134 research outputs found
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
In this paper, we study the problem of 3D object segmentation from raw point
clouds. Unlike all existing methods which usually require a large amount of
human annotations for full supervision, we propose the first unsupervised
method, called OGC, to simultaneously identify multiple 3D objects in a single
forward pass, without needing any type of human annotations. The key to our
approach is to fully leverage the dynamic motion patterns over sequential point
clouds as supervision signals to automatically discover rigid objects. Our
method consists of three major components, 1) the object segmentation network
to directly estimate multi-object masks from a single point cloud frame, 2) the
auxiliary self-supervised scene flow estimator, and 3) our core object geometry
consistency component. By carefully designing a series of loss functions, we
effectively take into account the multi-object rigid consistency and the object
shape invariance in both temporal and spatial scales. This allows our method to
truly discover the object geometry even in the absence of annotations. We
extensively evaluate our method on five datasets, demonstrating the superior
performance for object part instance segmentation and general object
segmentation in both indoor and the challenging outdoor scenarios.Comment: NeurIPS 2022. Code and data are available at:
https://github.com/vLAR-group/OG
Continuous-mode quantum key distribution with digital signal processing
Continuous-variable quantum key distribution (CVQKD) offers the specific
advantage of sharing keys remotely by the use of standard telecom components,
thereby promoting cost-effective and high-performance metropolitan
applications. Nevertheless, the introduction of high-rate spectrum broadening
has pushed CVQKD from a single-mode to a continuous-mode region, resulting in
the adoption of modern digital signal processing (DSP) technologies to recover
quadrature information from continuous-mode quantum states. However, the
security proof of DSP involving multi-point processing is a missing step. Here,
we propose a generalized method of analyzing continuous-mode state processing
by linear DSP via temporal-modes theory. The construction of temporal modes is
key in reducing the security proof to single-mode scenarios. The proposed
practicality oriented security analysis method paves the way for building
classical compatible digital CVQKD.Comment: 10 pages, 4 figure
Applying Back Propagation Algorithm and Analytic Hierarchy Process to Environment Assessment
This paper designs a new and scientific environmental quality assessment
method, and takes Saihan dam as an example to explore the environmental
improvement degree to the local and Beijing areas. AHP method is used to assign
values to each weight 7 primary indicators and 21 secondary indicators were
used to establish an environmental quality assessment model. The conclusion
shows that after the establishment of Saihan dam, the local environmental
quality has been improved by 7 times, and the environmental quality in Beijing
has been improved by 13%. Then the future environmental index is predicted.
Finally the Spearson correlation coefficient is analyzed, and it is proved that
correlation is 99% when the back-propagation algorithm is used to test and
prove that the error is little
Countermeasure for negative impact of practical source in continuous-variable measurement-device-independent quantum key distribution
Continuous-variable measurement-device-independent quantum key distribution
(CV-MDI QKD) can defend all attacks on the measurement devices fundamentally.
Consequently, higher requirements are put forward for the source of CV-MDI QKD
system. However, the imperfections of actual source brings practical security
risks to the CV-MDI QKD system. Therefore, the characteristics of the realistic
source must be controlled in real time to guarantee the practical security of
the CV-MDI QKD system. Here we propose a countermeasure for negative impact
introduced by the actual source in the CV-MDI QKD system based on
one-time-calibration method, not only eliminating the loophole induced from the
relative intensity noise (RIN) which is part of the source noise, but also
modeling the source noise thus improving the performance. In particular, three
cases in terms of whether the preparation noise of the practical sources are
defined or not, where only one of the users or both two users operate
monitoring on their respective source outputs, are investigated. The simulation
results show that the estimated secret key rate without our proposed scheme are
about 10.7 times higher than the realistic rate at 18 km transmission distance
when the variance of RIN is only 0.4. What's worse, the difference becomes
greater and greater with the increase of the variance of RIN. Thus, our
proposed scheme makes sense in further completing the practical security of
CV-MDI QKD system. In other words, our work enables CV-MDI QKD system not only
to resist all attacks against detectors, but also to close the vulnerability
caused by the actual source, thus making the scheme closer to practical
security
Experimental upstream transmission of continuous variable quantum key distribution access network
Continuous-variable quantum key distribution which can be implemented using
only low-cost and off-the-shelf components reveals great potential in the
practical large-scale realization. Access network as a modern network
necessity, connects multiple end-users to the network backbone. In this work,
we demonstrate the first upstream transmission quantum access networks using
continuous-variable quantum key distribution. A two-end-user quantum access
network is then experimentally realized. Through phase compensation, data
synchronization and other technical upgrades, we achieve 390kbps secret key
rate of the total network. In addition, we extend the case of two-end-user
quantum access network to the case of multiple users, and analyze the network
capacity in the case of multiple users by measuring the additive excess noise
from different time slots.Comment: 4 pages,3figure
Fast-HuBERT: An Efficient Training Framework for Self-Supervised Speech Representation Learning
Recent years have witnessed significant advancements in self-supervised
learning (SSL) methods for speech-processing tasks. Various speech-based SSL
models have been developed and present promising performance on a range of
downstream tasks including speech recognition. However, existing speech-based
SSL models face a common dilemma in terms of computational cost, which might
hinder their potential application and in-depth academic research. To address
this issue, we first analyze the computational cost of different modules during
HuBERT pre-training and then introduce a stack of efficiency optimizations,
which is named Fast-HuBERT in this paper. The proposed Fast-HuBERT can be
trained in 1.1 days with 8 V100 GPUs on the Librispeech 960h benchmark, without
performance degradation, resulting in a 5.2x speedup, compared to the original
implementation. Moreover, we explore two well-studied techniques in the
Fast-HuBERT and demonstrate consistent improvements as reported in previous
work
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