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479 results on '"Xu, Chenyu"'

4. Mapping effective connectivity by virtually perturbing a surrogate brain

Author

Luo, Zixiang, Peng, Kaining, Liang, Zhichao, Cai, Shengyuan, Xu, Chenyu, Li, Dan, Hu, Yu, Zhou, Changsong, and Liu, Quanying

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approaches, which infer EC from neural responses to stimulations, are not suited for mapping whole-brain EC in human due to being invasive and limited spatial coverage of stimulations. To address this gap, we present Neural Perturbational Inference (NPI), a data-driven framework designed to map EC across the entire brain. NPI employs an artificial neural network trained to learn large-scale neural dynamics as a computational surrogate of the brain. NPI maps EC by perturbing each region of the surrogate brain and observing the resulting responses in the rest of regions. NPI captures the directionality, strength, and excitatory/inhibitory properties of EC on a brain-wide scale. Our validation of NPI, using models with established EC, shows its superiority over Granger Causality and Dynamic Causal Modeling. Applying NPI to resting-state fMRI data from diverse datasets reveals consistent and structurally supported EC. Applications on a disease-specific dataset highlight the potential of using personalized EC as biomarkers for neurological diseases. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain's functional architecture and can facilitate neuroscience research and clinical applications.

Published
2022

5. MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis

Author

Zhang, Haodi, Xu, Chenyu, Liang, Peirou, Duan, Ke, Ren, Hao, Cheng, Weibin, and Wu, Kaishun

Subjects
Computer Science - Machine Learning, Computer Science - Symbolic Computation
Abstract

Recent studies show that deep learning models achieve good performance on medical imaging tasks such as diagnosis prediction. Among the models, multimodality has been an emerging trend, integrating different forms of data such as chest X-ray (CXR) images and electronic medical records (EMRs). However, most existing methods incorporate them in a model-free manner, which lacks theoretical support and ignores the intrinsic relations between different data sources. To address this problem, we propose a knowledge-driven and data-driven framework for lung disease diagnosis. By incorporating domain knowledge, machine learning models can reduce the dependence on labeled data and improve interpretability. We formulate diagnosis rules according to authoritative clinical medicine guidelines and learn the weights of rules from text data. Finally, a multimodal fusion consisting of text and image data is designed to infer the marginal probability of lung disease. We conduct experiments on a real-world dataset collected from a hospital. The results show that the proposed method outperforms the state-of-the-art multimodal baselines in terms of accuracy and interpretability.

Published
2022

9. First-Generation Inference Accelerator Deployment at Facebook

Author

Anderson, Michael, Chen, Benny, Chen, Stephen, Deng, Summer, Fix, Jordan, Gschwind, Michael, Kalaiah, Aravind, Kim, Changkyu, Lee, Jaewon, Liang, Jason, Liu, Haixin, Lu, Yinghai, Montgomery, Jack, Moorthy, Arun, Nadathur, Satish, Naghshineh, Sam, Nayak, Avinash, Park, Jongsoo, Petersen, Chris, Schatz, Martin, Sundaram, Narayanan, Tang, Bangsheng, Tang, Peter, Yang, Amy, Yu, Jiecao, Yuen, Hector, Zhang, Ying, Anbudurai, Aravind, Balan, Vandana, Bojja, Harsha, Boyd, Joe, Breitbach, Matthew, Caldato, Claudio, Calvo, Anna, Catron, Garret, Chandwani, Sneh, Christeas, Panos, Cottel, Brad, Coutinho, Brian, Dalli, Arun, Dhanotia, Abhishek, Duncan, Oniel, Dzhabarov, Roman, Elmir, Simon, Fu, Chunli, Fu, Wenyin, Fulthorp, Michael, Gangidi, Adi, Gibson, Nick, Gordon, Sean, Hernandez, Beatriz Padilla, Ho, Daniel, Huang, Yu-Cheng, Johansson, Olof, Juluri, Shishir, Kanaujia, Shobhit, Kesarkar, Manali, Killinger, Jonathan, Kim, Ben, Kulkarni, Rohan, Lele, Meghan, Li, Huayu, Li, Huamin, Li, Yueming, Liu, Cynthia, Liu, Jerry, Maher, Bert, Mallipedi, Chandra, Mangla, Seema, Matam, Kiran Kumar, Mehta, Jubin, Mehta, Shobhit, Mitchell, Christopher, Muthiah, Bharath, Nagarkatte, Nitin, Narasimha, Ashwin, Nguyen, Bernard, Ortiz, Thiara, Padmanabha, Soumya, Pan, Deng, Poojary, Ashwin, Ye, Qi, Raginel, Olivier, Rajagopal, Dwarak, Rice, Tristan, Ross, Craig, Rotem, Nadav, Russ, Scott, Shah, Kushal, Shan, Baohua, Shen, Hao, Shetty, Pavan, Skandakumaran, Krish, Srinivasan, Kutta, Sumbaly, Roshan, Tauberg, Michael, Tzur, Mor, Verma, Sidharth, Wang, Hao, Wang, Man, Wei, Ben, Xia, Alex, Xu, Chenyu, Yang, Martin, Zhang, Kai, Zhang, Ruoxi, Zhao, Ming, Zhao, Whitney, Zhu, Rui, Mathews, Ajit, Qiao, Lin, Smelyanskiy, Misha, Jia, Bill, and Rao, Vijay

Subjects
Computer Science - Hardware Architecture
Abstract

In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesses, large model sizes, as well as high compute, memory and network bandwidth requirements. We co-designed a high-performance, energy-efficient inference accelerator platform based on these requirements. We describe the inference accelerator platform ecosystem we developed and deployed at Facebook: both hardware, through Open Compute Platform (OCP), and software framework and tooling, through Pytorch/Caffe2/Glow. A characteristic of this ecosystem from the start is its openness to enable a variety of AI accelerators from different vendors. This platform, with six low-power accelerator cards alongside a single-socket host CPU, allows us to serve models of high complexity that cannot be easily or efficiently run on CPUs. We describe various performance optimizations, at both platform and accelerator level, which enables this platform to serve production traffic at Facebook. We also share deployment challenges, lessons learned during performance optimization, as well as provide guidance for future inference hardware co-design.

Published
2021

44. Unraveling active sites regulation and temperature-dependent thermodynamic mechanism in photothermocatalytic CO2 conversion with H2O.

Author

Zhang, Li, Li, Changqi, Liu, Yan, Xu, Chenyu, and Zhang, Yanwei

Subjects
TRANSITION metal oxides, METAL clusters, CATALYST supports, TRANSITION metals, TEMPERATURE effect, CHEMICAL reactions
Abstract

In the photothermal synergistic catalytic conversion of CO2 and H2O, the catalyst harnesses solar energy to accumulate heat, thereby elevating the reaction system's temperature. The influence of this temperature effect on surface chemical reactions remains an underexplored area. Here the impact of temperature on the surface-level thermodynamic reactions and conversion of CO2 with H2O on oxide semiconductors at the atomic scale was investigated using first-principle calculations. 13 different metal oxides and 5 transition metal clusters were used to introduce surface functional sites on the TiO2 supporting catalyst. The potential metal oxide cocatalysts that could be most beneficial to the following conversion of CO2 by H2O were initially screened by calculating the degrees of promotion of CO2 adsorption and activation of surface H to provide protons. The proton donation and hydrogen evolution difficulty from H2O were further analyzed, identifying transition metal cocatalysts that promote direct CO2 hydrogenation. Upon introducing bifunctional sites to facilitate adsorption and reduction, the production of CH3OH and CH4 could be further enhanced through the facilitation of the proton donation process of H2O. The results of Gibbs free-energy calculations revealed that increasing temperature enhances the reaction thermodynamics for each C1 product formation at different surface sites to varying degrees. These findings offer valuable theoretical insights for designing and regulating active sites on oxide semiconductor surfaces for efficient photothermal catalytic CO2 reduction by H2O. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Nicotine induced ototoxicity in rat cochlear organotypic cultures

Author

Zhao Yi, Liang Yue, Pan Chunchen, Tang Xiaomin, Sun Yuxuan, Xu Chenyu, Sun Jiaqiang, and Sun Jingwu

Subjects
nicotine, cochlea, hair cells, ototoxicity, organotypic culture, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Epidemiological evidence has shown that smoking is associated with an increased risk of hearing loss. However, the underlying mechanisms regarding the impact of nicotine on the cochlea remain unclear. This study aimed to investigate the cytotoxic effects of nicotine on cochlear cells using cultured cochlear basilar membranes. Cochlear basilar membranes were isolated from newborn rats, cultured, and treated with 1–100 ng/mL nicotine for 48 h. Cuticular plates and stereocilia bundle staining were used to evaluate hair cell (HC) loss. Spiral ganglion neuron and acoustic nerve fiber staining were assessed to evaluate cochlear neural injury. Scanning electron microscopy and transmission electron microscopy imaging were employed to examine cochlear ultrastructural changes. Our results showed that compared to spiral ganglia and nerve fibers, HCs are more susceptible to nicotine-induced toxicity. HC loss was more severe in the basal turn than in the middle and apical turns, while nerve fibers and spiral ganglion cells were morphologically maintained. Ultrastructural changes revealed disordered and damaged stereocilia, swelling and decreased mitochondrial density, swelling, and degranulation of the endoplasmic reticulum. Our results suggest that nicotine causes HCs’ degeneration and loss and may have implications for smoking-related hearing loss.

Published
2021
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