Companion to the exhibition “Japan’s Book Donation to the University of Louvain”, KU Leuven University Library, 28 October 2022 - 15 January 2023 With more than 3,000 titles in almost 14,000 volumes, ...the 1920s Japanese book donation to the University of Leuven/Louvain constitutes an invaluable time capsule of Japan’s pre-modern culture in all its diversity and richness. A century on, the time is right to take a new look at its contents, as well as its history and the political, social and cultural context surrounding the donation. To commemorate its centenary, the Katholieke Universiteit Leuven (KU Leuven) and the Université catholique de Louvain (UCLouvain) have joined forces to set up a special exhibition under the title “Japan’s Book Donation to the University of Louvain. Japanese Cultural Identity and Modernity in the 1920s” (October 2022–January 2023), at the University Library of KU Leuven. The present book has been compiled for the occasion of the exhibition, to serve as a durable guide to the magnificent book donation and its historical background, and as a reference for further research in the future. In five essays by historians of politics, media, culture, and arts of Japan, it offers a richly illustrated overview of the history of the donation and its wider historical context, providing illuminating insights into the vibrant 1920s in Japan, its politics, society, and popular culture. The reader is further invited to explore a sample of 65 remarkable and rare items from the donation, which were carefully selected for inclusion in the exhibition and are provided here with a detailed description. Moreover, the reader is introduced to 41 representative items, including visually captivating commercial and political posters related to Japan’s modernity in the 1920s, which represent mass culture, progress, and tensions, and highlight both imperial ambitions and a willingness to contribute to international cooperation.
Van katholieke propaganda en maatschappelijk belang tot wetenschappelijk onderzoek. Horen de natuurwetenschappen thuis aan een universiteit? Van bij haar oprichting in 1817 heeft de Leuvense ...faculteit voor wetenschappen moeten strijden om haar bestaan te rechtvaardigen. Daarbij moest ze steeds opnieuw het juiste evenwicht vinden tussen katholieke propaganda, maatschappelijk nut en zuivere wetenschap. Pas op het einde van de negentiende eeuw slaagde ze erin het roer in eigen handen te nemen. Wetenschappelijk onderzoek werd voortaan de norm. Sindsdien staat de faculteit vooraan in de verwetenschappelijking van de universiteit. Dit boek vertelt het verhaal van de Leuvense faculteit voor wetenschappen en haar steeds wisselende relatie tot universiteit en maatschappij.
Resource management in fog computing is a highly challenging issue. Resource constraints of fog devices, the dynamic and diverse nature of workloads such as data-intensive, computationally intensive, ...and latency-sensitive applications, and the unpredictability of fog computing environments make it more challenging. How to collaboratively schedule edge, and end multi-layer ubiquitous computing resources to break through single node scale and resource bottlenecks to further improve computing performance also needs to be addressed. In this paper, we leverage idle resources on the user side, and study the resource allocation problems in the D2D-assisted fog computing networks. Firstly, we use an improved Louvain algorithm to cluster users and select the D2D device with the highest priority in each cluster as the cluster head to assist fog computing, where cluster heads can act as both servers and relays. Secondly, considering that fog nodes and D2D devices have limited resources and are not obligated to share their resources for free, we propose a price incentive method to construct a three-layer Stackelberg game model that maximizes the benefits of fog nodes and cluster heads while minimizing user costs. Then, we use the Lagrange multiplier method and the Karush–Kuhn–Tucker (KKT) conditions to solve the equilibrium point of the model to achieve our goal. Finally, the simulation results show that our proposed scheme markedly decreases the overall system cost in comparison to the four benchmark schemes. Moreover, in various scenarios the total cost of our scheme consistently remains lower than the other four schemes, affirming the efficiency of the scheme proposed in this paper.
Community detection algorithms try to identify the underlying community structure (i.e., clearly distinguishable closely interacting groups of vertices) in a graph representing complex systems such ...as social networks, protein-protein interaction networks, and the World-Wide-Web. The Louvain algorithm iteratively moves vertices from one community to another to construct disjoint sets of vertices to form communities such that the vertices within the same community have more edges within themselves compared to their connections to the vertices outside the community. A property of the Louvain algorithm is that the number of vertex moves drops significantly just after the first few iterations because the community-membership also stabilizes quickly. In this paper, we present a parallel pull-and-push Louvain algorithm that exploits this property to prune unnecessary edge explorations without sacrificing the quality of the solution. We present a collection of parallel Louvain algorithms that prune many edges and vertices, speeding up convergence by an order of magnitude over the previously best-known implementation of the Louvain algorithm from Grappolo, while producing similar or better results.
Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we ...performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.
Focussing on an anomaly - highly controverisal, but at face value useless privileges granted to the university of Louvain -, this book explores the entanglement of material, political, religious and ...intellectual interests nurtured by early modern academics in the Confessional Age.
In most real-world networks, nodes/vertices tend to be organized into tightly-knit modules known as communities or clusters such that nodes within a community are more likely to be connected or ...related to one another than they are to the rest of the network. Community detection in a network (graph) is aimed at finding a partitioning of the vertices into communities. The goodness of the partitioning is commonly measured using modularity. Maximizing modularity is an NP-complete problem. In 2008, Blondel et al. introduced a multi-phase, multi-iteration heuristic for modularity maximization called the Louvain method. Owing to its speed and ability to yield high quality communities, the Louvain method continues to be one of the most widely used tools for serial community detection.
Distributed multi-GPU systems pose significant challenges and opportunities for efficient execution of parallel applications. Graph algorithms, in particular, have been known to be harder to parallelize on such platforms, due to irregular memory accesses, low computation to communication ratios, and load balancing problems that are especially hard to address on multi-GPU systems.
In this paper, we present our ongoing work on distributed-memory implementation of Louvain method on heterogeneous systems. We build on our prior work parallelizing the Louvain method for community detection on traditional CPU-only distributed systems without GPUs. Corroborated by an extensive set of experiments on multi-GPU systems, we demonstrate competitive performance to existing distributed-memory CPU-based implementation, up to 3.2× speedup using 16 nodes of OLCF Summit relative to two nodes, and up to 19× speedup relative to the NVIDIA RAPIDS® cuGraph® implementation on a single NVIDIA V100 GPU from DGX-2 platform, while achieving high quality solutions comparable to the original Louvain method. To the best of our knowledge, this work represents the first effort for community detection on distributed multi-GPU systems. Our approach and related findings can be extended to numerous other iterative graph algorithms on multi-GPU systems.
•Present cuVite, a distributed multi-GPU C++ library for community detection using the Louvain method as a serial template.•Discuss the various challenges in porting irregular applications on multi-GPU systems and present strategies to address these challenges.•Demonstrate up to 20× improvement relative to NVIDIA RAPIDS cuGraph on a single node, and about 1.6–3.2× strong scaling performance over 2–16 OLCF Summit nodes.•Demonstrate speedups of up to 6× on 2048 processes of ALCF Theta using eight real-world graphs, including characterization of different memory modes.•Demonstrate parity of solutions computed by cuVite with the solutions reported by serial and CPU-only implementations.
Asymptomatic and symptomatic Alzheimer’s disease (AD) subjects may present with equivalent neuropathological burdens but have significantly different antemortem cognitive decline rates. Using the ...transcriptome as a proxy for functional state, we selected 414 expression profiles of symptomatic AD subjects and age-matched non-demented controls from a community-based neuropathological study. By combining brain tissue-specific protein interactomes with gene networks, we identified functionally distinct composite clusters of genes that reveal extensive changes in expression levels in AD. Global expression for clusters broadly corresponding to synaptic transmission, metabolism, cell cycle, survival, and immune response were downregulated, while the upregulated cluster included largely uncharacterized processes. We propose that loss of EGR3 regulation mediates synaptic deficits by targeting the synaptic vesicle cycle. Our results highlight the utility of integrating protein interactions with gene perturbations to generate a comprehensive framework for characterizing alterations in the molecular network as applied to AD.
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•RNA expression profiling of 414 Alzheimer’s disease and non-demented controls•Integration of transcriptomic profiles with brain tissue-specific protein interactome•Revealed biologically distinct clusters by Louvain algorithm for community detection•Characterized transcriptional regulators across all clusters
Canchi et al. reveal the transcriptomic dynamics of clinically and neuropathologically confirmed Alzheimer’s disease subjects by integrating brain tissue-specific proteome data with gene network analysis. They identify perturbed biological processes and provide insights into the interactions between molecular mechanisms in symptomatic Alzheimer’s disease.
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