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Agent-based simulations of China inbound tourism network
[article]
2019
arXiv
pre-print
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Based on the results of a large-scale survey, we construct an agent-based network model for the independent inbound tourism of China and, by the approach of numerical simulation, investigate the dynamical responses of the tourist flows to external perturbations in different scenarios, including the closure of a tourist city, the opening of a new port in western China, and the increase of the tourism attractiveness of a specific city. Numerical results show that: (1) the closure of a single city
arXiv:1901.00080v1
fatcat:by3xkybr3rc4dh6vhv3kpwdtuu
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... in general will affect the tourist visitations of many other cities in the network and, comparing to the non-port cities, the overall visitation volume of the system is more influenced by closing a port city; (2) the opening of a new port city in western China will attract more tourists to the western cities, but has a negligible impact on either the overall visitation volume or the imbalanced tourist distribution; and (3) the increase of the tourism attractiveness of a non-port (port) city normally increases (decreases) the overall visitation volume, yet there are exceptions due to the spillover effect. Furthermore, by increasing the tourism attractiveness of a few cities simultaneously, we investigate also the strategy of multiple-city-upgrade in tourism development. Numerical results show that the overall tourist volume is better improved by upgrading important non-port cities that are geographically distant from each other. The study reveals the rich dynamics inherent in complex tourism network, and the findings could be helpful to the development and management of China inbound tourism.
Self-Supervised Scene De-occlusion
[article]
2020
arXiv
pre-print
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Natural scene understanding is a challenging task, particularly when encountering images of multiple objects that are partially occluded. This obstacle is given rise by varying object ordering and positioning. Existing scene understanding paradigms are able to parse only the visible parts, resulting in incomplete and unstructured scene interpretation. In this paper, we investigate the problem of scene de-occlusion, which aims to recover the underlying occlusion ordering and complete the
arXiv:2004.02788v1
fatcat:ppgf2zadlfegxfn23mhbqupfk4
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... e parts of occluded objects. We make the first attempt to address the problem through a novel and unified framework that recovers hidden scene structures without ordering and amodal annotations as supervisions. This is achieved via Partial Completion Network (PCNet)-mask (M) and -content (C), that learn to recover fractions of object masks and contents, respectively, in a self-supervised manner. Based on PCNet-M and PCNet-C, we devise a novel inference scheme to accomplish scene de-occlusion, via progressive ordering recovery, amodal completion and content completion. Extensive experiments on real-world scenes demonstrate the superior performance of our approach to other alternatives. Remarkably, our approach that is trained in a self-supervised manner achieves comparable results to fully-supervised methods. The proposed scene de-occlusion framework benefits many applications, including high-quality and controllable image manipulation and scene recomposition (see Fig. 1), as well as the conversion of existing modal mask annotations to amodal mask annotations.
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis
[article]
2021
arXiv
pre-print
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The advent of generative radiance fields has significantly promoted the development of 3D-aware image synthesis. The cumulative rendering process in radiance fields makes training these generative models much easier since gradients are distributed over the entire volume, but leads to diffused object surfaces. In the meantime, compared to radiance fields occupancy representations could inherently ensure deterministic surfaces. However, if we directly apply occupancy representations to generative
arXiv:2111.00969v1
fatcat:m2djb4q42bg75dveup27amusoa
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... models, during training they will only receive sparse gradients located on object surfaces and eventually suffer from the convergence problem. In this paper, we propose Generative Occupancy Fields (GOF), a novel model based on generative radiance fields that can learn compact object surfaces without impeding its training convergence. The key insight of GOF is a dedicated transition from the cumulative rendering in radiance fields to rendering with only the surface points as the learned surface gets more and more accurate. In this way, GOF combines the merits of two representations in a unified framework. In practice, the training-time transition of start from radiance fields and march to occupancy representations is achieved in GOF by gradually shrinking the sampling region in its rendering process from the entire volume to a minimal neighboring region around the surface. Through comprehensive experiments on multiple datasets, we demonstrate that GOF can synthesize high-quality images with 3D consistency and simultaneously learn compact and smooth object surfaces. Code, models, and demo videos are available at https://sheldontsui.github.io/projects/GOF
Channel Equilibrium Networks for Learning Deep Representation
[article]
2020
arXiv
pre-print
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., 2018; 2019; Pan et al., 2019) have investigated decorrelation (whitening) methods by using the covariance matrix, all of them are applied in the normalization layer. ...
Many normalization approaches also use decorrelation operation such as switchable whitening (SW) (Pan et al., 2019) and IterNorm (Huang et al., 2019) to stabilize the course of training . ...
arXiv:2003.00214v1
fatcat:tfrfhunicffl5bvg4ur35knkzm
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
[article]
2017
arXiv
pre-print
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Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored. These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, such as traffic lanes, which are often occluded or not even painted on the road
arXiv:1712.06080v1
fatcat:lrtvpp7nnfbmhn2ixc42b5wezi
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... urface as shown in Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes traditional deep layer-by-layer convolutions to slice-byslice convolutions within feature maps, thus enabling message passings between pixels across rows and columns in a layer. Such SCNN is particular suitable for long continuous shape structure or large objects, with strong spatial relationship but less appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a newly released very challenging traffic lane detection dataset and Cityscapse dataset. The results show that SCNN could learn the spatial relationship for structure output and significantly improves the performance. We show that SCNN outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet) in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an accuracy of 96.53%.
Talk-to-Edit: Fine-Grained Facial Editing via Dialog
[article]
2021
arXiv
pre-print
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The image is firstly inversed by the inversion method proposed by Pan et al. [36] . ...
enforce the field vector to be a valid vector that would not make the edited latent code fall into the outlier region of pretrained StyleGAN latent space, we adopt a regularization method proposed by Pan ...
arXiv:2109.04425v1
fatcat:76a7pompazdadbvt3bv4575mem
Comparative review of respiratory diseases caused by coronaviruses and influenza A viruses during epidemic season
2020
Microbes and infection
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The year in the brackets 351 indicates that the time of the virus was reported first. 352 References 354 [1] Pan X, Ojcius DM, Gao T, Li Z, Pan C, Pan C. ...
Virol J 2015;12:221. 379 [13] Yu F, Du L, Ojcius DM, Pan C, Jiang S. ...
doi:10.1016/j.micinf.2020.05.005
pmid:32405236
pmcid:PMC7217786
fatcat:opz2hfsqy5acflg4psmpo32ebu
Open Compound Domain Adaptation
[article]
2020
arXiv
pre-print
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A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a target domain (e.g., rainy weather). Whether the target contains a single homogeneous domain or multiple heterogeneous domains, existing works always assume that there exist clear distinctions between the domains, which is often not true in practice (e.g., changes in weather). We study an open compound domain
arXiv:1909.03403v2
fatcat:ax627aerszhxnk2fwziugjwdjy
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... n (OCDA) problem, in which the target is a compound of multiple homogeneous domains without domain labels, reflecting realistic data collection from mixed and novel situations. We propose a new approach based on two technical insights into OCDA: 1) a curriculum domain adaptation strategy to bootstrap generalization across domains in a data-driven self-organizing fashion and 2) a memory module to increase the model's agility towards novel domains. Our experiments on digit classification, facial expression recognition, semantic segmentation, and reinforcement learning demonstrate the effectiveness of our approach.
Unexpected custodial death due to acute epiglottitis
2018
Medicine
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Rationale: Acute epiglottitis is a potentially life-threaten disease, which makes it more challenging to save the life for doctors. Unexpected deaths in custody are a primary cause of concern for the forensic community and doctor worldwide. Patient concerns: We present a case of a 44-year-old male detainee who was clinically suspected of dying of acute epiglottitis. The man experienced failure of resuscitation and died after admitted to a hospital. Diagnoses: The autopsy, toxicological testing,
doi:10.1097/md.0000000000009941
pmid:29443781
pmcid:PMC5839830
fatcat:zauzj3xrrfhjxbdweanx2yqoyq
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... the test of immunoglobulin E and bacterial culture suggested the patient died of acute epiglottitis. Interventions: The bacterial culture was performed to imprecisely identify the cause of death. Outcomes: The bacterial culture of the patient's heart blood and nasal and throat swabs showed the presence of the pathogenic microorganism Haemophilus influenza type B. Lessons: We aim to provide a reference to the medical and forensic community and remind the local law enforcement agencies on the problems present within the correctional healthcare system through this case report. Additionally, we also aim to increase the current knowledge and understanding on custodial deaths caused by natural diseases. Abbreviations: CT = computed tomography, IgE = immunoglobulin E.
Nickel-Catalyzed Cross-Coupling ofgem-Difluoropropargyl Bromide with Aryl Boronic Acids
2015
Huaxue xuebao
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The gem-difluoropropargylated arenes play an important role in life and material sciences owning to the unique properties of the difluoromethylene group (CF 2 ). The traditional method to access such a kind of fluorinated structure relies on conversion of carbonyl group with aminosulfurtrifluorides, such as DAST and Deoxofluor. However, these reactions suffer from the use of expensive and toxic fluorinated reagents and the important functional group incompatibility. Hence, it is highly
doi:10.6023/a15010042
fatcat:rhlvaq5i2ffr3bn5iwxikprb5q
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... to develop new and efficient strategies and methods to prepare gem-difluoropropargylated arenes. As part of our ongoing interest in transition-metal-catalyzed difluoroalkylation reactions, herein, we report a nickel-catalyzed cross-coupling of gem-difluoropropargyl bromide with aryl boronic acids. The reaction uses low-cost Ni-catalyst and proceeds under mild reaction conditions with high efficiency and good functional group compatibility. It is also possible for gram-scale reaction and late stage gem-difluoropropargylation of bioactive natural product, thus providing a facile route for application in drug discovery and development. A representative procedure for nickel-catalyzed cross-coupling of gem-difluoropropargyl bromide with aryl boronic acids is as following: Phenylboronic acid 1a (1.5 equiv.), Ni(NO 3 ) 2 •6H 2 O or NiCl 2 •dppe (2.5 mol%), bpy (2.5 mol%), and K 2 CO 3 (2.0 equiv.) were subsequently added to a 25 mL of Schlenck tube. The resulting mixture was then evacuated and backfilled with Ar (3 times). gem-Difluoropropargyl bromide 2 (0.6 mmol, 1.0 equiv.) and 1,4-dioxane (4 mL) were then added. The Schlenck tube was screw capped and put into a preheated oil bath (80 ℃). After stirring for 24 h, the reaction mixture was cooled to room temperature. The yield was determined by 19 F NMR before working up. If necessary, the reaction mixture was diluted with EtOAc and filtered with a pad of cellite. The filtrate was concentrated, and the residue was purified with silica gel chromatography (100% Petroleum ether) to give product 3a.
CityNeRF: Building NeRF at City Scale
[article]
2021
arXiv
pre-print
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Neural Radiance Field (NeRF) has achieved outstanding performance in modeling 3D objects and controlled scenes, usually under a single scale. In this work, we make the first attempt to bring NeRF to city-scale, with views ranging from satellite-level that captures the overview of a city, to ground-level imagery showing complex details of an architecture. The wide span of camera distance to the scene yields multi-scale data with different levels of detail and spatial coverage, which casts great
arXiv:2112.05504v2
fatcat:lclx2xuyjzdfpemyjc7g37tlhe
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... hallenges to vanilla NeRF and biases it towards compromised results. To address these issues, we introduce CityNeRF, a progressive learning paradigm that grows the NeRF model and training set synchronously. Starting from fitting distant views with a shallow base block, as training progresses, new blocks are appended to accommodate the emerging details in the increasingly closer views. The strategy effectively activates high-frequency channels in the positional encoding and unfolds more complex details as the training proceeds. We demonstrate the superiority of CityNeRF in modeling diverse city-scale scenes with drastically varying views, and its support for rendering views in different levels of detail.
Functions, Roles, and Biological Processes of Ferroptosis-Related Genes in Renal Cancer: A Pan-Renal Cancer Analysis
2022
Frontiers in Oncology
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In this study, a pan-renal cancer analysis of ferroptosis-associated genes in renal cancer was performed to construct a multigene joint signature for predicting prognosis in renal cancer patients. ...
Finally, a pan-renal cancer analysis was combined to evaluate the prognostic significance of each gene signature in different renal cancer subtypes. ...
The pan-cancer expression profiles of SLC7A11, HMOX1, and MT1G were subsequently analyzed by integrating tumor samples in TCGA with normal samples in GTEx using the rank-sum test. ...
doi:10.3389/fonc.2021.697697
pmid:35360452
pmcid:PMC8962645
fatcat:43bic2gotvagllsejibe32w5rq
RCC Immune Microenvironment Subsequent to Targeted Therapy: A Friend or a Foe?
2020
Frontiers in Oncology
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Among the pan-carcinomas, RCC ranks one of the tumors with the highest degree of immune infiltration (7) . ...
Copyright © 2020 Chen, Pan and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). ...
doi:10.3389/fonc.2020.573690
pmid:33117708
pmcid:PMC7561377
fatcat:s62exdat45cy5bn6eshvh7dkwa
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
[chapter]
2018
Lecture Notes in Computer Science
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Convolutional neural networks (CNNs) have achieved great successes in many computer vision problems. Unlike existing works that designed CNN architectures to improve performance on a single task of a single domain and not generalizable, we present IBN-Net, a novel convolutional architecture, which remarkably enhances a CNN's modeling ability on one domain (e.g. Cityscapes) as well as its generalization capacity on another domain (e.g. GTA5) without finetuning. IBN-Net carefully integrates
doi:10.1007/978-3-030-01225-0_29
fatcat:3sukxuzgjjbmfioafptubahpwy
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... ce Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances. This work has three key contributions. (1) By delving into IN and BN, we disclose that IN learns features that are invariant to appearance changes, such as colors, styles, and virtuality/reality, while BN is essential for preserving content related information. (2) IBN-Net can be applied to many advanced deep architectures, such as DenseNet, ResNet, ResNeXt, and SENet, and consistently improve their performance without increasing computational cost. 1 (3) When applying the trained networks to new domains, e.g. from GTA5 to Cityscapes, IBN-Net achieves comparable improvements as domain adaptation methods, even without using data from the target domain. With IBN-Net, we won the 1st place on the WAD 2018 Challenge Drivable Area track, with an mIoU of 86.18%.
Meteorological rhythms of respiratory and circulatory diseases revealed by Harmonic Analysis
2020
Heliyon
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Xingang Fan, Siyi Wang: Analyzed and interpreted the data; Wrote the paper. Jin Fan: Performed the experiments; Wrote the paper. ...
doi:10.1016/j.heliyon.2020.e04034
pmid:32509988
pmcid:PMC7264065
fatcat:zq3jfzuw6jfshkvxqpcr3ofhje
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