Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can be modeled as a retrosynthesis planning problem with a catalyst allowed. This is a crucial step in efficient biomass conversion. The traditional computational chemistry approach to identifying possible reaction pathways involves computing the reaction energies of hundreds of intermediates, which is a critical bottleneck in silico reaction discovery. Deep reinforcement learning has shown in other domains that a well-trained agent with little or no prior human knowledge can surpass human performance. While some effort has been made to adapt machine learning techniques to the retrosynthesis planning problem, value-added chemical discovery presents unique challenges. Specifically, the reaction can occur in several different sites in a molecule, a subtle case that has never been treated in previous works. With a more versatile formulation of the problem as a Markov decision process, we address the problem using deep reinforcement learning techniques and present promising preliminary results.

Synchrotron light sources are powerful facilities that produce light in a variety of "colors," or wavelengths--from the infrared to X-rays--by accelerating electrons to emit light in controlled beams. Synchrotrons like the Advanced Light Source at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) allow scientists to explore samples in a variety of ways using this light, in fields ranging from materials science, biology, and chemistry to physics and environmental science. Researchers have found ways to upgrade these machines to produce more intense, focused, and consistent light beams that enable new, and more complex and detailed studies across a broad range of sample types. Many of these synchrotron facilities deliver different types of light for dozens of simultaneous experiments. And little tweaks to enhance light-beam properties at these individual beamlines can feed back into the overall light-beam performance across the entire facility.

More and more cities are looking to go green. And renewable energy is, if current trends hold, the future of the energy industry. But as renewable energy technologies like wind farms are implemented at larger scales than ever, local officials are running into their limitations. The energy production of wind farms is hard to predict, and this makes energy grid design difficult. Experts hope that machine learning can be applied to renewable energy to solve this problem. If it works, this new tech may make energy officials more enthusiastic about implementing renewables.

Conductor galloping is the high-amplitude, low-frequency oscillation of overhead power lines due to wind. Such movements may lead to severe damages to transmission lines, and hence pose significant risks to the power system operation. In this paper, we target to design a prediction framework for conductor galloping. The difficulty comes from imbalanced dataset as galloping happens rarely. By examining the impacts of data balance and data volume on the prediction performance, we propose to employ proper sample adjustment methods to achieve better performance. Numerical study suggests that using only three features, together with over sampling, the SVM based prediction framework achieves an F_1-score of 98.9%.

We study online optimization in a setting where an online learner seeks to optimize a per-round hitting cost, which may be non-convex, while incurring a movement cost when changing actions between rounds. We ask: \textit{under what general conditions is it possible for an online learner to leverage predictions of future cost functions in order to achieve near-optimal costs?} Prior work has provided near-optimal online algorithms for specific combinations of assumptions about hitting and switching costs, but no general results are known. In this work, we give two general sufficient conditions that specify a relationship between the hitting and movement costs which guarantees that a new algorithm, Synchronized Fixed Horizon Control (SFHC), provides a $1+O(1/w)$ competitive ratio, where $w$ is the number of predictions available to the learner. Our conditions do not require the cost functions to be convex, and we also derive competitive ratio results for non-convex hitting and movement costs. Our results provide the first constant, dimension-free competitive ratio for online non-convex optimization with movement costs. Further, we give an example of a natural instance, Convex Body Chasing (CBC), where the sufficient conditions are not satisfied and we can prove that no online algorithm can have a competitive ratio that converges to 1.

DUBLIN--(BUSINESS WIRE)--The "Innovations in Sensors, Quantum Dot Displays, Virtual Assistants, Cyber Analytics Tools, Artificial Intelligence, and Brain-computer Interfaces" report has been added to ResearchAndMarkets.com's offering. This edition of the Inside R&D TechVision Opportunity Engine (TOE) features trends and innovations in sensors, quantum dot displays, virtual assistants, cyber analytics tools, artificial intelligence, and brain-computer interfaces. The TOE also covers innovations in cancer detection and treatment of neurodegenerative diseases. Inside R&D TechVision Opportunity Engine covers global innovations that are in research and development in virtually all technology areas. We provide intelligence and insights on innovations spanning a wide variety of industry areas, including automation, electronics, sensors, information and communication technologies, manufacturing, health, wellness, medical devices, pharma, biotechnology, materials, coatings, renewable fuels, automotive, power systems, sustainable energy solutions and innovations that contribute to a cleaner and greener environment.

In recent times sustainability has become a buzz word amongst a large section of the world community and particularly amongst Business and Industry fraternity. In a rapidly globalized world faced with social inequity, environmental degradation and economic slowdown, sustainability has often been cited as the panacea for solving the ills of society. What is however not known is the fact that education provides the base knowledge for addressing humanity's footprint, and many of us at educational institutions often need to have a deeper understanding of the issue. Educational campuses are generally seen as the altar for teaching, learning and dissemination of knowledge. However, in recent times campuses have also started to move in the direction of addressing sustainability issues in terms of curriculum, research and operational aspects.

This will be an important topic discussed at POWERGEN International only two weeks away in New Orleans. Click here to learn more! Today, utility engineers spend a significant portion of their time completing repetitive administration tasks. Some organizations estimate that upwards of 40 percent of the time of highly trained engineers is spent on these mundane tasks. The maturation of artificial intelligence (AI) techniques such as machine learning and natural language processing (NLP) has made them increasingly viable for use in automating more complex and higher impact tasks.

This image shows the profile of an electron beam at Berkeley Lab's Advanced Light Source synchrotron, represented as pixels measured by a charged coupled device (CCD) sensor. When stabilized by a machine-learning algorithm, the beam has a horizontal size dimension of 49 microns (root mean squared) and vertical size dimension of 48 microns (root mean squared). Demanding experiments require that the corresponding light-beam size be stable on time scales ranging from less than seconds to hours to ensure reliable data. Synchrotron light sources are powerful facilities that produce light in a variety of "colors," or wavelengths – from the infrared to X-rays – by accelerating electrons to emit light in controlled beams. Synchrotrons like the Advanced Light Source at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) allow scientists to explore samples in a variety of ways using this light, in fields ranging from materials science, biology, and chemistry to physics and environmental science.

The "Innovations in Cancer Diagnosis and Treatment, Micro-LEDs, Renewable Energy Generation and Storage, and Fault Detection" report has been added to ResearchAndMarkets.com's offering. The edition also provides insights on the role of macropinocytosis in pancreatic cancer. The TOE covers use of ceramic electrodes for doubling energy density and a biosensor for earlier diagnosis of tumors.