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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 solvable model for spin polarizations with flow-momentum correspondence

We present an analytically solvable model based on the blast-wave picture of heavy-ion collisions with flow-momentum correspondence. It can describe the key features of spin polarizations in heavy-ion...

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The article presents a novel analytically solvable model that adds to the theoretical understanding of spin polarizations in heavy-ion collisions. Its use of flow-momentum correspondence highlights underlying mechanisms, and the results have broader implications for experimental and theoretical studies in nuclear physics. However, the practical applicability might depend on how well the model can be implemented in real-world scenarios.

Using Large Language Models for Expert Prior Elicitation in Predictive Modelling

Large language models (LLMs), trained on diverse data effectively acquire a breadth of information across various domains. However, their computational complexity, cost, and lack of transparency hinde...

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This article demonstrates a novel methodology for utilizing large language models in a practical setting, effectively bridging the gap between AI technologies and clinical research predictive modeling. The results exhibit significant improvements in predictive error reduction and cost-efficiency, showcasing substantial applicability in real-world scenarios. The methodological rigor and potential for interdisciplinary development enhance its relevance.

BadScan: An Architectural Backdoor Attack on Visual State Space Models

The newly introduced Visual State Space Model (VMamba), which employs \textit{State Space Mechanisms} (SSM) to interpret images as sequences of patches, has shown exceptional performance compared to V...

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The article introduces a novel backdoor attack, BadScan, targeting a cutting-edge model (VMamba) in computer vision, addressing a significant vulnerability that could have a profound impact on the security aspect of model deployment. The methodological rigor appears strong, with empirical testing on standard datasets validating the findings, highlighting both novelty and applicability, crucial for future research in adversarial attacks and security in AI. The potential to influence subsequent research addressing model vulnerabilities and security mechanisms is significant.

Social Distancing Induced Coronavirus Optimization Algorithm (COVO): Application to Multimodal Function Optimization and Noise Removal

The metaheuristic optimization technique attained more awareness for handling complex optimization problems. Over the last few years, numerous optimization techniques have been developed that are insp...

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The article presents a novel optimization algorithm inspired by a contemporary global event (COVID-19), adding a unique perspective to the field. Its empirical validation using benchmark functions points to methodological rigor, while the practicality in multimodal function optimization demonstrates applicability. However, the long-term impact on related methodology could be limited if no wide-ranging applications are established outside the current context.

Categorical Ambidexterity

We prove an ambidexterity result for $\infty$ -categories of $\infty$ -categories admitting a collection of colimits. This unifies and extends two known phenomena: the identification of ...

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This article presents a significant theoretical advancement in the area of higher category theory, specifically addressing ambidexterity in $ extit{infty}$-categories with colimits. The unification of existing phenomena is notable, as it offers new insights that could influence ongoing research. The use of a universal property for iterated spans is particularly innovative and suggests potential for further applications in related areas of mathematics.

High statistical computation of the Landau gauge ghost-gluon vertex

The lattice computation of the one-particle irreducible ghost-gluon Green function in the Landau gauge is revisited with a set of large gauge ensembles. The large statistical ensembles enable a precis...

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This article evaluates an important aspect of quantum field theories, specifically the ghost-gluon interactions in the context of lattice computations. The use of large statistical ensembles is an innovative approach that enhances the reliability of results and may lead to new insights into infrared and ultraviolet properties of gauge theories. Its methodological rigor and potential contributions to fundamental physics substantiate a high relevance score.

BESTAnP: Bi-Step Efficient and Statistically Optimal Estimator for Acoustic-n-Point Problem

We consider the acoustic-n-point (AnP) problem, which estimates the pose of a 2D forward-looking sonar (FLS) according to n 3D-2D point correspondences. We explore the nature of the measured partial s...

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The BESTAnP algorithm presents a significant advancement over existing methods in the acoustic-n-point problem by providing a closed-form solution for full pose estimation, which is crucial for applications in underwater navigation and robotics. The methodological approach is rigorous and the empirical validations through simulations and real-world experiments enhance its credibility. Its efficiency in terms of speed and resource demands is particularly relevant for practical implementations, which bodes well for its adoption in the field.

Interplay of magnetic field and chemical potential induced anisotropy and frame dependent chaos of a $Q\bar{Q}$ pair in holographic QCD

We investigate the role of both magnetic field and chemical potential on the emergence of chaotic dynamics in the QCD confining string from the holographic principle. An earlier developed bottom-up mo...

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This article presents a novel investigation into the interplay of magnetic fields and chemical potentials in quantum chromodynamics (QCD), adding depth to existing models via the holographic approach. The use of chaos theory concepts, such as Lyapunov exponents and Poincaré sections, adds a sophisticated analytical dimension and is of high relevance to theoretical physics. The findings reveal important dependencies on frames, which could influence future studies on QCD dynamics and materials under extreme conditions. Its integration of different fields enhances its interdisciplinary appeal.

The Exploration of Neural Collapse under Imbalanced Data

Neural collapse, a newly identified characteristic, describes a property of solutions during model training. In this paper, we explore neural collapse in the context of imbalanced data. We consider th...

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The article presents a novel exploration of neural collapse in the setting of imbalanced data, potentially enriching the understanding of neural networks under challenging data conditions. Theoretical analyses combined with empirical validation enhance its methodological rigor. Its focus on the geometric structures associated with classifier transformations and singular value estimations offers new insights that can influence future research in machine learning and data representation.

Minimizing Conservatism in Safety-Critical Control for Input-Delayed Systems via Adaptive Delay Estimation

Input delays affect systems such as teleoperation and wirelessly autonomous connected vehicles, and may lead to safety violations. One promising way to ensure safety in the presence of delay is to emp...

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The article presents a novel approach to improve the safety and efficiency of control strategies in systems affected by input delays, a significant issue in safety-critical applications. The adaptive delay estimation method shows methodological rigor, and the application in an automated connected truck scenario adds practical relevance. This research could lead to significant improvements in real-world systems where delays are critical, thus it holds high potential for future investigations and advancements in control theory.

Engineering Work Function to Stabilize Metal Oxides in Reactive Hydrogen

Hydrogen, crucial for the green energy transition, poses a challenge due to its tendency to degrade surrounding wall materials. To harness hydrogen's potential, it is essential to identify materia...

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The article presents a novel approach to controlling work function in transition metal oxides, which has significant implications for the stabilization of materials in reactive hydrogen environments. The research addresses a pressing challenge in green energy technologies, making it relevant and impactful. The methodological rigor is suggested by the control of TM compositions and their effects on material properties. However, additional experimental validation and broader applicability could enhance its impact.

Comb-referenced Doppler-free spectrometry of the $^{200}$ Hg and $^{202}$ Hg intercombination line at 254 nm

We report on precision spectroscopy of the 6s $^2$ $^1$ S $_0\to$ 6s6p $^3$ P $_1$ intercombination line of mercury in the deep ultraviolet, by means of a frequency-c...

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The article presents a novel approach to precision spectroscopy using advanced techniques, which pushes the boundaries of current methodologies in the field. The significant improvements in uncertainty over previous measurements and the exploration of the AC Stark effect demonstrate thorough methodology and originality. These findings have practical implications for metrology, quantum physics, and isotope research, ensuring high relevance for ongoing and future studies.

CleanVul: Automatic Function-Level Vulnerability Detection in Code Commits Using LLM Heuristics

Accurate identification of software vulnerabilities is crucial for system integrity. Vulnerability datasets, often derived from the National Vulnerability Database (NVD) or directly from GitHub, are e...

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The paper presents a novel approach to automatically identify vulnerability-fixing changes using LLMs combined with heuristics, addressing a significant problem in vulnerability detection due to noisy datasets. Its methodology, large-scale application, and presentation of a new high-quality dataset (CleanVul) with impressive performance metrics elevate its relevance. The results showcase improved accuracy and generalization of LLMs, indicating strong methodological rigor and applicability in real-world scenarios.

Special orientable sequences

Analogously to de Bruijn sequences, Orientable sequences have application in automatic position-location applications and, until recently, studies of these sequences focused on the binary case. In rec...

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The article presents a significant advancement in the construction of special orientable sequences, extending the previous work on binary cases to arbitrary finite alphabets. The novelty lies in its methodological developments and the potential applicability to position-location applications, which are critical in certain fields such as computer science and cryptography. However, while it contributes useful construction methods, the immediate impact on broader research may be limited without further application examples or integration into existing frameworks.

Reservoir computing with all-optical non-fading memory in a self-pulsing microresonator network

Photonic neuromorphic computing may offer promising applications for a broad range of photonic sensors, including optical fiber sensors, to enhance their functionality while avoiding loss of informati...

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This article presents a novel method for enhancing reservoir computing capabilities using an all-optical setup, a significant advancement in the field of neuromorphic computing. The integration of non-fading memory at multiple timescales addresses critical limitations in current systems, demonstrating both practical applicability and theoretical advancement. The experimental basis and the focus on energy efficiency and long-term memory make it a valuable contribution to the landscape of photonic computing and sensing technologies.

Broadcasting in Heterogeneous Tree Networks with Edge Weight Uncertainty

A broadcasting problem in heterogeneous tree networks with edge weight uncertainty under the postal model is considered in this paper. The broadcasting problem asks for a minmax-regret broadcast cente...

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The article addresses a specific and relevant problem in the field of network theory, particularly in heterogeneous tree networks. The introduction of edge weight uncertainty presents a novel challenge that enhances the complexity of traditional broadcasting problems. The methodological approach through an efficient algorithm demonstrates robust mathematical rigor. Its implications for optimizing network communication efficiency make it relevant for both theory and practical applications, though additional real-world validation could further enhance its impact.

An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese

In this paper, we aimed to develop a neural parser for Vietnamese based on simplified Head-Driven Phrase Structure Grammar (HPSG). The existing corpora, VietTreebank and VnDT, had around 15% of consti...

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The article presents a novel approach to developing a neural parser specifically for Vietnamese, showcasing methodological rigor through extensive experiments with two corpora. It also addresses a significant gap in the parsing of Vietnamese language data, suggesting a need for further interdisciplinary engagement with linguistic experts. The achievement of a new state-of-the-art F-score is a strong indicator of its potential impact.

CASBI -- Chemical Abundance Simulation-Based Inference for Galactic Archeology

Galaxies evolve hierarchically through merging with lower-mass systems and the remnants of destroyed galaxies are a key indicator of the past assembly history of our Galaxy. However, accurately measur...

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The article presents a novel method (CASBI) for inferring the properties of galactic assemblages, addressing a significant challenge in Galactic Archeology. It demonstrates methodological rigor and the potential for high-impact findings in understanding the formation history of the Milky Way. The application of Simulation-based Inference (SBI) adds a contemporary analytical framework that could invoke further research in related areas. Moreover, the successful inference from multi-dimensional chemical abundances is a significant advancement, suggesting broad applicability in similar studies.

Extension of adapted differentials on klt orbifolds

Given a geometric orbifold $(X,Δ)$ in the sense of Campana, adapted reflexive differentials with respect to this orbifold are defined on suitably ramified covers of $X$ . We show that i...

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The article presents a significant advancement in the theory of adapted reflexive differentials on klt orbifolds, which is an important area of study in algebraic geometry. The extension of differentials to regular forms on resolutions of singularities showcases methodological rigor and provides deep insights into the structure of geometric orbifolds. The results could inspire further research into resolution theories and their applications in complex algebraic geometry.

Polarization and Orbital Angular Momentum Encoded Quantum Toffoli Gate Enabled by Diffractive Neural Networks

Controlled quantum gates play a crucial role in enabling quantum universal operations by facilitating interactions between qubits. Direct implementation of three-qubit gates simplifies the design of q...

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This article presents a highly relevant and innovative approach to implementing a quantum Toffoli gate using polarization and orbital angular momentum of photons, enhancing the feasibility of three-qubit gate operations. The experimental demonstration shows high fidelity and visibility, indicating robustness and precision in quantum operations, which is crucial for quantum computing advancements. The use of diffractive neural networks adds a novel dimension to the design, suggesting potential for broader applications in quantum circuit design, thus showing significant interdisciplinary value.