721 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
Subequivariant Graph Reinforcement Learning in 3D Environments
Learning a shared policy that guides the locomotion of different agents is of
core interest in Reinforcement Learning (RL), which leads to the study of
morphology-agnostic RL. However, existing benchmarks are highly restrictive in
the choice of starting point and target point, constraining the movement of the
agents within 2D space. In this work, we propose a novel setup for
morphology-agnostic RL, dubbed Subequivariant Graph RL in 3D environments
(3D-SGRL). Specifically, we first introduce a new set of more practical yet
challenging benchmarks in 3D space that allows the agent to have full
Degree-of-Freedoms to explore in arbitrary directions starting from arbitrary
configurations. Moreover, to optimize the policy over the enlarged state-action
space, we propose to inject geometric symmetry, i.e., subequivariance, into the
modeling of the policy and Q-function such that the policy can generalize to
all directions, improving exploration efficiency. This goal is achieved by a
novel SubEquivariant Transformer (SET) that permits expressive message
exchange. Finally, we evaluate the proposed method on the proposed benchmarks,
where our method consistently and significantly outperforms existing approaches
on single-task, multi-task, and zero-shot generalization scenarios. Extensive
ablations are also conducted to verify our design. Code and videos are
available on our project page: https://alpc91.github.io/SGRL/.Comment: ICML 2023 Ora
Intelligent Agents for Negotiation and Recommendation in Mass Customization
Mass customization, as a means to meet individual consumer’s need on a large scale, has recently attracted the attention of both researchers and practitioners. However, as customers and their needs grow increasingly diverse, meeting every consumer’s need has become a surefire way to add unnecessary cost and complexity to operations. Furthermore, consumers are not all really ready for mass customization. They have to face inconveniences such as expensive price, delay delivery and they have to spend time “designing” their product. In order to solve this problem, we proposed a way of intelligent agent assisted negotiation and recommendation. The recommendation is a preference elicitation process, while the negotiation is a communication process based on the situation of manufacturer, such as the inventory level, production cost and lead time. With the aid of intelligent agent of negotiation and recommendation, a good balance between efficiency and customer satisfactions of mass customization can be reached
Prior Bilinear Based Models for Knowledge Graph Completion
Bilinear based models are powerful and widely used approaches for Knowledge
Graphs Completion (KGC). Although bilinear based models have achieved
significant advances, these studies mainly concentrate on posterior properties
(based on evidence, e.g. symmetry pattern) while neglecting the prior
properties. In this paper, we find a prior property named "the law of identity"
that cannot be captured by bilinear based models, which hinders them from
comprehensively modeling the characteristics of KGs. To address this issue, we
introduce a solution called Unit Ball Bilinear Model (UniBi). This model not
only achieves theoretical superiority but also offers enhanced interpretability
and performance by minimizing ineffective learning through minimal constraints.
Experiments demonstrate that UniBi models the prior property and verify its
interpretability and performance
Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules
Various faults can occur during the operation of PV arrays, and both the
dust-affected operating conditions and various diode configurations make the
faults more complicated. However, current methods for fault diagnosis based on
I-V characteristic curves only utilize partial feature information and often
rely on calibrating the field characteristic curves to standard test conditions
(STC). It is difficult to apply it in practice and to accurately identify
multiple complex faults with similarities in different blocking diodes
configurations of PV arrays under the influence of dust. Therefore, a novel
fault diagnosis method for PV arrays considering dust impact is proposed. In
the preprocessing stage, the Isc-Voc normalized Gramian angular difference
field (GADF) method is presented, which normalizes and transforms the resampled
PV array characteristic curves from the field including I-V and P-V to obtain
the transformed graphical feature matrices. Then, in the fault diagnosis stage,
the model of convolutional neural network (CNN) with convolutional block
attention modules (CBAM) is designed to extract fault differentiation
information from the transformed graphical matrices containing full feature
information and to classify faults. And different graphical feature
transformation methods are compared through simulation cases, and different
CNN-based classification methods are also analyzed. The results indicate that
the developed method for PV arrays with different blocking diodes
configurations under various operating conditions has high fault diagnosis
accuracy and reliability
The Adoption of Blockchain Technologies in Data Sharing: A State of the Art Survey
In the big data era, it is a significant need for data sharing in various industries. However, there are many weaknesses in the traditional centralized way of data sharing. It is easy to attack the centralized data storage center. As the process of data asset transactions is not transparent, there is a lack of trust in the percipients of data sharing. Blockchain technology offers a possibility to solve these problems in data sharing, as the blockchain can provide a decentralized, programmable, tamperproof, and anonymous data sharing environment. In this paper, we compare the blockchain-based data sharing with the traditional ways of data sharing, and analyze the scenarios in major industry applications. We survey the state of the art of the adoption of blockchain technologies in data sharing, and provide a summary about their technical frameworks and schemes
LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models
Creating graphic layouts is a fundamental step in graphic designs. In this
work, we present a novel generative model named LayoutDiffusion for automatic
layout generation. As layout is typically represented as a sequence of discrete
tokens, LayoutDiffusion models layout generation as a discrete denoising
diffusion process. It learns to reverse a mild forward process, in which
layouts become increasingly chaotic with the growth of forward steps and
layouts in the neighboring steps do not differ too much. Designing such a mild
forward process is however very challenging as layout has both categorical
attributes and ordinal attributes. To tackle the challenge, we summarize three
critical factors for achieving a mild forward process for the layout, i.e.,
legality, coordinate proximity and type disruption. Based on the factors, we
propose a block-wise transition matrix coupled with a piece-wise linear noise
schedule. Experiments on RICO and PubLayNet datasets show that LayoutDiffusion
outperforms state-of-the-art approaches significantly. Moreover, it enables two
conditional layout generation tasks in a plug-and-play manner without
re-training and achieves better performance than existing methods.Comment: Accepted by ICCV2023, project page: https://layoutdiffusion.github.i
Identifying Crypto Addresses with Gambling Behaviors: A Graph Neural Network Approach
The development of blockchain technology has brought prosperity to the cryptocurrency market and has made the blockchain platform a hotbed of crimes. As one of the most rampant crimes, crypto gambling has more high risk of illegal activities due to the lack of regulation. As a result, identifying crypto addresses with gambling behaviors has emerged as a significant research topic. In this work, we propose a novel detection approach based on Graph Neural Networks named CGDetector, consisting of Graph Construction, Subgraph Extractor, Statistical Feature Extraction, and Gambling Address Classification. Extensive experiments of large-scale and heterogeneous Ethereum transaction data are implemented to demonstrate that our proposed approach outperforms state-of-the-art address classifiers of traditional machine learning methods. This work makes the first attempt to detect suspicious crypto gambling addresses via Graph Neural Networks by all EVM-compatible blockchain systems, providing new insights into the field of cryptocurrency crime detection and blockchain security regulation
MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency
3D single object tracking (SOT) is an indispensable part of automated
driving. Existing approaches rely heavily on large, densely labeled datasets.
However, annotating point clouds is both costly and time-consuming. Inspired by
the great success of cycle tracking in unsupervised 2D SOT, we introduce the
first semi-supervised approach to 3D SOT. Specifically, we introduce two
cycle-consistency strategies for supervision: 1) Self tracking cycles, which
leverage labels to help the model converge better in the early stages of
training; 2) forward-backward cycles, which strengthen the tracker's robustness
to motion variations and the template noise caused by the template update
strategy. Furthermore, we propose a data augmentation strategy named SOTMixup
to improve the tracker's robustness to point cloud diversity. SOTMixup
generates training samples by sampling points in two point clouds with a mixing
rate and assigns a reasonable loss weight for training according to the mixing
rate. The resulting MixCycle approach generalizes to appearance matching-based
trackers. On the KITTI benchmark, based on the P2B tracker, MixCycle trained
with labels outperforms P2B trained with
labels, and achieves a precision improvement when using
labels. Our code will be released at
\url{https://github.com/Mumuqiao/MixCycle}.Comment: Accepted by ICCV2
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