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Ting He

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3 papers

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YNIMG Journal 2024 Journal Article

Children's oppositional defiant disorder symptoms and neural synchrony in mother-child interactions: An fNIRS study

Wenrui Zhang
Ting He
Nan Zhou
Lian Duan
Peilian Chi
Xiuyun Lin

Interpersonal neural synchrony (INS) between mothers and children responds to the temporal similarity of brain signals in joint behavior between dyadic partners and is considered an important neural indicator of the formation of adaptive social interaction bonds. Parent-child interactions are particularly important for the development and maintenance of oppositional defiant disorder (ODD) in children, but the underlying neurocognitive mechanisms are unknown. Therefore, in the current study we measured INS between mothers and children in interactions by using simultaneous functional Near-infrared Spectroscopy (fNIRS), and explored its association with ODD symptoms in children. Seventy-two mother-child dyads were recruited to participate in the study, including 35 children with ODD and 37 healthy children to be used as a control. Each mother-child dyad was measured for neural activity in frontal, parietal, and temporal lobe regions while completing free-play as well as positive, and negative topic discussion tasks. We used Phase-locked value to calculate the synchrony strength and then used the K-means algorithm and k-space based alignment tests to confirm the specific patterns of parent-child synchrony in different brain areas. The results showed that, in free-play (right MFG and bilateral SFG), positive (left TPJ and bilateral SFGdor), and negative (bilateral SFGmed, right ANG, and left MFG) topic discussions, the mother-child pairs showed different patterns of INS. These specific INS patterns were significantly lower in the ODD group compared to the control group and were negatively associated with ODD symptoms in children. Network analyses showed that these INS patterns were connected to different nodes in the ODD symptom network. Our findings suggest that ODD mother-child dyads exhibit lower neural synchrony across a wide range of parent-child interactions. Neural synchrony in the context of interpersonal interactions provides new insights into understanding the neural mechanisms of ODD and can be used as an indicator of neural and socio-environmental factors in the network of psychological disorder symptoms.

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NeurIPS Conference 2024 Conference Paper

IaC-Eval: A Code Generation Benchmark for Cloud Infrastructure-as-Code Programs

Patrick T. Kon
Jiachen Liu
Yiming Qiu
Weijun Fan
Ting He
Lei Lin
Haoran Zhang
Owen M. Park

Infrastructure-as-Code (IaC), an important component of cloud computing, allows the definition of cloud infrastructure in high-level programs. However, developing IaC programs is challenging, complicated by factors that include the burgeoning complexity of the cloud ecosystem (e. g. , diversity of cloud services and workloads), and the relative scarcity of IaC-specific code examples and public repositories. While large language models (LLMs) have shown promise in general code generation and could potentially aid in IaC development, no benchmarks currently exist for evaluating their ability to generate IaC code. We present IaC-Eval, a first step in this research direction. IaC-Eval's dataset includes 458 human-curated scenarios covering a wide range of popular AWS services, at varying difficulty levels. Each scenario mainly comprises a natural language IaC problem description and an infrastructure intent specification. The former is fed as user input to the LLM, while the latter is a general notion used to verify if the generated IaC program conforms to the user's intent; by making explicit the problem's requirements that can encompass various cloud services, resources and internal infrastructure details. Our in-depth evaluation shows that contemporary LLMs perform poorly on IaC-Eval, with the top-performing model, GPT-4, obtaining a pass@1 accuracy of 19. 36%. In contrast, it scores 86. 6% on EvalPlus, a popular Python code generation benchmark, highlighting a need for advancements in this domain. We open-source the IaC-Eval dataset and evaluation framework at https: //github. com/autoiac-project/iac-eval to enable future research on LLM-based IaC code generation.

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EAAI Journal 2021 Journal Article

Deep regression adaptation networks with model-based transfer learning for dynamic load identification in the frequency domain

Cheng Wang
Delei Chen
Jianwei Chen
Xiongming Lai
Ting He

Frequency-domain dynamic load identification methods based on neural network (NN) models construct models independently at each frequency, but are inaccurate and inefficient to train. To address these problems, a deep regression adaptation network (DRAN) with model-transfer learning is proposed for identifying dynamic loads in the frequency domain. The aim is to take advantage of the similarity of uncorrelated multi-source dynamic loads and multi-vibration response at adjacent frequencies. First, a DRAN model for load identification is established using the historical data for a specific frequency. Second, the trained DRAN parameters are transferred to the DRAN for the target frequency as the initial parameter values. Next, the transferred DRAN is fine-tuned with the historical data of the target frequency to obtain the load identification model of the target frequency. Finally, the trained DRAN parameters of the current target frequency are transferred to the next target frequency. This process is iterated until a DRAN model for all frequencies is established. Because a frequency response function is a continuous function varying with frequency, the relationships between the dynamic loads and response at adjacent frequencies are similar. DRAN can adapt the historical data of different frequencies to one neural network for training, and then extract the common feature information of different frequencies to improve the accuracy of the model. Moreover, instead of setting the initial weights randomly and training them independently for each DRAN model, model-transfer learning is used to obtain better initial weights from the trained weights of DRAN models of adjacent frequencies. The proposed method was evaluated on the experimental data of a cylindrical shell structure under acoustic vibration joint excitation. The results show that the proposed method can obtain better initial weights, higher accuracy, better noise robustness, and shorter training time than a neural network.

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