On the Role of Non-Localities in Fundamental Diagram Estimation

We consider the role of non-localities in speed-density data used to fit fundamental diagrams from vehicle trajectories. We demonstrate that the use of anticipated densities results in a clear classification of speed-density data into stationary and non-stationary points, namely, acceleration and deceleration regimes and their separating boundary. The separating boundary represents a locus of stationary traffic states, i.e., the fundamental diagram. To fit fundamental diagrams, we develop an enhanced cross entropy minimization method that honors equilibrium traffic physics. We illustrate the effectiveness of our proposed approach by comparing it with the traditional approach that uses local speed-density states and least squares estimation. Our experiments show that the separating boundary in our approach is invariant to varying trajectory samples within the same spatio-temporal region, providing further evidence that the separating boundary is indeed a locus of stationary traffic states

Exploring the Relationship between Architecture and Adversarially Robust Generalization

Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization gap on unseen testing adversaries, deemed as the adversarially robust generalization problem. Despite the preliminary understandings devoted to adversarially robust generalization, little is known from the architectural perspective. To bridge the gap, this paper for the first time systematically investigated the relationship between adversarially robust generalization and architectural design. Inparticular, we comprehensively evaluated 20 most representative adversarially trained architectures on ImageNette and CIFAR-10 datasets towards multiple `p-norm adversarial attacks. Based on the extensive experiments, we found that, under aligned settings, Vision Transformers (e.g., PVT, CoAtNet) often yield better adversarially robust generalization while CNNs tend to overfit on specific attacks and fail to generalize on multiple adversaries. To better understand the nature behind it, we conduct theoretical analysis via the lens of Rademacher complexity. We revealed the fact that the higher weight sparsity contributes significantly towards the better adversarially robust generalization of Transformers, which can be often achieved by the specially-designed attention blocks. We hope our paper could help to better understand the mechanism for designing robust DNNs. Our model weights can be found at http://robust.art

One-shot Implicit Animatable Avatars with Model-based Priors

Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic animatable 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can effortlessly estimate the body geometry and imagine full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pretrained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to generate text-conditioned unseen regions. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar. Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT has outperformed strong baseline methods of avatar creation when only a single image is available. The code is public for research purposes at https://huangyangyi.github.io/ELICIT/.Comment: To appear at ICCV 2023. Project website: https://huangyangyi.github.io/ELICIT

Single-cell RNA sequencing highlights the role of PVR/PVRL2 in the immunosuppressive tumour microenvironment in hepatocellular carcinoma

IntroductionThe conflict between cancer cells and the host immune system shapes the immune tumour microenvironment (TME) in hepatocellular carcinoma (HCC). A deep understanding of the heterogeneity and intercellular communication network in the TME of HCC will provide promising strategies to orchestrate the immune system to target and eradicate cancers.MethodsHere, we performed single-cell RNA sequencing (scRNA-seq) and computational analysis of 35786 unselected single cells from 3 human HCC tumour and 3 matched adjacent samples to elucidate the heterogeneity and intercellular communication network of the TME. The specific lysis of HCC cell lines was examined in vitro using cytotoxicity assays. Granzyme B concentration in supernatants of cytotoxicity assays was measured by ELISA.ResultsWe found that VCAN+ tumour-associated macrophages (TAMs) might undergo M2-like polarization and differentiate in the tumour region. Regulatory dendritic cells (DCs) exhibited immune regulatory and tolerogenic phenotypes in the TME. Furthermore, we observed intensive potential intercellular crosstalk among C1QC+ TAMs, regulatory DCs, regulator T (Treg) cells, and exhausted CD8+ T cells that fostered an immunosuppressive niche in the HCC TME. Moreover, we identified that the TIGIT-PVR/PVRL2 axis provides a prominent coinhibitory signal in the immunosuppressive TME. In vitro, antibody blockade of PVR or PVRL2 on HCC cell lines or TIGIT blockade on immune cells increased immune cell-mediated lysis of tumour cell. This enhanced immune response is paralleled by the increased secretion of Granzyme B by immune cells.DiscussionCollectively, our study revealed the functional state, clinical significance, and intercellular communication of immunosuppressive cells in HCC at single-cell resolution. Moreover, PVR/PVRL2, interact with TIGIT act as prominent coinhibitory signals and might represent a promising, efficacious immunotherapy strategy in HCC

COVID-19 in Japan: What could happen in the future? (Recent developments on inverse problems for partial differential equations and their applications)

This paper was finished in February, 2020 and posted in MedRxiv on Feb. 28th, 2020.COVID-19 has been impacting on the whole world critically and constantly Since December 2019. We have independently developed a novel statistical time delay dynamic model on the basis of the distribution models from CCDC. Based only on the numbers of confirmed cases in different regions in China, the model can clearly reveal that the containment of the epidemic highly depends on early and effective isolation. We apply the model on the epidemic in Japan and conclude that there could be a rapid outbreak in Japan if no effective quarantine measures are carried out immediately

The Hamilton neural network model: recognition of the color patterns

A 16-state Hamilton neural-network model is discussed. The storage capacity of the model is analyzed through theory and through a computer numerical simulation. The storage-capacity ratio of the presented model equals that of the Hopfield model. This 16-state neural network can be applied to the recognition of 16-level color patterns, and some examples are discussed

The computer simulation investigation of the Hamilton neural network model

University,Xiamen,Fujiang 361005,China) The Dirac symbol was used to represent the 16 level Hamilton discrete neural network.By using the computer simulation,the storage capacity and the error tolerance capacity of the model were studied and compared with the Hopfield model.The model was applied to recognize the 16 level gray or color patterns

Polymer Film Surface Fluctuation Dynamics in the Limit of Very Dense Branching

The surface fluctuation dynamics of melt films of densely branched comb polystyrene of thickness greater than 55 nm and at temperatures 23–58 °C above the bulk <i>T</i>_g can be rationalized using the hydrodynamic continuum theory (HCT) known to describe melts of unentangled linear and cyclic chains. Film viscosities (η_XPCS) inferred from fits of the HCT to X-ray photon correlation spectroscopy (XPCS) data are the same as those measured in bulk rheometry (η_bulk) for three combs. For the comb most like a star polymer and the comb closest to showing bulk entanglement behavior, η_XPCS > η_bulk. These discrepancies are much smaller than those seen for less densely branched polystyrenes. We conjecture that the smaller magnitude of η_XPCS – η_bulk for the densely grafted combs is due to a lack of interpenetration of the side chains when branching is most dense. Both <i>T</i>_g,bulk and the specific chain architecture play key roles in determining the surface fluctuations