In hacker spaces and at their homes, creative protesters are laser-cutting and 3D-printing tools to resist an occupation. As the US government's immigration crackdown expands across the country, anxious residents have mobilized to look out for each other. One way they're doing that is by finding ways to build the tools they need to be resilient against the surge of Immigration and Customs Enforcement agents empowered to kill with impunity . All over the country, makers are 3D-printing thousands of whistles to help people on the ground alert others to nearby ICE activity. But the whistles are far from the only tools being used to respond to the surge of federal agents.

U.S. District Court Judge Karin Immergut extends injunction blocking Trump administration from deploying National Guard troops to Portland until Friday.

Robby Starbuck speaks in an interview in New York in March. Robby Starbuck speaks in an interview in New York in March. Critics condemn Robby Starbuck, appointed in lawsuit settlement, for'peddling lies and pushing extremism' A prominent anti-DEI campaigner appointed by Meta in August as an adviser on AI bias has spent the weeks since his appointment spreading disinformation about shootings, transgender people, vaccines, crime, and protests. Robby Starbuck, 36, of Nashville, was appointed in August as an adviser by Meta - owner of Facebook, Instagram, WhatsApp, and other tech platforms - in an August lawsuit settlement. Since his appointment, Starbuck has baselessly claimed that individual shooters in the US were motivated by leftist ideology, described faith-based protest groups as communists, and without evidence tied Democratic lawmakers to murders.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. FBI investigators identified Robert Jacob Hoopes as a suspect in the injury of an ICE officer during protests in Portland, Ore., using facial recognition software, according to a criminal complaint from the case. In the criminal complaint, an unidentified FBI special agent said that a photo shared on OregonLive.com -- the online version of The Oregonian -- was put into "commercially available facial recognition software." The software allegedly provided 30 possible comparison photos from public databases. FBI Portland reviewed the photos and found one from a Reed College SmugMug page called "Canyon Day April '23," in which a tattoo on the suspect's forearm is visible.

'Bella the robot waitress won't replace our staff' 4 days agoShareSaveSophie CridlandReporting fromPortlandShareSaveBBCMike Deadman, from The View Cafe and Bar, said Bella was not being used to replace staff Bella carries multiple trays packed with food and drinks, deftly swerving any obstacles and delivering orders day in and day out to her customers. This is the latest recruit at The View Cafe and Bar at Portland's Heights hotel in Dorset. But Bella is no normal member of the waiting staff - she is a state-of-the art robot programmed to serve and even interact with the eatery's patrons. And costing a little under 9,000, it is hoped it can be an economical idea, as well as a novel one. But assistant manager Mike Deadman insists Bella - built by Chinese technology company Pudu - will not result in any job losses.

Parameter-level model merging is an emerging paradigm in multi-task learning with significant promise. Previous research has explored its connections with prediction-level model ensembling-commonly viewed as the upper bound for merging-to reveal the potential of achieving performance consistency between the two. However, this observation relies on certain preconditions, such as being limited to two models, using ViT-based models, and all models are fine-tuned from the same pre-trained checkpoint. To further understand the intrinsic connections between model merging and model ensembling, this paper explores an interesting possibility: If these restrictions are removed, can performance consistency still be achieved between merging and ensembling? To answer this question, we first theoretically establish a performance correlation between merging and ensembling. We find that even when previous restrictions are not met, there is still a way for model merging to attain a near-identical and superior performance similar to that of ensembling. To verify whether our findings are practical, we introduce a validation framework termed Neural Ligand (NeuLig). The learning process of NeuLig is meticulously designed with a specialized loss function supported by theoretical foundations. Experimental results demonstrate the robust resilience of NeuLig in terms of both model scale and the number of collaborating models. For instance, for the case involving 5 CLIP-ViT-B/32 models, parameter-level merging achieves the same performance as prediction-level ensembling (merging: 95.44% vs. ensembling: 95.46%).

Some people love buying Christmas presents. Polly Arrowsmith starts making a note of what her friends and family like, then hunts for bargains, slowly and carefully. Vie Portland begins her shopping in January and has a theme each year, from heart mirrors to inspirational books. And Betsy Benn spent so much time thinking about presents, she ended up opening her own online gift business. How would these gift-giving experts react to a trend that is either a timesaving brainwave or an appalling corruption of the Christmas spirit: asking ChatGPT to do it for them?

Chart-to-summary generation can help explore data, communicate insights, and help the visually impaired people. Multi-modal generative models have been used to produce fluent summaries, but they can suffer from factual and perceptual errors. In this work we present CHATS-CRITIC, a reference-free chart summarization metric for scoring faithfulness. CHATS-CRITIC is composed of an image-to-text model to recover the table from a chart, and a tabular entailment model applied to score the summary sentence by sentence. We find that CHATS-CRITIC evaluates the summary quality according to human ratings better than reference-based metrics, either learned or n-gram based, and can be further used to fix candidate summaries by removing not supported sentences. We then introduce CHATS-PI, a chart-to-summary pipeline that leverages CHATS-CRITIC during inference to fix and rank sampled candidates from any chart-summarization model. We evaluate CHATS-PI and CHATS-CRITIC using human raters, establishing state-of-the-art results on two popular chart-to-summary datasets.

Effective disaster response is critical for affected communities. Responders and decision-makers would benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and demands during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. We used Bidirectional Encoder Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data. Then, we conducted a temporal-spatial analysis to examine the distribution of these topics across different regions during the 2020 western U.S. wildfire season. Our results show that Twitter users mainly focused on three topics:"health impact," "damage," and "evacuation." We used the Susceptible-Infected-Recovered (SIR) theory to explore the magnitude and velocity of topic diffusion on Twitter. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The estimated parameters obtained from the SIR model in selected cities revealed that residents exhibited a high level of several concerns during the wildfire. Our study details how the SIR model and topic modeling using social media data can provide decision-makers with a quantitative approach to measure disaster response and support their decision-making processes.

We present a benchmark for assessing the capability of Large Language Models (LLMs) to discern intercardinal directions between geographic locations and apply it to three prominent LLMs: GPT-3.5, GPT-4, and Llama-2. This benchmark specifically evaluates whether LLMs exhibit a hierarchical spatial bias similar to humans, where judgments about individual locations' spatial relationships are influenced by the perceived relationships of the larger groups that contain them. To investigate this, we formulated 14 questions focusing on well-known American cities. Seven questions were designed to challenge the LLMs with scenarios potentially influenced by the orientation of larger geographical units, such as states or countries, while the remaining seven targeted locations less susceptible to such hierarchical categorization. Among the tested models, GPT-4 exhibited superior performance with 55.3% accuracy, followed by GPT-3.5 at 47.3%, and Llama-2 at 44.7%. The models showed significantly reduced accuracy on tasks with suspected hierarchical bias. For example, GPT-4's accuracy dropped to 32.9% on these tasks, compared to 85.7% on others. Despite these inaccuracies, the models identified the nearest cardinal direction in most cases, suggesting associative learning, embodying human-like misconceptions. We discuss the potential of text-based data representing geographic relationships directly to improve the spatial reasoning capabilities of LLMs.