Author name cluster

Mei Yang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

YNICL Journal 2022 Journal Article

Common gray matter loss in the frontal cortex in patients with methamphetamine-associated psychosis and schizophrenia

Xiaojian Jia
Jianhong Wang
Wentao Jiang
Zhi Kong
Huan Deng
Wentao Lai
Caihong Ye
Fen Guan

BACKGROUND AND HYPOTHESIS: Methamphetamine (MA)-associated psychosis has become a public concern. However, its mechanism is not clear. Investigating similarities and differences between MA-associated psychosis and schizophrenia in brain alterations would be informative for neuropathology. STUDY DESIGN: This study compared gray matter volumes of the brain across four participant groups: healthy controls (HC, n = 53), MA users without psychosis (MA, n = 22), patients with MA-associated psychosis (MAP, n = 34) and patients with schizophrenia (SCZ, n = 33). Clinical predictors of brain alterations, as well as association of brain alterations with psychotic symptoms and attention impairment were further investigated. STUDY RESULTS: Compared with the HC, the MAP and the SCZ showed similar gray matter reductions in the frontal cortex, particularly in prefrontal areas. Moreover, a stepwise extension of gray matter reductions was exhibited across the MA - MAP - SCZ. Duration of abstinence was associated with regional volumetric recovery in the MAP, while this amendment in brain morphometry was not accompanied with symptom's remission. Illness duration of psychosis was among the predictive factors of regional gray matter reductions in both psychotic groups. Volume reductions were found to be associated with attention impairment in the SCZ, while this association was reversed in the MAP in frontal cortex. CONCLUSIONS: This study suggested MA-associated psychosis and schizophrenia had common neuropathology in cognitive-related frontal cortices. A continuum of neuropathology between MA use and schizophrenia was tentatively implicated. Illness progressions and glial repairments could both play roles in neuropathological changes in MA-associated psychosis.

Details DOI

NeurIPS Conference 2022 Conference Paper

Fault-Aware Neural Code Rankers

Jeevana Priya Inala
Chenglong Wang
Mei Yang
Andres Codas
Mark Encarnación
Shuvendu Lahiri
Madanlal Musuvathi
Jianfeng Gao

Large language models (LLMs) have demonstrated an impressive ability to generate code for various programming tasks. In many instances, LLMs can generate a correct program for a task when given numerous trials. Consequently, a recent trend is to do large scale sampling of programs using a model and then filtering/ranking the programs based on the program execution on a small number of known unit tests to select one candidate solution. However, these approaches assume that the unit tests are given and assume the ability to safely execute the generated programs (which can do arbitrary dangerous operations such as file manipulations). Both of the above assumptions are impractical in real-world software development. In this paper, we propose CodeRanker, a neural ranker that can predict the correctness of a sampled program without executing it. Our CodeRanker is fault-aware i. e. , it is trained to predict different kinds of execution information such as predicting the exact compile/runtime error type (e. g. , an IndexError or a TypeError). We show that CodeRanker can significantly increase the pass@1 accuracy of various code generation models (including Codex, GPT-Neo, GPT-J) on APPS, HumanEval and MBPP datasets.

PDF Details

IROS Conference 2021 Conference Paper

Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems

Shan An
Fangru Zhou
Mei Yang
Haogang Zhu
Changhong Fu 0001
Konstantinos A. Tsintotas

Estimating a scene’s depth to achieve collision avoidance against moving pedestrians is a crucial and fundamental problem in the robotic field. This paper proposes a novel, low complexity network architecture for fast and accurate human depth estimation and segmentation in indoor environments, aiming to applications for resource-constrained platforms (including battery-powered aerial, micro-aerial, and ground vehicles) with a monocular camera being the primary perception module. Following the encoder-decoder structure, the proposed framework consists of two branches, one for depth prediction and another for semantic segmentation. Moreover, network structure optimization is employed to improve its forward inference speed. Exhaustive experiments on three self-generated datasets prove our pipeline’s capability to execute in real-time, achieving higher frame rates than contemporary state-of-the-art frameworks (114. 6 frames per second on an NVIDIA Jetson Nano GPU with TensorRT) while maintaining comparable accuracy.

Details