StradVision, an innovator in vision processing technology for Autonomous Vehicles, has announced it raised $27 million in its Series B funding round, led by Posco Capital. This round brings StradVision's total funding to $40 million. Other Series B investors include: IDG Capital; Industrial Bank of Korea; Lighthouse Combined Investment; LSS Private Equity; Mirae Asset Venture Investment; Neoplux; and Timefolio Asset Management. "StradVision's software solutions for Autonomous Vehicles and ADAS systems are proving successful and attractive to leading automakers and suppliers, as our latest round of funding strongly confirms." said Junhwan Kim, CEO of StradVision. "We appreciate all of our new investors coming on board, and StradVision will use this funding to take our groundbreaking products to the next level as we lead the advancement of camera technology in Autonomous Vehicles."

Machine learning is a highly effective tool for developing trading systems for Bitcoin and other cryptocurrencies. This post will explore some of the concepts that apply, potential issues you may encounter, and the competencies you'll need to develop your own machine-learning based trading system. Crypto-ML.com is a trade alert platform built on machine learning. We'll discuss the learnings and strategies it uses as well. Want to see the details of how we implement these concepts in our latest models? There is a lot of confusion as to what machine learning really is. Many people think it is "computers gone wild," but the reality is a little less sexy.

San Francisco (CNN Business)In 2010, artificial intelligence was more likely to pop up in dystopian science-fiction movies than in everyday life. And it certainly wasn't something people worried might take over their jobs in the near future.

AI's potential in medicine has already attracted large amounts of media and public attention. However, some of the specific uses and consequences of AI in the context of health care aren't particularly well known, at least not compared to awareness of the general utility of artificial intelligence. Some of these uses may be life-saving, and in more ways than one. In recent months, a small number of companies and researchers have begun using AI for the purposes of medical imaging, harnessing machine learning algorithms in order to construct 3D models out of 2D images. This may seem like a fairly standard employment of AI at first glance, but what's special about this emerging use is that it's focused on avoiding the need for MRI, CT and PET scans.

Who wants to live forever? Until recently, the quest to slow ageing or even reverse it was the stuff of legends – or scams. But, today, an evidence-based race to delay or prevent ageing is energising scientists worldwide. Scientists say there are already a number of things we can do to extend life and health, while promising that current and ongoing large-scale trials of drugs and other interventions mean the once-mythical goal of healthy, longer-lived lives is not far away. "Death is inevitable but ageing is not," said Dr Nir Barzilai, founding director of the Institute for Aging Research at the Albert Einstein College of Medicine, New York.

Hysteresis phenomena have been observed in different branches of physics and engineering sciences. Therefore, several models have been proposed for hysteresis simulation in different fields; however, almost neither of them can be utilized universally. In this paper by inspiring of Preisach Neural Network which was inspired by the Preisach model that basically stemmed from Madelungs rules and using the learning capability of the neural networks, an adaptive universal model for hysteresis is introduced and called Extended Preisach Neural Network Model. It is comprised of input, output and, two hidden layers. The input and output layers contain linear neurons while the first hidden layer incorporates neurons called Deteriorating Stop neurons, which their activation function follows Deteriorating Stop operator. Deteriorating Stop operators can generate non-congruent hysteresis loops. The second hidden layer includes Sigmoidal neurons. Adding the second hidden layer, helps the neural network learn non-Masing and asymmetric hysteresis loops very smoothly. At the input layer, besides input data the rate at which input data changes, is included as well in order to give the model the capability of learning rate-dependent hysteresis loops. Hence, the proposed approach has the capability of the simulation of both rate-independent and rate-dependent hysteresis with either congruent or non-congruent loops as well as symmetric and asymmetric loops. A new hybridized algorithm has been adopted for training the model which is based on a combination of the Genetic Algorithm and the optimization method of sub-gradient with space dilatation. The generality of the proposed model has been evaluated by applying it to various hysteresis from different areas of engineering with different characteristics. The results show that the model is successful in the identification of the considered hystereses.

Acquiring ground truth labels for unlabelled data can be a costly procedure, since it often requires manual labour that is error-prone. Consequently, the available amount of labelled data is increasingly reduced due to the limitations of manual data labelling. It is possible to increase the amount of labelled data samples by performing automated labelling or crowd-sourcing the annotation procedure. However, they often introduce noise or uncertainty in the labelset, that leads to decreased performance of supervised deep learning methods. On the other hand, weak supervision methods remain robust during noisy labelsets or can be effective even with low amounts of labelled data. In this paper we evaluate the effectiveness of a representation learning method that uses external categorical evidence called "Evidence Transfer", against low amount of corresponding evidence termed as incomplete evidence. Evidence transfer is a robust solution against external unknown categorical evidence that can introduce noise or uncertainty. In our experimental evaluation, evidence transfer proves to be effective and robust against different levels of incompleteness, for two types of incomplete evidence.

Motivated by their broad applications in reinforcement learning, we study the linear two-time-scale stochastic approximation, an iterative method using two different step sizes for finding the solutions of a system of two equations. Our main focus is to characterize the finite-time complexity of this method under time-varying step sizes and Markovian noise. In particular, we show that the mean square errors of the variables generated by the method converge to zero at a sublinear rate $\mathcal{O}(k^{2/3})$, where $k$ is the number of iterations. We then improve the performance of this method by considering the restarting scheme, where we restart the algorithm after a predetermined number of iterations. We show that using this restarting method the complexity of the algorithm under time-varying step sizes is as good as the one using constant step sizes, but still achieving an exact converge to the desired solution. Moreover, the restarting scheme also helps to prevent the step sizes from getting too small, which is useful for the practical implementation of the linear two-time-scale stochastic approximation.

Deep neural networks have gained tremendous success in a broad range of machine learning tasks due to its remarkable capability to learn semantic-rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption into unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications into critical domains where obtaining massive labelled data is prohibitively expensive. To enable downstream unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space. Random mapping is a theoretical proven approach to obtain approximately preserved distances. To well predict these random distances, the representation learner is optimised to learn genuine class structures that are implicitly embedded in the randomly projected space. Experimental results on 19 real-world datasets show our learned representations substantially outperform state-of-the-art competing methods in both anomaly detection and clustering tasks. Unsupervised representation learning aims at automatically extracting expressive feature representations from data without any manually labelled data. Due to the remarkable capability to learn semantic-rich features, deep neural networks have been becoming one widely-used technique to empower a broad range of machine learning tasks. One main issue with these deep learning techniques is that a massive amount of labelled data is typically required to successfully learn these expressive features. As a result, their transformation power is largely reduced for tasks that are unsupervised in nature, such as anomaly detection and clustering. This is also true to critical domains, such as healthcare and fintech, where collecting massive labelled data is prohibitively expensive and/or is impossible to scale. To bridge this gap, in this work we explore fully unsupervised representation learning techniques to enable downstream unsupervised learning methods on those critical domains. In recent years, many unsupervised representation learning methods (Mikolov et al., 2013a; Le & Mikolov, 2014; Misra et al., 2016; Lee et al., 2017; Gidaris et al., 2018) have been introduced, of which most are self-supervised approaches that formulate the problem as an annotation free pretext task.

Extracting insight from the enormous quantity of data generated from molecular simulations requires the identification of a small number of collective variables whose corresponding low-dimensional free-energy landscape retains the essential features of the underlying system. Data-driven techniques provide a systematic route to constructing this landscape, without the need for extensive a priori intuition into the relevant driving forces. In particular, autoencoders are powerful tools for dimensionality reduction, as they naturally force an information bottleneck and, thereby, a low-dimensional embedding of the essential features. While variational autoencoders ensure continuity of the embedding by assuming a unimodal Gaussian prior, this is at odds with the multi-basin free-energy landscapes that typically arise from the identification of meaningful collective variables. In this work, we incorporate this physical intuition into the prior by employing a Gaussian mixture variational autoencoder (GMVAE), which encourages the separation of metastable states within the embedding. The GMVAE performs dimensionality reduction and clustering within a single unified framework, and is capable of identifying the inherent dimensionality of the input data, in terms of the number of Gaussians required to categorize the data. We illustrate our approach on two toy models, alanine dipeptide, and a challenging disordered peptide ensemble, demonstrating the enhanced clustering effect of the GMVAE prior compared to standard VAEs. The resulting embeddings appear to be promising representations for constructing Markov state models, highlighting the transferability of the dimensionality reduction from static equilibrium properties to dynamics.