This paper presents an open-source dataset RflyMAD, a Multicopter Abnomal Dataset developed by Reliable Flight Control (Rfly) Group aiming to promote the development of research fields like fault detection and isolation (FDI) or health assessment (HA). The entire 114 GB dataset includes 11 types of faults under 6 flight statuses which are adapted from ADS-33 file to cover more occasions in which the multicopters have different mobility levels when faults occur. In the total 5629 flight cases, the fault time is up to 3283 minutes, and there are 2566 cases for software-in-the-loop (SIL) simulation, 2566 cases for hardware-in-the-loop (HIL) simulation and 497 cases for real flight. As it contains simulation data based on RflySim and real flight data, it is possible to improve the quantity while increasing the data quality. In each case, there are ULog, Telemetry log, Flight information and processed files for researchers to use and check. The RflyMAD dataset could be used as a benchmark for fault diagnosis methods and the support relationship between simulation data and real flight is verified through transfer learning methods. More methods as a baseline will be presented in the future, and RflyMAD will be updated with more data and types. In addition, the dataset and related toolkit can be accessed through https://rfly-openha.github.io/documents/4_resources/dataset.html.

Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented in clinical notes. Despite their vital role, no current oncology information representation and annotation schema fully encapsulates the diversity of information recorded within these notes. Although large language models (LLMs) have recently exhibited impressive performance on various medical natural language processing tasks, due to the current lack of comprehensively annotated oncology datasets, an extensive evaluation of LLMs in extracting and reasoning with the complex rhetoric in oncology notes remains understudied. We developed a detailed schema for annotating textual oncology information, encompassing patient characteristics, tumor characteristics, tests, treatments, and temporality. Using a corpus of 40 de-identified breast and pancreatic cancer progress notes at University of California, San Francisco, we applied this schema to assess the zero-shot abilities of three recent LLMs (GPT-4, GPT-3.5-turbo, and FLAN-UL2) to extract detailed oncological history from two narrative sections of clinical progress notes. Our team annotated 9028 entities, 9986 modifiers, and 5312 relationships. The GPT-4 model exhibited overall best performance, with an average BLEU score of 0.73, an average ROUGE score of 0.72, an exact-match F1-score of 0.51, and an average accuracy of 68% on complex tasks (expert manual evaluation on subset). Notably, it was proficient in tumor characteristic and medication extraction, and demonstrated superior performance in relational inference like adverse event detection. However, further improvements are needed before using it to reliably extract important facts from cancer progress notes needed for clinical research, complex population management, and documenting quality patient care.

Following the success of deep learning in computer vision, deep neural networks (DNNs) have now found their way to a wide range of imaging inverse problems [3, 19, 20]. In some applications, learning the distribution of images from data is the only option. In others, existing methods based on hand-crafted priors are well established. Magnetic resonance imaging (MRI) reconstruction, for which sparsity-based methods have been highly successful, is an example of the latter [17]. However, recent work suggests that image quality can be improved and computation times shortened significantly by the use of DNNs in MRI reconstruction [9]. At the same time, it is well known that DNNs trained for image classification admit socalled adversarial examples--images that have been altered in minor but very specific ways to change the label predicted by the network [4, 22]. In [2], it was discovered that DNNs used in inverse problems (MRI and computed tomography) exhibit similar behavior. Namely, the authors show that perturbing the measurements slightly can lead to undesirable artifacts in the image reconstructed by the network and that the same perturbations do not cause problems for state-of-the-art compressed sensing methods. On the other hand, [13] shows quantitatively that DNNs can be made robust, to a comparable level with total variation (TV) minimization, by injecting statistical noise to the measurement data during training.

Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. Hazard quantification for extreme wildfires is an important component of wildfire management, as it facilitates efficient resource distribution, adverse effect mitigation, and recovery efforts. However, although extreme wildfires are typically the most impactful, both small and moderate fires can still be devastating to local communities and ecosystems. Therefore, it is imperative to develop robust statistical methods to reliably model the full distribution of wildfire spread. We do so for a novel dataset of Australian wildfires from 1999 to 2019, and analyse monthly spread over areas approximately corresponding to Statistical Areas Level 1 and 2 (SA1/SA2) regions. Given the complex nature of wildfire ignition and spread, we exploit recent advances in statistical deep learning and extreme value theory to construct a parametric regression model using graph convolutional neural networks and the extended generalized Pareto distribution, which allows us to model wildfire spread observed on an irregular spatial domain. We highlight the efficacy of our newly proposed model and perform a wildfire hazard assessment for Australia and population-dense communities, namely Tasmania, Sydney, Melbourne, and Perth.

AI poses a grave threat to journalism, experts warned Congress at a hearing on Wednesday. Media executives and academic experts testified before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law about how AI is contributing to the big tech-fueled decline of journalism. They also talked about intellectual property issues arising from AI models being trained on the work of journalists, and raised alarms about the increasing dangers of AI-powered misinformation. "The rise of big tech has been directly responsible for the decline in local news," said Senator Richard Blumenthal, a Connecticut Democrat and chair of the subcommittee. "First, Meta, Google and OpenAI are using the hard work of newspapers and authors to train their AI models without compensation or credit. Adding insult to injury, those models are then used to compete with newspapers and broadcasters, cannibalizing readership and revenue from the journalistic institutions that generate the content in the first place."

Over the past few months, many have attempted to translate the uncanniness of Gov. Ron DeSantis' smile into words. After the Republican debates, it's been called "painfully weird" and said to look "like it's on his face upside down." It resembles "a Disney World animatronic" or "an A.I. trying to learn human emotions." It even inspired The Daily Show to put out a public service announcement about "Frownington's Disease," a made-up condition that causes a person's smile to resemble a wince one would make upon "sitting on his own testicles." As nice as it is that one expression has inspired such rich verbiage and creativity--Ron DeSantis, unlikely muse!--you might find yourself longing for a more technical explanation.

Fox News host Greg Gutfeld gives his take on Democrats' fears about former President Trump winning the 2024 election on'Gutfeld!' Former President Obama is becoming increasingly anxious about the closeness of the 2024 presidential election and fears former President Trump could take back the White House, according to a report. Former Attorney General Eric Holder, one of Obama's closest confidants, told USA Today that if Trump were to win the Republican nomination and beat President Biden this November, there could be "incalculable damage" brought upon the country. Holder confirmed Obama "absolutely" holds the same views when asked by the publication. "I think that's what motivates him. I think that's what will continue to motivate him," Holder responded.

Microsoft has been quietly debating the future of its advanced AI lab in China, sources say. The lab was opened in 1998 and has become one of the most important artificial intelligence hubs in the world, leading to advancements in the company's speech, image and facial recognition software. Microsoft Research Lab Asia (MSRA) opened at a time of optimism about China as an emerging democracy but as tensions between the US and the communist state have intensified, internal pressure has mounted to shut or scale it down. That pressure has only intensified in recent months, after the Biden administration banned US investments in Chinese tech ventures that might aid the rival superpower's'military, intelligence, surveillance, or cyber-enabled capabilities.' But, the tech giant's founder Bill Gates continues to defend the lab and has pushed to keep it open, alongside Microsoft's research leaders and its current president.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. New Jersey Democratic Gov. Phil Murphy on Tuesday announced a series of new measures he wants the newly expanded Democrat-led Legislature to adopt, including allowing 16-year-olds to vote in school board elections, reducing medical debt, expanding affordable housing and launching an artificial intelligence "moonshot." Murphy delivered his sixth state of the state address before a joint legislative session in the ornate Assembly chamber where Democrats picked up six seats in the November election. Murphy also reiterated calls he's made since his reelection in 2021 to further ease property taxes and expand free pre-K, among the measures that he says make the state "stronger and fairer."

The Israeli military has seemingly walked back its justification for targeting a vehicle in Gaza last week, killing two Al Jazeera journalists, United States broadcaster NBC reported. Hamza Dahdouh, the eldest son of Al Jazeera's Gaza bureau chief Wael Dahdouh, was killed in an Israeli missile strike on Sunday in Khan Younis, southern Gaza. Journalist Mustafa Thuraya was also killed in the attack, while a third passenger, journalist Hazem Rajab, was seriously injured. At the time of the attack, the Israeli army said it was targeting a "terrorist" in the vehicle. It confirmed in a statement that a military aircraft "identified and struck a terrorist who operated an aircraft that posed a threat to (Israeli) troops," adding that "we are aware of the reports that during the strike, two other suspects who were in the same vehicle as the terrorist were also hit".