Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks

Kavli Affiliate: Long Zhang | First 5 Authors: Jiayu Lin, Guanrong Chen, Bojun Jin, Chenyang Li, Shutong Jia | Summary: In this paper we present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023), and introduce the related datasets. We organize two tracks to handle the argumentative […]


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Matching-Driven Deep Reinforcement Learning for Energy-Efficient Transmission Parameter Allocation in Multi-Gateway LoRa Networks

Kavli Affiliate: Bo Gu | First 5 Authors: Ziqi Lin, Xu Zhang, Shimin Gong, Lanhua Li, Zhou Su | Summary: Long-range (LoRa) communication technology, distinguished by its low power consumption and long communication range, is widely used in the Internet of Things. Nevertheless, the LoRa MAC layer adopts pure ALOHA for medium access control, which […]


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Engineering Fractional Chern Insulators through Periodic Strain in Monolayer Graphene and Transition Metal Dichalcogenides

Kavli Affiliate: Zheng Zhu | First 5 Authors: Yuchen Liu, Zheng Zhu, , , | Summary: We propose the realization of interaction-driven insulators in periodically strained monolayer graphene and transition metal dichalcogenides (TMDs). By analyzing the tunable band structure and band geometry via strain, and performing extensive many-body exact diagonalization of a realistic model, we […]


Continue.. Engineering Fractional Chern Insulators through Periodic Strain in Monolayer Graphene and Transition Metal Dichalcogenides

Engineering Fractional Chern Insulators through Periodic Strain in Monolayer Graphene and Transition Metal Dichalcogenides

Kavli Affiliate: Zheng Zhu | First 5 Authors: Yuchen Liu, Zheng Zhu, , , | Summary: We propose the realization of interaction-driven insulators in periodically strained monolayer graphene and transition metal dichalcogenides (TMDs). By analyzing the tunable band structure and band geometry via strain, and performing extensive many-body exact diagonalization of a realistic model, we […]


Continue.. Engineering Fractional Chern Insulators through Periodic Strain in Monolayer Graphene and Transition Metal Dichalcogenides

Turning Generative Models Degenerate: The Power of Data Poisoning Attacks

Kavli Affiliate: Yi Zhou | First 5 Authors: Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou, Farhan Ahmed, Ling Cai | Summary: The increasing use of large language models (LLMs) trained by third parties raises significant security concerns. In particular, malicious actors can introduce backdoors through poisoning attacks to generate undesirable outputs. While such attacks have […]


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Motion and Structure from Event-based Normal Flow

Kavli Affiliate: Yi Zhou | First 5 Authors: Zhongyang Ren, Bangyan Liao, Delei Kong, Jinghang Li, Peidong Liu | Summary: Recovering the camera motion and scene geometry from visual data is a fundamental problem in the field of computer vision. Its success in standard vision is attributed to the maturity of feature extraction, data association […]


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OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models

Kavli Affiliate: Zheng Zhu | First 5 Authors: Zijian Zhou, Zheng Zhu, Holger Caesar, Miaojing Shi, | Summary: Panoptic Scene Graph Generation (PSG) aims to segment objects and recognize their relations, enabling the structured understanding of an image. Previous methods focus on predicting predefined object and relation categories, hence limiting their applications in the open […]


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Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates

Kavli Affiliate: Gang Su | First 5 Authors: Gang Su, Sun Yang, Zhishuai Li, , | Summary: The steam drum water level is a critical parameter that directly impacts the safety and efficiency of power plant operations. However, predicting the drum water level in boilers is challenging due to complex non-linear process dynamics originating from […]


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Event-Aided Time-to-Collision Estimation for Autonomous Driving

Kavli Affiliate: Yi Zhou | First 5 Authors: Jinghang Li, Bangyan Liao, Xiuyuan LU, Peidong Liu, Shaojie Shen | Summary: Predicting a potential collision with leading vehicles is an essential functionality of any autonomous/assisted driving system. One bottleneck of existing vision-based solutions is that their updating rate is limited to the frame rate of standard […]


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