Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, and the LucasCoordConv with an additional coordinates layer. Furthermore, we modify two existing 1D CNN approaches for the presented classification task. The code of all five CNN approaches is available on GitHub (Riese, 2019). We evaluate the performance of the CNN approaches and compare them to a random forest classifier. Thereby, we rely on the freely available LUCAS topsoil dataset. The CNN approach with the least depth turns out to be the best performing classifier. The LucasCoordConv achieves the best performance regarding the average accuracy. In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset.

In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it's almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning-based CF methods try to map users and items into a common representation space. In this case, the higher similarity between a user and an item in that space implies they match better. Matching function learning-based CF methods try to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the limited expressiveness of dot product and the weakness in capturing low-rank relations respectively. To this end, we propose a general framework named DeepCF, short for Deep Collaborative Filtering, to combine the strengths of the two types of methods and overcome such flaws. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DeepCF framework.

Objective: Predict individual septic children's personalized physiologic responses to vasoactive titrations by training a Recurrent Neural Network (RNN) using EMR data. Materials and Methods: This study retrospectively analyzed EMR of patients admitted to a pediatric ICU from 2009 to 2017. Data included charted time series vitals, labs, drugs, and interventions of children with septic shock treated with dopamine, epinephrine, or norepinephrine. A RNN was trained to predict responses in heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP) to 8,640 titrations during 652 septic episodes and evaluated on a holdout set of 3,883 titrations during 254 episodes. A linear regression model using titration data as its sole input was also developed and compared to the RNN model. Evaluation methods included the correlation coefficient between actual physiologic responses and RNN predictions, mean absolute error (MAE), and area under the receiver operating characteristic curve (AUC). Results: The actual physiologic responses displayed significant variability and were more accurately predicted by the RNN model than by titration alone (r=0.20 vs r=0.05, p<0.01). The RNN showed MAE and AUC improvements over the linear model. The RNN's MAEs associated with dopamine and epinephrine were 1-3% lower than the linear regression model MAE for HR, SBP, DBP, and MAP. Across all vitals vasoactives, the RNN achieved 1-19% AUC improvement over the linear model. Conclusion: This initial attempt in pediatric critical care to predict individual physiologic responses to vasoactive dose changes in children with septic shock demonstrated an RNN model showed some improvement over a linear model. While not yet clinically applicable, further development may assist clinical administration of vasoactive medications in children with septic shock.

The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems. The rich sensor data can be continuously monitored for intrusion events through anomaly detection. However, conventional threshold-based anomaly detection methods are inadequate due to the dynamic complexities of these systems, while supervised machine learning methods are unable to exploit the large amounts of data due to the lack of labeled data. On the other hand, current unsupervised machine learning approaches have not fully exploited the spatial-temporal correlation and other dependencies amongst the multiple variables (sensors/actuators) in the system for detecting anomalies. In this work, we propose an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs). Instead of treating each data stream independently, our proposed MAD-GAN framework considers the entire variable set concurrently to capture the latent interactions amongst the variables. We also fully exploit both the generator and discriminator produced by the GAN, using a novel anomaly score called DR-score to detect anomalies by discrimination and reconstruction. We have tested our proposed MAD-GAN using two recent datasets collected from real-world CPS: the Secure Water Treatment (SWaT) and the Water Distribution (WADI) datasets. Our experimental results showed that the proposed MAD-GAN is effective in reporting anomalies caused by various cyber-intrusions compared in these complex real-world systems.

We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a statistical inference framework that enables the end-to-end training of surrogate models on paired input-output observations that may be stochastic in nature, originate from different information sources of variable fidelity, or be corrupted by complex noise processes. The resulting surrogates can accommodate high-dimensional inputs and outputs and are able to return predictions with quantified uncertainty. The effectiveness our approach is demonstrated through a series of canonical studies, including the regression of noisy data, multi-fidelity modeling of stochastic processes, and uncertainty propagation in high-dimensional dynamical systems.

Due to the emerging technologies and the rapid integration of software development, many online retailers opt to design attractive webpages in order to influence consumer perceptions of the web environment and thus leading to more sales and profits [1]. An online 2017 UPS Pulse of the Online Shopper study with more than 6,400 European respondents from France, Germany, Italy, Poland, Spain, and the UK, discovered that 84% of shoppers will shop in the physical store instead of online [2]. The major reason is that they can look around and touch and feel the products. In the in-store environment, McCabe and Nowlis [3] found that the shoppers are less likely to pick up products with geometric properties as they highly rely on the sense of vision, which is believed to provide sufficient information of the products. For some of the products with geometric properties, especially packaged products, shoppers tends to evaluate the product through visually glancing instead of reach out or touch on the product [4]. The geometry properties of an agricultural product is often indicated by its weight as it is relatively easy and quick to be measured with a digital scale. An alternative solution to obtain the weight of the product is to measure its volume as the volume is a quantity defined by mass per unit density. There are several works published to measure the volume of the objects by fusing the computer vision technique with some mathematical modeling and derivations.

Any system which performs goal-directed continual learning must not only learn incrementally but process and absorb information incrementally. Such a system also has to understand when its goals have been achieved. In this paper, we consider these issues in the context of question answering. Current state-of-the-art question answering models reason over an entire passage, not incrementally. As we will show, naive approaches to incremental reading, such as restriction to unidirectional language models in the model, perform poorly. We present extensions to the DocQA [2] model to allow incremental reading without loss of accuracy. The model also jointly learns to provide the best answer given the text that is seen so far and predict whether this best-so-far answer is sufficient.

Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most machine learning techniques would expect, i.e. as a real-valued vector of a fixed, pre-specified size. In this work, we introduce a novel approach that compiles structured data into a satisfiability problem which has in its set of solutions at least (and often only) the input data. The satisfiability problem is constructed from constraints which are generated automatically a priori from a given signature, thus trivially allowing for a bag-of-words-esque vector representation of the input to be constructed. The method is demonstrated in two areas, automated reasoning and natural language processing, where it is shown to produce vector representations of natural-language sentences and first-order logic clauses that can be precisely translated back to their original, structured input forms.

This video is a part of a free online course that provides introduction to practical deep learning methods using MATLAB. In addition to short engaging videos, the course also contains interactive, in-browser MATLAB projects. For a 14-hour comprehensive course covering the theory and practice of deep learning using real-world image and sequence data, see: http://bit.ly/2DjaTdh

Modern machine learning world is going crazy over deep learning. People are stacking hundreds and thousands of interconnected artificial neurons to build the most complex of deep neural network than ever. These deep neural nets are able to create the most astonishing AIs that are outperforming humans in many tasks. But just like complex living forms, which evolved from the primitive cellular life, even the deep neural network had its humble beginning as a primitive artificial neuron dating back to McCulloch-Pitts Neuron Model in 1943. Neural Network Primitives is a series to understand the primitive forms of the artificial neural networks and how these were the first building blocks of modern deep learning.