Things to Do in L.A. Tap to enable a layout that focuses on the article. Students arrive for the first day of school at 24th Street Elementary School. This is read by an automated voice. Please report any issues or inconsistencies here . Kindergartners have California's highest chronic absenteeism rates, with 26% missing at least 10% of school days in 2023-24.

Video released Thursday by the LAPD shows police flying a drone to get a closer look at a man inside a van who had been shot by officers in Boyle Heights last month. Police said they shot the man after he ignored their commands to drop what appeared to be a rifle. The rifle, it turned out, was a battery-powered airsoft gun, which shoots plastic pellets. As the drone zoomed up to the white utility van, it captured a graphic image of Jeremy Flores, 26, slumped over the steering wheel, the fake gun lying on his lap. A SWAT team then approached and pulled Flores out of the vehicle.

Corpus Aware Training (CAT) leverages valuable corpus metadata during training by injecting corpus information into each training example, and has been found effective in the literature, commonly known as the "tagging" approach. Models trained with CAT inherently learn the quality, domain and nuance between corpora directly from data, and can easily switch to different inference behavior. To achieve the best evaluation, CAT models pre-define a group of high quality data before training starts which can be error-prone and inefficient. In this work, we propose Optimal Corpus Aware Training (OCAT), which fine-tunes a CAT pre-trained model by freezing most of the model parameters and only tuning small set of corpus-related parameters. We show that OCAT is lightweight, resilient to overfitting, and effective in boosting model accuracy. We use WMT23 English to Chinese and English to German translation tasks as our test ground and show +3.6 and +1.8 chrF improvement, respectively, over vanilla training. Furthermore, our approach is on-par or slightly better than other state-of-the-art fine-tuning techniques while being less sensitive to hyperparameter settings.

Wheel-legged robots integrate the agility of legs for navigating rough terrains while harnessing the efficiency of wheels for smooth surfaces. However, most existing designs do not fully capitalize on the benefits of both legged and wheeled structures, which limits overall system flexibility and efficiency. We present FLORES (reconfigured wheel-legged robot for enhanced steering and adaptability), a novel wheel-legged robot design featuring a distinctive front-leg configuration that sets it beyond standard design approaches. Specifically, FLORES replaces the conventional hip-roll degree of freedom (DoF) of the front leg with hip-yaw DoFs, and this allows for efficient movement on flat surfaces while ensuring adaptability when navigating complex terrains. This innovative design facilitates seamless transitions between different locomotion modes (i.e., legged locomotion and wheeled locomotion) and optimizes the performance across varied environments. To fully exploit FLORES's mechanical capabilities, we develop a tailored reinforcement learning (RL) controller that adapts the Hybrid Internal Model (HIM) with a customized reward structure optimized for our unique mechanical configuration. This framework enables the generation of adaptive, multi-modal locomotion strategies that facilitate smooth transitions between wheeled and legged movements. Furthermore, our distinctive joint design enables the robot to exhibit novel and highly efficient locomotion gaits that capitalize on the synergistic advantages of both locomotion modes. Through comprehensive experiments, we demonstrate FLORES's enhanced steering capabilities, improved navigation efficiency, and versatile locomotion across various terrains. The open-source project can be found at https://github.com/ZhichengSong6/FLORES-A-Reconfigured-Wheel-Legged-Robot-for-Enhanced-Steering-and-Adaptability.git.

Previous work has considered token overlap, or even similarity of token distributions, as predictors for multilinguality and cross-lingual knowledge transfer in language models. However, these very literal metrics assign large distances to language pairs with different scripts, which can nevertheless show good cross-linguality. This limits the explanatory strength of token overlap for knowledge transfer between language pairs that use distinct scripts or follow different orthographic conventions. In this paper, we propose subword token alignability as a new way to understand the impact and quality of multilingual tokenisation. In particular, this metric predicts multilinguality much better when scripts are disparate and the overlap of literal tokens is low. We analyse this metric in the context of both encoder and decoder models, look at data size as a potential distractor, and discuss how this insight may be applied to multilingual tokenisation in future work. We recommend our subword token alignability metric for identifying optimal language pairs for cross-lingual transfer, as well as to guide the construction of better multilingual tokenisers in the future. We publish our code and reproducibility details.

Open-source Large Language models (OsLLMs) propel the democratization of natural language research by giving the flexibility to augment or update model parameters for performance improvement. Nevertheless, like proprietary LLMs, Os-LLMs offer poorer performance on low-resource languages (LRLs) than high-resource languages (HRLs), owing to smaller amounts of training data and underrepresented vocabulary. On the other hand, continual pre-training (CPT) with large amounts of language-specific data is a costly proposition in terms of data acquisition and computational resources. Our goal is to drastically reduce CPT cost. To that end, we first develop a new algorithm to select a subset of texts from a larger corpus. We show the effectiveness of our technique using very little CPT data. In search of further improvement, we design a new algorithm to select tokens to include in the LLM vocabulary. We experiment with the recent Llama-3 model and nine Indian languages with diverse scripts and extent of resource availability. For evaluation, we use IndicGenBench, a generation task benchmark dataset for Indic languages. We experiment with various CPT corpora and augmented vocabulary size and offer insights across language families.

Election day is here and so too is the anxiety that has parked itself in the middle of the room. Some people are trying their best to meet the moment -- with mixed results -- while others have simply chosen not to let the race for president and control of Congress dominate their lives. For most all of the voters we spoke to, it's easier said than done. They planned to go for a hike and visit art galleries around downtown L.A. but admitted they were stressed about the election. "We're just sort of out walking around and trying to have a pleasant day and not think about it too much," Mark "I think we'll be glued to our TVs tonight to find out how the rest of our lives are gonna go."

Without a body to show in the 1996 San Luis Obispo cold case, the prosecution concluded its presentation in the Kristin Smart murder trial with a sexually explicit screenshot of another woman with a red ball gag in her mouth on the accused killer Paul Flores' San Pedro bed. The image came from Paul Flores' computer, experts testified. Judge Jennifer O'Keefe on Tuesday instructed Monterey County jurors that it was only to be considered as corroborating evidence to a single detail from the testimonies of two women who told the panel they were raped by Flores and that he owned a red ball gag. One warm Friday night in late spring 10 years ago, Kristin Denise Smart and three other young women started walking from their dorms at Cal Poly San Luis Obispo. Jurors had already heard those women testify that the man last seen walking with Kristin Smart on the Cal Poly San Luis Obispo campus on May 25, 1996, before she vanished, sexually assaulted them in Los Angeles separately in 2008 and 2011.

NerveTrack offers support similar to the way self-parking cars help drivers who struggle with parallel parking, Flores says. With some nerves as small as 2 millimeters in diameter, it can be time-consuming and difficult to identify them with an ultrasound machine. "What anesthesiologists find is twofold," Flores says. "One, they may need some assistance in finding that nerve block within that forearm. Or two, it's very tedious, and they want to automate that process so they can spend more time working with a patient."

"We can then hand that to a product team and to R&D and say, 'This is what customers are asking for--go develop a solution that we can then sell as a company,'" she says. The focus is on repeatable and scalable solutions, she emphasizes. The Atos team tries to understand a customer's needs and what their point of view is. It's "really important that we don't give our customers a point of view until after they've given us theirs," Flores says. It's key to build from that and really understand where someone is coming from for the best solutions. A unique approach of the lab is bringing in multiple stakeholders from different parts of the business.