Things to Do in L.A. Tap to enable a layout that focuses on the article. Among the evidence collected from the digital devices of Jonathan Rinderknecht of Florida, who was arrested in the Palisades fire, were images he generated on ChatGPT depicting a burning city, said acting U.S. Atty. This is read by an automated voice. Please report any issues or inconsistencies here . Jonathan Rinderknecht, 29, a one-time L.A. Uber driver and now Florida resident, was arrested by the FBI on Oct. 7 and charged with destruction of property for allegedly starting a Jan. 1 blaze known as the Lachman fire that smoldered for six days until it became the most destructive wildfire in Los Angeles history.

Ominous warning for humanity as birds suddenly adopt'unsettling' behavior Meghan is accused of'giggling as model stumbles on the catwalk': More Paris Fashion Week disasters emerge, including awkward moment with Kristin Scott Thomas More girls are starting their periods younger than ever before - scientists think they've finally found what's causing it The TRUTH to the doting mother who slaughtered her children and husband told by those she'd been quietly tormenting for years Insiders confirm what everyone suspects about Taylor Swift and Blake Lively... the private apology... and how any future friendship hangs on one humiliating condition Outrage as Baltimore's Dem mayor spends $164k of taxpayer cash on ultra-luxurious new SUV I have no sympathy for them - but this disturbing new trend isn't the answer: JANA HOCKING Taylor Swift reveals truth behind raunchy song about Travis Kelce's manhood Revealed: Which slimming jab REALLY works best. The doctors' ultimate expert guide on which to pick, how to save money, beat every side effect... and what you need to know about the'golden dose' Functioning alcoholics hide in plain sight... so are YOU one? Trump brands NFL's Bad Bunny Super Bowl halftime show selection'absolutely ridiculous' The troubled background of delivery man stabbed by Mark Sanchez... as he launches million-dollar lawsuit and sparks civil war at Fox Millions of Americans are facing a dangerous freeze warning on Tuesday as temperatures drop below freezing across multiple states. Sub-freezing temperatures as low as 22 to 30 F are expected in parts of Wisconsin, Minnesota, North Dakota, South Dakota, Michigan, Colorado, Wyoming and Idaho . The National Weather Service (NWS) issued the warning for tonight into Wednesday morning, ending between 8 and 10am local time, depending on the state and county .

Sparse autoencoders (SAEs) have been successfully used to discover sparse and human-interpretable representations of the latent activations of LLMs. However, we would ultimately like to understand the computations performed by LLMs and not just their representations. The extent to which SAEs can help us understand computations is unclear because they are not designed to "sparsify" computations in any sense, only latent activations. To solve this, we propose Jacobian SAEs (JSAEs), which yield not only sparsity in the input and output activations of a given model component but also sparsity in the computation (formally, the Jacobian) connecting them. With a na\"ive implementation, the Jacobians in LLMs would be computationally intractable due to their size. One key technical contribution is thus finding an efficient way of computing Jacobians in this setup. We find that JSAEs extract a relatively large degree of computational sparsity while preserving downstream LLM performance approximately as well as traditional SAEs. We also show that Jacobians are a reasonable proxy for computational sparsity because MLPs are approximately linear when rewritten in the JSAE basis. Lastly, we show that JSAEs achieve a greater degree of computational sparsity on pre-trained LLMs than on the equivalent randomized LLM. This shows that the sparsity of the computational graph appears to be a property that LLMs learn through training, and suggests that JSAEs might be more suitable for understanding learned transformer computations than standard SAEs.

Existing benchmarks for evaluating long-context language models (LCLMs) primarily focus on long-context recall, requiring models to produce short responses based on a few critical snippets while processing thousands of irrelevant tokens. We introduce LongProc (Long Procedural Generation), a new benchmark that requires both the integration of highly dispersed information and long-form generation. LongProc consists of six diverse procedural generation tasks, such as extracting structured information from HTML pages into a TSV format and executing complex search procedures to create travel plans. These tasks challenge LCLMs by testing their ability to follow detailed procedural instructions, synthesize and reason over dispersed information, and generate structured, long-form outputs (up to 8K tokens). Furthermore, as these tasks adhere to deterministic procedures and yield structured outputs, they enable reliable rule-based evaluation. We evaluate 17 LCLMs on LongProc across three difficulty levels, with maximum numbers of output tokens set at 500, 2K, and 8K. Notably, while all tested models claim a context window size above 32K tokens, open-weight models typically falter on 2K-token tasks, and closed-source models like GPT-4o show significant degradation on 8K-token tasks. Further analysis reveals that LCLMs struggle to maintain long-range coherence in long-form generations. These findings highlight critical limitations in current LCLMs and suggest substantial room for improvement. Data and code available at: https://princeton-pli.github.io/LongProc

The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. On the other hand, text-based methods struggle with the semantic gap between KG triples and natural language. Apart from triples, entity contexts (e.g., labels, descriptions, aliases) also play a significant role in augmenting KGs. To address these limitations, we propose KGR3, a context-enriched framework for KGC. KGR3 is composed of three modules. Firstly, the Retrieval module gathers supporting triples from the KG, collects plausible candidate answers from a base embedding model, and retrieves context for each related entity. Then, the Reasoning module employs a large language model to generate potential answers for each query triple. Finally, the Re-ranking module combines candidate answers from the two modules mentioned above, and fine-tunes an LLM to provide the best answer. Extensive experiments on widely used datasets demonstrate that KGR3 consistently improves various KGC methods. Specifically, the best variant of KGR3 achieves absolute Hits@1 improvements of 12.3% and 5.6% on the FB15k237 and WN18RR datasets.

Sensory substitution is an effective approach for displaying stable haptic feedback to a teleoperator under time delay. The finger is highly articulated, and can sense movement and force in many directions, making it a promising location for sensory substitution based on kinesthetic feedback. However, existing finger kinesthetic devices either provide only one-degree-of-freedom feedback, are bulky, or have low force output. Soft pneumatic actuators have high power density, making them suitable for realizing high force kinesthetic feedback in a compact form factor. We present a soft pneumatic handheld kinesthetic feedback device for the index finger that is controlled using a constant curvature kinematic model. \changed{It has respective position and force ranges of +-3.18mm and +-1.00N laterally, and +-4.89mm and +-6.01N vertically, indicating its high power density and compactness. The average open-loop radial position and force accuracy of the kinematic model are 0.72mm and 0.34N.} Its 3Hz bandwidth makes it suitable for moderate speed haptic interactions in soft environments. We demonstrate the three-dimensional kinesthetic force feedback capability of our device for sensory substitution at the index figure in a virtual telemanipulation scenario.

COVID-19 forecasting models have been used to inform decision making around resource allocation and intervention decisions e.g., hospital beds or stay-at-home orders. State of the art deep learning models often use multimodal data such as mobility or socio-demographic data to enhance COVID-19 case prediction models. Nevertheless, related work has revealed under-reporting bias in COVID-19 cases as well as sampling bias in mobility data for certain minority racial and ethnic groups, which could in turn affect the fairness of the COVID-19 predictions along race labels. In this paper, we show that state of the art deep learning models output mean prediction errors that are significantly different across racial and ethnic groups; and which could, in turn, support unfair policy decisions. We also propose a novel de-biasing method, DemOpts, to increase the fairness of deep learning based forecasting models trained on potentially biased datasets. Our results show that DemOpts can achieve better error parity that other state of the art de-biasing approaches, thus effectively reducing the differences in the mean error distributions across more racial and ethnic groups.

Los Angeles commuters: Don't be alarmed, but driverless taxis may soon become a more common site on local streets. On March 1, state regulators gave Waymo, the self-driving taxi company owned by Google's parent, Alphabet, the green light to expand its robotaxi service to Los Angeles County, clearing the way for the company's expansion into one of the biggest markets in the country. While local transportation agencies deal with day-to-day traffic operations in their respective jurisdictions, the California Public Utilities Commission oversees the regulation of driverless vehicles across the state, superseding local governments. Waymo has not disclosed a timeline for when its service will become widely available, but a handful of Waymo vehicles are already roaming about the county, including around the USC campus, as part of its ongoing testing and promotion program. Under its new approval agreement, Waymo's driverless fleet can operate in Los Angeles, Santa Monica, Beverly Hills, Inglewood, East Los Angeles, Compton and many more locales.

State regulators on Friday gave the green light for Waymo to expand into Los Angeles and San Mateo counties, clearing the way for the driverless taxi service to launch in the coming months. Exactly when Waymo services will be available in Los Angeles is still to be determined, but the decision by the California Public Utilities Commission will open the streets of America's second-largest city to a fleet of autonomous vehicles -- even as self-driving cars continue to be the subject of safety concerns and some public criticism. Waymo, formerly known as the Google self-driving car project, is owned by Google's parent company, Alphabet, and already operates in parts of San Francisco. Waymo's driverless taxi launch in Santa Monica attracted both excited enthusiasts and concerned critics. The company is allowed to operate fully autonomous vehicles and carry public passengers as part of its testing and promotion, and has been testing its driverless white Jaguars in Los Angeles for more than a year.

Another California community is raising concerns about plans to unleash the Waymo self-driving vehicle in its jurisdiction, following several incidents involving autonomous ride-hailing cars that resulted in injuries. San Mateo County, in the San Francisco Bay Area, has requested more information from state regulators before allowing Google-owned Waymo to operate its driverless vehicles in the county. San Mateo County made the request after Waymo submitted a letter Jan. 19 to the California Public Utilities Commission, asking the agency to approve its proposed expansion of its Automated Vehicle Passenger Services into portions of the San Francisco Peninsula, which includes San Mateo County, as well as the southwest region of Los Angeles County. The company has already been serving a portion of San Francisco, from Lands End to Bernal Heights. The autonomous car began offering rides for a limited time in November in Santa Monica, Century City, West Hollywood, Mid-City Koreatwon and downtown L.A., giving residents a chance at testing the driverless ride.