The Integrated Platform of Digital Cultural Heritage in China: a Proposed Model Based on Public’s Expectations

This poster attempts to propose an integrated platform of digital cultural heritage in China based on the public’s expectations and provide specific suggestions for policy makers. A questionnaire was designed and disseminated through online survey service website. From 6 October to November 2016, a total of 1,076 responses were collected. The data showed that the Chinese users expected a comprehensive, convenient, and unified one-stop online accessible portal to all types of digital cultural heritage from China. Based on user need analysis, an integrated platform model of digital cultural heritage has been proposed. Also the China’s digital cultural heritage integration management system has been proposed. In this system, the corporation between the Ministry of Culture and the State Archives Administration of China can be realized

Dynamical revival of phase coherence in a many-boson system

We study the quantum dynamics of cold Bose atoms in a double well. It is shown that self-trapping, as well as population oscillations are common phenomena associated to nonlinear interactions. For larger $U/t$, multi-particle tunneling is damped and the quantum dynamics is dominated by the single-particle tunneling. The many-body system can be effectively described in a truncated Fock space. It exhibits coherence-decoherence oscillations in the temporal evolution. We predict a novel phenomenon of dynamical revival and collapse of matter wave fields in optical lattices in regimes near the superfluid-Mott insulator phase boundary.Comment: 4 figure

Variable secondary porosity modeling of carbonate rocks based on μ-CT images

As an essential carbonate reservoir parameter, porosity is closely related to rock properties. Digital rock physics (DRP) technology can help us to build forward models and find out the relationship between porosity and physical properties. In order to prepare models for the rock physical simulations of carbonate rocks, digital rock models with different porosities and fractures are needed. Based on a three-dimensional carbonate digital rock image obtained by X-ray microtomography (μ-CT), we used erosion and dilation in mathematical morphology to modify the pores, and fractional Brownian motion model (FBM) to create fractures with different width and angles. The pores become larger after the erosion operation and become smaller after the dilation operation. Therefore, a series of models with different porosities are obtained. From the analysis of the rock models, we found out that the erosion operation is similar to the corrosion process in carbonate rocks. The dilation operation can be used to restore the matrix of the late stages. In both processes, the pore numbers decrease because of the pore surface area decreases. The porosity-permeability relation of the models is a power exponential function similar to the experimental results. The structuring element B’s radius can affect the operation results. The FBM fracturing method has been proved reliable in sandstones, and because it is based on mathematics, the usage of it can also be workable in carbonate rocks. We can also use the processes and workflows introduced in this paper in carbonate digital rocks reconstructed in other ways. The models we built in this research lay the foundation of the next step physical simulations

LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition

Self-supervised contrastive learning strategy has attracted remarkable attention due to its exceptional ability in representation learning. However, current contrastive learning tends to learn global coarse-grained representations of the image that benefit generic object recognition, whereas such coarse-grained features are insufficient for fine-grained visual recognition. In this paper, we present to incorporate the subtle local fine-grained feature learning into global self-supervised contrastive learning through a pure self-supervised global-local fine-grained contrastive learning framework. Specifically, a novel pretext task called Local Discrimination (LoDisc) is proposed to explicitly supervise self-supervised model's focus towards local pivotal regions which are captured by a simple-but-effective location-wise mask sampling strategy. We show that Local Discrimination pretext task can effectively enhance fine-grained clues in important local regions, and the global-local framework further refines the fine-grained feature representations of images. Extensive experimental results on different fine-grained object recognition tasks demonstrate that the proposed method can lead to a decent improvement in different evaluation settings. Meanwhile, the proposed method is also effective in general object recognition tasks.Comment: 11 pages, submitte

A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension

Pre-trained models have brought significant improvements to many NLP tasks and have been extensively analyzed. But little is known about the effect of fine-tuning on specific tasks. Intuitively, people may agree that a pre-trained model already learns semantic representations of words (e.g. synonyms are closer to each other) and fine-tuning further improves its capabilities which require more complicated reasoning (e.g. coreference resolution, entity boundary detection, etc). However, how to verify these arguments analytically and quantitatively is a challenging task and there are few works focus on this topic. In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e.g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task. Specifically, we identify five phenomena in MRC. According to pairwise probing tasks, we compare the performance of each layer's hidden representation of pre-trained and fine-tuned BERT. The proposed pairwise probe alleviates the problem of distraction from inaccurate model training and makes a robust and quantitative comparison. Our experimental analysis leads to highly confident conclusions: (1) Fine-tuning has little effect on the fundamental and low-level information and general semantic tasks. (2) For specific abilities required for downstream tasks, fine-tuned BERT is better than pre-trained BERT and such gaps are obvious after the fifth layer.Comment: e.g.: 4 pages, 1 figur

Measuring Significance of Community Structure in Complex Networks

Many complex systems can be represented as networks and separating a network into communities could simplify the functional analysis considerably. Recently, many approaches have been proposed for finding communities, but none of them can evaluate the communities found are significant or trivial definitely. In this paper, we propose an index to evaluate the significance of communities in networks. The index is based on comparing the similarity between the original community structure in network and the community structure of the network after perturbed, and is defined by integrating all the similarities. Many artificial networks and real-world networks are tested. The results show that the index is independent from the size of network and the number of communities. Moreover, we find the clear communities always exist in social networks, but don't find significative communities in proteins interaction networks and metabolic networks.Comment: 6 pages, 4 figures, 1 tabl

Multi-Sample Consensus Driven Unsupervised Normal Estimation for 3D Point Clouds

Deep normal estimators have made great strides on synthetic benchmarks. Unfortunately, their performance dramatically drops on the real scan data since they are supervised only on synthetic datasets. The point-wise annotation of ground truth normals is vulnerable to inefficiency and inaccuracies, which totally makes it impossible to build perfect real datasets for supervised deep learning. To overcome the challenge, we propose a multi-sample consensus paradigm for unsupervised normal estimation. The paradigm consists of multi-candidate sampling, candidate rejection, and mode determination. The latter two are driven by neighbor point consensus and candidate consensus respectively. Two primary implementations of the paradigm, MSUNE and MSUNE-Net, are proposed. MSUNE minimizes a candidate consensus loss in mode determination. As a robust optimization method, it outperforms the cutting-edge supervised deep learning methods on real data at the cost of longer runtime for sampling enough candidate normals for each query point. MSUNE-Net, the first unsupervised deep normal estimator as far as we know, significantly promotes the multi-sample consensus further. It transfers the three online stages of MSUNE to offline training. Thereby its inference time is 100 times faster. Besides that, more accurate inference is achieved, since the candidates of query points from similar patches can form a sufficiently large candidate set implicitly in MSUNE-Net. Comprehensive experiments demonstrate that the two proposed unsupervised methods are noticeably superior to some supervised deep normal estimators on the most common synthetic dataset. More importantly, they show better generalization ability and outperform all the SOTA conventional and deep methods on three real datasets: NYUV2, KITTI, and a dataset from PCV [1]

Influence of phacoemulsification on five sites of corneal endothelium of senile cataract after anti-glaucoma surgery

AIM: To study the influence of phacoemulsification on five sites of corneal endothelium of senile cataract in patients after anti-glaucoma surgery. METHODS: Patients with cataract after anti-glaucoma surgery were selected, and the surgery of phacoemulsification was performed by a same skilled surgeon, The superior, inferior, central, nasal, temporal endothelium cells were observed with a non-contact endothelium scope on pre-operation and seventh day, first month, third month and sixth month of post-operation. RESULTS: After operation, there were obvious differences of corneal endothelium of every sites between two groups(P<0.01). CONCLUSION: Endothelium cells of senile cataract in patients after anti-glaucoma surgery are easier to be damaged in the phacoemulsification, so preoperative evaluation, surgery manner and postoperative treatment are very important

Essential Role of MFG-E8 for Phagocytic Properties of Microglial Cells

Milk fat globule factor-E8 (MFG-E8) has been regarded as a key factor involved in the phagocytosis of apoptotic cells. We induced a lentivirus into the microglial cells for the augmentation or abrogation of MFG-E8 expression in mouse microglial cells, and investigated phagocytosis of phosphatidylserine tagged human red blood cells (hRBCs) in co-cultures. Increased MFG-E8 levels were associated with a significant increase in phagocytic activity compared to the controls. Conversely, phagocytosis dramitically decreased due to the abrogation of MFG-E8. In addition, the expression of the inflammatory cytokines, TNF-α and IL-1β, also increased or decreased in the microglial cells with the augmentation or abrogation of MFG-E8, respectively. Our findings indicate that the enhanced expression of MFG-E8 could increase phagocytosis of apoptotic cells; conversely, the rate of phagocytosis and the expression of inflammatory cytokines decreased when MFG-E8 expression was knocked down. Our results confirm that MFG-E8 plays an important role in phagocytosis, and possibly serves as an essential signal molecule for microglial cells