Bo Ren

Dalian Maritime University

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i-10 index

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Deqiang Jiang

Tencent (China)

Di Yin

Anhui Normal University

Ruizhi Qiao

Tencent (China)

Zhuoxuan Jiang

Shanghai Business School

Lingfeng Qiao

Harbin Institute of Technology

Lijie Cui

Air Force Engineering University

Cheng Wang

Huaqiao University

Ming‐Ming Cheng

Nankai University

Chen Wu

University of Science and Technology of China

Ye Liu

Nanjing University of Posts and Telecommunications

Cooperative Institutions

Tencent (China)

Chinese Academy of Sciences

Huazhong University of Science and Technology

Zhejiang University

University of Chinese Academy of Sciences

University of Science and Technology of China

University of Electronic Science and Technology of China

Tsinghua University

Shanghai Jiao Tong University

Peking University

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Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning

Zhuoxuan Jiang Lingfeng Qiao Di Yin Shanshan Feng Bo Ren

Headline

Duality (order theory)

10.18653/v1/2022.aacl-main.5

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Editorial: New advances in light oil/condensate geochemistry

Frontiers in Earth Science (2022)

Cheng Peng Xianming Xiao Bo Ren Tao Wen Yu Shuang

EDITORIAL article Front. Earth Sci., 05 December 2022Sec. Economic Geology Volume 10 - 2022 | https://doi.org/10.3389/feart.2022.1079834

10.3389/feart.2022.1079834

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OS-MSL: One Stage Multimodal Sequential Link Framework for Scene Segmentation and Classification

arXiv (Cornell University) (2022)

Ye Liu Lingfeng Qiao Di Yin Zhuoxuan Jiang Xinghua Jiang

Scene segmentation and classification (SSC) serve as a critical step towards the field of video structuring analysis. Intuitively, jointly learning of these two tasks can promote each other by sharing common information. However, scene segmentation concerns more on the local difference between adjacent shots while classification needs the global representation of scene segments, which probably leads to the model dominated by one of the two tasks in the training phase. In this paper, from an alternate perspective to overcome the above challenges, we unite these two tasks into one task by a new form of predicting shots link: a link connects two adjacent shots, indicating that they belong to the same scene or category. To the end, we propose a general One Stage Multimodal Sequential Link Framework (OS-MSL) to both distinguish and leverage the two-fold semantics by reforming the two learning tasks into a unified one. Furthermore, we tailor a specific module called DiffCorrNet to explicitly extract the information of differences and correlations among shots. Extensive experiments on a brand-new large scale dataset collected from real-world applications, and MovieScenes are conducted. Both the results demonstrate the effectiveness of our proposed method against strong baselines.

Leverage (statistics)

Link (geometry)

Structuring

Representation

Link analysis

10.48550/arxiv.2207.01241

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RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction

arXiv (Cornell University) (2022)

Liang Yuan Zhuoxuan Jiang Di Yin Bo Ren

In document-level event extraction (DEE) task, event arguments always scatter across sentences (across-sentence issue) and multiple events may lie in one document (multi-event issue). In this paper, we argue that the relation information of event arguments is of great significance for addressing the above two issues, and propose a new DEE framework which can model the relation dependencies, called Relation-augmented Document-level Event Extraction (ReDEE). More specifically, this framework features a novel and tailored transformer, named as Relation-augmented Attention Transformer (RAAT). RAAT is scalable to capture multi-scale and multi-amount argument relations. To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance. Extensive experiments demonstrate the effectiveness of the proposed method, which can achieve state-of-the-art performance on two public datasets. Our code is available at https://github. com/TencentYoutuResearch/RAAT.

Leverage (statistics)

Relationship extraction

10.48550/arxiv.2206.03377

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Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition

arXiv (Cornell University) (2022)

Helei Qiu Biao Hou Bo Ren Xiaohua Zhang

Capturing the dependencies between joints is critical in skeleton-based action recognition task. Transformer shows great potential to model the correlation of important joints. However, the existing Transformer-based methods cannot capture the correlation of different joints between frames, which the correlation is very useful since different body parts (such as the arms and legs in "long jump") between adjacent frames move together. Focus on this problem, A novel spatio-temporal tuples Transformer (STTFormer) method is proposed. The skeleton sequence is divided into several parts, and several consecutive frames contained in each part are encoded. And then a spatio-temporal tuples self-attention module is proposed to capture the relationship of different joints in consecutive frames. In addition, a feature aggregation module is introduced between non-adjacent frames to enhance the ability to distinguish similar actions. Compared with the state-of-the-art methods, our method achieves better performance on two large-scale datasets.

Action Recognition

10.48550/arxiv.2201.02849

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Citations (31)

Fair Value Measurement on the Basis of Economics

Journal of Social Science of Jiamusi University (2008)

Bo Ren

in the assets of the accounting measures used the fair value of properties is important manifestation of the system of accounting standards adjust to international practice.The use of fair value,reflecting the accounting measures to improve properties and has important theoretical and practical significance.This article attempts to balance the price of economics theory that the use of fair value measurement basis of the theory and its constraints,and then put the information on the development of the economics fair value,on the basis of policy recommendations.

Value (mathematics)

Historical cost

Value theory

Basis (linear algebra)

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MEMS Gyroscope Random Error Modeling and Filtering

Applied Mechanics and Materials (2010)

Bo Ren De Ming Zhang Huan Li

MEMS gyroscope is a new type of inertial device with small size, low cost, light weight, high reliability, but less precise and random error is relatively large. In this paper, from a practical engineering application point of view, first, the MEMS gyroscope random errors is real-time average filtered. Then, based on the basic principle of time series analysis of random sequence , the first-order AR model of MEMS gyroscope random errors is established. Finally, based on Markov characteristic of kalman filtering algorithm, each output of the MEMS gyroscope is multiple real-time filtered. Through the specific data processing, MEMS gyroscope random errors reduced to about two per cent of the original.

Rate integrating gyroscope

Random error

10.4028/www.scientific.net/amm.29-32.829

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Research on MEMS Gyroscope Random Drift Filtering

Journal of Shenyang Ligong University (2010)

Bo Ren

MEMS gyroscope is a new type of inertial sensor with small size,low cost,light weight,high reliability and so on.However,it is less precise with random errors.Aiming at application in practical engineering and dealing with MEMS gyroscope random errors,real-time average filtering is first processed,and then the first-order AR model of MEMS gyroscope random error is established on the basis of principles of time series analysis of random sequence.Finally,based an Markor properties of kalman filtering algorithm,a new method is proposed that each of the MEMS gyroscope output is multiple real-time filtered.Through the specific measurement data processing,MEMS gyroscope random errors are reduced to about two percent of the original one.

Rate integrating gyroscope

Allan variance

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A Novel Hybrid Approach for Water Resources Carrying Capacity Assessment by Integrating Fuzzy Comprehensive Evaluation and Analytical Hierarchy Process Methods with the Cloud Model

Water (2020)

Bo Ren Qiuwen Zhang Juanhui Ren Song Ye Fei Yan

The water resources carrying capacity (WRCC) shows remarkable fuzziness and randomness, which causes the uncertainty and instability of the WRCC assessment (WRCCA). In order to solve these problems, we proposed a novel hybrid approach for WRCCA, in which the fuzzy comprehensive evaluation (FCE) and analytical hierarchy process (AHP) methods were integrated with the cloud model (CM). Firstly, an evaluation indicator system of WRCC was constructed. Secondly, the AHP and FCE methods were subsequently improved with the CM. The CM was used to scale the relative importance and aggregate the judgment matrices, where the weights of the clouds were obtained. These integrations of AHP and CM greatly reduced the randomness in the weight calculation; the CM was used to describe the comment sets, calculate the membership degree matrices and determine the assignment clouds, the evaluation sets and the WRCCA index clouds were obtained. These integrations of FCE and CM effectively blurred the boundary fuzziness and gave more intuitive results. Finally, the hybrid FCE-AHP-CM approach was applied to a case study. It was concluded that the novel approach has particular advantages in dealing with the fuzziness and randomness comprehensively, and therefore could assess the WRCC and enhance the robustness and intuition of WRCCA results.

Intuition

Robustness

10.3390/w12113241

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Citations (23)

Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections

arXiv (Cornell University) (2023)

Jiaxiong Qiu Peng-Tao Jiang Yifan Zhu Ze-Xin Yin Ming‐Ming Cheng

Neural implicit methods have achieved high-quality 3D object surfaces under slight specular highlights. However, high specular reflections (HSR) often appear in front of target objects when we capture them through glasses. The complex ambiguity in these scenes violates the multi-view consistency, then makes it challenging for recent methods to reconstruct target objects correctly. To remedy this issue, we present a novel surface reconstruction framework, NeuS-HSR, based on implicit neural rendering. In NeuS-HSR, the object surface is parameterized as an implicit signed distance function (SDF). To reduce the interference of HSR, we propose decomposing the rendered image into two appearances: the target object and the auxiliary plane. We design a novel auxiliary plane module by combining physical assumptions and neural networks to generate the auxiliary plane appearance. Extensive experiments on synthetic and real-world datasets demonstrate that NeuS-HSR outperforms state-of-the-art approaches for accurate and robust target surface reconstruction against HSR. Code is available at https://github.com/JiaxiongQ/NeuS-HSR.

10.48550/arxiv.2304.08706

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