scSFCL:Deep clustering of scRNA-seq data with subspace feature confidence learning.

The rapid development of single-cell RNA sequencing(scRNA-seq) technology has spawned a variety of single-cell clustering methods. These methods combine statistics and bioinformatics to reveal differences in gene expression between cells and the diversity of cell types. Deep exploration of single-cell data is more challenging due to the high dimensionality, sparsity and noise of scRNA-seq data.

A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.

Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, inspired by the DNA-encoded compound library technique, we introduce DeepBlock, a deep learning approach for block-based ligand generation tailored to target protein sequences while enabling precise property control.

Multiprior Learning via Neural Architecture Search for Blind Face Restoration.

Blind face restoration (BFR) aims to recover high-quality (HQ) face images from low-quality (LQ) ones and usually resorts to facial priors for improving restoration performance. However, current methods still suffer from two major difficulties: 1) how to derive a powerful network architecture without extensive hand tuning and 2) how to capture complementary information from multiple facial priors in one network to improve restoration performance. To this end, we propose a face restoration searching network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality.

Vicinity Vision Transformer.

Vision transformers have shown great success on numerous computer vision tasks. However, their central component, softmax attention, prohibits vision transformers from scaling up to high-resolution images, due to both the computational complexity and memory footprint being quadratic. Linear attention was introduced in natural language processing (NLP) which reorders the self-attention mechanism to mitigate a similar issue, but directly applying existing linear attention to vision may not lead to satisfactory results.

Event-Aware Video Deraining via Multi-Patch Progressive Learning.

In this paper, we address the problem of video-based rain streak removal by developing an event-aware multi-patch progressive neural network. Rain streaks in video exhibit correlations in both temporal and spatial dimensions. Existing methods have difficulties in modeling the characteristics.

Strain-Driven Faceting of Graphene-Catalyst Interfaces.

The interfacial interaction of 2D materials with the substrate leads to striking surface faceting affecting its electronic properties. Here, we quantitatively study the orientation-dependent facet topographies observed on the catalyst under graphene using electron backscatter diffraction and atomic force microscopy. The original flat catalyst surface transforms into two facets: a low-energy low-index surface, e.

EDFace-Celeb-1 M: Benchmarking Face Hallucination With a Million-Scale Dataset.

Recent deep face hallucination methods show stunning performance in super-resolving severely degraded facial images, even surpassing human ability. However, these algorithms are mainly evaluated on non-public synthetic datasets. It is thus unclear how these algorithms perform on public face hallucination datasets.

Enhanced Spatio-Temporal Interaction Learning for Video Deraining: Faster and Better.

Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems. Despite the significant success which has been achieved for video deraining recently, two major challenges remain: 1) how to exploit the vast information among successive frames to extract powerful spatio-temporal features across both the spatial and temporal domains, and 2) how to restore high-quality derained videos with a high-speed approach. In this paper, we present a new end-to-end video deraining framework, dubbed Enhanced Spatio-Temporal Interaction Network (ESTINet), which considerably boosts current state-of-the-art video deraining quality and speed.

Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal.

Rain streaks and raindrops are two natural phenomena, which degrade image capture in different ways. Currently, most existing deep deraining networks take them as two distinct problems and individually address one, and thus cannot deal adequately with both simultaneously. To address this, we propose a Dual Attention-in-Attention Model (DAiAM) which includes two DAMs for removing both rain streaks and raindrops.

Deep Dense Multi-Scale Network for Snow Removal Using Semantic and Depth Priors.

Images captured in snowy days suffer from noticeable degradation of scene visibility, which degenerates the performance of current vision-based intelligent systems. Removing snow from images thus is an important topic in computer vision. In this paper, we propose a Deep Dense Multi-Scale Network (DDMSNet) for snow removal by exploiting semantic and depth priors.

Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding.

Single-image super-resolution (SR) and multi-frame SR are two ways to super resolve low-resolution images. Single-Image SR generally handles each image independently, but ignores the temporal information implied in continuing frames. Multi-frame SR is able to model the temporal dependency via capturing motion information.

Angular-Driven Feedback Restoration Networks for Imperfect Sketch Recognition.

Automatic hand-drawn sketch recognition is an important task in computer vision. However, the vast majority of prior works focus on exploring the power of deep learning to achieve better accuracy on complete and clean sketch images, and thus fail to achieve satisfactory performance when applied to incomplete or destroyed sketch images. To address this problem, we first develop two datasets that contain different levels of scrawl and incomplete sketches.

Adversarial Spatio-Temporal Learning for Video Deblurring.

Camera shake or target movement often leads to undesired blur effects in videos captured by a hand-held camera. Despite significant efforts having been devoted to video-deblur research, two major challenges remain: 1) how to model the spatio-temporal characteristics across both the spatial domain (i.e.

Time-averaged pressure drop induced by a jet pump in oscillatory flow.

Gedeon streaming is known to considerably deteriorate the thermal efficiency of a traveling-wave thermoacoustic engine with looped configuration. The time-average pressure drop induced by a jet pump can efficiently suppress the Gedeon streaming. In this study, such suppression mechanism of the jet pump is investigated, and the emphasis is put on the effects of the dimensionless rounding, the taper angle, and the cross-sectional area ratio.

Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks.

One key challenging issue of facial expression recognition is to capture the dynamic variation of facial physical structure from videos. In this paper, we propose a part-based hierarchical bidirectional recurrent neural network (PHRNN) to analyze the facial expression information of temporal sequences. Our PHRNN models facial morphological variations and dynamical evolution of expressions, which is effective to extract "temporal features" based on facial landmarks (geometry information) from consecutive frames.