Just like the air people breathe, some of the most important things in life are invisible. Now, can you imagine your company's supply chain becoming just as invisible as the air around us? We envision the supply chain of the future in the chemical industry will be self-driving, self-maneuvering and self-correcting, resulting in a seamless fulfillment capability that delivers chemicals at the right time, to the right place and with right quality--without manual human interaction. While leading retail giants invest in emerging technologies as a matter of course, historically this has not been the case for most chemical companies. However, the benefits of doing so are tremendous.

In May of last year, after a 13-month slumber, the ground beneath Washington's Puget Sound rumbled to life. The quake began more than 20 miles below the Olympic mountains and, over the course of a few weeks, drifted northwest, reaching Canada's Vancouver Island. It then briefly reversed course, migrating back across the US border before going silent again. All told, the monthlong earthquake likely released enough energy to register as a magnitude 6. By the time it was done, the southern tip of Vancouver Island had been thrust a centimeter or so closer to the Pacific Ocean.

A detailed study accumulated to offer Latest insights about acute features of the LegalTech Artificial Intelligence market. The report contains different market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. The report also offers a complete study of the future trends and developments of the market. It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary and SWOT analysis. Legal technology, also known as Legal Tech, refers to the use of technology and software to provide legal services.

What is'platformisation' and how does it relate to digital manufacturing? How can cloud-based design help steelmakers improve efficiency and reduce costs? How far can we go with'deep machine learning' without losing our grip on ethical responsibility and what exactly is'knowledge engineering'? These are all questions that need to be answered if steelmakers are going to gain a greater understanding of the world surrounding Industry 4.0 and its associated technologies. Augmented reality, robotics, cyber-enabled design and manufacturing – they are all subjects that need to be'top of mind' in the steel industry of the future.

The conference fee includes a 2-day conference programme, refreshments, a networking lunch and conference proceedings.

Having a compact, yet robust, structurally-based identifier or representation system for molecular structures is a key enabling factor for efficient sharing and dissemination of results within the research community. Such systems also lay down the essential foundations for machine learning and other data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system. For small molecules, the basic premise is that each distinct chemical species corresponds to a well-defined chemical structure. This does not hold for polymers.

FarmWise announced today that it has raised a $14.5 million Series A round of funding for its autonomous agriculture robots. The round was led by Calibrate Ventures with participation from Wilbur Ellis, Xplorer Capital and Alumni Ventures Group. This brings the total amount raised by FarmWise to $20.2 million. Farmwise builds self-driving robots that use a combination of computer vision and AI to identify weeds among crops and precision mechanical tools to remove them without the need for herbicides. According to the press release sent to The Spoon, FarmWise says its robots have removed weeds from more than 10 million plants.

Having a compact, yet robust, structurally-based identifier or representation system for molecular structures is a key enabling factor for efficient sharing and dissemination of results within the research community. Such systems also lay down the essential foundations for machine learning and other data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system. For small molecules, the basic premise is that each distinct chemical species corresponds to a well-defined chemical structure. This does not hold for polymers.

How are you enjoying the International Year of the Periodic Tables so far? Yes, tables – we should probably have been using the plural all along. Since Dmitri Mendeleev (and others) first sketched out the periodic relationships between the elements in the 1860s, it has been estimated that around a thousand different tables have appeared in print – and that's before considering all those on the internet. Even the T-shirts handed out at the opening ceremony in January (I grabbed one, naturally) offered a new version, courtesy of the European Chemical Society, with the elements colour-coded and given different-sized boxes according to their abundance and availability. Mostly these tables embody careful deliberation about what to put where, which information to prioritise, which message to convey.

Shoup: I'm going to share very little of my personal knowledge, in fact, none of it, but I'm going to talk about a cool paper that I really like. Then Gwen [Shapira] is going to talk about another cool paper and Roland [Meertens] is going to talk about yet another cool paper. The one I want to talk about is a paper that's around using machine learning to do database indexing better. This is a picture of my bookshelf at home. A while ago, I bought myself a box set of "The Art of Computer Programming", which has basically all of computer science algorithms written by or assembled by Don Knuth. There's 4a, so he's still working on completing the thing, hopefully, that will happen. When we're choosing a data structure, typically we're choosing it in this way, we are trying to look for time complexity, how fast is it going to run, and space complexity, how big is it going to be? We typically evaluate those things asymptotically, we're not looking as much at real-world workloads, but looking at what are the complexity characteristics of this thing at the limit when things get very large? We're also, and this is critical, looking at those things without having seen the data and without having seen typically the usage pattern. We're doing is we're saying what is the least worst time and space complexity, given an arbitrary data distribution and an arbitrary usage pattern? It seems like we could do a little better than that, that's what this paper is about. What we'd like to be able to ask or to be able to answer is how could we achieve the best time/space complexity given a specific real-world data distribution and a specific real-world usage pattern.