265 research outputs found
アドホックネットワークにおけるネットワーク生存性評価に関する研究
広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora
Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification
Deep learning-based multi-source unsupervised domain adaptation (MUDA) has
been actively studied in recent years. Compared with single-source unsupervised
domain adaptation (SUDA), domain shift in MUDA exists not only between the
source and target domains but also among multiple source domains. Most existing
MUDA algorithms focus on extracting domain-invariant representations among all
domains whereas the task-specific decision boundaries among classes are largely
neglected. In this paper, we propose an end-to-end trainable network that
exploits domain Consistency Regularization for unsupervised Multi-source domain
Adaptive classification (CRMA). CRMA aligns not only the distributions of each
pair of source and target domains but also that of all domains. For each pair
of source and target domains, we employ an intra-domain consistency to
regularize a pair of domain-specific classifiers to achieve intra-domain
alignment. In addition, we design an inter-domain consistency that targets
joint inter-domain alignment among all domains. To address different
similarities between multiple source domains and the target domain, we design
an authorization strategy that assigns different authorities to domain-specific
classifiers adaptively for optimal pseudo label prediction and self-training.
Extensive experiments show that CRMA tackles unsupervised domain adaptation
effectively under a multi-source setup and achieves superior adaptation
consistently across multiple MUDA datasets
Improved belief propagation decoding algorithm based on decoupling representation of Pauli operators for quantum LDPC codes
We propose a new method called decoupling representation to represent Pauli
operators as vectors over GF(2), based on which we propose partially decoupled
belief propagation and fully decoupled belief propagation decoding algorithm
for quantum low density parity-check codes. Under the assumption that there is
no measurement error, compared with traditional belief propagation algorithm in
symplectic representation over GF(2), within the same number of iterations, the
decoding accuracy of partially decoupled belief propagation and fully decoupled
belief propagation algorithm is significantly improved in pure Y noise channel
and depolarizing noise channel, which supports that decoding algorithms of
quantum error correcting codes might have better performance in decoupling
representation than in symplectic representation. The impressive performance of
fully decoupled belief propagation algorithm might promote the realization of
quantum error correcting codes in engineering
Direct observation of vacuum arc evolution with nanosecond resolution
Sufficiently high voltage applied between two metal electrodes, even in ultra high vacuum conditions, results in an inevitable discharge that lights up the entire gap, opening a conductive channel through the vacuum and parasitically consuming large amounts of energy. Despite many efforts to understand the processes that lead to this phenomenon, known as vacuum arc, there is still no consensus regarding the role of each electrode in the evolution of such a momentous process as lightning. Employing a high-speed camera, we capture the entire lightning process step-by-step with a nanosecond resolution and find which of the two electrodes holds the main responsibility for igniting the arc. The light that gradually expands from the positively charged electrode (anode), often is assumed to play the main role in the formation of a vacuum arc. However, both the nanosecond-resolution images of vacuum arc evolution and the corresponding theoretical calculations agree that the conductive channel between the electrodes is built in the form of cathodic plasma long before any significant activity develops in the anode. We show evidently that the anode illumination is weaker and plays a minor role in igniting and maintaining the conductive channel.Peer reviewe
Rethinking the competition between detection and ReID in Multi-Object Tracking
Due to balanced accuracy and speed, joint learning detection and ReID-based
one-shot models have drawn great attention in multi-object tracking(MOT).
However, the differences between the above two tasks in the one-shot tracking
paradigm are unconsciously overlooked, leading to inferior performance than the
two-stage methods. In this paper, we dissect the reasoning process of the
aforementioned two tasks. Our analysis reveals that the competition of them
inevitably hurts the learning of task-dependent representations, which further
impedes the tracking performance. To remedy this issue, we propose a novel
cross-correlation network that can effectively impel the separate branches to
learn task-dependent representations. Furthermore, we introduce a scale-aware
attention network that learns discriminative embeddings to improve the ReID
capability. We integrate the delicately designed networks into a one-shot
online MOT system, dubbed CSTrack. Without bells and whistles, our model
achieves new state-of-the-art performances on MOT16 and MOT17. Our code is
released at https://github.com/JudasDie/SOTS
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