Wandering intervals and absolutely continuous invariant probability measures of interval maps

For piecewise $C^1$ interval maps possibly containing critical points and discontinuities with negative Schwarzian derivative, under two summability conditions on the growth of the derivative and recurrence along critical orbits, we prove the nonexistence of wandering intervals, the existence of absolutely continuous invariant measures, and the bounded backward contraction property. The proofs are based on the method of proving the existence of absolutely continuous invariant measures of unimodal map, developed by Nowicki and van Strien.Comment: 16 pages, 2 figure

Mental Illness Campaigns

The target population of the research is people with mental diseases. It aims to study the symptoms of their illnesses and to understand the pain they encounter from the perspective of these patients. Based on that, this research combines with strong visual design and creative layout, presents art posters with impact and influence. The purpose of the research is to give mental patients recognition, encouragement and care to a certain extent through the art form of posters. This series consists of five posters, each poster describes the five most common mental illnesses in the United States, which are anxiety, obsessive-compulsive disorder, bipolar disorder, depression, and schizophrenia. This series of posters was exhibited at Imagine RIT in 2019, and was supported and affirmed by many academic groups and social personnel who came to visit. The same series of videos were also exhibited along with the poster. The video provided explanations for the poster while attracting more audiences. In this exhibition, a local psychological counseling agency issued invitations for cooperation, hoping to present the posters out of their respective clinics to help more patients. Moreover, many visitors also expressed their appreciation after understanding the concept behind the posters, and intuitively shared their experiences and feelings when fighting against mental illness

Revisiting Pre-Trained Models for Chinese Natural Language Processing

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models. In this paper, we target on revisiting Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pre-trained language model series to the community. We also propose a simple but effective model called MacBERT, which improves upon RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac). We carried out extensive experiments on eight Chinese NLP tasks to revisit the existing pre-trained language models as well as the proposed MacBERT. Experimental results show that MacBERT could achieve state-of-the-art performances on many NLP tasks, and we also ablate details with several findings that may help future research. Resources available: https://github.com/ymcui/MacBERTComment: 12 pages, to appear at Findings of EMNLP 202

CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation

The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims to track and segment multiple objects in video frames, has drawn much attention for its potential applications in various emerging areas such as autonomous driving, intelligent transportation, and smart retail. In this paper, we propose an effective framework for instance-level visual analysis on video frames, which can simultaneously conduct object detection, instance segmentation, and multi-object tracking. The core idea of our method is collaborative multi-task learning which is achieved by a novel structure, named associative connections among detection, segmentation, and tracking task heads in an end-to-end learnable CNN. These additional connections allow information propagation across multiple related tasks, so as to benefit these tasks simultaneously. We evaluate the proposed method extensively on KITTI MOTS and MOTS Challenge datasets and obtain quite encouraging results