Deformation capacity evaluation for flat slab seismic design

The authors acknowledge the dedicated and careful work carried out by the Associate Editor and Reviewers whose constructive criticism contributed to a very significant improvement of the quality of the paper. Authors wish to dedicate this work to the memory of their co-author Prof. Ion Radu Pascu, UTCB Bucharest, who passed away on June 10, 2021. Publisher Copyright: © 2021, The Author(s).In flat-slab frames, which are typically designed as secondary seismic structures, the shear failure of the slab around the column (punching failure) is typically the governing failure mode which limits the deformation capacity and can potentially lead to a progressive collapse of the structure. Existing rules to predict the capacity of flat slab frames to resist imposed lateral displacements without losing the capability to bear gravity loads have been derived empirically from the results of cyclic tests on thin members. These rules account explicitly only for the ratio between acting gravity loads and resistance against concentric punching shear (so-called Gravity Shear Ratio). Recent rational models to estimate the deformation capacity of flat slabs show that other parameters can play a major role and predict a significant size effect (reduced deformation for thick slabs). In this paper, a closed-form expression to predict the deformation capacity of internal slab-column connections as a function of the main parameters is derived from the same model that has been used to develop the punching shear formulae for the second generation of Eurocode 2 for concrete structures. This expression is compared to an existing database of isolated internal slab-column connections showing fine accuracy and allowing to resolve the shortcomings of existing rules. In addition, the results of a testing programme on a full-scale flat-slab frame with two stories and 12 columns are described. The differences between measured interstorey drifts and local slab rotations influencing their capacity to resist shear forces are presented and discussed. With respect to the observed deformation capacities, similar values are obtained as in the isolated specimens and the predictions are confirmed for the internal columns, but significant differences are observed between internal, edge and corner slab-column connections. The effects of punching shear reinforcement and of integrity reinforcement (required according to Eurocode 2 to prevent progressive collapse after punching) are also discussed.publishersversionpublishe

In Vitro Transformation of Primary Human CD34+ Cells by AML Fusion Oncogenes: Early Gene Expression Profiling Reveals Possible Drug Target in AML

Different fusion oncogenes in acute myeloid leukemia (AML) have distinct clinical and laboratory features suggesting different modes of malignant transformation. Here we compare the in vitro effects of representatives of 4 major groups of AML fusion oncogenes on primary human CD34+ cells. As expected from their clinical similarities, MLL-AF9 and NUP98-HOXA9 had very similar effects in vitro. They both caused erythroid hyperplasia and a clear block in erythroid and myeloid maturation. On the other hand, AML1-ETO and PML-RARA had only modest effects on myeloid and erythroid differentiation. All oncogenes except PML-RARA caused a dramatic increase in long-term proliferation and self-renewal. Gene expression profiling revealed two distinct temporal patterns of gene deregulation. Gene deregulation by MLL-AF9 and NUP98-HOXA9 peaked 3 days after transduction. In contrast, the vast majority of gene deregulation by AML1-ETO and PML-RARA occurred within 6 hours, followed by a dramatic drop in the numbers of deregulated genes. Interestingly, the p53 inhibitor MDM2 was upregulated by AML1-ETO at 6 hours. Nutlin-3, an inhibitor of the interaction between MDM2 and p53, specifically inhibited the proliferation and self-renewal of primary human CD34+ cells transduced with AML1-ETO, suggesting that MDM2 upregulation plays a role in cell transformation by AML1-ETO. These data show that differences among AML fusion oncogenes can be recapitulated in vitro using primary human CD34+ cells and that early gene expression profiling in these cells can reveal potential drug targets in AML

Towards Finite-Time Consensus with Graph Convolutional Neural Networks

Atrial electrograms are often used to gain understanding on the development of atrial fibrillation (AF). Using such electrograms, cardiologists can reconstruct how the depolarization wave-front propagates across the atrium. Knowing the exact moment at which the depolarization wavefront in the tissue reaches each electrode is an important aspect of such reconstruction. A common way to determine the LAT is based on the steepest deflection (SD) of the individual electrograms. However, the SD annotates each electrogram individually and is expected to be more prone to errors compared to approaches that would employ the data from the surrounding electrodes to estimate the LAT. As electrograms from neighboring electrodes tend to have rather similar morphology up to a delay, we propose in this paper to use the cross-correlation to find the pair-wise relative delays between electrograms. Instead of only using the direct neighbors we consider the array as a graph and involve higher order neighbors as well. Using a least-squares method, the absolute LATs can then be estimated from the calculated pair-wise relative delays. Simulated and clinically recorded electrograms are used to evaluate the proposed approach. From the simulated data it follows that the proposed approach outperforms the SD approach

Towards Finite-Time Consensus with Graph Convolutional Neural Networks

Atrial electrograms are often used to gain understanding on the development of atrial fibrillation (AF). Using such electrograms, cardiologists can reconstruct how the depolarization wave-front propagates across the atrium. Knowing the exact moment at which the depolarization wavefront in the tissue reaches each electrode is an important aspect of such reconstruction. A common way to determine the LAT is based on the steepest deflection (SD) of the individual electrograms. However, the SD annotates each electrogram individually and is expected to be more prone to errors compared to approaches that would employ the data from the surrounding electrodes to estimate the LAT. As electrograms from neighboring electrodes tend to have rather similar morphology up to a delay, we propose in this paper to use the cross-correlation to find the pair-wise relative delays between electrograms. Instead of only using the direct neighbors we consider the array as a graph and involve higher order neighbors as well. Using a least-squares method, the absolute LATs can then be estimated from the calculated pair-wise relative delays. Simulated and clinically recorded electrograms are used to evaluate the proposed approach. From the simulated data it follows that the proposed approach outperforms the SD approach.Multimedia Computin

Accuracy-diversity trade-off in recommender systems via graph convolutions

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns joint convolutional representations from a nearest neighbor and a furthest neighbor graph to establish a novel accuracy-diversity trade-off for recommender systems. The nearest neighbor graph connects entities (users or items) based on their similarities and is responsible for improving accuracy, while the furthest neighbor graph connects entities based on their dissimilarities and is responsible for diversifying recommendations. The information between the two convolutional modules is balanced already in the training phase through a regularizer inspired by multi-kernel learning. We evaluate the joint convolutional model on three benchmark datasets with different degrees of sparsity. The proposed method can either trade accuracy to improve substantially the catalog coverage or the diversity within the list; or improve both by a lesser amount. Compared with accuracy-oriented graph convolutional approaches, the proposed model shows diversity gains up to seven times by trading as little as 1% in accuracy. Compared with alternative accuracy-diversity trade-off solutions, the joint graph convolutional model retains the highest accuracy while offering a handle to increase diversity. To our knowledge, this is the first work proposing an accuracy-diversity trade-off with graph convolutions and opens the doors to learning over graphs approaches for improving such trade-off.</p

Accuracy-diversity trade-off in recommender systems via graph convolutions

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns joint convolutional representations from a nearest neighbor and a furthest neighbor graph to establish a novel accuracy-diversity trade-off for recommender systems. The nearest neighbor graph connects entities (users or items) based on their similarities and is responsible for improving accuracy, while the furthest neighbor graph connects entities based on their dissimilarities and is responsible for diversifying recommendations. The information between the two convolutional modules is balanced already in the training phase through a regularizer inspired by multi-kernel learning. We evaluate the joint convolutional model on three benchmark datasets with different degrees of sparsity. The proposed method can either trade accuracy to improve substantially the catalog coverage or the diversity within the list; or improve both by a lesser amount. Compared with accuracy-oriented graph convolutional approaches, the proposed model shows diversity gains up to seven times by trading as little as 1% in accuracy. Compared with alternative accuracy-diversity trade-off solutions, the joint graph convolutional model retains the highest accuracy while offering a handle to increase diversity. To our knowledge, this is the first work proposing an accuracy-diversity trade-off with graph convolutions and opens the doors to learning over graphs approaches for improving such trade-off.Multimedia ComputingIntelligent System

Task-Aware Connectivity Learning for Incoming Nodes Over Growing Graphs

Data processing over graphs is usually done on graphs of fixed size. However, graphs often grow with new nodes arriving over time. Knowing the connectivity information of these nodes, and thus, the expanded graph is crucial for processing data over the expanded graph. In its absence, its inference and the subsequent data processing become essential. This paper provides contributions along this direction by considering task-driven data processing for incoming nodes without connectivity information. We model the incoming node attachment as a random process dictated by the parameterized vectors of probabilities and weights of attachment. The attachment is driven by the existing graph topology, the corresponding graph signal, and an associated processing task. We consider two such tasks, one of interpolation at the incoming node, and that of graph signal smoothness. We show that the model bounds implicitly the spectral perturbation between the nominal topology of the expanded graph and the drawn realizations. In the absence of connectivity information our topology, task, and data-aware stochastic attachment performs better than purely data-driven and topology driven stochastic attachment rules, as is confirmed by numerical results over synthetic and real data.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia ComputingIntelligent System

Forecasting Multi-Dimensional Processes Over Graphs

The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector of quantities rather than a scalar one. To tackle this issue, we devise a new framework and propose new methodologies based on the graph vector autoregressive model. More explicitly, we leverage product graphs to model the high-dimensional graph data and develop multidimensional graph-based vector autoregressive models to forecast future trends with a number of parameters that is independent of the number of time series and a linear computational complexity. Numerical results demonstrating the prediction of moving point clouds corroborate our findings.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Circuits and SystemsMultimedia Computin

Filtering Random Graph Processes over Random Time-Varying Graphs

Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochasticity in both the graph topology and the signal itself. To bridge this gap, we examine the statistical behavior of the two key filter types, finite impulse response and autoregressive moving average graph filters, when operating on random time-varying graph signals (or random graph processes) over random time-varying graphs. Our analysis shows that 1) in expectation, the filters behave as the same deterministic filters operating on a deterministic graph, being the expected graph, having as input signal a deterministic signal, being the expected signal, and 2) there are meaningful upper bounds for the variance of the filter output. We conclude this paper by proposing two novel ways of exploiting randomness to improve (joint graph-time) noise cancellation, as well as to reduce the computational complexity of graph filtering. As demonstrated by numerical results, these methods outperform the disjoint average and denoise algorithm and yield a (up to) four times complexity reduction, with a very little difference from the optimal solution.Accepted Author ManuscriptCircuits and System