Search for: All records

Coronal mass ejections (CMEs) from pseudostreamers represent a significant fraction of large-scale eruptions from the Sun. In some cases, these CMEs take a narrow jet-like form reminiscent of coronal jets; in others, they have a much broader fan-shaped morphology like CMEs from helmet streamers. We present results from a magnetohydrodynamic simulation of a broad pseudostreamer CME. The early evolution of the eruption is initiated through a combination of breakout interchange reconnection at the overlying null point and ideal instability of the flux rope that forms within the pseudostreamer. This stage is characterized by a rolling motion and deflection of the flux rope toward the breakout current layer. The stretching out of the strapping field forms a flare current sheet below the flux rope; reconnection onset there forms low-lying flare arcade loops and the two-ribbon flare footprint. Once the CME flux rope breaches the rising breakout current layer, interchange reconnection with the external open field disconnects one leg from the Sun. This induces a whip-like rotation of the flux rope, generating the unstructured fan shape characteristic of pseudostreamer CMEs. Interchange reconnection behind the CME releases torsional Alfvén waves and bursty dense outflows into the solar wind. Our results demonstrate that pseudostreamer CMEs follow the same overall magnetic evolution as coronal jets, although they present different morphologies of their ejecta. We conclude that pseudostreamer CMEs should be considered a class of eruptions that are distinct from helmet-streamer CMEs, in agreement with previous observational studies.

 

We establish the first finite-time logarithmic regret bounds for the self-tuning regulation problem. We introduce a modified version of the certainty equivalence algorithm, which we call PIECE, that clips inputs in addition to utilizing probing inputs for exploration. We show that it has a ClogT upper bound on the regret after T time-steps for bounded noise, and Clog3T in the case of sub-Gaussian noise, unlike the LQ problem where logarithmic regret is shown to be not possible. The PIECE algorithm is also designed to address the critical challenge of poor initial transient performance of reinforcement learning algorithms for linear systems. Comparative simulation results illustrate the improved performance of PIECE. 

We study Markov potential games under the infinite horizon average reward criterion. Most previous studies have been for discounted rewards. We prove that both algorithms based on independent policy gradient and independent natural policy gradient converge globally to a Nash equilibrium for the average reward criterion. To set the stage for gradient-based methods, we first establish that the average reward is a smooth function of policies and provide sensitivity bounds for the differential value functions, under certain conditions on ergodicity and the second largest eigenvalue of the underlying Markov decision process (MDP). We prove that three algorithms, policy gradient, proximal-Q, and natural policy gradient (NPG), converge to an ϵ-Nash equilibrium with time complexity O(1ϵ2), given a gradient/differential Q function oracle. When policy gradients have to be estimated, we propose an algorithm with ~O(1mins,aπ(a|s)δ) sample complexity to achieve δ approximation error w.r.t~the ℓ2 norm. Equipped with the estimator, we derive the first sample complexity analysis for a policy gradient ascent algorithm, featuring a sample complexity of ~O(1/ϵ5). Simulation studies are presented. 

Interactive decision making, encompassing bandits, contextual bandits, and reinforcement learning, has recently been of interest to theoretical studies of experimentation design and recommender system algorithm research. Recently, it has been shown that the wellknown Graves-Lai constant being zero is a necessary and sufficient condition for achieving bounded (or constant) regret in interactive decision making. As this condition may be a strong requirement for many applications, the practical usefulness of pursuing bounded regret has been questioned. In this paper, we show that the condition of the Graves-Lai constant being zero is also necessary to achieve delay model robustness when reward delays are unknown (i.e., when feedbacks are anonymous). Here, model robustness is measured in terms of ✏-robustness, one of the most widely used and one of the least adversarial robustness concepts in the robust statistics literature. In particular, we show that ✏-robustness cannot be achieved for a consistent (i.e., uniformly sub-polynomial regret) algorithm however small the nonzero ✏ value is when the Grave-Lai constant is not zero. While this is a strongly negative result, we also provide a positive result for linear rewards models (Linear contextual bandits, Reinforcement learning with linear MDP) that the Grave-Lai constant being zero is also sufficient for achieving bounded regret without any knowledge of delay models, i.e., the best of both the efficiency world and the delay robustness world. 

Compound drought and heatwave (CDHW) events have garnered increased attention due to their significant impacts on agriculture, energy, water resources, and ecosystems. We quantify the projected future shifts in CDHW characteristics (such as frequency, duration, and severity) due to continued anthropogenic warming relative to the baseline recent observed period (1982 to 2019). We combine weekly drought and heatwave information for 26 climate divisions across the globe, employing historical and projected model output from eight Coupled Model Intercomparison Project 6 GCMs and three Shared Socioeconomic Pathways. Statistically significant trends are revealed in the CDHW characteristics for both recent observed and model simulated future period (2020 to 2099). East Africa, North Australia, East North America, Central Asia, Central Europe, and Southeastern South America show the greatest increase in frequency through the late 21st century. The Southern Hemisphere displays a greater projected increase in CDHW occurrence, while the Northern Hemisphere displays a greater increase in CDHW severity. Regional warmings play a significant role in CDHW changes in most regions. These findings have implications for minimizing the impacts of extreme events and developing adaptation and mitigation policies to cope with increased risk on water, energy, and food sectors in critical geographical regions.

 

We provide data on daily social contact intensity of clusters of people at different types of Points of Interest (POI) by zip code in Florida and California. This data is obtained by aggregating fine-scaled details of interactions of people at the spatial resolution of 10 m, which is then normalized as a social contact index. We also provide the distribution of cluster sizes and average time spent in a cluster by POI type. This data will help researchers perform fine-scaled, privacy-preserving analysis of human interaction patterns to understand the drivers of the COVID-19 epidemic spread and mitigation. Current mobility datasets either provide coarse-level metrics of social distancing, such as radius of gyration at the county or province level, or traffic at a finer scale, neither of which is a direct measure of contacts between people. We use anonymized, de-identified, and privacy-enhanced location-based services (LBS) data from opted-in cell phone apps, suitably reweighted to correct for geographic heterogeneities, and identify clusters of people at non-sensitive public areas to estimate fine-scaled contacts.

 

Abstract Buildings consume over 40% of global energy in their construction and operations contributing to over 39% of global carbon emission each year. This huge environmental footprint presents an excellent opportunity to reduce energy use and help deliver an environmentally sustainable built environment. Most of the energy is consumed by buildings as embodied energy (EE) and operational energy (OE). EE is used directly and indirectly during buildings’ initial construction, maintenance and replacement, and demolition phases through construction products and services. OE is used in the processes of heating, cooling, water heating, lighting, and operating building equipment. Most environmental optimization research has been centered on energy and carbon emission overlooking another critical sustainability aspect, water use. Each building also consumes a significant amount of freshwater as embodied water (EW) or virtual water in its initial construction, maintenance and replacement, and demolition phases. Since each primary and secondary energy source depletes water in its extraction, refinement or production, there is also a water expense associated with EE and OE use that must also be included in total EW use. The total EW, therefore, includes both non-energy and energy related water use. Research suggests that there are tradeoffs between EE and EW that may complicate design decisions such as material selection for environmental sustainability. In other words, a material selected for its lower EE may have higher EW and selecting such a material may not help reach environmental sustainability goals since water scarcity is becoming a grave problem. In this paper, we created an input-output-based hybrid (IOH) model for calculating and comparing EE and EW of building materials frequently used in building construction. The main goal is to examine and highlight any tradeoffs that may exist when selecting one material over another. The results reveal that there is a weak correlation between EE and total EW that is the sum of energy and non-energy water use, which means that a design decision made solely based on EE may conflict with EW. The share of energy related water use in total EW of construction materials also varies significantly (2.5%-31.2%), indicating that reducing energy use alone may not be sufficient to reduce freshwater use; additional efforts may be needed to decrease the use of materials and processes that are water intensive. The results of this study are significant to achieving the goal of creating a truly sustainable built environment.