5,082 research outputs found
Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing
Multi-stage architectures have exhibited efficacy in image dehazing, which
usually decomposes a challenging task into multiple more tractable sub-tasks
and progressively estimates latent hazy-free images. Despite the remarkable
progress, existing methods still suffer from the following shortcomings: (1)
limited exploration of frequency domain information; (2) insufficient
information interaction; (3) severe feature redundancy. To remedy these issues,
we propose a novel Mutual Information-driven Triple interaction Network
(MITNet) based on spatial-frequency dual domain information and two-stage
architecture. To be specific, the first stage, named amplitude-guided haze
removal, aims to recover the amplitude spectrum of the hazy images for haze
removal. And the second stage, named phase-guided structure refined, devotes to
learning the transformation and refinement of the phase spectrum. To facilitate
the information exchange between two stages, an Adaptive Triple Interaction
Module (ATIM) is developed to simultaneously aggregate cross-domain,
cross-scale, and cross-stage features, where the fused features are further
used to generate content-adaptive dynamic filters so that applying them to
enhance global context representation. In addition, we impose the mutual
information minimization constraint on paired scale encoder and decoder
features from both stages. Such an operation can effectively reduce information
redundancy and enhance cross-stage feature complementarity. Extensive
experiments on multiple public datasets exhibit that our MITNet performs
superior performance with lower model complexity.The code and models are
available at https://github.com/it-hao/MITNet.Comment: Accepted in ACM MM 202
Adaptive Dynamic Filtering Network for Image Denoising
In image denoising networks, feature scaling is widely used to enlarge the
receptive field size and reduce computational costs. This practice, however,
also leads to the loss of high-frequency information and fails to consider
within-scale characteristics. Recently, dynamic convolution has exhibited
powerful capabilities in processing high-frequency information (e.g., edges,
corners, textures), but previous works lack sufficient spatial contextual
information in filter generation. To alleviate these issues, we propose to
employ dynamic convolution to improve the learning of high-frequency and
multi-scale features. Specifically, we design a spatially enhanced kernel
generation (SEKG) module to improve dynamic convolution, enabling the learning
of spatial context information with a very low computational complexity. Based
on the SEKG module, we propose a dynamic convolution block (DCB) and a
multi-scale dynamic convolution block (MDCB). The former enhances the
high-frequency information via dynamic convolution and preserves low-frequency
information via skip connections. The latter utilizes shared adaptive dynamic
kernels and the idea of dilated convolution to achieve efficient multi-scale
feature extraction. The proposed multi-dimension feature integration (MFI)
mechanism further fuses the multi-scale features, providing precise and
contextually enriched feature representations. Finally, we build an efficient
denoising network with the proposed DCB and MDCB, named ADFNet. It achieves
better performance with low computational complexity on real-world and
synthetic Gaussian noisy datasets. The source code is available at
https://github.com/it-hao/ADFNet.Comment: 9 pages, Accepted in AAAI Conference on Artificial Intelligence
(AAAI) 202
(R P,R P)-Bis[(3-menthyloxy)(phenyl)ÂphosphinoÂyl] disulfide
The molecule of the title compound, C32H48O4P2S2, has 2 symmetry, the mid-point of the S—S bond being located on a twofold rotation axis. The two tetraÂhedral P units are linked by a S—S bond with a P—S—S—P torsion angle is 131.19 (6)°. The dihedral angle between two phenyl rings is 12.66 (13)°. The cycloÂhexane ring of the menthoxyl group displays a chair conformation. Weak interÂmolecular C—H⋯O hydrogen bonding is present in the crystal structure
Time scales of epidemic spread and risk perception on adaptive networks
Incorporating dynamic contact networks and delayed awareness into a contagion
model with memory, we study the spreading patterns of infectious diseases in
connected populations. It is found that the spread of an infectious disease is
not only related to the past exposures of an individual to the infected but
also to the time scales of risk perception reflected in the social network
adaptation. The epidemic threshold is found to decrease with the rise
of the time scale parameter s and the memory length T, they satisfy the
equation .
Both the lifetime of the epidemic and the topological property of the evolved
network are considered. The standard deviation of the degree
distribution increases with the rise of the absorbing time , a power-law
relation is found
Recent Advances in Flame Retardant and Mechanical Properties of Polylactic Acid: A Review.
The large-scale application of ecofriendly polymeric materials has become a key focus of scientific research with the trend toward sustainable development. Mechanical properties and fire safety are two critical considerations of biopolymers for large-scale applications. Polylactic acid (PLA) is a flammable, melt-drop carrying, and strong but brittle polymer. Hence, it is essential to achieve both flame retardancy and mechanical enhancement to improve safety and broaden its application. This study reviews the recent research on the flame retardant functionalization and mechanical reinforcement of PLA. It classifies PLA according to the type of the flame retardant strategy employed, such as surface-modified fibers, modified nano/micro fillers, small-molecule and macromolecular flame retardants, flame retardants with fibers or polymers, and chain extension or crosslinking with other flame retardants. The functionalization strategies and main parameters of the modified PLA systems are summarized and analyzed. This study summarizes the latest advances in the fields of flame retardancy and mechanical reinforcement of PLA.pre-print3656 K
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