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!

Properties of the phase boundary in the parabolic problem with hysteresis

We study solutions of parabolic equations with a discontinuous hysteresis operator, described by a free interface boundary. It is established that for spatially transverse initial data from the space ...

Useful Fields:

This article presents a compelling exploration of parabolic equations with hysteresis, highlighting new findings regarding the continuity and boundary conditions of solutions. The focus on initial data spaces and the implications for interface behavior demonstrates methodological rigor and provides a fresh perspective in the study of hysteresis, particularly in the context of phase transitions and boundary conditions.

Training Hamiltonian neural networks without backpropagation

Neural networks that synergistically integrate data and physical laws offer great promise in modeling dynamical systems. However, iterative gradient-based optimization of network parameters is often c...

Useful Fields:

This article presents a novel approach to training Hamiltonian neural networks that significantly improves computational efficiency and training speed without relying on backpropagation, which is a common bottleneck in neural network training. The empirical results indicating over 100 times speedup and high accuracy in chaotic systems demonstrate both methodological rigor and practical applicability, potentially transforming how dynamical systems are modeled.

Exact and Heuristic Approaches for the Covering Tour Location Routing Problem

The Covering Tour Location Routing Problem (CTLRP) unites the well-known location routing problem with the possibility of covering customers through intermediary facilities. The objective is to select...

Useful Fields:

The article addresses a significant gap in routing and location problems by integrating intermediary facilities into the covering tour model, providing both exact and heuristic solutions. The methodological rigor is high as it includes a mixed-integer programming model and a matheuristic approach, along with comprehensive computational experiments that demonstrate the effectiveness of the proposed methods. This dual focus on theory and practical application enhances its relevance to real-world scenarios in logistics and supply chain management.

On the affine invariant of hypersemitoric systems

Hypersemitoric systems are a class of integrable systems on $4$ -dimensional symplectic manifolds which only have mildly degenerate singularities and where one of the integrals induces an effec...

Useful Fields:

The article introduces an innovative concept (the affine invariant) for a specific class of integrable systems, which may significantly enhance the understanding of hypersemitoric systems and their applications. The methodological rigor demonstrated through the computation and visualization of the invariant adds robustness to the study, making it a valuable contribution to the field.

Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute

Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as state-of-the-art (SotA) model-based techniques rely on precise knowledge of the vehicle's parameters. Yet, system id...

Useful Fields:

The paper presents a novel algorithm that addresses significant challenges in autonomous racing by improving tire parameter identification using machine learning. It demonstrates excellent methodological rigor with impressive empirical results, showcasing a substantial reduction in RMSE compared to traditional techniques. Its ability to operate in dynamic conditions with minimal data input is a substantial advancement, indicating potential for wide applicability in real-world scenarios and inspiring further research in related areas.

Scanning Thermal Microscopy in Air and Vacuum: A Comparison

We present measurements comparing scanning thermal microscopy in air and vacuum. Signal levels are compared and resolution is probed by scanning over the edge of a nanofabricated Ag square embedded in...

Useful Fields:

The article presents new insights into the influence of environmental conditions (air vs. vacuum) on scanning thermal microscopy, a technique increasingly used in nanotechnology and materials science. The presented results enable better understanding of heat exchange mechanisms, which is critical for improving the accuracy and reliability of thermal measurements at the nanoscale. Its methodological rigor and comparative approach enhance its value for future research.

Neural network modelling of kinematic and dynamic features for signature verification

Online signature parameters, which are based on human characteristics, broaden the applicability of an automatic signature verifier. Although kinematic and dynamic features have previously been sugges...

Useful Fields:

This article presents an innovative use of neural networks for signature verification, which is a growing field in biometric security. The dual approach of combining physical robotic simulation with neural network estimation is novel and potentially impactful. The rigorous testing across multiple datasets strengthens its methodological rigor and applicability. However, the paper's impact may depend on the robustness of the experimental results and the practicality of deploying the methods in real-world applications.

Resonant Inductive Coupling Power Transfer for Mid-Sized Inspection Robot

This paper presents a wireless power transfer (WPT) for a mid-sized inspection mobile robot. The objective is to transmit 100 W of power over 1 meter of distance, achieved through lightweight Litz wir...

Useful Fields:

The paper addresses a significant challenge in the field of wireless power transfer (WPT) relevant to mobile robotics, particularly the transmission efficiency and distance. The novel application of lightweight Litz wire coils and the efficient design for misalignment is key, showcasing practical applicability. The experimental results demonstrating close-to-optimal efficiency lend credibility, though future improvements could enhance efficiency further. Overall, the work is methodologically sound and paves the way for advancements in robotic systems reliant on wireless power solutions.

Superconductivity in WBe2

WBe2, which occurs in space group 194, with hexagonal symmetry P63/mmc, is prepared by arc-melting at temperatures above 2200 C, where Be vapor loss is significant. This study is motivated by recent w...

Useful Fields:

The study presents novel findings on the superconducting properties of WBe2, particularly its transition temperatures and characterization methods. The methodological rigor in characterizing its superconducting properties under controlled conditions adds credibility. Its relevance is enhanced by the context provided by existing literature on superconductors under high pressure, and the potential implications of understanding superconductivity in different compounds. However, the findings may have limited applicability outside superconducting materials and high-pressure physics, which lowers the overall impact score.

Impact of Scalar NSI on Spatial and Temporal Correlations in Neutrino Oscillations

Neutrino oscillation experiments are gradually approaching an era of precision, where subleading effects can also be tested. One such subleading effect is Non-Standard Interactions (NSI), which can pl...

Useful Fields:

The study addresses a significant and emerging concept in neutrino physics by focusing on the less-explored scalar Non-Standard Interactions (NSI). This topic's novelty adds a fresh perspective to the understanding of neutrino oscillations. The methodological approach appears rigorous, considering both spatial and temporal non-locality, which could inspire further research into subleading effects in various settings. The article's focus on quantum correlations enhances its relevance beyond traditional neutrino physics, bridging connections to quantum information science.

Confidence-Aware Deep Learning for Load Plan Adjustments in the Parcel Service Industry

This study develops a deep learning-based approach to automate inbound load plan adjustments for a large transportation and logistics company. It addresses a critical challenge for the efficient and r...

Useful Fields:

The article presents a novel application of deep learning techniques specifically tailored for the parcel service industry, addressing a crucial operational challenge with practical implications. The methodological rigor is demonstrated through extensive comparative experiments with traditional machine learning methods. The use of conformal prediction to enhance decision confidence adds significant innovative value, making the research highly relevant and impactful. Its potential to reshape operational practices in logistics illustrates its importance in advancing the field.

Inference Scaling $\scriptsize\mathtt{F}$ Laws: The Limits of LLM Resampling with Imperfect Verifiers

Recent research has generated hope that inference scaling could allow weaker language models to match or exceed the accuracy of stronger models, such as by repeatedly sampling solutions to a coding pr...

Useful Fields:

This paper addresses an important and nuanced limitation in the current paradigm of inference scaling within machine learning, specifically in language models. It builds on existing research while providing empirical evidence to support its claims, thus contributing to a deeper understanding of the trade-offs involved. Its findings could have significant implications for future model training and evaluation methodologies, making it both novel and methodologically rigorous.

No evidence for fast, galaxy-wide ionised outflows in a nearby quasar -- the importance of accounting for beam smearing

To test the scenario that outflows accelerated by active galactic nuclei (AGN) have a major impact on galaxy-wide scales, we have analysed deep VLT/MUSE data for the type-2 quasar/ultraluminous infrar...

Useful Fields:

This article presents a significant methodological advancement by highlighting the impact of beam smearing on the analysis of AGN-driven outflows. Its findings challenge existing notions of galaxy-wide outflows, creating potential for a paradigm shift in understanding AGN feedback mechanisms. The detailed exploration of the observational techniques used enhances methodological rigor, which is valuable for future research in the field.

Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithms

TSF is growing in various domains including manufacturing. Although numerous TSF algorithms have been developed recently, the validation and evaluation of algorithms hold substantial value for researc...

Useful Fields:

The article provides a comprehensive evaluation of state-of-the-art time-series forecasting (TSF) algorithms specifically in the context of smart manufacturing systems. Its methodological rigor and applicability in real-world settings enhance its relevance. The contrasting performances of both complex and simple algorithms challenge existing assumptions and contribute to practical decision-making, increasing its potential impact.

Simplification of Polyhedral Reductions in Practice

Reductions combine collections of inputs with an associative (and here, also commutative) operator to produce collections of outputs. When the same value contributes to multiple outputs, there is an o...

Useful Fields:

The article presents a significant advancement in compiler technology by introducing a novel push-button implementation of reduction simplification. This method not only automates a process previously reliant on manual optimization, but it also reveals new algorithms, emphasizing its innovative aspect. The evaluations conducted across real-world applications indicate robust methodological rigor and the potential for impactful contributions to various domains.

Observation of droplets in dimer model on a triangular lattice

In this paper, we consider the formation of droplets in the dimer model on a triangular lattice. The droplets in the dimer model are superposition polygons formed as two overlapping configurations of ...

Useful Fields:

The study presents a novel examination of droplet formation within the dimer model, which is particularly relevant given the connections drawn to the Ising model. The methodological rigor surrounding the exploration of local energies and low-temperature effects is sound and adds significant to our understanding of statistical mechanics. Furthermore, the potential implications for broader applications in phase transitions underscore its relevance.

$μ$ SR evidence of a marked exchange interaction effect on the local spin dynamics of Tb-based molecular nanomagnets

We report on the spin dynamics of two Terbium-based molecular nanomagnets, Tb-SQ and Tb-Trp, investigated by means of longitudinal muon spin relaxation ( $μ$ SR) measurements as a function of ap...

Useful Fields:

The study presents novel insights into the local spin dynamics of Tb-based molecular nanomagnets, showcasing the effects of exchange interactions via rigorous experimental methods like $μ$SR and AC susceptibility. This work fills a significant gap in understanding how different coordination spheres influence magnetic properties and spin dynamics behavior, indicating potential implications for the design of future magnetic materials.

A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts

Life insurance, like other forms of insurance, relies heavily on large volumes of data. The business model is based on an exchange where companies receive payments in return for the promise to provide...

Useful Fields:

The manuscript presents a novel approach to anomaly detection specifically tailored for life insurance contracts, a field that is increasingly reliant on data integrity. It addresses the critical issue of data reliability in a business model that depends on trust, which has high relevance in the insurance sector. The discussion of classical and modern unsupervised methods, along with the comparative analysis across datasets, reflects methodological rigor. Moreover, the focus on automating these methods for accessibility is noteworthy, potentially increasing the usability of the research. However, the effectiveness of the methods in real-world scenarios and the extent to which they can be generalized could be further explored.

On the rank index of projective curves of almost minimal degree

In this article, we investigate the rank index of projective curves $\mathscr{C} \subset \mathbb{P}^r$ of degree $r+1$ when $\mathscr{C} = π_p (\tilde{\mathscr{C}})$ for the st...

Useful Fields:

The article presents a significant advancement in the understanding of the rank index of projective curves, which is a relatively underexplored area in algebraic geometry. The methodological focus on specific degrees and properties surrounding projections from rational normal curves introduces a novel perspective that could inspire future research on curves and their associated ideals. Its mathematical rigor solidifies its contributions to the field, making it a profound resource for future explorations.

Ubiquitous van der Waals altermagnetism with sliding/moire ferroelectricity

According to the recent studies on sliding/moire ferroelectricity, most 2D van der Waals nonferroelectric monolayers can become ferroelectric via multilayer stacking. In this paper we propose that sim...

Useful Fields:

The paper presents a novel approach to combining van der Waals altermagnetism with sliding and moire ferroelectricity, representing a significant advance in the understanding of 2D materials and their magnetic and electric properties. The findings are underpinned by robust first-principles calculations, enhancing the methodological rigor. This interdisciplinary work not only expands the theoretical landscape but also paves the way for promising applications in spintronics and memory devices, making it a potentially influential study in the field.