Montreal-based My Intelligent Machines (MIMS), which is developing a platform to help life sciences companies maximize their R&D and production activities, has closed a $2.6 million seed round. "MIMS will contribute to life sciences companies that are ready to welcome the artificial intelligence era." The round saw participation from Anges Québec, Anges Québec Capital, Consortium MedTeq, Desjardins Capital, Real Ventures, and StandUp Ventures. With this funding, MIMS intends to develop solutions that allow life scientists to stratify human, animal, and plant populations through artificial intelligence and big data, without needing expertise in data science or bioinformatics. "MIMS sets itself apart from other AI startups, because of our ability to leverage what we call'fat data' using AI, which is very difficult to do," said Sarah Jenna, co-founder and CEO of MIMS.

Inside a sunny conference room on the Microsoft campus in Redmond, Wash., a small team of employees is describing how technology can save the world. Microsoft's Digital Diplomacy unit consists of two dozen policy experts who work on everything from the ethical use of artificial intelligence to protecting the 2020 presidential election from foreign cyberinterference. Brad Smith, Microsoft's president, sits in the middle of the table, sipping coffee from a mug bearing the name of his hometown, Appleton, Wis. The group updates Smith on a tech-industry initiative co-founded by Microsoft to combat terrorist messaging on the Internet. Smith pushes for more ideas. "We need something that will create a new mold," he says.

How likely is it that a few initial node activations are amplified to produce large response cascades that span a considerable part of an entire network? Our answer to this question relies on the Independent Cascade Model for weighted directed networks. In using this model, most of our insights have been derived from the study of average effects. Here, we shift the focus on the full probability distribution of the final cascade size. This shift allows us to explore both typical cascade outcomes and improbable but relevant extreme events. We present an efficient message passing algorithm to compute the final cascade size distribution and activation probabilities of nodes conditional on the final cascade size. Our approach is exact on trees but can be applied to any network topology. It approximates locally treelike networks well and can lead to surprisingly good performance on more dense networks, as we show using real world data, including a miRNAmiRNA probabilistic interaction network for gastrointestinal cancer. We demonstrate the utility of our algorithms for clustering of nodes according to their functionality and influence maximization. Introduction The Independent Cascade Model (ICM) is a cornerstone in the study of spreading processes on networks. Many related optimization algorithms require sampling from the model.

The performance of land surface models (LSMs) strongly depends on their unknown parameter variables so that it is necessary to optimize them. Here I present a globally applicable and computationally efficient method for parameter optimization and uncertainty assessment of the LSM by combining Markov Chain Monte Carlo (MCMC) with machine learning. First, I performed the long-term ensemble simulation of the LSM, in which each ensemble member has different parameters' variables, and calculated the gap between simulation and observation, or the cost function, for each ensemble member. Second, I developed the statistical machine learning based surrogate model, which is computationally cheap but accurately mimics the relationship between parameters and the cost function, by applying the Gaussian process regression to learn the model simulation. Third, we applied MCMC by repeatedly driving the surrogate model to get the posterior probabilistic distribution of parameters. Using satellite passive microwave brightness temperature observations, both synthetic and real-data experiments were performed to optimize unknown soil and vegetation parameters of the LSM. The primary findings are (1) the proposed method is 50,000 times as fast as the direct application of MCMC to the full LSM; (2) the skill of the LSM to simulate both soil moisture and vegetation dynamics can be improved; (3) I successfully quantify the characteristics of equifinality by obtaining the full non-parametric probabilistic distribution of parameters.

One of the key issues for imitation learning lies in making policy learned from limited samples to generalize well in the whole state-action space. This problem is much more severe in high-dimensional state environments, such as game playing with raw pixel inputs. Under this situation, even state-of-the-art adversary based imitation learning algorithms fail. Through theoretical and empirical studies, we find that the main cause lies in the failure of training a powerful discriminator to generate meaningful rewards in high-dimensional environments. Theoretical results are provided to suggest the necessity of dimensionality reduction. However, since preserving important discriminative information via feature transformation is a non-trivial task, a straightforward application of off-the-shelf methods cannot achieve desirable performance. To address the above issues, we propose HashReward, which is a novel imitation learning algorithm utilizing the idea of supervised hashing to realize effective training of the discriminator. As far as we are aware, HashReward is the first pure imitation learning approach to achieve expert comparable performance in Atari game environments with raw pixel inputs.

The biomanufacturing industry is growing rapidly and becoming one of the key drivers of personalized medicine and life science. However, biopharmaceutical production faces critical challenges, including complexity, high variability, long lead time and rapid changes in technologies, processes, and regulatory environment. Driven by these challenges, we explore the biotechnology domain knowledge and propose a rigorous risk and sensitivity analysis framework for biomanufacturing innovation. Built on the causal relationships of raw material quality attributes, production process, and bio-drug properties in safety and efficacy, we develop a Bayesian Network (BN) to model the complex probabilistic interdependence between process parameters and quality attributes of raw materials/in-process materials/drug substance. It integrates various sources of data and leads to an interpretable probabilistic knowledge graph of the end-to-end production process. Then, we introduce a systematic risk analysis to assess the criticality of process parameters and quality attributes. The complex production processes often involve many process parameters and quality attributes impacting on the product quality variability. However, the real-world (batch) data are often limited, especially for customized and personalized bio-drugs. We propose uncertainty quantification and sensitivity analysis to analyze the impact of model risk. Given very limited process data, the empirical results show that we can provide reliable and inter-Corresponding author Email addresses: w.xie@northeastern.edu Thus, the proposed framework can provide the science-and risk-based guidance on the process monitoring, data collection, and process parameters specifications to facilitate the production process learning and stability control. Keywords: Decision analysis, biomanufacturing, Bayesian network, production process risk analysis, sensitivity analysis 2017 MSC: 00-01, 99-00 1. Introduction In the past decades, pharmaceutical companies have invested billions of dollars in the research and development (R&D) of new biomedicines for the treatment of many severe illnesses, including cancer cells and adult blindness. More than 40 percent of the overall pharmaceutical industry R&D and products in the development pipeline are biopharmaceuticals and this percentage is expected to continuously increase. Compared to the classical pharmaceutical manufacturing, biopharmaceutical production faces several challenges, including complexity, high variability, long lead time and rapid changes in technologies, processes, and regulatory environment (Kaminsky & Wang, 2015). Biotechnology products are produced in living organisms, which induces a lot of uncertainty in the production process.

Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here we propose super learning (a type of ensemble learning) by combining 10 machine learning algorithms. We apply the proposed algorithm in one-step ahead forecasting mode. For the application, we exploit a big dataset consisting of 10-year long time series of daily streamflow, precipitation and temperature from 511 basins. The super learner improves over the performance of the linear regression algorithm by 20.06%, outperforming the "hard to beat in practice" equal weight combiner. The latter improves over the performance of the linear regression algorithm by 19.21%. The best performing individual machine learning algorithm is neural networks, which improves over the performance of the linear regression algorithm by 16.73%, followed by extremely randomized trees (16.40%), XGBoost (15.92%), loess (15.36%), random forests (12.75%), polyMARS (12.36%), MARS (4.74%), lasso (0.11%) and support vector regression (-0.45%). Based on the obtained large-scale results, we propose super learning for daily streamflow forecasting.

F ORCE FIELD TERMINOLOGY Taking into consideration a classical mechanics approach, a force is defined as a vectorial quantity that acts on a body to cause a change in its state of motion [25]. Forces can be classified in action-reaction (when bodies, which are in contact, change their momenta [25]) and action-at-a-distance forces (when objects interact without being physically touched). Considering that social interactions can be often modeled as contact-less, it becomes possible to explain social phenomena in a certain environment by modeling interactions between entities with action-at-a-distance forces. A force field null F is defined as a vector point-function which has the property that at every point of the space takes a particular value related to the magnitude and direction of a force acting on a particle of unit of mass placed there [26]. Accordingly, in this work, the particles of unit of mass affected by force fields will be called agents. A central force field null F f ( r)ˆr is a special case of force field in which the motion of agents is affected depending on the distance r to a center of force, which is generally associated with the center of mass of the object that produces the force field.

Operator-theoretic analysis of nonlinear dynamical systems has attracted much attention in a variety of engineering and scientific fields, endowed with practical estimation methods using data such as dynamic mode decomposition. In this paper, we address a lifted representation of nonlinear dynamical systems with random noise based on transfer operators, and develop a novel Krylov subspace method for estimating it using finite data, with consideration of the unboundedness of operators. For this purpose, we first consider Perron-Frobenius operators with kernel-mean embeddings for such systems. Then, we extend the Arnoldi method, which is the most classical type of Kryov subspace methods, so that it can be applied to the current case. Meanwhile, the Arnoldi method requires the assumption that the operator is bounded, which is not necessarily satisfied for transfer operators on nonlinear systems. We accordingly develop the shift-invert Arnoldi method for the Perron-Frobenius operators to avoid this problem. Also, we describe a way of evaluating the predictive accuracy by estimated operators on the basis of the maximum mean discrepancy, which is applicable, for example, to anomaly detection in complex systems. The empirical performance of our methods is investigated using synthetic and real-world healthcare data.

Automatic differentiation(AD) evaluates derivatives or gradients of any functions specified by computer programs[ 1 ]. It is implemented by propagating derivatives of primitive operations v ia chain rules. Such approach is different from classical symbolic or numerical differentiations. Symbolic differentiat ion faces the challenge of converting a complicated computer program into expressions, while numerical differentiation faces the difficulty of numerical errors in the discretization. Besides, both symbolic and numerical methods have problems in calculating higher order derivatives and are also slow at computing gradients with respect to lots of input s variables, e.g. in the case for gradient-based optimization algorithms.