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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!

Jet-shaped filamentary ejecta in common envelope evolution

We conduct three-dimensional (3D) hydrodynamical simulations of common envelope evolution (CEE) of a neutron star (NS) that launches jets as it spirals in inside the envelope of a rotating red supergi...

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The study presents novel 3D hydrodynamical simulations that incorporate envelope rotation in the common envelope evolution framework, providing significant insights into the dynamics of neutron star interactions with red supergiant envelopes. The findings on Rayleigh-Taylor instabilities and their role in filamentary ejecta are particularly original and could shift the understanding of mass ejection mechanisms in such stellar environments. The methodological rigor is evident in the detailed simulation approach and the exploration of non-radial oscillations. Overall, this research enhances theoretical models of stellar evolution and identifies new avenues for investigation.

SHORES: Serendipitous H-ATLAS-fields Observations of Radio Extragalactic Sources with the ATCA. I: catalog generation and analysis

We introduce the Serendipitous H-ATLAS-fields Observations of Radio Extragalactic Sources (SHORES) multiple pencil beam survey that observed at 2.1 GHz with the Australia Telescope Compact Array (ATCA...

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This paper presents a novel large-scale survey that not only catalogs a significant number of radio sources but also engages with deeper aspects of polarization and the FIR-radio correlation. Its rigorous methodology and substantial data output deliver great potential for future investigations in extragalactic astrophysics, specifically concerning unresolved sources and polarization behavior, which remain critical areas in the field.

Approaching optimal microwave-acoustic transduction on lithium niobate using SQUID arrays

Electronic devices exploiting acoustic vibrations are ubiquitous in classical and quantum technologies. Central to these devices is the transducer, which enables the exchange of signals between electr...

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The article presents a novel approach utilizing SQUID arrays for piezoelectric microwave-acoustic transduction, addressing the significant limitations of traditional transducers. The high efficiency-bandwidth product achieved, along with in-situ tunability, highlights its potential for both classical and quantum technologies. The study is methodologically rigorous, demonstrating practical applications in emerging fields such as quantum-limited phonon detection, suggesting high relevance for future research.

Peierls bounds from random Toom contours

For deterministic monotone cellular automata on the $d$ -dimensional integer lattice, Toom (1980) has given necessary and sufficient conditions for the all-one fixed point to be stable against ...

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This article addresses a significant open problem in the field of cellular automata by extending established results in novel contexts, specifically introducing random contours to tackle complex estimations. The methodological innovation and theoretical contributions are noteworthy, enhancing both the academic discourse around stability in cellular automata and providing potential pathways for future research. However, the complexity and potential limitations of the results, such as the constructed counterexample, temper universal applicability.

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training

The dynamical evolution of a neural network during training has been an incredibly fascinating subject of study. First principal derivation of generic evolution of variables in statistical physics sys...

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This paper offers a novel approach by applying concepts from statistical physics, specifically Fokker-Planck dynamics, to the training of neural networks. The derivation of relevant partial differential equations (PDEs) adds theoretical depth, while the empirical validation strengthens its contribution. The interdisciplinary nature, merging physics with deep learning, enhances its potential impact across multiple fields. However, the scope is somewhat limited to specific architectures and may benefit from broader applicability.

A symmetry-protected topological optical lattice clock

We theoretically propose a tunable implementation of symmetry-protected topological phases in a synthetic superlattice, taking advantage of the long coherence time and exquisite spectral resolutions o...

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This article presents a novel theoretical approach to combining symmetry-protected topological phases with optical lattice clocks, showcasing significant innovation in quantum sensing technology. The proposal's methodological rigor, as well as clear applicability to enhance measurement sensitivity and robustness against imperfections, positions it as highly impactful in advancing its field. The findings not only have immediate implications for clock precision but also suggest broader applications in quantum technologies, particularly in precision measurement contexts.

On the Energy Consumption of Test Generation

Research in the area of automated test generation has seen remarkable progress in recent years, resulting in several approaches and tools for effective and efficient generation of test cases. In parti...

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This article presents a novel investigation into the energy consumption of automated test generation, an area that has seen little previous research. The focus on energy metrics adds a fresh dimension to the evaluation of test generation tools, which typically emphasize effectiveness metrics such as code coverage. The methodological rigor in measuring energy across various algorithms and comparisons with manually written tests enhances the robustness of the findings. Additionally, the implications of the results for optimizing both the EvoSuite tool and the underlying programs suggest practical applications and avenues for future research.

Shock-type singularity of the hyperbolic-parabolic chemotaxis system

This paper deals with the hyperbolic-parabolic chemotaxis (HPC) model, which is a hydrodynamic model describing vascular network formation at the early stage of the vasculature. We study analytically ...

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The paper presents a notable investigation into singularity formation in the hyperbolic-parabolic chemotaxis system, contributing significantly to the understanding of vascular network formation. Its analytical approach to studying blow-up profiles is methodologically rigorous and offers insights into a phenomenon not fully addressed in existing literature. This could have far-reaching implications for both theoretical and applied research in the fields mentioned.

A Survey of Research in Large Language Models for Electronic Design Automation

Within the rapidly evolving domain of Electronic Design Automation (EDA), Large Language Models (LLMs) have emerged as transformative technologies, offering unprecedented capabilities for optimizing a...

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This article provides a timely and comprehensive survey of the application of Large Language Models in Electronic Design Automation, a field that is ripe for innovation. Its extensive exploration of model architectures, implications of size, and customization techniques demonstrates methodological rigor. The focus on practical challenges and opportunities also makes it highly relevant to both researchers and industry professionals, suggesting that LLMs can play a transformative role in EDA. Overall, the combination of novelty, applicability, and significance to ongoing research justifies a high relevance score.

Stacking disorder in novel ABAC-stacked brochantite

In geometrically frustrated magnetic systems, weak interactions or slight changes to the structure can tip the delicate balance of exchange interactions, sending the system into a different ground sta...

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The article provides insights into frustrated magnetism through a novel polytype of brochantite and reveals how structural factors influence magnetic properties. The growth of synthetic crystals represents a significant advancement that addresses previous research limitations, allowing further exploration of the material's properties. Its contributions to understanding stacking disorder in magnetic systems are both novel and methodologically robust.

The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models

The recent rise in the popularity of large language models has spurred the development of extensive code datasets needed to train them. This has left limited code available for collection and use in t...

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The article presents a highly relevant and novel dataset, The Heap, which addresses a critical issue in the field of machine learning and natural language processing (NLP): contamination in code datasets for training language models. The methodological rigor in deduplication and multilingual support enhances its applicability across diverse research areas. Its impact will likely extend to fair evaluations and benchmarking of large language models, a significant challenge in current research.

Active particle in a very thin interfacial droplet

A single light-driven Janus particle confined in a very thin oil droplet at an air--water interface displays intriguing dynamics. While laser activation induces rapid horizontal motion (1mm/s--1cm/s) ...

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This article presents a novel investigation into the dynamics of active particles at the air-water interface, utilizing a combination of laser activation and droplet mechanics. The discovery of periodic and irregular motion, coupled with a simple model that addresses complex interactions in the system, enhances its impact. The methodological rigor in the experimental setup and modeling further supports its applicability in various scientific fields.

Electron scale magnetic holes generation driven by Whistler-to-Bernstein mode conversion in fully kinetic plasma turbulence

Magnetic holes (MHs) are coherent structures characterized by a strong and localized magnetic field amplitude dip, commonly observed in the solar wind and planetary magnetosheaths. These structures co...

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The article presents a novel mechanism for the generation of electron scale magnetic holes (MHs) through turbulence, backed by fully kinetic simulations. Its implications for understanding plasma behavior in space environments add substantial value to the field. The methodological rigor and relevance of findings to real-world observations enhance its impact.

Aging of colloidal gels in microgravity

We study the aging of colloidal gels using light microscopy movies of depletion gels from the International Space Station. Under such microgravity conditions, we observe a slowdown in particle dynamic...

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The study offers novel insights into the aging dynamics of colloidal gels in microgravity, a relatively unexplored area of research. The use of International Space Station data enables unique methodologies not available under normal gravitational conditions, contributing to the depth of understanding in both fundamental and applied contexts. The combination of microgravity and comparative ground-based experiments strengthens the findings' relevance.

Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments

Online motion planning is a challenging problem for intelligent robots moving in dense environments with dynamic obstacles, e.g., crowds. In this work, we propose a novel approach for optimal and safe...

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The article presents a novel integration of Monte Carlo Tree Search with Velocity Obstacles, which is highly relevant in the context of motion planning for intelligent robots. The emphasis on minimal information about dynamic obstacles makes it particularly unique and applicable in real-world scenarios where complete data may not be feasible. Its rigorous experimental validation against state-of-the-art techniques establishes its robustness and potential for practical applications.

Statistical inference for interacting innovation processes and related general results

Given the importance of understanding how different innovation processes affect each other, we have introduced a model for a finite system of interacting innovation processes. The present work focuses...

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The article presents a novel statistical model for analyzing interacting innovation processes, highlighting its theoretical advancements and practical applications. By employing real datasets, it demonstrates methodological rigor and relevance to current challenges in understanding innovation dynamics. The focus on second-order asymptotic properties enhances its robustness, providing a strong basis for future research in this area.

Constraining the Secluded and Catalyzed Annihilation Dark Matter with Fermi-LAT and Planck Data

We propose a dark matter (DM) model with a complex scalar charged under a hidden gauge symmetry, denoted as $U(1)_D$ . The scalar field is the DM candidate while the $U(1)_D$ gauge fiel...

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The article presents a novel dark matter model with significant implications for both theoretical and observational aspects of cosmology and particle physics. The use of Fermi-LAT and Planck data to challenge existing constraints indicates a potential shift in the understanding of dark matter interactions. The rigorous analysis and extension to various dark matter spin types enhance the robustness and applicability of the findings.

NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes

In many real-world applications, agents must make sequential decisions in environments where conditions are subject to change due to various exogenous factors. These non-stationary environments pose s...

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NS-Gym addresses a critical gap in the research on non-stationary Markov decision processes by providing a dedicated toolkit for simulation and standard benchmarks. Its innovative separation of environment dynamics from the decision-making process allows for greater flexibility and is likely to foster further advancements in algorithm development. The open-source nature of the tool enhances its accessibility and potential for widespread usage within the research community, promising significant impact on future studies in adaptive decision-making. The methodological rigor demonstrated through the benchmark testing adds credibility to the findings, enhancing its relevance further.

CarMem: Enhancing Long-Term Memory in LLM Voice Assistants through Category-Bounding

In today's assistant landscape, personalisation enhances interactions, fosters long-term relationships, and deepens engagement. However, many systems struggle with retaining user preferences, lead...

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The article presents a novel approach to enhancing long-term memory in voice assistants, addressing critical issues of user engagement and privacy. The methodological rigor is solid due to the introduction of a synthetic dataset grounded in real data, which significantly contributes to robustness in experimental validation. Its potential for practical application in personalized voice assistant systems marks it as highly relevant in the field.

Planetary Rhythms: Synchronous Circulation on Variably Irradiated Asynchronous Planets

Tidal locking of planets to their host stars results in an atmospheric circulation with a hotspot fixed to the frame of reference of the planet. On the other hand, asynchronously rotating planets feat...

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The article introduces a novel concept of 'beating' in atmospheric circulation on asynchronously rotating planets, contributing valuable insights into planetary climate dynamics. The correlation between diurnal rotation and variations in instellation presents unique implications for understanding habitable zones. The methodological approach appears rigorous, engaging with observational data from Kepler and TESS, which enhances its reliability and applicability. However, the phenomenon's complexity could limit broader applicability without further empirical validation.