Here's what you need to know as you start your day Fauci says'hopefully' making young kids wear masks won't have'lasting negative impact' White House Chief Health Adviser Dr. Anthony Fauci said Monday that "hopefully" making young kids wear face masks won't have any "lasting negative impact" on them. During an interview with conservative radio host Hugh Hewitt, Fauci said it's important to keep an "open mind" about masking after the Centers for Disease Control and Prevention recommended that unvaccinated children ages 2 and older wear masks and that students wear masks in all K-12 schools, regardless of vaccination status, in light of the rapid spread of the COVID-19 delta variant. "It's not comfortable, obviously, for children to wear masks, particularly the younger children," he said. "But you know, what we're starting to see, Hugh, and I think it's going to unfold even more as the weeks go by, that this virus not only is so extraordinarily transmissible, but we're starting to see pediatric hospitals get more and more younger people and kids not only numerically, but what seems to be more severe disease. "Now we're tracking that, the CDC is tracking that really very carefully, so it's going to be a balance that we would feel very badly if we all of a sudden said OK, kids, don't wear masks, then you find out retrospectively that this virus in a very, very strange and unusual way is really hitting kids really hard," he continued. "But hopefully, this will be a temporary thing, temporary enough that it doesn't have any lasting negative impact on them." Hewitt pushed back, citing an editorial Sunday by The Wall Street Journal, titled, "The Case Against Masks for Children," which argues that long-term masking can cause physical and developmental issues in children and that there's little evidence to back up a mandate. "Facial expressions are integral to human connection, particularly for younger children who are only learning how to signal fear, confusion and happiness," Hewitt said. "Covering a child's face mutes these nonverbal forms of communication, can result in robotic and emotionless interaction.

Tree squirrels are the Olympic divers of the rodent world, leaping gracefully among branches and structures high above the ground. And as with human divers, a squirrel's success in this competition requires both physical strength and mental adaptability. Two species – the eastern gray squirrel (Sciurus carolinensis) and the fox squirrel (Sciurus niger) – thrive on campus landscapes and are willing participants in our behavioral experiments. They are also masters in two- and three-dimensional spatial orientation – using sensory cues to move through space. In a newly published study, we show that squirrels leap and land without falling by making trade-offs between the distance they have to cover and the springiness of their takeoff perch.

Paige is a software company helping pathologists and clinicians make faster, more informed diagnostic and treatment decisions by mining decades of data from the world's experts in cancer care. We are leading a digital transformation in pathology by leveraging advanced Artificial Intelligence (AI) technology to create value for the oncology clinical team. We are the first company to develop clinical grade AI tools for the pathologist, which resulted in our receiving FDA breakthrough designation for our first product. Paige has also received FDA-clearance for our digital viewer, FullFocus . We have also established multiple relationships with biopharma, laboratory, and equipment manufacturers that enables Paige to develop an ecosystem ready to help patients receive better diagnoses and treatment.

All the sessions from Transform 2021 are available on-demand now. Before diving into cybersecurity and how the industry is using AI at this point, let's define the term AI first. Artificial intelligence (AI), as the term is used today, is the overarching concept covering machine learning (supervised, including deep learning, and unsupervised), as well as other algorithmic approaches that are more than just simple statistics. These other algorithms include the fields of natural language processing (NLP), natural language understanding (NLU), reinforcement learning, and knowledge representation. These are the most relevant approaches in cybersecurity.

Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US Census survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to study temporal shift and geographic variation. We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions. Our datasets are available at https://github.com/zykls/folktables.

Scene understanding is crucial for autonomous systems which intend to operate in the real world. Single task vision networks extract information only based on some aspects of the scene. In multi-task learning (MTL), on the other hand, these single tasks are jointly learned, thereby providing an opportunity for tasks to share information and obtain a more comprehensive understanding. To this end, we develop UniNet, a unified scene understanding network that accurately and efficiently infers vital vision tasks including object detection, semantic segmentation, instance segmentation, monocular depth estimation, and monocular instance depth prediction. As these tasks look at different semantic and geometric information, they can either complement or conflict with each other. Therefore, understanding inter-task relationships can provide useful cues to enable complementary information sharing. We evaluate the task relationships in UniNet through the lens of adversarial attacks based on the notion that they can exploit learned biases and task interactions in the neural network. Extensive experiments on the Cityscapes dataset, using untargeted and targeted attacks reveal that semantic tasks strongly interact amongst themselves, and the same holds for geometric tasks. Additionally, we show that the relationship between semantic and geometric tasks is asymmetric and their interaction becomes weaker as we move towards higher-level representations.

Researchers often spend weeks sifting through decades of unlabeled satellite imagery(on NASA Worldview) in order to develop datasets on which they can start conducting research. We developed an interactive, scalable and fast image similarity search engine (which can take one or more images as the query image) that automatically sifts through the unlabeled dataset reducing dataset generation time from weeks to minutes. In this work, we describe key components of the end to end pipeline. Our similarity search system was created to be able to identify similar images from a potentially petabyte scale database that are similar to an input image, and for this we had to break down each query image into its features, which were generated by a classification layer stripped CNN trained in a supervised manner. To store and search these features efficiently, we had to make several scalability improvements. To improve the speed, reduce the storage, and shrink memory requirements for embedding search, we add a fully connected layer to our CNN make all images into a 128 length vector before entering the classification layers. This helped us compress the size of our image features from 2048 (for ResNet, which was initially tried as our featurizer) to 128 for our new custom model. Additionally, we utilize existing approximate nearest neighbor search libraries to significantly speed up embedding search. Our system currently searches over our entire database of images at 5 seconds per query on a single virtual machine in the cloud. In the future, we would like to incorporate a SimCLR based featurizing model which could be trained without any labelling by a human (since the classification aspect of the model is irrelevant to this use case).

EuroVoc is a multilingual thesaurus that was built for organizing the legislative documentary of the European Union institutions. It contains thousands of categories at different levels of specificity and its descriptors are targeted by legal texts in almost thirty languages. In this work we propose a unified framework for EuroVoc classification on 22 languages by fine-tuning modern Transformer-based pretrained language models. We study extensively the performance of our trained models and show that they significantly improve the results obtained by a similar tool - JEX - on the same dataset. The code and the fine-tuned models were open sourced, together with a programmatic interface that eases the process of loading the weights of a trained model and of classifying a new document.

NASA has been waiting six months for Perseverance to collect its inaugural Mars core samples, but the American space Agency will have to wait a little longer after the rover failed during its first attempt. The percussive drill, coring bit and sample tube processing all worked'as intended, but data shows the sample tube was empty following extraction. The rover carries 43 titanium sample tubes, and is exploring Jezero Crater, where it will be gathering samples of rock and regolith (broken rock and dust) for future analysis on Earth. NASA scientists are currently analyzing the data with the hopes of finding the blunder and hopes to have a better understanding of what may occurred in the next few weeks. Jennifer Trosper, project manager for Perseverance at JPL, said in a statement: 'The initial thinking is that the empty tube is more likely a result of the rock target not reacting the way we expected during coring, and less likely a hardware issue with the Sampling and Caching System.

A ed-tech nonprofit will join four universities in launching a new institute dedicated to creating artificial intelligence tools that can be applied to human …