130 research outputs found
ANALYSIS AND GUIDANCE OF AGGREGATION INFORMATION PLATFORM ON THE FORMATION OF USERS’ ANXIETY UNDER THE ENVIRONMENT OF IDEOLOGICAL AND POLITICAL NEW MEDIA
Minimal Clifford Shadow Estimation by Mutually Unbiased Bases
Predicting properties of large-scale quantum systems is crucial for the
development of quantum science and technology. Shadow estimation is an
efficient method for this task based on randomized measurements, where
many-qubit random Clifford circuits are used for estimating global properties
like quantum fidelity. Here we introduce the minimal Clifford measurement (MCM)
to reduce the number of possible random circuits to the minimum, while keeping
the effective post-processing channel in shadow estimation. In particular, we
show that MCM requires distinct Clifford circuits, and it can be
realized by Mutually Unbiased Bases (MUB), with as the total qubit number.
By applying the Z-Tableau formalism, this ensemble of circuits can be
synthesized to the structure, which can be composed by
\emph{fixed} circuit modules, and the total circuit depth is at most
. Compared to the original Clifford measurements, our MCM significantly
reduces the circuit complexity and the compilation costs. In addition, we find
the sampling advantage of MCM on estimating off-diagonal operators, and extend
this observation to the biased-MCM scheme to enhance the sampling improvement
further.Comment: 10+15 pages, 9 figures. Comments are welcom
ANALYSIS AND GUIDANCE OF AGGREGATION INFORMATION PLATFORM ON THE FORMATION OF USERS’ ANXIETY UNDER THE ENVIRONMENT OF IDEOLOGICAL AND POLITICAL NEW MEDIA
REFLECTIONS ON THE MODE OF INTEGRATING MENTAL HEALTH EDUCATION INTO COLLEGE STUDENT MANAGEMENT
DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion
Human de-occlusion, which aims to infer the appearance of invisible human
parts from an occluded image, has great value in many human-related tasks, such
as person re-id, and intention inference. To address this task, this paper
proposes a dynamic mask-aware transformer (DMAT), which dynamically augments
information from human regions and weakens that from occlusion. First, to
enhance token representation, we design an expanded convolution head with
enlarged kernels, which captures more local valid context and mitigates the
influence of surrounding occlusion. To concentrate on the visible human parts,
we propose a novel dynamic multi-head human-mask guided attention mechanism
through integrating multiple masks, which can prevent the de-occluded regions
from assimilating to the background. Besides, a region upsampling strategy is
utilized to alleviate the impact of occlusion on interpolated images. During
model learning, an amodal loss is developed to further emphasize the recovery
effect of human regions, which also refines the model's convergence. Extensive
experiments on the AHP dataset demonstrate its superior performance compared to
recent state-of-the-art methods
Reliability analysis of the impact of thickness measurement accuracy on the longitudinal strength assessment of CAP
The reliability analysis of the longitudinal bending strength of ships is conducted using the total probability method, taking into account and analyzing the accuracy error in thickness measurement of transverse section members on existing servicing ships. A mathematical model for calculating failure probability CAP ratings is established. By calculating the CAP rating of an aged LPG carrier, it is observed that when the deterministic method reaches the target rating, the failure probability in rating calculation by the reliability analysis method is high. This indicates a significant influence of thickness measurement accuracy on CAP rating accuracy
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