Search Results for author: Zhou Zhou

Found 24 papers, 6 papers with code

Differences between Two Maximal Principal Strain Rate Calculation Schemes in Traumatic Brain Analysis with in-vivo and in-silico Datasets

no code implementations12 Sep 2024 Xianghao Zhan, Zhou Zhou, Yuzhe Liu, Nicholas J. Cecchi, Marzieh Hajiahamemar, Michael M. Zeineh, Gerald A. Grant, David Camarillo

The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and the recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPSxSR, enhances the accuracy of TBI prediction.

Benchmarking Neural Decoding Backbones towards Enhanced On-edge iBCI Applications

no code implementations8 Jun 2024 Zhou Zhou, Guohang He, Zheng Zhang, Luziwei Leng, Qinghai Guo, Jianxing Liao, Xuan Song, Ran Cheng

We executed a series of neural decoding experiments involving nonhuman primates engaged in random reaching tasks, evaluating four prospective models, Gated Recurrent Unit (GRU), Transformer, Receptance Weighted Key Value (RWKV), and Selective State Space model (Mamba), across several metrics: single-session decoding, multi-session decoding, new session fine-tuning, inference speed, calibration speed, and scalability.

Benchmarking Mamba

Exposing Text-Image Inconsistency Using Diffusion Models

1 code implementation28 Apr 2024 Mingzhen Huang, Shan Jia, Zhou Zhou, Yan Ju, Jialing Cai, Siwei Lyu

In the battle against widespread online misinformation, a growing problem is text-image inconsistency, where images are misleadingly paired with texts with different intent or meaning.

Misinformation

AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics

1 code implementation14 Apr 2023 Shan Jia, Mingzhen Huang, Zhou Zhou, Yan Ju, Jialing Cai, Siwei Lyu

To achieve this, we propose a new approach that leverages the DALL-E2 language-image model to automatically generate and splice masked regions guided by a text prompt.

Image and Video Forgery Detection Image Generation

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

2 code implementations7 Nov 2022 Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He

While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.

Image Super-Resolution

Superhedging duality for multi-action options under model uncertainty with information delay

no code implementations29 Nov 2021 Anna Aksamit, Ivan Guo, Shidan Liu, Zhou Zhou

We consider the superhedging price of an exotic option under nondominated model uncertainty in discrete time in which the option buyer chooses some action from an (uncountable) action space at each time step.

RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection

no code implementations3 Oct 2021 Jiarui Xu, Zhou Zhou, Lianjun Li, Lizhong Zheng, Lingjia Liu

The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity.

Learning to Equalize OTFS

no code implementations17 Jul 2021 Zhou Zhou, Lingjia Liu, Jiarui Xu, Robert Calderbank

Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain.

Scheduling

Federated Dynamic Spectrum Access

no code implementations28 Jun 2021 Yifei Song, Hao-Hsuan Chang, Zhou Zhou, Shashank Jere, Lingjia Liu

In this article, we introduce a Federated Learning (FL) based framework for the task of DSA, where FL is a distributive machine learning framework that can reserve the privacy of network terminals under heterogeneous data distributions.

Federated Learning Multi-agent Reinforcement Learning

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

no code implementations9 Feb 2021 Xianghao Zhan, Yiheng Li, Yuzhe Liu, August G. Domel, Hossein Vahid Alizadeh, Zhou Zhou, Nicholas J. Cecchi, Samuel J. Raymond, Stephen Tiernan, Jesse Ruan, Saeed Barbat, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo

To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e. g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events.

Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing

no code implementations6 Feb 2021 Zhou Zhou, Kangjun Bai, Nima Mohammadi, Yang Yi, Lingjia Liu

This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems.

Harnessing Tensor Structures -- Multi-Mode Reservoir Computing and Its Application in Massive MIMO

no code implementations25 Jan 2021 Zhou Zhou, Lingjia Liu, Jiarui Xu

In this paper, we introduce a new neural network (NN) structure, multi-mode reservoir computing (Multi-Mode RC).

Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks

no code implementations2 Dec 2020 Zhou Zhou, Yan Xin, Hao Chen, Charlie Zhang, Lingjia Liu

In this paper, we consider jointly optimizing cell load balance and network throughput via a reinforcement learning (RL) approach, where inter-cell handover (i. e., user association assignment) and massive MIMO antenna tilting are configured as the RL policy to learn.

Reinforcement Learning (RL)

Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection

no code implementations1 Dec 2020 Zhou Zhou, Shashank Jere, Lizhong Zheng, Lingjia Liu

In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system.

Binary Classification

Learning for Integer-Constrained Optimization through Neural Networks with Limited Training

no code implementations NeurIPS Workshop LMCA 2020 Zhou Zhou, Shashank Jere, Lizhong Zheng, Lingjia Liu

In this paper, we investigate a neural network-based learning approach towards solving an integer-constrained programming problem using very limited training.

RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training

no code implementations15 Mar 2020 Zhou Zhou, Lingjia Liu, Shashank Jere, Jianzhong, Zhang, Yang Yi

In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC).

Quantization

Equilibrium concepts for time-inconsistent stopping problems in continuous time

no code implementations3 Sep 2019 Erhan Bayraktar, Jingjie Zhang, Zhou Zhou

A \emph{new} notion of equilibrium, which we call \emph{strong equilibrium}, is introduced for time-inconsistent stopping problems in continuous time.

IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation

1 code implementation24 Jul 2019 Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, Xiaofei He

In this work, we collect abundant relationships from common user behaviors and item information, and propose a novel framework named IntentGC to leverage both explicit preferences and heterogeneous relationships by graph convolutional networks.

Network Embedding

Optimal Bookmaking

no code implementations1 Jul 2019 Matthew Lorig, Zhou Zhou, Bin Zou

We introduce a general framework for continuous-time betting markets, in which a bookmaker can dynamically control the prices of bets on outcomes of random events.

Learning for Detection: MIMO-OFDM Symbol Detection through Downlink Pilots

no code implementations25 Jun 2019 Zhou Zhou, Lingjia Liu, Hao-Hsuan Chang

Reservoir computing (RC) is a special recurrent neural network which consists of a fixed high dimensional feature mapping and trained readout weights.

Decoder

Bayesian Compressive Sensing Using Normal Product Priors

no code implementations24 Aug 2017 Zhou Zhou, Kaihui Liu, Jun Fang

In this paper, we introduce a new sparsity-promoting prior, namely, the "normal product" prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework.

Compressive Sensing

Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems

1 code implementation12 Sep 2016 Zhou Zhou, Jun Fang, Linxiao Yang, Hongbin Li, Zhi Chen, Rick S. Blum

Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems.

Information Theory Information Theory

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