Graph Neural Networks (GNNs) demonstrate superior performance in various graph learning tasks, yet their wider real-world application is hindered by the computational overhead when applied to large-scale graphs. To address the issue, the Graph Lottery Hypothesis (GLT) has been proposed, advocating the identification of subgraphs and subnetworks, \textit{i.e.}, winning tickets, without compromising performance. The effectiveness of current GLT methods largely stems from the use of iterative magnitude pruning (IMP), which offers higher stability and better performance than one-shot pruning. However, identifying GLTs is highly computationally expensive, due to the iterative pruning and retraining required by IMP. In this paper, we reevaluate the correlation between one-shot pruning and IMP: while one-shot tickets are suboptimal compared to IMP, they offer a \textit{fast track} to tickets with a stronger performance. We introduce a one-shot pruning and denoising framework to validate the efficacy of the \textit{fast track}. Compared to current IMP-based GLT methods, our framework achieves a double-win situation of graph lottery tickets with \textbf{higher sparsity} and \textbf{faster speeds}. Through extensive experiments across 4 backbones and 6 datasets, our method demonstrates $1.32\% - 45.62\%$ improvement in weight sparsity and a $7.49\% - 22.71\%$ increase in graph sparsity, along with a $1.7-44 \times$ speedup over IMP-based methods and $95.3\%-98.6\%$ MAC savings.

One of the most widely used buzzwords in the logistics sector in 2022 is "digitalization." The word is a useful umbrella term for the evolution to computer-based processes from manual procedures that relied on pencils and clipboards in the warehouse or printed manifests at the loading dock. But references to the trend nearly always ignore the tactical steps needed to make digitalization happen. Your DC probably doesn't have a magic wand that transforms basic paper checklists into cloud-based software platforms. So how are practitioners driving toward the goal of pulling logistics processes into the 21st century?

Can artificial intelligence (AI) be used in judicial processes to reduce the pendency of cases? In response to this unstarred question in the Lok Sabha during the first part of the Budget session of Parliament, Law Minister Kiren Rijiju said that while implementing phase two of the eCourts projects, under operation since 2015, a need was felt to adopt new, cutting edge technologies of Machine Learning (ML) and Artificial Intelligence (AI) to increase the efficiency of the justice delivery system. "To explore the use of AI in judicial domain, the Supreme Court of India has constituted Artificial Intelligence Committee which has mainly identified application of AI technology in Translation of judicial documents; Legal research assistance and Process automation," Mr. Rijiju stated. Several law firms are now keen try out new technologies for a quick reference on judicial precedents and pronouncements on cases with similar legal issues at stake. Mumbai-based Riverus, a "legal tech" firm, has developed ML applications that peruse troves of cases, "understand" them, and parse cases that are similar in content -- very much like a human expert would do -- in a fraction of the time.

To make ALMDVs a daily reality, the first step is legislation. There are various ways to categorize AVs: people-carriers or goods-carriers; operating on public roads or private property; high speed or low speed, and so on. But which type of regulations should be apply to the Autonomous Last Mile Delivery Vehicle (ALMDV)? Is it a vehicle, a non-motor vehicle, a personal delivery device, or a robot? The answer to this question ultimately determines which lane an ALMDV will be allowed to drive.

The use of artificial intelligence (AI) in our daily lives was predicted in Hollywood movies decades ago and began to come true with Siri, Alexa and Smartphones. According to a white paper released recently by Mitchell International (the parent company of NAGS), artificial intelligence use in automotive claims is growing fast as a result of the COVID-19 pandemic, which made a transition to digital essential to decrease the spread of the virus from human to human. "As insurers embrace AI and its ability to improve the claims process, they are devoting a larger portion of their technology budgets to AI-enabled solutions. In fact, according to one report, 87% of carriers are now spending in excess of $5 million annually on these technologies, which is more than in the banking and retail sectors," Mitchell reported. Although new to the auto insurance industry, the science behind AI has existed for more than 50 years.

The use of artificial intelligence (AI) in our daily lives was predicted in Hollywood movies decades ago and began to come true with Siri, Alexa and smartphones. According to a white paper released recently by Mitchell International, parent company of NAGS, artificial intelligence use in automotive claims is growing fast as a result of the COVID-19 pandemic, which made a transition to digital essential to decrease the spread of the virus from human to human. "As insurers embrace AI and its ability to improve the claims process, they are devoting a larger portion of their technology budgets to AI-enabled solutions. In fact, according to one report, 87% of carriers are now spending in excess of $5 million annually on these technologies, which is more than in the banking and retail sectors," Mitchell reported. Although new to the auto insurance industry, the science behind AI has existed for more than 50 years.

Are you ready for it? According to Accenture, only 7% of organizations today have made the necessary investments in data science and AI to be considered "future-ready," and they will reap enormous rewards from the work they have put in (even with COVID-19). But the good news is the system integrator sees a rising tide of all boats thanks to data and AI in the future. In its new report, titled "Fast Track to Future-Ready Performance," Accenture surveyed 1,100 executives in 11 countries and 13 industries to gauge how their digital transformation journeys correlate with use of emerging technologies, like AI, analytics, cloud, blockchain, and robotic process automation (RPA). Accenture found that, three years ago, about four in five organizations fell in the bottom half of the spectrum.

MACHINE-LEARNING SYSTEMS USE DATA TO UNDERSTAND PATTERNS and make predictions. When the system is predicting which photos are of cats, you may not care how certain it is about its results. But if it's predicting the fastest route to the hospital, the amount of uncertainty becomes critically important. "Imagine the system tells you'Route A takes 9 minutes' and'Route B takes 10 minutes.' Route A sounds better," says Tamara Broderick, an associate professor in the Department of Electrical Engineering and Computer Science. "But now it turns out that Route A takes 9 minutes plus-or-minus 5, and Route B takes 10 minutes plus-or-minus 1. If you need a life-saving procedure in 12 minutes, suddenly your decision making really changes."

Foundation today announces general availability of the newest, fastest, most powerful BeagleBoard.org Built on our proven open source Linux approach, BeagleBone AI fills the gap between small single board computers (SBCs) and more powerful industrial computers. Leveraging the Texas Instruments Sitara AM5729 processor, developers have access to powerful machine learning capabilities with the ease of the BeagleBone Black header and mechanical compatibility. BeagleBone AI makes it easy to explore how artificial intelligence (AI) and machine learning can be used in everyday life. Through BeagleBone AI, developers can take advantage of the TI C66x digital-signal-processor (DSP) cores and embedded-vision-engine (EVE) cores on the Sitara AM5729 processor.

D. Tyler McQuade, Ph.D., a professor in the Virginia Commonwealth University College of Engineering, is principal investigator of a multi-university project seeking to use artificial intelligence to help scientists come up with the perfect molecule for everything from a better shampoo to coatings on advanced microchips. The project is one of the first in the U.S. to be selected for $994,433 in funding as part of a new pilot project of the National Science Foundation called the Convergence Accelerator (C-Accel). McQuade and his collaborators will pitch their prototype in March 2020 in a bid for additional funding of up to $5 million over five years. Adam Luxon, a Ph.D. student in the Department of Chemical and Life Science Engineering who has been involved from the beginning, explained it this way: "We want to essentially make the Alexa of chemistry." Just as Amazon, Google and Netflix use data algorithms to suggest customized predictions, the team plans to build an open network that can combine and help users make sense of molecular sciences data pulled from a range of sources including academia, industry and government.