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SPACE-SUIT: An Artificial Intelligence based chromospheric feature extractor and classifier for SUIT

The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehe...

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The article presents a novel AI-based approach for automated chromospheric feature detection using advanced neural network techniques. The method addresses significant challenges in solar imaging analysis and demonstrates strong performance metrics, indicating its potential impact on solar research. The combination of machine learning with astrophysical applications shows methodological rigor and applicability, making it a substantial contribution to the field.

A stress tensor for asymptotically flat spacetime

In this article, we propose a procedure for calculating the boundary stress tensor of a gravitational theory in asymptotic flat spacetime. As a case study, the stress tensor correctly reproduces the B...

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This article presents a novel procedure for calculating boundary stress tensors in asymptotically flat spacetimes, which is pivotal for understanding gravitational theories in general relativity. The methodology appears rigorous, as it accurately reproduces existing known results (Brown-York charges), indicating a solid theoretical foundation. The connection to BMS symmetries further enhances its relevance in current gravitational research, making it a potentially influential work for future studies. Furthermore, the comparative analysis with established methods (Wald-Zoupas) lends credibility and situates the findings within the broader research context, suggesting applicability in various theoretical explorations.

Advancing Single- and Multi-task Text Classification through Large Language Model Fine-tuning

Both encoder-only models (e.g., BERT, RoBERTa) and large language models (LLMs, e.g., Llama3) have been widely used for text classification tasks. However, there is a lack of systematic studies compar...

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The article provides significant novelty by systematically comparing encoder-only and large language models in text classification tasks, employing rigorous methodology with a diverse range of models and evaluations on multiple datasets. Its findings on fine-tuning and multi-tasking capabilities address gaps in current research, providing a comprehensive benchmark that can influence future studies in text classification.

Asymptotically good CSS-T codes exist

We give a new construction of binary quantum codes that enables the generation of a CSS-T code from any given CSS code. Using this construction, we prove the existence of asymptotically good binary CS...

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The article presents a significant advancement in the field of quantum error correction by resolving a previously open problem regarding CSS-T codes. Its novel construction of binary quantum codes not only enhances theoretical understanding but also has practical implications for quantum computing. The combination of new methods and application to existing issues adds to its robustness and relevance.

TURBOATTENTION: Efficient Attention Approximation For High Throughputs LLMs

Large language model (LLM) inference demands significant amount of computation and memory, especially in the key attention mechanism. While techniques, such as quantization and acceleration algorithms...

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This article presents a novel approach to improving the efficiency of attention mechanisms in large language models by introducing innovative techniques that significantly reduce memory usage and computational demands. The methodological rigor is supported by experimental results demonstrating meaningful performance improvements over existing state-of-the-art methods. Its focus on quantization and execution efficiency is highly relevant given the growing size and complexity of LLMs, indicating strong applicability and potential impact in the field of AI research.

Efficient search of a minimum tree on points in a space with the $l_1$ -norm

In this paper, we consider the minimum spanning tree problem (for short, MSTP) on an arbitrary set of $n$ points of $d$ -dimensional space in $l_1$ -norm. For this problem, for e...

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This article presents a significant improvement in computational complexity for solving the Minimum Spanning Tree Problem (MSTP) within a $d$-dimensional space using the $l_1$-norm. The introduction of a new algorithm that reduces complexity to $O(n imes ext{log}^{d-1} n)$ is a notable advancement, especially in higher dimensions (d ≥ 6), which will enhance efficiency in applications that require high-dimensional data analysis. The novelty and rigorous methodology of the approach establish a strong foundation for future enhancements in algorithm design and optimization techniques.

A Principled Solution to the Disjunction Problem of Diagrammatic Query Representations

Finding unambiguous diagrammatic representations for first-order logical formulas and relational queries with arbitrarily nested disjunctions has been a surprisingly long-standing unsolved problem. We...

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This article addresses a fundamental problem in the domain of diagrammatic representations, offering a unified solution that significantly advances prior methodologies. Its focus on preserving relational patterns while managing the complexities of nested disjunctions showcases its novelty and methodological rigor. Furthermore, the potential for more succinct representations could greatly impact computational efficiency in various applications involving database queries and logic programming.

Automated Soap Opera Testing Directed by LLMs and Scenario Knowledge: Feasibility, Challenges, and Road Ahead

Exploratory testing (ET) harnesses tester's knowledge, creativity, and experience to create varying tests that uncover unexpected bugs from the end-user's perspective. Although ET has proven e...

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The study explores a novel approach to automated exploratory testing, specifically using LLMs and scenario knowledge, which may significantly advance the field of software testing. The introduction of multi-agent systems to automate the testing process is innovative and provides robust insights into the potential and challenges of this automation. Its implications for reducing manual testing efforts and enhancing software reliability are particularly noteworthy. The contribution of insights regarding neural-symbolic synergy and human-AI co-learning further enhances its relevance in both software engineering and AI fields.

Sparse Signature Coefficient Recovery via Kernels

Central to rough path theory is the signature transform of a path, an infinite series of tensors given by the iterated integrals of the underlying path. The signature poses an effective way to capture...

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This article presents a novel approach to efficiently recover sparse signature coefficients using signature kernels and PDE methods. The application of the signature transform in analyzing paths is theoretically significant, providing a new tool for researchers. The methodology appears rigorous and is supported by empirical results, enhancing its potential impact and relevance within the domain.

Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting

As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address ...

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The article presents a novel approach to systematic reviews using advanced machine learning and natural language processing, addressing current challenges in academic literature management. The methodological rigor seems sound, and the focus on efficiency and scalability is highly relevant for the social sciences. The insights on explainability further enhance its significance, particularly as transparency in ML becomes increasingly important. However, while the application is innovative, the broader impacts may still depend on the generalizability of the proposed methods across different domains.

Mel-Refine: A Plug-and-Play Approach to Refine Mel-Spectrogram in Audio Generation

Text-to-audio (TTA) model is capable of generating diverse audio from textual prompts. However, most mainstream TTA models, which predominantly rely on Mel-spectrograms, still face challenges in produ...

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The article presents a novel and practical solution to a critical problem in the text-to-audio domain by improving audio generation through enhancements in Mel-spectrograms. The methodology is rigorous, and the positive results in performance metrics indicate significant contributions to the field.

Positivity Proofs for Linear Recurrences through Contracted Cones

Deciding the positivity of a sequence defined by a linear recurrence with polynomial coefficients and initial condition is difficult in general. Even in the case of recurrences with constant coefficie...

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The article presents a significant advancement in understanding the positivity of sequences defined by linear recurrences, particularly through the novel approach of using contracted cones and extending Perron-Frobenius theory. Such methodological innovation and extension of existing frameworks demonstrate a high degree of novelty and applicability, with implications for potentially broad classes of linear recurrences. The rigorous description of the algorithm implies a strong methodological foundation, making the findings impactful for future work in related areas.

Annotation-Efficient Task Guidance for Medical Segment Anything

Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training da...

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The article introduces SAM-Mix, a novel multitask learning framework aimed at improving the efficiency of medical image segmentation. Its significant advancements in reducing the required labeled data and training epochs—while still improving accuracy—demonstrates high methodological rigor and applicability in a crucial area of medical imaging. The results not only indicate a substantial improvement over existing methods but also enhance generalization capabilities, making this work both impactful and inspiring for future research in this domain.

Learning Sketch Decompositions in Planning via Deep Reinforcement Learning

In planning and reinforcement learning, the identification of common subgoal structures across problems is important when goals are to be achieved over long horizons. Recently, it has been shown that ...

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This article presents a novel approach to the limitations of existing methods for learning sketch decompositions in planning via deep reinforcement learning. It combines principles from different fields (planning, reinforcement learning, and computer science) creating potential for interdisciplinary applications. The evaluation of the approach across multiple domains is a strong methodological choice, enhancing its relevance across varied contexts. However, the lack of interpretable sketches may limit applicability in some scenarios.

TryOffAnyone: Tiled Cloth Generation from a Dressed Person

The fashion industry is increasingly leveraging computer vision and deep learning technologies to enhance online shopping experiences and operational efficiencies. In this paper, we address the challe...

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The paper presents a novel application of deep learning in the fashion industry, specifically addressing the growing demand for enhanced online shopping experiences through high-fidelity image generation. The use of a fine-tuned StableDiffusion model, along with a streamlined single-stage network design, reflects methodological rigor and innovation, potentially setting new standards in virtual try-on technologies. Additionally, the availability of code and model for public use enhances its impact on future research. Overall, the paper effectively bridges technological advancements with industry needs.

Proportionally Fair Makespan Approximation

We study fair mechanisms for the classic job scheduling problem on unrelated machines with the objective of minimizing the makespan. This problem is equivalent to minimizing the egalitarian social cos...

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This article presents a significant advancement in the area of fair job scheduling, providing a tight approximation for a previously unresolved problem. The introduction of a proportional mechanism for minimizing makespan while incorporating payment strategies indicates a novel approach that may influence future research directions. The methodological rigor is strong due to the comprehensive characterization of allocation functions, making this work robust and applicable across related contexts.

Impact-parameter-dependent solutions to the Balitsky-Kovchegov equation at next-to-leading order

A stable numerical solution of the impact-parameter-dependent next-to-leading order Balitsky-Kovchegov equation is presented for the first time. The rapidity evolution of the dipole amplitude is discu...

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The article introduces a novel numerical solution to the next-to-leading order Balitsky-Kovchegov equation, addressing a significant gap in existing methods. The detailed exploration of dipole amplitude properties and impact parameters enhances its robustness, while strong comparisons with previous solutions indicate methodological rigor. The implications for understanding better the rapidity evolution in high-energy physics mark it as a considerable contribution to the field.

Interplay of coupling, residual, and quasiparticle losses for the frequency- and temperature-dependent quality factor of superconducting resonators

The overall, loaded quality factor $Q_\mathrm{L}$ quantifies the loss of energy stored in a resonator. Here we discuss on general grounds how $Q_\mathrm{L}$ of a planar microwave reson...

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This article presents pioneering insights into the complex behavior of the quality factor in superconducting resonators, a topic critical for the advancement of superconducting qubits and microwave applications. The detailed experimentation over various conditions is rigorous and supports the theoretical models proposed. Its findings have implications for the design and optimization of superconducting devices, making it relevant to both fundamental research and applied technology.

Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness

Soft robots have the potential to interact with sensitive environments and perform complex tasks effectively. However, motion plans and trajectories for soft manipulators are challenging to calculate ...

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The article presents a novel and efficient method for real-time trajectory generation in soft robotics, addressing a significant challenge in the field. The methodological rigor is evident through the demonstration of differential flatness and real-time performance metrics that outperform existing methods. This approach enhances the applicability of soft robots in practical settings, such as safety-critical environments. Its implications for better motion planning in soft manipulators represent a major advancement that could influence future research in robot control and soft robotics.

Identifiability of the instrumental variable model with the treatment and outcome missing not at random

The instrumental variable model of Imbens and Angrist (1994) and Angrist et al. (1996) allow for the identification of the local average treatment effect, also known as the complier average causal eff...

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This article addresses a significant gap in the empirical application of instrumental variable models, particularly under the challenging scenarios of missing data. By providing robust findings that unify existing literature, it presents a novel contribution to the understanding of missing data mechanisms specifically in the context of causal inference. The methodological rigor involved in tackling such a complex issue enhances its relevance and potential impact in the field.