Improved mechanical and electrical properties in electrospun polyimide/multiwalled carbon nanotubes nanofibrous composites

Highly aligned polyimide (PI) and PI/multi-walled carbon nanotubes (PI/MWCNTs) nanofibrous composites by incorporating poly(ethylene oxide) as the dispersing medium were fabricated using electrospinning technique. The morphology, mechanical, and electrical properties of the electrospun nanofibrous composites were investigated. Scanning electron microscope showed that the functionalized MWCNTs (f-MWCNTs) were well dispersed and oriented along the nanofiber axis. Analysis of electrical properties indicated a remarkable improvement on the alternating current conductivity by introduction of the aligned f-MWCNTs. Besides, with addition of 3 vol.% f-MWCNTs, the obvious enhancement of tensile modulus and strength was achieved. Thus, the electrospun PI/MWCNTs nanofibrous composites have great potential applications in multifunctional engineering materials

LEMON: Learning 3D Human-Object Interaction Relation from 2D Images

Learning 3D human-object interaction relation is pivotal to embodied AI and interaction modeling. Most existing methods approach the goal by learning to predict isolated interaction elements, e.g., human contact, object affordance, and human-object spatial relation, primarily from the perspective of either the human or the object. Which underexploit certain correlations between the interaction counterparts (human and object), and struggle to address the uncertainty in interactions. Actually, objects' functionalities potentially affect humans' interaction intentions, which reveals what the interaction is. Meanwhile, the interacting humans and objects exhibit matching geometric structures, which presents how to interact. In light of this, we propose harnessing these inherent correlations between interaction counterparts to mitigate the uncertainty and jointly anticipate the above interaction elements in 3D space. To achieve this, we present LEMON (LEarning 3D huMan-Object iNteraction relation), a unified model that mines interaction intentions of the counterparts and employs curvatures to guide the extraction of geometric correlations, combining them to anticipate the interaction elements. Besides, the 3D Interaction Relation dataset (3DIR) is collected to serve as the test bed for training and evaluation. Extensive experiments demonstrate the superiority of LEMON over methods estimating each element in isolation.Comment: accept by CVPR202

Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation

Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. Recently, a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve pixel-level localization. While existing FPM-based methods use cross-entropy to evaluate the foreground prediction map and to guide the learning of the generator, this paper presents two astonishing experimental observations on the object localization learning process: For a trained network, as the foreground mask expands, 1) the cross-entropy converges to zero when the foreground mask covers only part of the object region. 2) The activation value continuously increases until the foreground mask expands to the object boundary. Therefore, to achieve a more effective localization performance, we argue for the usage of activation value to learn more object regions. In this paper, we propose a Background Activation Suppression (BAS) method. Specifically, an Activation Map Constraint (AMC) module is designed to facilitate the learning of generator by suppressing the background activation value. Meanwhile, by using foreground region guidance and area constraint, BAS can learn the whole region of the object. In the inference phase, we consider the prediction maps of different categories together to obtain the final localization results. Extensive experiments show that BAS achieves significant and consistent improvement over the baseline methods on the CUB-200-2011 and ILSVRC datasets. In addition, our method also achieves state-of-the-art weakly supervised semantic segmentation performance on the PASCAL VOC 2012 and MS COCO 2014 datasets. Code and models are available at https://github.com/wpy1999/BAS-Extension.Comment: Accepted by IJCV. arXiv admin note: text overlap with arXiv:2112.0058

Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation

Dynamic Range (DR) is a pivotal characteristic of imaging systems. Current frame-based cameras struggle to achieve high dynamic range imaging due to the conflict between globally uniform exposure and spatially variant scene illumination. In this paper, we propose AsynHDR, a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging and the unique event-generating mechanism of Dynamic Vision Sensors (DVS). Our proposed AsynHDR system integrates the DVS with a set of LCD panels. The LCD panels modulate the irradiance incident upon the DVS by altering their transparency, thereby triggering the pixel-independent event streams. The HDR image is subsequently decoded from the event streams through our temporal-weighted algorithm. Experiments under standard test platform and several challenging scenes have verified the feasibility of the system in HDR imaging task

Alisol C 23-acetate from the rhizome of Alisma orientale

The title compound [systematic name: 11β-hydr­oxy-24,25-ep­oxy-3,16-oxo-protost-13 (17)-en-23-yl acetate], C32H48O6, a protostane-type triterpenoid, was isolated from the Chinese herbal medicine alismatis rhizoma (the rhizome of Alisma orientalis Juzep). The mol­ecule contains four trans-fused rings, viz. three six-membered and one five-membered ring. Two of the six-membered rings have slightly distorted half-chair conformations, while the third exhibits a chair conformation. The five-membered ring is almost planar. An inter­molecular O—H⋯O hydrogen bond between the hydr­oxy and ep­oxy groups and intra- and intermolecular C—H⋯O hydrogen bonds are observed

High-Temperature Polyimide Dielectric Materials for Energy Storage

The availability of high-temperature dielectrics is key to develop advanced electronics and power systems that operate under extreme environmental conditions. In the past few years, many improvements have been made and many exciting developments have taken place. However, currently available candidate materials and methods still do not meet the applicable standards. Polyimide (PI) was found to be the preferred choice for high-temperature dielectric films development due to its thermal stability, dielectric properties, and flexibility. However, it has disadvantages such as a relatively low dielectric permittivity. This chapter presents an overview of recent progress on PI dielectric materials for high-temperature capacitive energy storage applications. In this way, a new molecular design of the skeleton structure of PI should be performed to balance size and thermal stability and to optimize energy storage property for high-temperature application. The improved performance can be generated via incorporation of inorganic units into polymers to form organic-inorganic hybrid and composite structures