In this work, we study the emergence of sparsity and multiway structures in second-order statistical characterizations of dynamical processes governed by partial differential equations (PDEs). We consider several state-of-the-art multiway covariance and inverse covariance (precision) matrix estimators and examine their pros and cons in terms of accuracy and interpretability in the context of physics-driven forecasting when incorporated into the ensemble Kalman filter (EnKF). In particular, we show that multiway data generated from the Poisson and the convection-diffusion types of PDEs can be accurately tracked via EnKF when integrated with appropriate covariance and precision matrix estimators.

This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understanding of the potential risks posed by these models is needed. A wide range of established and anticipated risks are analysed in detail, drawing on multidisciplinary expertise and literature from computer science, linguistics, and social sciences. We outline six specific risk areas: I. Discrimination, Exclusion and Toxicity, II. Information Hazards, III. Misinformation Harms, V. Malicious Uses, V. Human-Computer Interaction Harms, VI. Automation, Access, and Environmental Harms. The first area concerns the perpetuation of stereotypes, unfair discrimination, exclusionary norms, toxic language, and lower performance by social group for LMs. The second focuses on risks from private data leaks or LMs correctly inferring sensitive information. The third addresses risks arising from poor, false or misleading information including in sensitive domains, and knock-on risks such as the erosion of trust in shared information. The fourth considers risks from actors who try to use LMs to cause harm. The fifth focuses on risks specific to LLMs used to underpin conversational agents that interact with human users, including unsafe use, manipulation or deception. The sixth discusses the risk of environmental harm, job automation, and other challenges that may have a disparate effect on different social groups or communities. In total, we review 21 risks in-depth. We discuss the points of origin of different risks and point to potential mitigation approaches. Lastly, we discuss organisational responsibilities in implementing mitigations, and the role of collaboration and participation. We highlight directions for further research, particularly on expanding the toolkit for assessing and evaluating the outlined risks in LMs.

USB drives are used by ransomware attackers to distribute malware across the air gap that all industrial distribution, manufacturing, and utility firms rely on as their first line of defense against cyber attacks. According to Honeywell's Industrial Cybersecurity USB Threat Report 2021, 79 percent of USB assaults have the potential to damage operational technologies (OT) that power industrial processing plants. The incidence of malware-based USB attacks is one of the most rapidly developing and difficult-to-detect threat vectors that process industries such as public utilities confront today, according to the research. As the Colonial Pipeline and JBS Foods demonstrate, this type of attack vector is particularly effective. Utility companies are also being targeted by ransomware criminals, as the thwarted water treatment plant attacks in Florida and Northern California illustrate.

Given the expansive scope of inspections in different environments, industries are in need of a mixed fleet of specialist robots that are tailored to these conditions. Our robot-agnostic solution enables industries to manage a mixed fleet of robots in different environments (incl. ATEX/IECEx Zone 1 areas) through one single interface. In this session, we will present a live-demo of autonomous inspection and delve into how these robots can be equipped with extensible sensors and skills that match your inspection needs. Join our webinar and watch robots perform inspection missions autonomously. What to look forward to in the webinar? 1. Live demo of inspection missions by our robot fleet including Spot, from Boston Dynamics, and ExR-2 from ExRobotics 2. The need for a mixed fleet of specialist robots and how you can manage them through a single interface 3. Learn about industrial use-cases and problems solved through autonomous robots 4. Best practices derived from 2 years of our experience in deploying 50+ robots in the brownfield industry with over 50,000 hours of deployment 5. Value added by autonomous inspections in terms of operational efficiency, workplace safety and cost effectiveness Be a part of the Webinar and learn how we push the boundaries of what is possible and extract the full potential of robots. About us: Energy Robotics provides an end-to-end, robot-agnostic software solution for autonomous inspections in capital-intensive industries such as oil & gas, chemical and energy.

Learning-based predictive control is a promising alternative to optimization-based MPC. However, efficiently learning the optimal control policy, the optimal value function, or the Q-function requires suitable function approximators. Often, artificial neural networks (ANN) are considered but choosing a suitable topology is also non-trivial. Against this background, it has recently been shown that tailored ANN allow, in principle, to exactly describe the optimal control policy in linear MPC by exploiting its piecewise affine structure. In this paper, we provide a similar result for representing the optimal value function and the Q-function that are both known to be piecewise quadratic for linear MPC.

Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.

The multi-task learning (MTL) paradigm can be traced back to an early paper of Caruana (1997) in which it was argued that data from multiple tasks can be used with the aim to obtain a better performance over learning each task independently. A solution of MTL with conflicting objectives requires modelling the trade-off among them which is generally beyond what a straight linear combination can achieve. A theoretically principled and computationally effective strategy is finding solutions which are not dominated by others as it is addressed in the Pareto analysis. Multi-objective optimization problems arising in the multi-task learning context have specific features and require adhoc methods. The analysis of these features and the proposal of a new computational approach represent the focus of this work. Multi-objective evolutionary algorithms (MOEAs) can easily include the concept of dominance and therefore the Pareto analysis. The major drawback of MOEAs is a low sample efficiency with respect to function evaluations. The key reason for this drawback is that most of the evolutionary approaches do not use models for approximating the objective function. Bayesian Optimization takes a radically different approach based on a surrogate model, such as a Gaussian Process. In this thesis the solutions in the Input Space are represented as probability distributions encapsulating the knowledge contained in the function evaluations. In this space of probability distributions, endowed with the metric given by the Wasserstein distance, a new algorithm MOEA/WST can be designed in which the model is not directly on the objective function but in an intermediate Information Space where the objects from the input space are mapped into histograms. Computational results show that the sample efficiency and the quality of the Pareto set provided by MOEA/WST are significantly better than in the standard MOEA.

Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile devices due to its small size and affordable price. Previous works have mainly focused on quality improvement for I-ToF imaging especially curing the effect of Multi Path Interference (MPI). These investigations are typically done in specifically constrained scenarios at close distance, indoors and under little ambient light. Surprisingly little work has investigated I-ToF quality improvement in real-life scenarios where strong ambient light and far distances pose difficulties due to an extreme amount of induced shot noise and signal sparsity, caused by the attenuation with limited sensor power and light scattering. In this work, we propose a new learning based end-to-end depth prediction network which takes noisy raw I-ToF signals as well as an RGB image and fuses their latent representation based on a multi step approach involving both implicit and explicit alignment to predict a high quality long range depth map aligned to the RGB viewpoint. We test our approach on challenging real-world scenes and show more than 40% RMSE improvement on the final depth map compared to the baseline approach.

The following news release was issued by the U.S. Department of Energy (DOE). One of the projects receiving funding is aimed at developing intelligent experiments through real-time artificial intelligence (AI) to achieve fast data processing and autonomous detector control for the sPHENIX detector at the Relativistic Heavy Ion Collider (RHIC) -- a DOE Office of Science user facility at DOE's Brookhaven National Laboratory -- and for future detectors at the Electron-Ion Collider (EIC). Another project will support AI-driven detector design for the EIC. Both AI projects will be led by scientists at other DOE laboratories and universities across the U.S. Schematic for the sPHENIX detector at the Relativistic Heavy Ion Collider (left) and a preliminary concept for a future Electron-Ion Collider detector (right). WASHINGTON, D.C. - Today, the U.S. Department of Energy (DOE) announced $5.7 million for six projects that will implement artificial intelligence methods to accelerate scientific discovery in nuclear physics research.

Precognition, the ability to see events in the future, has always fascinated humankind. We probably will get there someday, but time series forecasting gets you close. The human brain is naturally trained to anticipate future events by analyzing the past, but the brain often makes only linear predictions because it can't analyze the amount of data generated in a modern enterprise. How about letting a machine record those past sequences of events from millions of sources, analyze the data, and make predictions for your business? Let's take for example a software as a service (SaaS) provider that has thousands of customers from different industries, including online retail, oil and gas, and airline.