Gauge theory description of Rydberg atom arrays with a tunable blockade radius

We discuss a Rydberg atom chain with a tunable blockade radius from the gauge theoretic perspective. When the blockade radius is one lattice spacing, this system can be formulated in terms of the PXP model, and there is a $\mathbb{Z}_2$ Ising phase transition known to be equivalent to a confinement-deconfinement transition in a gauge theory, the lattice Schwinger model. Further increasing the blockade radius, one can add a next-nearest neighbor (NNN) interaction into the PXP model. We discuss the interpretation of NNN interaction in terms of the gauge theory and how finite NNN interaction alters the deconfinement behavior and propose a corresponding experimental protocol. When the blockade radius reaches two lattice spacing, the model reduces to the PPXPP model. A novel gauge theory equivalent to the PPXPP model is formulated, and the phases in the two formulations are delineated. These results are readily explored experimentally in Rydberg quantum simulators.Comment: 7 pages, 4 figures; a few more references added compared to the published versio

Euler--Chern Correspondence via Topological Superconductivity

The Fermi sea topology is characterized by the Euler characteristics $\chi_F$. In this paper, we examine how $\chi_F$ of the metallic state is inhereted by the topological invariant of the superconducting state. We establish a correspondence between the Euler characteristic and the Chern number $C$ of $p$-wave topological superconductors without time-reversal symmetry in two dimensions. By rewriting the pairing potential $\Delta_{\bf k}=\Delta_1-i\Delta_2$ as a vector field ${\bf u}=(\Delta_1,\Delta_2)$, we found that $\chi_F=C$ when ${\bf u}$ and fermion velocity ${\bf v}$ can be smoothly deformed to be parallel or antiparallel on each Fermi surface. We also discuss a similar correspondence between Euler characteristic and 3D winding number of time-reversal-invariant $p$-wave topological superconductors in three dimensions.Comment: 6 pages, 3 figur

Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear

Developing embodied agents in simulation has been a key research topic in recent years. Exciting new tasks, algorithms, and benchmarks have been developed in various simulators. However, most of them assume deaf agents in silent environments, while we humans perceive the world with multiple senses. We introduce Sonicverse, a multisensory simulation platform with integrated audio-visual simulation for training household agents that can both see and hear. Sonicverse models realistic continuous audio rendering in 3D environments in real-time. Together with a new audio-visual VR interface that allows humans to interact with agents with audio, Sonicverse enables a series of embodied AI tasks that need audio-visual perception. For semantic audio-visual navigation in particular, we also propose a new multi-task learning model that achieves state-of-the-art performance. In addition, we demonstrate Sonicverse's realism via sim-to-real transfer, which has not been achieved by other simulators: an agent trained in Sonicverse can successfully perform audio-visual navigation in real-world environments. Sonicverse is available at: https://github.com/StanfordVL/Sonicverse.Comment: In ICRA 2023. Project page: https://ai.stanford.edu/~rhgao/sonicverse/. Code: https://github.com/StanfordVL/sonicverse. Gao and Li contributed equally to this work and are in alphabetical orde

Genetic Diversity Analysis of Hypsizygus marmoreus

Hypsizygus marmoreus is an industrialized edible mushroom. In the present paper, the genetic diversity among 20 strains collected from different places of China was evaluated by target region amplification polymorphism (TRAP) analysis; the common fragment of TRAPs was sequenced and analyzed. Six fixed primers were designed based on the analysis of H. marmoreus sequences from GenBank database. The genomic DNA extracted from H. marmoreus was amplified with 28 TRAP primer combinations, which generated 287 bands. The average of amplified bands per primer was 10.27 (mean polymorphism is 69.73%). The polymorphism information content (PIC) value for TRAPs ranged from 0.32 to 0.50 (mean PIC value per TRAP primer combination is 0.48), which indicated a medium level of polymorphism among the strains. A total of 36 sequences were obtained from TRAP amplification. Half of these sequences could encode the known or unknown proteins. According to the phylogenetic analysis based on TRAP result, the 20 strains of H. marmoreus were classified into two main groups

Modeling Dynamic Environments with Scene Graph Memory

Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial information. We pose this as a new type of link prediction problem: link prediction on partially observable dynamic graphs. Our graph is a representation of a scene in which rooms and objects are nodes, and their relationships are encoded in the edges; only parts of the changing graph are known to the agent at each timestep. This partial observability poses a challenge to existing link prediction approaches, which we address. We propose a novel state representation -- Scene Graph Memory (SGM) -- with captures the agent's accumulated set of observations, as well as a neural net architecture called a Node Edge Predictor (NEP) that extracts information from the SGM to search efficiently. We evaluate our method in the Dynamic House Simulator, a new benchmark that creates diverse dynamic graphs following the semantic patterns typically seen at homes, and show that NEP can be trained to predict the locations of objects in a variety of environments with diverse object movement dynamics, outperforming baselines both in terms of new scene adaptability and overall accuracy. The codebase and more can be found at https://www.scenegraphmemory.com

Evolution of Urban Spatial Clusters in China: A Graph-Based Method Using Nighttime Light Data

An urban spatial cluster (USC) describes one or more geographic agglomerations and the linkages among cities. USCs are conventionally delineated based on predefined administrative boundaries of cities, without considering the dynamic and evolving nature of the spatial extent of USCs. This study uses Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light (NTL) satellite images to quantitatively detect and characterize the evolution of USCs. We propose a dynamic minimum spanning tree (DMST) and a subgraph partitioning method to identify the evolving USCs over time, which considers both the spatial proximity of urban built-up areas and their affiliations with USCs at the previous snapshot. China is selected as a case study for its rapid urbanization process and the cluster-based economic development strategy. Four DMSTs are generated for China using the urban built-up areas extracted from DMSP/OLS NTL satellite images collected in 2000, 2004, 2008, and 2012. Each DMST is partitioned into various subtrees and the urban built-up areas connected by the same subtree are identified as a potential USC. By inspecting the evolution of USCs over time, three different types of USCs are obtained, including newly emerging, single-core, and multicore clusters. Using the rank-size distribution, we find that large-sized USCs have greater development than medium- and small-sized USCs. A clear directionality and heterogeneity are observed in the expansions of the ten largest USCs. Our study provides further insight for the understanding of urban system and its spatial structures, and assists policymakers in their planning practices at national and regional scales