162 research outputs found
PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features
Event cameras are motion-activated sensors that capture pixel-level
illumination changes instead of the intensity image with a fixed frame rate.
Compared with the standard cameras, it can provide reliable visual perception
during high-speed motions and in high dynamic range scenarios. However, event
cameras output only a little information or even noise when the relative motion
between the camera and the scene is limited, such as in a still state. While
standard cameras can provide rich perception information in most scenarios,
especially in good lighting conditions. These two cameras are exactly
complementary. In this paper, we proposed a robust, high-accurate, and
real-time optimization-based monocular event-based visual-inertial odometry
(VIO) method with event-corner features, line-based event features, and
point-based image features. The proposed method offers to leverage the
point-based features in the nature scene and line-based features in the
human-made scene to provide more additional structure or constraints
information through well-design feature management. Experiments in the public
benchmark datasets show that our method can achieve superior performance
compared with the state-of-the-art image-based or event-based VIO. Finally, we
used our method to demonstrate an onboard closed-loop autonomous quadrotor
flight and large-scale outdoor experiments. Videos of the evaluations are
presented on our project website: https://b23.tv/OE3QM6
Skin Mast Cells Contribute to Sporothrix schenckii Infection
Background: Sporothrix schenckii (S. schenckii), a dimorphic fungus, causes sporotrichosis. Mast cells (MCs) have been described to be involved in skin fungal infections. The role of MCs in cutaneous sporotrichosis remains largely unknown.
Objectives: To characterize the role and relevance of MCs in cutaneous sporotrichosis.
Methods: We analyzed cutaneous sporotrichosis in wild-type (WT) mice and two different MC-deficient strains. In vitro, MCs were assessed for S. schenckii-induced cytokine production and degranulation after incubation with S. schenckii. We also explored the role of MCs in human cutaneous sporotrichosis.
Results: WT mice developed markedly larger skin lesions than MC-deficient mice (> 1.5 fold) after infection with S. schenckii, with significantly increased fungal burden. S. schenckii induced the release of tumor necrosis factor alpha (TNF), interleukin (IL)-6, IL-10, and IL-1β by MCs, but not degranulation. S. schenckii induced larger skin lesions and higher release of IL-6 and TNF by MCs as compared to the less virulent S. albicans. In patients with sporotrichosis, TNF and IL-6 were increased in skin lesions, and markedly elevated levels in the serum were linked to disease activity.
Conclusions: These findings suggest that cutaneous MCs contribute to skin sporotrichosis by releasing cytokines such as TNF and IL-6
Exploring the Diversity of Music Experiences for Deaf and Hard of Hearing People
Sensory substitution or enhancement techniques have been proposed to enable
deaf or hard of hearing (DHH) people to listen to and even compose music.
However, little is known about how such techniques enhance DHH people's music
experience. Since deafness is a spectrum -- as are DHH people's preferences and
perceptions of music -- a more situated understanding of their interaction with
music is needed. To understand the music experience of this population, we
conducted social media analyses, both qualitatively and quantitatively, in the
deaf and hard of hearing Reddit communities. Our content analysis revealed that
DHH people leveraged sign language and visual/haptic cues to feel the music and
preferred familiar, non-lyrical, instrument-heavy, and loud music. In addition,
hearing aids were not customized for music, and the visual/haptic techniques
developed were not widely adopted by DHH people, leading to their suboptimal
music experiences. The DHH community embodied mutual support among music
lovers, evidenced by active information sharing and Q&A around music and
hearing loss. We reflect on design justice for DHH people's music experience
and propose practical design implications to create a more accessible music
experience for them
ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation
Recently, foundational models such as CLIP and SAM have shown promising
performance for the task of Zero-Shot Anomaly Segmentation (ZSAS). However,
either CLIP-based or SAM-based ZSAS methods still suffer from non-negligible
key drawbacks: 1) CLIP primarily focuses on global feature alignment across
different inputs, leading to imprecise segmentation of local anomalous parts;
2) SAM tends to generate numerous redundant masks without proper prompt
constraints, resulting in complex post-processing requirements. In this work,
we innovatively propose a CLIP and SAM collaboration framework called ClipSAM
for ZSAS. The insight behind ClipSAM is to employ CLIP's semantic understanding
capability for anomaly localization and rough segmentation, which is further
used as the prompt constraints for SAM to refine the anomaly segmentation
results. In details, we introduce a crucial Unified Multi-scale Cross-modal
Interaction (UMCI) module for interacting language with visual features at
multiple scales of CLIP to reason anomaly positions. Then, we design a novel
Multi-level Mask Refinement (MMR) module, which utilizes the positional
information as multi-level prompts for SAM to acquire hierarchical levels of
masks and merges them. Extensive experiments validate the effectiveness of our
approach, achieving the optimal segmentation performance on the MVTec-AD and
VisA datasets.Comment: 17 pages,17 figure
Spiking PointNet: Spiking Neural Networks for Point Clouds
Recently, Spiking Neural Networks (SNNs), enjoying extreme energy efficiency,
have drawn much research attention on 2D visual recognition and shown gradually
increasing application potential. However, it still remains underexplored
whether SNNs can be generalized to 3D recognition. To this end, we present
Spiking PointNet in the paper, the first spiking neural model for efficient
deep learning on point clouds. We discover that the two huge obstacles limiting
the application of SNNs in point clouds are: the intrinsic optimization
obstacle of SNNs that impedes the training of a big spiking model with large
time steps, and the expensive memory and computation cost of PointNet that
makes training a big spiking point model unrealistic. To solve the problems
simultaneously, we present a trained-less but learning-more paradigm for
Spiking PointNet with theoretical justifications and in-depth experimental
analysis. In specific, our Spiking PointNet is trained with only a single time
step but can obtain better performance with multiple time steps inference,
compared to the one trained directly with multiple time steps. We conduct
various experiments on ModelNet10, ModelNet40 to demonstrate the effectiveness
of Spiking PointNet. Notably, our Spiking PointNet even can outperform its ANN
counterpart, which is rare in the SNN field thus providing a potential research
direction for the following work. Moreover, Spiking PointNet shows impressive
speedup and storage saving in the training phase.Comment: Accepted by NeurIP
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks
The Spiking Neural Network (SNN), as one of the biologically inspired neural
network infrastructures, has drawn increasing attention recently. It adopts
binary spike activations to transmit information, thus the multiplications of
activations and weights can be substituted by additions, which brings high
energy efficiency. However, in the paper, we theoretically and experimentally
prove that the binary spike activation map cannot carry enough information,
thus causing information loss and resulting in accuracy decreasing. To handle
the problem, we propose a ternary spike neuron to transmit information. The
ternary spike neuron can also enjoy the event-driven and multiplication-free
operation advantages of the binary spike neuron but will boost the information
capacity. Furthermore, we also embed a trainable factor in the ternary spike
neuron to learn the suitable spike amplitude, thus our SNN will adopt different
spike amplitudes along layers, which can better suit the phenomenon that the
membrane potential distributions are different along layers. To retain the
efficiency of the vanilla ternary spike, the trainable ternary spike SNN will
be converted to a standard one again via a re-parameterization technique in the
inference. Extensive experiments with several popular network structures over
static and dynamic datasets show that the ternary spike can consistently
outperform state-of-the-art methods. Our code is open-sourced at
https://github.com/yfguo91/Ternary-Spike.Comment: Accepted by AAAI202
Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey
Urban air mobility (UAM) has the potential to revolutionize transportation in
metropolitan areas, providing a new mode of transportation that could alleviate
congestion and improve accessibility. However, the integration of UAM into
existing transportation systems is a complex task that requires a thorough
understanding of its impact on traffic flow and capacity. In this paper, we
conduct a survey to investigate the current state of research on UAM in
metropolitan-scale traffic using simulation techniques. We identify key
challenges and opportunities for the integration of UAM into urban
transportation systems, including impacts on existing traffic patterns and
congestion; safety analysis and risk assessment; potential economic and
environmental benefits; and the development of shared infrastructure and routes
for UAM and ground-based transportation. We also discuss the potential benefits
of UAM, such as reduced travel times and improved accessibility for underserved
areas. Our survey provides a comprehensive overview of the current state of
research on UAM in metropolitan-scale traffic using simulation and highlights
key areas for future research and development
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