The $20 million National Science Foundation award will help UAB and eight other Alabama-based universities build research infrastructure. UABs share will be about $2 million.

Yogesh Vohra Yogesh Vohra, Ph.D., is a co-principal investigator on a National Science Foundation award that will bring the University of Alabama at Birmingham about $2 million over five years.

The total NSF EPSCoR Research Infrastructure Improvement Program award of $20 million with its principal investigator Gary Zank, Ph.D., based at the University of Alabama in Huntsville will help strengthen research infrastructure at UAB, UAH, Auburn University, Tuskegee University, the University of South Alabama, Alabama A&M University, Alabama State University, Oakwood University, and the University of Alabama.

The award, Future technologies and enabling plasma processes, or FTPP, aims to develop new technologies using plasma in hard and soft biomaterials, food safety and sterilization, and space weather prediction. This project will build plasma expertise, research and industrial capacity, as well as a highly trained and capable plasma science and engineering workforce, across Alabama.

Unlike solids, liquids and gas, plasma the fourth state of matter does not exist naturally on Earth. This ionized gaseous substance can be made by heating neutral gases. At UAB, Vohra, a professor and university scholar in the UAB Department of Physics, has employed microwave-generated plasmas to create thin diamond films that have many potential uses, including super-hard coatings and diamond-encapsulated sensors for extreme environments. This new FTPP grant will support research into plasma synthesis of materials that maintain their strength at high temperatures, superconducting thin films and developing plasma surface modifications that incorporate antimicrobial materials in biomedical implants.

Vohra says the UAB Department of Physics will mostly use its share of the award to support faculty in the UAB Center for Nanoscale Materials and Biointegration and two full-time postdoctoral scholars, and support hiring of a new faculty member in computational physics with a background in machine-learning. The machine-learning predictions using the existing databases on materials properties will enable our research team to reduce the time from materials discovery to actual deployment in real-world applications, Vohra said.

The NSF EPSCoR Research Infrastructure Improvement Program helps establish partnerships among academic institutions to make sustainable improvements in research infrastructure, and research and development capacity. EPSCoR is the acronym for Established Program to Stimulate Competitive Research, an effort to level the playing field for states, territories and a commonwealth that historically have received lesser amounts of federal research and development funding.

Jurisdictions can compete for NSF EPSCoR awards if their five-year level of total NSF funding is less than 0.75 percent of the total NSF budget. Current qualifiers include Alabama, 22 other states, and Guam, the U.S. Virgin Islands and Puerto Rico.

Besides Alabama, the other four 2022 EPSCoR Research Infrastructure Improvement Program awardees are Hawaii, Kansas, Nevada and Wyoming.

In 2017, UAB was part of another five-year, $20 million NSF EPSCoR award to Alabama universities.

The Department of Physics is part of the UAB College of Arts and Sciences.

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NSF award will boost UAB research in machine-learning-enabled plasma synthesis of novel materials - University of Alabama at Birmingham

In our latest Innovation Spotlight, Cognex introduces a vision system that automates error detection in minutes without PC or programming skills.

Increasingly, packaging products require their own custom inspection systems to perfect quality, eliminate false rejects, improve throughput, and eliminate the risk of a recall. Some of the foundational machine vision applications along a packaging line include verifying that a label on a package is present, correct, straight, and readable. Other simple packaging inspections involve presence, position, quality, and readability on a label.

But packaging like bottles, cans, cases, and boxes cant always be accurately inspected by traditional machine vision. For applications that present variable, unpredictable defects on confusing surfaces such as those that are highly patterned or suffer from specular glare, manufacturers have typically relied on the flexibility and judgment-based decision-making of human inspectors. Yet human inspectors have some very large tradeoffs for the modern consumer packaged goods industry: they arent necessarily scalable.

Deep Learning expands the range of possible inspection applications

For applications which resist automation yet demand high quality and throughput, Deep Learning technology is a flexible tool that application engineers can have confidence in as their packaging needs grow and change.

Deep Learning technology from Cognex can handle all different types of packaging surfaces, including paper, glass, plastics, and ceramics, as well as their labels. Be it a specific defect on a printed label or the cutting zone for a piece of packaging, Deep Learning solutions can identify all these regions of interest simply by learning the varying appearance of the targeted zone.

Using an array of tools, Deep Learning can then locate and count complex objects or features, detect anomalies, and classify said objects or even entire scenes. And finally, it can recognize and verify alphanumeric characters using a pre-trained font library.

A simple solution, even for complex tasks

While manufacturers recognize the importance of digitalizing their processes using artificial intelligence, many are still hesitant to invest in them because of a lack of resources. Yet the combination of machine vision and Deep Learning is the on-ramp for companies to adopt smarter technologies that will give them the scale, precision, efficiency, and financial growth for the next generation.

A new, full-featured vision system now puts the power of Deep Learning-based image analysis into an easy-to-use package that gets error-proofing applications running in minutes.

The In-Sight 2800 system can be trained with just a few images to automate everything from simple pass/fail inspections to advanced classification and sorting - no PC or programming is needed. The interface guides users through the application development process step-by-step, making it simple for even new vision users to set up any job.

Changes in products and materials or line speed? Not a problem!

The combination of Deep Learning and traditional vision tools gives users the flexibility to solve a broad range of inspection applications. Tools can be used individually for simple jobs or chained together for more complex logic sequences. A powerful classifying tool can be trained using as few as five images to identify and sort defects into different categories and correctly identify parts with variation.

The new In-Sight 2800 system also offers a wide variety of accessories and field-changeable components to help users adapt quickly to changes such as new parts, faster line speeds and higher quality standards.

Watch this video to see why In-Sight 2800 is the easy choice for your next machine vision deployment and enter for your chance to win an In-Sight 2800:

https://connect.cognex.com/IS2800-Giveaway-LP-EN?src=7014W000000urCPQAY

This content was sponsored by Cognex.

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Leveraging the benefits of deep learning in the smart factory - Packaging Europe

LONDON, May 24, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Companys research report on the artificial intelligence (AI) in drug discovery market, the rising adoption of cloud-based applications and services by pharmaceutical companies will contribute to the growth of AI in the drug discovery market. Among the various end-users of cloud-based drug discovery platforms, pharmaceutical vendors are likely to be major stakeholders, holding a high-value share of the global cloud-based drug discovery platform market. An opportunity analysis of the global market reveals that leading software vendors have already adopted cloud-based drug discovery platforms to facilitate seamless research and development processes. Moreover, the cloud-based drug discovery platform revolution will witness significant growth in the coming years, thereby creating better opportunities for software vendors for growth and expansion. For example, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company, announced in December 2021 that it is collaborating with Pfizer to develop innovative, cloud-based solutions that have the potential to improve how new medicines are developed, manufactured, and distributed for clinical trials. The companies are exploring these advances through their newly created Pfizer Amazon Collaboration Team (PACT) initiative, which applies AWS capabilities in analytics, machine learning, compute, storage, security, and cloud data warehousing to Pfizer laboratory, clinical manufacturing, and clinical supply chain efforts. Thus, the increasing adoption of cloud-based applications and services by pharmaceutical companies will contribute positively to the AI drug discovery market size.

Request for a sample of the global artificial intelligence (AI) in drug discovery market report

The global artificial intelligence in drug discovery market size is expected to grow from $0.79 billion in 2021 to $1.04 billion in 2022 at a compound annual growth rate (CAGR) of 31.6%. The growth in the market is mainly due to the companies resuming their operations and adapting to the new normal while recovering from the COVID-19 impact, which had earlier led to restrictive containment measures involving social distancing, remote working, and the closure of commercial activities that resulted in operational challenges. The AI in drug discovery market is expected to reach $2.99 billion in 2026 at a CAGR of 30.2%.

Use of AI through Machine Learning (ML) is a trend in assessing pre-clinical studies during the drug development process. Pre-clinical studies are non-clinical studies for novel drug substances to establish clinical efficacy and safety in a controlled environment before testing with a final target population. ML modelling pharmacokinetic (PK) and pharmacodynamic (PD) methodologies are applied in in-vitro and preclinical PK studies to successfully anticipate the dose concentration response relationship of pipeline assets. In addition, deep learning methodologies are employed as In-Silico methods for successfully predicting the therapeutic/pharmacological properties of novel molecules by utilizing transcriptomic data, which includes various biological systems and controlled conditions. Besides the drug discovery market, machine learning technology finds its application in the AI in medical diagnostics market as well as AI in medical imaging market.

Major players in the artificial intelligence for drug discovery and development market are IBM Corporation, Microsoft, Atomwise Inc., Deep Genomics, Cloud Pharmaceuticals, Insilico Medicine, Benevolent AI, Exscientia, Cyclica, and BIOAGE.

The global artificial intelligence in drug discovery market report is segmented by technology into deep learning, machine learning; by drug type into small molecule, large molecules; by disease type into metabolic disease, cardiovascular disease, oncology, neurodegenerative diseases, others; by end-users into pharmaceutical companies, biopharmaceutical companies, academic and research institutes, others.

In 2021, North America was the largest region in the artificial intelligence (AI) in drug discovery market. It was followed by the Asia-Pacific, Western Europe, and then the other regions. The regions covered in the AI in drug discovery market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, and Africa.

Artificial Intelligence (AI) In Drug Discovery Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide artificial intelligence (AI) in drug discovery market overviews, artificial intelligence (AI) in drug discovery market analyze and forecast market size and growth for the whole market, artificial intelligence (AI) in drug discovery market segments and geographies, artificial intelligence (AI) in drug discovery market trends, artificial intelligence (AI) in drug discovery market drivers, artificial intelligence (AI) in drug discovery market restraints, artificial intelligence (AI) in drug discovery market leading competitors revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors approaches.

Not the market you are looking for? Check out some similar market intelligence reports:

AI In Pharma Global Market Report 2022 By Technology (Context-Aware Processing, Natural Language Processing, Querying Method, Deep Learning), By Drug Type (Small Molecule, Large Molecules), By Application (Diagnosis, Clinical Trial Research, Drug Discovery, Research And Development, Epidemic Prediction) Market Size, Trends, And Global Forecast 2022-2026

Artificial Intelligence In Healthcare Global Market Report 2022 By Offering (Hardware, Software), By Algorithms (Deep Learning, Querying Method, Natural Language Processing, Context Aware Processing), By Application (Robot-Assisted Surgery, Virtual Nursing Assistant, Administrative Workflow Assistance, Fraud Detection, Dosage Error Reduction, Clinical Trial Participant Identifier, Preliminary Diagnosis), By End User(Hospitals And Diagnostic Centers, Pharmaceutical And Biopharmaceutical Companies, Healthcare Payers, Patients) Market Size, Trends, And Global Forecast 2022-2026

Cloud Services Global Market Report 2022 By Type (Software As A Service (SaaS), Platform As A Service (PaaS), Infrastructure As A Service (IaaS), Business Process As A Service (BPaaS)), By End-User Industry (BFSI, Media And Entertainment, IT And Telecommunications, Energy And Utilities, Government And Public Sector, Retail And Consumer Goods, Manufacturing), By Application (Storage, Backup, And Disaster Recovery, Application Development And Testing, Database Management, Business Analytics, Integration And Orchestration, Customer Relationship Management), By Deployment Model (Public Cloud, Private Cloud, Hybrid Cloud), By Organization Size (Large Enterprises, Small And Medium Enterprises) Market Size, Trends, And Global Forecast 2022-2026

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Artificial Intelligence (AI) In Drug Discovery Market Growth Is Driven At A 30% Rate With Increasing Adoption Of Cloud-Based Applications And Services...

The future of high-performance computing will be virtualized, VMware's Uday Kurkure has told The Register.

Kurkure, the lead engineer for VMware's performance engineering team, has spent the past five years working on ways to virtualize machine-learning workloads running on accelerators. Earlier this month his team reported "near or better than bare-metal performance" for Bidirectional Encoder Representations from Transformers (BERT) and Mask R-CNN two popular machine-learning workloads running on virtualized GPUs (vGPU) connected using Nvidia's NVLink interconnect.

NVLink enables compute and memory resources to be shared across up to four GPUs over a high-bandwidth mesh fabric operating at 6.25GB/s per lane compared to PCIe 4.0's 2.5GB/s. The interconnect enabled Kurkure's team to pool 160GB of GPU memory from the Dell PowerEdge system's four 40GB Nvidia A100 SXM GPUs.

"As the machine learning models get bigger and bigger, they don't fit into the graphics memory of a single chip, so you need to use multiple GPUs," he explained.

Support for NVLink in VMware's vSphere is a relatively new addition. By toggling NVLink on and off in vSphere between tests, Kurkure was able to determine how large of an impact the interconnect had on performance.

And in what should be a surprise to no one, the large ML workloads ran faster, scaling linearly with additional GPUs, when NVLink was enabled.

Testing showed Mask R-CNN training running 15 percent faster in a twin GPU, NVLink configuration, and 18 percent faster when using all four A100s. The performance delta was even greater in the BERT natural language processing model, where the NVLink-enabled system performed 243 percent faster when running on all four GPUs.

What's more, Kurkure says the virtualized GPUs were able to achieve the same or better performance compared to running the same workloads on bare metal.

"Now with NVLink being supported in vSphere, customers have the flexibility where they can combine multiple GPUs on the same host using NVLink so they can support bigger models, without a significant communication overhead," Kurkure said.

Based on the results of these tests, Kurkure expects most HPC workloads will be virtualized moving forward. The HPC community is always running into performance bottlenecks that leaves systems underutilized, he added, arguing that virtualization enables users to make much more efficient use of their systems.

Kurkure's team was able to achieve performance comparable to bare metal while using just a fraction of the dual-socket system's CPU resources.

"We were only using 16 logical cores out of 128 available," he said. "You could use that CPU resources for other jobs without affecting your machine-learning intensive graphics modules. This is going to improve your utilization, and bring down the cost of your datacenter."

By toggling on and off NVLink between GPUs, additional platform flexibility can be achieved by enabling multiple isolated AI/ML workloads to be spread across the GPUs simultaneously.

"One of the key takeaways of this testing was that because of the improved utilization offered by vGPUs connected over a NVLink mesh network, VMware was able to achieve bare-metal-like performance while freeing idle resources for other workloads," Kurkure said.

VMWare expects these results to improve resource utilization in several applications, including investment banking, pharmaceutical research, 3D CAD, and auto manufacturing. 3D CAD is a particularly high-demand area for HPC virtualization, according to Kurkure, who cited several customers looking to implement machine learning to assist with the design process.

And while it's possible to run many of these workloads on GPUs in the cloud, he argued that cost and/or intellectual property rules may prevent them from doing so.

An important note is VMware's tests were conducted using Nvidia's vGPU Manager in vSphere as opposed to the hardware-level partitioning offered by multi-instance GPU (MIG) on the A100. MIG essentially allows the A100 to behave like up to seven less-powerful GPUs.

By comparison, vGPUs are defined in the hypervisor and are time-sliced. You can think of this as multitasking where the GPU rapidly cycles through each vGPU workload until they're completed.

The benefit of vGPUs is users can scale well beyond seven GPU instances at the cost of potential overheads associated with rapid context switching, Kurkure explained. However, at least in his testing, the use of vGPUs didn't appear to have a negative impact on performance compared to running on bare metal with the GPUs passed through to the VM.

Whether MIG would change this dynamic remains to be seen and is the subject of another ongoing investigation by Kurkure's team. "It's not clear when you should be using vGPU and when we should be running in MIG mode," he said.

With vGPU with NVLink validated for scale-up workloads, VMware is now exploringoptions such as how these workloads scale across multiple systems and racks over RDMA over converged Ethernet (RoCE). Here, he says, networking becomes a major consideration.

"The natural extension of this is scale out," he said. "So, we'll have a number of hosted connected by RoCE."

VMware is also investing how virtualized GPUs perform with even larger AI/ML models,

Kurkure's team is also investigating how these architectures scale with even larger AI/ML, like GPT-3, as well as how they can be applied to telco workloads running at the edge.

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VMware claims 'bare-metal' performance on virtualized GPUs - The Register