arXiv subjects

Jason Shepherd

Publications and source records attributed to Jason Shepherd.

3 recordsLinked to original sources

arXiv2026

Agentic Digital Twins: A Taxonomy of Capabilities for Understanding Possible Futures

As digital twins (DTs) evolve to become more agentic through the integration of artificial intelligence (AI), they acquire capabilities that extend beyond dynamic representation of their target systems. This paper presents a taxonomy of agentic DTs organised around three fundamental dimensions: the locus of agency (external, internal, distributed), the tightness of coupling (loose, tight, constitutive), and model evolution (static, adaptive, reconstructive). From the resulting 27-configuration space, we identify nine illustrative configurations grouped into three clusters: "The Present" (existing tools and emerging steering systems), "The Threshold" (where emergent properties appear and coupling becomes constitutive), and "The Frontier" (where systems gain reconstructive capabilities). Our analysis explores how agentic DTs exercise performative power--not merely representing physical systems but actively participating in constituting them. Using traffic navigation systems as examples, we show how even passive tools can exhibit emergent performativity, while advanced configurations risk performative lock-in. Drawing on performative prediction theory, we trace a progression from passive tools through active steering to ontological reconstruction, examining how constitutive coupling enables systems to create self-validating realities. Understanding these configurations is essential for navigating the transformation from DTs as mirror worlds to DTs as architects of new ontologies.

cs.CY↗

arXiv2020

3D Computational Cannula Fluorescence Microscopy enabled by Artificial Neural Networks

Computational Cannula Microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and fluorescence emission, where computational methods are used for image visualization. Here, we enhance CCM with artificial neural networks to enable 3D imaging of cultured neurons and fluorescent beads, the latter inside a volumetric phantom. We experimentally demonstrate transverse resolution of ~6um, field of view ~200um and axial sectioning of ~50um for depths down to ~700um, all achieved with computation time of ~3ms/frame on a laptop computer.

eess.IV↗

arXiv2020

Computational Cannula Microscopy of neurons using Neural Networks

Computational Cannula Microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable fast, power-efficient image reconstructions that are more efficiently scalable to larger fields of view. Specifically, we demonstrate widefield fluorescence microscopy of cultured neurons and fluorescent beads with field of view of 200$\mu$m (diameter) and resolution of less than 10$\mu$m using a cannula of diameter of only 220$\mu$m. In addition, we show that this approach can also be extended to macro-photography.

eess.IV↗