Thesis Tide - Find relevant and interesting papers

Simple and fast method for the analysis of $^{236}$ U, $^{237}$ Np, $^{239}$ Pu and $^{240}$ Pu from seawater samples by Accelerator Mass Spectrometry

A simple and fast radiochemical procedure for the sequential extraction of U, Np and Pu from small-volume seawater samples (<10 L) is presented. The method has been developed and optimize...

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This article presents a novel and efficient method for the analysis of important actinides in seawater, crucial for environmental monitoring and nuclear science. The methodological rigor, including robust validation and low detection limits, indicates significant advancement in this analytical technique. This could lead to more widespread application in oceanographic research and radiological studies, enhancing our understanding of nuclear contaminants in marine environments.

Attention based Bidirectional GRU hybrid model for inappropriate content detection in Urdu language

With the increased use of the internet and social networks for online discussions, the spread of toxic and inappropriate content on social networking sites has also increased. Several studies have bee...

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The article addresses a significant gap in the literature regarding the detection of inappropriate content specifically in the Urdu language using deep learning techniques. It utilizes a novel hybrid approach that combines an attention mechanism with Bidirectional GRU, which showcases methodological rigor and innovation. The performance comparison with existing models enhances its applicability and relevance to the field, particularly for researchers focused on natural language processing (NLP) for low-resource languages.

Hybridization between surface flat bands and bulk bands in the topological nodal-line semimetal Sn $_{0.15}$ NbSe $_{1.75}$ probed via soft-point-contact spectroscopy

We report a detailed study of soft-point-contact spectroscopy of the superconducting topological nodal-line semimetal Sn $_{0.15}$ NbSe $_{1.75}$ with the superconducting transition tempe...

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The paper presents significant novel findings on the hybridization of surface flat bands and bulk bands in a topological semimetal, which could advance the understanding of superconducting states in these materials. The use of soft-point-contact spectroscopy is methodologically rigorous and applicable to broader studies in topological materials. The results could stimulate further research into similar semimetals and their electronic properties.

A Simple Aerial Detection Baseline of Multimodal Language Models

The multimodal language models (MLMs) based on generative pre-trained Transformer are considered powerful candidates for unifying various domains and tasks. MLMs developed for remote sensing (RS) have...

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This article presents a novel approach to applying multimodal language models to aerial detection, an area that has not been extensively explored. The introduction of a normalization method aligns detection outputs with MLM frameworks showcases methodological innovation. The comparison with conventional object detection models enhances its robustness. Overall, the study has the potential to inspire further research in both remote sensing and multimodal AI applications, particularly in enhancing understanding of RS images.

Comparative Insights from 12 Machine Learning Models in Extracting Economic Ideology from Political Text

This study conducts a systematic assessment of the capabilities of 12 machine learning models and model variations in detecting economic ideology. As an evaluation benchmark, I use manifesto data span...

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The article presents a systematic assessment of various machine learning models applied to a politically significant task—detecting economic ideology from political texts. Its comprehensive benchmarking against real-world manifesto data, including insights on model performance and methodological rigor, makes it highly relevant. The findings have implications for both machine learning and political science, as they address practical concerns regarding accessibility and scalability in applying such technologies for analyzing political texts.

FLOL: Fast Baselines for Real-World Low-Light Enhancement

Low-Light Image Enhancement (LLIE) is a key task in computational photography and imaging. The problem of enhancing images captured during night or in dark environments has been well-studied in the im...

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The article presents a novel lightweight neural network solution for low-light image enhancement, addressing significant challenges in efficiency and robustness. Its combination of frequency and spatial domain processing is innovative, potentially setting a new standard in practical applications of LLIE. The empirical results demonstrating state-of-the-art performance on widely used datasets enhance its credibility and applicability. However, the impact may be limited to this specific niche within image processing unless it inspires broader methodology enhancements.

Running EFT-hedron with null constraints at loop level

Implications of general properties of quantum field theory, such as causality, unitarity, and locality include constraints on the couplings of the effective field theory (EFT) coefficients. These cons...

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The article extends existing theoretical frameworks by incorporating loop-level amplitudes into the study of effective field theory (EFT) constraints. This advancement is significant as it challenges the traditional understanding of positivity bounds and introduces new insights into the behavior of EFT couplings under different assumptions. The methodological rigor, stemming from a detailed exploration of massless particle loops, enhances its credibility and relevance in the field.

Intelligent OLSR Routing Protocol Optimization for VANETs

Recent advances in wireless technologies have given rise to the emergence of vehicular ad hoc networks (VANETs). In such networks, the limited coverage of WiFi and the high mobility of the nodes gener...

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The article presents a significant contribution by optimizing the OLSR protocol specifically for VANETs, addressing a critical challenge in this field. The use of various metaheuristic algorithms for parameter tuning adds novelty and methodological rigor. The real-world application and evaluation scenarios enhance its relevance, promising to improve the QoS in vehicular networks.

Large moiré superstructure of stacked incommensurate charge density waves

Recent advances in van der Waals heterostructures have opened the new frontier of moiré physics, whereby tuning the interlayer twist angle or adjusting lattice parameter mismatch have led to a plethor...

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The article presents substantial advancements in moiré physics, particularly through the exploration of incommensurate charge density waves and their implications for material properties. The novelty of combining moiré engineering with incommensurate orders contributes significantly to the understanding of layered materials and opens new avenues for future research. The methodological rigor is evident through the use of high-momentum-resolution X-ray diffraction, enhancing the reliability of findings. The potential applications in superconductivity and quantum materials could lead to transformative developments in these fields, which adds to its relevance.

A Unified View of Vacuum Decay Channels

The tunneling potential formalism, an alternative to the standard Coleman Euclidean approach, offers in a natural way a unified view of vacuum decays. In particular, I show in this talk how Coleman...

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This article presents a novel perspective on vacuum decay processes by utilizing a tunneling potential formalism, which extends beyond traditional models like the Coleman approach. The unified view of different decay channels is significant for theoretical physics and may simplify complex discussions about vacuum stability in quantum field theories. Its implications for understanding various cosmological scenarios add to its importance, while its robust methodological approach bolsters its credibility.

Distributionally Fair Peer-to-Peer Electricity Trading

Peer-to-peer energy trading platforms enable direct electricity exchanges between peers who belong to the same energy community. In a semi-decentralized system, a community manager adheres to grid res...

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The article presents a novel mechanism for ensuring fairness in peer-to-peer electricity trading, addressing a significant gap in current systems where inequality in participation may exist. The methodological rigor demonstrated through simulations on a substantial number of peers and the use of a recognized grid structure (IEEE 33-bus) add credibility to its findings. Furthermore, the implications of energy poverty and social welfare optimization are highly relevant in today's energy transition discussions, enhancing its practical applicability.

Converse bounds for quantum hypothesis exclusion: A divergence-radius approach

Hypothesis exclusion is an information-theoretic task in which an experimenter aims at ruling out a false hypothesis from a finite set of known candidates, and an error occurs if and only if the hypot...

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The paper presents a novel approach to quantum hypothesis exclusion, employing a divergence-radius technique that offers an alternative perspective to previously established bounds. This methodological rigor, combined with implications for quantum state and channel exclusion, highlights its potential to advance the field significantly. Additionally, given that hypothesis testing is a fundamental aspect of quantum information theory, the findings could influence future research directions and inspire additional exploration in quantum complexities.

Space of ancient caloric functions on some manifolds beyond volume doubling

Under a condition that breaks the volume doubling barrier, we obtain a time polynomial structure result on the space of ancient caloric functions with polynomial growth on manifolds. As a byproduct,...

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This article addresses significant gaps in the understanding of ancient caloric functions on manifolds under novel conditions, particularly breaking the volume doubling barrier. The findings are robust, showcasing methodological rigor through the analysis of harmonic functions with polynomial growth. This could lead to further exploration in geometric analysis and PDEs, thus showing high relevance for both theoretical and practical advancements in these areas.

On equidistant single-orbit cyclic and quasi-cyclic subspace codes

A code is said to be equidistant if the distance between any two distinct codewords of the code is the same. In this paper, we have studied equidistant single-orbit cyclic and quasi-cyclic subspace co...

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The article presents novel findings in coding theory, specifically detailing equidistant codes within cyclic and quasi-cyclic subspace contexts. Its exploration of trivial equidistant orbit codes and relation to cyclic difference sets demonstrates methodological rigor and offers fresh insights into the structure of these codes, making it likely impactful for further research and applications in error correction and cryptography.

CyberMentor: AI Powered Learning Tool Platform to Address Diverse Student Needs in Cybersecurity Education

Many non-traditional students in cybersecurity programs often lack access to advice from peers, family members and professors, which can hinder their educational experiences. Additionally, these stude...

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The paper introduces CyberMentor, which directly addresses the educational needs of underrepresented groups in cybersecurity, making a significant impact on accessibility and mentorship in this field. Its use of advanced technologies (LLMs and RAG) illustrates methodological rigor while providing personalized learning experiences, which is crucial for modern education. The open-source nature of the platform adds to its potential for broader applicability across disciplines, enhancing its relevance and impact on future research and educational practices.

Belavkin-Staszewski Quantum Markov Chains

It is well-known that the conditional mutual information of a quantum state is zero if, and only if, the quantum state is a quantum Markov chain. Replacing the Umegaki relative entropy in the definiti...

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The article introduces a novel concept by generalizing the notion of quantum Markov chains through the lens of Belavkin-Staszewski (BS) relative entropy, potentially offering new insights into quantum information theory. The methodological rigor is evident through the establishment of correspondence and recovery maps, indicating significant theoretical advancement. The findings related to Gibbs states in quantum spin chains add practical relevance, suggesting broad applicability in quantum statistical mechanics. However, the implications may be limited to niche areas of quantum information.

The Goofus & Gallant Story Corpus for Practical Value Alignment

Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are be...

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This article introduces a novel multi-modal dataset specifically designed for training AI systems to align with human values, a critical area in AI ethics. The incorporation of both natural language and artistic images enhances its applicability for AI training. The emphasis on social normative behavior and education for young children adds a unique angle, addressing the ethical implications of AI in the context of societal values. Methodological rigor in curating this dataset may significantly impact AI training processes, aligning technology with human principles.

Domain Adaptation of Foundation LLMs for e-Commerce

We present the e-Llama models: 8 billion and 70 billion parameter large language models that are adapted towards the e-commerce domain. These models are meant as foundation models with deep knowledge ...

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The study presents a novel approach to domain adaptation using large language models, specifically targeting the e-commerce sector, which is a growing area of research. The methodological rigor demonstrated through extensive ablation studies and multilingual evaluation tasks reinforces its applicability. The adaptation of foundation models like Llama 3.1 to niche areas can inspire further research and development in domain-specific AI applications, encouraging exploration of similar methodologies in other fields of commerce and technology.

Practical Continual Forgetting for Pre-trained Vision Models

For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios, erasure requests originate at any time f...

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The paper addresses a timely and critical issue in the field of computer vision—how to effectively erase unwanted information from pre-trained models while preserving the integrity of existing knowledge. The methodological innovation of Group Sparse LoRA (GS-LoRA) and its practical implications for real-world scenarios add significant value. The rigorous experimental evaluation enhances its credibility, but its ultimate impact will depend on further validation across diverse datasets.

Infinity norm bounds for the inverse of Nekrasov matrices using scaling matrices

For many applications, it is convenient to have good upper bounds for the norm of the inverse of a given matrix. In this paper, we obtain such bounds when A is a Nekrasov matrix, by means of a scaling...

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The paper presents a significant advancement in obtaining upper bounds for the inverses of Nekrasov matrices, which are pertinent in various applications across mathematical optimization and numerical analysis. The novelty lies in the application of scaling matrices to achieve strictly diagonally dominant matrices. The robustness of the methodology is bolstered by numerical comparisons and the derivation of new error bounds for linear complementarity problems, which adds to its applicability.