Ferroelectricity of Ice Nanotubes inside Carbon Nanotubes

We report that ice nanotubes with odd number of side faces inside carbon nanotubes exhibit spontaneous electric polarization along its axes direction by using molecular dynamics simulations. The mechanism of this nanoscale quasi-one-dimensional ferroelectricity is due to low dimensional confinement and the orientational order of hydrogen bonds. These ferroelectric fiber structural materials are different from traditional perovskite structural bulk materials.Comment: 4 pages and 4 figure

Bulk-fragment and tube-like structures of AuN (N=2-26)

Using the relativistic all-electron density-functional calculations on the AuN (N=2-26) in the generalized gradient approximation, combined with the guided simulated annealing, we have found that the two- to three-dimensional structural transition for AuN occurs between N=13 and 15, and the AuN (16<= N <=25) prefer also the pyramid-based bulk fragment structures in addition to the Au20. More importantly, the tubelike structures are found to be the most stable for Au24 and Au26, offering another powerful structure competitor with other isomers, e.g., amorphous, bulk fragment, and gold fullerene. The mechanism to cause these unusual AuN may be attributed to the stronger s-d hybridization and the d-d interaction enhanced by the relativistic effects.Comment: 12 pages and 3 figure

Method for Extracting the Glueball Wave Function

We describe a nonperturbative method for calculating the QCD vacuum and glueball wave functions, based on an eigenvalue equation approach to Hamiltonian lattice gauge theory. Therefore, one can obtain more physical information than the conventional simulation methods. For simplicity, we take the 2+1 dimensional U(1) model as an example. The generalization of this method to 3+1 dimensional QCD is straightforward.Comment: 3 pages, Latex. Presented at Lattice 97: 15th International Symposium on Lattice Field Theory, Edinburgh, Scotland, 22-26 Jul 1997, to appear in Nucl. Phys. B(Proc. Suppl.

Structure-dependent ferroelectricity of niobium clusters (NbN, N=2-52)

The ground-state structures and ferroelectric properties of NbN (N=2-52) have been investigated by a combination of density-functional theory (DFT) in the generalized gradient approximation (GGA) and an unbiased global search with the guided simulated annealing. It is found that the electric dipole moment (EDM) exists in the most of NbN and varies considerably with their sizes. And the larger NbN (N>=25) prefer the amorphous packing. Most importantly, our numerical EDM values of NbN (N>=38) exhibit an extraordinary even-odd oscillation, which is well consistent with the experimental observation, showing a close relationship with the geometrical structures of NbN. Finally, an inverse coordination number (ICN) function is proposed to account for the structural relation of the EDM values, especially their even-odd oscillations starting from Nb38.Comment: 11 pages and 4 figure

Motion-state Alignment for Video Semantic Segmentation

In recent years, video semantic segmentation has made great progress with advanced deep neural networks. However, there still exist two main challenges \ie, information inconsistency and computation cost. To deal with the two difficulties, we propose a novel motion-state alignment framework for video semantic segmentation to keep both motion and state consistency. In the framework, we first construct a motion alignment branch armed with an efficient decoupled transformer to capture dynamic semantics, guaranteeing region-level temporal consistency. Then, a state alignment branch composed of a stage transformer is designed to enrich feature spaces for the current frame to extract static semantics and achieve pixel-level state consistency. Next, by a semantic assignment mechanism, the region descriptor of each semantic category is gained from dynamic semantics and linked with pixel descriptors from static semantics. Benefiting from the alignment of these two kinds of effective information, the proposed method picks up dynamic and static semantics in a targeted way, so that video semantic regions are consistently segmented to obtain precise locations with low computational complexity. Extensive experiments on Cityscapes and CamVid datasets show that the proposed approach outperforms state-of-the-art methods and validates the effectiveness of the motion-state alignment framework.Comment: Accepted by CVPR Workshops 202

Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation

Recently, instance segmentation has made great progress with the rapid development of deep neural networks. However, there still exist two main challenges including discovering indistinguishable objects and modeling the relationship between instances. To deal with these difficulties, we propose a novel object mining framework for instance segmentation. In this framework, we first introduce the semantics perceiving subnetwork to capture pixels that may belong to an obvious instance from the bottom up. Then, we propose an object excavating mechanism to discover indistinguishable objects. In the mechanism, preliminary perceived semantics are regarded as original instances with classifications and locations, and then indistinguishable objects around these original instances are mined, which ensures that hard objects are fully excavated. Next, an instance purifying strategy is put forward to model the relationship between instances, which pulls the similar instances close and pushes away different instances to keep intra-instance similarity and inter-instance discrimination. In this manner, the same objects are combined as the one instance and different objects are distinguished as independent instances. Extensive experiments on the COCO dataset show that the proposed approach outperforms state-of-the-art methods, which validates the effectiveness of the proposed object mining framework.Comment: Accepted by CVPR Workshops 202

Text2Street: Controllable Text-to-image Generation for Street Views

Text-to-image generation has made remarkable progress with the emergence of diffusion models. However, it is still a difficult task to generate images for street views based on text, mainly because the road topology of street scenes is complex, the traffic status is diverse and the weather condition is various, which makes conventional text-to-image models difficult to deal with. To address these challenges, we propose a novel controllable text-to-image framework, named \textbf{Text2Street}. In the framework, we first introduce the lane-aware road topology generator, which achieves text-to-map generation with the accurate road structure and lane lines armed with the counting adapter, realizing the controllable road topology generation. Then, the position-based object layout generator is proposed to obtain text-to-layout generation through an object-level bounding box diffusion strategy, realizing the controllable traffic object layout generation. Finally, the multiple control image generator is designed to integrate the road topology, object layout and weather description to realize controllable street-view image generation. Extensive experiments show that the proposed approach achieves controllable street-view text-to-image generation and validates the effectiveness of the Text2Street framework for street views

Microbial mediated arsenic biotransformation in wetlands

Arsenic (As) is a pervasive environmental toxin and carcinogenic metalloid. It ranks at the top of the US priority List of Hazardous Substances and causes worldwide human health problems. Wetlands, including natural and artificial ecosystems (i.e. paddy soils) are highly susceptible to As enrichment; acting not only as repositories for water but a host of other elemental/chemical moieties. While macro-scale processes (physical and geological) supply As to wetlands, it is the micro-scale biogeochemistry that regulates the fluxes of As and other trace elements from the semi-terrestrial to neighboring plant/aquatic/atmospheric compartments. Among these fine-scale events, microbial mediated As biotransformations contribute most to the element’s changing forms, acting as the ‘switch’ in defining a wetland as either a source or sink of As. Much of our understanding of these important microbial catalyzed reactions follows relatively recent scientific discoveries. Here we document some of these key advances, with focuses on the implications that wetlands and their microbial mediated transformation pathways have on the global As cycle, the chemistries of microbial mediated As oxidation, reduction and methylation, and future research priorities areas