Things to Do in L.A. Tap to enable a layout that focuses on the article. Woman gives birth in a driverless Waymo taxi in San Francisco. Waymo taxis navigate a street in San Francisco in 2023. This is read by an automated voice. Please report any issues or inconsistencies here .

While the US has precision missiles that can shoot these drones down, they don't always succeed: A drone attack killed three US soldiers and injured dozens more at a base in the Jordanian desert last year. And each American missile costs orders of magnitude more than its targets, which limits their supply; countering thousand-dollar drones with missiles that cost hundreds of thousands, or even millions, of dollars per shot can only work for so long, even with a defense budget that could reach a trillion dollars next year. The US armed forces are now hunting for a solution--and they want it fast. Every branch of the service and a host of defense tech startups are testing out new weapons that promise to disable drones en masse. There are drones that slam into other drones like battering rams; drones that shoot out nets to ensnare quadcopter propellers; precision-guided Gatling guns that simply shoot drones out of the sky; electronic approaches, like GPS jammers and direct hacking tools; and lasers that melt holes clear through a target's side.

Oysters are ecologically and commercially important species that require frequent monitoring to track population demographics (e.g. abundance, growth, mortality). Current methods of monitoring oyster reefs often require destructive sampling methods and extensive manual effort. Therefore, they are suboptimal for small-scale or sensitive environments. A recent alternative, the ODYSSEE model, was developed to use deep learning techniques to identify live oysters using video or images taken in the field of oyster reefs to assess abundance. The validity of this model in identifying live oysters on a reef was compared to expert and non-expert annotators. In addition, we identified potential sources of prediction error. Although the model can make inferences significantly faster than expert and non-expert annotators (39.6 s, $2.34 \pm 0.61$ h, $4.50 \pm 1.46$ h, respectively), the model overpredicted the number of live oysters, achieving lower accuracy (63\%) in identifying live oysters compared to experts (74\%) and non-experts (75\%) alike. Image quality was an important factor in determining the accuracy of the model and the annotators. Better quality images improved human accuracy and worsened model accuracy. Although ODYSSEE was not sufficiently accurate, we anticipate that future training on higher-quality images, utilizing additional live imagery, and incorporating additional annotation training classes will greatly improve the model's predictive power based on the results of this analysis. Future research should address methods that improve the detection of living vs. dead oysters.

Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-purpose Large Language Models (LLM) in supporting ME problem formulation tasks, specifically focusing on stakeholder identification. We identify a relevant reference problem, a NASA space mission design challenge, and document ChatGPT-3.5's ability to perform stakeholder identification tasks. We execute multiple parallel attempts and qualitatively evaluate LLM outputs, focusing on both their quality and variability. Our findings portray a nuanced picture. We find that the LLM performs well in identifying human-focused stakeholders but poorly in recognizing external systems and environmental factors, despite explicit efforts to account for these. Additionally, LLMs struggle with preserving the desired level of abstraction and exhibit a tendency to produce solution specific outputs that are inappropriate for problem formulation. More importantly, we document great variability among parallel threads, highlighting that LLM outputs should be used with caution, ideally by adopting a stochastic view of their abilities. Overall, our findings suggest that, while ChatGPT could reduce some expert workload, its lack of consistency and domain understanding may limit its reliability for problem formulation tasks.

According to legend, Singer Vehicle Design founder and executive chairman Rob Dickinson was a young boy the first time his dad pointed out a Porsche 911. Dickinson turned that passion into a multi-million dollar business, reimagining classic Porsche models with his own twist. To be perfectly clear, Singer is not sponsored, approved, endorsed by, or in any way associated or affiliated with Porsche. Customers bring their own 911 to the Singer shop--not just any old 911, but an air-cooled 964 version model from 1989-1994--for a complete makeover. The cars are completely disassembled and modified around the original chassis with a process driven by Singer's obsessive attention to detail.

Abstract-- This paper considers cooperative control of robots involving two different testbed systems in remote locations with communication on the internet. This provides us the capability to exchange robots status like positions, velocities and directions needed for the swarming algorithm. The results show that all robots properly follow some leader defined one of the testbeds. Measurement of data exchange rates show no loss of packets, and average transfer delays stay within tolerance limits for practical applications. I. INTRODUCTION The efficient co-operation between multiple agents situated at distinct locations while pursuing common While the topic raises fundamental questions related to a variety of fields such as communication systems and distributed co-operative control, it is of immense practical of California Los Angeles (UCLA) Applied Mathematics interest as well.

The recent, rapid development and popularization of Large Language Models (LLM) have transformed the panorama of Natural Language Processing (NLP) and, more generally, of Artificial Intelligence (AI), permeating into society and transforming the way many tasks are performed, being now either supported or automated with the help of LLM-based tools. Along with the challenges of hallucinations, lack of reasoning capabilities, inability to perform numerical calculations, natural aging of the training data, and improper traceability and citation of information sources, another intrinsic challenge of LLMs, tightly related to their architecture and training, concerns their limited context window and maximum token output (Kaddour et al., 2023). Indeed, the context window is the cornerstone for LLM-based applications which require the previous interactions in the conversation to be preserved and considered by the LLM. This, being true for long conversations, is of particular importance in the engineering design field when an LLM is used to support engineers in the design of a system, going from high-level concept generation to lower-level system requirements or technical specifications generation. This application requires previous decisions as well as the decision-making process to be considered in later stages.

In this study, we present a quantitative and comprehensive analysis of social gaze in people with autism spectrum disorder (ASD). Diverging from traditional first-person camera perspectives based on eye-tracking technologies, this study utilizes a third-person perspective database from the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) interview videos, encompassing ASD participants and neurotypical individuals as a reference group. Employing computational models, we extracted and processed gaze-related features from the videos of both participants and examiners. The experimental samples were divided into three groups based on the presence of social gaze abnormalities and ASD diagnosis. This study quantitatively analyzed four gaze features: gaze engagement, gaze variance, gaze density map, and gaze diversion frequency. Furthermore, we developed a classifier trained on these features to identify gaze abnormalities in ASD participants. Together, we demonstrated the effectiveness of analyzing social gaze in people with ASD in naturalistic settings, showcasing the potential of third-person video perspectives in enhancing ASD diagnosis through gaze analysis.

How we perceive objects around us depends on what we actively attend to, yet our eye movements depend on the perceived objects. Still, object segmentation and gaze behavior are typically treated as two independent processes. Drawing on an information processing pattern from robotics, we present a mechanistic model that simulates these processes for dynamic real-world scenes. Our image-computable model uses the current scene segmentation for object-based saccadic decision-making while using the foveated object to refine its scene segmentation recursively. To model this refinement, we use a Bayesian filter, which also provides an uncertainty estimate for the segmentation that we use to guide active scene exploration. We demonstrate that this model closely resembles observers' free viewing behavior, measured by scanpath statistics, including foveation duration and saccade amplitude distributions used for parameter fitting and higher-level statistics not used for fitting. These include how object detections, inspections, and returns are balanced and a delay of returning saccades without an explicit implementation of such temporal inhibition of return. Extensive simulations and ablation studies show that uncertainty promotes balanced exploration and that semantic object cues are crucial to form the perceptual units used in object-based attention. Moreover, we show how our model's modular design allows for extensions, such as incorporating saccadic momentum or pre-saccadic attention, to further align its output with human scanpaths.

In this work, we introduce Multiple Embedding Model for EHR (MEME), an approach that views Electronic Health Records (EHR) as multimodal data. This approach incorporates "pseudo-notes", textual representations of tabular EHR concepts such as diagnoses and medications, and allows us to effectively employ Large Language Models (LLMs) for EHR representation. This framework also adopts a multimodal approach, embedding each EHR modality separately. We demonstrate the effectiveness of MEME by applying it to several tasks within the Emergency Department across multiple hospital systems. Our findings show that MEME surpasses the performance of both single modality embedding methods and traditional machine learning approaches. However, we also observe notable limitations in generalizability across hospital institutions for all tested models.