378 research outputs found
Simulation of a Solar Jet Formed from an Untwisting Flux Rope Interacting with a Null Point
Coronal jets are eruptions identified by a collimated, sometimes twisted
spire. They are small-scale energetic events compared with flares. Using
multi-wavelength observations from the Solar Dynamics Observatory/Atmospheric
Imaging Assembly (SDO/AIA) and a magnetogram from Hinode/Spectro-Polarimeter
(Hinode/SP), we study the formation and evolution of a jet occurring on 2019
March 22 in the active region NOAA 12736. A zero- magnetohydrodynamic
(MHD) simulation is conducted to probe the initiation mechanisms and appearance
of helical motion during this jet event. As the simulation reveals, there are
two pairs of field lines at the jet base, indicating two distinct magnetic
structures. One structure outlines a flux rope lying low above the photosphere
in the north of a bald patch region and the other structure shows a null point
high in the corona in the south. The untwisting motions of the observed flux
rope was recovered by adding an anomalous (artificial) resistivity in the
simulation. A reconnection occurs at the bald patch in the flux rope structure,
which is moving upwards and simultaneously encounters the field lines of the
null point structure. The interaction of the two structures results in the jet
while the twist of the flux rope is transferred to the jet by the reconnected
field lines. The rotational motion of the flux rope is proposed to be an
underlying trigger of this process and responsible for helical motions in the
jet spire.Comment: 17pages, 9 figures. Accepted for publication in The Astrophysical
Journa
Adaptive 3D Mesh Steganography Based on Feature-Preserving Distortion
3D mesh steganographic algorithms based on geometric modification are
vulnerable to 3D steganalyzers. In this paper, we propose a highly adaptive 3D
mesh steganography based on feature-preserving distortion (FPD), which
guarantees high embedding capacity while effectively resisting 3D steganalysis.
Specifically, we first transform vertex coordinates into integers and derive
bitplanes from them to construct the embedding domain. To better measure the
mesh distortion caused by message embedding, we propose FPD based on the most
effective sub-features of the state-of-the-art steganalytic feature set. By
improving and minimizing FPD, we can efficiently calculate the optimal
vertex-changing distribution and simultaneously preserve mesh features, such as
steganalytic and geometric features, to a certain extent. By virtue of the
optimal distribution, we adopt the Q-layered syndrome trellis coding (STC) for
practical message embedding. However, when Q varies, calculating bit
modification probability (BMP) in each layer of Q-layered will be cumbersome.
Hence, we contrapuntally design a universal and automatic BMP calculation
approach. Extensive experimental results demonstrate that the proposed
algorithm outperforms most state-of-the-art 3D mesh steganographic algorithms
in terms of resisting 3D steganalysis.Comment: IEEE TVCG major revisio
TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces
This report describes the solution that secured the first place in the "View
Synthesis Challenge for Human Heads (VSCHH)" at the ICCV 2023 workshop. Given
the sparse view images of human heads, the objective of this challenge is to
synthesize images from novel viewpoints. Due to the complexity of textures on
the face and the impact of lighting, the baseline method TensoRF yields results
with significant artifacts, seriously affecting facial reconstruction. To
address this issue, we propose TI-Face, which improves facial reconstruction
through tensorial radiance fields (T-Face) and implicit surfaces (I-Face),
respectively. Specifically, we employ an SAM-based approach to obtain the
foreground mask, thereby filtering out intense lighting in the background.
Additionally, we design mask-based constraints and sparsity constraints to
eliminate rendering artifacts effectively. The experimental results demonstrate
the effectiveness of the proposed improvements and superior performance of our
method on face reconstruction. The code will be available at
https://github.com/RuijieZhu94/TI-Face.Comment: 1st place solution in the View Synthesis Challenge for Human Heads
(VSCHH) at the ICCV 2023 worksho
Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic Enhancement
The design and development of text-based knowledge graph completion (KGC)
methods leveraging textual entity descriptions are at the forefront of
research. These methods involve advanced optimization techniques such as soft
prompts and contrastive learning to enhance KGC models. The effectiveness of
text-based methods largely hinges on the quality and richness of the training
data. Large language models (LLMs) can utilize straightforward prompts to alter
text data, thereby enabling data augmentation for KGC. Nevertheless, LLMs
typically demand substantial computational resources. To address these issues,
we introduce a framework termed constrained prompts for KGC (CP-KGC). This
CP-KGC framework designs prompts that adapt to different datasets to enhance
semantic richness. Additionally, CP-KGC employs a context constraint strategy
to effectively identify polysemous entities within KGC datasets. Through
extensive experimentation, we have verified the effectiveness of this
framework. Even after quantization, the LLM (Qwen-7B-Chat-int4) still enhances
the performance of text-based KGC methods \footnote{Code and datasets are
available at
\href{https://github.com/sjlmg/CP-KGC}{https://github.com/sjlmg/CP-KGC}}. This
study extends the performance limits of existing models and promotes further
integration of KGC with LLMs.Comment: new versio
Pedestrian Accessible Infrastructure Inventory: Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types
In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure
segmentation workflow is designed and optimized, which is capable of
efficiently processing multi-sourced geospatial data including LiDAR data and
satellite imagery data. We used an expanded definition of pedestrian
infrastructure inventory which goes beyond the traditional transportation
elements to include street furniture objects that are important for
accessibility but are often omitted from the traditional definition. Our
contributions lie in producing the necessary knowledge to answer the following
two questions. First, which data representation can facilitate zero-shot
segmentation of infrastructure objects with SAM? Second, how well does the
SAM-based method perform on segmenting pedestrian infrastructure objects? Our
findings indicate that street view images generated from mobile LiDAR point
cloud data, when paired along with satellite imagery data, can work efficiently
with SAM to create a scalable pedestrian infrastructure inventory approach with
immediate benefits to GIS professionals, city managers, transportation owners,
and walkers, especially those with travel-limiting disabilities, such as
individuals who are blind, have low vision, or experience mobility
disabilities
Design of multifunctional color routers with Kerker switching using generative adversarial networks
To achieve optoelectronic devices with high resolution and efficiency, there
is a pressing need for optical structural units that possess an ultrasmall
footprint yet exhibit strong controllability in both the frequency and spatial
domains. For dielectric nanoparticles, the overlap of electric and magnetic
dipole moments can scatter light completely forward or backward, which is
called Kerker theory. This effect can expand to any multipoles and any
directions, re-named as generalized Kerker effect, and realize controllable
light manipulation at full space and full spectrum using well-designed
dielectric structures. However, the complex situations of multipole couplings
make it difficult to achieve structural design. Here, generative artificial
intelligence (AI) is utilized to facilitate multi-objective-oriented structural
design, wherein we leverage the concept of "combined spectra" that consider
both spectra and direction ratios as labels. The proposed generative
adversarial network (GAN) is named as DDGAN (double-discriminator GAN) which
discriminates both images and spectral labels. Using trained networks, we
achieve the simultaneous design for scattering color and directivities, RGB
color routers, as well as narrowband light routers. Notably, all generated
structures possess a footprint less than 600x600 nm indicating their potential
applications in optoelectronic devices with ultrahigh resolution
Anisodamine combined with lidocaine improves healing of myocardial ischemia reperfusion injury in rats via PI3K/Akt signaling pathway
Purpose: To study the effects of anisodamine (Ad) combined with lidocaine (Ldc) on myocardial ischemia-reperfusion injury (MIRI) in rats, and its correlation with PI3K/AKT signaling pathway.Methods: A total of 70 healthy rats were randomly divided into S group, M group, Ad group, Ldc group, Ad + Ldc group, Ad + Ldc + LY group, and LY group. The cardiac hemodynamic indices in each group were determined, and the area of myocardial infarction measured. Serum biochemical indices were also determined. Furthermore, the protein expressions of p-Akt, T-Akt, Bcl-2, and Bax in myocardial cells were determined by Western blotting.Results: Compared with those in M group, Ad group, Ldc group, Ad + Ldc + LY group, and LY group, cardiac hemodynamic indices significantly improved, while the area of myocardial infarction was significantly reduced (p < 0.01). Furthermore, serum malondialdehyde (MDA) concentration but the activities of CK, CK-MB, TNF-α, and IL-6 declined, while the activities of superoxide dismutase (SOD), CAT and GSH-Px rose in Ad + Ldc group (p < 0.01). In Ad + Ldc group, p-Akt, T-Akt, and Bcl-2 increased, while Bax significantly decreased. Through comparison LY294002 significantly inhibited the protective effect of Ad combined with Ldc against MIRI in rats (p < 0.01).Conclusion: Anisodamine combination with lidocaine has a protective effect against MIRI in rats via PI3K/Akt signaling pathway, thus indicating that it is a potential therapeutic strategy for the management of myocardial ischemia-reperfusion
Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework
As the demands for immediate and effective responses increase in both
civilian and military domains, the unmanned aerial vehicle (UAV) swarms emerge
as effective solutions, in which multiple cooperative UAVs can work together to
achieve specific goals. However, how to manage such complex systems to ensure
real-time adaptability lack sufficient researches. Hence, in this paper, we
propose the cooperative cognitive dynamic system (CCDS), to optimize the
management for UAV swarms. CCDS leverages a hierarchical and cooperative
control structure that enables real-time data processing and decision.
Accordingly, CCDS optimizes the UAV swarm management via dynamic
reconfigurability and adaptive intelligent optimization. In addition, CCDS can
be integrated with the biomimetic mechanism to efficiently allocate tasks for
UAV swarms. Further, the distributed coordination of CCDS ensures reliable and
resilient control, thus enhancing the adaptability and robustness. Finally, the
potential challenges and future directions are analyzed, to provide insights
into managing UAV swarms in dynamic heterogeneous networking
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