MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond

Neural radiance fields (NeRF) and its subsequent variants have led to remarkable progress in neural rendering. While most of recent neural rendering works focus on objects and small-scale scenes, developing neural rendering methods for city-scale scenes is of great potential in many real-world applications. However, this line of research is impeded by the absence of a comprehensive and high-quality dataset, yet collecting such a dataset over real city-scale scenes is costly, sensitive, and technically difficult. To this end, we build a large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches. Leveraging the Unreal Engine 5 City Sample project, we develop a pipeline to easily collect aerial and street city views, accompanied by ground-truth camera poses and a range of additional data modalities. Flexible controls over environmental factors like light, weather, human and car crowd are also available in our pipeline, supporting the need of various tasks covering city-scale neural rendering and beyond. The resulting pilot dataset, MatrixCity, contains 67k aerial images and 452k street images from two city maps of total size $28km^2$. On top of MatrixCity, a thorough benchmark is also conducted, which not only reveals unique challenges of the task of city-scale neural rendering, but also highlights potential improvements for future works. The dataset and code will be publicly available at our project page: https://city-super.github.io/matrixcity/.Comment: Accepted to ICCV 2023. Project page: $\href{https://city-super.github.io/matrixcity/}{this\, https\, URL}

DUSP6: Potential interactions with FXR1P in the nervous system

229-237Fragile X syndrome (FXS) is a leading genetic cause of autism intellectual disorder and autism spectrum disorder (ASD), with either limited treatment options or incurable. Fragile X-related gene 1 (FXR1) is a homolog of the Fragile X mental retardation gene 1 (FMR1), the causative gene of FXS, and both are highly homologous and functionally identical. In FXS, both PI3K (AKT/mTOR signaling pathway) and ERK1/2 (MAPK signaling pathway) expression levels were abnormal. Dual specificity phosphatase 6 (DUSP6) is a member of the mitogen-activated protein kinases (MAPKs) that participates in the crosstalk between the two signaling systems of MEK/ERK and mTOR. By interacting with multiple nodes of MAPK and PI3K/AKT signaling pathways (including the mTOR complex), DUSP6 regulates cellular growth, proliferation, metabolism and participates in pathological processes of cancer and cognitive impairment. However, whether there is an interaction between FXR1P and DUSP6 and the effects of DUSP6 on the growth of SK-N-SH cells remains elusive. As demonstrated by our results, FXR1P was identified in the cytoplasm and nucleus of SK-N-SH cells co-localized with DUSP6, which might have regulated ERK1/2 signaling pathways in SK-N-SH cells. To a certain extent, FXR1P may reverse the negative regulation of ERK1/2 by DUSP6. Moreover, we discovered that not only does DUSP6 inhibit proliferation, but it also promotes the apoptosis of SK-N-SH cells

Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling

Autonomous cars are indispensable when humans go further down the hands-free route. Although existing literature highlights that the acceptance of the autonomous car will increase if it drives in a human-like manner, sparse research offers the naturalistic experience from a passenger's seat perspective to examine the human likeness of current autonomous cars. The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars (driven by either human or AI drivers) as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. In contrast, when the human driver drove the car, the passengers' judgement was around chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin's field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers' humanness rating behaviour. We employed affective transition between pre-study baseline emotions and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers' ascription of humanness would increase with the greater affective transition. Our study suggested an important role of affective transition in passengers' ascription of humanness, which might become a future direction for autonomous driving.Comment: 16 pages, 9 figures, 3 table

Decrease of Plasma Platelet-Activating Factor Acetylhydrolase Activity in Lipopolysaccharide Induced Mongolian Gerbil Sepsis Model

Platelet-activating factor (PAF) plays an important role in the pathogenesis of sepsis, and the level of plasma PAF acetylhydrolase (pPAF-AH), which inactivates PAF, decreases in sepsis patients except for the sepsis caused by severe leptospirosis. Usually, increase of pPAF-AH activity was observed in lipopolysaccharide (LPS)-induced Syrian hamster and rat sepsis models, while contradictory effects were reported for mouse model in different studies. Here, we demonstrated the in vivo effects of LPS upon the change of pPAF-AH activity in C57BL/6 mice and Mongolian gerbils. After LPS-treatment, the clinical manifestations of Mongolian gerbil model were apparently similar to that of C57BL/6 mouse sepsis model. The pPAF-AH activity increased in C57BL/6 mice after LPS induction, but decreased in Mongolian gerbils, which was similar to that of the human sepsis. It thus suggests that among the LPS-induced rodent sepsis models, only Mongolian gerbil could be used for the study of pPAF-AH related to the pathogenesis of human sepsis. Proper application of this model might enable people to clarify the underline mechanism accounted for the contradictory results between the phase II and phase III clinical trials for the administration of recombinant human pPAF-AH in the sepsis therapy

Attentive Mask CLIP

Image token removal is an efficient augmentation strategy for reducing the cost of computing image features. However, this efficient augmentation strategy has been found to adversely affect the accuracy of CLIP-based training. We hypothesize that removing a large portion of image tokens may improperly discard the semantic content associated with a given text description, thus constituting an incorrect pairing target in CLIP training. To address this issue, we propose an attentive token removal approach for CLIP training, which retains tokens with a high semantic correlation to the text description. The correlation scores are computed in an online fashion using the EMA version of the visual encoder. Our experiments show that the proposed attentive masking approach performs better than the previous method of random token removal for CLIP training. The approach also makes it efficient to apply multiple augmentation views to the image, as well as introducing instance contrastive learning tasks between these views into the CLIP framework. Compared to other CLIP improvements that combine different pre-training targets such as SLIP and MaskCLIP, our method is not only more effective, but also much more efficient. Specifically, using ViT-B and YFCC-15M dataset, our approach achieves $43.9\%$ top-1 accuracy on ImageNet-1K zero-shot classification, as well as $62.7/42.1$ and $38.0/23.2$ I2T/T2I retrieval accuracy on Flickr30K and MS COCO, which are $+1.1\%$, $+5.5/+0.9$, and $+4.4/+1.3$ higher than the SLIP method, while being $2.30\times$ faster. An efficient version of our approach running $1.16\times$ faster than the plain CLIP model achieves significant gains of $+5.3\%$, $+11.3/+8.0$, and $+9.5/+4.9$ on these benchmarks