160 research outputs found
Surgical technique and clinical results for scapular allograft reconstruction following resection of scapular tumors
Expectation-Maximizing Network Reconstruction and MostApplicable Network Types Based on Binary Time Series Data
Based on the binary time series data of social infection dynamics, we propose
a general framework to reconstruct 2-simplex complexes with two-body and
three-body interactions by combining the maximum likelihood estimation in
statistical inference and introducing the expectation maximization. In order to
improve the code running efficiency, the whole algorithm adopts vectorization
expression. Through the inference of maximum likelihood estimation, the
vectorization expression of the edge existence probability can be obtained, and
through the probability matrix, the adjacency matrix of the network can be
estimated. We apply a two-step scheme to improve the effectiveness of network
reconstruction while reducing the amount of computation significantly. The
framework has been tested on different types of complex networks. Among them,
four kinds of networks can obtain high reconstruction effectiveness. Besides,
we study the influence of noise data or random interference and prove the
robustness of the framework, then the effects of two kinds of hyper-parameters
on the experimental results are tested. Finally, we analyze which type of
network is more suitable for this framework, and propose methods to improve the
effectiveness of the experimental results
Rumor Detection with Diverse Counterfactual Evidence
The growth in social media has exacerbated the threat of fake news to
individuals and communities. This draws increasing attention to developing
efficient and timely rumor detection methods. The prevailing approaches resort
to graph neural networks (GNNs) to exploit the post-propagation patterns of the
rumor-spreading process. However, these methods lack inherent interpretation of
rumor detection due to the black-box nature of GNNs. Moreover, these methods
suffer from less robust results as they employ all the propagation patterns for
rumor detection. In this paper, we address the above issues with the proposed
Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD). Our
intuition is to exploit the diverse counterfactual evidence of an event graph
to serve as multi-view interpretations, which are further aggregated for robust
rumor detection results. Specifically, our method first designs a subgraph
generation strategy to efficiently generate different subgraphs of the event
graph. We constrain the removal of these subgraphs to cause the change in rumor
detection results. Thus, these subgraphs naturally serve as counterfactual
evidence for rumor detection. To achieve multi-view interpretation, we design a
diversity loss inspired by Determinantal Point Processes (DPP) to encourage
diversity among the counterfactual evidence. A GNN-based rumor detection model
further aggregates the diverse counterfactual evidence discovered by the
proposed DCE-RD to achieve interpretable and robust rumor detection results.
Extensive experiments on two real-world datasets show the superior performance
of our method. Our code is available at https://github.com/Vicinity111/DCE-RD
Experimental demonstration of PAM-DWMT for passive optical network
We experimentally demonstrate a discrete wavelet multitone (DWMT) modulation scheme based on pulse amplitude modulation (PAM) for next generation passive optical network (PON), which offers high tolerance against chromatic dispersion, high spectral efficiency, low peak to average power ratio (PAPR) and low side lobes. The experimental results show the chromatic dispersion induced power penalties are negligible after 20km fiber transmission. Compared with orthogonal frequency division multiplexing (OFDM), DWMT offers a better receiver sensitivity
Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network
Leveraging the low-power, event-driven computation and the inherent temporal
dynamics, spiking neural networks (SNNs) are potentially ideal solutions for
processing dynamic and asynchronous signals from event-based sensors. However,
due to the challenges in training and the restrictions in architectural design,
there are limited examples of competitive SNNs in the realm of event-based
dense prediction when compared to artificial neural networks (ANNs). In this
paper, we present an efficient spiking encoder-decoder network designed for
large-scale event-based semantic segmentation tasks. This is achieved by
optimizing the encoder using a hierarchical search method. To enhance learning
from dynamic event streams, we harness the inherent adaptive threshold of
spiking neurons to modulate network activation. Moreover, we introduce a
dual-path Spiking Spatially-Adaptive Modulation (SSAM) block, specifically
designed to enhance the representation of sparse events, thereby considerably
improving network performance. Our proposed network achieves a 72.57% mean
intersection over union (MIoU) on the DDD17 dataset and a 57.22% MIoU on the
recently introduced, larger DSEC-Semantic dataset. This performance surpasses
the current state-of-the-art ANNs by 4%, whilst consuming significantly less
computational resources. To the best of our knowledge, this is the first study
demonstrating SNNs outperforming ANNs in demanding event-based semantic
segmentation tasks, thereby establishing the vast potential of SNNs in the
field of event-based vision. Our source code will be made publicly accessible
Computational Optics Meet Domain Adaptation: Transferring Semantic Segmentation Beyond Aberrations
Semantic scene understanding with Minimalist Optical Systems (MOS) in mobile
and wearable applications remains a challenge due to the corrupted imaging
quality induced by optical aberrations. However, previous works only focus on
improving the subjective imaging quality through computational optics, i.e.
Computational Imaging (CI) technique, ignoring the feasibility in semantic
segmentation. In this paper, we pioneer to investigate Semantic Segmentation
under Optical Aberrations (SSOA) of MOS. To benchmark SSOA, we construct
Virtual Prototype Lens (VPL) groups through optical simulation, generating
Cityscapes-ab and KITTI-360-ab datasets under different behaviors and levels of
aberrations. We look into SSOA via an unsupervised domain adaptation
perspective to address the scarcity of labeled aberration data in real-world
scenarios. Further, we propose Computational Imaging Assisted Domain Adaptation
(CIADA) to leverage prior knowledge of CI for robust performance in SSOA. Based
on our benchmark, we conduct experiments on the robustness of state-of-the-art
segmenters against aberrations. In addition, extensive evaluations of possible
solutions to SSOA reveal that CIADA achieves superior performance under all
aberration distributions, paving the way for the applications of MOS in
semantic scene understanding. Code and dataset will be made publicly available
at https://github.com/zju-jiangqi/CIADA.Comment: Code and dataset will be made publicly available at
https://github.com/zju-jiangqi/CIAD
Automotive Object Detection via Learning Sparse Events by Temporal Dynamics of Spiking Neurons
Event-based sensors, with their high temporal resolution (1us) and dynamical
range (120dB), have the potential to be deployed in high-speed platforms such
as vehicles and drones. However, the highly sparse and fluctuating nature of
events poses challenges for conventional object detection techniques based on
Artificial Neural Networks (ANNs). In contrast, Spiking Neural Networks (SNNs)
are well-suited for representing event-based data due to their inherent
temporal dynamics. In particular, we demonstrate that the membrane potential
dynamics can modulate network activity upon fluctuating events and strengthen
features of sparse input. In addition, the spike-triggered adaptive threshold
can stabilize training which further improves network performance. Based on
this, we develop an efficient spiking feature pyramid network for event-based
object detection. Our proposed SNN outperforms previous SNNs and sophisticated
ANNs with attention mechanisms, achieving a mean average precision (map50) of
47.7% on the Gen1 benchmark dataset. This result significantly surpasses the
previous best SNN by 9.7% and demonstrates the potential of SNNs for
event-based vision. Our model has a concise architecture while maintaining high
accuracy and much lower computation cost as a result of sparse computation. Our
code will be publicly available
CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity
Roadside camera-driven 3D object detection is a crucial task in intelligent
transportation systems, which extends the perception range beyond the
limitations of vision-centric vehicles and enhances road safety. While previous
studies have limitations in using only depth or height information, we find
both depth and height matter and they are in fact complementary. The depth
feature encompasses precise geometric cues, whereas the height feature is
primarily focused on distinguishing between various categories of height
intervals, essentially providing semantic context. This insight motivates the
development of Complementary-BEV (CoBEV), a novel end-to-end monocular 3D
object detection framework that integrates depth and height to construct robust
BEV representations. In essence, CoBEV estimates each pixel's depth and height
distribution and lifts the camera features into 3D space for lateral fusion
using the newly proposed two-stage complementary feature selection (CFS)
module. A BEV feature distillation framework is also seamlessly integrated to
further enhance the detection accuracy from the prior knowledge of the
fusion-modal CoBEV teacher. We conduct extensive experiments on the public 3D
detection benchmarks of roadside camera-based DAIR-V2X-I and Rope3D, as well as
the private Supremind-Road dataset, demonstrating that CoBEV not only achieves
the accuracy of the new state-of-the-art, but also significantly advances the
robustness of previous methods in challenging long-distance scenarios and noisy
camera disturbance, and enhances generalization by a large margin in
heterologous settings with drastic changes in scene and camera parameters. For
the first time, the vehicle AP score of a camera model reaches 80% on
DAIR-V2X-I in terms of easy mode. The source code will be made publicly
available at https://github.com/MasterHow/CoBEV.Comment: The source code will be made publicly available at
https://github.com/MasterHow/CoBE
Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation
In this paper, we address panoramic semantic segmentation which is
under-explored due to two critical challenges: (1) image distortions and object
deformations on panoramas; (2) lack of semantic annotations in the 360-degree
imagery. To tackle these problems, first, we propose the upgraded Transformer
for Panoramic Semantic Segmentation, i.e., Trans4PASS+, equipped with
Deformable Patch Embedding (DPE) and Deformable MLP (DMLPv2) modules for
handling object deformations and image distortions whenever (before or after
adaptation) and wherever (shallow or deep levels). Second, we enhance the
Mutual Prototypical Adaptation (MPA) strategy via pseudo-label rectification
for unsupervised domain adaptive panoramic segmentation. Third, aside from
Pinhole-to-Panoramic (Pin2Pan) adaptation, we create a new dataset (SynPASS)
with 9,080 panoramic images, facilitating Synthetic-to-Real (Syn2Real)
adaptation scheme in 360-degree imagery. Extensive experiments are conducted,
which cover indoor and outdoor scenarios, and each of them is investigated with
Pin2Pan and Syn2Real regimens. Trans4PASS+ achieves state-of-the-art
performances on four domain adaptive panoramic semantic segmentation
benchmarks. Code is available at https://github.com/jamycheung/Trans4PASS.Comment: Extended version of CVPR 2022 paper arXiv:2203.01452. Code is
available at https://github.com/jamycheung/Trans4PAS
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