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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!

Nonreciprocal spin-wave dispersion in magnetic bilayers

Nonreciprocal spin-wave propagation in bilayer ferromagnetic systems has attracted significant attention due to its potential to precisely quantify material parameters as well as for applications in m...

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This study presents novel insights into nonreciprocal spin-wave propagation in magnetic bilayers, with robust experimental validation through Brillouin light scattering. The focus on manipulating material properties for practical applications in magnonic logic enhances its relevance. The methodology is sound, and the findings have significant implications for future research and device engineering in the field.

Deuteron- ${}^{3}\mathrm{He}$ scattering using nucleon- ${}^{3}\mathrm{He}$ optical potentials fitted to four-body amplitudes

Deuteron- ${}^{3}\mathrm{He}$ reactions in the 15 to 40 MeV range are studied using a three-body model where the constructed nonlocal optical potentials rely on rigorous nucleon-${}^{3}\mat...

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The article presents a novel approach to modeling deuteron-${}^{3} ext{He}$ scattering using rigorously fitted nonlocal optical potentials, which is significant for the field of nuclear interaction studies. Its methodological rigor and the demonstrated predictive capability for differential cross sections enhance its relevance. The identification of the Pauli term's role in complex potentials adds an important dimension to theoretical nuclear physics, potentially influencing future scattering models.

Series solutions for clamped peridynamic beams using fourth-order eigenfunctions

We propose an analytical approach to solving nonlocal generalizations of the Euler--Bernoulli beam. Specifically, we consider a version of the governing equation recently derived under the theory of p...

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This article presents a novel analytical approach to solving peridynamic beam theory problems, which is relatively underexplored. The use of fourth-order eigenfunctions offers significant methodological advances over previously established methods (e.g., Fourier sine series), indicating potential improvements in computational efficiency and accuracy. The rigorous comparison with other techniques enhances the robustness of the conclusions. However, the niche focus might limit broader applications beyond structural engineering and peridynamics.

CUAL: Continual Uncertainty-aware Active Learner

AI deployed in many real-world use cases should be capable of adapting to novelties encountered after deployment. Here, we consider a challenging, under-explored and realistic continual adaptation pro...

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The article presents a significant advancement in active learning, specifically addressing the challenges of continual learning in the presence of both known and unknown classes. The use of uncertainty estimation in active learning is novel and holds great potential for applications in dynamic real-world scenarios where new data distributions may emerge. The methodological rigor is indicated by the evaluation across multiple datasets and various models, which enhances the reliability and applicability of the findings.

Rotating convection with a melting boundary: an application to the icy moons

A better understanding of the ice-ocean couplings is required to better characterise the hydrosphere of the icy moons. Using global numerical simulations in spherical geometry, we have investigated he...

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The article demonstrates significant novelty by applying a new phase field formulation to study the dynamics of rotating convection in icy moons, which presents a new approach to understanding ice-ocean couplings, an area that lacks comprehensive exploration. The methodological rigor is underscored by the use of global numerical simulations and the validation of the proposed model. Its findings on the interplay between convection and ice melting could have important implications for future planetary science research, particularly in astrobiology concerning ocean worlds.

Neutrinos from explosive transients at the dawn of multi-messenger astronomy

With the advent of time-domain astronomy and the game-changing next generation of telescopes, we have unprecedented opportunities to explore the most energetic events in our Universe through electroma...

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The article presents a comprehensive review of neutrinos in the context of multi-messenger astronomy, a rapidly evolving field. Its emphasis on the interplay between neutrino physics and explosive astrophysical events marks it as a valuable resource for researchers seeking to understand fundamental processes in the universe. The systematic approach to addressing outstanding questions and optimization strategies indicates methodological rigor and a forward-looking perspective essential for future research directions.

Langevin Monte Carlo Beyond Lipschitz Gradient Continuity

We present a significant advancement in the field of Langevin Monte Carlo (LMC) methods by introducing the Inexact Proximal Langevin Algorithm (IPLA). This novel algorithm broadens the scope of proble...

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The introduction of the Inexact Proximal Langevin Algorithm (IPLA) represents a meaningful advancement in Langevin Monte Carlo methods, allowing for a broader range of potential applications. Its ability to maintain computational cost while expanding LMC's applicability signifies a significant improvement in methodological rigor. Furthermore, the provision of analytical bounds for the Markov chain adds depth to the study, indicating that a comprehensive understanding has been achieved.

Evaluating time-specific treatment effects in matched-pairs studies

This study develops methods for evaluating a treatment effect on a time-to-event outcome in matched-pair studies. While most methods for paired right-censored outcomes allow determining an overall tre...

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The study offers novel methodologies for analyzing time-specific treatment effects in matched-pair designs, addressing a significant gap in existing statistical methods. Its rigorous simulation analysis validates its robustness, suggesting practical applicability to real-world scenarios. Additionally, the extension to observational studies increases its relevance by providing tools for researchers in various fields, enhancing its methodological contribution.

Soybean Maturity Prediction using 2D Contour Plots from Drone based Time Series Imagery

Plant breeding programs require assessments of days to maturity for accurate selection and placement of entries in appropriate tests. In the early stages of the breeding pipeline, soybean breeding pro...

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The article proposes a novel machine-learning approach to assess soybean maturity using UAV-based imagery, which significantly enhances current practices by reducing subjectivity and time involved in traditional methods. The methodological rigor demonstrated through a comprehensive dataset spanning three years adds credibility to the findings. The potential impact on crop breeding practices and decision-making processes in agriculture positions this work as highly relevant for future advancements in both agricultural science and machine learning applications in crop management.

Codes in algebras of direct products of groups and associated quantum codes

In this paper we obtain the Wedderburn-Artin decomposition of a semisimple group algebra associated to a direct product of finite groups. We also provide formulae for the number of all possible group ...

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This article presents significant findings in the area of algebra and quantum computing by exploring the structure of group algebras and their applications in quantum error correction. The Wedderburn-Artin decomposition and detailed analysis of various group algebras demonstrate a high level of methodological rigor and contribute novelty to existing literature. Additionally, the link established between algebraic structures and quantum codes suggests a promising interdisciplinary application, thereby enhancing its relevance.

Omni-ID: Holistic Identity Representation Designed for Generative Tasks

We introduce Omni-ID, a novel facial representation designed specifically for generative tasks. Omni-ID encodes holistic information about an individual's appearance across diverse expressions and...

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Omni-ID provides a novel approach to facial representation specifically tailored for generative applications, distinguishing itself from existing benchmarks like CLIP and ArcFace. The methodological rigor, evidenced by the multi-decoder framework and the unique few-to-many identity reconstruction paradigm, supports its potential for significant advancements in generating diverse facial expressions and poses. The dataset created (MFHQ) could also be influential in future research, enhancing the dataset landscape in facial recognition. However, potential issues with the generalizability and scalability of the approach may need thorough investigation in subsequent studies.

Low two-level-system noise in hydrogenated amorphous silicon

At sub-Kelvin temperatures, two-level systems (TLS) present in amorphous dielectrics source a permittivity noise, degrading the performance of a wide range of devices using superconductive resonators ...

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The article presents significant advancements in reducing two-level system noise in hydrogenated amorphous silicon (a-Si:H), which has implications for improving the performance of superconductive devices. The methodological rigor in the use of experimental measurements for various deposition recipes adds to its reliability. The improvement noted, over 5 times better noise performance than previously reported, positions this work as a notable contribution to the field, enhancing both theoretical understanding and practical applications. The potential for interdisciplinary applications in quantum computing and high precision measurements further enhances its impact.

TOAP: Towards Better Robustness in Universal Transferable Anti-Facial Retrieval

Deep hash-based retrieval techniques are widely used in facial retrieval systems to improve the efficiency of facial matching. However, it also brings the risk of privacy leakage. Deep hash models are...

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The article presents a novel approach (TOAP) that addresses a critical issue of privacy in facial recognition systems by enhancing robustness against adversarial attacks. It combines deep-hash model analysis with innovative optimization techniques and meta-learning, showcasing methodological rigor and significant empirical results. The potential for real-world application in OSNs emphasizes its relevance.

Towards the phase diagram of fermions coupled with $SO(3)$ quantum links in $(2+1)$ -D

Quantum link models (QLMs) are generalizations of Wilson's lattice gauge theory formulated with finite-dimensional link Hilbert spaces. In certain cases, the non-Abelian Gauss Law constraint can b...

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The article presents a novel extension of quantum link models to (2+1)-D, which is a significant theoretical advancement. The ability to address a rich phase diagram and phenomena related to confinement and chiral symmetry breaking processes aligns closely with areas of interest in gauge theories and quantum simulations, enhancing its potential impact. The use of exact diagonalization strengthens its methodological rigor, given the complexity of the systems studied. However, while the results are promising, they appear to be preliminary, which limits immediate application in empirical settings.

Full Magnetometer and Gyroscope Bias Estimation using Angular Rates: Theory and Experimental Evaluation of a Factor Graph-Based Approach

Despite their widespread use in determining system attitude, Micro-Electro-Mechanical Systems (MEMS) Attitude and Heading Reference Systems (AHRS) are limited by sensor measurement biases. This paper ...

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The proposed MAGYC method is innovative in its application of factor graphs for calibrating sensor biases, addressing significant limitations in the current technology utilized in MEMS AHRS. The rigorous validation via both simulations and real-world applications enhances its credibility and applicability in practical scenarios, marking a potential leap in achieving more accurate navigation systems. Its reduced dependency on external magnetic field knowledge further positions it as a versatile tool in various operational contexts.

A $C^0$ interior penalty method for the stream function formulation of the surface Stokes problem

We propose a $C^0$ interior penalty method for the fourth-order stream function formulation of the surface Stokes problem. The scheme utilizes continuous, piecewise polynomial spaces defined o...

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The article presents a novel approach to solving the surface Stokes problem using an innovative $C^0$ interior penalty method. The positive definiteness and detailed error estimates add significant methodological rigor. The independence from Gauss curvature could broaden the applicability of the method, making it particularly relevant for complex geometries. However, its practical implementation and comparative performance against existing methods could benefit from further exploration in future studies.

Formal Languages and TQFTs with Defects

A construction that assigns a Boolean 1D TQFT with defects to a finite state automaton was recently developed by Gustafson, Im, Kaldawy, Khovanov, and Lihn. We show that the construction is functorial...

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The article presents a significant advancement in the connection between formal languages and topological quantum field theories (TQFTs), especially through the establishment of a functorial relationship. The novelty lies in its methodological rigor and the generalization of findings from finite automata to context-free grammars. Its implications for both computability and quantum physics make it particularly impactful for the field.

DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations

Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial on resource-constrain...

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The article presents a novel approach to deep quantization that specifically targets low-bit activation representation, which is particularly relevant for resource-constrained environments. The methodology is simplified compared to existing techniques, which increases its applicability on devices with limited computational power. Furthermore, the significant accuracy improvements over current methods demonstrate its potential impact on operational efficiency within this domain. However, further validation across a wider range of tasks and models would enhance its robustness.

The Cost of Replicability in Active Learning

Active learning aims to reduce the required number of labeled data for machine learning algorithms by selectively querying the labels of initially unlabeled data points. Ensuring the replicability of ...

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The article addresses a pertinent challenge in machine learning concerning the trade-off between replicability and label efficiency. The integration of statistical query subroutines with a classical active learning method is a novel approach that provides theoretical backing for practical applications, making it significant for both academic research and practical use. It enhances our understanding of how to maintain result consistency without excessively raising resource requirements, which could stimulate further research on algorithmic efficiency and robustness in machine learning.

Rainbow chains and numerical renormalisation group for accurate chiral conformal spectra

Based on the relationship between reduced and thermal density matrices in conformal field theory (CFT), we show that the entanglement spectrum of a conformal critical chain with exponentially decaying...

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The article presents a novel approach to extracting conformal field theory (CFT) spectra, utilizing the entanglement spectrum and a robust methodology involving the Wilsonian numerical renormalisation group. This combination addresses important challenges in conformal critical systems, offering an alternative to traditional methods. Its implications on obtaining detailed CFT spectra with reduced assumptions and finite-size effects highlight its potential impact on future research in this area.