Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving

Aiming towards a holistic understanding of multiple downstream tasks simultaneously, there is a need for extracting features with better transferability. Though many latest self-supervised pre-training methods have achieved impressive performance on various vision tasks under the prevailing pretrain-finetune paradigm, their generalization capacity to multi-task learning scenarios is yet to be explored. In this paper, we extensively investigate the transfer performance of various types of self-supervised methods, e.g., MoCo and SimCLR, on three downstream tasks, including semantic segmentation, drivable area segmentation, and traffic object detection, on the large-scale driving dataset BDD100K. We surprisingly find that their performances are sub-optimal or even lag far behind the single-task baseline, which may be due to the distinctions of training objectives and architectural design lied in the pretrain-finetune paradigm. To overcome this dilemma as well as avoid redesigning the resource-intensive pre-training stage, we propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training, where the off-the-shelf pretrained models can be effectively adapted without increasing the training overhead. During the adapt stage, we utilize learnable multi-scale adapters to dynamically adjust the pretrained model weights supervised by multi-task objectives while leaving the pretrained knowledge untouched. Furthermore, we regard the vision-language pre-training model CLIP as a strong complement to the pretrain-adapt-finetune paradigm and propose a novel adapter named LV-Adapter, which incorporates language priors in the multi-task model via task-specific prompting and alignment between visual and textual features.Comment: Accepted at NeurIPS 202

Phycocyanin relieves myocardial ischemia-reperfusion injury in rats by inhibiting oxidative stress

Purpose: To investigate the effect of phycocyanin on myocardial ischemia-reperfusion injury, and the possible mechanisms involved. Methods: Twenty-four Sprague-Dawley (SD) rats were randomly divided into Sham group (only threading without ligation), IRI group (myocardial ischemia-reperfusion injury group) and phycocyanin group (phycocyanin pretreatment + myocardial ischemia-reperfusion injury group). The heart was harvested and cardiomyocytes were isolated. Colorimetry was used to determine the contents of cardiomyocyte serum creatine phospho-MB (CK-MB), lactate dehydrogenase (LDH) and malondialdehyde (MDA), and the activities of total antioxidant capacity (T-AOC), catalase (CAT), glutathione (GSH), total superoxide dismutase (SOD) and other related oxidative stress indicators. Furthermore, apoptosis was evaluated using TUNEL staining. Protein levels of cardiac factor E2 related factor 2 (Nrf2), heme oxygenase-1 (HO-1), human NADPH dehydrogenase 1 (NQO1) and nuclear factor-κB (NF-κB) were evaluated by Western blot and immunohistochemistry. Results: Compared with the myocardial IRI group, the contents of CK-MB, LDH, MAD and ROS in the treated group were significantly decreased (p < 0.05), but the activities of SOD, GSH, SOD, CAT, and T-AOC in the myocardial tissues were significantly enhanced (p < 0.05). Moreover, the pathological changes in myocardial tissue were significantly reduced. In addition, the expression levels of Nrf2, HO-1 and NQO-1 were significantly up-regulated after phycocyanin pretreatment, while expression of NF-κB was significantly down-regulated (p < 0.05). Conclusion: Phycocyanin improves myocardial anti-oxidative stress via activation of Nrf2 signaling pathway, and also protects rats from myocardial ischemia-reperfusion injury by reducing inflammatory response via inhibition of NF-κB signaling pathway

Multi-layered Semantic Representation Network for Multi-label Image Classification

Multi-label image classification (MLIC) is a fundamental and practical task, which aims to assign multiple possible labels to an image. In recent years, many deep convolutional neural network (CNN) based approaches have been proposed which model label correlations to discover semantics of labels and learn semantic representations of images. This paper advances this research direction by improving both the modeling of label correlations and the learning of semantic representations. On the one hand, besides the local semantics of each label, we propose to further explore global semantics shared by multiple labels. On the other hand, existing approaches mainly learn the semantic representations at the last convolutional layer of a CNN. But it has been noted that the image representations of different layers of CNN capture different levels or scales of features and have different discriminative abilities. We thus propose to learn semantic representations at multiple convolutional layers. To this end, this paper designs a Multi-layered Semantic Representation Network (MSRN) which discovers both local and global semantics of labels through modeling label correlations and utilizes the label semantics to guide the semantic representations learning at multiple layers through an attention mechanism. Extensive experiments on four benchmark datasets including VOC 2007, COCO, NUS-WIDE, and Apparel show a competitive performance of the proposed MSRN against state-of-the-art models

A Survey of Recent Trends in Chinese FDI in Denmark

China embarked on outward FDI (OFDI) with the advent of economic reforms in 1979 but it was not until 2000 that Chinese OFDI began to grow significantly. By that year, the Chinese government proposed a new national strategy, “Go global” and issued several policy incentives, including simplified procedures for government approval of investments, encouragement of state-owned banks to provide funding for OFDI, and tax incentives for OFDI (Yu & Jiao, 2011). As a result, FDI surged. In recent years, China has challenged the stereotype of being an exporter of cheap products by embarking on massive foreign direct investment (FDI) in both developing and developed countries. And the European Union (EU) is one of the most favored destinations (Hanemann & Huotari, 2015). Especially after 2008, Chinese FDI in EU started to increase rapidly partly fueled by the Eurozone debt crisis which, inter alia, led to depreciating asset prices in the EU (Ma & Overbeek, 2015)

MO-VLN: A Multi-Task Benchmark for Open-set Zero-Shot Vision-and-Language Navigation

Given a natural language, a general robot has to comprehend the instruction and find the target object or location based on visual observations even in unexplored environments. Most agents rely on massive diverse training data to achieve better generalization, which requires expensive labor. These agents often focus on common objects and fewer tasks, thus are not intelligent enough to handle different types of instructions. To facilitate research in open-set vision-and-language navigation, we propose a benchmark named MO-VLN, aiming at testing the effectiveness and generalization of the agent in the multi-task setting. First, we develop a 3D simulator rendered by realistic scenarios using Unreal Engine 5, containing more realistic lights and details. The simulator contains three scenes, i.e., cafe, restaurant, and nursing house, of high value in the industry. Besides, our simulator involves multiple uncommon objects, such as takeaway cup and medical adhesive tape, which are more complicated compared with existing environments. Inspired by the recent success of large language models (e.g., ChatGPT, Vicuna), we construct diverse high-quality data of instruction type without human annotation. Our benchmark MO-VLN provides four tasks: 1) goal-conditioned navigation given a specific object category (e.g., "fork"); 2) goal-conditioned navigation given simple instructions (e.g., "Search for and move towards a tennis ball"); 3) step-by-step instruction following; 4) finding abstract object based on high-level instruction (e.g., "I am thirsty").Comment: 18 page

Competition between DNA Methylation, Nucleotide Synthesis, and Antioxidation in Cancer versus Normal Tissues

Global DNA hypomethylation occurs in many cancer types, but there is no explanation for its differential occurrence or possible impact on cancer cell physiology. Here we address these issues with a computational study of genome-scale DNA methylation in 16 cancer types. Specifically, we identified (i) a possible determinant for global DNA methylation in cancer cells and (ii) a relationship between levels of DNA methylation, nucleotide synthesis, and intracellular oxidative stress in cells. We developed a system of kinetic equations to capture the metabolic relations among DNA methylation, nucleotide synthesis, and antioxidative stress response, including their competitions for methyl and sulfur groups, based on known information about one-carbon metabolism and trans-sulfuration pathways. We observed a kinetic-based regulatory mechanism that controls reaction rates of the three competing processes when their shared resources are limited, particularly when the nucleotide synthesis rates or oxidative states are high. The combination of this regulatory mechanism and the need for rapid nucleotide synthesis, as well as high production of glutathione dictated by cancer-driving forces, led to the nearly universal observations of reduced global DNA methylation in cancer. Our model provides a natural explanation for differential global DNA methylation levels across cancer types and supports the observation that more malignant cancers tend to exhibit reduced DNA methylation levels. Insights obtained from this work provide useful information about the complexities of cancer due to interplays among competing, dynamic biological processes

Perspectives and challenges of applying the water-food-energy nexus approach to lake eutrophication modelling

Embargo until August 4, 2023The water-food-energy (WFE) nexus is about balancing competing interests to secure the sustainability of services provided by interconnected sectors. Ignoring the interconnections could cause serious consequences. For example, eutrophication caused by overemphasizing on food production maximization could threaten water security. Worldwide eutrophication intensification is one of the most important causes of the lake water quality deteriorations. Water quality models are usually important decision making tools for policy makers. This study attempts to explore the possibilities of applying the WFE nexus concept into water quality models. We propose the most significant challenge is lack of a common modelling framework to streamline connections between up- and downstream models. As the most important water quality issue, eutrophication modeling should increase its visibility in the United Nations Sustainable Develop Goals.acceptedVersio

Genome-wide analysis of Dongxiang wild rice (Oryza rufipogon Griff.) to investigate lost/acquired genes during rice domestication

This file reports the functional annotation of 99,092 DXWR transcripts from the NCBI NR database using the software blast2go. This file is in the tab delimited format and can be opened using the software Excel. (TXT 12649 kb

Cell-scale hemolysis evaluation of intervenient ventricular assist device based on dissipative particle dynamics

Most of the existing hemolysis mechanism studies are carried out on the macro flow scale. They assume that the erythrocyte membranes with different loads will suffer the same damage, which obviously has limitations. Thus, exploring the hemolysis mechanism through the macroscopic flow field information is a tough challenge. In order to further understand the non-physiological shear hemolysis phenomenon at the cell scale, this study used the coarse-grained erythrocytes damage model at the mesoscopic scale based on the transport dissipative particle dynamics (tDPD) method. Combined with computational fluid dynamics the hemolysis of scalarized shear stress (τ) in the clearance of “Impella 5.0” was evaluated under the Lagrange perspective and Euler perspective. The results from the Lagrange perspective showed that the change rate of scaled shear stress (τ˙) was the most critical factor in damaging RBCs in the rotor region of “Impella 5.0”and other transvalvular micro-axial blood pumps. Then, we propose a dimensionless number Dk with time integration based on τ˙ to evaluate hemolysis. The Dissipative particle dynamics simulation results are consistent with the Dk evaluation results, so τ˙ may be an important factor in the hemolysis of VADs. Finally, we tested the hemolysis of 30% hematocrit whole blood in the “Impella 5.0” shroud clearance from the Euler perspective. Relevant results indicate that because of the wall effect, the RBCs near the impeller side are more prone to damage, and most of the cytoplasm is also gathered at the rotor side