Inductive Graph Neural Networks for Spatiotemporal Kriging

Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis. Recent research on graph neural networks has made substantial progress in time series forecasting, while little attention has been paid to the kriging problem -- recovering signals for unsampled locations/sensors. Most existing scalable kriging methods (e.g., matrix/tensor completion) are transductive, and thus full retraining is required when we have a new sensor to interpolate. In this paper, we develop an Inductive Graph Neural Network Kriging (IGNNK) model to recover data for unsampled sensors on a network/graph structure. To generalize the effect of distance and reachability, we generate random subgraphs as samples and reconstruct the corresponding adjacency matrix for each sample. By reconstructing all signals on each sample subgraph, IGNNK can effectively learn the spatial message passing mechanism. Empirical results on several real-world spatiotemporal datasets demonstrate the effectiveness of our model. In addition, we also find that the learned model can be successfully transferred to the same type of kriging tasks on an unseen dataset. Our results show that: 1) GNN is an efficient and effective tool for spatial kriging; 2) inductive GNNs can be trained using dynamic adjacency matrices; 3) a trained model can be transferred to new graph structures and 4) IGNNK can be used to generate virtual sensors.Comment: AAAI 202

Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach

Time series analysis is a fundamental task in various application domains, and deep learning approaches have demonstrated remarkable performance in this area. However, many real-world time series data exhibit significant periodic or quasi-periodic dynamics that are often not adequately captured by existing deep learning-based solutions. This results in an incomplete representation of the underlying dynamic behaviors of interest. To address this gap, we propose an unsupervised method called Floss that automatically regularizes learned representations in the frequency domain. The Floss method first automatically detects major periodicities from the time series. It then employs periodic shift and spectral density similarity measures to learn meaningful representations with periodic consistency. In addition, Floss can be easily incorporated into both supervised, semi-supervised, and unsupervised learning frameworks. We conduct extensive experiments on common time series classification, forecasting, and anomaly detection tasks to demonstrate the effectiveness of Floss. We incorporate Floss into several representative deep learning solutions to justify our design choices and demonstrate that it is capable of automatically discovering periodic dynamics and improving state-of-the-art deep learning models.Comment: 12 page

On-the-fly scheduling vs. reservation-based scheduling for unpredictable workflows

International audienceScientific insights in the coming decade will clearly depend on the effective processing of large datasets generated by dynamic heterogeneous applications typical of workflows in large data centers or of emerging fields like neuroscience. In this paper, we show how these big data workflows have a unique set of characteristics that pose challenges for leveraging HPC methodologies, particularly in scheduling. Our findings indicate that execution times for these workflows are highly unpredictable and are not correlated with the size of the dataset involved or the precise functions used in the analysis. We characterize this inherent variability and sketch the need for new scheduling approaches by quantifying significant gaps in achievable performance. Through simulations, we show how on-the-fly scheduling approaches can deliver benefits in both system-level and user-level performance measures. On average, we find improvements of up to 35% in system utilization and up to 45% in average stretch of the applications, illustrating the potential of increasing performance through new scheduling approaches

Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures

Crohn's disease (CD) is a chronic and relapsing inflammatory condition that affects segments of the gastrointestinal tract. CD activity is determined by histological findings, particularly the density of neutrophils observed on Hematoxylin and Eosin stains (H&E) imaging. However, understanding the broader morphometry and local cell arrangement beyond cell counting and tissue morphology remains challenging. To address this, we characterize six distinct cell types from H&E images and develop a novel approach for the local spatial signature of each cell. Specifically, we create a 10-cell neighborhood matrix, representing neighboring cell arrangements for each individual cell. Utilizing t-SNE for non-linear spatial projection in scatter-plot and Kernel Density Estimation contour-plot formats, our study examines patterns of differences in the cellular environment associated with the odds ratio of spatial patterns between active CD and control groups. This analysis is based on data collected at the two research institutes. The findings reveal heterogeneous nearest-neighbor patterns, signifying distinct tendencies of cell clustering, with a particular focus on the rectum region. These variations underscore the impact of data heterogeneity on cell spatial arrangements in CD patients. Moreover, the spatial distribution disparities between the two research sites highlight the significance of collaborative efforts among healthcare organizations. All research analysis pipeline tools are available at https://github.com/MASILab/cellNN.Comment: Submitted to SPIE Medical Imaging. San Diego, CA. February 202

Rubber Toughened and Nanoparticle Reinforced Epoxy Composites

Epoxy resins have achieved acceptance as adhesives, coatings, and potting compounds, but their main application is as matrix to produce reinforced composites. However, their usefulness in this field still limited due to their brittle nature. Some studies have been done to increase the toughness of epoxy composites, of which the most successful one is the modification of the polymer matrix with a second toughening phase. Resin Transfer Molding (RTM) is one of the most important technologies to manufacture fiber reinforced composites. In the last decade it has experimented new impulse, due to its favorable application to produce large surface composites with good technical properties and at relative low cost. This research work focuses on the development of novel modified epoxy matrices, with enhanced mechanical and thermal properties, suitable to be processed by resin transfer molding technology, to manufacture Glass Fiber Reinforced Composites (GFRC’s) with improved performance in comparison to the commercially available ones. In the first stage of the project, a neat epoxy resin (EP) was modified using two different nano-sized ceramics: silicium dioxide (SiO2) and zirconium dioxide (ZrO2); and micro-sized particles of silicone rubber (SR) as second filler. Series of nanocomposites and hybrid modified epoxy resins were obtained by systematic variation of filler contents. The rheology and curing process of the modified epoxy resins were determined in order to define their aptness to be processed by RTM. The resulting matrices were extensively characterized qualitatively and quantitatively to precise the effect of each filler on the polymer properties. It was shown that the nanoparticles confer better mechanical properties to the epoxy resin, including modulus and toughness. It was possible to improve simultaneously the tensile modulus and toughness of the epoxy matrix in more than 30 % and 50 % respectively, only by using 8 vol.-% nano-SiO2 as filler. A similar performance was obtained by nanocomposites containing zirconia. The epoxy matrix modified with 8 vol.-% ZrO2 recorded tensile modulus and toughness improved up to 36% and 45% respectively regarding EP. On the other hand, the addition of silicone rubber to EP and nanocomposites results in a superior toughness but has a slightly negative effect on modulus and strength. The addition of 3 vol.-% SR to the neat epoxy and nanocomposites increases their toughness between 1.5 and 2.5 fold; but implies also a reduction in their tensile modulus and strength in range 5-10%. Therefore, when the right proportion of nanoceramic and rubber were added to the epoxy resin, hybrid epoxy matrices with fracture toughness 3 fold higher than EP but also with up to 20% improved modulus were obtained. Widespread investigations were carried out to define the structural mechanisms responsible for these improvements. It was stated, that each type of filler induces specific energy dissipating mechanisms during the mechanical loading and fracture processes, which are closely related to their nature, morphology and of course to their bonding with the epoxy matrix. When both nanoceramic and silicone rubber are involved in the epoxy formulation, a superposition of their corresponding energy release mechanisms is generated, which provides the matrix with an unusual properties balance. From the modified matrices glass fiber reinforced RTM-plates were produced. The structure of the obtained composites was microscopically analyzed to determine their impregnation quality. In all cases composites with no structural defects (i.e. voids, delaminations) and good superficial finish were reached. The composites were also properly characterized. As expected the final performance of the GFRCs is strongly determined by the matrix properties. Thus, the enhancement reached by epoxy matrices is translated into better GFRC´s macroscopical properties. Composites with up to 15% enhanced strength and toughness improved up to 50%, were obtained from the modified epoxy matrices

Association Between Whole Blood-Derived Mitochondrial Dna Copy Number, Low-Density Lipoprotein Cholesterol, and Cardiovascular Disease Risk

Background The relationship between mitochondrial DNA copy number (mtDNA CN) and cardiovascular disease remains elusive. Methods and Results We performed cross-sectional and prospective association analyses of blood-derived mtDNA CN and cardiovascular disease outcomes in 27 316 participants in 8 cohorts of multiple racial and ethnic groups with whole-genome sequencing. We also performed Mendelian randomization to explore causal relationships of mtDNA CN with coronary heart disease (CHD) and cardiometabolic risk factors (obesity, diabetes, hypertension, and hyperlipidemia)

Stem cell-based ischemic stroke therapy: Novel modifications and clinical challenges

Ischemic stroke (IS) causes severe disability and high mortality worldwide. Stem cell (SC) therapy exhibits unique therapeutic potential for IS that differs from current treatments. SC's cell homing, differentiation and paracrine abilities give hope for neuroprotection. Recent studies on SC modification have enhanced therapeutic effects for IS, including gene transfection, nanoparticle modification, biomaterial modification and pretreatment. These methods improve survival rate, homing, neural differentiation, and paracrine abilities in ischemic areas. However, many problems must be resolved before SC therapy can be clinically applied. These issues include production quality and quantity, stability during transportation and storage, as well as usage regulations. Herein, we reviewed the brief pathogenesis of IS, the “multi-mechanism” advantages of SCs for treating IS, various SC modification methods, and SC therapy challenges. We aim to uncover the potential and overcome the challenges of using SCs for treating IS and convey innovative ideas for modifying SCs