Discriminating the matched named entity pairs or identifying the entities' canonical forms are critical in text mining tasks. More precise named entity normalization in text mining will benefit other subsequent text analytic applications. We built the named entity normalization model with a novel Edge Weight Updating Neural Network. Our proposed model when tested on four different datasets achieved state-of-the-art results. We, next, verify our model's performance on NCBI Disease, BC5CDR Disease, and BC5CDR Chemical databases, which are widely used named entity normalization datasets in the bioinformatics field. We also tested our model with our own financial named entity normalization dataset to validate the efficacy for more general applications. Using the constructed dataset, we differentiate named entity pairs. Our model achieved the highest named entity normalization performances in terms of various evaluation metrics.

Abstract: Conversational agents are a recent trend in human-computer interaction, deployed in multidisciplinary applications to assist the users. In this paper, we introduce "Atreya", an interactive bot for chemistry enthusiasts, researchers, and students to study the ChEMBL database. Atreya is hosted by Telegram, a popular cloud-based instant messaging application. This user-friendly bot queries the ChEMBL database, retrieves the drug details for a particular disease, targets associated with that drug, etc. This paper explores the potential of using a conversational agent to assist chemistry students and chemical scientist in complex information seeking process.

Litigating Artificial Intelligence: When Does AI Violate Our Legal Rights? Read full article May 27, 2021, 3:20 PM ·3 min read From the minds of Canada's leading law and technology experts comes a playbook for understanding the multi-faceted intersection of AI and the law TORONTO, May 27, 2021 (GLOBE NEWSWIRE) -- We are living in an Artificial Intelligence (AI) boom. Self-driving cars, personal voice assistants, and facial recognition technology are just a few of the AI-enabled technologies permeating into everyday life. But what happens when AI causes harm or violates our rights? If your self-driving car gets into an accident while on autopilot, are you responsible? Emond Publishing, Canada's leading independent legal publisher, today announced the release of Litigating Artificial Intelligence, a book examining AI-informed legal determinations, AI-based lawsuits, and AI-enabled litigation tools. Anchored by the expertise of general editors Jill R. Presser, Jesse Beatson, and Gerald Chan, this title offers practical insights regarding AI's decision-making capabilities, position in evidence law and product-based lawsuits, role in automating legal work, and use by the courts, tribunals, and government agencies. For example, can government agencies use AI-powered facial recognition software to identify BLM protestors and Capitol rioters, or does this violate privacy rights? Who is liable, users, developers, or AI? What laws are in place to prevent AI-related crimes, and how do litigators prosecute the responsible parties?

If you could soar high in the sky, as red kites often do in search of prey, and look down at the domain of all things known and yet to be known, you would see something very curious: a vast class of things that science has so far almost entirely neglected. These things are central to our understanding of physical reality, both at the everyday level and at the level of the most fundamental phenomena in physics--yet they have traditionally been regarded as impossible to incorporate into fundamental scientific explanations. They are facts not about what is--"the actual"--but about what could or could not be. In order to distinguish them from the actual, they are called counterfactuals. Suppose that some future space mission visited a remote planet in another solar system, and that they left a stainless-steel box there, containing among other things the critical edition of, say, William Blake's poems. That the poetry book is subsequently sitting somewhere on that planet is a factual property of it. That the words in it could be read is a counterfactual property, which is true regardless of whether those words will ever be read by anyone.

After all, mined minerals have become essential for your cell phones, electric vehicles, solar panels, wind turbines, your computers, you name it. Plus, with a growing population, urbanization, demand for green energy, buildings, cars, and even more electronic gadgets, we could very well see an increased need for metals. What could make mining even more valuable, though, is that we already seem to be coming up short on essential metals, like copper, silver, platinum, palladium, nickel, cobalt, and rhodium. Goldman Sachs, for one, appeared to warn of a looming shortage of copper.(2) Also, "Fitch Ratings has revised some of its metals and mining price assumptions as prices for many commodities will benefit in the short term from returning demand while the supply response remains slow and inventories are running low," the company said in a report.(3)

MIT engineers have discovered a new way of generating electricity using tiny carbon particles that can create a current simply by interacting with liquid surrounding them. The liquid, an organic solvent, draws electrons out of the particles, generating a current that could be used to drive chemical reactions or to power micro- or nanoscale robots, the researchers say. "This mechanism is new, and this way of generating energy is completely new," says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT. "This technology is intriguing because all you have to do is flow a solvent through a bed of these particles. This allows you to do electrochemistry, but with no wires." In a new study describing this phenomenon, the researchers showed that they could use this electric current to drive a reaction known as alcohol oxidation -- an organic chemical reaction that is important in the chemical industry.

Exyn Technologies says it's achieved the highest level of drone autonomy ever, which the company has classified at autonomy level 4A. That's two levels short of full autonomy, but it does enable sophisticated transport, delivery, security, inspection, and research tasks, as well as new collaborative modes with other drones as well as land-based robots. "The operator's just giving very, very high level mission parameters to the drone and leaving it to the drone to figure out how it's going to fly itself -- not just going from point A to point B, but just figuring out how it's going to complete the mission from there," Exyn CEO Nader Elm told me on a recent episode of the TechFirst podcast. "We call this'Scoutonomy' and we've just launched the first iteration of that, and that's our Level 4A." Level zero, as you might expect, is a pilot flying a drone. So are levels one through three, although they add increasing doses of automated stabilization, crash avoidance, position sensing, as well as pilot assistance and warnings as you progress through those levels.

Effectively predicting molecular interactions has the potential to accelerate molecular dynamics by multiple orders of magnitude and thus revolutionize chemical simulations. Graph neural networks (GNNs) have recently shown great successes for this task, overtaking classical methods based on fixed molecular kernels. However, they still appear very limited from a theoretical perspective, since regular GNNs cannot distinguish certain types of graphs. In this work we close this gap between theory and practice. We show that GNNs with directed edge embeddings and two-hop message passing are indeed universal approximators for predictions that are invariant to global rotation and translation, and equivariant to permutation. We then leverage these insights and multiple structural improvements to propose the geometric message passing neural network (GemNet). We demonstrate the benefits of the proposed changes in multiple ablation studies. GemNet outperforms previous models on the COLL and MD17 molecular dynamics datasets by 34 % and 40 %, performing especially well on the most challenging molecules.

As data accessibility and analysis capabilities have rapidly advanced in recent years, new digital platforms driven by artificial intelligence (AI) and machine learning (ML) are increasingly finding practical applications in industry. "Data are so readily available now. Several years ago, we didn't have the manipulation capability, the broad platform or cloud capacity to really work with large volumes of data. We've got that now, so that has been huge in making AI more practical," says Paige Morse, industry marketing director for chemicals at Aspen Technology, Inc. (Bedford, Mass.; www.aspentech.com). While AI and ML have been part of the digitalization discussion for many years, these technologies have not seen a great deal of practical application in the chemical process industries (CPI) until relatively recently, says Don Mack, global alliance manager at Siemens Industry, Inc. (Alpharetta, Ga.; www.industry.usa.siemens.com). "In order for AI to work correctly, it needs data. Control systems and historians in chemical plants have a lot of data available, but in many cases, those data have just been sitting dormant, not really being put to good use. However, new digitalization tools enable us to address some use cases for AI that until recently just weren't possible." This convergence of technologies, from smart sensors to high-performance computing and cloud storage, along with advances in data science, deep learning and access to free and open-source software, have enabled the field of industrial AI to move beyond pure research to practical applications with business benefits, says Samvith Rao, chemical and petroleum industry manager at MathWorks (Natick, Mass.; www.mathworks.com).

Recently, the combination of robust one-dimensional convolutional neural networks (1-D CNNs) and Raman spectroscopy has shown great promise in rapid identification of unknown substances with good accuracy. Using this technique, researchers can recognize a pure compound and distinguish it from unknown substances in a mixture. The novelty of this approach is that the trained neural network operates automatically without any pre- or post-processing of data. Some studies have attempted to extend this technique to the classification of pure compounds in an unknown mixture. However, the application of 1-D CNNs has typically been restricted to binary classifications of pure compounds. Here we will highlight a new approach in spectral recognition and quantification of chemical components in a multicomponent mixture. Two 1-D CNN models, RaMixNet I and II, have been developed for this purpose. The former is for rapid classification of components in a mixture while the latter is for quantitative determination of those constituents. In the proposed method, there is no limit to the number of compounds in a mixture. A data augmentation method is also introduced by adding random baselines to the Raman spectra. The experimental results revealed that the classification accuracy of RaMixNet I and II is 100% for analysis of unknown test mixtures; at the same time, the RaMixNet II model may achieve a regression accuracy of 88% for the quantification of each component.