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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!

Test-time Cost-and-Quality Controllable Arbitrary-Scale Super-Resolution with Variable Fourier Components

Super-resolution (SR) with arbitrary scale factor and cost-and-quality controllability at test time is essential for various applications. While several arbitrary-scale SR methods have been proposed, ...

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The article presents a novel approach to super-resolution that effectively addresses limitations in current methods by utilizing an RNN to allow for controllable quality and cost at test time. This innovation has potential broad applications in image processing and computer vision, lending it significant relevance. The experimental validation of improved PSNR scores adds methodological rigor, making it a valuable contribution to the field.

Measuring Consumer Sensitivity to Audio Advertising: A Long-Run Field Experiment on Pandora Internet Radio

A randomized experiment with almost 35 million Pandora listeners enables us to measure the sensitivity of consumers to advertising, an important topic of study in the era of ad-supported digital conte...

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The paper offers a comprehensive long-term experimental analysis of consumer behavior in response to audio advertising, providing novel insights that challenge traditional observational methods. Its robust experimental design allows for a deeper understanding of ad sensitivity over time, which is crucial in the evolving landscape of digital advertising. The findings not only have significant implications for advertisers but also contribute to theoretical frameworks in consumer behavior and marketing strategies. This level of empirical evidence can drive future research on advertising efficacy and consumer choice.

Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to gen...

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This article presents a novel approach to generating reward functions directly from videos, addressing a significant challenge in legged robot behavior learning. The integration of video inputs for reward generation is both innovative and practical, and the iterative refinement scheme adds a layer of methodological rigor. The reported performance improvements over existing methods by a substantial margin indicate that this research can have a major impact in robotics and reinforcement learning, making it highly relevant for advancing the field.

Magnonics along the wall in Bimeron Chain Domain Walls

We demonstrate that domain walls built from bimeron chains (bc-DW) in two-dimensional systems constitute a spontaneously assembled medium that holds magnonic excitations along its direction. We prove ...

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The study presents a novel concept of bimeron chain domain walls (bc-DW) and their interaction with magnons, which has significant implications for topological materials and magnonics. The focus on edge states being robust against disorder underscores the methodological rigor and potential applicability in real-world nanoscale devices, marking its importance in advancing the field.

Partially Synchronous BFT Consensus Made Practical in Wireless Networks

Consensus is becoming increasingly important in wireless networks. Partially synchronous BFT consensus, a significant branch of consensus, has made considerable progress in wired networks. However, it...

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The article presents a significant advancement in the application of partially synchronous Byzantine Fault Tolerance (BFT) consensus in the challenging environment of wireless networks. The introduction of the ReduceCatch protocol showcases novelty in addressing reliability and communication complexities specific to wireless networks. Moreover, the implementation and evaluation of various consensus protocols provide robust empirical data supporting its effectiveness, which is crucial for validating theoretical approaches. The practical implications of this work are significant for future research on consensus mechanisms in decentralized systems, especially in dynamic environments.

Anomalous and nonlinear Hall effects in two-dimensional CrPS $_{4}$

Few-layer CrPS $_{4}$ is a two-dimensional (2D) magnetic material with excellent stability in ambient environment, which attracted significant interest in recent research. Here, via first-princ...

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The article presents significant findings on the anomalous Hall effects in a novel two-dimensional magnetic material, CrPS$_{4}$. The use of first-principles calculations ensures methodological rigor, while the discovery of layer-dependent transport phenomena introduces a new perspective in the field. The implications for electronic and spintronic applications further posit its relevance. However, the specific application claims could be elaborated to enhance impact assessment.

Travel groupoids on complete multipartite graphs

A travel groupoid is an algebraic system satisfying two suitable conditions, which has a relation to graphs. In this article, we characterize travel groupoids on finite complete multipartite graphs, a...

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The article presents a characterization of travel groupoids in relation to complete multipartite graphs, which is a specific and novel contribution to algebraic structures and graph theory. While the topic is quite niche, the potential applicability in combinatorics and algebra may stimulate further research in these areas, though the impact may be limited due to the specialized nature of the subject.

Weighted Shift operators on spaces of analytic functions over an annulus -- I

We initiate a study of the forward weighted shift operator, denoted by $F_w$ , defined on the Banach spaces $\ell^p_{a,b}(\mathtt{A})$ and $c_{0,a,b}(\mathtt{A})$ of analytic fu...

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The article presents significant advancements in the theory of weighted shift operators on spaces of analytic functions, specifically addressing conditions for boundedness, hypercyclicity, and chaos of the operator and its adjoint. The results are novel and expand existing knowledge in functional analysis and operator theory, potentially influencing further studies in these areas.

Optimizing Returns from Experimentation Programs

Experimentation in online digital platforms is used to inform decision making. Specifically, the goal of many experiments is to optimize a metric of interest. Null hypothesis statistical testing can b...

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This article presents a novel approach to optimizing experimentation in online platforms, merging empirical Bayes analyses with dynamic programming. The significance lies in altering traditional A/B testing methods to favor optimization over mere hypothesis testing, which can greatly enhance decision-making in digital environments. The methodology is robust, promising advancements in applied statistics and practical implementation in industry settings.

AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration

Robot description models are essential for simulation and control, yet their creation often requires significant manual effort. To streamline this modeling process, we introduce AutoURDF, an unsupervi...

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The article presents an innovative unsupervised approach to robot modeling that addresses significant challenges in the field. Its methodological rigor is highlighted by comprehensive validation across various robots using both synthetic and real-world data. Furthermore, the development of AutoURDF has practical implications for reducing the manual effort in generating robot models, thus indicating high applicability and potential for widespread use in robotics.

Distributional Solution and Spectral Shift Function of Heun Differential Equation

In this work, the Heun operator is written as an element in the universal enveloping algebra of the Lie algebra $\mathscr{G}=\mathscr{L}(G)$ of the Lie group $G=SL(2,\mathbb{C})$ . The ...

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The article presents a novel approach to the Heun differential equation using Lie algebraic techniques, which adds depth to the understanding of this important mathematical structure. The use of universal enveloping algebra and the connection to spectral functions suggests a solid mathematical groundwork, adding to its methodological rigor. Additionally, the results may have implications for both theoretical and applied physics, particularly in quantum mechanics, enhancing its relevance.

Ranking of Large Language Model with Nonparametric Prompts

We consider the inference for the ranking of large language models (LLMs). Alignment arises as a big challenge to mitigate hallucinations in the use of LLMs. Ranking LLMs has been shown as a well-perf...

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The article presents a novel inferential framework for ranking large language models (LLMs) based on nonparametric prompts, addressing a significant challenge in model alignment and hallucination mitigation. Its methodology is rigorously articulated, incorporating advanced statistical techniques and extending existing theory, which showcases both novelty and methodological rigor. The implications for model alignment in practical applications enhance its relevance.

Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search

Spiking Neural Networks (SNNs) are amenable to deployment on edge devices and neuromorphic hardware due to their lower dissipation. Recently, SNN-based transformers have garnered significant interest,...

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This article presents a novel approach to enhancing the efficiency of Spiking Neural Networks (SNNs), which is critical due to the growing demand for running AI models on resource-constrained edge devices. The use of heterogeneous quantization for compressing spiking transformers demonstrates both innovation and practical applicability, crucial conditions for advancing the field. The study’s rigorous methodology—optimizing quantization at a layer level—shows potential for significant performance gains without a substantial accuracy loss, making it an impactful contribution to the research on efficient model deployment.

Towards a Theory for the Formation of Chimera Patterns in Complex Networks

Chimera states, marked by the coexistence of order and disorder in systems of coupled oscillators, have captivated researchers with their existence and intricate patterns. Despite ongoing advances, a ...

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This article presents a novel theoretical framework for understanding chimera patterns, leveraging empirical and simulation data to support its claims. The rigorous methodology, including spectral analysis, enhances the robustness of the findings, making it a valuable contribution to both theoretical and applied aspects of complex networks.

Critical scaling profile for trees and connected subgraphs on the complete graph

We analyse generating functions for trees and for connected subgraphs on the complete graph, and identify a single scaling profile which applies for both generating functions in a critical window. Our...

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The article presents a novel approach to scaling profiles for generating functions of trees and connected subgraphs, indicating an important intersection between combinatorics and statistical physics. Its implications for high-dimensional analysis contribute significantly to the field, and the conjecture about dimensions provides a compelling avenue for future work. The rigorous mathematical analysis strengthens its robustness.

EC-Chain: Cost-Effective Storage Solution for Permissionless Blockchains

Permissionless blockchains face considerable challenges due to increasing storage demands, driven by the proliferation of Decentralized Applications (DApps). This paper introduces EC-Chain, a cost-eff...

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The article addresses a critical issue in the blockchain sector regarding storage efficiency for permissionless systems, which is increasingly relevant as the demand for DApps grows. Its proposed solutions are novel and leverage existing technologies in innovative ways, exhibiting a clear methodological rigor. The significant potential for storage reduction demonstrates applicability and relevance to ongoing blockchain development.

Computational models of learning and synaptic plasticity

Many mathematical models of synaptic plasticity have been proposed to explain the diversity of plasticity phenomena observed in biological organisms. These models range from simple interpretations of ...

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The article presents a comprehensive overview of computational models linking synaptic plasticity to various learning paradigms. Its emphasis on both basic and complex models offers valuable insights into a critical aspect of neuroscience. The methodological rigor in discussing theoretical frameworks suggests its applicability to various experimental setups. However, the operationalization of these models could be further refined to enhance practical applicability.

Syzygies of canonical ribbons on higher genus curves

We study the syzygies of the canonical embedding of a ribbon $\widetilde{C}$ on a curve $C$ of genus $g \geq 1$ . We show that the linear series Clifford index and the resolutio...

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This article presents a significant advancement in the study of syzygies related to canonical embeddings of ribbons on higher genus curves. It connects to several critical conjectures in algebraic geometry, such as the gonality conjecture and Green's conjecture, suggesting its findings could have implications for ongoing research in these areas. Its methodological rigor and focus on specific cases enhance its applicability. However, the narrow focus might limit its broader impact in very divergent fields, preventing a perfect score.

SplaXBERT: Leveraging Mixed Precision Training and Context Splitting for Question Answering

SplaXBERT, built on ALBERT-xlarge with context-splitting and mixed precision training, achieves high efficiency in question-answering tasks on lengthy texts. Tested on SQuAD v1.1, it attains an Exact ...

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The article introduces SplaXBERT, which innovatively combines mixed precision training and context-splitting, a novel approach that could significantly enhance efficiency in NLP tasks, particularly in handling lengthy texts. The performance metrics (Exact Match and F1 Score) indicate a promising leap over existing BERT models, suggesting strong applicability and potential improvements in efficiency and accuracy that can inspire further research in NLP optimization methods.

Accelerating Proximal Gradient Descent via Silver Stepsizes

Surprisingly, recent work has shown that gradient descent can be accelerated without using momentum -- just by judiciously choosing stepsizes. An open question raised by several papers is whether this...

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The article presents a novel contribution to the field of optimization by extending the concept of stepsize-based acceleration to constrained and composite convex optimization, thus addressing an open question in the literature. The introduction of the 'silver stepsize schedule' and its analytically proven advantages suggests significant advancements for the field. The rigor in methodology and the potential impact of the findings, particularly in improving optimization algorithms, substantiate a high relevance score.