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1. Degree Distribution based Spiking Graph Networks for Domain Adaptation

Author

Wang, Yingxu, Liu, Siwei, Wang, Mengzhu, Liang, Shangsong, and Yin, Nan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Spiking Graph Networks (SGNs) have garnered significant attraction from both researchers and industry due to their ability to address energy consumption challenges in graph classification. However, SGNs are only effective for in-distribution data and cannot tackle out-of-distribution data. In this paper, we first propose the domain adaptation problem in SGNs, and introduce a novel framework named Degree-aware Spiking Graph Domain Adaptation for Classification. The proposed DeSGDA addresses the spiking graph domain adaptation problem by three aspects: node degree-aware personalized spiking representation, adversarial feature distribution alignment, and pseudo-label distillation. First, we introduce the personalized spiking representation method for generating degree-dependent spiking signals. Specifically, the threshold of triggering a spike is determined by the node degree, allowing this personalized approach to capture more expressive information for classification. Then, we propose the graph feature distribution alignment module that is adversarially trained using membrane potential against a domain discriminator. Such an alignment module can efficiently maintain high performance and low energy consumption in the case of inconsistent distribution. Additionally, we extract consistent predictions across two spaces to create reliable pseudo-labels, effectively leveraging unlabeled data to enhance graph classification performance. Extensive experiments on benchmark datasets validate the superiority of the proposed DeSGDA compared with competitive baselines.

Published
2024

2. Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks

Author

Wang, Luoyu, Tao, Yitian, Yang, Qing, Liang, Yan, Liu, Siwei, Shi, Hongcheng, Shen, Dinggang, and Zhang, Han

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Simultaneous functional PET/MR (sf-PET/MR) presents a cutting-edge multimodal neuroimaging technique. It provides an unprecedented opportunity for concurrently monitoring and integrating multifaceted brain networks built by spatiotemporally covaried metabolic activity, neural activity, and cerebral blood flow (perfusion). Albeit high scientific/clinical values, short in hardware accessibility of PET/MR hinders its applications, let alone modern AI-based PET/MR fusion models. Our objective is to develop a clinically feasible AI-based disease diagnosis model trained on comprehensive sf-PET/MR data with the power of, during inferencing, allowing single modality input (e.g., PET only) as well as enforcing multimodal-based accuracy. To this end, we propose MX-ARM, a multimodal MiXture-of-experts Alignment and Reconstruction Model. It is modality detachable and exchangeable, allocating different multi-layer perceptrons dynamically ("mixture of experts") through learnable weights to learn respective representations from different modalities. Such design will not sacrifice model performance in uni-modal situation. To fully exploit the inherent complex and nonlinear relation among modalities while producing fine-grained representations for uni-modal inference, we subsequently add a modal alignment module to line up a dominant modality (e.g., PET) with representations of auxiliary modalities (MR). We further adopt multimodal reconstruction to promote the quality of learned features. Experiments on precious multimodal sf-PET/MR data for Mild Cognitive Impairment diagnosis showcase the efficacy of our model toward clinically feasible precision medicine., Comment: 11 pages

Published
2024

4. PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction

Author

Lu, Junjian, Liu, Siwei, Kobylianski, Dmitrii, Dreyer, Etienne, Gross, Eilam, and Liang, Shangsong

Subjects
High Energy Physics - Phenomenology, Computer Science - Machine Learning
Abstract

In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations (LCAG) matrix, to encode the particle decay tree structure. Then, we introduce a perturbative augmentation technique applied to node features, aiming to mimic experimental uncertainties and increase data diversity. We further propose a supervised graph contrastive learning algorithm to utilize the information of inter-particle relations from multiple decay processes. Extensive experiments show that our proposed supervised graph contrastive learning with perturbative augmentation (PASCL) method outperforms state-of-the-art baseline models on an existing physics-based dataset, significantly improving the reconstruction accuracy. This method provides a more effective training strategy for models with the same parameters and makes way for more accurate and efficient high-energy particle physics data analysis.

Published
2024

5. A Robbins--Monro Sequence That Can Exploit Prior Information For Faster Convergence

Author

Liu, Siwei, Ma, Ke, and Goetz, Stephan M.

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis, Mathematics - Optimization and Control, Mathematics - Probability, Mathematics - Statistics Theory, Statistics - Methodology, Statistics - Machine Learning, 62L20, 62L05, 62L10, 60G99, 60-08, 65B99, 65C99, 90C15
Abstract

We propose a new method to improve the convergence speed of the Robbins-Monro algorithm by introducing prior information about the target point into the Robbins-Monro iteration. We achieve the incorporation of prior information without the need of a -- potentially wrong -- regression model, which would also entail additional constraints. We show that this prior-information Robbins-Monro sequence is convergent for a wide range of prior distributions, even wrong ones, such as Gaussian, weighted sum of Gaussians, e.g., in a kernel density estimate, as well as bounded arbitrary distribution functions greater than zero. We furthermore analyse the sequence numerically to understand its performance and the influence of parameters. The results demonstrate that the prior-information Robbins-Monro sequence converges faster than the standard one, especially during the first steps, which are particularly important for applications where the number of function measurements is limited, and when the noise of observing the underlying function is large. We finally propose a rule to select the parameters of the sequence., Comment: 26 pages, 5 figures

Published
2024

7. Interparental Conflict and Early Adolescent Depressive Symptoms: Parent-Child Triangulation as the Mediator and Grandparent Support as the Moderator.

Author

Wang, Meiping, Sun, Shan, Liu, Xiaojie, Yang, Yang, Liu, Chunyu, Huang, Aodi, and Liu, Siwei

Subjects
Depressive symptoms, Early adolescent, Grandparent support, Interparental conflict, Parent-child triangulation, Female, Humans, Adolescent, Male, Family Conflict, Depression, Grandparents, Parents, Parent-Child Relations
Abstract

A notable ambiguity persists concerning whether distinct forms of parent-child triangulation (unstable coercive coalition, stable coalition, detouring-attacking, detouring supportive, parentification) might mediate the association between interparental conflict and early adolescent depressive symptoms similarly within the context of Chinese Confucianism. Filling this research gap, this study aimed to examine the mediating role of the five dimensions of parent-child triangulation in the association between interparental conflict and early adolescent depressive symptoms, as well as the moderating effect of grandparent support on this mediating pathway. Data were drawn from a sample of 761 Chinese adolescents (M age = 12.82 ± 0.47, 49.1% girls). Structural equation model analyses indicated that unstable coercive coalition, stable coalition, and detouring-attacking behaviors partially mediated the association between interparental conflict and adolescent depressive symptoms, while detouring-supportive behaviors and parentification did not demonstrate such mediating effects. Unlike Western societies, a negative correlation was observed between interparental conflict and parentification in the context of China. Grandparent support mitigated the adverse effects of both interparental conflict and the unstable coercive coalition on early adolescent depressive symptoms.

Published
2024

9. Demultiplexing-free ultra-compact WDM-compatible multimode optical switch assisted by mode exchanger

Author

Liu Siwei, Fu Xin, Niu Jiaqi, Huo Yujie, Cheng Chuang, and Yang Lin

Subjects
mode-division multiplexing, optical switch, on-chip optical interconnects, silicon photonics, Physics, QC1-999
Abstract

Silicon-based optical switches are integral to on-chip optical interconnects, and mode-division multiplexing (MDM) technology has enabled modes to function as carriers in routing, further boosting optical switches’ link capacity. However, traditional multimode optical switches, which typically use Mach–Zehnder interferometer (MZI) structures and mode (de)multiplexers, are complex and occupy significant physical space. In this paper, we propose and experimentally demonstrate a novel demultiplexing-free dual-mode 3 × 3 thermal-optical switch based on micro-rings (MRs) and mode exchangers (MEs). All MRs are designed to handle TE1 mode, while the ME converts TE0 mode to TE1 mode, enabling separate routing of both modes. Bezier curves are employed to optimize not only the ME, but also the dual-mode 45° and 90° waveguide bends, which facilitate the flexible and compact layout design. Moreover, our structure can support multiple wavelength channels and spacings by adding pairs of MRs, exhibiting strong WDM compatibility. The switch has an ultra-compact footprint of 0.87 × 0.52 mm2. Under both “all-bar” and “all-cross” configurations, its insertion losses (ILs) remain below 8.7 dB at 1,551 nm, with optical signal-to-noise ratios (OSNRs) exceeding 13.0 dB. Also, 32 Gbps data transmission experiments validate the switch’s high-speed transmission capability.

Published
2024
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10. Effective graph representation learning for ranking-based recommendation

Author

Liu, Siwei

Subjects
QA75 Electronic computers. Computer science
Abstract

Ranking-based recommender systems are designed to generate a personalised ranking list of items for a given user to address the information overload problem. An effective and efficient ranking-based recommender system can benefit users by providing them with items of interest as well as service providers by increasing their exposure and profits. Since more and more users and providers of items have been increasingly interacting with online platforms, the underlying recommendation algorithms are facing more challenges. For example, traditional collaborative filtering-based recommender systems cannot generate effective recommendations to cold-start users due to the lack of sufficient interactions. In addition, although recommender systems can leverage deep learning-based techniques to enhance their effectiveness, they are not robust enough against variances in the models' initialisations, which can degrade the users' satisfaction. Furthermore, when incorporating these complex deep models, the training phases of recommender systems become less efficient, which might slower the online platforms from quickly capturing the users' interests. Graph representation learning includes techniques that can leverage graph-structured data and generate latent representations for the nodes, graphs/sub-graphs and edges between nodes. Since the user-item interaction matrix is in fact a bipartite graph, we can use these graph-based techniques to leverage the interaction matrix and generate more effective node representations for the users and items. Therefore, this thesis aims to enhance the ranking-based recommendations by proposing novel recommender systems based on graph representation learning. In particular, this thesis uses heterogeneous graph representation learning, graph pre-training and graph contrastive learning to improve the effectiveness of ranking-based recommendations while alleviating the aforementioned cold-start problem as well as the low-robustness and low training-efficiency issues. To enhance the effectiveness of ranking-based recommendations and alleviate the cold-start problem, we propose to use the heterogeneous graph representation learning technique to encode the typical side information of the users and items, which are usually defined as the attributes of users and the descriptions of items. For example, a user-item interaction matrix, social relations are one of the most naturally available relations that can be used to enrich such an interaction matrix. Therefore, we choose the social relations among different types of side information to build the heterogeneous graph. We propose a novel recommender system, the Social-aware Gaussian Pre-trained model (SGP), which encodes the user social relations and interaction data using the heterogeneous graph representation learning technique. Next, in the subsequent fine-tuning stage, our SGP model adopts a Gaussian Mixture Model (GMM) to factorise these pre-trained embeddings for further training. Our extensive experiments on three public datasets show that SGP can alleviate the cold-start problem while also ensuring effective recommendations for regular users. To alleviate the low-robustness issue and enhance the recommendation effectiveness, we propose to leverage multiple types of side information using the graph pre-training technique. In particular, we aim to generalise the pre-training technique used by SGP for multiple types of side information associated with both users and items. Specifically, we propose two novel pre-training schemes, namely Single-P and Multi-P, to leverage side information such as the ages and occupations of users and the textual reviews and categories of items. Instead of jointly training with two objectives, our pre-training schemes first pre-train a representation model under the users and items' multi/single relational graphs constructed by their side information and then fine-tune their embeddings under an existing general representation-based recommendation model. Extensive experiments on three public datasets show that the graph pre-training technique can effectively enhance the effectiveness of ranking-based recommender systems and alleviates the cold-start problem. In addition, our pre-training schemes can provide more ef-fective initialisations for both the users and items; hence the robustness of fine-tuning models namely MF, NCF, NGCF and LightGCN, can be improved. Finally, to enhance the training efficiency of graph-based recommenders while ensuring their effectiveness, we propose to use the graph contrastive learning technique to improve the traditional random negative sampling approach. In particular, we propose a dynamic negative sampling (DNS) approach that leverages the graph contrastive learning technique to replace the randomly sampled negative items with more informative negative items. Our experiments show that DNS can improve the recommendation effectiveness of four competitive recommenders. Next, we further propose a novel graph-based model, i.e. MLP-CGRec, that leverages a multiple sampling approach to enhance the training efficiency of the graph-based recommender system. In particular, MLP-CGRec uses DNS to sample contrastive negative items and an efficient graph-based sampling method to select pseudo-positive samples. Experimental results on three public datasets show that MLP-CGRec can maintain competitive effectiveness and achieve the best efficiency compared with state-of-the-art recommender systems.

Published
2023
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11. K\'ahler Finsler manifolds with curvatures bounded from below

Author

Chen, Bin, Li, Nan, and Liu, Siwei

Subjects
Mathematics - Differential Geometry, 53C60, 53B40
Abstract

We obtain a partial parallelism of the complex structure on K\"ahler Finsler manifolds. As applications, we prove Synge-Tsukamoto theorem and Bonnet-Myers theorem for positively curved K\"ahler Finsler manifolds. Moreover, we generalize a comparison theorem due to Ni-Zheng by introducing the notion of orthogonal Ricci curvature to K\"ahler Finsler geometry.

Published
2022

12. A New Frontier for Studying Within-Person Variability: Bayesian Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models

Author

Rast, Philippe, Martin, Stephen R, Liu, Siwei, and Williams, Donald R

Subjects
Applied and Developmental Psychology, Social and Personality Psychology, Mathematical Sciences, Statistics, Psychology, Brain Disorders, Humans, Bayes Theorem, Time Factors, multivariate GARCH, BEKK, DCC, within-person variance, individual models, Cognitive Sciences, Social Sciences Methods, Applied and developmental psychology, Social and personality psychology
Abstract

Research on individual variation has received increased attention. The bulk of the models discussed in psychological research so far, focus mainly on the temporal development of the mean structure. We expand the view on within-person residual variability and present a new model parameterization derived from classic multivariate GARCH models used to predict and forecast volatility in financial time-series. We propose a new pdBEKK and a modified dynamic conditional correlation (DCC) model that accommodate external time-varying predictors for the within-person variance. The main goal of this work is to evaluate the potential usefulness of MGARCH models for research in within-person variability. MGARCH models partition the within-person variance into, at least, 3 components: An overall constant and unconditional baseline variance, a process that introduces variance conditional on previous innovations, or random shocks, and a process that governs the carry-over effects of previous conditional variance, similar to an AR model. These models allow for variance spillover effects from one time-series to another. We illustrate the pdBEKK- and the DCC-MGARCH on two individuals who have rated their daily positive and negative affect over 100 consecutive days. The full models comprised a multivariate ARMA(1,1) model for the means and included physical activity as moderator of the overall baseline variance. Overall, the pdBEKK seems to result in a more straightforward psychological interpretation, but the DCC is generally easier to estimate and can accommodate more simultaneous time-series. Both models require rather large amounts of datapoints to detect nonzero parameters. We provide an R-package bmgarch that facilitates the estimation of these types of models. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Published
2022

24. Testing the empirical integration of threat-deprivation and harshness-unpredictability dimensional models of adversity.

Author

Usacheva, Maria, Choe, Daniel, Liu, Siwei, Timmer, Susan, and Belsky, Jay

Subjects
Humans, Substance-Related Disorders, Sexual Behavior, Cognition, adaptation, dimensional adversity, evolutionary-developmental, integrative model, risk factors, Behavioral and Social Science, Prevention, Mental Health, Violence Research, Basic Behavioral and Social Science, Aetiology, 2.3 Psychological, social and economic factors, Psychology, Cognitive Sciences, Developmental & Child Psychology
Abstract

Recent dimensional models of adversity informed by a neurobiological deficit framework highlights threat and deprivation as core dimensions, whereas models informed by an evolutionary, adaptational and functional framework calls attention to harshness and unpredictability. This report seeks to evaluate an integrative model of threat, deprivation, and unpredictability, drawing on the Fragile Families Study. Confirmatory factor analysis of presumed multiple indicators of each construct reveals an adequate three-factor structure of adversity. Theory-based targeted predictions of the developmental sequelae of each dimension also received empirical support, with deprivation linked to health problems and cognitive ability; threat linked to aggression; and unpredictability to substance use and sexual risk-taking. These findings lend credibility to utility of the three-dimensional integrative framework of adversity. It could thus inform development of dimensional measures of risk assessment and exploration of multidimensional adversity profiles, sensitive to individual differences in lived experiences, supporting patient-centered, strength-based approaches to services.

Published
2022

25. Graph Neural Pre-training for Enhancing Recommendations using Side Information

Author

Meng, Zaiqiao, Liu, Siwei, Macdonald, Craig, and Ounis, Iadh

Subjects
Computer Science - Information Retrieval
Abstract

Leveraging the side information associated with entities (i.e. users and items) to enhance the performance of recommendation systems has been widely recognized as an important modelling dimension. While many existing approaches focus on the integration scheme to incorporate entity side information -- by combining the recommendation loss function with an extra side information-aware loss -- in this paper, we propose instead a novel pre-training scheme for leveraging the side information. In particular, we first pre-train a representation model using the side information of the entities, and then fine-tune it using an existing general representation-based recommendation model. Specifically, we propose two pre-training models, named GCN-P and COM-P, by considering the entities and their relations constructed from side information as two different types of graphs respectively, to pre-train entity embeddings. For the GCN-P model, two single-relational graphs are constructed from all the users' and items' side information respectively, to pre-train entity representations by using the Graph Convolutional Networks. For the COM-P model, two multi-relational graphs are constructed to pre-train the entity representations by using the Composition-based Graph Convolutional Networks. An extensive evaluation of our pre-training models fine-tuned under four general representation-based recommender models, i.e. MF, NCF, NGCF and LightGCN, shows that effectively pre-training embeddings with both the user's and item's side information can significantly improve these original models in terms of both effectiveness and stability.

Published
2021

32. Goals for Reaching Optimal Wellness (GROWell): A clinical trial protocol of a digital dietary intervention for pregnant and postpartum people with prenatal overweight or obesity

Author

Simmons, Leigh Ann, Phipps, Jennifer E, Overstreet, Courtney, Smith, Paige M, Bechard, Elizabeth, Liu, Siwei, Walker, Cheryl, and Noonan, Devon

Subjects
Clinical Research, Nutrition, Behavioral and Social Science, Clinical Trials and Supportive Activities, Pediatric, Obesity, Prevention, 7.1 Individual care needs, Management of diseases and conditions, Cardiovascular, Metabolic and endocrine, Stroke, Reproductive health and childbirth, Oral and gastrointestinal, Cancer, Generic health relevance, Good Health and Well Being, Female, Gestational Weight Gain, Goals, Humans, Infant, Overweight, Postpartum Period, Pregnancy, Pregnancy Complications, Randomized Controlled Trials as Topic, Gestational weight gain, Postpartum weight retention, Mobile health intervention, Medical and Health Sciences, General Clinical Medicine, Public Health
Abstract

BackgroundExcess gestational weight gain (EGWG) is associated with multiple pregnancy complications and health risks for birthing people and their infants. Likewise, postpartum weight retention (PPWR), or not losing all pregnancy weight, has long-term health consequences. EGWG among people who enter pregnancy with overweight or obesity have worse obstetric outcomes and increased PPWR compared to women who gain within Institute of Medicine guidelines.MethodsThis study protocol describes the details of a blinded, randomized clinical trial of GROWell: Goals for Reaching Optimal Wellness, a mHealth tool designed to improve diet quality among people who enter pregnancy with overweight or obese BMIs to help them achieve appropriate GWG and safe postpartum pregnancy weight loss. Individuals with overweight and obesity will be randomly assigned to an attention control or intervention arm. The intervention group will receive personalized, goal-oriented text messages regarding dietary choices, while the attention control group will receive text messages about healthy pregnancy, labor, delivery, and early infancy. Both groups will complete online surveys at baseline, follow up, 3 and 6 months postpartum.Results and discussionCurrently, 162 subjects have been enrolled. Outcomes associated with GWG and pregnancy are expected in late 2023, while outcomes on postpartum weight retention GROWell adherence are expected in late 2024. The results of this trial will support the use of an evidence-based mHealth tool to be integrated into clinical practice to reduce EGWG and PPWR among pregnant people with overweight and obese BMIs, a resource that is currently lacking.Trial registrationClinicalTrials.gov identifier: NCT04449432. Registered on June 26, 2020.

Published
2022

33. BPLF: A Bi-Parallel Linear Flow Model for Facial Expression Generation from Emotion Set Images

Author

Xu, Gao, Long, Yuanpeng, Liu, Siwei, Yang, Lijia, Xu, Shimei, Yao, Xiaoming, and Shu, Kunxian

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The flow-based generative model is a deep learning generative model, which obtains the ability to generate data by explicitly learning the data distribution. Theoretically its ability to restore data is stronger than other generative models. However, its implementation has many limitations, including limited model design, too many model parameters and tedious calculation. In this paper, a bi-parallel linear flow model for facial emotion generation from emotion set images is constructed, and a series of improvements have been made in terms of the expression ability of the model and the convergence speed in training. The model is mainly composed of several coupling layers superimposed to form a multi-scale structure, in which each coupling layer contains 1*1 reversible convolution and linear operation modules. Furthermore, this paper sorted out the current public data set of facial emotion images, made a new emotion data, and verified the model through this data set. The experimental results show that, under the traditional convolutional neural network, the 3-layer 3*3 convolution kernel is more conducive to extracte the features of the face images. The introduction of principal component decomposition can improve the convergence speed of the model., Comment: 20 pages, 10 figures

Published
2021

34. An optimized Capsule-LSTM model for facial expression recognition with video sequences

Author

Liu, Siwei, Long, Yuanpeng, Xu, Gao, Yang, Lijia, Xu, Shimei, Yao, Xiaoming, and Shu, Kunxian

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includingcapsule encoders, capsule decoders and LSTM network. The capsule encoder extracts the spatial information of facial expressions in video frames. Capsule decoder reconstructs the images to optimize the network. LSTM extracts the temporal information between video frames and analyzes the differences in expression changes between frames. The experimental results from the MMI dataset show that the Capsule-LSTM model proposed in this paper can effectively improve the accuracy of video expression recognition., Comment: 14pages,4 figurews

Published
2021

35. EEG-Based Motor Imagery Classification with Deep Adversarial Learning

Author

Liu, Dezheng, Liu, Siwei, Wu, Hanrui, Zhang, Jia, Long, Jinyi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Guo, Yinzhang, editor, Song, Xiaoxia, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Gao, Liping, editor, and Du, Bowen, editor

Published
2023
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43. Long-term mental health services use in children referred to a clinical intervention.

Author

Usacheva, Maria, Timmer, Susan, Choe, Daniel, Liu, Siwei, Thurston, Holly, and Urquiza, Anthony

Subjects
Humans, Parent-Child Relations, Mental Health Services, Child Abuse, Child Welfare, Child, Child, Preschool, Referral and Consultation, Female, Male, Surveys and Questionnaires, Child Protective Services, Child mental health, Intervention, Mandated treatment, PCIT, Pediatric services use, Voluntary treatment, Clinical Trials and Supportive Activities, Behavioral and Social Science, Mental Health, Violence Research, Pediatric, Health Services, Youth Violence, Child Abuse and Neglect Research, Prevention, Clinical Research, Good Health and Well Being, Criminology, Social Work, Psychology, Developmental & Child Psychology
Abstract

BackgroundRecent advancements in pediatric mental health (MH) increased accessibility of evidence-based interventions. Yet, accessibility alone does not explain the rise in MH services use (MHSU). Maltreatment-related adversity, symptom severity, and access to early interventions have been linked to ongoing need for services, yet their joint contributions to continuities in MHSU remain unclear.ObjectiveThe study examines the role of maltreatment, externalizing symptom severity, and referral for early intervention in pediatric MHSU across five years. To evaluate engagement in treatment, we accounted for treatment progress and referral type, comparing MHSU in court-mandated and voluntary participants.Participants and settingParticipants were 321 children (M = 4.3 years; 58.9 % boys) referred to parent-child interaction therapy (PCIT), an evidence-based intervention for families struggling with child disruptive behaviors and maltreating families involved with child welfare. Services were provided at a university-affiliated clinic in a metropolitan county.MethodSymptom severity was assessed with standardized questionnaires; maltreatment history and referral type were obtained from casefile reviews. MHSU was tracked through county behavioral health diagnostic reports. The data were analyzed using structural equation modeling.ResultsResults indicated that for the 44.9 % of children with onward referrals, the frequency of service use, but not progress in treatment, predicted ongoing services. Maltreatment emerged as a universal predictor, while externalizing predicted MHSU only in court-mandated participants, suggesting referral type contributes to quantifiable differences in MH needs.ConclusionsFindings emphasize importance of ongoing funding for pediatric MH services, and the need to explore mechanisms underlying continuous MHSU in vulnerable children.

Published
2021

48. Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations Among Affective Traits, States, and Physical Activity

Author

Williams, Donald R, Martin, Stephen R, Liu, Siwei, and Rast, Philippe

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Clinical Sciences, Neurosciences, Behavioral and Social Science, Prevention, Basic Behavioral and Social Science, Clinical Research, Aetiology, 2.5 Research design and methodologies (aetiology), Mental health, personality assessment, personality traits and states, multivariate mixed-effect location scale model, intraindividual variability, Bayesian inference, Psychology, Social Psychology, Biochemistry and cell biology, Clinical sciences
Abstract

Intensive longitudinal studies and experience sampling methods are becoming more common in psychology. While they provide a unique opportunity to ask novel questions about within-person processes relating to personality, there is a lack of methods specifically built to characterize the interplay between traits and states. We thus introduce a Bayesian multivariate mixed-effects location scale model (M-MELSM). The formulation can simultaneously model both personality traits (the location) and states (the scale) for multivariate data common to personality research. Variables can be included to predict either (or both) the traits and states, in addition to estimating random effects therein. This provides correlations between location and scale random effects, both across and within each outcome, which allows for characterizing relations between any number of personality traits and the corresponding states. We take a fully Bayesian approach, not only to make estimation possible, but also because it provides the necessary information for use in psychological applications such as hypothesis testing. To illustrate the model we use data from 194 individuals that provided daily ratings of negative and positive affect, as well as their physical activity in the form of step counts over 100 consecutive days. We describe the fitted model, where we emphasize, with visualization, the richness of information provided by the M-MELSM. We demonstrate Bayesian hypothesis testing for the correlations between the random effects. We conclude by discussing limitations of the MELSM in general and extensions to the M-MELSM specifically for personality research.

Published
2020

49. Integrating a multilevel approach to examine family conflict and parent-adolescent physiological synchrony.

Author

Li, Zhi, Sturge-Apple, Melissa L, Liu, Siwei, and Davies, Patrick T

Subjects
Pediatric, Good Health and Well Being, Adolescent, Adult, Child, Family Conflict, Female, Humans, Male, Multilevel Analysis, Parent-Child Relations, Respiratory Sinus Arrhythmia, physiological synchrony, coparenting conflict, respiratory sinus arrhythmia, parent-child relationship, adolescence, Psychology, Family Studies
Abstract

The present study investigated physiological synchrony across mothers, fathers, and adolescents during a conflict discussion. In particular, a multilevel, within-dyad approach was used to parameterize synchrony within the parasympathetic nervous system. Moreover, we examined how domains of conflict within the larger family system influenced the level of synchrony between family members. Participants were 191 families with adolescents (M age = 12.4 years), whose respiratory sinus arrhythmia (RSA) were measured during a triadic family conflict discussion. On the minute-to-minute basis, mothers and adolescents as well as mothers and fathers exhibited positive RSA concurrent synchrony, whereas no such concordance was observed between adolescents and fathers. In addition, the presence of conflict between parents with respect to coparenting moderated the level of mother-adolescent synchrony such that no concordant RSA synchrony emerged between mother and adolescents under high levels of coparenting conflict. In contrast, general interparental conflict did not moderate levels of physiological synchrony among any of the dyads. Findings suggest that mothers may be particularly physiologically in tune with family members in the context of conflict discussions and specific domains of family conflict may influence concordant physiological dynamics. Taken together, this is one of the first studies to examine physiological synchrony during the adolescent period and results suggest this may be an important developmental period for these dynamics. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Published
2020

50. A new frontier for studying within-person variability: Bayesian multivariate generalized autoregressive conditional heteroskedasticity models.

Author

Rast, Philippe, Martin, Stephen R, Liu, Siwei, and Williams, Donald R

Subjects
Social Sciences Methods, Psychology, Cognitive Sciences
Abstract

Research on individual variation has received increased attention. The bulk of the models discussed in psychological research so far, focus mainly on the temporal development of the mean structure. We expand the view on within-person residual variability and present a new model parameterization derived from classic multivariate GARCH models used to predict and forecast volatility in financial time-series. We propose a new pdBEKK and a modified dynamic conditional correlation (DCC) model that accommodate external time-varying predictors for the within-person variance. The main goal of this work is to evaluate the potential usefulness of MGARCH models for research in within-person variability. MGARCH models partition the within-person variance into, at least, 3 components: An overall constant and unconditional baseline variance, a process that introduces variance conditional on previous innovations, or random shocks, and a process that governs the carry-over effects of previous conditional variance, similar to an AR model. These models allow for variance spillover effects from one time-series to another. We illustrate the pdBEKK- and the DCC-MGARCH on two individuals who have rated their daily positive and negative affect over 100 consecutive days. The full models comprised a multivariate ARMA(1,1) model for the means and included physical activity as moderator of the overall baseline variance. Overall, the pdBEKK seems to result in a more straightforward psychological interpretation, but the DCC is generally easier to estimate and can accommodate more simultaneous time-series. Both models require rather large amounts of datapoints to detect nonzero parameters. We provide an R-package bmgarch that facilitates the estimation of these types of models. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Published
2020

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