This is a experimental project. Feel free to send feedback!

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!

Angular bispectrum of matter number counts in cosmic structures

The bispectrum of galaxy number counts is a key probe of large-scale structure (LSS), offering insights into the initial conditions of the Universe, the nature of gravity, and cosmological parameters....

Useful Fields:

This article introduces a novel approach to calculating the angular bispectrum of galaxy number counts, integrating both Newtonian and relativistic effects without the Limber approximation. This methodological advancement is significant as it enhances our understanding of cosmic structures and the dynamics of the Universe. The inclusion of redshift binning and the development of an accessible code for evaluation further increase its applicability and potential impact on future research.

Active Microrheology and Dynamic Phases for Pattern Forming Systems with Competing Interactions

We consider the driven dynamics of a probe particle moving through an assembly of particles with competing long-range repulsive and short-range attractive interactions, which form crystal, stripe, lab...

Useful Fields:

The study presents novel insights into the dynamics of probe particles within patterned systems characterized by competing interactions. Its exploration of depinning thresholds and nonmonotonic behavior in particle velocity and interactions provides a solid foundation for future research in both theoretical and applied contexts. The methodology appears rigorous, leveraging complex interactions to understand fundamental physical behaviors. This research may inspire further studies on micro-scale interactions, materials science applications, and the development of smart materials.

RoboPanoptes: The All-seeing Robot with Whole-body Dexterity

We present RoboPanoptes, a capable yet practical robot system that achieves whole-body dexterity through whole-body vision. Its whole-body dexterity allows the robot to utilize its entire body surface...

Useful Fields:

The article presents a groundbreaking robot system that enhances dexterity and visual feedback through innovative design. The integration of whole-body dexterity with whole-body vision offers significant improvements in robot manipulation capabilities, addressing real-world challenges in constrained environments. Furthermore, the learning approach utilizing human demonstrations adds a novel dimension to the development of robotics, making it both practical and relevant for various applications.

Caroli-de Gennes-Matricon Analogs in Full-Shell Hybrid Nanowires

We report tunneling spectroscopy of Andreev subgap states in hybrid nanowires with a thin superconducting full-shell surrounding a semiconducting core. The combination of the quantized fluxoid of the ...

Useful Fields:

This article presents a novel exploration of Andreev subgap states in hybrid nanowires, introducing a significant analog to well-established superconducting phenomena. The rigorous experimental approach combined with the theoretical implications of the findings suggests a meaningful advancement in the understanding of superconductivity and hybrid nanostructures. The work has strong potential for influencing future research in material science and quantum physics due to its innovative approach and the exploration of quasiparticle dynamics.

Virtual-Work Based Shape-Force Sensing for Continuum Instruments with Tension-Feedback Actuation

Continuum instruments are integral to robot-assisted minimally invasive surgery (MIS), with tendon-driven mechanisms being the most common. Real-time tension feedback is crucial for precise articulati...

Useful Fields:

This article presents novel advancements in shape and force sensing for continuum instruments, addressing critical challenges in robotic surgery. The integration of torque cells into pulley modules for real-time tension feedback represents a significant innovation that enhances the capabilities of minimally invasive surgical tools. The methodological rigor is evident in the experimental validation and comparison with conventional approaches, showing clear improvements in performance. Its impact on the field of robotic surgery is substantial, potentially influencing future designs and applications in minimally invasive techniques.

Incoherent Diffraction Imaging with a Pseudo-Thermal Light Source

Incoherent Diffraction Imaging - IDI - is a diffraction-based imaging technique that has been recently proposed to exploit the partial coherence of incoherently scattered light to retrieve structural ...

Useful Fields:

The article presents a novel approach to Incoherent Diffraction Imaging using a Pseudo-Thermal Light Source, which could broaden the accessibility of this imaging technique beyond high-energy facilities. The proposed methodology introduces an innovative experimental setup that may demystify complex imaging processes, thus enhancing practical applications. The benchmarking of existing models indicates a strong methodological rigor, supporting its potential impact on the field of imaging.

Analytical control of the exchange interaction in periodically driven Mott insulators

The manipulation of electronic structure through periodic electric fields enables the reversible control of effective interactions in extended antiferromagnetic Mott insulators on ultrafast timescales...

Useful Fields:

This article offers novel insights into the control of electronic structure through periodic electric fields, addressing a significant gap in understanding the dynamics of Mott insulators and their interactions. The methodological rigor is underscored by the use of exact summation formulas and Bessel functions, showcasing both theoretical depth and practical applicability. The implications of the findings on ultrafast manipulation of magnetic properties are likely to inspire future experimental and theoretical studies in condensed matter physics.

Uncertainty-aware Knowledge Tracing

Knowledge Tracing (KT) is crucial in education assessment, which focuses on depicting students' learning states and assessing students' mastery of subjects. With the rise of modern online lear...

Useful Fields:

The article introduces a novel approach to Knowledge Tracing that addresses a critical limitation in existing models by incorporating uncertainty into the learning process. This innovative methodology, along with rigorous experimental validation on multiple datasets, suggests it could significantly enhance the accuracy of educational assessments. Its potential applicability in real-world online learning environments further underscores its relevance.

LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation

Existing benchmarks for evaluating long-context language models (LCLMs) primarily focus on long-context recall, requiring models to produce short responses based on a few critical snippets while proce...

Useful Fields:

This paper introduces a novel benchmark (LongProc) tailored specifically for evaluating long-context language models in procedural generation tasks, addressing a significant gap in existing evaluations. Its focus on both integration of dispersed information and generating long-form outputs is impactful for advancing research in language modeling. The rigorous evaluation of 17 LCLMs across different task difficulties highlights current models' limitations and sets a foundation for future improvements. The structured outputs and reliable evaluation criteria further enhance its applicability. Overall, its methodological rigor and potential to inspire further research in this field warrant a high relevance score.

Seeing Sound: Assembling Sounds from Visuals for Audio-to-Image Generation

Training audio-to-image generative models requires an abundance of diverse audio-visual pairs that are semantically aligned. Such data is almost always curated from in-the-wild videos, given the cross...

Useful Fields:

This article presents a novel approach to audio-to-image generation by challenging the traditional reliance on aligned audio-visual pairs. Its proposal for a scalable image sonification framework could significantly expand the dataset's size and diversity, influencing future generative model capabilities. The methodology appears rigorous, and the demonstration of competitive results against state-of-the-art models bolsters its relevance. Furthermore, the exploration of auditory capabilities in a cross-modal context may inspire interdisciplinary research in human-computer interaction and multimedia processing.

Search-based Testing of Simulink Models with Requirements Tables

Search-based software testing (SBST) of Simulink models helps find scenarios that demonstrate that the system can reach a state that violates one of its requirements. However, many SBST techniques for...

Useful Fields:

The article presents a novel black-box testing approach for Simulink models that addresses a significant barrier for industry adoption of SBST techniques. Its integration with the Requirements Table is a crucial advancement, suggesting practical applicability and relevance. The comprehensive evaluation, showing a 70% success rate in generating failure-revealing test cases, indicates robustness in the proposed methodology. The findings could inspire further research into practical approaches for requirement-based testing techniques and their incorporation into existing engineering workflows.

Adaptive Path-Planning for Autonomous Robots: A UCH-Enhanced Q-Learning Approach

Q-learning methods are widely used in robot path planning but often face challenges of inefficient search and slow convergence. We propose an Improved Q-learning (IQL) framework that enhances standard...

Useful Fields:

The article proposes a novel approach to improving Q-learning for autonomous robot path-planning, addressing key limitations such as inefficient search and slow convergence. The introduction of both the PACO algorithm and UCH mechanism demonstrates methodological innovation and potential real-world applicability, which are crucial for advancing the field. Additionally, the empirical validation through extensive experiments adds to the robustness of the findings, making this work highly relevant for both theoretical and practical advancements in robotics.

Not-Quite-Transcendental Functions For Logarithmic Interpolation of Tabulated Data

From tabulated nuclear and degenerate equations of state to photon and neutrino opacities, to nuclear reaction rates: tabulated data is ubiquitous in computational astrophysics. The dynamic range that...

Useful Fields:

The article presents a novel strategy for interpolating tabulated data, which is crucial in various computational astrophysics applications. The focus on dynamic ranges and performance enhancement adds significant value. The method's comparison with existing linear interpolation techniques highlights its potential practical applicability, which may inspire future research in numerical methods and data handling within astrophysics and other fields that rely heavily on tabulated data.

TimeRL: Efficient Deep Reinforcement Learning with Polyhedral Dependence Graphs

Modern deep learning (DL) workloads increasingly use complex deep reinforcement learning (DRL) algorithms that generate training data within the learning loop. This results in programs with several ne...

Useful Fields:

TimeRL offers transformative advancements in the execution efficiency of deep reinforcement learning algorithms by integrating dynamic dependency management with optimization techniques. The innovative use of polyhedral dependence graphs for mapping and optimizing DRL workloads demonstrates its methodological rigor and novelty, making it highly applicable to contemporary DL challenges. The substantial performance improvements noted indicate significant practical implications for researchers and practitioners in the field.

On-line Policy Improvement using Monte-Carlo Search

We present a Monte-Carlo simulation algorithm for real-time policy improvement of an adaptive controller. In the Monte-Carlo simulation, the long-term expected reward of each possible action is statis...

Useful Fields:

The article presents a novel Monte-Carlo simulation algorithm that shows significant improvements in policy optimization, specifically in adaptive control and decision-making. The applicability of the method across different domains, alongside its parallelizability, adds to its robustness. The promising results in backgammon suggest the algorithm’s potential in both game theory and real-world applications, positioning it as a significant contribution to the field of adaptive systems.

Innovative Designs and Insights into Quantum Thermal Machines

We present a comprehensive theoretical investigation about the operational regions of quantum systems, specifically examining their roles as working media functioning between two thermal reservoirs in...

Useful Fields:

This article presents a significant theoretical advancement in the understanding of quantum thermal machines (QTMs), introducing new designs and a comprehensive classification scheme. The novelty and applicability of the findings, particularly the treatment of lasers as QTMs, contribute to its strong impact. The rigorous theoretical framework enhances its credibility, making it a valuable contribution to quantum thermodynamics.

Data-driven methods to discover stable linear models of the helicity injectors on HIT-SIU

Accurate and efficient circuit models are necessary to control the power electronic circuits found on plasma physics experiments. Tuning and controlling the behavior of these circuits is inextricably ...

Useful Fields:

This article presents an innovative approach by applying data-driven methods to improve the modeling of circuit systems in plasma physics. The use of Bagging Optimized Dynamic Mode Decomposition (BOP-DMD) offers a novel technique that could greatly enhance control applications by providing interpretable models. The relevance is high due to the intersection of control theory and plasma physics, addressing a key challenge in the field.

Aspects of Propagator Sparsening in Lattice QCD

In lattice field theory, field sparsening aims to replace quantum fields, or objects constructed from them, with approximations that preserve the appropriate symmetries and maintain many aspects of th...

Useful Fields:

The article presents a novel methodology for optimizing lattice QCD calculations through propagator sparsening, which addresses significant computational challenges in the field. Its rigorous examination of correlation functions and emphasis on preserving physical properties enhances its applicability. The potential for reducing computational costs while maintaining accuracy marks a critical advance, making this paper valuable for both current researchers and future investigations in lattice QCD.

TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts

Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to generate data from one...

Useful Fields:

The article presents a novel approach to time series generation by incorporating multi-domain capabilities using domain prompts, which addresses a notable gap in existing literature that primarily focuses on single-domain generation. The methodological rigor is strong, demonstrated by empirical results that showcase superior performance compared to baseline models. Its potential applications in data augmentation and privacy preservation further emphasize its relevance and applicability across various disciplines.

Edge modes in modulated metamaterials based on the three-gap theorem

We present a new paradigm for generating complex structured materials based on the three-gap theorem that unifies and generalises several key concepts in the study of localised edge states. Our model ...

Useful Fields:

The article introduces a novel framework based on the three-gap theorem to explore localized edge states in metamaterials, presenting significant methodological advancements. The study's integration of existing theories with new concepts is likely to inspire further research into edge modes and metamaterial applications. However, additional empirical validation could enhance its impact.