Marc Bara

Marc Bara

This note extends Restrepo (2025)'s model of economic growth under AGI by incorporating Moravec's Paradox -the observation that tasks requiring sensorimotor skills remain computationally expensive relative to cognitive tasks. We partition the task space into cognitive and physical components with differential automation costs, allowing infinite costs for some physical bottlenecks. Our key result shows that when physical tasks constitute economic bottlenecks with sufficiently high (or infinite) computational requirements, the labor share of income converges to a positive constant in the finite-compute regime (rather than zero). This fundamentally alters the distributional implications of AGI while preserving the growth dynamics for cognitive-intensive economies.

Marc Bara

Dataless neural networks represent a paradigm shift in applying neural architectures to combinatorial optimization problems, eliminating the need for training datasets by encoding problem instances directly into network parameters. Despite the pioneering work of Alkhouri et al. (2022) demonstrating the viability of dataless approaches for the Maximum Independent Set problem, our comprehensive literature review reveals that no published work has extended these methods to the Resource-Constrained Project Scheduling Problem (RCPSP). This paper addresses this gap by presenting the first dataless neural network approach for RCPSP, providing a complete mathematical framework that transforms discrete scheduling constraints into differentiable objectives suitable for gradient-based optimization. Our approach leverages smooth relaxations and automatic differentiation to unlock GPU parallelization for project scheduling, traditionally a domain of sequential algorithms. We detail the mathematical formulation for both precedence and renewable resource constraints, including a memory-efficient dense time-grid representation. Implementation and comprehensive experiments on PSPLIB benchmark instances (J30, J60, and J120) are currently underway, with empirical results to be reported in an updated version of this paper.

Marc Bara

We present the first theoretical framework for applying spiking neural networks (SNNs) to synthetic aperture radar (SAR) interferometric phase unwrapping. Despite extensive research in both domains, our comprehensive literature review confirms that SNNs have never been applied to phase unwrapping, representing a significant gap in current methodologies. As Earth observation data volumes continue to grow exponentially (with missions like NISAR expected to generate 100PB in two years) energy-efficient processing becomes critical for sustainable data center operations. SNNs, with their event-driven computation model, offer potential energy savings of 30-100x compared to conventional approaches while maintaining comparable accuracy. We develop spike encoding schemes specifically designed for wrapped phase data, propose SNN architectures that leverage the spatial propagation nature of phase unwrapping, and provide theoretical analysis of computational complexity and convergence properties. Our framework demonstrates how the temporal dynamics inherent in SNNs can naturally model the spatial continuity constraints fundamental to phase unwrapping. This work opens a new research direction at the intersection of neuromorphic computing and SAR interferometry, offering a complementary approach to existing algorithms that could enable more sustainable large-scale InSAR processing.