Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendous efforts have been made to recall precise and complete locations for each class, existing methods still commonly suffer from the unsolicited Out-of-Candidate (OC) error predictions that not belongs to the label candidates, which could be avoidable since the contradiction with image-level class tags is easy to be detected. In this paper, we develop a group ranking-based Out-of-Candidate Rectification (OCR) mechanism in a plug-and-play fashion. Firstly, we adaptively split the semantic categories into In-Candidate (IC) and OC groups for each OC pixel according to their prior annotation correlation and posterior prediction correlation. Then, we derive a differentiable rectification loss to force OC pixels to shift to the IC group. Incorporating our OCR with seminal baselines (e.g., AffinityNet, SEAM, MCTformer), we can achieve remarkable performance gains on both Pascal VOC (+3.2%, +3.3%, +0.8% mIoU) and MS COCO (+1.0%, +1.3%, +0.5% mIoU) datasets with negligible extra training overhead, which justifies the effectiveness and generality of our OCR.Comment: Accepted to CVPR202

The Dlk1-Gtl2 Locus Preserves LT-HSC Function by Inhibiting the PI3K-mTOR Pathway to Restrict Mitochondrial Metabolism

The mammalian imprinted Dlk1-Gtl2 locus produces multiple non-coding RNAs (ncRNAs) from the maternally inherited allele, including the largest miRNA cluster in the mammalian genome. This locus has characterized functions in some types of stem cell, but its role in hematopoietic stem cells (HSCs) is unknown. Here, we show that the Dlk1-Gtl2 locus plays a critical role in preserving long-term repopulating HSCs (LT-HSCs). Through transcriptome profiling in 17 hematopoietic cell types, we found that ncRNAs expressed from the Dlk1-Gtl2 locus are predominantly enriched in fetal liver HSCs and the adult LT-HSC population and sustain long-term HSC functionality. Mechanistically, the miRNA mega-cluster within the Dlk1-Gtl2 locus suppresses the entire PI3K-mTOR pathway. This regulation in turn inhibits mitochondrial biogenesis and metabolic activity and protects LT-HSCs from excessive reactive oxygen species (ROS) production. Our data therefore show that the imprinted Dlk1-Gtl2 locus preserves LT-HSC function by restricting mitochondrial metabolism

Exploring the relationship between abnormally high expression of NUP205 and the clinicopathological characteristics, immune microenvironment, and prognostic value of lower-grade glioma

Nuclear pore complex (NPC) is a major transport pivot for nucleocytoplasmic molecule exchange. Nucleoporin 205 (NUP205)—a main component of NPC—plays a key regulatory role in tumor cell proliferation; however, few reports document its effect on the pathological progression of lower-grade glioma (LGG). Therefore, we conducted an integrated analysis using 906 samples from multiple public databases to explore the effects of NUP205 on the prognosis, clinicopathological characteristics, regulatory mechanism, and tumor immune microenvironment (TIME) formation in LGG. First, multiple methods consistently showed that the mRNA and protein expression levels of NUP205 were higher in LGG tumor tissue than in normal brain tissue. This increased expression was mainly noted in the higher WHO Grade, IDH-wild type, and 1p19q non-codeleted type. Second, various survival analysis methods showed that the highly expressed NUP205 was an independent risk indicator that led to reduced survival time of patients with LGG. Third, GSEA analysis showed that NUP205 regulated the pathological progress of LGG via the cell cycle, notch signaling pathway, and aminoacyl-tRNA biosynthesis. Ultimately, immune correlation analysis suggested that high NUP205 expression was positively correlated with the infiltration of multiple immune cells, particularly M2 macrophages, and was positively correlated with eight immune checkpoints, particularly PD-L1. Collectively, this study documented the pathogenicity of NUP205 in LGG for the first time, expanding our understanding of its molecular function. Furthermore, this study highlighted the potential value of NUP205 as a target of anti-LGG immunotherapy

Overcoming Wnt–β-catenin dependent anticancer therapy resistance in leukaemia stem cells

Leukaemia stem cells (LSCs) underlie cancer therapy resistance but targeting these cells remains difficult. The Wnt–β-catenin and PI3K–Akt pathways cooperate to promote tumorigenesis and resistance to therapy. In a mouse model in which both pathways are activated in stem and progenitor cells, LSCs expanded under chemotherapy-induced stress. Since Akt can activate β-catenin, inhibiting this interaction might target therapy-resistant LSCs. High-throughput screening identified doxorubicin (DXR) as an inhibitor of the Akt–β-catenin interaction at low doses. Here we repurposed DXR as a targeted inhibitor rather than a broadly cytotoxic chemotherapy. Targeted DXR reduced Akt-activated β-catenin levels in chemoresistant LSCs and reduced LSC tumorigenic activity. Mechanistically, β-catenin binds multiple immune-checkpoint gene loci, and targeted DXR treatment inhibited expression of multiple immune checkpoints specifically in LSCs, including PD-L1, TIM3 and CD24. Overall, LSCs exhibit distinct properties of immune resistance that are reduced by inhibiting Akt-activated β-catenin. These findings suggest a strategy for overcoming cancer therapy resistance and immune escape

Distributed Optimal Random Access Scheme for Energy Harvesting Devices in Satellite Communication Networks

This paper considers satellite communication networks where each satellite terminal is equipped with energy harvesting (EH) devices to supply energy continuously, and randomly transmits bursty packets to a geostationary satellite over a shared wireless channel. Packet replicas combined with a successive iteration cancellation scheme can reduce the negative impact of packet collisions but consume more energy. Hence, appropriate energy management policies are required to mitigate the adverse effect of energy outages. Although centralized access schemes can provide better performance on the networks’ throughput, they expend extra signallings to allocate the resources, which leads to non-negligible communication latencies, especially for the satellite communication networks. In order to reduce the communication overhead and delay, a distributed random access (RA) scheme considering the energy constraints is studied. Each EH satellite terminal (EH-ST) decides whether to transmit the packet and how many replicas are transmitted according to its local energy and EH rates to maximize the average long-term network throughput. Owing to the nonconvexity of this problem, we adopted a game theoretic method to approximate the optimal solution. By forcing all the EH-STs to employ the same policy, we characterized and proved the existence and uniqueness of the symmetric Nash equilibrium (NE) of the game. Moreover, an efficient algorithm is proposed to calculate the symmetric NE by combining a policy iteration algorithm and the bisection method. The performance of the proposed RA scheme was investigated via numerous simulations. Simulation results showed that the proposed RA scheme is applicable to the EH devices in the future low-cost interactive satellite communication system

Asynchronous Flipped Grant-Free SCMA for Satellite-Based Internet of Things Communication Networks

Sparse code multiple access (SCMA) is a promising code domain non-orthogonal multiple-access scheme which is able to support massive connectivity and grant-free transmission in future satellite-based Internet of Things (IoT) communication networks. Traditional grant-free SCMA is based on time synchronization, which is no longer favorable in such satellite communication networks since the amount of signaling generated to keep all transmitters’ time synchronized is impractical for large networks. Moreover, without centralized codebook assignment, grant-free SCMA suffers from codebook collisions which mean more than one terminal selecting the same codebook being interfered. Motivated by these issues, a novel uplink grant-free asynchronous flipped SCMA scheme named AF-SCMA is proposed in this paper. With the concept of flipped diversity, a specific SCMA-encoded packet is transmitted with its flipped replica together. Successive interference cancellation technique combined with a sliding window is adopted to resolve the packet collisions including codebook collisions at the gateway station. The performance of AF-SCMA is investigated via both mathematical analysis and simulations. Simulation results show that the proposed AF-SCMA provides remarkable performance in terms of throughput and packet loss ratio (PLR), and can benefit from the received signal power unbalance

Video Parsing Based on Head Tracking and Face Recognition

In this paper, we describe a fully automatic video retrieval prototype system that uses an image or a video sequence of an interested identity as probe. The system is based on face vision techniques including face detection and tracking, face alignment and recognition. Given a film or TV sitcom, first face trajectories are extracted in video by head tracking that decompose the video into segments corresponding to certain identity, then frames containing faces of higher quality are selected and normalized according to face alignment results, and finally different segments are associated by face recognition. Experiments are carried out on news video, feature length film video and TV sitcom to show its effectiveness. Potential usage of our system includes intelligent DVD/VCD browsing, video database retrieval, meeting record browsing, etc

Generalizing Energy-based Generative ConvNets from Particle Evolution Perspective

Compared with Generative Adversarial Networks (GAN), Energy-Based generative Models (EBMs) possess two appealing properties: i) they can be directly optimized without requiring an auxiliary network during the learning and synthesizing; ii) they can better approximate underlying distribution of the observed data by learning explicitly potential functions. This paper studies a branch of EBMs, i.e., energy-based Generative ConvNets (GCNs), which minimize their energy function defined by a bottom-up ConvNet. From the perspective of particle physics, we solve the problem of unstable energy dissipation that might damage the quality of the synthesized samples during the maximum likelihood learning. Specifically, we firstly establish a connection between classical FRAME model [1] and dynamic physics process and generalize the GCN in discrete flow with a certain metric measure from particle perspective. To address KL-vanishing issue, we then reformulate GCN from the KL discrete flow with KL divergence measure to a Jordan-Kinderleher-Otto (JKO) discrete flow with Wasserastein distance metric and derive a Wasserastein GCN (wGCN). Based on these theoretical studies on GCN, we finally derive a Generalized GCN (GGCN) to further improve the model generalization and learning capability. GGCN introduces a hidden space mapping strategy by employing a normal distribution for the reference distribution to address the learning bias issue. Due to MCMC sampling in GCNs, it still suffers from a serious time-consuming issue when sampling steps increase; thus a trainable non-linear upsampling function and an amortized learning are proposed to improve the learning efficiency. Our proposed GGCN is trained in a symmetrical learning manner. Our method surpass the existing models in both model stability and the quality of generated samples on several widely-used face and natural image datasets.Comment: after the submission we found there is fatal error in this paper. From one respect we missed cite some important reference work, from another our pipeline of cooperative learning is totally wron