970 research outputs found
Dualities and non-Abelian mechanics
Dualities are mathematical mappings that reveal unexpected links between
apparently unrelated systems or quantities in virtually every branch of
physics. Systems that are mapped onto themselves by a duality transformation
are called self-dual and they often exhibit remarkable properties, as
exemplified by an Ising magnet at the critical point. In this Letter, we unveil
the role of dualities in mechanics by considering a family of so-called twisted
Kagome lattices. These are reconfigurable structures that can change shape
thanks to a collapse mechanism easily illustrated using LEGO. Surprisingly,
pairs of distinct configurations along the mechanism exhibit the same spectrum
of vibrational modes. We show that this puzzling property arises from the
existence of a duality transformation between pairs of configurations on either
side of a mechanical critical point. This critical point corresponds to a
self-dual structure whose vibrational spectrum is two-fold degenerate over the
entire Brillouin zone. The two-fold degeneracy originates from a general
version of Kramers theorem that applies to classical waves in addition to
quantum systems with fermionic time-reversal invariance. We show that the
vibrational modes of the self-dual mechanical systems exhibit non-Abelian
geometric phases that affect the semi-classical propagation of wave packets.
Our results apply to linear systems beyond mechanics and illustrate how
dualities can be harnessed to design metamaterials with anomalous symmetries
and non-commuting responses.Comment: See http://home.uchicago.edu/~vitelli/videos.html for Supplementary
Movi
Kink-antikink asymmetry and impurity interactions in topological mechanical chains
We study the dynamical response of a diatomic periodic chain of rotors
coupled by springs, whose unit cell breaks spatial inversion symmetry. In the
continuum description, we derive a nonlinear field theory which admits
topological kinks and antikinks as nonlinear excitations but where a
topological boundary term breaks the symmetry between the two and energetically
favors the kink configuration. Using a cobweb plot, we develop a fixed-point
analysis for the kink motion and demonstrate that kinks propagate without the
Peierls-Nabarro potential energy barrier typically associated with lattice
models. Using continuum elasticity theory, we trace the absence of the
Peierls-Nabarro barrier for the kink motion to the topological boundary term
which ensures that only the kink configuration, and not the antikink, costs
zero potential energy. Further, we study the eigenmodes around the kink and
antikink configurations using a tangent stiffness matrix approach appropriate
for pre-stressed structures to explicitly show how the usual energy degeneracy
between the two no longer holds. We show how the kink-antikink asymmetry also
manifests in the way these nonlinear excitations interact with impurities
introduced in the chain as disorder in the spring stiffness. Finally, we
discuss the effect of impurities in the (bond) spring length and build
prototypes based on simple linkages that verify our predictions.Comment: 20 pages, 21 figure
Computational Geometry Teaching Tool
When students are taking Computational Geometry course which covers many geometry algorithms, most of them are difficult to follow because these algorithms are very abstract even if authors draw pictures to illustrate. In order to help students to get a better understanding of these algorithms, we decide to design Computational Geometry Teaching Tool. This tool is a web application that covers 8 geometry algorithms : Graham Scan, Quick Hull, Line Segment Intersection, Dual, Line Arrangement, Voronoi Diagram, Incremental Delaunay Triangulation and Kd Tree. First, this tool is developed by using JavaScript so that users don\u27t need to install any software or package. Furthermore, it breaks down the algorithm and go step by step so that students can move forward and backward on their own pace. Finally, all demos in this tool have same layout so that when students learn how to use the first one, they will know how to use others
"El miedo es una de nuestras emociones primarias" : Entrevista a Mariana Enriquez
Entrevista a Mariana EnriquezInterview with Mariana Enrique
Not a Simple 'Anthropocene' Story in Contemporary China: Unveiling the Entanglement of Chinese Social and Environmental Issues
I developed this thesis, that addresses current social issues and environmental issues in contemporary China, in response to the pursuit of economic development and urbanization, before and after the political-ideological reorientation of the Chinese Communist Party led by Deng Xiaoping and his Reform and Openness policy at the end of the 1970s. I explore the cinematic reflection of environmental problems and social issues in three films of Jia Zhangke, a famous sixth-generation director who adopts realistic aesthetic and artistic pursuit in his films. My reflection is accompanied by a critical discourse analysis of newspaper articles from People’s Daily. Through these two methods, I examine the reality of contemporary China through an economic, cultural, political, social and natural lens to shed light on the root causes of various social and environmental issues in contemporary China.
Guided by Anthropocene thinking, I engage in a discussion on tensions within human- nature relationships in China. This research introduces a way of understanding the human- nature relationship within the Anthropocene framework by considering elements including the social structure of human society, the social stratification of different social groups, and the ultimate hegemony of powerholders. In this thesis, I argue that proletariats and ordinary Chinese people in contemporary China who struggle with their personal existential crises do not have the social and political power to make changes to their social reality, neither do they have the power to interfere with the decisions of powerholders, where these decisions are influential in causing social changes, changes to Chinese people’s daily life, changes of Chinese natural landscapes and the human-nature relationship in China
Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G
User location is a piece of critical information for network management and
control. However, location uncertainty is unavoidable in certain settings
leading to localization errors. In this paper, we consider the user location
uncertainty in the mmWave networks, and investigate joint vision-aided sensing
and communications using deep reinforcement learning-based beam management for
future 6G networks. In particular, we first extract pixel characteristic-based
features from satellite images to improve localization accuracy. Then we
propose a UK-medoids based method for user clustering with location
uncertainty, and the clustering results are consequently used for the beam
management. Finally, we apply the DRL algorithm for intra-beam radio resource
allocation. The simulations first show that our proposed vision-aided method
can substantially reduce the localization error. The proposed UK-medoids and
DRL based scheme (UKM-DRL) is compared with two other schemes: K-means based
clustering and DRL based resource allocation (K-DRL) and UK-means based
clustering and DRL based resource allocation (UK-DRL). The proposed method has
17.2% higher throughput and 7.7% lower delay than UK-DRL, and more than doubled
throughput and 55.8% lower delay than K-DRL
Sampled-Data Control of Singular Systems with Time Delays
This paper is concerned with sampled-data controller design for singular systems with time delay. It is assumed that the sampling periods are arbitrarily varying but bounded. A time-dependent Lyapunov function is proposed, which is positive definite at sampling times but not necessarily positive definite inside the sampling intervals. Combining input delay approach with Lyapunov method, sufficient conditions are derived which guarante that the singular system is regular, impulse free, and exponentially stable. Then, the existence conditions of desired sampled-data controller can be obtained, which are formulated in terms of strict linear matrix inequality. Finally, numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method
Hybrid Data-driven Framework for Shale Gas Production Performance Analysis via Game Theory, Machine Learning and Optimization Approaches
A comprehensive and precise analysis of shale gas production performance is
crucial for evaluating resource potential, designing field development plan,
and making investment decisions. However, quantitative analysis can be
challenging because production performance is dominated by a complex
interaction among a series of geological and engineering factors. In this
study, we propose a hybrid data-driven procedure for analyzing shale gas
production performance, which consists of a complete workflow for dominant
factor analysis, production forecast, and development optimization. More
specifically, game theory and machine learning models are coupled to determine
the dominating geological and engineering factors. The Shapley value with
definite physical meanings is employed to quantitatively measure the effects of
individual factors. A multi-model-fused stacked model is trained for production
forecast, on the basis of which derivative-free optimization algorithms are
introduced to optimize the development plan. The complete workflow is validated
with actual production data collected from the Fuling shale gas field, Sichuan
Basin, China. The validation results show that the proposed procedure can draw
rigorous conclusions with quantified evidence and thereby provide specific and
reliable suggestions for development plan optimization. Comparing with
traditional and experience-based approaches, the hybrid data-driven procedure
is advanced in terms of both efficiency and accuracy.Comment: 37 pages, 15 figures, 6 table
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