22 papers · ranked by Valyu relevance
Niklas Kowallik, Trever Schirmer, David Bermbach
Function-as-a-Service (FaaS) has become a central paradigm in serverless cloud computing, yet optimizing FaaS deployments remains challenging. Using function fusion, multiple functions can be combined into a single deployment unit, which can be used to reduce cost and latency of complex serverless applications…
Marlena Duda, Bradley Baker, Jessica Turner, Theo Van Erp + 1 more
Multimodal data fusion is a powerful technique for extracting shared and complementary information about the brain that is captured across neuroimaging modalities. Independent component analysis (ICA)-based approaches are among the most widely utilized methods for multimodal fusion, as they are data-driven, robust to…
Bingao Zhang, Xinyan You, Yiding Liu, Jingjing Xu + 2 more
The emerging paradigm of “fusion of lifeforms” represents a transformative shift from conventional human-machine interfaces toward deeply integrated symbiotic systems, where biological and artificial components co-adapt structurally, energetically, informationally, and cognitively. This review systematically classifies…
Daria Maslennikova, Harry Baird, Alessia Loffreda, Kaustubh Ramachandran + 10 more
DYT1 early-onset dystonia is a severe, incurable disorder of the central nervous system caused by mutations in the gene encoding Torsin1A (Tor1A, DYT1). Torsins are ER-resident AAA^+^-ATPases implicated in lipid metabolism, nuclear pore complex (NPC) biogenesis, and lipoprotein secretion, yet their molecular function…
Kunjing Yang, Zhiwei Wang, Minru Bai
Image fusion aims to integrate structural and complementary information from multi-source images. However, existing fusion methods are often either highly task-specific, or general frameworks that apply uniform strategies across diverse tasks, ignoring their distinct fusion mechanisms. To address this issue, we propose…
Ana-Maria Gherghelas, Christopher P. Toseland
Breast cancer is characterised by profound genetic heterogeneity, with oestrogen receptor alpha (ERα, encoded by ESR1) being a central driver in ~70% of cases. While point mutations in the ligand-binding domain of ESR1 are well recognised mediators of endocrine resistance, a growing body of evidence highlights ESR1…
Fabian Morelli, Stephan Eckstein
Ensembles of neural networks typically outperform individual networks but incur large computational costs, whereas weight aggregation produces less costly, yet also less accurate, aggregate models. We introduce partial fusion of networks, which interpolates between ensembles and weight aggregation and thus allows for a…
Junmin Hua, Evan S. Krystofiak, Andrew D. Pumford, Andrea Page-McCaw + 1 more
Tissue wounds comprise both dead and damaged cells. In epithelial wounds, repair is accomplished by cells at the wound edges, which are themselves often damaged. In the Drosophila pupal notum, wound-adjacent epithelial cells with plasma membrane damage often fuse to form syncytia; when plasma membrane damage is…
Yuhao Liu, Antonio Riveiro Rodríguez
Metamaterials can achieve extraordinary properties unattainable in natural materials through sophisticated artificial structural design. This study constructs novel tri-periodic minimal surface configurations based on fundamental structures. By employing surface boundary capture techniques to capture infinitely…
Authors not listed
Membrane fusion underpins many fundamental cellular processes, yet the design of artificial fusogens that can interact with specific cell surface markers in a precise and predictable manner remains a major challenge. Here we demonstrate that cholera toxin B-subunit (CTB), a naturally occurring glycolipid-binding…
Sajjita Saha, Aiswarya Sajeevan, Laura Merlini, Vincent Vincenzetti + 2 more
The conserved Cdc42 GTPase is a key driver of symmetry breaking and polarized growth, forming zones of activity that locally recruit effectors to organize the cytoskeleton and polarize secretion. Here, we show that Cdc42 also functions in cell-cell fusion during Schizosaccharomyces pombe sexual reproduction, but…
Nadine S. Kurz, Irem Berna Güven, Tim Beißbarth, Jürgen Dönitz
Accurate prediction of gene fusion pathogenicity is critical for understanding oncogenic mechanisms and advancing precision oncology. While existing computational methods provide valuable insights, their performance remains limited by incomplete integration of multi-scale biological features and lack of…
ChangChao Liu, Jinzhe Han, Yue Zhang, Zhizheng Zhang + 2 more
In the field of intelligent fault diagnosis, graph neural networks (GNNs) can create a richer fault feature space by modeling dependencies between sensor signals and embedding them in a structural attribute graph. However, existing GNNs models typically use monitoring signals to directly construct graphical datasets…
Manan Kharwar
We present FusionCore, an open-source ROS 2 sensor fusion package that fuses IMU, wheel encoder odometry, GPS, and Visual SLAM pose into a single 100 Hz odometry stream using a 23-state Unscented Kalman Filter (UKF). The 23rd state is an online estimate of the wheel encoder's systematic yaw rate bias, identified…
Arya Kaul, Fernando Rossine, Karel Břinda, Michael Baym
Bacteria are hosts to enormous genic diversity. How new genes emerge, functionalize, and spread remain longstanding questions. Here, we explore a mechanism by which adaptive deletions fuse distant gene fragments. Unlike other gene birth mechanisms that begin with rare, neutral mutations, these “deletion-born fusions”…
Tao Zhoua, Yunlong Liu, Qinghui Chen, Zekai Zhang + 5 more
Infrared and visible image fusion aims to generate a composite image that retains significant target information and preserves detailed textures, integrating two heterogeneous modalities. Previous image fusion methods typically adopt a single-module stacking approach to extract features from the two modalities.…
Zhu, Huayi, Shu, Xiu + 12 more
—Current multi-modal image fusion methods typically rely on task-specific models, leading to high training costs and limited scalability. While generative methods provide a unified modeling perspective, they often suffer from slow inference due to the complex sampling trajectories from noise to image. To address this…
Shuni Feng, Qingzhou Wu, Kailin Zhang, Yu Song + 1 more
Hearing-impaired people face challenges in expressing and perceiving emotions, and traditional single-modal emotion recognition methods demonstrate limited effectiveness in complex environments. To enhance recognition performance, this paper proposes a multimodal fusion neural network based on a multimodal multi-head…
Chang Liu, Xiaoyan Wang, Mostafa Orban, Alexander Vartanov + 5 more
This paper reviews recent advances in assistive devices based on multimodal information fusion control, designed for individuals with motor dysfunction. The prevalence of motor dysfunction is increasingly concerning amidst global population aging. Information fusion technology, widely adopted in rehabilitation…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
Early prediction of drug-induced organ toxicity remains a major bottleneck in drug discovery and clinical pharmacotherapy. Most data-driven toxicity models behave as endpoint predictors: they output a label but provide limited transparency about why a compound is risky or which evidence channel dominated the decision.…
Torbjörn Nur Olsson, Atsarina Larasati Anindya, María-José García-Bonete, Elwin Vrouwe + 10 more
Understanding the complex landscape of protein interactions, especially those involving intrinsically disordered proteins (IDPs), is fundamental yet challenging due to their structural heterogeneity and flexibility. Traditional sequence-based homology methods frequently fall short in characterizing IDP functions and…