Let's dig deeper into the details of the algorithm. The authors used a Weisfeiler-Lehman Network, a type of graph-convolutional neural network, as depicted in the figure below. First, for each atom-atom pair (including pairs of atoms that are not bound or are located in different reactants) the neural network predicts the likelihood of the bond order to change. The starting point is from a graph representation of reactants (A), where atoms are featurized by atomic number, formal charge, degree of connectivity, valence and aromaticity, and bonds are featurized by bond order and ring status. These atom-level features are iteratively updated (B) by incorporating information from neighbor atoms.
The core objective of plant population ecology is to understand changes in numbers of individuals/organisms across time and space.1 Achieving this depends on methods that permit plants to be mapped and monitored at informative scales.2-4 Surveys of plant populations have been undertaken using a variety of different methods such as transect sampling, quadrat sampling and with unmanned aerial systems (UAS).5-7 Each of these methods has an inherent trade‐off between the area that can be surveyed and the intensity at which the subjects in that area can be studied.8 Transect and quadrat sampling can be used for either small area, high‐intensity studies or large area, low‐intensity studies, but typically not both.9 UAS present a unique opportunity for ecological monitoring because, potentially, they can yield data across both large spatial areas and at high survey intensity.
One of the most interesting demos at this week's Google I/O keynote featured a new version of Google's voice assistant that's due out later this year. A Google employee asked the Google Assistant to bring up her photos and then show her photos with animals. She tapped one and said, "Send it to Justin." The photo was dropped into the messaging app. From there, things got more impressive.
The competitive video gaming industry has become a serious business over the past few years. As prize pools and viewership continue to grow year after year the industry is attracting more and more players. With new players coming in every day, the competition is getting saturated with talented professionals. How does one gain an edge in such a competitive world? Well, some would say to just practice as much as possible but I think we are getting to the point where that will not be enough.
The model is significantly faster to train and to make predictions, yet still requires a set of candidate regions to be proposed along with each input image. Python and C (Caffe) source code for Fast R-CNN as described in the paper was made available in a GitHub repository. The model architecture was further improved for both speed of training and detection by Shaoqing Ren, et al. at Microsoft Research in the 2016 paper titled "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." The architecture was the basis for the first-place results achieved on both the ILSVRC-2015 and MS COCO-2015 object recognition and detection competition tasks. The architecture was designed to both propose and refine region proposals as part of the training process, referred to as a Region Proposal Network, or RPN. These regions are then used in concert with a Fast R-CNN model in a single model design. These improvements both reduce the number of region proposals and accelerate the test-time operation of the model to near real-time with then state-of-the-art performance.
AI and automation are changing the business environment across industries, delivering new opportunities through intelligent, automated solutions. Some companies are ahead of the curve, while others are stagnating in adopting the technology. Operators and enterprises are aware of the benefits of AI and automation, but the questions that always remain are, "What does it bring to my business? How will it solve my problems?" Artificial intelligence (AI) is a constellation of technologies that describes the processes of intelligent automation, like machine learning, natural language processing (NLP), cognitive computing, and deep learning.
The mission of building one-to-one communication and engagement is not a new concept. Back in 1993, Don Peppers and Martha Rogers, Ph.D., proposed that organizations could use technology to gather information about, and to communicate directly with, individuals to form a personal bond. The book, The One to One Future: Building Relationships One Customer at a Time, stated that technology had made it possible and affordable to track individual consumers, to understand each person's individual journey, and to provide contextual offers at the optimal time of need. Six years later, internationally recognized best-selling author Seth Godin published Permission Marketing. He built a logical case for creating incentives for consumers to accept advertising voluntarily.
For those who like their dessert first: here's the finished model, and here's the colab for this example. A rather empty user-interface should show up on your screen. In the sidebar, click the Library-dropdown, and select TensorFlow. Now the code for our model will use TensorFlow instead of PyTorch. Next, click on the Theme-dropdown and select "orange".