A Unified Distributed Method for Constrained Networked Optimization via Saddle-Point Dynamics

This paper develops a unified distributed method for solving two classes of constrained networked optimization problems, i.e., optimal consensus problem and resource allocation problem with non-identical set constraints. We first transform these two constrained networked optimization problems into a unified saddle-point problem framework with set constraints. Subsequently, two projection-based primal-dual algorithms via Optimistic Gradient Descent Ascent (OGDA) method and Extra-gradient (EG) method are developed for solving constrained saddle-point problems. It is shown that the developed algorithms achieve exact convergence to a saddle point with an ergodic convergence rate $O(1/k)$ for general convex-concave functions. Based on the proposed primal-dual algorithms via saddle-point dynamics, we develop unified distributed algorithm design and convergence analysis for these two networked optimization problems. Finally, two numerical examples are presented to demonstrate the theoretical results

Associations of the Triglyceride and Glucose Index With Hypertension Stages, Phenotypes, and Their Progressions Among Middle-Aged and Older Chinese

Objectives: To assess the associations of the triglyceride and glucose (TyG) index with hypertension stages, phenotypes, and their progressions.Methods: The data originated from the China Health and Retirement Longitudinal Study. Multinomial logistic regression investigated the associations of the TyG index with hypertension stages (stage 1, stage 2), phenotypes (isolated systolic hypertension [ISH], isolated diastolic hypertension [IDH], systolic diastolic hypertension [SDH]), their progressions.Results: Compared with the lowest quartile of TyG index, the highest quartile was associated with increased risks of stage 1 hypertension (OR 1.71, 95% CI 1.38–2.13), stage 2 (1.74, 1.27–2.38), ISH (1.66, 1.31–2.11), IDH (2.52, 1.26–5.05), and SDH (1.65, 1.23–2.23). Similar results were found when TyG index was a continuous variable. From 2011 to 2015, a higher baseline TyG index was associated with normotension to stage 1 (per-unit: 1.39, 1.16–1.65), normotension to ISH (per-unit: 1.28, 1.04–1.56), and normotension to IDH (per-unit: 1.94, 1.27–2.97).Conclusion: The TyG index was associated with different hypertension stages, phenotypes, their progressions, and could be served as a surrogate indicator for early hypertension management

Estimating Causal Effects using a Multi-task Deep Ensemble

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks.Comment: 18 pages, 7 figures, 3 tables, published at the 40th International Conference on Machine Learning (ICML 2023

TESSP: Text-Enhanced Self-Supervised Speech Pre-training

Self-supervised speech pre-training empowers the model with the contextual structure inherent in the speech signal while self-supervised text pre-training empowers the model with linguistic information. Both of them are beneficial for downstream speech tasks such as ASR. However, the distinct pre-training objectives make it challenging to jointly optimize the speech and text representation in the same model. To solve this problem, we propose Text-Enhanced Self-Supervised Speech Pre-training (TESSP), aiming to incorporate the linguistic information into speech pre-training. Our model consists of three parts, i.e., a speech encoder, a text encoder and a shared encoder. The model takes unsupervised speech and text data as the input and leverages the common HuBERT and MLM losses respectively. We also propose phoneme up-sampling and representation swapping to enable joint modeling of the speech and text information. Specifically, to fix the length mismatching problem between speech and text data, we phonemize the text sequence and up-sample the phonemes with the alignment information extracted from a small set of supervised data. Moreover, to close the gap between the learned speech and text representations, we swap the text representation with the speech representation extracted by the respective private encoders according to the alignment information. Experiments on the Librispeech dataset shows the proposed TESSP model achieves more than 10% improvement compared with WavLM on the test-clean and test-other sets. We also evaluate our model on the SUPERB benchmark, showing our model has better performance on Phoneme Recognition, Acoustic Speech Recognition and Speech Translation compared with WavLM.Comment: 9 pages, 4 figure

H-RNet: hybrid rlation network for few-shot learning-based hyperspectral image classification.

Deep network models rely on sufficient training samples to perform reasonably well, which has inevitably constrained their application in classification of hyperspectral images (HSIs) due to the limited availability of labeled data. To tackle this particular challenge, we propose a hybrid relation network, H-RNet, by combining three-dimensional (3-D) convolution neural networks (CNN) and two-dimensional (2-D) CNN to extract the spectral–spatial features whilst reducing the complexity of the network. In an end-to-end relation learning module, the sample pairing approach can effectively alleviate the problem of few labeled samples and learn correlations between samples more accurately for more effective classification. Experimental results on three publicly available datasets have fully demonstrated the superior performance of the proposed model in comparison to a few state-of-the-art methods

Spousal concordance in adverse childhood experiences and the association with depressive symptoms in middle-aged and older adults: findings across China, the US, and Europe

BackgroundAdverse childhood experiences (ACEs) are associated with higher depressive risks in adulthood. Whether respondents’ ACEs are associated with their own depressive symptoms in adulthood and whether this association extends to their spouses’ depressive symptoms remain unexplored.MethodsData were from China Health and Retirement Longitudinal Study (CHARLS), the Health and Retirement Study (HRS), and the Survey of Health, Ageing and Retirement in Europe (SHARE). ACEs were categorized into overall, intra-familial, and extra-familial ACEs. Correlations of couples’ ACEs were calculated using Cramer’s V and partial Spearman’s correlation. Associations of respondents’ ACEs with spousal depressive symptoms were assessed using logistic regression, and mediation analyses were conducted to explore the mediating role of respondents’ depressive symptoms.ResultsSignificant associations between husbands’ ACEs and wives’ depressive symptoms, with odds ratios (ORs) and 95% confidence intervals (CIs) of 2.09 (1.36–3.22) for 4 or more ACEs in CHARLS, and 1.25 (1.06–1.48) and 1.38 (1.06–1.79) for 2 or more ACEs in HRS and SHARE. However, wives’ ACEs were associated with husbands’ depressive symptoms only in CHARLS and SHARE. Findings in intra-familial and extra-familial ACEs were consistent with our main results. Additionally, respondents’ depressive symptoms mediated more than 20% of the effect of respondents’ ACEs on spousal depressive symptoms.ConclusionWe found that ACEs were significantly correlated between couples. Respondents’ ACEs were associated with spousal depressive symptoms, with respondents’ depressive symptoms mediating the association. The bidirectional implications of ACEs on depressive symptoms should be considered within household and effective interventions are warranted

Automatic Aesthetics Evaluation of Robotic Dance Poses Based on Hierarchical Processing Network.

Vision plays an important role in the aesthetic cognition of human beings. When creating dance choreography, human dancers, who always observe their own dance poses in a mirror, understand the aesthetics of those poses and aim to improve their dancing performance. In order to develop artificial intelligence, a robot should establish a similar mechanism to imitate the above human dance behaviour. Inspired by this, this paper designs a way for a robot to visually perceive its own dance poses and constructs a novel dataset of dance poses based on real NAO robots. On this basis, this paper proposes a hierarchical processing network-based approach to automatic aesthetics evaluation of robotic dance poses. The hierarchical processing network first extracts the primary visual features by using three parallel CNNs, then uses a synthesis CNN to achieve high-level association and comprehensive processing on the basis of multi-modal feature fusion, and finally makes an automatic aesthetics decision. Notably, the design of this hierarchical processing network is inspired by the research findings in neuroaesthetics. Experimental results show that our approach can achieve a high correct ratio of aesthetic evaluation at 82.3%, which is superior to the existing methods

An immunogenic cell death-related classification predicts prognosis and response to immunotherapy in kidney renal clear cell carcinoma

IntroductionImmunogenic cell death (ICD) is a form of regulated cell death that activates an adaptive immune response in an immunocompetent host and is particularly sensitive to antigens from tumor cells. Kidney clear cell carcinoma (KIRC) is an immunogenic tumor with extensive tumor heterogeneity. However, no reliable predictive biomarkers have been identified to reflect the immune microenvironment and therapeutic response of KIRC.MethodsTherefore, we used the CIBERSORT and ESTIMATE algorithms to define three ICD clusters based on the expression of ICD-related genes in 661 KIRC patients. Subsequently, we identified three different ICD gene clusters based on the overlap of differentially expressed genes (DEGs) within the ICD clusters. In addition, principal component analysis (PCA) was performed to calculate the ICD scores.ResultsThe results showed that patients with reduced ICD scores had a poorer prognosis and reduced transcript levels of immune checkpoint genes regulated with T cell differentiation. Furthermore, the ICD score was negatively correlated with the tumor mutation burden (TMB) value of KICD. patients with higher ICD scores showed clinical benefits and advantages of immunotherapy, indicating that the ICD score is an accurate and valid predictor to assess the effect of immunotherapy.DiscussionOverall, our study presents a comprehensive KICD immune-related ICD landscape that can provide guidance for current immunotherapy and predict patient prognosis to help physicians make judgments about the patient’s disease and treatment modalities, and can guide current research on immunotherapy strategies for KICD