arXiv:2510.25550v1 Announce Type: cross 
Abstract: We propose a method for variable selection in the intensity function of spatial point processes that combines sparsity-promoting estimation with noise-robust model selection. As high-resolution spatial data becomes increasingly available through remote sensing and automated image analysis, identifying spatial covariates that influence the localization of events is crucial to understand the underlying mechanism. However, results from automated acquisition techniques are often noisy, for example due to measurement uncertainties or detection errors, which leads to spurious displacements and missed events. We study the impact of such noise on sparse point-process estimation across different models, including Poisson and Thomas processes. To improve noise robustness, we propose to use stability selection based on point-process subsampling and to incorporate a non-convex best-subset penalty to enhance model-selection performance. In extensive simulations, we demonstrate that such an approach reliably recovers true covariates under diverse noise scenarios and improves both selection accuracy and stability. We then apply the proposed method to a forestry data set, analyzing the distribution of trees in relation to elevation and soil nutrients in a tropical rain forest. This shows the practical utility of the method, which provides a systematic framework for robust variable selection in spatial point-process models under noise, without requiring additional knowledge of the process.

تم تقديم طريقة جديدة لاختيار المتغيرات في عمليات النقاط المكانية، تجمع بين التقدير الذي يعزز الندرة واختيار النموذج المقاوم للضوضاء. هذا مهم حيث تصبح البيانات المكانية عالية الدقة الناتجة عن الاستشعار عن بعد وتحليل الصور الآلي أكثر شيوعًا، مما يسمح للباحثين بتحديد المتغيرات المكانية التي تؤثر على تحديد مواقع الأحداث بشكل أفضل. فهم هذه العوامل ضروري لفهم الآليات الأساسية، مما يجعل هذا التقدم مساهمة قيمة في هذا المجال.

Se ha introducido un nuevo método para la selección de variables en procesos de puntos espaciales, que combina eficazmente la estimación que promueve la escasez con la selección de modelos robusta al ruido. Esto es significativo ya que los datos espaciales de alta resolución provenientes de la teledetección y el análisis de imágenes automatizado se vuelven más comunes, lo que permite a los investigadores identificar mejor las covariables espaciales que influyen en la localización de eventos. Comprender estos factores es esencial para captar los mecanismos subyacentes, lo que convierte este avance en una contribución valiosa al campo.

Une nouvelle méthode de sélection de variables pour les processus de points spatiaux a été introduite, combinant efficacement l'estimation favorisant la parcimonie avec une sélection de modèle robuste au bruit. Cela est significatif alors que les données spatiales haute résolution issues de la télédétection et de l'analyse d'image automatisée deviennent plus courantes, permettant aux chercheurs de mieux identifier les covariables spatiales qui influencent la localisation des événements. Comprendre ces facteurs est essentiel pour saisir les mécanismes sous-jacents, faisant de cette avancée une contribution précieuse au domaine.

A new method for variable selection in spatial point processes has been introduced, which effectively combines sparsity-promoting estimation with noise-robust model selection. This is significant as high-resolution spatial data from remote sensing and automated image analysis becomes more prevalent, allowing researchers to better identify the spatial covariates that influence event localization. Understanding these factors is essential for grasping the underlying mechanisms at play, making this advancement a valuable contribution to the field.

Robust variable selection for spatial point processes observed with noise

arXiv:2601.08446v1 Announce Type: cross 
Abstract: The development of reliable methods for multi-label classification (MLC) has become a prominent research direction in remote sensing (RS). As the scale of RS data continues to expand, annotation procedures increasingly rely on thematic products or crowdsourced procedures to reduce the cost of manual annotation. While cost-effective, these strategies often introduce multi-label noise in the form of partially incorrect annotations. In MLC, label noise arises as additive noise, subtractive noise, or a combination of both in the form of mixed noise. Previous work has largely overlooked this distinction and commonly treats noisy annotations as supervised signals, lacking mechanisms that explicitly adapt learning behavior to different noise types. To address this limitation, we propose NAR, a noise-adaptive regularization method that explicitly distinguishes between additive and subtractive noise within a semi-supervised learning framework. NAR employs a confidence-based label handling mechanism that dynamically retains label entries with high confidence, temporarily deactivates entries with moderate confidence, and corrects low confidence entries via flipping. This selective attenuation of supervision is integrated with early-learning regularization (ELR) to stabilize training and mitigate overfitting to corrupted labels. Experiments across additive, subtractive, and mixed noise scenarios demonstrate that NAR consistently improves robustness compared with existing methods. Performance improvements are most pronounced under subtractive and mixed noise, indicating that adaptive suppression and selective correction of noisy supervision provide an effective strategy for noise robust learning in RS MLC.

تم اقتراح طريقة جديدة تُدعى التنظيم التكيفي للضوضاء (NAR) لتحسين التصنيف متعدد التسميات في استشعار عن بُعد، حيث تتناول التحديات التي تطرحها التعليقات المزعجة التي قد تنشأ من طرق جمع البيانات ذات التكلفة المنخفضة. يميز NAR بين الضوضاء المضافة والضوضاء المخصومة ضمن إطار التعلم شبه الموجه، مما يعزز من قوة تصنيف الصور.

Se ha propuesto un nuevo método llamado Regularización Adaptativa al Ruido (NAR) para mejorar la clasificación multi-etiqueta en la teledetección, abordando los desafíos que presentan las anotaciones ruidosas que pueden surgir de métodos de recolección de datos rentables. NAR distingue entre ruido aditivo y ruido sustractivo dentro de un marco de aprendizaje semi-supervisado, mejorando así la robustez de la clasificación de imágenes.

Une nouvelle méthode appelée Régularisation Adaptative au Bruit (NAR) a été proposée pour améliorer la classification multi-étiquettes dans la télédétection, en abordant les défis posés par les annotations bruyantes qui peuvent découler de méthodes de collecte de données rentables. NAR distingue entre le bruit additif et le bruit soustractif dans un cadre d'apprentissage semi-supervisé, renforçant ainsi la robustesse de la classification d'images.

A new method called Noise-Adaptive Regularization (NAR) has been proposed to improve multi-label classification in remote sensing, addressing the challenges posed by noisy annotations that can arise from cost-effective data collection methods. NAR distinguishes between additive and subtractive noise within a semi-supervised learning framework, enhancing the robustness of image classification.

Noise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classification

arXiv:2503.03244v3 Announce Type: replace 
Abstract: Around 10% of newborns require some help to initiate breathing, and 5\% need ventilation assistance. Accurate Time of Birth (ToB) documentation is essential for optimizing neonatal care, as timely interventions are vital for proper resuscitation. However, current clinical methods for recording ToB often rely on manual processes, which can be prone to inaccuracies. In this study, we present a novel two-stream fusion system that combines the power of image and video analysis to accurately detect the ToB from thermal recordings in the delivery room and operating theater. By integrating static and dynamic streams, our approach captures richer birth-related spatiotemporal features, leading to more robust and precise ToB estimation. We demonstrate that this synergy between data modalities enhances performance over single-stream approaches. Our system achieves 95.7% precision and 84.8% recall in detecting birth within short video clips. Additionally, with the help of a score aggregation module, it successfully identifies ToB in 100% of test cases, with a median absolute error of 2 seconds and an absolute mean deviation of 4.5 seconds compared to manual annotations.

تم تطوير نظام جديد للدمج ثنائي التدفق لتحسين دقة الكشف عن وقت الولادة (ToB) في رعاية حديثي الولادة، باستخدام كل من تحليل الصور والفيديو من التسجيلات الحرارية في غرف الولادة. يهدف هذا النظام إلى معالجة الأخطاء المرتبطة بأساليب التسجيل اليدوي الحالية لـ ToB، والتي تعتبر حاسمة للتدخلات السريعة في رعاية حديثي الولادة.

Se ha desarrollado un nuevo sistema de fusión de dos flujos para mejorar la precisión de la detección del Tiempo de Nacimiento (ToB) en el cuidado neonatal, utilizando tanto análisis de imágenes como de video a partir de grabaciones térmicas en las salas de parto. Este sistema busca abordar las inexactitudes asociadas con los métodos manuales actuales de registro del ToB, que son críticos para intervenciones neonatales oportunas.

Un nouveau système de fusion à deux flux a été développé pour améliorer la précision de la détection du moment de la naissance (ToB) dans les soins néonatals, en utilisant à la fois l'analyse d'images et de vidéos à partir d'enregistrements thermiques dans les salles d'accouchement. Ce système vise à remédier aux inexactitudes associées aux méthodes manuelles actuelles d'enregistrement du ToB, qui sont critiques pour des interventions néonatales en temps opportun.

A novel two-stream fusion system has been developed to enhance the accuracy of Time of Birth (ToB) detection in neonatal care, utilizing both image and video analysis from thermal recordings in delivery rooms. This system aims to address the inaccuracies associated with current manual ToB recording methods, which are critical for timely neonatal interventions.

Robust variable selection for spatial point processes observed with noise

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