174 research outputs found
Design, Actuation, and Functionalization of Untethered Soft Magnetic Robots with Life-Like Motions: A Review
Soft robots have demonstrated superior flexibility and functionality than
conventional rigid robots. These versatile devices can respond to a wide range
of external stimuli (including light, magnetic field, heat, electric field,
etc.), and can perform sophisticated tasks. Notably, soft magnetic robots
exhibit unparalleled advantages among numerous soft robots (such as untethered
control, rapid response, and high safety), and have made remarkable progress in
small-scale manipulation tasks and biomedical applications. Despite the
promising potential, soft magnetic robots are still in their infancy and
require significant advancements in terms of fabrication, design principles,
and functional development to be viable for real-world applications. Recent
progress shows that bionics can serve as an effective tool for developing soft
robots. In light of this, the review is presented with two main goals: (i)
exploring how innovative bioinspired strategies can revolutionize the design
and actuation of soft magnetic robots to realize various life-like motions;
(ii) examining how these bionic systems could benefit practical applications in
small-scale solid/liquid manipulation and therapeutic/diagnostic-related
biomedical fields
Neural network encoded variational quantum algorithms
We introduce a general framework called neural network (NN) encoded
variational quantum algorithms (VQAs), or NN-VQA for short, to address the
challenges of implementing VQAs on noisy intermediate-scale quantum (NISQ)
computers. Specifically, NN-VQA feeds input (such as parameters of a
Hamiltonian) from a given problem to a neural network and uses its outputs to
parameterize an ansatz circuit for the standard VQA. Combining the strengths of
NN and parameterized quantum circuits, NN-VQA can dramatically accelerate the
training process of VQAs and handle a broad family of related problems with
varying input parameters with the pre-trained NN. To concretely illustrate the
merits of NN-VQA, we present results on NN-variational quantum eigensolver
(VQE) for solving the ground state of parameterized XXZ spin models. Our
results demonstrate that NN-VQE is able to estimate the ground-state energies
of parameterized Hamiltonians with high precision without fine-tuning, and
significantly reduce the overall training cost to estimate ground-state
properties across the phases of XXZ Hamiltonian. We also employ an
active-learning strategy to further increase the training efficiency while
maintaining prediction accuracy. These encouraging results demonstrate that
NN-VQAs offer a new hybrid quantum-classical paradigm to utilize NISQ resources
for solving more realistic and challenging computational problems.Comment: 4.4 pages, 5 figures, with supplemental material
DeepInteraction: 3D Object Detection via Modality Interaction
Existing top-performance 3D object detectors typically rely on the
multi-modal fusion strategy. This design is however fundamentally restricted
due to overlooking the modality-specific useful information and finally
hampering the model performance. To address this limitation, in this work we
introduce a novel modality interaction strategy where individual per-modality
representations are learned and maintained throughout for enabling their unique
characteristics to be exploited during object detection. To realize this
proposed strategy, we design a DeepInteraction architecture characterized by a
multi-modal representational interaction encoder and a multi-modal predictive
interaction decoder. Experiments on the large-scale nuScenes dataset show that
our proposed method surpasses all prior arts often by a large margin.
Crucially, our method is ranked at the first position at the highly competitive
nuScenes object detection leaderboard.Comment: To appear at NeurIPS 2022. 16 pages, 7 figur
On the Robustness of Average Losses for Partial-Label Learning
Partial-label (PL) learning is a typical weakly supervised classification
problem, where a PL of an instance is a set of candidate labels such that a
fixed but unknown candidate is the true label. For PL learning, there are two
lines of research: (a) the identification-based strategy (IBS) purifies each
label set and extracts the true label; (b) the average-based strategy (ABS)
treats all candidates equally for training. In the past two decades, IBS was a
much hotter topic than ABS, since it was believed that IBS is more promising.
In this paper, we theoretically analyze ABS and find it also promising in the
sense of the robustness of its loss functions. Specifically, we consider five
problem settings for the generation of clean or noisy PLs, and we prove that
average PL losses with bounded multi-class losses are always robust under mild
assumptions on the domination of true labels, while average PL losses with
unbounded multi-class losses (e.g., the cross-entropy loss) may not be robust.
We also conduct experiments to validate our theoretical findings. Note that IBS
is heuristic, and we cannot prove its robustness by a similar proof technique;
hence, ABS is more advantageous from a theoretical point of view, and it is
worth paying attention to the design of more advanced PL learning methods
following ABS
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Vehicle-to-everything (V2X) autonomous driving opens up a promising direction
for developing a new generation of intelligent transportation systems.
Collaborative perception (CP) as an essential component to achieve V2X can
overcome the inherent limitations of individual perception, including occlusion
and long-range perception. In this survey, we provide a comprehensive review of
CP methods for V2X scenarios, bringing a profound and in-depth understanding to
the community. Specifically, we first introduce the architecture and workflow
of typical V2X systems, which affords a broader perspective to understand the
entire V2X system and the role of CP within it. Then, we thoroughly summarize
and analyze existing V2X perception datasets and CP methods. Particularly, we
introduce numerous CP methods from various crucial perspectives, including
collaboration stages, roadside sensors placement, latency compensation,
performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover,
we conduct extensive experimental analyses to compare and examine current CP
methods, revealing some essential and unexplored insights. Specifically, we
analyze the performance changes of different methods under different
bandwidths, providing a deep insight into the performance-bandwidth trade-off
issue. Also, we examine methods under different LiDAR ranges. To study the
model robustness, we further investigate the effects of various simulated
real-world noises on the performance of different CP methods, covering
communication latency, lossy communication, localization errors, and mixed
noises. In addition, we look into the sim-to-real generalization ability of
existing CP methods. At last, we thoroughly discuss issues and challenges,
highlighting promising directions for future efforts. Our codes for
experimental analysis will be public at
https://github.com/memberRE/Collaborative-Perception.Comment: 19 page
Bubble Trajectory Tracking Based on ORB Algorithm
The system of gas-liquid two-phase bubbly flows is widely found in many industrial fields, such as nuclear energy, chemical, petroleum, and refrigeration. Bubbly two-phase flows measuring including detection and tracking affects the specific engineering problem solving to a great extent. The particle tracking velocity (PTV) algorithm is generally used for the tracking of the particles in the flow field. However, it does not take the shape change of particles into account in the process of flow. In this paper, a kind of bubble feature matching method based on ORB algorithm is proposed, and the edge detection method of findContours in OpenCV is used to extract the bubble contour in the image. The proposed algorithm implements the trajectory tracking of the bubbles with shape change when moving up in liquid. The feasibility of bubble trajectory tracking is shown by displaying of different bubble tracks in the plan, 3D plots and contour changing plots
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