With the emergence of online social networks (OSNs), the way people create and share information has changed, which becomes faster and broader than traditional social media. Understanding how... Show moreWith the emergence of online social networks (OSNs), the way people create and share information has changed, which becomes faster and broader than traditional social media. Understanding how information (both good and harmful) spreads through OSNs, as well as what elements drive the success of information diffusion, has significant implications for a wide range of real-world applications. In this thesis, we conduct research to analysis the diffusion of information in OSNs via using deep representation learning. Specifically, we aim to develop deep learning- based models to solve two specific tasks, i.e., information cascades modeling and rumor detection. Show less
Existing methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network... Show moreExisting methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network differences. In this work, a novel method, permutation-based Network True Discovery Proportions (NetTDP), is proposed to quantify the number of edges (correlations) or nodes (genes) for which the co-expression networks are different. In the NetTDP method, we propose an edge-level statistic and a node-level statistic, and detect true discoveries of edges and nodes in the sense of differential co-expression network, respectively, by the permutation-based sumSome method. Furthermore, the NetTDP method could further localize the differences by inferring the TDPs for edge or gene subsets of interest, which can be selected post hoc. Our NetTDP method allows inference on data-driven modules or biology-driven gene sets, and remains valid even when these sub-networks are optimized using the same data. Experimental results on both simulation data sets and five real data sets show the effectiveness of the proposed method in inferring the quantification and localization of differential co-expression networks. The R code is available at hrips://github.com/LiminLi-xjtu/NetTDP. Show less
Simultaneous inference allows for the exploration of data while deciding on criteria for proclaiming discoveries. It was recently proved that all admissible post hoc inference methods for the true... Show moreSimultaneous inference allows for the exploration of data while deciding on criteria for proclaiming discoveries. It was recently proved that all admissible post hoc inference methods for the true discoveries must employ closed testing. In this paper, we investigate efficient closed testing with local tests of a special form: thresholding a function of sums of test scores for the individual hypotheses. Under this special design, we propose a new statistic that quantifies the cost of multiplicity adjustments, and we develop fast (mostly linear-time) algorithms for post hoc inference. Paired with recent advances in global null tests based on generalized means, our work instantiates a series of simultaneous inference methods that can handle many dependence structures and signal compositions. We provide guidance on the method choices via theoretical investigation of the conservativeness and sensitivity for different local tests, as well as simulations that find analogous behavior for local tests and full closed testing. Show less
Ye, C.; Raaijman, S.J.; Chen, X.; Koper, M.T.M. 2022
Developing active and selective catalysts that convert CO2 into valuable products remains a critical challenge for further application of the electrochemical CO2 reduction reaction (CO2RR).... Show moreDeveloping active and selective catalysts that convert CO2 into valuable products remains a critical challenge for further application of the electrochemical CO2 reduction reaction (CO2RR). Catalytic tuning with organic additives/films has emerged as a promising strategy to tune CO2RR activity and selectivity. Herein, we report a facile method to significantly change CO2RR selectivity and activity of copper and gold electrodes. We found improved selectivity toward HCOOH at low overpotentials on both polycrystalline Cu and Au electrodes after chemical modification with a poly(4-vinylpyridine) (P4VP) layer. In situ attenuated total reflection surface-enhanced infrared reflection-adsorption spectroscopy and contact angle measurements indicate that the hydrophobic nature of the P4VP layer limits mass transport of HCO3- and H2O, whereas it has little influence on CO2 mass transport. Moreover, the early onset of HCOOH formation and the enhanced formation of HCOOH over CO suggest that P4VP modification promotes a surface hydride mechanism for HCOOH formation on both electrodes. Show less
Jong, T.A. de; Chen, X.; Jobst, J.; Krasovskii, E.E.; Tromp, R.M.; Molen, S.J. van der 2022
Stacking domain boundaries occur in Van der Waals heterostacks whenever there is a twist angle or lattice mismatch between subsequent layers. Not only can these domain boundaries host topological... Show moreStacking domain boundaries occur in Van der Waals heterostacks whenever there is a twist angle or lattice mismatch between subsequent layers. Not only can these domain boundaries host topological edge states, imaging them has been instrumental to determine local variations in twisted bilayer graphene. Here, we analyse the mechanisms causing stacking domain boundary contrast in Bright Field Low-Energy Electron Microscopy (BF-LEEM) for both graphene on SiC, where domain boundaries are caused by strain and for twisted few layer graphene. We show that when domain boundaries are between the top two graphene layers, BF-LEEM contrast is observed due to amplitude contrast and corresponds well to calculations of the contrast based purely on the local stacking in the domain boundary. Conversely, for deeper-lying domain boundaries, amplitude contrast only provides a weak distinction between the inequivalent stackings in the domains themselves. However, for small domains phase contrast, where electrons from different parts of the unit cell interfere causes a very strong contrast. We derive a general rule-of-thumb of expected BF-LEEM contrast for domain boundaries in Van der Waals materials. Show less
Chen, X.; Granda Marulanda, L.P.; McCrum, I.T.; Koper, M.T.M. 2022
Researchers, enterprises, and governments have made great efforts to detect misinformation promptly and accurately. Traditional solutions either examine complicated hand-crafted features or rely... Show moreResearchers, enterprises, and governments have made great efforts to detect misinformation promptly and accurately. Traditional solutions either examine complicated hand-crafted features or rely heavily on the constructed credibility networks to extract useful indicators for discerning false information. However, such approaches require insightful domain expert knowledge and intensive feature engineering that are often non-generalizable. Recent advances in deep learning techniques have spurred learning high-level representations from textual and image content and discovering diffusion patterns with various neural networks. Despite the progress made by these methods, they still face the problem of overdependence on the content features and fail to discriminate against the influence of each user involved in the process of rumor spreading. Different user-aspect information plays different roles in various stages of rumor diffusion, effectively extract features from each aspect, and aggregate the learned features into a unique representation, which has not been well investigated. To address these limitations, we propose a novel model, UMLARD (User-aspect Multi-view Learning with Attention for Rumor Detection), to effectively learn the representation of different views of the users who engaged in spreading the tweet, and fuse the learned features through the distinguishable fusion mechanism. Finally, we concatenate the learned user-aspect features with content features to form a unique representation and feed it into a fully connected layer to predict the label of rumors. Our experiments conducted on real-world datasets demonstrate that UMLARD significantly improves the rumor detection performance compared to state-of-the-art baselines. It also allows explainability of the model behavior and the predicted results. Show less
