OVSNet : Towards One-Pass Real-Time Video Object Segmentation

Video object segmentation aims at accurately segmenting the target object regions across consecutive frames. It is technically challenging for coping with complicated factors (e.g., shape deformations, occlusion and out of the lens). Recent approaches have largely solved them by using backforth re-identification and bi-directional mask propagation. However, their methods are extremely slow and only support offline inference, which in principle cannot be applied in real time. Motivated by this observation, we propose a efficient detection-based paradigm for video object segmentation. We propose an unified One-Pass Video Segmentation framework (OVS-Net) for modeling spatial-temporal representation in a unified pipeline, which seamlessly integrates object detection, object segmentation, and object re-identification. The proposed framework lends itself to one-pass inference that effectively and efficiently performs video object segmentation. Moreover, we propose a maskguided attention module for modeling the multi-scale object boundary and multi-level feature fusion. Experiments on the challenging DAVIS 2017 demonstrate the effectiveness of the proposed framework with comparable performance to the state-of-the-art, and the great efficiency about 11.5 FPS towards pioneering real-time work to our knowledge, more than 5 times faster than other state-of-the-art methods.Comment: 10 pages, 6 figure

Optimal Mixed Strategies to the Zero-sum Linear Differential Game

This paper exploits the weak approximation method to study a zero-sum linear differential game under mixed strategies. The stochastic nature of mixed strategies poses challenges in evaluating the game value and deriving the optimal strategies. To overcome these challenges, we first define the mixed strategy based on time discretization given the control period $\delta$. Then, we design a stochastic differential equation (SDE) to approximate the discretized game dynamic with a small approximation error of scale $\mathcal{O}(\delta^2)$ in the weak sense. Moreover, we prove that the game payoff is also approximated in the same order of accuracy. Next, we solve the optimal mixed strategies and game values for the linear quadratic differential games. The effect of the control period is explicitly analyzed when the payoff is a terminal cost. Our results provide the first implementable form of the optimal mixed strategies for a zero-sum linear differential game. Finally, we provide numerical examples to illustrate and elaborate on our results

Image Clustering with External Guidance

The core of clustering is incorporating prior knowledge to construct supervision signals. From classic k-means based on data compactness to recent contrastive clustering guided by self-supervision, the evolution of clustering methods intrinsically corresponds to the progression of supervision signals. At present, substantial efforts have been devoted to mining internal supervision signals from data. Nevertheless, the abundant external knowledge such as semantic descriptions, which naturally conduces to clustering, is regrettably overlooked. In this work, we propose leveraging external knowledge as a new supervision signal to guide clustering, even though it seems irrelevant to the given data. To implement and validate our idea, we design an externally guided clustering method (Text-Aided Clustering, TAC), which leverages the textual semantics of WordNet to facilitate image clustering. Specifically, TAC first selects and retrieves WordNet nouns that best distinguish images to enhance the feature discriminability. Then, to improve image clustering performance, TAC collaborates text and image modalities by mutually distilling cross-modal neighborhood information. Experiments demonstrate that TAC achieves state-of-the-art performance on five widely used and three more challenging image clustering benchmarks, including the full ImageNet-1K dataset

Graph-guided Architecture Search for Real-time Semantic Segmentation

Designing a lightweight semantic segmentation network often requires researchers to find a trade-off between performance and speed, which is always empirical due to the limited interpretability of neural networks. In order to release researchers from these tedious mechanical trials, we propose a Graph-guided Architecture Search (GAS) pipeline to automatically search real-time semantic segmentation networks. Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with new search space where a lightweight model can be effectively explored through the cell-level diversity and latencyoriented constraint. Specifically, to produce the cell-level diversity, the cell-sharing constraint is eliminated through the cell-independent manner. Then a graph convolution network (GCN) is seamlessly integrated as a communication mechanism between cells. Finally, a latency-oriented constraint is endowed into the search process to balance the speed and performance. Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS achieves the new state-of-the-art trade-off between accuracy and speed. In particular, on Cityscapes dataset, GAS achieves the new best performance of 73.5% mIoU with speed of 108.4 FPS on Titan Xp.Comment: CVPR202

Synergistic Exacerbation of Mitochondrial and Synaptic Dysfunction and Resultant Learning and Memory Deficit in a Mouse Model of Diabetic Alzheimer’s Disease

Diabetes is considered to be a risk factor in Alzheimer’s disease (AD) pathogenesis. Although recent evidence indicates that diabetes exaggerates pathologic features of AD, the underlying mechanisms are not well understood. To determine whether mitochondrial perturbation is associated with the contribution of diabetes to AD progression, we characterized mouse models of streptozotocin (STZ)-induced type 1 diabetes and transgenic AD mouse models with diabetes. Brains from mice with STZ-induced diabetes revealed a significant increase of cyclophilin D (CypD) expression, reduced respiratory function, and decreased hippocampal long-term potentiation (LTP); these animals had impaired spatial learning and memory. Hyperglycemia exacerbated the upregulation of CypD, mitochondrial defects, synaptic injury, and cognitive dysfunction in the brains of transgenic AD mice overexpressing amyloid-β as shown by decreased mitochondrial respiratory complex I and IV enzyme activity and greatly decreased mitochondrial respiratory rate. Concomitantly, hippocampal LTP reduction and spatial learning and memory decline, two early pathologic indicators of AD, were enhanced in the brains of diabetic AD mice. Our results suggest that the synergistic interaction between effects of diabetes and AD on mitochondria may be responsible for brain dysfunction that is in common in both diabetes and AD

DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities

Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded by designated rule-based detectors that heavily rely on empirical expertise, requiring tremendous effort from software experts to generate rule repositories for large code corpus. Recent advances in deep learning, especially Graph Neural Networks (GNN), have uncovered the feasibility of automatic detection of a wide range of software vulnerabilities. However, prior learning-based works only break programs down into a sequence of word tokens for extracting contextual features of codes, or apply GNN largely on homogeneous graph representation (e.g., AST) without discerning complex types of underlying program entities (e.g., methods, variables). In this work, we are one of the first to explore heterogeneous graph representation in the form of Code Property Graph and adapt a well-known heterogeneous graph network with a dual-supervisor structure for the corresponding graph learning task. Using the prototype built, we have conducted extensive experiments on both synthetic datasets and real-world projects. Compared with the state-of-the-art baselines, the results demonstrate promising effectiveness in this research direction in terms of vulnerability detection performance (average F1 improvements over 10\% in real-world projects) and transferability from C/C++ to other programming languages (average F1 improvements over 11%)

Inhibitory Effect of Cinobufagin on L-Type C a

Cinobufagin (CBG), a major bioactive ingredient of the bufanolide steroid compounds of Chan Su, has been widely used to treat coronary heart disease. At present, the effect of CBG on the L-type Ca2+ current (ICa-L) of ventricular myocytes remains undefined. The aim of the present study was to characterize the effect of CBG on intracellular Ca2+ ([Ca2+]i) handling and cell contractility in rat ventricular myocytes. CBG was investigated by determining its influence on ICa-L, Ca2+ transient, and contractility in rat ventricular myocytes using the whole-cell patch-clamp technique and video-based edge-detection and dual-excitation fluorescence photomultiplier systems. The dose of CBG (10−8 M) decreased the maximal inhibition of CBG by 47.93%. CBG reduced ICa-L in a concentration-dependent manner with an IC50 of 4 × 10−10 M, upshifted the current-voltage curve of ICa-L, and shifted the activation and inactivation curves of ICa-L leftward. Moreover, CBG diminished the amplitude of the cell shortening and Ca2+ transients with a decrease in the time to peak (Tp) and the time to 50% of the baseline (Tr). CBG inhibited L-type Ca2+ channels, and reduced [Ca2+]i and contractility in adult rat ventricular myocytes. These findings contribute to the understanding of the cardioprotective efficacy of CBG