11,601 research outputs found

    A study of BPS and near-BPS black holes via AdS/CFT

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    In the settings of various AdS/CFT dual pairs, we use results from supersymmetric localiza tion to gain insights into the physics of asymptotically-AdS, BPS black holes in 5 dimensions, and near-BPS black holes in 4 dimensions. We first begin with BPS black holes embedded in the known examples of AdS5/CFT4 dualities. Using the Bethe Ansatz formulation, we compute the superconformal index at large N with arbitrary chemical potentials for all charges and angular momenta, for general N = 1 four-dimensional conformal theories with a holographic dual. We conjecture and bring some evidence that a particular universal contribution to the sum over Bethe vacua dominates the index at large N. For N = 4 SYM, this contribution correctly leads to the entropy of BPS Kerr-Newman black holes in AdS5 × S 5 for arbitrary values of the conserved charges, thus completing the microscopic derivation of their microstates. We also consider theories dual to AdS5 × SE5, where SE5 is a Sasaki-Einstein manifold. We first check our results against the so-called universal black hole. We then explicitly construct the near-horizon geometry of BPS Kerr-Newman black holes in AdS5 × T 1,1 , charged under the baryonic symmetry of the conifold theory and with equal angular momenta. We compute the entropy of these black holes using the attractor mechanism and find complete agreement with field theory predictions. Next, we consider the 3d Chern-Simons matter theory that is holographically dual to massive Type IIA string theory on AdS4 × S 6 . By Kaluza-Klein reducing on S 2 with a background that is dual to the asymptotics of static dyonic BPS black holes in AdS4, we construct a N = 2 supersymmetric gauged quantum mechanics whose ground-state degener acy reproduces the entropy of BPS black holes. We expect its low-lying spectrum to contain information about near-extremal horizons. Interestingly, the model has a large number of statistically-distributed couplings, reminiscent of SYK models

    Peak Estimation of Time Delay Systems using Occupation Measures

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    This work proposes a method to compute the maximum value obtained by a state function along trajectories of a Delay Differential Equation (DDE). An example of this task is finding the maximum number of infected people in an epidemic model with a nonzero incubation period. The variables of this peak estimation problem include the stopping time and the original history (restricted to a class of admissible histories). The original nonconvex DDE peak estimation problem is approximated by an infinite-dimensional Linear Program (LP) in occupation measures, inspired by existing measure-based methods in peak estimation and optimal control. This LP is approximated from above by a sequence of Semidefinite Programs (SDPs) through the moment-Sum of Squares (SOS) hierarchy. Effectiveness of this scheme in providing peak estimates for DDEs is demonstrated with provided examplesComment: 34 pages, 14 figures, 3 table

    Technology for Low Resolution Space Based RSO Detection and Characterisation

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    Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment

    Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

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    A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities. Our approach, calibrated Q-learning (Cal-QL), accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from offline data, while also ensuring that the learned Q-values are at a reasonable scale. We refer to this property as calibration, and define it formally as providing a lower bound on the true value function of the learned policy and an upper bound on the value of some other (suboptimal) reference policy, which may simply be the behavior policy. We show that a conservative offline RL algorithm that also learns a calibrated value function leads to effective online fine-tuning, enabling us to take the benefits of offline initializations in online fine-tuning. In practice, Cal-QL can be implemented on top of the conservative Q learning (CQL) for offline RL within a one-line code change. Empirically, Cal-QL outperforms state-of-the-art methods on 9/11 fine-tuning benchmark tasks that we study in this paper. Code and video are available at https://nakamotoo.github.io/projects/Cal-QLComment: project page: https://nakamotoo.github.io/projects/Cal-Q

    Control-mode as a Grid Service in Software-defined Power Grids: GFL vs GFM

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    In power systems with high penetration of power electronics, grid-forming control is proposed to replace traditional Grid-Following Converter (GFL) in order to improve the overall system strength and resist small-signal instability in weak grids by directly forming the terminal voltage. However, sufficient headroom of both active and reactive power must be made available for Grid-Forming Converter (GFM) to operate, potentially leading to sub-optimal operation in steady states. This presents a new research problem to optimally allocate between GFM and GFL to balance the ability of GFMs to improve the grid strength and the potential economic loss resulting from reserved headroom. An optimization framework under software-defined grids is proposed, for the first time, to dynamically determine the optimal allocation of GFMs and GFLs in power systems at each time step of system scheduling according to system conditions, which ensures both system stability and minimum operational cost. To achieve this, the system scheduling model is expanded to simultaneously consider the constraints related to active and reactive power reserves for GFMs, as well as the system level stability. Case studies conducted on the modified IEEE 30-bus system demonstrate significant economic benefits in that the optimal proportion of GFMs in the power system can be dynamically determined while ensuring power reserve and grid stability constraints

    Complicated objects: artifacts from the Yuanming Yuan in Victorian Britain

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    The 1860 spoliation of the Summer Palace at the close of the Second Opium War by British and French troops was a watershed event within the development of Britain as an imperialist nation, which guaranteed a market for opium produced in its colony India and demonstrated the power of its armed forces. The distribution of the spoils to officers and diplomatic corps by campaign leaders in Beijing was also a sign of the British Army’s rising power as an instrument of the imperialist state. These conditions would suggest that objects looted from the site would be integrated into an imperialist aesthetic that reflected and promoted the material benefits of military engagement overseas and foregrounded the circumstances of their removal to Britain for campaign members and the British public. This study mines sources dating to the two decades following the war – including British newspapers, auction house records, exhibition catalogs and works of art – to test this hypothesis. Findings show that initial movements of looted objects through the military and diplomatic corps did reinforce notions of imperialist power by enabling campaign members to profit from the spoliation through sales of looted objects and trophy displays. However, material from the Summer Palace arrived at a moment when British manufacturers and cultural leaders were engaged in a national effort to improve the quality of British goods to compete in the international marketplace and looted art was quickly interpolated in this national conversation. Ironically, the same “free trade” imperatives that motivated the invasion energized a new design movement that embraced Chinese ornament. As a consequence, political interpretations of the material outside of military collections were quickly joined by a strong response to Chinese ornament from cultural institutions and design leaders. Art from the Summer Palace held a prominent place at industrial art exhibitions of the postwar period and inspired new designs in a number of mediums. While the availability of Chinese imperial art was the consequence of a military invasion and therefore a product of imperialist expansion, evidence presented here shows that the design response to looted objects was not circumscribed by this political reality. Chinese ornament on imperial wares was ultimately celebrated for its formal qualities and acknowledged links to the Summer Palace were an indicator of good design, not a celebration of victory over a failed Chinese state. Therefore, the looting of the Summer Palace was ultimately an essential factor in the development of modern design, the essence of which is a break with Classical ornament

    Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

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    Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms. Most C-MARL algorithms use a primal-dual approach to enforce constraints through a penalty function added to the reward. In this paper, we study the structural effects of this penalty term on the MARL problem. First, we show that the standard practice of using the constraint function as the penalty leads to a weak notion of safety. However, by making simple modifications to the penalty term, we can enforce meaningful probabilistic (chance and conditional value at risk) constraints. Second, we quantify the effect of the penalty term on the value function, uncovering an improved value estimation procedure. We use these insights to propose a constrained multiagent advantage actor critic (C-MAA2C) algorithm. Simulations in a simple constrained multiagent environment affirm that our reinterpretation of the primal-dual method in terms of probabilistic constraints is effective, and that our proposed value estimate accelerates convergence to a safe joint policy.Comment: 19 pages, 8 figures. Presented at L4DC 202

    Spectral methods for solving elliptic PDEs on unknown manifolds

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    In this paper, we propose a mesh-free numerical method for solving elliptic PDEs on unknown manifolds, identified with randomly sampled point cloud data. The PDE solver is formulated as a spectral method where the test function space is the span of the leading eigenfunctions of the Laplacian operator, which are approximated from the point cloud data. While the framework is flexible for any test functional space, we will consider the eigensolutions of a weighted Laplacian obtained from a symmetric Radial Basis Function (RBF) method induced by a weak approximation of a weighted Laplacian on an appropriate Hilbert space. Especially, we consider a test function space that encodes the geometry of the data yet does not require us to identify and use the sampling density of the point cloud. To attain a more accurate approximation of the expansion coefficients, we adopt a second-order tangent space estimation method to improve the RBF interpolation accuracy in estimating the tangential derivatives. This spectral framework allows us to efficiently solve the PDE many times subjected to different parameters, which reduces the computational cost in the related inverse problem applications. In a well-posed elliptic PDE setting with randomly sampled point cloud data, we provide a theoretical analysis to demonstrate the convergent of the proposed solver as the sample size increases. We also report some numerical studies that show the convergence of the spectral solver on simple manifolds and unknown, rough surfaces. Our numerical results suggest that the proposed method is more accurate than a graph Laplacian-based solver on smooth manifolds. On rough manifolds, these two approaches are comparable. Due to the flexibility of the framework, we empirically found improved accuracies in both smoothed and unsmoothed Stanford bunny domains by blending the graph Laplacian eigensolutions and RBF interpolator.Comment: 8 figure

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
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