Using multiple agents in uncertainty minimization of ablating target sources

The objective of this research effort is to provide an efficient methodology for a multi-agent robotic system to observe moving targets that are generated from an ablation process. An ablation process is a process where a larger mass is reduced in volume as a result of erosion; this erosion results in smaller, independent masses. An example of such a process is the natural process that gives rise to icebergs, which are generated through an ablation process referred to as ice calving. Ships that operate in polar regions continue to face the threat of floating ice sheets and icebergs generated from the ice ablation process. Although systems have been implemented to track these threats with varying degrees of success, many of these techniques require that the operations are conducted outside of some boundary where the icebergs are known not to drift. Since instances where polar operations must be conducted within such a boundary line do exist (e.g., resource exploration), methods for situational awareness of icebergs for these operations are necessary. In this research, efficacy of these methods is correlated to the initial acquisition time of observing newly ablated targets, as it provides for the ability to enact early countermeasures. To address the research objective, the iceberg tracking problem is defined such that it is re-cast within a class of robotic, multiagent target-observation problems. From this new definition, the primary contributions of this research are obtained: 1) A definition of the iceberg observation problem that extends an existing robotic observation problem to the requirements for the observation of floating ice masses; 2) A method for modeling the activity regions on an ablating source to extract ideal search regions to quickly acquire newly ablated targets; 3) A method for extracting metrics for this model that can be used to assess performance of observation algorithms and perform resource allocation. A robot controller is developed that implements the algorithms that result from these contributions and comparisons are made to existing target acquisition techniques.Ph.D

Coordinated Unmanned Aerial Vehicles for Surveillance of Targets

PhDThis thesis investigates the coordination approaches of multiple mobile and autonomous robots, especially resource-limited small-scale UAVs, for the surveillance of pre-de ned ground targets in a given environment. A key research issue in surveillance task is the coordination among the robots to determine the target's time varying locations. The research focuses on two applications of surveillance: (i) cooperative search of stationary targets, and (ii) cooperative observation of moving targets. The objective in cooperative search is to minimize the time and errors in nding the locations of stationary targets. The objective of cooperative observation is to maximize the collective time and quality of observation of moving targets. The thesis presents a survey of the approaches in a larger domain of multi-robot systems for the surveillance of pre-de ned targets in a given environment. This survey identi es various factors and application scenarios that a ect the performance of multi-robot surveillance systems. The thesis proposes a distributed strategy for merging delayed and incomplete information, which is a result of sensing and communication limitations, collected by di erent UAVs. An analytic derivation of the number of required observations is provided to declare the absence or existence of a target in a region. This number of required observations is integrated into an iterative use of Travelling Salesman Problem (TSP) and Multiple Travelling Salesmen Problem (MTSP) for autonomous path planning of UAVs. Additionally, it performs an exploration of the algorithmic design space and analyzes the e ects of centralized and distributed coordination on the cooperative search of stationary targets in the presence of sensing and communication limitations. The thesis also proposes the application of UAVs for observing multiple moving targets with di erent resolutions. A key contribution is to use the quad-tree data-structure for modelling the environment and movement of UAVs. This modelling has helped in the dynamic sensor placement of UAVs to maximize the observation of the number of moving targets as well as the resolution of observation.European Regional Development Fund and the Carinthian Economic Promotion Fund (KWF) under grant 20214/21530/32602

Technology for Future NASA Missions: Civil Space Technology Initiative (CSTI) and Pathfinder

Information is presented in viewgraph form on a number of related topics. Information is given on orbit transfer vehicles, spacecraft instruments, spaceborne experiments, university/industry programs, spacecraft propulsion, life support systems, cryogenics, spacecraft power supplies, human factors engineering, spacecraft construction materials, aeroassist, aerobraking and aerothermodynamics

SSTAC/ARTS review of the draft Integrated Technology Plan (ITP). Volume 8: Aerothermodynamics Automation and Robotics (A/R) systems sensors, high-temperature superconductivity

Viewgraphs of briefings presented at the SSTAC/ARTS review of the draft Integrated Technology Plan (ITP) on aerothermodynamics, automation and robotics systems, sensors, and high-temperature superconductivity are included. Topics covered include: aerothermodynamics; aerobraking; aeroassist flight experiment; entry technology for probes and penetrators; automation and robotics; artificial intelligence; NASA telerobotics program; planetary rover program; science sensor technology; direct detector; submillimeter sensors; laser sensors; passive microwave sensing; active microwave sensing; sensor electronics; sensor optics; coolers and cryogenics; and high temperature superconductivity

The Young Supernova Experiment: Survey Goals, Overview, and Operations

Time domain science has undergone a revolution over the past decade, with tens of thousands of new supernovae (SNe) discovered each year. However, several observational domains, including SNe within days or hours of explosion and faint, red transients, are just beginning to be explored. Here, we present the Young Supernova Experiment (YSE), a novel optical time-domain survey on the Pan-STARRS telescopes. Our survey is designed to obtain well-sampled $griz$ light curves for thousands of transient events up to $z \approx 0.2$. This large sample of transients with 4-band light curves will lay the foundation for the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope, providing a critical training set in similar filters and a well-calibrated low-redshift anchor of cosmologically useful SNe Ia to benefit dark energy science. As the name suggests, YSE complements and extends other ongoing time-domain surveys by discovering fast-rising SNe within a few hours to days of explosion. YSE is the only current four-band time-domain survey and is able to discover transients as faint $\sim$21.5 mag in $gri$ and $\sim$20.5 mag in $z$, depths that allow us to probe the earliest epochs of stellar explosions. YSE is currently observing approximately 750 square degrees of sky every three days and we plan to increase the area to 1500 square degrees in the near future. When operating at full capacity, survey simulations show that YSE will find $\sim$5000 new SNe per year and at least two SNe within three days of explosion per month. To date, YSE has discovered or observed 8.3% of the transient candidates reported to the International Astronomical Union in 2020. We present an overview of YSE, including science goals, survey characteristics and a summary of our transient discoveries to date.Comment: ApJ, in press; more information at https://yse.ucsc.edu

Towards Deep Learning with Competing Generalisation Objectives

The unreasonable effectiveness of Deep Learning continues to deliver unprecedented Artificial Intelligence capabilities to billions of people. Growing datasets and technological advances keep extending the reach of expressive model architectures trained through efficient optimisations. Thus, deep learning approaches continue to provide increasingly proficient subroutines for, among others, computer vision and natural interaction through speech and text. Due to their scalable learning and inference priors, higher performance is often gained cost-effectively through largely automatic training. As a result, new and improved capabilities empower more people while the costs of access drop. The arising opportunities and challenges have profoundly influenced research. Quality attributes of scalable software became central desiderata of deep learning paradigms, including reusability, efficiency, robustness and safety. Ongoing research into continual, meta- and robust learning aims to maximise such scalability metrics in addition to multiple generalisation criteria, despite possible conflicts. A significant challenge is to satisfy competing criteria automatically and cost-effectively. In this thesis, we introduce a unifying perspective on learning with competing generalisation objectives and make three additional contributions. When autonomous learning through multi-criteria optimisation is impractical, it is reasonable to ask whether knowledge of appropriate trade-offs could make it simultaneously effective and efficient. Informed by explicit trade-offs of interest to particular applications, we developed and evaluated bespoke model architecture priors. We introduced a novel architecture for sim-to-real transfer of robotic control policies by learning progressively to generalise anew. Competing desiderata of continual learning were balanced through disjoint capacity and hierarchical reuse of previously learnt representations. A new state-of-the-art meta-learning approach is then proposed. We showed that meta-trained hypernetworks efficiently store and flexibly reuse knowledge for new generalisation criteria through few-shot gradient-based optimisation. Finally, we characterised empirical trade-offs between the many desiderata of adversarial robustness and demonstrated a novel defensive capability of implicit neural networks to hinder many attacks simultaneously

Hepatocellular Carcinoma

This open access book offers a comprehensive review of hepatocellular carcinoma (HCC) with a particular focus on the pathobiology and clinical aspects of the disease, including diagnosis and treatment. HCC is becoming one of the most common causes of cancer-related death worldwide. It is the fifth most common malignancy in men and the ninth in women, with an estimated 500,000 to 1 million new cases annually around the world. Independent of its cause, cirrhosis is considered a major clinical and histopathological risk factor for HCC development. Five percent of all cirrhotic patients develop HCC every year. Diagnostic tools for HCC include blood tests, high-quality imaging studies and liver biopsy. The treatment of HCC depends on the size and location of the HCC and includes surgical resection, liver transplantation, endovascular approaches, percutaneous ablation, and medical treatments. The book is organized into four parts – overview, diagnosis, management strategies, and recommendations – and aims to provide surgeons and clinicians with a valuable resource for complete and up-to-date research on the clinical aspects and management of HCC