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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!

Labeled Datasets for Research on Information Operations

Social media platforms have become a hub for political activities and discussions, democratizing participation in these endeavors. However, they have also become an incubator for manipulation campaign...

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The article presents novel labeled datasets crucial for research into information operations, a pressing issue in current political discourse due to the misuse of social media for manipulation. Its methodological rigor in providing extensive IO and control data positions it as a significant resource for academics and practitioners developing detection algorithms. The datasets' potential for facilitating comparative analyses enhances its applicability and interdisciplinary value.

MLAN: Language-Based Instruction Tuning Improves Zero-Shot Generalization of Multimodal Large Language Models

We present a novel instruction tuning recipe to improve the zero-shot task generalization of multimodal large language models. In contrast to existing instruction tuning mechanisms that heavily rely o...

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This article presents a significant advancement in the field of multimodal AI by proposing a novel method for instruction tuning that emphasizes language input over visual input, thereby enhancing training efficiency and generalization capabilities. The methodology is innovative and tackles the existing limitations of current approaches that rely heavily on visual instructions. Its comprehensive evaluation across various datasets adds to its robustness and applicability, making it relevant for ongoing and future research in multimodal AI.

Star Log-extended eMulation: a method for efficient computation of the Tolman-Oppenheimer-Volkoff equations

We emulate the Tolman-Oppenheimer-Volkoff (TOV) equations, including tidal deformability, for neutron stars using a novel hybrid method based upon the Dynamic Mode Decomposition (DMD) for the first ti...

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The article introduces a novel computational method (SLM) for efficiently solving the TOV equations, which is pivotal for astrophysics studies involving neutron stars. Its significant speed-up in computation can greatly benefit ongoing research in gravitational wave astronomy and nuclear physics, while the open-source policy enhances reproducibility and community engagement. The exploration of both fixed and freely-varying EOS using this approach adds robustness and versatility to the findings, making it a potentially influential contribution.

Efficient inference for differential equation models without numerical solvers

Parameter inference is essential when interpreting observational data using mathematical models. Standard inference methods for differential equation models typically rely on obtaining repeated numeri...

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The article presents a novel approach that sidesteps traditional reliance on numerical solvers for parameter inference in differential equation models, addressing a critical issue related to numerical truncation error. This innovation is significant as it could enhance the accuracy of analyses in various fields where differential equations are prevalent. The provision of open-access tools adds a practical dimension that could facilitate widespread adoption and application of the method across multiple disciplines.

Super-Leading Logarithms in $pp\to2$ Jets

Jet observables at hadron colliders feature ''super-leading'' logarithms, double-logarithmic corrections resulting from a breakdown of color coherence due to complex phases in hard-sca...

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The analysis of 'super-leading' logarithms represents a novel contribution to the understanding of high-energy hadronic collisions, and the findings enhance the precision of jet observables significantly. The methodological approach is robust, including a comprehensive treatment of various partonic channels and interference effects, which points to solid theoretical groundwork. This study will likely encourage further investigation into similar logarithmic corrections in related processes, making it highly relevant for both theoretical and experimental physics at hadron colliders.

WIA-SZZ: Work Item Aware SZZ

Many software engineering maintenance tasks require linking a commit that induced a bug with the commit that later fixed that bug. Several existing SZZ algorithms provide a way to identify the potenti...

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This article introduces a novel heuristic for identifying work items related to bug-induced commits, which provides a significant improvement over traditional SZZ algorithms in accuracy. The methodological rigor is supported by comprehensive evaluations on multiple repositories, demonstrating practical applicability in software maintenance tasks. The concept of integrating logical work items with the existing SZZ framework showcases innovation that can inspire further research in software engineering and maintenance algorithms.

Improving the solver for the Balitsky-Kovchegov evolution equation with Automatic Differentiation

The Balitsky-Kovchegov (BK) evolution equation is an equation derived from perturbative Quantum Chromodynamics that allows one to calculate the scattering amplitude of a pair of quark and antiquark of...

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This article introduces a significant methodological improvement to the Balitsky-Kovchegov evolution equation by incorporating automatic differentiation, which enhances the fitting process for experimental data and facilitates the calculation of derivatives critical for understanding TMDs. This innovation can streamline research in high-energy physics, particularly within Quantum Chromodynamics (QCD).

Tiling randomly perturbed bipartite graphs

A perfect $H$ -tiling in a graph $G$ is a collection of vertex-disjoint copies of a graph $H$ in $G$ that covers all vertices of $G$ . Motivated by papers of Bush...

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The article addresses a significant problem in graph theory related to perfect matchings, specifically focusing on random perturbations in bipartite graphs. It builds on previous works and identifies a threshold, which is a novel contribution to the field. The methodological rigor in determining this threshold indicates strong analytical skills and impacts combinatorial optimization and random graph theories.

Effects of Neutron Radiation on the Thermal Conductivity of Highly Oriented Pyrolitic Graphite

Highly Ordered Pyrolytic Graphite (HOPG) has been extensively researched due to its chemical and physical properties that make it suitable for applications in several technologies. Its high thermal co...

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This article presents novel findings on the effects of neutron radiation on the properties of HOPG, providing valuable insights for nuclear applications. The methodological rigor in the study, particularly in the detailed morphological and crystallographic analyses, supports its relevance. The implications for thermal management in advanced nuclear technologies are significant, making this research impactful for current and future studies in materials science and nuclear engineering.

ACING: Actor-Critic for Instruction Learning in Black-Box Large Language Models

The effectiveness of Large Language Models (LLMs) in solving tasks vastly depends on the quality of the instructions, which often require fine-tuning through extensive human effort. This highlights th...

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The article presents a novel approach to optimizing instruction prompts for Large Language Models (LLMs) using reinforcement learning techniques in a black-box setting. This addresses a significant challenge in the field and demonstrates strong empirical results. The methodological rigor and clear validation of the approach across multiple tasks enhance its reliability and applicability for future research endeavors.

The More the Merrier: On Evolving Five-valued Spectra Boolean Functions

Evolving Boolean functions with specific properties is an interesting optimization problem since, depending on the combination of properties and Boolean function size, the problem can range from very ...

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The article tackles a niche yet intriguing optimization problem in the realm of Boolean functions, presenting novel findings regarding the evolution of five-valued spectra Boolean functions. Its methodological rigor, demonstrated through extensive experimentation with various encodings and fitness functions, indicates the potential for broader applicability and deeper insights into function properties that could influence future studies. Furthermore, the identification of superior encoding methods could pave the way for advancements in cryptography and information security.

Soft Robotic Dynamic In-Hand Pen Spinning

Dynamic in-hand manipulation remains a challenging task for soft robotic systems that have demonstrated advantages in safe compliant interactions but struggle with high-speed dynamic tasks. In this wo...

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The article presents a novel approach to dynamic manipulation in soft robotics, focusing on learning from real-world trials rather than simulations. The high success rate achieved with various objects indicates significant robustness and generalizability. This work not only addresses an important challenge in the field but also provides a practical solution that could inspire further advancements in soft robotic manipulations and reach various applications.

On the microscopics of proximity effects in one-dimensional superconducting hybrid systems

Investigating the microscopic details of the proximity effect is crucial for both key experimental applications and fundamental inquiries into nanoscale devices featuring superconducting elements. In ...

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This article presents a robust theoretical framework that elucidates the proximity effects in superconducting hybrid systems, which are crucial for both practical applications and fundamental physics. The use of advanced theoretical tools (Keldysh non-equilibrium Green's functions) indicates a high level of methodological rigor, and the connection made to recent experimental results enhances its relevance. The identification of a disorder-induced crossover indicates potential implications for understanding nanoscale device behavior, which is a current hot topic in condensed matter physics.

Benchmarking Positional Encodings for GNNs and Graph Transformers

Recent advances in Graph Neural Networks (GNNs) and Graph Transformers (GTs) have been driven by innovations in architectures and Positional Encodings (PEs), which are critical for augmenting node fea...

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The article presents a systematic benchmarking of positional encodings in GNNs and Graph Transformers, addressing a significant gap in current research where the isolated impact of PEs is often difficult to ascertain due to their coupling with novel architectures. The theoretical contributions and the introduction of a new attention mechanism add innovation and depth. The comprehensive nature of the study and the availability of the code further enhance its utility for future research, making it highly relevant.

Finite-size scaling on the torus with periodic projected entangled-pair states

An efficient algorithm is constructed for contracting two-dimensional tensor networks under periodic boundary conditions. The central ingredient is a novel renormalization step that scales linearly wi...

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The article presents a novel algorithm that significantly enhances the efficiency of contracting 2D tensor networks, which is crucial for quantum physics simulations. The combination of its computational cost-effectiveness, numerical accuracy, and applicability to critical properties estimation indicates a high relevance to ongoing research and future advancements in quantum systems.

Testing classical properties from quantum data

Many properties of Boolean functions can be tested far more efficiently than the function can be learned. However, this advantage often disappears when testers are limited to random samples--a natural...

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The article presents novel quantum algorithms for testing properties of Boolean functions using quantum data, addressing a significant gap in classical testing methods limited to random samples. Its findings showcase substantial efficiency improvements that could significantly influence both theoretical and applied aspects of quantum computation and computational complexity. The rigorous upper and lower bounds provide strong methodological validation, emphasizing the robustness of the results. Furthermore, it opens avenues for future exploration in quantum data applications, which adds to its overall impact.

Precise study of triply charmed baryons ( $Ω_{ccc}$ )

We present the most precise results for the ground state mass of the triply-charmed spin- $3/2$ baryon using lattice quantum chromodynamics. The calculations are performed on six $N_f=2+1+1...

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This article provides highly precise measurements of the mass of a triply charmed baryon, a topic that is of significant interest in the field of particle physics, especially for understanding baryon spectroscopy and the interactions of heavy quarks. The methodological rigor displayed through the use of lattice quantum chromodynamics enhances the credibility of the findings, while the thorough examination of systematic uncertainties adds robustness to the conclusions. The findings could inspire further theoretical and experimental work in particle physics, particularly in tests of the Standard Model or potential new physics beyond it.

Information Theory of Meaningful Communication

In Shannon's seminal paper, entropy of printed English, treated as a stationary stochastic process, was estimated to be roughly 1 bit per character. However, considered as a means of communication...

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This article presents a novel approach to quantifying communication in narratives through an information-theoretic lens, utilizing large language models. Its potential to redefine how we measure linguistic information creates a pathway for further exploration and impact in the fields of linguistics and artificial intelligence. The methodological rigor mentioned, through the use of advanced models, supports its relevance and applicability. However, the novelty hinges on the effectiveness of the proposed methods, which may require extensive validation and broader acceptance within the field.

High-fidelity entanglement of metastable trapped-ion qubits with integrated erasure conversion

We present metastable qubits in trapped ions as potential erasure qubits for which most fundamental algorithm errors can be converted into erasures. We first implement an erasure conversion scheme whi...

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This article introduces a novel approach by utilizing metastable qubits in trapped ions, significantly enhancing error correction in quantum computing. The reported high fidelity and effective error conversion method represent a substantial methodological advancement in the field. The potential for improved gate efficiency further underscores its impact. The rigorous experimental results and forward-looking applications mark this work as highly relevant and influential.

LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport

We present LazyDINO, a transport map variational inference method for fast, scalable, and efficiently amortized solutions of high-dimensional nonlinear Bayesian inverse problems with expensive paramet...

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The article introduces a novel method, LazyDINO, for Bayesian inversion that addresses efficiency and scalability in high-dimensional inverse problems. The use of derivative-informed surrogates and transport map variational inference signifies a strong advancement in computational techniques in this area. The rigorous methodological approach and significant improvements in performance against existing methods suggest high potential for real-world applications and further innovations, making it highly relevant for its field.