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New process for high optical quality InAs quantum dots grown on patterned GaAs(001) substrates

This work presents a selective ultraviolet (UV)-ozone oxidation-chemical etching process that has been used, in combination with laser interference lithography (LIL), for the preparation of GaAs patte...

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This article introduces a novel process that significantly enhances the optical quality of InAs quantum dots through a new patterning technology, showcasing both practical application and methodological advancement in semiconductor fabrication. The combination of selective oxidation and laser interference lithography is particularly noteworthy for its potential to influence future research in quantum dot applications. The findings have strong implications for optoelectronics and quantum computing, making it a potentially transformative advancement in the field.

Structural and optical changes induced by incorporation of antimony into InAs/GaAs(001) quantum dots

We present experimental evidence of Sb incorporation inside InAs/GaA(001) quantum dots exposed to an antimony flux immediately before capping with GaAs. The Sb composition profile inside the nanostruc...

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The article presents a novel finding regarding the incorporation of antimony in InAs/GaAs quantum dots, which can significantly enhance their optical properties. The methodological rigor demonstrated through advanced imaging techniques adds credibility to the findings. The implications for improved quantum dot performance in optoelectronic applications make this research highly relevant for future studies in nanotechnology and materials science.

Line Edge Roughness Effects on the Thermoelectric Properties of Armchair Black Phosphorene Nanoribbons

This study delves into the thermoelectric properties of armchair black phosphorene nanoribbons while considering the presence of line edge roughness. Employing the tight-binding method in conjunction ...

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The study presents novel insights into the thermoelectric properties of armchair black phosphorene nanoribbons, especially under the influence of line edge roughness, which is a critical factor in materials science. The use of advanced theoretical methods (tight-binding, non-equilibrium Green's functions) enhances the methodological rigor. The work is likely to impact the design and optimization of thermoelectric materials, making it relevant for future research in material science and nanotechnology. However, the predictability of the results and the need for experimental validation could be points for improvement.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection

The rapid development of Deepfake technology has enabled the generation of highly realistic manipulated videos, posing severe social and ethical challenges. Existing Deepfake detection methods primari...

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The article presents a novel approach to Deepfake detection through an innovative dual-stream framework that integrates both spatial and temporal features, addressing a critical gap in existing methodologies. The methodological rigor is reinforced by extensive experimental validation showing superior performance against current state-of-the-art techniques, which enhances its relevance and applicability in real-world scenarios. Additionally, the focus on subtle forgery detection will likely inspire further research in related areas, including security and ethical implications.

Compaction of Granular Columns under Thermal Cycling

Granular materials undergo compaction under periodic temperature fluctuations, leading to various engineering and geological phenomena from landslides to silo compaction. Although thermal effects on g...

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The article presents a novel examination of the thermally induced compaction of granular materials, addressing a significant gap in the understanding of the underlying physical mechanisms. The use of established fitting models adds methodological rigor, while the potential implications for various engineering and geological phenomena enhance its applicability. However, the reliance on specific materials like glass beads and sand might limit its generalizability to other types of granular media.

Quantum computational advantage of noisy boson sampling with partially distinguishable photons

Boson sampling stands out as a promising approach toward experimental demonstration of quantum computational advantage. However, the presence of physical noise in near-term experiments hinders the rea...

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This article presents significant advancements in understanding the impact of noise on boson sampling, offering a quantitative threshold for maintaining classical intractability in noisy environments. The methodological rigor in analyzing partial distinguishability is noteworthy, as it tackles a critical barrier in the implementation of quantum computing. The implications for future experimental setups are substantial, making it a strong contender for advancing both theoretical and practical aspects of quantum computation.

Emotion estimation from video footage with LSTM

Emotion estimation in general is a field that has been studied for a long time, and several approaches exist using machine learning. in this paper, we present an LSTM model, that processes the blend-s...

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The article presents a novel application of LSTM (Long Short-Term Memory) networks for real-time facial emotion estimation, showcasing an innovative integration with MediaPipe and a benchmark dataset (FER2013). The achieved accuracy and f1-score suggest practical applicability, but the results may not significantly surpass existing methodologies in this domain, which limits its overall impact.

Optimizing the Trade-off Between Throughput and PAoI Outage Exponents

This paper investigates the trade-off between throughput and peak age of information (PAoI) outage probability in a multi-sensor information collection system. Each sensor monitors a physical process,...

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The paper addresses a significant and emerging issue in real-time information systems, especially in contexts where timely data delivery is crucial. The novel optimization approach proposed for balancing throughput and PAoI is methodologically rigorous, providing both theoretical insight and practical applicability. Additionally, the closed-form approximation derived enhances its utility for large-scale applications, suggesting a broader relevance.

Wasserstein-regularized Conformal Prediction under General Distribution Shift

Conformal prediction yields a prediction set with guaranteed $1-α$ coverage of the true target under the i.i.d. assumption, which may not hold and lead to a gap between $1-α$ and the a...

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This article presents a novel approach to conformal prediction methodologies, addressing a significant limitation in existing research related to general distribution shifts. The introduction of Wasserstein distance as a measure provides a strong theoretical foundation, and the new algorithm (WR-CP) demonstrates practical effectiveness through experiments. The potential to balance accuracy and efficiency positions this work as impactful for both theoretical development and practical applications.

Proton decay matrix elements on PACS configurations

We report the preliminary results of lattice computation for the proton decay matrix elements in $N_f=2+1$ physical point with Wilson-clover fermion. We perform it on the PACS configurations o...

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This article addresses a fundamental aspect of particle physics—proton decay—using rigorous lattice computation methods. The focus on systematic uncertainties and the detailed examination of various transition modes enhances its credibility. The research also compares its findings with existing literature, providing context and validating its contributions. Its novelty lies in the precise estimation of decay matrix elements, which could influence subsequent theoretical models and experiments.

On the ADM mass of critical area-normalized capacitors

In this note, we prove mass-capacity inequalities for asymptotically flat manifolds whose boundary capacity potential satisfies an overdetermined problem, referred to as critical area-normalized capac...

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The article presents novel proof techniques related to ADM mass and capacity inequalities, addressing open questions in general relativity specifically regarding uniqueness problems in spacetimes. Its findings potentially advance theoretical understanding and stimulate further research into asymptotic boundary conditions and spacetimes with critical structures.

Auto-Prompting SAM for Weakly Supervised Landslide Extraction

Weakly supervised landslide extraction aims to identify landslide regions from remote sensing data using models trained with weak labels, particularly image-level labels. However, it is often challeng...

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The proposed approach, APSAM, demonstrates a novel integration of prompt engineering with the Segment Anything Model (SAM) to tackle the specific problem of weakly supervised landslide extraction. The methodology appears rigorous, with clear experimental results showing significant improvements over existing methods. The innovative use of adaptive prompt generation to enhance segmentation accuracy is particularly noteworthy, suggesting a high potential for practical applications in remote sensing. However, further validation across diverse environments could enhance its generalizability.

Higher-order multiscale method and its convergence analysis for nonlinear thermo-electric coupling problems of composite structures

This paper proposes a higher-order multiscale computational method for nonlinear thermo-electric coupling problems of composite structures, which possess temperature-dependent material properties and ...

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This article presents a novel methodology in higher-order multiscale computational techniques, specifically targeting complex nonlinear thermo-electric coupling problems which are significant within materials science and engineering. Its rigorous approach to convergence analysis enhances its applicability and reliability, making it a potentially transformative contribution for simulation practices in composite materials. The computational efficiency highlighted through numerical experiments adds to its practicality in real-world applications.

Universal deformation rings and derived equivalences

In this paper, we show that stable functors of derived equivalences preserve the isomorphism classes of versal deformation rings of finitely generated Gorenstein-projective modules over finite dimensi...

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The paper tackles an innovative intersection of derived categories and deformation theory, providing significant advancements in understanding how stable functors connect with the structure of Gorenstein-projective modules. This provides both theoretical implications and potential applications in representation theory, opening avenues for further exploration in module theory and algebraic geometry. The methodological rigor is affirmed by generalizing existing results, which illustrates the adaptability of the framework. However, as the concepts involved can be highly specialized, it may not immediately induce broad change across diverse fields.

General Error Estimates of Non Conforming Approximation of System of Reaction-Diffusion Equations

This paper aims to establish a first general error estimate for numerical approximations of the system of reaction-diffusion equations (SRDEs), using reasonable regularity assumptions on the exact sol...

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The article presents novel contributions to error estimates in numerical approximations of reaction-diffusion equations, which is a significant area in mathematical modeling and numerical analysis. The utilization of a general gradient discretisation method enhances its applicability across various schemes, indicating robust methodological rigor. The findings not only address fundamental theoretical questions but also provide practical insights, evidenced by numerical results. However, the novelty may be somewhat limited to specialists in numerical analysis, thus affecting broader impact.

Atmospheric Noise-Resilient Image Classification in a Real-World Scenario: Using Hybrid CNN and Pin-GTSVM

Parking space occupation detection using deep learning frameworks has seen significant advancements over the past few years. While these approaches effectively detect partial obstructions and adapt to...

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This article presents a novel approach to improve image classification under atmospheric noise conditions, specifically for parking space detection—an area ripe for practical applications in smart cities. The integration of a hybrid model using deep learning with advanced classification techniques (Pin-GTSVM) is innovative, particularly in its ability to operate effectively without pre-processing for haze. The empirical evaluation against established methods strengthens the article’s methodological rigor.

Perceived Fairness of the Machine Learning Development Process: Concept Scale Development

In machine learning (ML) applications, unfairness is triggered due to bias in the data, the data curation process, erroneous assumptions, and implicit bias rendered during the development process. It ...

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This article addresses an increasingly important issue in machine learning—fairness—through a well-structured methodological approach that includes qualitative and theoretical frameworks. Its innovative concept scale for perceived fairness can facilitate more equitable ML systems. The multidimensional framework contributes novel insights that are likely to inspire future work in fairness assessment in ML.

LVFace: Large Vision model for Face Recogniton

Recently, large vision models have demonstrated powerful representation capabilities in the field of computer vision. However, we unexpectedly found that face recognition research is still mainly focu...

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The article presents a novel approach to face recognition using large vision models, which is a significant shift from the conventional CNN architectures. This is important as it addresses a gap in existing methodologies and showcases the performance enhancement over SOTA models. The methodological rigor demonstrated by empirical validation on a large dataset further strengthens its contribution to the field.

A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions

Language in the Arab world presents a complex diglossic and multilingual setting, involving the use of Modern Standard Arabic, various dialects and sub-dialects, as well as multiple European languages...

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This survey provides a comprehensive overview of code-switched Arabic NLP, addressing a significant gap in the existing literature. The systematic review characterizes progress and challenges in a rapidly evolving area, making it potentially influential for researchers and practitioners. The focus on multilingualism and diglossia positions the work at the intersection of computational linguistics and sociolinguistics, enhancing its relevance. It offers clear implications for the development of language technologies in Arabic-speaking regions, potentially guiding future research and applications.

Rethinking the Sample Relations for Few-Shot Classification

Feature quality is paramount for classification performance, particularly in few-shot scenarios. Contrastive learning, a widely adopted technique for enhancing feature quality, leverages sample relati...

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The article presents a novel approach (MGRCL) to few-shot classification, addressing a critical gap in existing methods by considering semantic similarity discrepancies across multiple granularity levels. Its methodological rigor and the addition of Transformation Consistency Learning and Class Contrastive Learning demonstrate significant improvements over existing techniques, thereby potentially influencing future research and practices in the field. The ability to integrate this model with existing frameworks enhances its practical relevance.