Most people don't buy a jar of relish every week. But when they decide to buy one from Ocado--the world's largest online-only grocery retailer--they don't have to scrabble at the back of the store. Instead, they call on robots and artificial intelligence to have it delivered to their door. Ocado claims that its 350,000-square-foot warehouse in Dorden, near the U.K.'s second city of Birmingham, is more heavily automated than Amazon's warehouse facilities. The company's task is certainly more challenging in many respects: most of the 48,000 lines of goods that it sells are perishable, and many must be chilled or frozen.

There are a number of machine learning models to choose from. We can use Linear Regression to predict a value, Logistic Regression to classify distinct outcomes, and Neural Networks to model non-linear behaviors. When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output. But even if this model can accurately predict a value from historical data, how do we know it will work as well on new data? Or more plainly, how do we evaluate whether a machine learning model is actually "good"?

Stream water temperature is considered both a dominant factor in determining the longitudinal distribution pattern of aquatic biota and as a general metabolic indicator for the water body, since so many biological processes are temperature dependent. Moreover, the plunging depth of stream water, its associated pollutant load, and its potential impact on lake/reservoir ecology is dependent on water temperature. Lack of detailed datasets and knowledge on physical processes of the stream system limits the use of a phenomenological model to estimate stream temperature. Rather, empirical models have been used as viable alternatives. In this study, an empirical model (artificial neural networks (ANN)), a statistical model (multiple regression analysis (MRA)), and the chaotic non-linear dynamic algorithms (CNDA) were examined to predict the stream water temperature from the available solar radiation and air temperature.

SYS-CON Events announced today that Technologic Systems Inc., an embedded systems solutions company, will exhibit at SYS-CON's @ThingsExpo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Technologic Systems is an embedded systems company with headquarters in Fountain Hills, Arizona. They have been in business for 32 years, helping more than 8,000 OEM customers and building over a hundred COTS products that have never been discontinued. Technologic Systems' product base consists of a wide variety of off-the-shelf PC/104 single board computers, computer-on-modules, touch panel computers, peripherals and industrial controllers. They also offer custom configurations and design services.

We propose communication-efficient distributed estimation and inference methods for the transelliptical graphical model, a semiparametric extension of the elliptical distribution in the high dimensional regime. In detail, the proposed method distributes the $d$-dimensional data of size $N$ generated from a transelliptical graphical model into $m$ worker machines, and estimates the latent precision matrix on each worker machine based on the data of size $n=N/m$. It then debiases the local estimators on the worker machines and send them back to the master machine. Finally, on the master machine, it aggregates the debiased local estimators by averaging and hard thresholding. We show that the aggregated estimator attains the same statistical rate as the centralized estimator based on all the data, provided that the number of machines satisfies $m \lesssim \min\{N\log d/d,\sqrt{N/(s^2\log d)}\}$, where $s$ is the maximum number of nonzero entries in each column of the latent precision matrix. It is worth noting that our algorithm and theory can be directly applied to Gaussian graphical models, Gaussian copula graphical models and elliptical graphical models, since they are all special cases of transelliptical graphical models. Thorough experiments on synthetic data back up our theory.

Following the very recent line of work on the ``generalized min-max'' (GMM) kernel, this study proposes the ``generalized intersection'' (GInt) kernel and the related ``normalized generalized min-max'' (NGMM) kernel. In computer vision, the (histogram) intersection kernel has been popular, and the GInt kernel generalizes it to data which can have both negative and positive entries. Through an extensive empirical classification study on 40 datasets from the UCI repository, we are able to show that this (tuning-free) GInt kernel performs fairly well. The empirical results also demonstrate that the NGMM kernel typically outperforms the GInt kernel. Interestingly, the NGMM kernel has another interpretation --- it is the ``asymmetrically transformed'' version of the GInt kernel, based on the idea of ``asymmetric hashing''. Just like the GMM kernel, the NGMM kernel can be efficiently linearized through (e.g.,) generalized consistent weighted sampling (GCWS), as empirically validated in our study. Owing to the discrete nature of hashed values, it also provides a scheme for approximate near neighbor search.

The mixture of Gaussian distributions, a soft version of k-means , is considered a state-of-the-art clustering algorithm. It is widely used in computer vision for selecting classes, e.g., color, texture, and shapes. In this algorithm, each class is described by a Gaussian distribution, defined by its mean and covariance. The data is described by a weighted sum of these Gaussian distributions. We propose a new method, inspired by quantum interference in physics. Instead of modeling each class distribution directly, we model a class wave function such that its magnitude square is the class Gaussian distribution. We then mix the class wave functions to create the mixture wave function. The final mixture distribution is then the magnitude square of the mixture wave function. As a result, we observe the quantum class interference phenomena, not present in the Gaussian mixture model. We show that the quantum method outperforms the Gaussian mixture method in every aspect of the estimations. It provides more accurate estimations of all distribution parameters, with much less fluctuations, and it is also more robust to data deformations from the Gaussian assumptions. We illustrate our method for color segmentation as an example application.

Inference in complex and high-dimensional statistical models is a very challenging problem that is ubiquitous in applications such as climate informatics [Monteleoni et al., 2013], bioinformatics [Cohen, 2004] and machine learning [Wainwright and Jordan, 2008], to mention a few. We are interested in sequential Bayesian inference in settings where we have a sequence of posterior distributions that we need to compute. To be specific, we are focusing on settings where the model (or state variable) is high-dimensional, but where there are local dependencies. One example of the type of models we consider are the so-called spatiotemporal models [Wikle, 2015, Cressie and Wikle, 2011, Rue and Held, 2005]. Sequential Monte Carlo (SMC) methods comprise one of the most successful methodologies for sequential Bayesian inference. However, SMC struggles in high dimensions and these methods are rarely used for dimensions, say, higher than ten [Rebeschini and van Handel, 2015].

Learning from examples is one of the key problems in science and engineering. It deals with function reconstruction from a finite set of direct and noisy samples. Regularization in reproducing kernel Hilbert spaces (RKHSs) is widely used to solve this task and includes powerful estimators such as regularization networks. Recent achievements include the proof of the statistical consistency of these kernel- based approaches. Parallel to this, many different system identification techniques have been developed but the interaction with machine learning does not appear so strong yet. One reason is that the RKHSs usually employed in machine learning do not embed the information available on dynamic systems, e.g. BIBO stability. In addition, in system identification the independent data assumptions routinely adopted in machine learning are never satisfied in practice. This paper provides new results which strengthen the connection between system identification and machine learning. Our starting point is the introduction of RKHSs of dynamic systems. They contain functionals over spaces defined by system inputs and allow to interpret system identification as learning from examples. In both linear and nonlinear settings, it is shown that this perspective permits to derive in a relatively simple way conditions on RKHS stability (i.e. the property of containing only BIBO stable systems or predictors), also facilitating the design of new kernels for system identification. Furthermore, we prove the convergence of the regularized estimator to the optimal predictor under conditions typical of dynamic systems.

Predictive State Representations (PSRs) are powerful techniques for modelling dynamical systems, which represent a state as a vector of predictions about future observable events (tests). In PSRs, one of the fundamental problems is the learning of the PSR model of the underlying system. Recently, spectral methods have been successfully used to address this issue by treating the learning problem as the task of computing an singular value decomposition (SVD) over a submatrix of a special type of matrix called the Hankel matrix. Under the assumptions that the rows and columns of the submatrix of the Hankel Matrix are sufficient (which usually means a very large number of rows and columns, and almost fails in practice) and the entries of the matrix can be estimated accurately, it has been proven that the spectral approach for learning PSRs is statistically consistent and the learned parameters can converge to the true parameters. However, in practice, due to the limit of the computation ability, only a finite set of rows or columns can be chosen to be used for the spectral learning. While different sets of columns usually lead to variant accuracy of the learned model, in this paper, we propose an approach for selecting the set of columns, namely basis selection, by adopting a concept of model entropy to measure the accuracy of the learned model. Experimental results are shown to demonstrate the effectiveness of the proposed approach.