Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation

This paper considers a joint multi-graph inference and clustering problem for simultaneous inference of node centrality and association of graph signals with their graphs. We study a mixture model of filtered low pass graph signals with possibly non-white and low-rank excitation. While the mixture model is motivated from practical scenarios, it presents significant challenges to prior graph learning methods. As a remedy, we consider an inference problem focusing on the node centrality of graphs. We design an expectation-maximization (EM) algorithm with a unique low-rank plus sparse prior derived from low pass signal property. We propose a novel online EM algorithm for inference from streaming data. As an example, we extend the online algorithm to detect if the signals are generated from an abnormal graph. We show that the proposed algorithms converge to a stationary point of the maximum-a-posterior (MAP) problem. Numerical experiments support our analysis

Detecting Central Nodes from Low-rank Excited Graph Signals via Structured Factor Analysis

This paper treats a blind detection problem to identify the central nodes in a graph from filtered graph signals. Unlike prior works which impose strong restrictions on the data model, we only require the underlying graph filter to satisfy a low pass property with a generic low-rank excitation model. We treat two cases depending on the low pass graph filter's strength. When the graph filter is strong low pass, i.e., it has a frequency response that drops sharply at the high frequencies, we show that the principal component analysis (PCA) method detects central nodes with high accuracy. For general low pass graph filter, we show that the graph signals can be described by a structured factor model featuring the product between a low-rank plus sparse factor and an unstructured factor. We propose a two-stage decomposition algorithm to learn the structured factor model via a judicious combination of the non-negative matrix factorization and robust PCA algorithms. We analyze the identifiability conditions for the model which lead to accurate central nodes detection. Numerical experiments on synthetic and real data are provided to support our findings. We demonstrate significant performance gains over prior works

Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling

This paper considers learning a product graph from multi-attribute graph signals. Our work is motivated by the widespread presence of multilayer networks that feature interactions within and across graph layers. Focusing on a product graph setting with homogeneous layers, we propose a bivariate polynomial graph filter model. We then consider the topology inference problems thru adapting existing spectral methods. We propose two solutions for the required spectral estimation step: a simplified solution via unfolding the multi-attribute data into matrices, and an exact solution via nearest Kronecker product decomposition (NKD). Interestingly, we show that strong inter-layer coupling can degrade the performance of the unfolding solution while the NKD solution is robust to inter-layer coupling effects. Numerical experiments show efficacy of our methods.Comment: 6 pages, 4 figures, submitted to ICASSP 202

Role of microphysical parameterizations with droplet relative dispersion in IAP AGCM 4.1

Previous studies have shown that accurate descriptions of the cloud droplet effective radius (R (e)) and the autoconversion process of cloud droplets to raindrops (A (r)) can effectively improve simulated clouds and surface precipitation, and reduce the uncertainty of aerosol indirect effects in GCMs. In this paper, we implement cloud microphysical schemes including two-moment A (r) and R (e) considering relative dispersion of the cloud droplet size distribution into version 4.1 of the Institute of Atmospheric Physics's atmospheric GCM (IAP AGCM 4.1), which is the atmospheric component of the Chinese Academy of Sciences' Earth System Model. Analysis of the effects of different schemes shows that the newly implemented schemes can improve both the simulated shortwave and longwave cloud radiative forcings, as compared to the standard scheme, in IAP AGCM 4.1. The new schemes also effectively enhance the large-scale precipitation, especially over low latitudes, although the influences of total precipitation are insignificant for different schemes. Further studies show that similar results can be found with the Community Atmosphere Model, version 5.1

NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference

The inherent diversity of computation types within individual deep neural network (DNN) models necessitates a corresponding variety of computation units within hardware processors, leading to a significant constraint on computation efficiency during neural network execution. In this study, we introduce NeuralMatrix, a framework that transforms the computation of entire DNNs into linear matrix operations, effectively enabling their execution with one general-purpose matrix multiplication (GEMM) accelerator. By surmounting the constraints posed by the diverse computation types required by individual network models, this approach provides both generality, allowing a wide range of DNN models to be executed using a single GEMM accelerator and application-specific acceleration levels without extra special function units, which are validated through main stream DNNs and their variant models.Comment: 12 pages, 4figures, Submitted to 11th International Conference on Learning Representation

Improving Audio-Visual Segmentation with Bidirectional Generation

The aim of audio-visual segmentation (AVS) is to precisely differentiate audible objects within videos down to the pixel level. Traditional approaches often tackle this challenge by combining information from various modalities, where the contribution of each modality is implicitly or explicitly modeled. Nevertheless, the interconnections between different modalities tend to be overlooked in audio-visual modeling. In this paper, inspired by the human ability to mentally simulate the sound of an object and its visual appearance, we introduce a bidirectional generation framework. This framework establishes robust correlations between an object's visual characteristics and its associated sound, thereby enhancing the performance of AVS. To achieve this, we employ a visual-to-audio projection component that reconstructs audio features from object segmentation masks and minimizes reconstruction errors. Moreover, recognizing that many sounds are linked to object movements, we introduce an implicit volumetric motion estimation module to handle temporal dynamics that may be challenging to capture using conventional optical flow methods. To showcase the effectiveness of our approach, we conduct comprehensive experiments and analyses on the widely recognized AVSBench benchmark. As a result, we establish a new state-of-the-art performance level in the AVS benchmark, particularly excelling in the challenging MS3 subset which involves segmenting multiple sound sources. To facilitate reproducibility, we plan to release both the source code and the pre-trained model.Comment: Dawei Hao and Yuxin Mao contribute equality to this paper. Yiran Zhong is the corresponding author. The code will be released at https://github.com/OpenNLPLab/AVS-bidirectiona

Nedd4-2-dependent ubiquitination potentiates the inhibition of human NHE3 by cholera toxin and enteropathogenic Escherichia coli

BACKGROUND & AIMS: Diarrhea is one of the most common illnesses and is often caused by bacterial infection. Recently, we have shown that human Naþ/Hþ exchanger NHE3 (hNHE3), but not non-human NHE3s, interacts with the E3 ubiquitin ligase Nedd4-2. We hypothesize that this property of hNHE3 contributes to the increased severity of diarrhea in humans. METHODS: We used humanized mice expressing hNHE3 in the intestine (hNHE3int) to compare the contribution of hNHE3 and mouse NHE3 to diarrhea induced by cholera toxin (CTX) and enteropathogenic Escherichia coli (EPEC). We measured Naþ/ Hþ exchange activity and fluid absorption. The role of Nedd4-2 on hNHE3 activity and ubiquitination was determined by knockdown in Caco-2bbe cells. The effects of protein kinase A (PKA), the primary mediator of CTX-induced diarrhea, on Nedd4-2 and hNHE3 phosphorylation and their interaction were determined. RESULTS: The effects of CTX and EPEC were greater in hNHE3int mice than in control wild-type (WT) mice, resulting in greater inhibition of NHE3 activity and increased fluid accumulation in the intestine, the hallmark of diarrhea. Activation of PKA increased ubiquitination of hNHE3 and enhanced interaction of Nedd4-2 with hNHE3 via phosphorylation of Nedd4-2 at S342. S342A mutation mitigated the Nedd4-2–hNHE3 interaction and blocked PKA-induced inhibition of hNHE3. Unlike non-human NHE3s, inhibition of hNHE3 by PKA is independent of NHE3 phosphorylation, suggesting a distinct mechanism of hNHE3 regulation. CONCLUSIONS: The effects of CTX and EPEC on hNHE3 are amplified, and the unique properties of hNHE3 may contribute to diarrheal symptoms occurring in humans

Aloperine attenuates high glucose-induced oxidative injury in Schwann cells via activation of NRF2/HO-1 pathway

Purpose: To determine the involvement of nuclear factor erythroid 2-related factor 2 (NRF2) and heme oxygenase-1 (HO-1) in the action of aloperine on Schwann cell injury caused by high glucose (HG).Methods: Cell viability was determined using MTT assay while the release of lactate dehydrogenase (LDH) was determined by biochemical assay. Apoptosis was assessed using flow cytometry, while the levels of malondialdehyde (MDA) were determined by Annexin V-FIT staining. Glutathione Stransferase (GST), glutathione peroxidase (GPX), and reactive oxygen species (ROS) were determined using enzyme-linked immunosorbent assay.Results: Treatment with HG suppressed RSC96 cell viability and increased LDH release, while aloperine reversed these results (p < 0.05). Apoptosis of RSC96 cells was induced by HG stimulation, but was abolished by aloperine. The levels of ROS, MDA, and GST were enhanced in cells followingtreatment with HG, but was reversed by aloperine (p < 0.05). The decreased level of GPX caused by HG in RSC96 cells was elevated by aloperine. Moreover, aloperine upregulated NRF2 and HO-1 in RSC96 cells treated with HG (p < 0.05).Conclusion: Aloperine attenuates HG-induced oxidative injury in Schwann cells via activation of NRF2/HO-1 pathway, suggesting its potential as a potent drug for the management of diabetic peripheral neuropathy. Keywords: Aloperine, Schwann cells, High glucose, Oxidative stress, NRF2, HO-