Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not an easy task owing to their variability and ambiguity due to thousands of micro and pico-plankton species. Therefore, the aim of this paper is to build a framework for identifying phytoplankton species and to perform a comparison on different features types and classifiers. We propose a new features type extracted from raw signals of phytoplankton species. We then analyze the performance of various classifiers on the proposed features type as well as two other features types for finding the robust one. Through experiments, it is found that Random Forest using the proposed features gives the best classification results with average accuracy up to 98.24%.

In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.

We consider the demixing problem of two (or more) high-dimensional vectors from nonlinear observations when the number of such observations is far less than the ambient dimension of the underlying vectors. Specifically, we demonstrate an algorithm that stably estimate the underlying components under general \emph{structured sparsity} assumptions on these components. Specifically, we show that for certain types of structured superposition models, our method provably recovers the components given merely $n = \mathcal{O}(s)$ samples where $s$ denotes the number of nonzero entries in the underlying components. Moreover, our method achieves a fast (linear) convergence rate, and also exhibits fast (near-linear) per-iteration complexity for certain types of structured models. We also provide a range of simulations to illustrate the performance of the proposed algorithm.

We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning have allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the data. We propose a discriminative probabilistic manifold embedding where locally linear mappings transform data points from high-dimensional space to corresponding low-dimensional coordinates. A discriminant adjacency matrix is constructed to maximize the separation between progressive and non-progressive groups of patients diagnosed with scoliosis, while minimizing the distance in latent variables belonging to the same class. To predict the evolution of deformation, a baseline reconstruction is projected onto the manifold, from which a spatiotemporal regression model is built from parallel transport curves inferred from neighboring exemplars. Rate of progression is modulated from the spine flexibility and curve magnitude of the 3D spine deformation. The method was tested on 745 reconstructions from 133 subjects using longitudinal 3D reconstructions of the spine, with results demonstrating the discriminatory framework can identify between progressive and non-progressive of scoliotic patients with a classification rate of 81% and prediction differences of 2.1$^{o}$ in main curve angulation, outperforming other manifold learning methods. Our method achieved a higher prediction accuracy and improved the modeling of spatiotemporal morphological changes in highly deformed spines compared to other learning methods.

We develop a framework for rendering photographic images, taking into account display limitations, so as to optimize perceptual similarity between the rendered image and the original scene. We formulate this as a constrained optimization problem, in which we minimize a measure of perceptual dissimilarity, the Normalized Laplacian Pyramid Distance (NLPD), which mimics the early stage transformations of the human visual system. When rendering images acquired with higher dynamic range than that of the display, we find that the optimized solution boosts the contrast of low-contrast features without introducing significant artifacts, yielding results of comparable visual quality to current state-of-the art methods with no manual intervention or parameter settings. We also examine a variety of other display constraints, including limitations on minimum luminance (black point), mean luminance (as a proxy for energy consumption), and quantized luminance levels (halftoning). Finally, we show that the method may be used to enhance details and contrast of images degraded by optical scattering (e.g., fog).

Lung cancer is one of the most common types of cancer, with nearly 225,000 new cases of the disease expected in the U.S. in 2016. Using a data set of high-resolution scans of lungs provided by the National Cancer Institute, participants will develop artificial intelligence algorithms to accurately determine when lesions in the lungs are cancerous. This will dramatically reduce the false positive rate that prevents low-dose CT scans from being widely used for lung cancer detection. Competition results have the potential to advance our understanding of how all types of cancer develop and spread in the body. They'll also free radiologists to spend more time with patients.

If, for example, you're diagnosed with cancer, you might benefit from the platform, Watson for Oncology. "Normally it's up to a specialist doctor to meet with cancer patients, and to spend time reviewing their notes – which would arrive on paper format or in a string of emails," says Balkizas. "A doctor's decision will be limited to their individual experience and the information available in front of them." However, now the Memorial Sloan Cancer Treatment Centre in New York is training IBM Watson to be able to quickly provide evidence based recommendations to time poor clinicians. "It takes all those unstructured notes and restructures it in a way that the doctor can check easily, with treatment recommendations of which drug to give, which radiation or dosage," says Balkizas.

In this week's Abundance Insider: Self-organizing drone swarms, synthetic stem cells, and an AI that can detect heart failure better than human doctors. I'm launching an online course with SUCCESS Magazine called Xponential Advantage. It's aimed to inspire, educate and guide a new breed of "Exponential Entrepreneurs," and is an expansion of my core content from Abundance and BOLD, the keynotes I give to Fortune 500 executive teams, and some of the material I teach executives who attend Singularity University. These are the areas I truly believe an exponential entrepreneur can leverage to have a billion-person impact. What it is: Scientists at the London Institute of Medical Services have created an AI capable of predicting with 80% accuracy which patients would die of pulmonary hypertension within a year, beating the average doctor's prediction accuracy by about 20%.

What do you think of when you think about chatbots? If you're like many others, it's possible that you think of them as nothing more than that annoying little window that pops up when you're visiting a website. You know what I'm talking about - the one that claims to be able to answer your questions. You may have had both positive and negatives experiences with chatbots. Some of you may even ignore them altogether.