289 research outputs found
Gauss-Bonnet solution with a cloud of strings in de Sitter and anti-de Sitter space
In this paper, we present exact spherically symmetric Gauss-Bonnet black hole
solutions surrounded by a cloud of strings fluid with cosmological constant in
dimensions. Both charged and uncharged cases are considered. We focus on
the de Sitter solutions in the main text and leave the Anti-de Sitter solutions
in the appendix. We analyze the features of thermodynamic properties of the
black hole solutions. The mass, Hawking temperature as well as thermal
stability and the phase transitions are discussed. Moreover, the equation of
state and critical phenomena associated with these solutions are also explored.Comment: 16 pages, 7 figure
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-level features to plan a local collision-free action, which
can be computationally prohibitive and not robust. More importantly, in
practice the performance of these methods are much lower than their centralized
counterparts.
We present a decentralized sensor-level collision avoidance policy for
multi-robot systems, which directly maps raw sensor measurements to an agent's
steering commands in terms of movement velocity. As a first step toward
reducing the performance gap between decentralized and centralized methods, we
present a multi-scenario multi-stage training framework to find an optimal
policy which is trained over a large number of robots on rich, complex
environments simultaneously using a policy gradient based reinforcement
learning algorithm. We validate the learned sensor-level collision avoidance
policy in a variety of simulated scenarios with thorough performance
evaluations and show that the final learned policy is able to find time
efficient, collision-free paths for a large-scale robot system. We also
demonstrate that the learned policy can be well generalized to new scenarios
that do not appear in the entire training period, including navigating a
heterogeneous group of robots and a large-scale scenario with 100 robots.
Videos are available at https://sites.google.com/view/drlmac
Beads-on-String Model for Virtual Rectum Surgery Simulation
A beads-on-string model is proposed to handle the deformation and collision of the rectum in virtual surgery simulation. The idea is firstly inspired by the observation of the similarity in shape shared by a rectum with regular bulges and a string of beads. It is beneficial to introduce an additional layer of beads, which provides an interface to map the deformation of centreline to the associated mesh in an elegant manner and a bounding volume approximation in collision handling. Our approach is carefully crafted to achieve high computational efficiency and retain its physical basis. It can be implemented for real time surgery simulation application
SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation
The Segment Anything Model (SAM) is a powerful foundation model that has
revolutionised image segmentation. To apply SAM to surgical instrument
segmentation, a common approach is to locate precise points or boxes of
instruments and then use them as prompts for SAM in a zero-shot manner.
However, we observe two problems with this naive pipeline: (1) the domain gap
between natural objects and surgical instruments leads to poor generalisation
of SAM; and (2) SAM relies on precise point or box locations for accurate
segmentation, requiring either extensive manual guidance or a well-performing
specialist detector for prompt preparation, which leads to a complex
multi-stage pipeline. To address these problems, we introduce SurgicalSAM, a
novel end-to-end efficient-tuning approach for SAM to effectively integrate
surgical-specific information with SAM's pre-trained knowledge for improved
generalisation. Specifically, we propose a lightweight prototype-based class
prompt encoder for tuning, which directly generates prompt embeddings from
class prototypes and eliminates the use of explicit prompts for improved
robustness and a simpler pipeline. In addition, to address the low inter-class
variance among surgical instrument categories, we propose contrastive prototype
learning, further enhancing the discrimination of the class prototypes for more
accurate class prompting. The results of extensive experiments on both
EndoVis2018 and EndoVis2017 datasets demonstrate that SurgicalSAM achieves
state-of-the-art performance while only requiring a small number of tunable
parameters. The source code will be released at
https://github.com/wenxi-yue/SurgicalSAM.Comment: Technical Report. The source code will be released at
https://github.com/wenxi-yue/SurgicalSA
Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms
Robust audio anti-spoofing has been increasingly challenging due to the
recent advancements on deepfake techniques. While spectrograms have
demonstrated their capability for anti-spoofing, complementary information
presented in multi-order spectral patterns have not been well explored, which
limits their effectiveness for varying spoofing attacks. Therefore, we propose
a novel deep learning method with a spectral fusion-reconstruction strategy,
namely S2pecNet, to utilise multi-order spectral patterns for robust audio
anti-spoofing representations. Specifically, spectral patterns up to
second-order are fused in a coarse-to-fine manner and two branches are designed
for the fine-level fusion from the spectral and temporal contexts. A
reconstruction from the fused representation to the input spectrograms further
reduces the potential fused information loss. Our method achieved the
state-of-the-art performance with an EER of 0.77% on a widely used dataset:
ASVspoof2019 LA Challenge
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