Large-scale Weakly Supervised Learning for Road Extraction from Satellite Imagery

Automatic road extraction from satellite imagery using deep learning is a viable alternative to traditional manual mapping. Therefore it has received considerable attention recently. However, most of the existing methods are supervised and require pixel-level labeling, which is tedious and error-prone. To make matters worse, the earth has a diverse range of terrain, vegetation, and man-made objects. It is well known that models trained in one area generalize poorly to other areas. Various shooting conditions such as light and angel, as well as different image processing techniques further complicate the issue. It is impractical to develop training data to cover all image styles. This paper proposes to leverage OpenStreetMap road data as weak labels and large scale satellite imagery to pre-train semantic segmentation models. Our extensive experimental results show that the prediction accuracy increases with the amount of the weakly labeled data, as well as the road density in the areas chosen for training. Using as much as 100 times more data than the widely used DeepGlobe road dataset, our model with the D-LinkNet architecture and the ResNet-50 backbone exceeds the top performer of the current DeepGlobe leaderboard. Furthermore, due to large-scale pre-training, our model generalizes much better than those trained with only the curated datasets, implying great application potential

Suppressing electron disorder-induced heating of ultracold neutral plasma via optical lattice

Disorder-induced heating (DIH) prevents ultracold neutral plasma into electron strong coupling regime. Here we propose a scheme to suppress electronic DIH via optical lattice. We simulate the evolution dynamics of ultracold neutral plasma constrained by three-dimensional optical lattice using classical molecular dynamics method. The results show that for experimentally achievable condition, electronic DIH is suppressed by a factor of 1.3, and the Coulomb coupling strength can reach to 0.8 which is approaching the strong coupling regime. Suppressing electronic DIH via optical lattice may pave a way for the research of electronic strongly coupled plasma

Navigation as Attackers Wish? Towards Building Byzantine-Robust Embodied Agents under Federated Learning

Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training. However, since the local data is inaccessible to the server under federated learning, attackers may easily poison the training data of the local client to build a backdoor in the agent without notice. Deploying such an agent raises the risk of potential harm to humans, as the attackers may easily navigate and control the agent as they wish via the backdoor. Towards Byzantine-robust federated embodied agent learning, in this paper, we study the attack and defense for the task of vision-and-language navigation (VLN), where the agent is required to follow natural language instructions to navigate indoor environments. First, we introduce a simple but effective attack strategy, Navigation as Wish (NAW), in which the malicious client manipulates local trajectory data to implant a backdoor into the global model. Results on two VLN datasets (R2R and RxR) show that NAW can easily navigate the deployed VLN agent regardless of the language instruction, without affecting its performance on normal test sets. Then, we propose a new Prompt-Based Aggregation (PBA) to defend against the NAW attack in federated VLN, which provides the server with a ''prompt'' of the vision-and-language alignment variance between the benign and malicious clients so that they can be distinguished during training. We validate the effectiveness of the PBA method on protecting the global model from the NAW attack, which outperforms other state-of-the-art defense methods by a large margin in the defense metrics on R2R and RxR

Transcriptomic analysis of cell envelope inhibition by prodigiosin in methicillin-resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is a leading threat to public health as it is resistant to most currently available antibiotics. Prodigiosin is a secondary metabolite of microorganisms with broad-spectrum antibacterial activity. This study identified a significant antibacterial effect of prodigiosin against MRSA with a minimum inhibitory concentration as low as 2.5 mg/L. The results of scanning electron microscopy, crystal violet staining, and confocal laser scanning microscopy indicated that prodigiosin inhibited biofilm formation in S. aureus USA300, while also destroying the structure of the cell wall and cell membrane, which was confirmed by transmission electron microscopy. At a prodigiosin concentration of 1.25 mg/L, biofilm formation was inhibited by 76.24%, while 2.5 mg/L prodigiosin significantly reduced the vitality of MRSA cells in the biofilm. Furthermore, the transcriptomic results obtained at 1/8 MIC of prodigiosin indicated that 235and 387 genes of S. aureus USA300 were significantly up- and downregulated, respectively. The downregulated genes were related to two-component systems, including the transcriptional regulator LytS, quorum sensing histidine kinases SrrB, NreA and NreB, peptidoglycan biosynthesis enzymes (MurQ and GlmU), iron-sulfur cluster repair protein ScdA, microbial surface components recognizing adaptive matrix molecules, as well as the key arginine synthesis enzymes ArcC and ArgF. The upregulated genes were mainly related to cell wall biosynthesis, as well as two-component systems including vancomycin resistance-associated regulator, lipoteichoic acid biosynthesis related proteins DltD and DltB, as well as the 9 capsular polysaccharide biosynthesis proteins. This study elucidated the molecular mechanisms through which prodigiosin affects the cell envelope of MRSA from the perspectives of cell wall synthesis, cell membrane and biofilm formation, providing new potential targets for the development of antimicrobials for the treatment of MRSA

The M-T hook structure increases the potency of HIV-1 fusion inhibitor sifuvirtide and overcomes drug resistance

Objectives Peptides derived from the C-terminal heptad repeat (CHR) of HIV-1 gp41 are potent fusion inhibitors. We have recently demonstrated that the unique M-T hook structure preceding the pocket-binding motif of CHR peptide-based inhibitors can greatly improve their antiviral activity. In this study, we applied the M-T hook structure to optimize sifuvirtide (SFT), a potent CHR-derived inhibitor currently under Phase III clinical trials in China. Methods The peptide MT-SFT was generated by incorporating two M-T hook residues (Met-Thr) into the N-terminus of sifuvirtide. Multiple structural and functional approaches were used to determine the biophysical properties and antiviral activity of MT-SFT. Results The high-resolution crystal structure of MT-SFT reveals a highly conserved M-T hook conformation. Compared with sifuvirtide, MT-SFT exhibited a significant improvement in the ability to bind to the N-terminal heptad repeat, to block the formation of the six helix bundle and to inhibit HIV-1 Env-mediated cell fusion, viral entry and infection. Importantly, MT-SFT was fully active against sifuvirtide- and enfuvirtide (T20)-resistant HIV-1 variants and displayed a high genetic barrier to developing drug resistance. Conclusions Our studies have verified that the M-T hook structure offers a general strategy for designing novel HIV-1 fusion inhibitors and provide new insights into viral entry and inhibitio