Parametric POMDPs for planning in continuous state spaces

This thesis is concerned with planning and acting under uncertainty in partially-observable continuous domains. In particular, it focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that a single state, such as the mode of this distribution, is correct. In scenarios involving significant uncertainty, this can lead to serious control errors. It is generally agreed that the reliability of navigation in uncertain environments would be greatly improved by the ability to consider the entire distribution when acting, rather than the single most likely state. The framework adopted in this thesis for modelling navigation problems mathematically is the Partially Observable Markov Decision Process (POMDP). An exact solution to a POMDP problem provides the optimal balance between reward-seeking behaviour and information-seeking behaviour, in the presence of sensor and actuation noise. Unfortunately, previous exact and approximate solution methods have had difficulty scaling to real applications. The contribution of this thesis is the formulation of an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. In order to apply the solution algorithm to real-world problems, a number of novel improvements are proposed. Specifically, Monte Carlo methods are employed to estimate distributions over future parameterised beliefs, improving planning accuracy without a loss of efficiency. Conditional independence assumptions are exploited to simplify the problem, reducing computational requirements. Scalability is further increased by focussing computation on likely beliefs, using metric indexing structures for efficient function approximation. Local online planning is incorporated to assist global offline planning, allowing the precision of the latter to be decreased without adversely affecting solution quality. Finally, the algorithm is implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. We argue that this task is substantially more challenging and realistic than previous problems to which POMDP solution methods have been applied. Results show that POMDP planning, which considers the evolution of the entire probability distribution over robot poses, produces significantly more robust behaviour when compared with a heuristic planner which considers only the most likely states and outcomes

The Role of Process History in Reducing False Alarms

PresentationProcess history is essential when reviewing operator alarm limits in the context of alarm stewardship and formal rationalization. Far too often limits are implemented on a ‘try-it-and-see’ approach that leads to higher operator load weakening the operators’ trust in the alarm system, potentially leading to delays in acting, and adding extra work later in re-reviewing the limits. Reasons for not making full use of the process history currently in review may include the complexity of the data and perceived overhead of including it in the review. In this paper we demonstrate techniques of data analysis based on the parallel coordinate plot that streamline and improve this, enabling the inclusion and reference to process operating envelopes in all alarm reviews. Operator alarms are essential for the economic operation of process plants, avoiding process downtime and contribute to increased process safety. There has been much recent attention on these systems and the introduction of the EEMUA 191 guidelines and IEC62682 standard for the management systems concerned with alarms. Little detail is provided for the practice of setting these alarm limits. In most approaches to alarm limit setting and philosophies, while attention is paid to consequences and consequence threshold, current process performance and capability is rarely considered in this process. This leads to alarm sets that cause unacceptable operator performance and do not contribute to improved operation or safety. Including historic process data in the alarm rationalization process is required to avoid these pitfalls. The size of the required datasets, and the difficulty of visualizing, let alone interrogating, this data using traditional methods, has led to adapting the parallel coordinate projection as the enabling technique for visualizing sets of alarm limits and their relationship with operating history, operating envelopes, and operator response. Using interrogative visualization of process history in the alarm review context increases effectiveness, producing limits that already consider process operation, and identifying early in the process issues that are usually only seen after the new limits have been put in place, allowing necessary operational and engineering changes to be investigates months or more earlier than now, while producing a set of limits consistent with this operation. These methods also increase the speed of the review, allowing smaller teams to perform most of the work independently and providing a common framework for communication. Pitfalls and issues that can be identified by using the historic data include mis-sized equipment, poor control, lack of capability and failed equipment. We demonstrate how these are identified in the context of alarm review

Technology Addictions and Technostress: An Examination of Hong Kong and the U.S.

In today’s technology-centric world, people are becoming increasingly dependent on the Internet. The most common use of the Internet is through social media, which are used to communicate, share, collaborate, and connect. However, continued usage of a hedonic system can be linked with compulsion or addiction. Since problematic usage/behaviors can lead to negative outcomes, this manuscript aims to determine differential effects of Internet and social media addictions on social media-related technostress. This is examined in two different cultures: the U.S. and Hong Kong. The results support the association between Internet and social media addictions with increases in social media-related technostress. Additionally, these effects are moderated by culture. Implications for research and practice are discussed along with future directions for this stream

Search for Gamma-ray Emission from Dark Matter Annihilation in the Large Magellanic Cloud with the Fermi Large Area Telescope

At a distance of 50 kpc and with a dark matter mass of $\sim10^{10}$ M$_{\odot}$, the Large Magellanic Cloud (LMC) is a natural target for indirect dark matter searches. We use five years of data from the Fermi Large Area Telescope (LAT) and updated models of the gamma-ray emission from standard astrophysical components to search for a dark matter annihilation signal from the LMC. We perform a rotation curve analysis to determine the dark matter distribution, setting a robust minimum on the amount of dark matter in the LMC, which we use to set conservative bounds on the annihilation cross section. The LMC emission is generally very well described by the standard astrophysical sources, with at most a $1-2\sigma$ excess identified near the kinematic center of the LMC once systematic uncertainties are taken into account. We place competitive bounds on the dark matter annihilation cross section as a function of dark matter particle mass and annihilation channel.Comment: 33 pages, 22 figures Version 2: minor corrections and clarifications after journal peer review proces

Search for Gamma-ray Emission from Dark Matter Annihilation in the Small Magellanic Cloud with the Fermi Large Area Telescope

The Small Magellanic Cloud (SMC) is the second-largest satellite galaxy of the Milky Way and is only 60 kpc away. As a nearby, massive, and dense object with relatively low astrophysical backgrounds, it is a natural target for dark matter indirect detection searches. In this work, we use six years of Pass 8 data from the Fermi Large Area Telescope to search for gamma-ray signals of dark matter annihilation in the SMC. Using data-driven fits to the gamma-ray backgrounds, and a combination of N-body simulations and direct measurements of rotation curves to estimate the SMC DM density profile, we found that the SMC was well described by standard astrophysical sources, and no signal from dark matter annihilation was detected. We set conservative upper limits on the dark matter annihilation cross section. These constraints are in agreement with stronger constraints set by searches in the Large Magellanic Cloud and approach the canonical thermal relic cross section at dark matter masses lower than 10 GeV in the $b\bar{b}$ and $\tau^+\tau^-$ channels.Comment: 17 pages, 11 figures. Accepted by PR

Evidence-based implementation practices applied to the intensive treatment of eating disorders: Summary of research and illustration of principles using a case example

Implementation of evidence‐based practices (EBPs) in intensive treatment settings poses a major challenge in the field of psychology. This is particularly true for eating disorder (ED) treatment, where multidisciplinary care is provided to a severe and complex patient population; almost no data exist concerning best practices in these settings. We summarize the research on EBP implementation science organized by existing frameworks and illustrate how these practices may be applied using a case example. We describe the recent successful implementation of EBPs in a community‐based intensive ED treatment network, which recently adapted and implemented transdiagnostic, empirically supported treatment for emotional disorders across its system of residential and day‐hospital programs. The research summary, implementation frameworks, and case example may inform future efforts to implement evidence‐based practice in intensive treatment settings.Published versio

Measuring the response of macroeconomic uncertainty to shocks

Recent research documents the importance of uncertainty in determining macroeconomic outcomes, but little is known about the transmission of uncertainty across such outcomes. This paper examines the response of uncertainty about inflation and output growth to shocks documenting statistically significant size and sign bias and spillover effects. Uncertainty about inflation is a determinant of output uncertainty, whereas higher growth volatility tends to raise inflation volatility. Both inflation and growth volatility respond asymmetrically to positive and negative shocks. Negative growth and inflation shocks lead to higher and more persistent uncertainty than shocks of equal magnitude but opposite sign

A Synthetic Loop Replacement Peptide That Blocks Canonical NFâ κB Signaling

Aberrant canonical NFâ κB signaling is implicated in diseases from autoimmune disorders to cancer. A major therapeutic challenge is the need for selective inhibition of the canonical pathway without impacting the many nonâ canonical NFâ κB functions. Here we show that a selective peptideâ based inhibitor of canonical NFâ κB signaling, in which a hydrogen bond in the NBD peptide is synthetically replaced by a nonâ labile bond, shows an about 10â fold increased potency relative to the original inhibitor. Not only is this molecule, NBD2, a powerful tool for dissection of canonical NFâ κB signaling in disease models and healthy tissues, the success of the synthetic loop replacement suggests that the general strategy could be useful for discovering modulators of the many proteinâ protein interactions mediated by such structures.A peptideâ based inhibitor of canonical NFâ κB signaling, in which a hydrogen bond in the NBD peptide is synthetically replaced by a nonâ labile bond, shows an about 10â fold increased potency relative to the original inhibitor. The success of the synthetic replacement of a peptide loop suggests that the general strategy could be broadly useful for discovering modulators of many proteinâ protein interactions mediated by such structures.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135096/1/anie201607990.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135096/2/anie201607990-sup-0001-misc_information.pd

Multi-Objective GFlowNets

We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learning such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. Moreover, these objectives are often imperfect evaluations of some underlying property of interest, making it important to generate diverse candidates to have multiple options for expensive downstream evaluations. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse Pareto optimal solutions, based on GFlowNets. We introduce two variants of MOGFNs: MOGFN-PC, which models a family of independent sub-problems defined by a scalarization function, with reward-conditional GFlowNets, and MOGFN-AL, which solves a sequence of sub-problems defined by an acquisition function in an active learning loop. Our experiments on wide variety of synthetic and benchmark tasks demonstrate advantages of the proposed methods in terms of the Pareto performance and importantly, improved candidate diversity, which is the main contribution of this work.Comment: 23 pages, 8 figures. ICML 2023. Code at: https://github.com/GFNOrg/multi-objective-gf