to theoretical and experimental research in the field. The ability to control entanglement and provide a framework for future studies on correlated quantum matter in higher dimensions enhances its novelty and applicability across various quantum applications.

$c=1$ , $R=1$ and $N\gg 1$ : ZZ instantons in 2D String Theory and Matrix Integrals

We explore the non-perturbative aspects of $c=1$ string with compactified Euclidean time, its $0+0$ dimensional matrix model duals (at self-dual radius), and $0+1$ dimensional ...

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This article presents a solid exploration into non-perturbative aspects of 2D string theory, which is a rich area for theoretical physics. The use of string field theory insights to derive instanton calculations signals methodological rigor and potential for novel contributions to the field. Additionally, the connections made between string theory and matrix models highlight the interdisciplinary relevance, which may inspire further research into dualities and instanton effects. However, limitations may exist in terms of applicability to more complex models beyond $c=1$.

The rate of extreme coronal line emitters in the Baryon Oscillation Spectroscopic Survey LOWZ sample

Extreme coronal line emitters (ECLEs) are a rare class of galaxy that exhibit strong, high-ionization iron coronal emission lines in their spectra. In some cases, these lines are transient and may be ...

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The paper provides novel insights into the rates of extreme coronal line emitters (ECLEs) within the context of tidal disruption events, a relatively unexplored area in astrophysical research. The methodology involves extensive spectral analysis and follow-up observations, demonstrating robust experimental design. The comparison with previous samples adds depth to the findings, indicating potential shifts in understanding transient astronomical events. This research is likely to inspire further studies on ECLEs and their connection to TDEs, thereby expanding the field. Overall, the significance of the results and their implications for understanding galaxy phenomena strengthen its relevance.

Precise Predictions for Event Shapes in Diphoton Production at the LHC

Photon pair production is an important benchmark process at the LHC, entering Higgs boson studies and new physics searches. It has been measured to high accuracy, allowing for detailed studies of even...

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This article addresses a significant topic in high-energy physics, providing advanced calculations of QCD corrections for diphoton production, which is pivotal for Higgs boson studies and new physics searches. The methodological rigor of second-order corrections adds robustness to the predictions, likely advancing precision measurements in the field.

Connectivity-aware Synthesis of Quantum Algorithms

We present a general method for the implementation of quantum algorithms that optimizes both gate count and circuit depth. Our approach introduces connectivity-adapted CNOT-based building blocks calle...

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This article presents a novel method that significantly improves the implementation of quantum algorithms through connectivity-aware synthesis, which addresses a current challenge in quantum computing—optimizing gate count and circuit depth. The introduction of Parity Twine chains represents a valuable advancement over existing methodologies, evidenced by the rigorous proofs of optimality for specific cases. Moreover, the broad applicability across various types of quantum hardware enhances its relevance immensely.

The Three Hundred Project hydrodynamical simulations: Hydrodynamical Weak-Lensing Cluster Mass Biases and Richnesses using different hydro models

The mass of galaxy clusters derived from weak-lensing observations is sensitive to projection effects, and, on average, it is biased low with respect to the true cluster mass, with a mass and redshift...

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This article presents innovative research through the use of state-of-the-art hydrodynamical simulations to investigate weak-lensing biases in cluster mass estimates. The novelty lies in the comparison of baryonic and dark matter-only simulations, contributing significantly to the understanding of weak-lensing techniques. Its results have implications for future observational cosmology. The methodology appears robust, with acknowledged uncertainties that enhance the credibility of the findings.

Chiral Effective Field Theories for Strong and Weak Dynamics

The chiral effective field theory (ChEFT) is an extension of the chiral perturbation theory that includes the nuclear forces and weak currents at the hadronic and nuclear scales. We propose a systemat...

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The article introduces a systematic and novel approach to understanding chiral effective field theory, which is critical in nuclear and particle physics. The combination of advanced mathematical techniques (Weinberg counting, Young tensor method) with a practical application to strong and weak dynamics showcases methodological rigor. Its potential impact lies in expanding theoretical frameworks and providing tools for future explorations in nuclear interactions.

Exact generalized Bethe eigenstates of the non-integrable alternating Heisenberg chain

Exact solutions of quantum lattice models serve as useful guides for interpreting physical phenomena in condensed matter systems. Prominent examples of integrability appear in one dimension, including...

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This article presents a novel approach by extending the concept of Bethe eigenstates into non-integrable systems, offering unique insights into quantum many-body scar states. The use of exact solutions is underpinned by strong methodological rigor, increasing its value for both theoretical exploration and practical applications. Its implications could challenge existing paradigms and inspire new research directions.

Highly reflective white clouds on the western dayside of an exo-Neptune

Highly-irradiated gas giant exoplanets are predicted to show circulation patterns dominated by day-to-night heat transport and a spatial distribution of clouds that is driven by advection and local he...

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This article presents original findings on a less-explored type of exoplanet, the ultra-hot Neptune, utilizing robust spectroscopic techniques enabled by JWST. The discovery of reflective white clouds and a unique temperature distribution indicates significant atmospheric dynamics, contributing to the understanding of exoplanet atmospheres. Its findings may inspire future studies into similar planets and enhance the modeling of their atmospheres.

Apples to Apples in Jet Quenching: robustness of Machine Learning classification of quenched jets to Underlying Event contamination

Progress in the theoretical understanding of parton branching dynamics within an expanding Quark Gluon Plasma relies on detailed and fair comparisons with experimental data for reconstructed jets. Suc...

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The article presents a thorough investigation into the robustness of Machine Learning techniques in the context of jet quenching, addressing a critical challenge in high-energy physics. The methodological rigor and significant insight into the interplay between background contamination and jet classification impact the field's understanding of the Quark Gluon Plasma dynamics.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading

Large Language Models (LLMs) can perform zero-shot learning on unseen tasks and few-shot learning on complex reasoning tasks. However, resource-limited mobile edge networks struggle to support long-co...

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The article presents a novel approach for optimizing the deployment of long-context LLMs in resource-limited mobile edge networks, which is highly relevant given the increasing demand for efficient LLM operation. The use of test-time deep reinforcement learning is particularly innovative, as it addresses dynamic context management effectively. The proposed model caching and inference offloading framework appears methodologically rigorous and demonstrates significant cost reductions in practical settings, indicating its applicability in real-world scenarios. Overall, the combination of cutting-edge techniques and practical relevance makes it a pivotal contribution to the field.

INDIGO+: A Unified INN-Guided Probabilistic Diffusion Algorithm for Blind and Non-Blind Image Restoration

Generative diffusion models are becoming one of the most popular prior in image restoration (IR) tasks due to their remarkable ability to generate realistic natural images. Despite achieving satisfact...

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The proposed INDIGO algorithm presents a novel approach that leverages the strengths of invertible neural networks and generative diffusion models, significantly enhancing flexibility and performance in image restoration tasks. The methodological rigor is demonstrated through comprehensive experiments on both synthetic and real images, suggesting strong applicability in practical scenarios. This breakthrough in handling various degradation processes can inspire further exploration in the field.

Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation

Multiphase CT studies are routinely obtained in clinical practice for diagnosis and management of various diseases, such as cancer. However, the CT studies can be acquired with low radiation doses, di...

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This article presents a novel approach by leveraging multiphase CT scans to enhance the quality of specific phases, which is a step forward in CT imaging technology. The innovative use of a 3D PFNL network demonstrates methodological rigor, and the results show a tangible improvement in a clinically relevant application (pancreas segmentation). The findings could influence future research on image processing and radiology, although further validation in diverse clinical settings would strengthen its applicability.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks

Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes. However, constructing a high-quality surrogate model often demands extensive data ac...

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The article presents a novel approach to transfer learning specifically for non-differentiable surrogate models, a topic that has not been extensively explored. The application of domain affine transformation is both innovative and practical, particularly for scenarios where data acquisition is costly, making it relevant both theoretically and practically. The evaluation across synthetic and real-world benchmarks provides strong methodological rigor and evidential support for its claims.