332 research outputs found
Quantum Signatures of Topological Phase in Bosonic Quadratic System
Quantum entanglement and classical topology are two distinct phenomena that
are difficult to be connected together. Here we discover that an open bosonic
quadratic chain exhibits topology-induced entanglement effect. When the system
is in the topological phase, the edge modes can be entangled in the steady
state, while no entanglement appears in the trivial phase. This finding is
verified through the covariance approach based on the quantum master equations,
which provide exact numerical results without truncation process. We also
obtain concise approximate analytical results through the quantum Langevin
equations, which perfectly agree with the exact numerical results. We show the
topological edge states exhibit near-zero eigenenergies located in the band gap
and are separated from the bulk eigenenergies, which match the
system-environment coupling (denoted by the dissipation rate) and thus the
squeezing correlations can be enhanced. Our work reveals that the stationary
entanglement can be a quantum signature of the topological phase in bosonic
systems, and inversely the topological quadratic systems can be powerful
platforms to generate robust entanglement.Comment: 14 pages, 7 figure
LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation
This paper presents a lightweight, efficient calibration neural network model
for denoising low-cost microelectromechanical system (MEMS) gyroscope and
estimating the attitude of a robot in real-time. The key idea is extracting
local and global features from the time window of inertial measurement units
(IMU) measurements to regress the output compensation components for the
gyroscope dynamically. Following a carefully deduced mathematical calibration
model, LGC-Net leverages the depthwise separable convolution to capture the
sectional features and reduce the network model parameters. The Large kernel
attention is designed to learn the long-range dependencies and feature
representation better. The proposed algorithm is evaluated in the EuRoC and
TUM-VI datasets and achieves state-of-the-art on the (unseen) test sequences
with a more lightweight model structure. The estimated orientation with our
LGC-Net is comparable with the top-ranked visual-inertial odometry systems,
although it does not adopt vision sensors. We make our method open-source at:
https://github.com/huazai665/LGC-Ne
Augmenting Iterative Trajectory for Bilevel Optimization: Methodology, Analysis and Extensions
In recent years, there has been a surge of machine learning applications
developed with hierarchical structure, which can be approached from Bi-Level
Optimization (BLO) perspective. However, most existing gradient-based methods
overlook the interdependence between hyper-gradient calculation and Lower-Level
(LL) iterative trajectory, focusing solely on the former. Consequently,
convergence theory is constructed with restrictive LL assumptions, which are
often challenging to satisfy in real-world scenarios. In this work, we
thoroughly analyze the constructed iterative trajectory, and highlight two
deficiencies, including empirically chosen initialization and default use of
entire trajectory for hyper-gradient calculation. To address these issues, we
incrementally introduce two augmentation techniques including Initialization
Auxiliary (IA) and Pessimistic Trajectory Truncation (PTT), and investigate
various extension strategies such as prior regularization, different iterative
mapping schemes and acceleration dynamics to construct Augmented Iterative
Trajectory (AIT) for corresponding BLO scenarios (e.g., LL convexity and LL
non-convexity). Theoretically, we provide convergence analysis for AIT and its
variations under different LL assumptions, and establish the first convergence
analysis for BLOs with non-convex LL subproblem. Finally, we demonstrate the
effectiveness of AIT through three numerical examples, typical learning and
vision applications (e.g., data hyper-cleaning and few-shot learning) and more
challenging tasks such as neural architecture search.Comment: 16 page
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark
Recently significant progress has been made in human action recognition and
behavior prediction using deep learning techniques, leading to improved
vision-based semantic understanding. However, there is still a lack of
high-quality motion datasets for small bio-robotics, which presents more
challenging scenarios for long-term movement prediction and behavior control
based on third-person observation. In this study, we introduce RatPose, a
bio-robot motion prediction dataset constructed by considering the influence
factors of individuals and environments based on predefined annotation rules.
To enhance the robustness of motion prediction against these factors, we
propose a Dual-stream Motion-Scenario Decoupling (\textit{DMSD}) framework that
effectively separates scenario-oriented and motion-oriented features and
designs a scenario contrast loss and motion clustering loss for overall
training. With such distinctive architecture, the dual-branch feature flow
information is interacted and compensated in a decomposition-then-fusion
manner. Moreover, we demonstrate significant performance improvements of the
proposed \textit{DMSD} framework on different difficulty-level tasks. We also
implement long-term discretized trajectory prediction tasks to verify the
generalization ability of the proposed dataset.Comment: Rat, Video Position Predictio
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