Ore deposits are generally formed by hydrothermal activity, which also forms various types of alteration zones in the vicinity of such deposits. Identifying alteration zones can clarify the mineralization mechanism and provides information indispensable for mineral resource exploration. The application of an alteration halo accompanied by the alteration of host rocks to the exploration of the Kuroko ore deposits produced many results in Japan (see, e.g., [1]). It is important to identify the alteration zone in the exploration of porphyry copper deposits. More than 50% of global copper production is from porphyry copper deposits, making them the most important copper resource.

The study of causal effects of an intervention or treatment on a specific outcome based on observational data is a fundamental problem in many applications. Examples include understanding the effects of massive wildfires on a person's mental health, of teaching methods on a student's employability, or of disease outbreaks on the global stock market. A critical problem of causal inference from observational data is confounding. A variable that affects both the treatment and the outcome is known as a confounder of the treatment effects on the outcome. Standard ways to measure observable confounders include propensity score matching and their variants (Rubin, 2005). However, if a confounder is hidden, the treatment effect on the outcome cannot be directly estimated without further assumptions (Pearl, 2009; Louizos et al., 2017). For example, household income, which cannot be easily measured, can affect both the therapy options available to a patient and the health condition after therapy of that patient.

Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named "Shapelet transform", which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly "white-box" machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.

Everguard.ai, a developer of AI-based worker safety solutions, announced the commercial release of Sentri360 – an end-to-end safety solution that alerts management and workers in real-time to potential safety hazards. Worker safety is a critical concern for employers and underwriters representing major morale, productivity, and economic impacts. Annually in the US, there are more than 100,000 injuries, 5,000-plus fatalities, which results in more than $60 billion in direct costs and $30 billion-plus in worker compensation claims. Sentri360, developed for the steel industry, will improve worker safety and in addition improve operations and margins as well. Sentri360 solution is tackling the problem of workplace safety by continuously monitoring worker behavior while generating a more complete situational understanding of the worker's environment.

Measurements in Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors, such as the MicroBooNE detector at Fermilab, feature large, high fidelity event images. Deep learning techniques have been extremely successful in classification tasks of photographs, but their application to LArTPC event images is challenging, due to the large size of the events. Events in these detectors are typically two orders of magnitude larger than images found in classical challenges, like recognition of handwritten digits contained in the MNIST database or object recognition in the ImageNet database. Ideally, training would occur on many instances of the entire event data, instead of many instances of cropped regions of interest from the event data. However, such efforts lead to extremely long training cycles, which slow down the exploration of new network architectures and hyperparameter scans to improve the classification performance. We present studies of scaling a LArTPC classification problem on multiple architectures, spanning multiple nodes. The studies are carried out on simulated events in the MicroBooNE detector. We emphasize that it is beyond the scope of this study to optimize networks or extract the physics from any results here. Institutional computing at Pacific Northwest National Laboratory and the SummitDev machine at Oak Ridge National Laboratory's Leadership Computing Facility have been used. To our knowledge, this is the first use of state-of-the-art Convolutional Neural Networks for particle physics and their attendant compute techniques onto the DOE Leadership Class Facilities. We expect benefits to accrue particularly to the Deep Underground Neutrino Experiment (DUNE) LArTPC program, the flagship US High Energy Physics (HEP) program for the coming decades.

In this study, we propose a machine-learning-based approach to identify the modal parameters of the output only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independence feature of each mode, we use the principle of unsupervised learning, making the training process of the deep neural network becomes the process of modal separation. A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then we use a complex cost function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The deep neural network is essentially a nonlinear objective function optimization problem. A novel loss function is proposed to constrain the independent feature with consideration of uncorrelation and non-Gaussianity to restrict the designed neural network to obtain the structural modal parameters. A numerical example of a simple structure and an example of actual SHM data from a cable-stayed bridge are presented to illustrate the modal parameter identification ability of the proposed approach. The results show the approach s good capability in blindly extracting modal information from system responses.

Mercury Systems, Inc., a leader in trusted, secure mission-critical technologies for aerospace and defense, announced it received a $4.7 million order from a leading defense prime contractor to provide artificial intelligence (AI) processing technology for integration into an advanced airborne electro-optic system. The order was booked in the Company's fiscal 2020 third quarter and is expected to be shipped over the next several quarters. "Mercury solutions are designed to be the most rugged, durable and highest performing available to meet the rigorous demands of military and commercial customers," said Joe Plunkett, Mercury's Vice President and General Manager for Mercury's Sensor Processing group. "Our ability to provide datacenter-quality processing architecture in an embedded solution allows our customers to quickly extract critical information from electro-optical/infrared (EO/IR) imagery using the latest AI techniques. This technology underscores our commitment to Innovation That Matters by enabling our military to see further and with greater clarity, giving them a decisive edge in all tactical situations."

Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures; maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Damage may accumulate on structures due to different environmental and human-induced factors. Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Considerable effort has been put into vibration-based methods, which utilize the vibration response of the monitored structure to assess its condition and identify structural damage. Meanwhile, with emerging computing power and sensing technology in the last decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have become more feasible and extensively used in vibration-based structural damage detection with elegant performance and often with rigorous accuracy. While there have been multiple review studies published on vibration-based structural damage detection, there has not been a study where the transition from traditional methods to ML and DL methods are described and discussed. This paper aims to fulfill this gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.

Blockchain-enabled Federated Learning (BFL) enables model updates of Federated Learning (FL) to be stored in the blockchain in a secure and reliable manner. However, the issue of BFL is that the training latency may increase due to the blockchain mining process. The other issue is that mobile devices in BFL have energy and CPU constraints that may reduce the system lifetime and training efficiency. To address these issues, the Machine Learning Model Owner (MLMO) needs to (i) decide how much data and energy that the mobile devices use for the training and (ii) determine the mining difficulty to minimize the training latency and energy consumption while achieving the target model accuracy. Under the uncertainty of the BFL environment, it is challenging for the MLMO to determine the optimal decisions. We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for the MLMO.

We present Catalyst.RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research. Main features of Catalyst.RL include large-scale asynchronous distributed training, efficient implementations of various RL algorithms and auxiliary tricks, such as n-step returns, value distributions, hyperbolic reinforcement learning, etc. To demonstrate the effectiveness of Catalyst.RL, we applied it to a physics-based reinforcement learning challenge "NeurIPS 2019: Learn to Move -- Walk Around" with the objective to build a locomotion controller for a human musculoskeletal model. The environment is computationally expensive, has a high-dimensional continuous action space and is stochastic. Our team took the 2nd place, capitalizing on the ability of Catalyst.RL to train high-quality and sample-efficient RL agents in only a few hours of training time. The implementation along with experiments is open-sourced so results can be reproduced and novel ideas tried out.