Things to Do in L.A. Tap to enable a layout that focuses on the article. Various items at the Metro Lost & Found office on March 5, 2026. This is read by an automated voice. Please report any issues or inconsistencies here . If you've ever lost something valuable on a Metro bus or train and assumed it was gone forever, take heart: There is a system for reuniting riders with their possessions.

Due to their high predictive performance, predictive maintenance applications have increasingly been approached with Deep Learning techniques in recent years. However, as in other real-world application scenarios, the need for explainability is often stated but not sufficiently addressed. This study will focus on predicting failures on Metro trains in Porto, Portugal. While recent works have found high-performing deep neural network architectures that feature a parallel explainability pipeline, the generated explanations are fairly complicated and need help explaining why the failures are happening. This work proposes a simple online rule-based explainability approach with interpretable features that leads to straightforward, interpretable rules. We showcase our approach on MetroPT2 and find that three specific sensors on the Metro do Porto trains suffice to predict the failures present in the dataset with simple rules. The most straightforward approach, corrective maintenance, merely replaces machine parts whenever they break.

The computational generation of poems is a complex task, which involves several sound, prosodic and rhythmic resources. In this work we present PROPOE 2, with the extension of structural and rhythmic possibilities compared to the original system, generating poems from metered sentences extracted from the prose of Brazilian literature, with multiple rhythmic assembly criteria. These advances allow for a more coherent exploration of rhythms and sound effects for the poem. Results of poems generated by the system are demonstrated, with variations in parameters to exemplify generation and evaluation using various criteria.

Vice presidential nominee and Minnesota Gov. Tim Walz discusses Israel's right to defend itself and Kamala Harris' economic policies on'Fox News Sunday.' A deepfake video disparaging vice presidential candidate Tim Walz was created by "Russian influence actors" who are trying to undermine Kamala Harris' campaign, U.S. intelligence community officials told Fox News. The video circulating on social media purports to show former Mankato West High School student Matthew Metro claiming that he was groped and kissed by Walz in 1997 when the Minnesota governor was a teacher there. Except the allegations are completely fabricated. "Based on newly available intelligence analysis conducted over the weekend, Russian influence actors manufactured and amplified the content," the officials told Fox News, adding that the video fit a pattern used by Russian actors in which the subject was "staged direct to camera and trying to make them go viral." These intelligence community officials also pointed out that they believe Russia is likely to be more aggressive in its efforts to sow division in the U.S. post-election if Harris wins, because Russia prefers that former President Trump win the 2024 race.

Intel has tacked an additional dozen games to its Intel Application Optimization technology, meaning that several popular games will receive a higher framerate if you're using a supported processor and the technology is turned on. Better yet, it should work on older processors now, with a little legwork. Intel updated the technology in conjunction with the launch of the Core i9-14900KS -- which, at speeds of up to 6.2GHz, should already be exceptionally fast by itself. But IAO adds an even higher speed boost -- up to 11 percent faster performance in Metro: Exodus, according to an Intel presentation. Intel's IAO launched alongside the 14th-gen Core lineup with only support for Metro: Exodus and Tom Clancy's Rainbow Six Siege included, but the new updates has added World of Warcraft, Red Dead Redemption 2, Dirt 5, F1:22 and several additional titles.

The decision making involved behind the mode choice is critical for transportation planning. While statistical learning techniques like discrete choice models have been used traditionally, machine learning (ML) models have gained traction recently among the transportation planners due to their higher predictive performance. However, the black box nature of ML models pose significant interpretability challenges, limiting their practical application in decision and policy making. This study utilised a dataset of $1350$ households belonging to low and low-middle income bracket in the city of Bengaluru to investigate mode choice decision making behaviour using Multinomial logit model and ML classifiers like decision trees, random forests, extreme gradient boosting and support vector machines. In terms of accuracy, random forest model performed the best ($0.788$ on training data and $0.605$ on testing data) compared to all the other models. This research has adopted modern interpretability techniques like feature importance and individual conditional expectation plots to explain the decision making behaviour using ML models. A higher travel costs significantly reduce the predicted probability of bus usage compared to other modes (a $0.66\%$ and $0.34\%$ reduction using Random Forests and XGBoost model for $10\%$ increase in travel cost). However, reducing travel time by $10\%$ increases the preference for the metro ($0.16\%$ in Random Forests and 0.42% in XGBoost). This research augments the ongoing research on mode choice analysis using machine learning techniques, which would help in improving the understanding of the performance of these models with real-world data in terms of both accuracy and interpretability.

Retrosynthesis involves the strategic breakdown of complex molecules into simpler precursors, paving the way for the synthesis of novel molecules. Recently, there has been a development of AI-based methods for retrosynthesis, which allow learning reaction rules from the data of historically performed reactions. A central component of such systems is a model for single-step retrosynthesis that predicts what reactions could lead to a considered target molecule. Two dominant methodologies are used for single-step retrosynthesis. Template-based methods use a set of translation rules that represent the possible chemical transformations. Although these methods are characterized by speed and interpretability, they may require an extensive set of templates to cover a large space of chemical reactions, which limits their generalization capacity. Conversely, template-free approaches can produce arbitrary reactions without such constraints but are often computationally demanding, largely due to their dependency on autoregressive decoding [1, 2, 3, 4].

Transformers are a neural network architecture originally designed for natural language processing that it is now a mainstream tool for solving a wide variety of problems, including natural language processing, sound, image, reinforcement learning, and other problems with heterogeneous input data. Its distinctive feature is its self-attention system, based on attention to one's own sequence, which derives from the previously introduced attention system. This article provides the reader with the necessary context to understand the most recent research articles and presents the mathematical and algorithmic foundations of the elements that make up this type of network. The different components that make up this architecture and the variations that may exist are also studied, as well as some applications of the transformer models. This article is in Spanish to bring this scientific knowledge to the Spanish-speaking community.

In the US alone, the Department of Agriculture estimates 30 to 40 percent of food ends up in landfills. A startup called Wasteless thinks machine learning can play a part in addressing the issue. The company has developed an AI-powered system for automatically reducing the price of perishable food items as they spend more time on store shelves. The closer a product is to its best before date, the cheaper it will be to buy. And you'll see all of that reflected in the price tags grocery stores have on their shelves.

When most people think of the connection between technology and jobs, they think of robots and automation taking over relatively unskilled jobs like factory work. And thus, the biggest toll from these technological advances would be on already hard-hit manufacturing regions of the Rust Belt. But a new wave of developments in artificial intelligence may have a greater effect on high-skilled jobs and high-tech knowledge regions. The study by Mark Muro, Jacob Whiton, and Robert Maxim takes a close look at the potential of artificial intelligence--or AI--to automate tasks that until now have required human intelligence and decision-making. As they put it: "Unlike robotics (associated with the factory floor) and computers (associated with routine office activities), AI has a distinctly white-collar bent."