Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage by Hyeok-Jin Bak, Eun-Ji Kim, Ji-Hyeon Lee, Sungyul Chang, Dongwon Kwon, Woo-Jin Im, Woon-Ha Hwang, Jae-Ki Chang, Nam-Jin Chung, Wan-Gyu Sang

“…Time-series panicle coverage data, extracted from the segmented images, were fitted to a piecewise function to model their growth and decline dynamics. …”
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Internet Memes of 2019—2021 as Current Phenomenon of Internet Communication: Meanings of Unity and Disunity by L. N. Rebrina

“…The object of the study is borrowed and appeared in the Runet functioning in the specified period Internet memes that translate the meanings of unity and isolation. …”
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ConvNeXt embedded U-Net for semantic segmentation in urban scenes of multi-scale targets by Yanyan Wu, Qian Li

“…The improved method proposed in this paper is superior to other methods in the semantic segmentation of urban scenes. The main advantage of this algorithm is to explore the specific loss function and segmentation strategy suitable for urban scene in the face of the complexity and diversity of urban scene images.…”
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LA-ResUNet: An Efficient Linear Attention Mechanism in ResUNet for the Semantic Segmentation of Pulmonary Nodules by P. C. Sarah Prithvika, L. Jani Anbarasi

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XPolypNet: A U-Net-Based Model for Semantic Segmentation of Gastrointestinal Polyps With Explainable AI by Arjun Kumar Bose Arnob, Muhammad Mostafa Monowar, Md. Abdul Hamid, M. F. Mridha

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Remote Sensing Image Semantic Segmentation Sample Generation Using a Decoupled Latent Diffusion Framework by Yue Xu, Honghao Liu, Ruixia Yang, Zhengchao Chen

“…In the label generation stage, we fine-tune a pretrained latent diffusion model with LoRA to generate semantic label masks from textual descriptions. A novel proportion-aware loss function explicitly penalizes deviations from the desired class distribution in the generated mask. …”
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CSNet: A Remote Sensing Image Semantic Segmentation Network Based on Coordinate Attention and Skip Connections by Jiahao Li, Hongguo Zhang, Liang Chen, Binbin He, Huaixin Chen

“…Additionally, a hybrid loss function combining weighted cross-entropy and Dice loss is employed. …”
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Quantum Classification Outside the Promised Class by Theodore Andronikos, Constantinos Bitsakos, Konstantinos Nikas, Georgios I. Goumas, Nectarios Koziris

“…This paper studies the important problem of quantum classification of Boolean functions from an entirely novel perspective. Typically, quantum classification algorithms allow us to classify functions with a probability of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.0</mn></mrow></semantics></math></inline-formula>, if we are promised that they meet specific unique properties. …”
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Existence and Uniqueness of Solutions to Non-Local Problems of Brézis–Oswald Type and Its Application by Yun-Ho Kim

“…The main features of the present paper are the lack of the continuity of the Kirchhoff function in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>[</mo><mn>0</mn><mo>,</mo><mo>∞</mo><mo>)</mo></mrow></semantics></math></inline-formula> and the localization of a positive solution.…”
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Identification and Estimation in Linear Models with Endogeneity Through Time-Varying Volatility by Shih-Tang Hwu

“…The identification strategy exploits time-varying volatility to address the multicollinearity problem that arises in conventional control function methods when instruments are weak. We establish the identification conditions and show that the proposed method is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msqrt><mi>T</mi></msqrt></semantics></math></inline-formula>-consistent and asymptotically normal. …”
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Lexical Neologisms in English: Formation, Trends, and Cultural Impact by Oksana Melnyk, Iryna Kyselova

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Logistics Services Sector and Economic Recession in Greece: Challenges and Opportunities by Dimitrios Folinas, Naoum Tsolakis, Dimitrios Aidonis

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Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data by Guihua Zhang, Jiayue Liu, Yuxin Wu, Guangxi Yue

“…However, the model’s ability to capture turbulent combustion interactions is limited by the shape of the presumed probability density function (PDF) of the mixture fraction and progress variable. …”
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On the <i>t</i>-Transformation of Free Convolution by Shokrya S. Alshqaq, Ohud A. Alqasem, Raouf Fakhfakh

“…In this paper, the notion of the <i>t</i>-transformation of a measure and convolution is studied from the perspective of families and their relative variance functions (VFs). Using analytical and algebraic approaches, we aim to develop a deeper understanding of how the <i>t</i>-transformation shapes the behavior of probability measures, with possible implications in current probabilistic models. …”
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TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments by Yanru Bai, Qi Tang, Ran Zhao, Hongxing Liu, Shuming Zhang, Mingkun Guo, Minghan Guo, Junjie Wang, Changjian Wang, Mu Xing, Guangjian Ni, Dong Ming

“…Abstract Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. …”
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Contestable AI for criminal intelligence analysis: improving decision-making through semantic modeling and human oversight by Falk Maoro, Michaela Geierhos

“…As a proof of concept, we developed an AI-driven information extraction system that incorporates contestability features and provides multiple functionalities for data analysis. Our findings highlight three key perspectives essential for contestability in AI-driven investigations: information provision, interactive controls, and quality assurance. …”
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Assessment of Building Compactness at Initial Design Stage of Single-Family Houses by Edwin Koźniewski

“…These examples show the functioning of the indicators analyzed by the author in earlier papers and indicate the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi><mi>C</mi></mrow><mrow><mi>s</mi><mi>q</mi></mrow></msub></mrow></semantics></math></inline-formula> indicator that best reflects the assessment of building compactness in percentage points in relation to the ideal shape of the building plan, which is a square. …”
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Musical Expertise Reshapes Cross-Domain Semantic Integration: ERP Evidence from Language and Music Processing by Xing Wang, Tao Zeng

“…Crucially, this study is the first to demonstrate that musical expertise may gradually reorganize these shared mechanisms, enabling two initially independent but functionally analogous semantic mechanisms into a domain-general processing system. …”
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Limit Laws for Sums of Logarithms of <i>k</i>-Spacings by Paul Deheuvels

“…Let <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>Z</mi><mo>=</mo><msub><mi>Z</mi><mn>1</mn></msub><mo>,</mo><mo>…</mo><mo>,</mo><msub><mi>Z</mi><mi>n</mi></msub></mrow></semantics></math></inline-formula> be an i.i.d. sample from the absolutely continuous distribution function <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mo>(</mo><mi>z</mi><mo>)</mo><mo>:</mo><mo>=</mo><mi>P</mi><mo>(</mo><mi>Z</mi><mo>≤</mo><mi>z</mi><mo>)</mo></mrow></semantics></math></inline-formula>, with density <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>f</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow><mo>:</mo><mo>=</mo><mstyle scriptlevel="0" displaystyle="true"><mfrac><mi mathvariant="normal">d</mi><mrow><mi mathvariant="normal">d</mi><mi>z</mi></mrow></mfrac></mstyle><mi>F</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula>. …”
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Spectral ℝ-Linear Problems: Applications to Complex Permittivity of Coated Cylinders by Zhanat Zhunussova, Vladimir Mityushev

“…This curve divides the plane <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi mathvariant="double-struck">R</mi><mn>2</mn></msup></semantics></math></inline-formula> into two regions, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>D</mi><mo>+</mo></msup></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>D</mi><mo>−</mo></msup><mo>∋</mo><mo>∞</mo></mrow></semantics></math></inline-formula>. …”
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