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Journal articles on the topic 'Modèle latent'

Author: Grafiati

Published: 1 February 2025

Last updated: 31 July 2025

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1

Cialdella, P., O. Chambon, J. P. Boissel, and E. Ravet-Cialdella. "La recherche d’une mesure unidimensionnelle de la dépression: à propos de l’échelle de dépression de Hamilton." Psychiatry and Psychobiology 4, no. 4 (1989): 203–10. http://dx.doi.org/10.1017/s0767399x00002765.

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RésuméL’utilisation du score global d’une échelle d’évaluation suppose l’unidimensionnalité de l’instrument, avantage censé permettre une plus grande puissance des tests statistiques. Classiquement, la vérification de cette propriété reposait sur l’isolement par l’analyse factorielle d’un facteur général, en rapport avec le pourcentage de variance extraite, mais aucun critère de choix du nombre de facteurs n’a été universellement accepté. De plus, les résultats d’une analyse donnée sont très dépendants des caractéristiques de l’échantillon, et non généralisables. Les récentes théories du trait

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Korahiré, Joël Awouhidia. "Dynamiques socioéconomiques dans les villages riverains au ranch de gibier de Nazinga au Burkina Faso." Articles 40, no. 2 (2011): 53–70. http://dx.doi.org/10.7202/1001388ar.

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Afin d’inciter les populations riveraines à la conservation des ressources fauniques, le Ranch de Gibier de Nazinga a adopté un modèle de gestion pour l’amélioration de leur condition de vie. Le but de cette recherche est d’étudier l’environnement socioéconomique dans lequel évoluent ces populations depuis la création dudit ranch. En utilisant principalement une démarche qualitative, la réalisation de cet objectif se fait en deux temps, soit primo en examinant les changements structurels socioéconomiques engendrés par le ranch dans la zone d’étude, puis secundo, en identifiant les impacts soci

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Descheneau-Guay, Amélie. "Les séries jeunesse et les stéréotypes sexuels : la récupération de l’idée d’émancipation et l’émergence d’une culture du consensus." Notes de recherche 19, no. 2 (2007): 143–54. http://dx.doi.org/10.7202/014846ar.

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Cette note de recherche rapporte une exploration des stéréotypes sexuels féminins présents dans quatre séries télévisuelles que de jeunes québécois âgés de 7 à 12 ans ont dit préférer lors d’une enquête sur les systèmes représentationnels liés à la télévision. Dans une démarche mettant l’accent sur le contenu discursif latent, les séries sont décrites brièvement et les principaux thèmes sont relevés. Deux grands constats se dégagent du travail d’interprétation, soit l’émergence d’une culture du consensus dépolitisante et la récupération de l’idée féministed’émancipationcollective en l’idéologi

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Childs, Ruth A., Orlena Broomes, and Monique B. Herbert. "Deciding Whether to Respond: A Latent Class Analysis of Nonresponse on Ontario’s Grade 9 Assessment of Mathematics." Alberta Journal of Educational Research 64, no. 1 (2018): 70–87. http://dx.doi.org/10.55016/ojs/ajer.v64i1.56472.

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This study investigates nonresponse on Ontario’s Grade 9 Assessment of Mathematics – in particular, whether or not students responded to all multiple-choice or all open-response items in two test booklets. Whether students responded to all items of one type (multiple-choice or open-response) by booklet (for the first or second day of testing) was modeled, with and without proportion correct scores by item type as covariates, using latent class analysis. Both a 3-class model without the covariates and a 4-class model with the covariates but without direct effects distinguished among students wh

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Burton, Réginald. "Influence des distributions du trait latent et de la difficulté des items sur les estimations du modèle de Birnbaum : une étude du type Monte-Carlo." Mesure et évaluation en éducation 27, no. 3 (2004): 41. http://dx.doi.org/10.7202/1087787ar.

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Nyambiya, T. E., T. Muromo, and K. Muchena. "‘My blood is now old and exhausted’: A Qualitative Study of Adults’ Behavioural Beliefs About Donating Blood in Harare, Zimbabwe." Africa Sanguine 22, no. 1 (2020): 14–22. http://dx.doi.org/10.4314/asan.v22i1.3.

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Introduction: In Zimbabwe, though adults consume 80% of donated blood, their contribution to the national blood bank remains low.Considering that blood is an essential but scarce national resource it is important to gain insight into the beliefs that influence adults’ decisionsto donate blood or refrain from doing so.Aims and objectives: We sought to identify and describe the behavioural beliefs underlying adults’ blood donation intentions in Harare.Materials and methods: We used a qualitative study design based on the Integrated Behavioural Model. We interviewed 32 participants usinga semi-st

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Fine, David. "Nursing’s latest module." Nursing Standard 6, no. 23 (1992): 51. http://dx.doi.org/10.7748/ns.6.23.51.s53.

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Chaudhary, Neha, and Priti Dimri. "LATENT FINGERPRINT IMAGE ENHANCEMENT BASED ON OPTIMIZED BENT IDENTITY BASED CONVOLUTIONAL NEURAL NETWORK." Indian Journal of Computer Science and Engineering 12, no. 5 (2021): 1477–93. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205124.

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Fingerprints are unique biometric systems (BSs) in which none of the human possesses similar fingerprint structures. It is one of the most significant biometric processes used in the identification of criminals. Latent fingerprints or latents are generated mainly by the finger sweat or oil deposits which is left by the suspects unintentionally. The impressions of latents are blurred or smudgy in nature and not viewed by naked eye. These fingerprints are of low quality, corrupted by noise, degraded by technological factors and exhibit minor details. Latents display consistent structural info wh

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Joo, Hyungjun, Hyeonggeun Han, Sehwan Kim, Sangwoo Hong, and Jungwoo Lee. "Constructing Fair Latent Space for Intersection of Fairness and Explainability." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4156–65. https://doi.org/10.1609/aaai.v39i4.32436.

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As the use of machine learning models has increased, numerous studies have aimed to enhance fairness. However, research on the intersection of fairness and explainability remains insufficient, leading to potential issues in gaining the trust of actual users. Here, we propose a novel module that constructs a fair latent space, enabling faithful explanation while ensuring fairness. The fair latent space is constructed by disentangling and redistributing labels and sensitive attributes, allowing the generation of counterfactual explanations for each type of information. Our module is attached to

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Seol, Hyun-Soo. "Development and Application of Latent Transition Analysis Module in jamovi." Korean Human Resource Development Strategy Institute 19, no. 4 (2024): 27–42. https://doi.org/10.21329/khrd.2024.19.4.27.

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This study presents the development and implementation of a novel statistical module for latent transition analysis within the jamovi software environment. jamovi is an open-source platform designed to make statistical analysis accessible to users with varying levels of expertise. This module integrates latent transition analysis, a complex method used for examining changes in latent variables over time. This integration significantly enhances the software's analytical capabilities. The primary objective of this development is to democratize access to advanced statistical methods, thereby faci

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Jin Dai, Jin Dai, and Zhifang Zheng Jin Dai. "Disentangling Representation of Variational Autoencoders Based on Cloud Models." 電腦學刊 34, no. 6 (2023): 001–14. http://dx.doi.org/10.53106/199115992023123406001.

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<p>Variational autoencoder (VAE) has the problem of uninterpretable data generation process, because the features contained in the VAE latent space are coupled with each other and no mapping from the latent space to the semantic space is established. However, most existing algorithms cannot understand the data distribution features in the latent space semantically. In this paper, we propose a cloud model-based method for disentangling semantic features in VAE latent space by adding support vector machines (SVM) to feature transformations of latent variables, and we propose to use the clo

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Chen, Fangdong, Yumeng Xu, and Li Wang. "Two-Stage Octave Residual Network for End-to-End Image Compression." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 3922–29. http://dx.doi.org/10.1609/aaai.v36i4.20308.

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Octave Convolution (OctConv) is a generic convolutional unit that has already achieved good performances in many computer vision tasks. Recent studies also have shown the potential of applying the OctConv in end-to-end image compression. However, considering the characteristic of image compression task, current works of OctConv may limit the performance of the image compression network due to the loss of spatial information caused by the sampling operations of inter-frequency communication. Besides, the correlation between multi-frequency latents produced by OctConv is not utilized in current

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Jiang, Xu, Xiaoling Bai, and Lifeng Yin. "The Latest Research Progress of Attention Mechanism in Deep Learning." Journal of Electronic Research and Application 9, no. 3 (2025): 82–89. https://doi.org/10.26689/jera.v9i3.10597.

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With the development of artificial intelligence and deep learning, the attention mechanism has become a key technology for enhancing the performance of complex tasks. This paper reviews the evolution of attention mechanisms, including soft attention, hard attention, and recent innovations such as multi-head latent attention and cross-attention. It focuses on the latest research outcomes, such as lightning attention, the PADRe polynomial attention replacement algorithm, the context anchor attention module, and improvements in attention mechanisms for large models. These advancements improve the

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Bellecci, C., and M. Conti. "Transient behaviour analysis of a latent heat thermal storage module." International Journal of Heat and Mass Transfer 36, no. 15 (1993): 3851–57. http://dx.doi.org/10.1016/0017-9310(93)90065-e.

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15

de Jong, John H. A. L. "Le Modele De Rasch." Taaltoetsen 31 (January 1, 1988): 57–70. http://dx.doi.org/10.1075/ttwia.31.07jon.

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This paper provides an elementary introduction to the one parameter psychometric model known as the Rasch model. It explains the basic principles underlying the model and the concepts of unidimensionality, local stochastic independence, and additivity in non-mathematical terms. The requirements of measurement procedures, the measurement of latent traits, the control on model fit, and the definition of a trait are discussed. It is argued that the Rasch model is particularly appropriate to understand the mutual dependence of test reliability and validity. Examples from foreign language listening

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Kapłon, Robert. "Modele analizy czynnikowej z dwoma zmiennymi ukrytymi." Przegląd Statystyczny. Statistical Review 2011, no. 3-4 (2011): 242–55. http://dx.doi.org/10.59139/ps.2011.03-04.6.

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The goal of factor analysis is to reduce the redundancy among variables by using smaller number of factors that are treated as constructs or latent variables. Unfortunately, if we face with data heterogeneity, the estimates of a single set of means, factor loadings and specific variances may be misleading.One way of accounting for unobserved heterogeneity is to include another latent variable in a factor analysis model. As a consequence, the observations in a samples are assumed to arise from two or more subpopulations that are mixed in unknown proportions. Since putting some restrictions on p

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Erek, Aytunc, and Ibrahim Dincer. "An approach to entropy analysis of a latent heat storage module." International Journal of Thermal Sciences 47, no. 8 (2008): 1077–85. http://dx.doi.org/10.1016/j.ijthermalsci.2007.08.002.

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Al-Saadi, Saleh Nasser, and Zhiqiang (John) Zhai. "A new validated TRNSYS module for simulating latent heat storage walls." Energy and Buildings 109 (December 2015): 274–90. http://dx.doi.org/10.1016/j.enbuild.2015.10.013.

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Wu, Siqi, Yinda Chen, Dong Liu, and Zhihai He. "Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12863–71. https://doi.org/10.1609/aaai.v39i12.33403.

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In this paper, we study how to synthesize a dynamic reference from an external dictionary to perform conditional coding of the input image in the latent domain and how to learn the conditional latent synthesis and coding modules in an end-to-end manner. Our approach begins by constructing a universal image feature dictionary using a multi-stage approach involving modified spatial pyramid pooling, dimension reduction, and multi-scale feature clustering. For each input image, we learn to synthesize a conditioning latent by selecting and synthesizing relevant features from the dictionary, which s

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Xiang, Zhengtao, Xing Wan, Libo Xu, Xin Yu, and Yuhan Mao. "A Training-Free Latent Diffusion Style Transfer Method." Information 15, no. 10 (2024): 588. http://dx.doi.org/10.3390/info15100588.

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Diffusion models have attracted considerable scholarly interest for their outstanding performance in generative tasks. However, current style transfer techniques based on diffusion models still rely on fine-tuning during the inference phase to optimize the generated results. This approach is not merely laborious and resource-demanding but also fails to fully harness the creative potential of expansive diffusion models. To overcome this limitation, this paper introduces an innovative solution that utilizes a pretrained diffusion model, thereby obviating the necessity for additional training ste

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Nam, Woojun, Jinho Choi, Gyugwang Kim, Jinhee Hyun, Hyungkeun Ahn, and Neungsoo Park. "Predicting Photovoltaic Module Lifespan Based on Combined Stress Tests and Latent Heat Analysis." Energies 18, no. 2 (2025): 304. https://doi.org/10.3390/en18020304.

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In this study, long-term reliability tests for high-power-density photovoltaic (PV) modules were introduced and analyzed in accordance with IEC 61215 and light-combined damp heat cycles, such as DIN 75220. The results indicated that post light soaking procedure, light-combined damp heat cycles caused a 3.51% power drop, while IEC standard tests (DH1000 and TC200) caused only 0.87% and 1.32% power drops, respectively. IEC 61215 failed to assess the long-term reliability of the high-power-density PV module, such as the passivated emitter rear cell. Additionally, based on the combined test, the l

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Chen, Yujun, Xin Tan, Zhizhong Zhang, Yanyun Qu, and Yuan Xie. "Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (2024): 1245–53. http://dx.doi.org/10.1609/aaai.v38i2.27887.

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As the exorbitant expense of labeling autopilot datasets and the growing trend of utilizing unlabeled data, semi-supervised segmentation on point clouds becomes increasingly imperative. Intuitively, finding out more ``unspoken words'' (i.e., latent instance information) beyond the label itself should be helpful to improve performance. In this paper, we discover two types of latent labels behind the displayed label embedded in LiDAR and image data. First, in the LiDAR Branch, we propose a novel augmentation, Cylinder-Mix, which is able to augment more yet reliable samples for training. Second,

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Yarmchuk, E. J., C. W. Cline, and D. C. Bruen. "Latent defect screening for high-reliability glass-ceramic multichip module copper interconnects." IBM Journal of Research and Development 49, no. 4.5 (2005): 677–85. http://dx.doi.org/10.1147/rd.494.0677.

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GARNER, JAMES, and SCOTT CROSSLEY. "A Latent Curve Model Approach To Studying L2 N-Gram Development." Modern Language Journal 102, no. 3 (2018): 494–511. http://dx.doi.org/10.1111/modl.12494.

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Zhang, Rongchao, Yiwei Lou, Dexuan Xu, Yongzhi Cao, Hanpin Wang, and Yu Huang. "A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16803–11. http://dx.doi.org/10.1609/aaai.v38i15.29621.

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The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realis

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SARITA KUMARI and SOMNATH BAIDYA ROY. "Impact of dynamic vegetation on near-surface meteorology using a newly developed WRF_NOAHMP_SUCROS coupled model." Journal of Agrometeorology 24, no. 4 (2022): 335–47. http://dx.doi.org/10.54386/jam.v24i4.1892.

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The study attempts to quantitatively understand the impact of dynamic vegetation on land-surface atmosphere interactions over spring wheat croplands in India. A new modeling tool capable of simulating these interactions was developed by incorporating the crop growth module of the Simple and Universal Crop growth Simulator (SUCROS) crop model into the Weather Research and Forecasting (WRF) mesoscale model. An earlier study had calibrated and evaluated the stand-alone SUCROS crop model with observed data for spring wheat collected from an experimental site in northwestern India. The crop growth

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Bao, Yuting, Yuwen Tao, and Pengjiang Qian. "Image Compression Based on Hybrid Domain Attention and Postprocessing Enhancement." Computational Intelligence and Neuroscience 2022 (March 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/4926124.

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Deep learning-based image compression methods have made significant achievements recently, of which the two key components are the entropy model for latent representations and the encoder-decoder network. Both the inaccurate estimation of the entropy estimation model and the existence of information redundancy in latent representations lead to a reduction in the compression efficiency. To address these issues, the study suggests an image compression method based on a hybrid domain attention mechanism and postprocessing improvement. This study embeds hybrid domain attention modules as nonlinear

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Chen, Geng, Wuyuan Xie, Di Lin, Ye Liu, and Miaohui Wang. "mmFAS: Multimodal Face Anti-Spoofing Using Multi-Level Alignment and Switch-Attention Fusion." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 58–66. https://doi.org/10.1609/aaai.v39i1.31980.

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The increasing number of presentation attacks on reliable face matching has raised concerns and garnered attention towards face anti-spoofing (FAS). However, existing methods for FAS modeling commonly fuse multiple visual modalities (e.g., RGB, Depth, and Infrared) in a straightforward manner, disregarding latent feature gaps that can hinder representation learning. To address this challenge, we propose a novel multimodal FAS framework (mmFAS) that focuses on explicit alignment and fusion of latent features across different modalities. Specifically, we develop a multimodal alignment module to

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Zhang, Jian, Jin Yuan, Chuanzhen Li, and Bin Li. "An Inverse Design Framework for Isotropic Metasurfaces Based on Representation Learning." Electronics 11, no. 12 (2022): 1844. http://dx.doi.org/10.3390/electronics11121844.

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A hybrid framework for solving the non-uniqueness problem in the inverse design of isomorphic metasurfaces is proposed. The framework consists of a representation learning (RL) module and a variational autoencoder-particle swarm optimization (VAE-PSO) algorithm module. The RL module is used to reduce the complex high-dimensional space into a low-dimensional space with obvious features, with the purpose of eliminating the many-to-one relationship between the original design space and response space. The VAE-PSO algorithm first encodes all meta-atoms into a continuous latent space through VAE an

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Chen, Yueqiao, Wenxia Yang, Xi Fang, and Huan Han. "EC-GAN: Emotion-Controllable GAN for Face Image Completion." Applied Sciences 13, no. 13 (2023): 7638. http://dx.doi.org/10.3390/app13137638.

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Image completion methods based on deep learning, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have succeeded in producing semantically plausible results. However, existing facial image completion methods can either produce only one result or, although they can provide multiple results, cannot attribute particular emotions to the results. We propose EC-GAN, a novel facial Emotion-Controllable GAN-based image completion model that can infer and customize generative facial emotions. We propose an emotion inference module that infers the emotions of faces bas

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Yao, Kouassi Paul Antoine Tanoh, Hubert Gallée, Adama Diawara, et al. "Sensibilité du schéma de surface SISVAT à la fraction verte de la végétation sur 2 sites d’Afrique de l’Ouest." Climatologie 22 (2024): 3. https://doi.org/10.1051/climat/202422003.

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Dans le but d’évaluer les performances du modèle de surface SISVAT (Soil Ice Snow Vegetation Atmosphere Transfer), une simulation unidimensionnelle des flux de chaleur latente et sensible sur quatre ans (2006 à 2009) est effectuée. Les résultats de la simulation sont comparés aux observations de deux stations de mesures en Afrique de l’Ouest (Nalohou au Bénin et Wankama au Niger). Ces deux sites ont été sélectionnés pour leur emplacement géographique, la différence entre leur climat respectif et la disponibilité d’une longue série de données météorologiques, permettant une analyse approfondie

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Goldsworth, J. "United Kingdom: Pensions: latest trustee toolkit module tackles fund management." Trusts & Trustees 13, no. 4 (2007): 110. http://dx.doi.org/10.1093/tandt/ttm009.

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Fang, Zhiyu, Xiaobin Zhu, Chun Yang, Zheng Han, Jingyan Qin, and Xu-Cheng Yin. "Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6605–13. http://dx.doi.org/10.1609/aaai.v36i6.20614.

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Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC). However, due to the lack of fine-grained instance-wise annotations, it still easily suffer from the domain shift problem for the discrepancy between the visual representation of diversified images and the semantic representation of fixed attributes. In this paper, we propose an innovative autoencoder network by learning Aligned Cross-Modal Representations (dubbed ACMR) for GZSC. Specifically, we propose a novel Vision-Semantic Al

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Cho, Sun-Joo, Youngsuk Suh, and Woo-yeol Lee. "An NCME Instructional Module on Latent DIF Analysis Using Mixture Item Response Models." Educational Measurement: Issues and Practice 35, no. 1 (2015): 48–61. http://dx.doi.org/10.1111/emip.12093.

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Nakhla, David, Hossam Sadek, and James S. Cotton. "Melting performance enhancement in latent heat storage module using solid extraction electrohydrodynamics (EHD)." International Journal of Heat and Mass Transfer 81 (February 2015): 695–704. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2014.10.016.

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Selvakumar, R. Deepak, Jian Wu, and Ahmed K. Alkaabi. "Electrohydrodynamic acceleration of charging process in a latent heat thermal energy storage module." Applied Thermal Engineering 242 (April 2024): 122475. http://dx.doi.org/10.1016/j.applthermaleng.2024.122475.

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Sun, Long, Haonan Zhou, Li Yang, Dengyang Zhao, and Dongping Zhang. "LD-DEM: Latent Diffusion with Conditional Decoding for High-Precision Planetary DEM Generation from RGB Satellite Images." Aerospace 12, no. 8 (2025): 658. https://doi.org/10.3390/aerospace12080658.

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A Digital Elevation Model (DEM) provides accurate topographic data for planetary exploration (e.g., Moon and Mars), essential for tasks like lander navigation and path planning. This study proposes the first latent diffusion-based algorithm for DEM generation, leveraging a conditional decoder to enhance reconstruction accuracy from RGB satellite images. The algorithm performs the diffusion process in the latent space and uses a conditional decoder module to enhance the decoding accuracy of the DEM latent vectors. Experimental results show that the proposed algorithm outperforms the baseline al

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Zhang, Guoqing, Ruixin Zhou, Yuhui Zheng, and Baozhu Li. "Binary Noise Guidance Learning for Remote Sensing Image-to-Image Translation." Remote Sensing 16, no. 1 (2023): 65. http://dx.doi.org/10.3390/rs16010065.

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Image-to-image translation (I2IT) is an important visual task that aims to learn a mapping of images from one domain to another while preserving the representation of the content. The phenomenon known as mode collapse makes this task challenging. Most existing methods usually learn the relationship between the data and latent distributions to train more robust latent models. However, these methods often ignore the structural information among latent variables, leading to patterns in the data being obscured during the process. In addition, the inflexibility of data modes caused by ignoring the

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Li, Jingwen, Jianan Sun, Mingyu Shao, Yinghui Lai, and Chen Chen. "A New Multidimensional Computerized Testing Approach: On-the-Fly Assembled Multistage Adaptive Testing Based on Multidimensional Item Response Theory." Mathematics 13, no. 4 (2025): 594. https://doi.org/10.3390/math13040594.

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Unidimensional on-the-fly assembled multistage adaptive testing (OMST), a flexible testing method, integrates the strengths of the adaptive test assembly of computerized adaptive testing (CAT) and the modular test administration of multistage adaptive testing (MST). Since numerous latent trait structures in practical applications are inherently multidimensional, extending the realm from unidimensional to multidimensional is necessary. Multidimensional item response theory (MIRT), a branch of mathematical and statistical latent variable modeling research, has an important position in the intern

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Namatēvs, Ivars, Artūrs Ņikuļins, Anda Slaidiņa, Laura Neimane, Oskars Radziņš, and Kaspars Sudars. "Towards Explainability of the Latent Space by Disentangled Representation Learning." Information Technology and Management Science 26 (November 30, 2023): 41–48. http://dx.doi.org/10.7250/itms-2023-0006.

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Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They are also often criticised as “black boxes” because their decision-making process is often not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms that structure observational data is still a challenge. To further explore the latent space and achieve generic processing, we propose a pipeline for discovering the explainable directions in the latent space of generative models. Since the latent space contains se

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Chen, Hui-O., Yuan-Chi Cui, Peng-Chan Lin, and Jung-Hsien Chiang. "An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction." Journal of Personalized Medicine 14, no. 7 (2024): 694. http://dx.doi.org/10.3390/jpm14070694.

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By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we present a predictive model for drug response that incorporates diverse types of omics data, comprising genetic mutation, copy number variation, methylation, and gene expression data. This study proposes latent alignment for information mismatch in integration, which is ach

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Mohamed Abdul Cader, Akmal Jahan, Jasmine Banks, and Vinod Chandran. "Fingerprint Systems: Sensors, Image Acquisition, Interoperability and Challenges." Sensors 23, no. 14 (2023): 6591. http://dx.doi.org/10.3390/s23146591.

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The fingerprint is a widely adopted biometric trait in forensic and civil applications. Fingerprint biometric systems have been investigated using contact prints and latent and contactless images which range from low to high resolution. While the imaging techniques are advancing with sensor variations, the input fingerprint images also vary. A general fingerprint recognition pipeline consists of a sensor module to acquire images, followed by feature representation, matching and decision modules. In the sensor module, the image quality of the biometric traits significantly affects the biometric

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Singh, Babita, and Waseem Ahmad. "Implementation of Latent Fingerprint Matching System." International Journal of Advance Research and Innovation 2, no. 2 (2014): 43–47. http://dx.doi.org/10.51976/ijari.221406.

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"Biometrics" means "life measurement" .The term is usually associated with the use of unique physiological characteristics to identify an individual. Biometrics is used in computer science as a means of identification and access control. It is also used to identify individuals in groups that are under surveillance. Latent finger prints are inadvertent impressions left by fingers on surfaces of objects. The main difficulties in latent fingerprint matching are unclear ridge structure, small finger area, and large non-linear distortion while rolled fingerprint are of larger size and contain more

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Szaszák, Norbert, and Ákos Pozsa. "Experimental investigation of a high efficiency electric heater and dehumidifier prototype unit." Analecta Technica Szegedinensia 14, no. 2 (2020): 43–49. http://dx.doi.org/10.14232/analecta.2020.2.43-49.

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In this paper the principle of operation and preliminary laboratory measurements of a prototype of a high-efficiency electrical air heater unit is presented. Unlike conventional heaters, which apply Joule-heat formed by electrical resistance, the developed device uses thermoelectric modules for heating ambient air. Just like in case of resistance heaters, most of the heat is produced as a result of the internal ohmic resistance of the thermoelectric module (resistance heating), however, in case of appropriate air conditions our device is capable of transforming the latent heat of the air moist

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Gong, Zhen-Xiang, and A. S. Mujumdar. "A New Solar Receiver Thermal Store for Space-Based Activities Using Multiple Composite Phase-Change Materials." Journal of Solar Energy Engineering 117, no. 3 (1995): 215–20. http://dx.doi.org/10.1115/1.2847798.

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A new solar receiver latent thermal energy storage module for space-based activities is proposed. It is different from a conventional module (which uses a single phase-change material (PCM)); this module uses a composite configuration of multiple PCMs. A finite element model is presented which simulates the transient cyclic thermal process involved. Computations have been carried out to investigate the potential advantages of a composite multi-PCM store over a single PCM store. Numerical results indicate that variation of the fluid outlet temperature can be significantly reduced by appropriate

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Bian, Cheng, Xiaoyu Li, Qi Bi, et al. "Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11087–95. http://dx.doi.org/10.1609/aaai.v38i10.28985.

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Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management. Notwithstanding its potential, the invasive nature of ABP measurements confines their utility primarily to clinical environments, limiting their applicability for continuous monitoring beyond medical facilities. The conversion of photoplethysmography (PPG) signals into ABP equivalents has garnered significant attention due to its potential in revolutionizing cardiovascular disease management. Recent strides in PPG-to-ABP prediction encompass the integration of generative and discriminative m

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Li, Hui, Tianpeng Pan, and Lili Zhang. "Enhanced Remote Sensing Image Compression Method Using Large Network with Sparse Extracting Strategy." Electronics 13, no. 13 (2024): 2677. http://dx.doi.org/10.3390/electronics13132677.

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Deep neural networks based on hyper-encoders play a critical role in estimating prior distributions in remote sensing image compression issues. However, most of the existing encoding methods suffer from a problem on the hyper-encoding side, namely the mismatch of extraction ability with the encoder. This ability bias results in likelihood features that fail to extract sufficient information from latent representations. To solve this problem, the feature extraction capabilities of the hyper-encoder are enhanced to better estimate the Gaussian likelihood of the latent representation in end-to-en

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Grubor, Aleksandar, and Nikola Milićević. "The implementation of hierarchical latent models in the measurement of perceived service quality." Marketing 50, no. 3 (2019): 179–85. http://dx.doi.org/10.5937/markt1903179g.

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Wei, Tingting, Hongpeng Wu, Lei Dong, and Frank Tittel. "Acoustic Detection Module Design of a Quartz-Enhanced Photoacoustic Sensor." Sensors 19, no. 5 (2019): 1093. http://dx.doi.org/10.3390/s19051093.

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This review aims to discuss the latest advancements of an acoustic detection module (ADM) based on quartz-enhanced photoacoustic spectroscopy (QEPAS). Starting from guidelines for the design of an ADM, the ADM design philosophy is described. This is followed by a review of the earliest standard quartz tuning fork (QTF)-based ADM for laboratory applications. Subsequently, the design of industrial fiber-coupled and free-space ADMs based on a standard QTF for near-infrared and mid-infrared laser sources respectively are described. Furthermore, an overview of the latest development of a QEPAS ADM

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Kim, Youn-Su, and Gwang-Sang Yoon. "The Efficiency and Repeatability of Multispectral Module for Close-up Photography to Latent Fingerprints." Korean Academy of Scientific Criminal Investigation 15, no. 3 (2021): 197–204. http://dx.doi.org/10.20297/jsci.2021.15.3.197.

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