Once COVID-19 vaccines are available to children, Los Angeles students will have to be immunized before they can return to campus, Supt. He did not, however, suggest that campuses remain closed until the vaccines are available. Instead, he said, the state should set the standards for reopening schools, explain the reasoning behind the standards, and then require campuses to open when these standards are achieved. A COVID-19 vaccine requirement would be "no different than students who are vaccinated for measles or mumps," Beutner said in a pre-recorded briefing. He also compared students, staff and others getting a COVID-19 vaccine to those who "are tested for tuberculosis before they come on campus. That's the best way we know to keep all on a campus safe."

Faux burgers and chicken nuggets are having a moment. In 2018, Impossible Foods, the Silicon Valley–based alternative meat company known for its plant-based burger that "bleeds" like the real thing, made its nationwide fast food debut at all White Castle locations in the US. A year later, KFC partnered with Los Angeles–based alt-protein producer Beyond Meat to create a plant-based fried chicken that is "finger lickin' good." And since the onset of the pandemic, sales of plant-based meats from supermarkets have more than doubled. Once reserved only for hippies and PETA employees, protein alternatives have finally entered the mainstream. But that doesn't mean industrial animal agriculture is on its way out.

Saudi Crown Prince Mohammed bin Salman is promising to build a network of smart cities that won't have any cars or roads. It's called The Line, due to its arrangement of "hyper-connected future communities," and will form part of NEOM, a $500 billion project announced in October 2017. According to the prince, the development will offer "ultra-high-speed transit," autonomous vehicles and an urban layout that ensures basic facilities, such as schools and medical clinics, are never more than a five-minute walk away. "It is expected no journey will be longer than 20 minutes," the project's organizers claimed in a press release today. One million people are supposed to live inside The Line.

Mr. Trump signed an executive order on Tuesday banning transactions with eight Chinese software applications, including Alipay. It was the latest escalation of the president's economic war with China. Details and the start of the ban will fall to Mr. Biden, who could decide not to follow through on the idea. Separately, the Trump administration has also banned the import of some cotton from the Xinjiang region, where China has detained vast numbers of people who are members of ethnic minorities and forced them to work in fields and factories. In another move, the administration prohibited several Chinese companies, including the chip maker SMIC and the drone maker DJI, from buying American products.

Here's what you need to know as you start your day ... Pelosi: Trump'imminent threat' to'our Democracy,' lawmakers moving forward with impeachment The House will be moving forward with a resolution to impeach President Trump, said House Speaker Nancy Pelosi, referring to the president in a letter to colleagues as an "imminent threat" to both the U.S. Constitution and democracy. In the letter Sunday, Pelosi said the House will act with "great solemnity" with less than two weeks remaining before Trump is set to leave office. "In protecting our Constitution and our Democracy, we will act with urgency, because this President represents an imminent threat to both," she said. Pelosi said the House will try to force Vice President Mike Pence and the Cabinet to oust Trump by invoking the 25th Amendment. On Monday, House leaders will work to swiftly pass legislation to do that.

We present observations of a region of the Galactic plane taken during the Early Science Program of the Australian Square Kilometre Array Pathfinder (ASKAP). In this context, we observed the SCORPIO field at 912 MHz with an uncompleted array consisting of 15 commissioned antennas. The resulting map covers a square region of ~40 deg^2, centred on (l, b)=(343.5{\deg}, 0.75{\deg}), with a synthesized beam of 24"x21" and a background rms noise of 150-200 {\mu}Jy/beam, increasing to 500-600 {\mu}Jy/beam close to the Galactic plane. A total of 3963 radio sources were detected and characterized in the field using the CAESAR source finder. We obtained differential source counts in agreement with previously published data after correction for source extraction and characterization uncertainties, estimated from simulated data. The ASKAP positional and flux density scale accuracy were also investigated through comparison with previous surveys (MGPS, NVSS) and additional observations of the SCORPIO field, carried out with ATCA at 2.1 GHz and 10" spatial resolution. These allowed us to obtain a measurement of the spectral index for a subset of the catalogued sources and an estimated fraction of (at least) 8% of resolved sources in the reported catalogue. We cross-matched our catalogued sources with different astronomical databases to search for possible counterparts, finding ~150 associations to known Galactic objects. Finally, we explored a multiparametric approach for classifying previously unreported Galactic sources based on their radio-infrared colors.

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics.

"Listen Josef," said the Czech playwright Karel Čapek to his brother. "I have an idea for a play." Josef, an artist of some renown, was painting furiously and unimpressed by his brother's intrusion. "What kind of play?" he asked, sharply. Karel set out the plot.

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.

A large fraction of textual data available today contains various types of 'noise', such as OCR noise in digitized documents, noise due to informal writing style of users on microblogging sites, and so on. To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i.e., for cleaning different kinds of noise in the text. There have been several efforts towards cleaning or normalizing noisy text; however, many of the existing text normalization methods are supervised and require language-dependent resources or large amounts of training data that is difficult to obtain. We propose an unsupervised algorithm for text normalization that does not need any training data / human intervention. The proposed algorithm is applicable to text over different languages, and can handle both machine-generated and human-generated noise. Experiments over several standard datasets show that text normalization through the proposed algorithm enables better retrieval and stance detection, as compared to that using several baseline text normalization methods. Implementation of our algorithm can be found at https://github.com/ranarag/UnsupClean.