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Chemical engineers and materials scientists are continuously looking for the following groundbreaking material, chemical, or medication. The emergence of machine-learning technologies has accelerated the discovery process, which may typically take years. Ideally, the objective is to train a machine-learning model on a few known chemical samples and then let it build as many manufacturable molecules of the same class with predictable physical attributes as feasible. You can develop new molecules with ideal characteristics if you have all of these components and the know-how to synthesize them.

However, current approaches need large datasets for training models. Many class-specific chemical databases only contain a few example compounds, restricting their capacity to generalize and construct biological molecules that might be generated in the real world.

This issue was addressed by a team of researchers from MIT and IBM by employing a generative graph model to create new synthesizable compounds within the same training data’s chemical class. The research was presented in a research paper. They model the production of atoms and chemical bonds as a graph and create a graph grammar — a linguistic analog of systems and structures for word ordering — that provides a set of rules for constructing compounds like monomers and polymers.

It relies on a new “freeze-thaw” design. A recent study has just been published by U.S. scientists who have managed to develop an aluminum-nickel molten salt battery that can retain over 90% of its initial capacity over a period of up to 12 weeks. Having an energy density of 260 W/hour per kg, the new battery was built with an aluminum anode and a nickel cathode, immersed in a molten-salt electrolyte.


The breakthrough could have many applications in soft robotics including in the Metaverse.

The researchers were inspired by actual skin. Researchers have been working on robot dexterity for several years now trying to give the machines human-like sensitivity. This has been no easy task as even the most advanced machines struggle with this concept.


Now the team is working on making the artificial fingertip as sensitive to fine detail as the real thing. Currently, the 3D-printed skin is thicker than real skin which may be hindering this process. As such, Lepora’s team is now working on 3D-printing structures on the microscopic scale of human skin.

“Our aim is to make artificial skin as good – or even better — than real skin,” concluded Professor Lepora. The end result could have many applications in soft robotics including in the Metaverse.

Today Amazon and The Johns Hopkins University announced the creation of the JHU + Amazon Initiative for Interactive AI (AI2AI). The collaboration will focus on … See more.


Amazon and Johns Hopkins University (JHU) today announced the creation of the JHU + Amazon Initiative for Interactive AI (AI2AI).

The Amazon-JHU collaboration will focus on driving ground-breaking AI advances with an emphasis on machine learning, computer vision, natural language understanding, and speech processing. Sanjeev Khudanpur, an associate professor in the Department of Electrical and Computer Engineering, will serve as the founding director of the initiative.

Amazon’s sponsorship of AI2AI, which will be housed in JHU’s Whiting School of Engineering, underscores its commitment to partnering with academia to address the most complex challenges in Al, democratizing access to the benefits of Al innovations, and broadening participation in research from diverse, interdisciplinary scholars, and other innovators.

A pair of researchers working in the Personal Robotics Laboratory at Imperial College London has taught a robot to put a surgical gown on a supine mannequin. In their paper published in the journal Science Robotics, Fan Zhang and Yiannis Demiris described the approach they used to teach the robot to partially dress the mannequin. Júlia Borràs, with Institut de Robòtica i Informàtica Industrial, CSIC-UPC, has published a Focus piece in the same journal issue outlining the difficulties in getting robots to handle soft material and the work done by the researchers on this new effort.

As researchers and engineers continue to improve the state of robotics, one area has garnered a lot of attention—using robots to assist with health care. In this instance, the focus was on assisting patients in a who have lost the use of their limbs. In such cases, dressing and undressing falls to healthcare workers. Teaching a robot to dress patients has proven to be challenging due to the nature of the soft materials used to make clothes. They change in a near infinite number of ways, making it difficult to teach a robot how to deal with them. To overcome this problem in a clearly defined setting, Zhang and Demiris used a new approach.

The setting was a simulated hospital room with a mannequin lying face up on a bed. Nearby was a hook affixed to the wall holding a surgical gown that is worn by pushing arms forward through sleeves and tying in the back. The task for the robot was to remove the gown from the hook, maneuver it to an optimal position, move to the bedside, identify the “patient” and its orientation and then place the gown on the patient by lifting each arm one at a time and pulling the gown over each in a natural way.

Cybersecurity researchers have detailed a “simple but efficient” persistence mechanism adopted by a relatively nascent malware loader called Colibri, which has been observed deploying a Windows information stealer known as Vidar as part of a new campaign.

“The attack starts with a malicious Word document deploying a Colibri bot that then delivers the Vidar Stealer,” Malwarebytes Labs said in an analysis. “The document contacts a remote server at (securetunnel[.]co) to load a remote template named ‘trkal0.dot’ that contacts a malicious macro,” the researchers added.

First documented by FR3D.HK and Indian cybersecurity company CloudSEK earlier this year, Colibri is a malware-as-a-service (MaaS) platform that’s engineered to drop additional payloads onto compromised systems. Early signs of the loader appeared on Russian underground forums in August 2021.

Say cheese! Researchers have developed a tiny camera that takes amazingly clear photos. Just don’t sneeze while it’s in your hand. At the size of a coarse grain of salt, you may never find it again.

Smaller cameras could mean lighter smartphones and new James Bond–style gadgets. But that’s not all. Cameras on this scale could swim through the body, hitch a ride on an insect, scope out your brain or monitor hostile environments. And those are just a few of the possibilities.

How do you pack that much picture-taking power into something the size of a crumb? It takes a “radically different approach” to making a camera lens, says Felix Heide. He’s a computer scientist at Princeton University in New Jersey. His lab developed the camera with colleagues from the University of Washington in Seattle. The team shared its work in Nature Communications in November.