Precursor-Confined Chemical Vapor Deposition of 2D Single-Crystalline SexTe1-x Nanosheets for p-Type Transistors and Inverters.

Two-dimensional (2D) tellurium (Te) is emerging as a promising p-type candidate for constructing complementary metal-oxide-semiconductor (CMOS) architectures. However, its small bandgap leads to a high leakage current and a low on/off current ratio. Although alloying Te with selenium (Se) can tune its bandgap, thermally evaporated SexTe1-x thin films often suffer from grain boundaries and high-density defects. Herein, we introduce a precursor-confined chemical vapor deposition (CVD) method for synthesizing single-crystalline SexTe1-x alloy nanosheets. These nanosheets, with tunable compositions, are ideal for high-performance field-effect transistors (FETs) and 2D inverters. The preformation of Se-Te frameworks in our developed CVD method plays a critical role in the growth of SexTe1-x nanosheets with high crystallinity. Optimizing the Se composition resulted in a Se0.30Te0.70 nanosheet-based p-type FET with a large on/off current ratio of 4 × 105 and a room-temperature hole mobility of 120 cm2·V-1·s-1, being eight times higher than thermally evaporated SexTe1-x with similar composition and thickness. Moreover, we successfully fabricated an inverter based on p-type Se0.30Te0.70 and n-type MoS2 nanosheets, demonstrating a typical voltage transfer curve with a gain of 30 at an operation voltage of Vdd = 3 V.

Unified Cross-Structural Motion Retargeting for Humanoid Characters.

Motion retargeting for animation characters has potential applications in fields such as animation production and virtual reality. However, current methods either assume that the source and target characters have the same skeletal structure, or require designing and training specific model architectures for each structure. In this paper, we aim to address the challenge of motion retargeting across previously unseen skeletal structures with a unified dynamic graph network. The proposed approach utilizes a dynamic graph transformation module to dynamically transfer latent motion features to different structures. We also take into consideration for intricate hand movements and model both torso and hand joints as graphs in a unified manner for whole-body motion retargeting. Our model allows the use of motion data from different structures to train a unified model and learns cross-structural motion retargeting in an unsupervised manner with unpaired data. Experimental results demonstrate the superiority of the proposed method in terms of data efficiency and performance on both seen and unseen structures.

Multiple Instance Differentiation Learning for Active Object Detection.

Despite the substantial progress of active learning for image recognition, there lacks a systematic investigation of instance-level active learning for object detection. In this paper, we propose to unify instance uncertainty calculation with image uncertainty estimation for informative image selection, creating a multiple instance differentiation learning (MIDL) method for instance-level active learning. MIDL consists of a classifier prediction differentiation module and a multiple instance differentiation module. The former leverages two adversarial instance classifiers trained on the labeled and unlabeled sets to estimate instance uncertainty of the unlabeled set. The latter treats unlabeled images as instance bags and re-estimates image-instance uncertainty using the instance classification model in a multiple instance learning fashion. Through weighting the instance uncertainty using instance class probability and instance objectness probability under the total probability formula, MIDL unifies the image uncertainty with instance uncertainty in the Bayesian theory framework. Extensive experiments validate that MIDL sets a solid baseline for instance-level active learning. On commonly used object detection datasets, it outperforms other state-of-the-art methods by significant margins, particularly when the labeled sets are small.

Growth of Tellurium Nanobelts on h-BN for p-type Transistors with Ultrahigh Hole Mobility.

The lack of stable p-type van der Waals (vdW) semiconductors with high hole mobility severely impedes the step of low-dimensional materials entering the industrial circle. Although p-type black phosphorus (bP) and tellurium (Te) have shown promising hole mobilities, the instability under ambient conditions of bP and relatively low hole mobility of Te remain as daunting issues. Here we report the growth of high-quality Te nanobelts on atomically flat hexagonal boron nitride (h-BN) for high-performance p-type field-effect transistors (FETs). Importantly, the Te-based FET exhibits an ultrahigh hole mobility up to 1370 cm2 V-1 s-1 at room temperature, that may lay the foundation for the future high-performance p-type 2D FET and metal-oxide-semiconductor (p-MOS) inverter. The vdW h-BN dielectric substrate not only provides an ultra-flat surface without dangling bonds for growth of high-quality Te nanobelts, but also reduces the scattering centers at the interface between the channel material and the dielectric layer, thus resulting in the ultrahigh hole mobility .

Index Networks.

We show that existing upsampling operators in convolutional networks can be unified using the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can often recover boundary details considerably better than other upsampling operators such as bilinear interpolation. By viewing the indices as a function of the feature map, we introduce the concept of 'learning to index', and present a novel index-guided encoder-decoder framework where indices are learned adaptively from data and are used to guide downsampling and upsampling stages, without extra training supervision. At the core of this framework is a new learnable module, termed Index Network (IndexNet), which dynamically generates indices conditioned on the feature map. IndexNet can be used as a plug-in, applicable to almost all convolutional networks that have coupled downsampling and upsampling stages, enabling the networks to dynamically capture variations of local patterns. In particular, we instantiate and investigate five families of IndexNet. We highlight their superiority in delivering spatial information over other upsampling operators with experiments on synthetic data, and demonstrate their effectiveness on four dense prediction tasks, including image matting, image denoising, semantic segmentation, and monocular depth estimation. Code and models are available at https://git.io/IndexNet.