Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.

Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values. Users' demands, implying strong purchase intents, can be the most useful way to promote products sales if well utilized. Previous recommendation models mainly focused on user's general interests to find the right products. However, the aspect of meeting users' demands at the right time has been much less explored. To address this problem, we propose a novel Long-Short Demands-aware Model (LSDM), in which both user's interests towards items and user's demands over time are incorporated. We summarize two aspects: termed as long-time demands (e.g., purchasing the same product repetitively showing a long-time persistent interest) and short-time demands (e.g., co-purchase like buying paintbrushes after pigments). To utilize such long-short demands of users, we create different clusters to group the successive product purchases together according to different time spans, and use recurrent neural networks to model each sequence of clusters at a time scale. The long-short purchase demands with multi-time scales are finally aggregated by joint learning strategies. Experimental results on three real-world commerce datasets demonstrate the effectiveness of our model for next-item recommendation, showing the usefulness of modeling users' long-short purchase demands of items with multi-time scales.

This article was written by Harry Chiang, a financial analyst at I Know First. Most humans would understand, perhaps even intuitively, that when he or she runs there is a certain path of movement and way in which he or she is interacting with the environment. The athlete will follow the curvature of the track. They dictate how his or her body moves and how his or her feet must move along the rubber. A machine, however, would struggle to understand all these small details.

This is a guest post from Paul Pilotte, technical marketing manager for data science and predictive analytics. Gartner recognizes MathWorks as a Visionary in its January 2019 Magic Quadrant for Data Science and Machine Learning Platforms Deep learning and AI are top of mind in many organizations we work with at MathWorks. It's inspiring for us to see many engineers and scientists learning and applying deep learning in applications from UAVs using AI for object detection in satellite imagery to improved pathology diagnosis for early disease detection during cancer screenings. If you've followed this blog, you've seen how MATLAB offers a comprehensive deep learning workflow that simplifies and automates data synthesis, labeling, training, tuning, and deploying deep learning to AI-driven systems, including enterprise applications, embedded functionality, and edge systems. This makes AI accessible to engineers and scientists without previous data science experience.

Data scientist just topped Glassdoor's list of the 50 Best Jobs in America for the fourth year in a row, with entry-, mid-, and senior-level jobs alike offering competitive salaries. And with virtually every industry in need of professionals who can crunch customers' data -- not to mention ongoing innovations in the field of machine learning -- demand for data scientists has increased almost 30 percent year over year, according to an analysis by the job sites Indeed and Dice. For a limited time, Entrepreneur readers can get in on this lucrative profession on the cheap by enrolling in the Machine Learning & Data Science Certification Training Bundle, an eight-part education on that spans 48 hours of content. Kicking off the bundle's extensive learning roster are a pair of courses on Tensorflow and Keras, two frameworks within the popular, general-purpose programming language Python. After giving you a comprehensive introduction to data science techniques in Python, these classes will help you master Tensorflow and Keras installation, then show you how to use both libraries to create artificial neural networks and deep learning structures.

Artificial intelligence has not only become an international arms race, competition has now heated up as companies look to adopt machine learning/deep learning at an unprecedented pace. But the conversation about AI has largely focused on pitting humanity against AI, instead of what happens when we bring together humans *with* AI. In this talk by a16z operating partner Frank Chen, given at the annual a16z Summit, Chen goes beyond the hype to look forward at how AI and automation will augment, enhance, create, and yes, replace humans.... but also highlighting what it is that makes us human to begin with.

The U.S. Army Research Laboratory has partnered with Technica to develop a system that would use artificial intelligence algorithms to collect data from multiple devices and allow soldiers to exchange information in areas where connectivity is limited, Nextgov reported Friday. "We want to try to put deep learning and AI into the hands of the soldier," Brendon Unland, senior technology architect at Technica, told Nextgov in an interview. ARL awarded the company a $1M research and development contract in mid-January to build a "fog computing" platform designed to support data processing even at locations without network access or in the cloud. The system, called SmartFog, would use AI or machine learning to analyze incoming data streams in real-time and aid soldiers in decisionmaking. Unland added the company will develop algorithms that could be trained to detect vehicle maintenance problems, network attacks or other anomalies.

An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo. OUTLINE: 0:00 - Introduction 0:53 - Deep learning in one slide 4:55 - History of ideas and tools 9:43 - Simple example in TensorFlow 11:36 - TensorFlow in one slide 13:32 - Deep learning is representation learning 16:02 - Why deep learning (and why not) 22:00 - Challenges for supervised learning 38:27 - Key low-level concepts 46:15 - Higher-level methods 1:06:00 - Toward artificial general intelligence CONNECT: - If you enjoyed this video, please subscribe to this channel.

We provide fast and extremely competitive machine learning services. With systems that can utilize our pre-trained models or even systems that harness our raw engine based structure simulations, that you can insert and use as your own. Our "iStein technology" services have better training performance and increased accuracy compared to almost any other competitive deep learning system. Our massively parallel architecture, known as "The Many Integrated Core (MIC) architecture system" is now available as a cloud service to bring unmatched scale and speed to your business applications. As our systems are API based you can launch and use them on any platform.

Formally known as Audio Source Separation, the problem we are trying to solve here consists in recovering or reconstructing one or more source signals that, through some -linear or convolutive- process, have been mixed with other signals. The field has many practical applications including but not limited to speech denoising and enhancement, music remixing, spatial audio, remastering, etc. In the context of music production, it is sometimes referred to as unmixing or demixing. For a nice walkthrough on the first two, you can check out these tutorial mini-series from CCRMA, which I found very useful back in the day. As someone who's been working in signal & image processing for a while and prior to the'deep-learning-solves-it-all' boom, I will introduce the solution as a Feature Engineering journey and show you why, for this particular problem, an artificial neural network ends up being the best approach.