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Damien Rouchouse

Publications and source records attributed to Damien Rouchouse.

2 recordsLinked to original sources

Reprogrammable origami through bistable buckled hinges

Origami structures typically have a multiplicity of folded states that are connected to a flat sheet, making the folding protocol for specific end shapes challenging to design and deploy. Here, we introduce reprogrammable origami hinges that use bistable buckled shims to reversibly control their preferred folding direction. A shim embedded across a hinge produces an asymmetric torque-angle response that favors either mountain or valley folding. Switching the shim between its two stable states reverses this response, allowing the folding direction of each hinge to be reprogrammed after fabrication. By independently controlling the states and geometries of the shims, we enable a single origami sheet to access multiple folding branches and transform into prescribed three-dimensional shapes. We further introduce self-switching hinges in which folding causes the shims to snap between their stable states. These elements allow the sheet to reprogram its folding pathway under global mechanical inputs applied to the boundaries. Our approach embeds both shape selection and transition rules directly within the mechanics of the hinges, providing a versatile framework for creating multifunctional, deployable, and reconfigurable structures.

cond-mat.soft↗

Non-Vacuous Generalization Bounds: Can Rescaling Invariances Help?

A central challenge in understanding generalization is to obtain non-vacuous guarantees that go beyond worst-case complexity over data or weight space. Among existing approaches, PAC-Bayes bounds stand out as they can provide tight, data-dependent guarantees even for large networks. However, in ReLU networks, rescaling invariances mean that different weight distributions can represent the same function while leading to arbitrarily different PAC-Bayes complexities. We propose to study PAC-Bayes bounds in an invariant, lifted representation that resolves this discrepancy. This paper explores both the guarantees provided by this approach (invariance, tighter bounds via data processing) and the algorithmic aspects of KL-based rescaling-invariant PAC-Bayes bounds.

stat.ML↗