Kleber Mendonça Filho wants his films to reclaim lost history. For Kleber Mendonça Filho, filmmaking is an act of both provocation and preservation. Mendonça was born in 1968, in the early years of a ruthless military dictatorship--a time when cinema, like much else, was harshly constrained. His mother, Joselice Jucá, was a historian who studied Brazil's abolitionist movement, and she taught him that filling gaps in the cultural memory was a way to expose concealed truths. His relationship with film is inextricably linked with his home town, Recife--a port city where attractive beaches and high-rise developments coexist with sprawling favelas and rampant crime. In his youth, Mendonça was fascinated by the city's grand cinema palaces. He carried a Super 8 camera to the tops of marquees and shot dizzying images; he spent hours in projection booths, learning the mechanics of how films reached the screen. Over time, Mendonça watched those theatres fall into decline, an experience that he likened to being aboard a ship as it wrecked. But even as Recife lost its allure, he made the city a fixture of his films--a way of vindicating its place in history. His first narrative feature, "Neighboring Sounds," takes place on a street where he lived as a child, a setting that he spent years documenting. Later, he made "Pictures of Ghosts," a documentary about Recife told largely through its cinemas.

In this work, we revisit dictionary-based sparse regression, in particular, Sequential Threshold Least Squares (STLS), and propose a score-guided library selection to provide practical guidance for data-driven modeling, with emphasis on SINDy-type algorithms. STLS is an algorithm to solve the $\ell_0$ sparse least-squares problem, which relies on splitting to efficiently solve the least-squares portion while handling the sparse term via proximal methods. It produces coefficient vectors whose components depend on both the projected reconstruction errors, here referred to as the scores, and the mutual coherence of dictionary terms. The first contribution of this work is a theoretical analysis of the score and dictionary-selection strategy. This could be understood in both the original and weak SINDy regime. Second, numerical experiments on ordinary and partial differential equations highlight the effectiveness of score-based screening, improving both accuracy and interpretability in dynamical system identification. These results suggest that integrating score-guided methods to refine the dictionary more accurately may help SINDy users in some cases to enhance their robustness for data-driven discovery of governing equations.

A unique mating call led biologists to this newly discovered pint-sized amphibian. 'Brachycephalus lulai' is a tiny pumpkin toadlet measuring less than 14 millimeters in length. It is sitting on a pencil tip for scale. Breakthroughs, discoveries, and DIY tips sent every weekday. A new pumpkin toadlet species was recently discovered in the mountains of southern Brazil. is just over one centimeter (only 0.39 inches) long and the size of a pencil tip.

Large Language Models (LLMs) excel in many Natural Language Processing (NLP) tasks through in-context learning but often under-perform in Named Entity Recognition (NER), especially for lower-resource languages like Portuguese. While open-weight LLMs enable local deployment, no single model dominates all tasks, motivating ensemble approaches. However, existing LLM ensembles focus on text generation or classification, leaving NER under-explored. In this context, this work proposes a novel three-step ensemble pipeline for zero-shot NER using similarly capable, locally run LLMs. Our method outperforms individual LLMs in four out of five Portuguese NER datasets by leveraging a heuristic to select optimal model combinations with minimal annotated data. Moreover, we show that ensembles obtained on different source datasets generally outperform individual LLMs in cross-dataset configurations, potentially eliminating the need for annotated data for the current task.

HE development of autonomous vehicles (A Vs) is on the verge of passing beyond the laboratory and simulation tests and is shifting towards addressing the challenges that limit their practicality in today's society. While there is still need for further technological improvements to enable safe and smooth operation of a single A V, a great deal of research attention is being focused on the emerging challenge of operating multiple A Vs and the co-existence of A Vs and human-driven vehicles (HVs) [1], [2]. A realistic outlook for the adoption of autonomous vehicles on the roads is a mixed-traffic scenario in which human drivers with different driving styles and social preferences share the road with A Vs that are perhaps built by different manufacturers and hence follow different policies [3], [4]. In this work, we seek a solution that can ensure the safety and robustness of A Vs in the presence of human drivers with heterogeneous behavioral traits. Connected & autonomous vehicles (CA Vs) via vehicle-to-vehicle (V2V) communication allow vehicles to directly communicate with their neighbors, creating an extended perception that enables explicit coordination among vehicles to overcome the limitations of an isolated agent [5]-[11]. While planning in a fully A V scenario is relatively easy to achieve, coordination in the presence of HVs is a significantly more challenging task, as the A Vs not only need to react to road objects but also need to consider the behaviors of HVs [3], [4], [12]. We start by identifying the major challenges in the domain of behavior planning and prediction for A Vs in mixed-autonomy traffic.

Two armed men have stolen eight engravings by French artist Matisse and at least another five by Brazilian painter Cândido Portinari from a library in São Paulo. Brazilian officials say the thieves held up a security guard and an elderly couple who were visiting the library before making off with the artworks on foot. They reportedly entered the library by the main entrance at 10:00 (13:00 GMT) on Sunday, and left by the same route, heading towards the nearest metro station. The heist comes less than two months after the art world was rocked by a brazen break-in at the Louvre museum in Paris, where thieves made off with priceless jewels. The engravings stolen from Biblioteca Mário de Andrade on Sunday formed part of a joint exhibition with the São Paulo Museum of Modern Art.

This extended abstract introduces Self-Explaining Contrastive Evidence Re-Ranking (CER), a novel method that restructures retrieval around factual evidence by fine-tuning embeddings with contrastive learning and generating token-level attribution rationales for each retrieved passage. Hard negatives are automatically selected using a subjectivity-based criterion, forcing the model to pull factual rationales closer while pushing subjective or misleading explanations apart. As a result, the method creates an embedding space explicitly aligned with evidential reasoning. We evaluated our method on clinical trial reports, and initial experimental results show that CER improves retrieval accuracy, mitigates the potential for hallucinations in RAG systems, and provides transparent, evidence-based retrieval that enhances reliability, especially in safety-critical domains.

Recent advancements in Large Language Models (LLMs) have significantly improved their problem-solving capabilities. However, these models still struggle when faced with complex multi-step reasoning tasks. In this paper, we propose the Multi-Layered Self-Reflection with Auto-Prompting (MAPS) framework, a novel approach designed to enhance multi-step mathematical reasoning in LLMs by integrating techniques such as Chain of Thought (CoT), Self-Reflection, and Auto-Prompting. Unlike traditional static prompting methods, MAPS employs an iterative refinement process. Initially, the model generates a solution using CoT prompting. When errors are detected, an adaptive self-reflection mechanism identifies and analyzes them, generating tailored prompts to guide corrections. These dynamically adjusted prompts enable the model to iteratively refine its reasoning. Experiments on four well-established benchmarks across multiple LLMs show that MAPS significantly outperforms standard CoT and achieves competitive results with reasoning-optimized models. In addition, MAPS enables general-purpose LLMs to reach performance levels comparable to specialized reasoning models. While deeper reflection layers improve accuracy, they also increase token usage and costs. To balance this trade-off, MAPS strategically limits reflection depth, ensuring an optimal balance between cost and reasoning performance.