Drawing From the Past

Drawing From the Past is a short, 3D animated film that portrays the different thoughts about drawing between two generations and is based on my personal experience. Some art students get less encouragement from their families than other students. In Asian traditional thinking, majoring in art was not the correct career direction, which is correct in that period， because of the economy. Majoring in art means students need to pay more money than other majors. In addition, art is not necessary for some people who worry about food. In the previous period, many people worry about their lives. That means it is hard for art students to find a job. However, with the development of the economy, society has started focusing on spiritual satisfaction. Art is a good method to achieve this goal. In this animation, a woman prevents her daughter from drawing based on the traditional education she got from her mother. The daughter insists on drawing, although her mother keeps stopping her. The effort of the girl reminds the woman of her childhood. The woman loved drawing, but her mother also forced her to learn medicine. At her graduation ceremony, she is the only one who is unhappy. Her daughter’s actions move her. This film discusses which is suitable for the current situation when traditional and modern thoughts disagree. The film ends with the mother agreeing with her daughter’s dream and supporting her. The conventional thought broke from the mother’s generation. The mother did not major in what she loves. Therefore, she wants her daughter tomajor in her favorite thing

Learning from Noisy Crowd Labels with Logics

This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd Labels (Logic-LNCL), an EM-alike iterative logic knowledge distillation framework that learns from both noisy labeled data and logic rules of interest. Unlike traditional EM methods, our framework contains a ``pseudo-E-step'' that distills from the logic rules a new type of learning target, which is then used in the ``pseudo-M-step'' for training the classifier. Extensive evaluations on two real-world datasets for text sentiment classification and named entity recognition demonstrate that the proposed framework improves the state-of-the-art and provides a new solution to learning from noisy crowd labels.Comment: 12 pages, 7 figures, accepted by ICDE-202

Statistical modeling of polarimetric SAR data: a survey and challenges

Knowledge of the exact statistical properties of the signal plays an important role in the applications of Polarimetric Synthetic Aperture Radar (PolSAR) data. In the last three decades, a considerable research effort has been devoted to finding accurate statistical models for PolSAR data, and a number of distributions have been proposed. In order to see the differences of various models and to make a comparison among them, a survey is provided in this paper. Texture models, which could capture the non-Gaussian behavior observed in high resolution data, and yet keep a compact mathematical form, are mainly explained. Probability density functions for the single look data and the multilook data are reviewed, as well as the advantages and applicable context of those models. As a summary, challenges in the area of statistical analysis of PolSAR data are also discussed.Peer ReviewedPostprint (published version

Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.</p

Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler

We propose a neuralized undirected graphical model called Neural-Hidden-CRF to solve the weakly-supervised sequence labeling problem. Under the umbrella of probabilistic undirected graph theory, the proposed Neural-Hidden-CRF embedded with a hidden CRF layer models the variables of word sequence, latent ground truth sequence, and weak label sequence with the global perspective that undirected graphical models particularly enjoy. In Neural-Hidden-CRF, we can capitalize on the powerful language model BERT or other deep models to provide rich contextual semantic knowledge to the latent ground truth sequence, and use the hidden CRF layer to capture the internal label dependencies. Neural-Hidden-CRF is conceptually simple and empirically powerful. It obtains new state-of-the-art results on one crowdsourcing benchmark and three weak-supervision benchmarks, including outperforming the recent advanced model CHMM by 2.80 F1 points and 2.23 F1 points in average generalization and inference performance, respectively.Comment: 13 pages, 4 figures, accepted by SIGKDD-202

Suboptimal Filtering of Networked Discrete-Time Systems with Random Observation Losses

This paper studies the remote filtering problem over a packet-dropping network. A general multiple-input-multiple-output (MIMO) discrete-time system is considered. The multiple measurements are sent over different communication channels every time step, and the packet loss phenomenon in every communication channel is described by an independent and identically distributed (i.i.d) Bernoulli process. A suboptimal filter is obtained which can minimize the mean squared estimation error. The convergence properties of the estimation error covariance are studied, and mean square stability of the suboptimal filter is proved under standard assumptions. A simulation example is exploited to demonstrate the effectiveness of the results

CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require an explicit view transformation between BEV and front view, which itself is still a challenging problem. In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step. Specifically, we formulate 3D lane detection as a curve propagation problem by using curve queries. A 3D lane query is represented by a dynamic and ordered anchor point set. In this way, queries with curve representation in Transformer decoder iteratively refine the 3D lane detection results. Moreover, a curve cross-attention module is introduced to compute the similarities between curve queries and image features. Additionally, a context sampling module that can capture more relative image features of a curve query is provided to further boost the 3D lane detection performance. We evaluate our method for 3D lane detection on both synthetic and real-world datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art approaches. The effectiveness of each component is validated via ablation studies as well

Probiotics treatment ameliorated mycophenolic acid-induced colitis by enhancing intestinal barrier function and improving intestinal microbiota dysbiosis in mice

BackgroundMycophenolic acid (MPA)-induced colitis was still a severe side effect and challenge faced by solid transplant recipients. We aimed to explore the function and mechanism of probiotics in the treatment of MPA-induced colitis.MethodsIn this study, 15 mice (C57BL/6) were randomly assigned into three groups: control (CNTL) group (n = 5), MPA group (n = 5) and the MPA + Probiotic group (n = 5). Bifid Triple Viable capsules, which were drugs for clinical use and consisted of Bifidobacterium longum, Lactobacillus acidophilus, and Enterococcus faecalis, were used in Probiotic group. Body weight change, stool scores, colon histopathology and morphology were used to evaluate the disease severity. The intestinal mucosal barrier function was assessed by measuring the expression level of secretory immunoglobulin A (sIgA), Zonula occludens-1 (ZO-1) and Occludin. Finally, 16S rDNA sequencing and bioinformatics analysis were performed on mice feces to compare the different intestinal microbial composition and diversity among three groups.ResultsCompared with the CNTL group, the mice in MPA group showed a significantly decreased body weight, increased stool scores, shortened colon length and severe colon inflammation. However, probiotics treated MPA mice reversed the disease severity, indicating that probiotics ameliorated MPA-induced colitis in mice. Mechanistically, probiotics improved the intestinal barrier function by up-regulating the expression of sIgA, ZO-1 and Occludin. Moreover, MPA-induced colitis led to intestinal microbiota dysbiosis, including the change of Firmicutes/Bacteroidetes ratio, α- and β-diversity. But probiotic treated group showed mild change in these microbial features. Additionally, we found that Clostridiales was the most significantly different microbiota flora in MPA group.ConclusionProbiotics treatment ameliorated MPA-induced colitis by enhancing intestinal barrier function and improving intestinal microbiota dysbiosis. Clostridiales might be the dominant functional intestinal microflora and serve as the potential therapy target in MPA-induced colitis