arXiv:2210.14351v2 Announce Type: replace-cross 
Abstract: We address the problem of simultaneously estimating arc travel times in a network \emph{and} parameters of route choice models for strategic and tactical network planning purposes. Hitherto, these interdependent tasks have been approached separately in the literature on road traffic networks. We illustrate that ignoring this interdependence can lead to erroneous route choice model parameter estimates. We propose a method for maximum likelihood estimation to solve the simultaneous estimation problem that is applicable to any differentiable route choice model. Moreover, our approach allows to naturally mix observations at varying levels of granularity, including noisy or partial path data. Numerical results based on real taxi data from New York City show strong performance of our method, even in comparison to a benchmark method focused solely on arc travel time estimation.

تقدم دراسة جديدة طريقة لتقدير أوقات السفر للأقواس ومعلمات نماذج اختيار المسار في وقت واحد، مما يعالج فجوة في أبحاث الشبكات المرورية حيث تم التعامل مع هذه المهام بشكل منفصل. تُظهر هذه الطريقة، المستندة إلى تقدير الاحتمالية القصوى، أداءً قويًا باستخدام بيانات حقيقية من سيارات الأجرة في مدينة نيويورك، مما يبرز أهمية مراعاة الترابط في هذه التقديرات.

Un nuevo estudio propone un método para estimar simultáneamente los tiempos de viaje de los arcos y los parámetros de los modelos de elección de rutas, abordando una brecha en la investigación sobre redes de tráfico donde estas tareas se han tratado por separado. Este método, basado en la estimación de máxima verosimilitud, muestra un rendimiento sólido utilizando datos reales de taxis de Nueva York, destacando la importancia de considerar la interdependencia en estas estimaciones.

Une nouvelle étude propose une méthode pour estimer simultanément les temps de trajet des arcs et les paramètres des modèles de choix de route, comblant une lacune dans la recherche sur les réseaux de trafic où ces tâches ont été traitées séparément. Cette méthode, basée sur l'estimation du maximum de vraisemblance, montre de bonnes performances en utilisant des données réelles de taxis de New York, soulignant l'importance de considérer l'interdépendance dans ces estimations.

A new study proposes a method for simultaneously estimating arc travel times and route choice model parameters, addressing a gap in traffic network research where these tasks have been treated separately. This method, based on maximum likelihood estimation, shows strong performance using real taxi data from New York City, highlighting the importance of considering interdependence in these estimations.

Arc travel time and path choice model estimation subsumed

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Samsung's QN90F QLED delivers great streaming and gaming performance, making it a strong holiday value.

Why I recommend this Samsung QLED TV over pricier OLED models in 2025 - and don't regret it

<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

AI Amnesia: Erasing Knowledge Without a Trace

Baseus' Enercore CG11 travel adapter is one of the better designed ones I've tested, although its best feature isn't immediately apparent.

Traveling soon? Why this one charger is the only one you'll ever need to pack

Turning materials like wood chips, crop residues and municipal solid waste into fuels and chemicals is important for our country's energy independence.

طور الباحثون نماذج حاسوبية متقدمة تهدف إلى تحسين التنبؤات لعمليات طحن الكتلة الحيوية. تركز هذه الابتكارات على تحويل مواد مثل رقائق الخشب، بقايا المحاصيل، والنفايات الصلبة البلدية إلى وقود ومواد كيميائية قيمة، وهو أمر مهم لتعزيز استقلالية الطاقة في البلاد. من المتوقع أن تعمل النماذج على تحسين كفاءة معالجة الكتلة الحيوية، مما يساهم في حلول الطاقة المستدامة.

Investigadores han desarrollado modelos informáticos avanzados destinados a mejorar las predicciones para los procesos de molienda de biomasa. Esta innovación se centra en convertir materiales como astillas de madera, residuos de cultivos y residuos sólidos municipales en combustibles y productos químicos valiosos, lo que es crucial para mejorar la independencia energética del país. Se espera que los modelos optimicen la eficiencia del procesamiento de biomasa, contribuyendo así a soluciones energéticas sostenibles.

Des chercheurs ont développé des modèles informatiques avancés visant à améliorer les prévisions pour les processus de broyage de la biomasse. Cette innovation se concentre sur la conversion de matériaux tels que les copeaux de bois, les résidus de culture et les déchets solides municipaux en combustibles et produits chimiques précieux, ce qui est crucial pour renforcer l'indépendance énergétique du pays. Les modèles devraient optimiser l'efficacité du traitement de la biomasse, contribuant ainsi à des solutions énergétiques durables.

Researchers have developed advanced computer models aimed at improving predictions for biomass milling processes. This innovation focuses on converting materials such as wood chips, crop residues, and municipal solid waste into valuable fuels and chemicals, which is crucial for enhancing energy independence in the country. The models are expected to optimize the efficiency of biomass processing, thereby contributing to sustainable energy solutions.

Researchers develop computer models for better biomass milling predictions

The most interesting aspect of the latest Even Realities glasses may be their limitations.

I've tried several AI smart glasses (including Meta Ray-Bans) in 2025 - these are the most comfortable

arXiv:2506.08740v2 Announce Type: replace 
Abstract: Graph neural networks (GNNs) are widely used in urban spatiotemporal forecasting, such as predicting infrastructure problems. In this setting, government officials wish to know in which neighborhoods incidents like potholes or rodent issues occur. The true state of incidents (e.g., street conditions) for each neighborhood is observed via government inspection ratings. However, these ratings are only conducted for a sparse set of neighborhoods and incident types. We also observe the state of incidents via crowdsourced reports, which are more densely observed but may be biased due to heterogeneous reporting behavior. First, for such settings, we propose a multiview, multioutput GNN-based model that uses both unbiased rating data and biased reporting data to predict the true latent state of incidents. Second, we investigate a case study of New York City urban incidents and collect, standardize, and make publicly available a dataset of 9,615,863 crowdsourced reports and 1,041,415 government inspection ratings over 3 years and across 139 types of incidents. Finally, we show on both real and semi-synthetic data that our model can better predict the latent state compared to models that use only reporting data or models that use only rating data, especially when rating data is sparse and reports are predictive of ratings. We also quantify demographic biases in crowdsourced reporting, e.g., higher-income neighborhoods report problems at higher rates. Our analysis showcases a widely applicable approach for latent state prediction using heterogeneous, sparse, and biased data.

تُستخدم الشبكات العصبية الرسومية (GNN) بشكل متزايد في التنبؤات الحضرية الزمانية والمكانية، وخاصةً في التنبؤ بمشاكل البنية التحتية مثل الحفر ومشاكل القوارض. توفر تقييمات التفتيش الحكومية رؤى حول حالة الحوادث في أحياء مختلفة، لكن هذه التقييمات تقتصر على مجموعة محدودة من المناطق. لتحسين دقة التنبؤات، يقترح نموذج جديد يعتمد على GNN متعدد المناظر ومخرجات، يدمج بين بيانات التقييم الحكومية والتقارير المجمعة، مما يعالج التحيزات في سلوك الإبلاغ ويحسن فهم الحوادث الحضرية.

Las redes neuronales gráficas (GNN) se utilizan cada vez más en la previsión espaciotemporal urbana, especialmente para predecir problemas de infraestructura como baches y problemas de roedores. Las calificaciones de inspección gubernamental proporcionan información sobre el estado de los incidentes en varios vecindarios, pero estas calificaciones están limitadas a una selección escasa de áreas. Para mejorar la precisión de las predicciones, un nuevo modelo GNN de múltiples vistas y salidas integra tanto las calificaciones gubernamentales como los informes crowdsourcados, abordando los sesgos …

Les réseaux de neurones graphiques (GNN) sont de plus en plus utilisés dans les prévisions spatiotemporelles urbaines, notamment pour prédire des problèmes d'infrastructure tels que les nids-de-poule et les problèmes de rongeurs. Les évaluations des inspections gouvernementales fournissent des informations sur l'état des incidents dans divers quartiers, mais ces évaluations sont limitées à un nombre restreint de zones. Pour améliorer la précision des prévisions, un nouveau modèle GNN à plusieurs vues et sorties intègre à la fois les évaluations gouvernementales et les rapports crowdsourcés, ab…

Graph neural networks (GNNs) are increasingly utilized in urban spatiotemporal forecasting, particularly for predicting infrastructure issues like potholes and rodent problems. Government inspection ratings provide insights into the state of incidents in various neighborhoods, but these ratings are limited to a sparse selection of areas. To enhance prediction accuracy, a new multiview, multioutput GNN model integrates both government ratings and crowdsourced reports, addressing biases in reporting behavior and improving the understanding of urban incidents.

Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports

arXiv:2511.06854v2 Announce Type: replace-cross 
Abstract: Irregularly sampled time series (ISTS), characterized by non-uniform time intervals with natural missingness, are prevalent in real-world applications. Existing approaches for ISTS modeling primarily rely on observed values to impute unobserved ones or infer latent dynamics. However, these methods overlook a critical source of learning signal: the reconstruction error inherently produced during model training. Such error implicitly reflects how well a model captures the underlying data structure and can serve as an informative proxy for unobserved values. To exploit this insight, we propose iTimER, a simple yet effective self-supervised pre-training framework for ISTS representation learning. iTimER models the distribution of reconstruction errors over observed values and generates pseudo-observations for unobserved timestamps through a mixup strategy between sampled errors and the last available observations. This transforms unobserved timestamps into noise-aware training targets, enabling meaningful reconstruction signals. A Wasserstein metric aligns reconstruction error distributions between observed and pseudo-observed regions, while a contrastive learning objective enhances the discriminability of learned representations. Extensive experiments on classification, interpolation, and forecasting tasks demonstrate that iTimER consistently outperforms state-of-the-art methods under the ISTS setting.

يقدم المقال iTimER، وهو إطار عمل للتدريب الذاتي الموجه مصمم لتعلم تمثيلات السلاسل الزمنية المأخوذة بشكل غير منتظم (ISTS). تتميز ISTS بفترات زمنية غير متساوية وغياب طبيعي، مما يشكل تحديات أمام أساليب النمذجة التقليدية. يستفيد iTimER من خطأ إعادة البناء الناتج أثناء تدريب النموذج لإنشاء ملاحظات زائفة للعلامات الزمنية غير الملاحظة، مما يعزز قدرة النموذج على التقاط الهياكل الأساسية للبيانات وتحسين دقة التنبؤات.

El artículo presenta iTimER, un marco de preentrenamiento auto-supervisado diseñado para aprender representaciones de series temporales muestreadas de manera irregular (ISTS). Las ISTS se caracterizan por intervalos de tiempo no uniformes y faltantes naturales, lo que plantea desafíos para los enfoques de modelado tradicionales. iTimER aprovecha el error de reconstrucción generado durante el entrenamiento del modelo para crear pseudo-observaciones para marcas de tiempo no observadas, mejorando la capacidad del modelo para capturar las estructuras subyacentes de los datos y mejorar la precisión…

L'article présente iTimER, un cadre de pré-entraînement auto-supervisé conçu pour apprendre des représentations de séries temporelles échantillonnées de manière irrégulière (ISTS). Les ISTS se caractérisent par des intervalles de temps non uniformes et des manques naturels, ce qui pose des défis pour les approches de modélisation traditionnelles. iTimER exploite l'erreur de reconstruction générée pendant l'entraînement du modèle pour créer des pseudo-observations pour les horodatages non observés, améliorant ainsi la capacité du modèle à capturer les structures sous-jacentes des données et à a…

The article presents iTimER, a self-supervised pre-training framework designed for learning representations of irregularly sampled time series (ISTS). ISTS are characterized by non-uniform time intervals and natural missingness, which pose challenges for traditional modeling approaches. iTimER leverages the reconstruction error generated during model training to create pseudo-observations for unobserved timestamps, enhancing the model's ability to capture underlying data structures and improve forecasting accuracy.

Beyond Observations: Reconstruction Error-Guided Irregularly Sampled Time Series Representation Learning

arXiv:2503.09887v2 Announce Type: replace-cross 
Abstract: We develop a novel semigroup stability analysis based on Lyapunov techniques and contraction coefficients to prove exponential convergence of Sinkhorn equations on weighted Banach spaces. This operator-theoretic framework yields exponential decays of Sinkhorn iterates towards Schr\"odinger bridges with respect to general classes of $\phi$-divergences and Kantorovich-type criteria, including the relative entropy, squared Hellinger integrals, $\alpha$-divergences as well as weighted total variation norms and Wasserstein distances. To the best of our knowledge, these contraction inequalities are the first results of this type in the literature on entropic transport and the Sinkhorn algorithm.
  We also provide Lyapunov contractions principles under minimal regularity conditions that allow to provide quantitative exponential stability estimates for a large class of Sinkhorn semigroups. We apply this novel framework in a variety of situations, ranging from polynomial growth potentials and heavy tailed marginals on general normed spaces to more sophisticated boundary state space models, including semi-circle transitions, Beta, Weibull, exponential marginals as well as semi-compact models. Last but not least, our approach also allows to consider statistical finite mixture of the above models, including kernel-type density estimators of complex data distributions arising in generative modeling.

On the contraction properties of Sinkhorn semigroups

arXiv:2511.13049v1 Announce Type: cross 
Abstract: We study a matrix completion problem where both the ground truth $R$ matrix and the unknown sampling distribution $P$ over observed entries are low-rank matrices, and \textit{share a common subspace}. We assume that a large amount $M$ of \textit{unlabeled} data drawn from the sampling distribution $P$ is available, together with a small amount $N$ of labeled data drawn from the same distribution and noisy estimates of the corresponding ground truth entries. This setting is inspired by recommender systems scenarios where the unlabeled data corresponds to `implicit feedback' (consisting in interactions such as purchase, click, etc. ) and the labeled data corresponds to the `explicit feedback', consisting of interactions where the user has given an explicit rating to the item. Leveraging powerful results from the theory of low-rank subspace recovery, together with classic generalization bounds for matrix completion models, we show error bounds consisting of a sum of two error terms scaling as $\widetilde{O}\left(\sqrt{\frac{nd}{M}}\right)$ and $\widetilde{O}\left(\sqrt{\frac{dr}{N}}\right)$ respectively, where $d$ is the rank of $P$ and $r$ is the rank of $M$. In synthetic experiments, we confirm that the true generalization error naturally splits into independent error terms corresponding to the estimations of $P$ and and the ground truth matrix $\ground$ respectively. In real-life experiments on Douban and MovieLens with most explicit ratings removed, we demonstrate that the method can outperform baselines relying only on the explicit ratings, demonstrating that our assumptions provide a valid toy theoretical setting to study the interaction between explicit and implicit feedbacks in recommender systems.

يتناول المقال مشكلة إكمال المصفوفات حيث تكون كل من المصفوفة الحقيقية وتوزيع العينة غير المعروف مصفوفات منخفضة الرتبة تشترك في فضاء فرعي مشترك. يبرز توافر كمية كبيرة من البيانات غير المعلّمة المستمدة من توزيع العينة، إلى جانب مجموعة أصغر من البيانات المعلّمة مع تقديرات مشوشة. هذا السيناريو ذو صلة خاصة في أنظمة التوصية، حيث تمثل البيانات غير المعلّمة تعليقات ضمنية وتمثل البيانات المعلّمة تعليقات صريحة. تستفيد الدراسة من نتائج قوية من نظرية استرداد الفضاءات الفرعية المنخفضة الرتبة، إلى جانب حدود التعميم الكلاسيكية لنماذج إكمال المصفوفات، لتظهر حدود الخطأ.

El artículo aborda un problema de completado de matrices donde tanto la matriz verdadera como la distribución de muestreo desconocida son matrices de bajo rango que comparten un subespacio común. Se destaca la disponibilidad de una gran cantidad de datos no etiquetados de la distribución de muestreo, junto con un conjunto más pequeño de datos etiquetados con estimaciones ruidosas. Este escenario es particularmente relevante en sistemas de recomendación, donde los datos no etiquetados representan retroalimentación implícita y los datos etiquetados representan retroalimentación explícita. El est…

L'article traite d'un problème de complétion de matrice où la matrice réelle et la distribution d'échantillonnage inconnue sont des matrices de faible rang partageant un sous-espace commun. Il souligne la disponibilité d'une grande quantité de données non étiquetées provenant de la distribution d'échantillonnage, ainsi qu'un ensemble plus petit de données étiquetées avec des estimations bruitées. Ce scénario est particulièrement pertinent dans les systèmes de recommandation, où les données non étiquetées représentent un retour implicite et les données étiquetées un retour explicite. L'étude s'…

The article discusses a matrix completion problem where both the true matrix and the unknown sampling distribution are low-rank matrices sharing a common subspace. It highlights the availability of a large amount of unlabeled data from the sampling distribution, alongside a smaller set of labeled data with noisy estimates. This scenario is particularly relevant in recommender systems, where unlabeled data represents implicit feedback and labeled data represents explicit feedback. The study leverages low-rank subspace recovery theory and generalization bounds for matrix completion models to dem…

Arc travel time and path choice model estimation subsumed

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