Organizers say the "No Kings" protests drew more than 7 million people across 2,700 cities. The crowds included high-profile politicians, A-list celebrities, and more than a few creative inflatables. On Saturday, crowds gathered in cities across the United States to protest President Donald Trump and his administration. Organizers of the No Kings rallies claim that more than 7 million people attended in all, across 2,700 cities in the Unites States and beyond. The gatherings provided a clear picture not only of how widespread the resistance to the Trump administration has become, but also the diversity of the coalition driving it.

Errors or failures in a high-volume manufacturing environment can have significant impact that can result in both the loss of time and money. Identifying such failures early has been a top priority for manufacturing industries and various rule-based algorithms have been developed over the years. However, catching these failures is time consuming and such algorithms cannot adapt well to changes in designs, and sometimes variations in everyday behavior. More importantly, the number of units to monitor in a high-volume manufacturing environment is too big for manual monitoring or for a simple program. Here we develop a novel program that combines both rule-based decisions and machine learning models that can not only learn and adapt to such day-to-day variations or long-term design changes, but also can be applied at scale to the high number of manufacturing units in use today. Using the current state-of-the-art technologies, we then deploy this program at-scale to handle the needs of ever-increasing demand from the manufacturing environment.

Artificial intelligence can already write poems and make movie recommendations. What's it going to do next? High school students in Longmont, Colorado, are learning how to design their own AI model projects at the St. Vrain Valley School District Innovation Center. The program started this past fall. Mai Vu, the A.I. Program manager at St. Vrain Valley School District, said the AI program's goal is to teach students how to use AI to solve real-world problems.

Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient outcomes. However, much of the processing in HSI uses simplified linear methods that are unable to capture the non-linear, wavelength-dependent phenomena that must be modeled for accurate recovery of fluorophore abundances. We therefore propose two deep learning models for correction and unmixing, which can account for the nonlinear effects and produce more accurate estimates of abundances. Both models use an autoencoder-like architecture to process the captured spectra. One is trained with protoporphyrin IX (PpIX) concentration labels. The other undergoes semi-supervised training, first learning hyperspectral unmixing self-supervised and then learning to correct fluorescence emission spectra for heterogeneous optical and geometric properties using a reference white-light reflectance spectrum in a few-shot manner. The models were evaluated against phantom and pig brain data with known PpIX concentration; the supervised model achieved Pearson correlation coefficients (R values) between the known and computed PpIX concentrations of 0.997 and 0.990, respectively, whereas the classical approach achieved only 0.93 and 0.82. The semi-supervised approach's R values were 0.98 and 0.91, respectively. On human data, the semi-supervised model gives qualitatively more realistic results than the classical method, better removing bright spots of specular reflectance and reducing the variance in PpIX abundance over biopsies that should be relatively homogeneous. These results show promise for using deep learning to improve HSI in fluorescence-guided neurosurgery.

To mitigate the potential misuse of large language models (LLMs), recent research has developed watermarking algorithms, which restrict the generation process to leave an invisible trace for watermark detection. Due to the two-stage nature of the task, most studies evaluate the generation and detection separately, thereby presenting a challenge in unbiased, thorough, and applicable evaluations. In this paper, we introduce WaterBench, the first comprehensive benchmark for LLM watermarks, in which we design three crucial factors: (1) For \textbf{benchmarking procedure}, to ensure an apples-to-apples comparison, we first adjust each watermarking method's hyper-parameter to reach the same watermarking strength, then jointly evaluate their generation and detection performance. (2) For \textbf{task selection}, we diversify the input and output length to form a five-category taxonomy, covering $9$ tasks. (3) For \textbf{evaluation metric}, we adopt the GPT4-Judge for automatically evaluating the decline of instruction-following abilities after watermarking. We evaluate $4$ open-source watermarks on $2$ LLMs under $2$ watermarking strengths and observe the common struggles for current methods on maintaining the generation quality. The code and data are available at \url{https://github.com/THU-KEG/WaterBench}.

Jason Allen, a video game designer in Pueblo, Colorado, spent roughly 80 hours working on his entry to the Colorado State Fair's digital arts competition. Judges awarded him first place, which came with a $300 prize. But when Allen posted about his win on social media late last month, his artwork went viral--for all the wrong reasons. Allen's victory took a turn when he revealed online that he'd created his prize-winning art using Midjourney, an artificial intelligence program that can turn text descriptions into images. He says he also made that clear to state fair officials when he dropped off his submission, called Théâtre D'opéra Spatial.

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ProJo can also help students work together and assess their growth and weaknesses, in both robot form and on a computer screen. It is one of a variety of teaching aids in development, boosted by artificial intelligence, that scientists and educators say could support tomorrow's classrooms. Typically, AI education products serve one function, such as assessing a student's literacy, tailoring tools to individual learners or performing administrative functions such as grading. Next-generation tools may do all of this in a single platform, serving at times as a peer learning partner, a group facilitator and a monitor for educators--a sort of superpowered teacher's assistant personalized for each student. A look at how innovation and technology are transforming the way we live, work and play.

Anomalous change detection (ACD) is an important problem in remote sensing image processing. Detecting not only pervasive but also anomalous or extreme changes has many applications for which methodologies are available. This paper introduces a nonlinear extension of a full family of anomalous change detectors. In particular, we focus on algorithms that utilize Gaussian and elliptically contoured (EC) distribution and extend them to their nonlinear counterparts based on the theory of reproducing kernels' Hilbert space. We illustrate the performance of the kernel methods introduced in both pervasive and ACD problems with real and simulated changes in multispectral and hyperspectral imagery with different resolutions (AVIRIS, Sentinel-2, WorldView-2, and Quickbird). A wide range of situations is studied in real examples, including droughts, wildfires, and urbanization. Excellent performance in terms of detection accuracy compared to linear formulations is achieved, resulting in improved detection accuracy and reduced false-alarm rates. Results also reveal that the EC assumption may be still valid in Hilbert spaces. We provide an implementation of the algorithms as well as a database of natural anomalous changes in real scenarios http://isp.uv.es/kacd.html.

T-Mobile hasn't been quick to fulfill its promises of launching TV service, but it finally has something to show following all the early hype: it's launching TVision Home, a rebranded and retuned version of Layer3's broadband-based IPTV service. It's not the fully independent streaming service you might have hoped for (that's coming later in 2019). However, the telecom is hoping to bring a dash of its straightforward "Uncarrier" strategy to the TV world -- provided you're willing to pay. To start, T-Mobile is bringing its no-shock billing to TV. You'll pay $100 per month ($90 if you're also a phone customer) for 150-plus channels and $10 per connected TV, but there are no hidden fees used to jack up the real price.