The novel two-dimensional semiconductors with high carrier mobility and excellent stability are essential to the next-generation high-speed and low-power nanoelectronic devices. Because of the natural abundance, intrinsic gap, and chemical stability, metal oxides were also recently suggested as potential candidates for electronic materials. However, their carrier mobilities are typically on the order of tens of square centimeters per volt per second, much lower than that for commonly used silicon. By using first-principles calculations and deformation potential theory, we have predicted few-layer MoO as chemically stable wide-band-gap semiconductors with a considerably high acoustic-phonon-limited carrier mobility above 3000 cm V s, which makes them promising candidates for both electron- and hole-transport applications. Moreover, we also find a large in-plane anisotropy of the carrier mobility with a ratio of about 20-30 in this unusual system. Further analysis indicates that, because of the unique charge density distribution of whole valence electrons and the states near the band edge, both the elastic modulus and deformation potential are strongly directionally dependent. Also, the predicted high-mobility transport anisotropy of few-layer MoO can be attributed to the synergistic effect of the anisotropy of the elastic modulus and deformation potential. Our results not only give an insightful understanding for the high carrier mobility observed in few-layer MoO systems but also reveal the importance of the carrier-transport direction to the device performance.

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Two-dimensional (2D) layered materials and their van der Waals (vdW) heterostructures are promising candidates for highly efficient renewable energy application. On the basis of density functional theory, we investigated systematically the structure, stability, and electronic and optical properties of the group-VA trihalides AI3 (A = As, Sb) single layers and their vdW heterostructure. Our results suggest that the AI3 (A = As, Sb) single layers can be exfoliated from their bulk crystal easily and are also dynamically stable. Standard PBE predicts that the band gap of AI3 increases with element number of A, which is in conflict with the experimental results of the bulk. This unreasonable trend can be corrected when the spin–orbit coupling (SOC) effect is considered. The inconsistence between PBE and PBE+SOC calculations can be understood by the competition of two contrary effects for gap variation induced by lattice expansion and relativistic effect. Our PBE+SOC calculations indicate the AsI3 and SbI3 monolayers are potential photocatalysts for water splitting with indirect band gaps of 2.00 and 1.89 eV and moderate electron mobility (∼102 cm2 V–1 s–1). By stacking AsI3 and SbI3 vertically, a strongly binding vdW heterostructure with a type-II band alignment can be formed. Excitingly, the indirect band gap is reduced to 1.63 eV, and the absolute band edges still straddle the water redox potentials, implying that it can be used as a potential photocatalyst with strong adsorption for visible light. Moreover, such a vdW heterostructure can also be an effective excitonic solar cell material with theoretical power conversion efficiency up to 18%. These results show that the AI3 (A = As, Sb) single layers and their vdW heterostructure are potential candidates for future solar energy conversion applications.

In an underwater imaging system, a perspective camera is often placed outside a tank or in waterproof housing with a flat glass window. The refraction of light occurs when a light ray passes through the water-glass and air-glass interface, rendering the conventional multiple view geometry based on the single viewpoint (SVP) camera model invalid. While most recent underwater vision studies mainly focus on the challenging topic of calibrating such systems, no previous work has systematically studied the influence of refraction on underwater three-dimensional (3D) reconstruction. This paper demonstrates the possibility of using the SVP camera model in underwater 3D reconstruction through theoretical analysis of refractive distortion and simulations. Then, the performance of the SVP camera model in multiview underwater 3D reconstruction is quantitatively evaluated. The experimental results reveal a rather surprising and useful yet overlooked fact that the SVP camera model with radial distortion correction and focal length adjustment can compensate for refraction and achieve high accuracy in multiview underwater 3D reconstruction (within 0.7 mm for an object of dimension 200 mm) compared with the results of land-based systems. Such an observation justifies the use of the SVP camera model in underwater application for reconstructing reliable 3D scenes. Our results can be used to guide the selection of system parameters in the design of an underwater 3D imaging setup.

This work addresses a new challenge of understanding human nonverbal interaction in social contexts. Nonverbal signals pervade virtually every communicative act. Our gestures, facial expressions, postures, gaze, even physical appearance all convey messages, without anything being said. Despite their critical role in social life, nonverbal signals receive very limited attention as compared to the linguistic counterparts, and existing solutions typically examine nonverbal cues in isolation. Our study marks the first systematic effort to enhance the interpretation of multifaceted nonverbal signals. First, we contribute a novel large-scale dataset, called NVI, which is meticulously annotated to include bounding boxes for humans and corresponding social groups, along with 22 atomic-level nonverbal behaviors under five broad interaction types. Second, we establish a new task NVI-DET for nonverbal interaction detection, which is formalized as identifying triplets in the form ⟨individual, group, interaction⟩ from images. Third, we propose a nonverbal interaction detection hypergraph (NVI-DEHR), a new approach that explicitly models high-order nonverbal interactions using hypergraphs. Central to the model is a dual multi-scale hypergraph that adeptly addresses individual-to-individual and group-to-group correlations across varying scales, facilitating interactional feature learning and eventually improving interaction prediction. Extensive experiments on NVI show that NVI-DEHR improves various baselines significantly in NVI-DET. It also exhibits leading performance on HOI-DET, confirming its versatility in supporting related tasks and strong generalization ability. We hope that our study will offer the community new avenues to explore nonverbal signals in more depth.