Computational Sketch
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Sketch-to-Layout: A Human-Centric Computational Agent for Constraint-Aware Synthesis of Modular Photobioreactors
arXiv:2606.09849v1 Announce Type: new Abstract: Building-integrated photobioreactors (PBRs) offer a pathway for carbon-neutral architecture, yet deployment is hindered by configuration complexity and biological maintenance. This paper presents a modular PBR facade system powered by a computational framework reconciling design intent with physical validity. We introduce 'carbon-neutralization bricks' featuring integrated vessel-and-conduit geometry; monolithic fluid channels enable...
The Fast Mixing Mechanism for Differential Privacy
Announce Type: new Abstract: Randomized sketching is a central tool for compressing large-scale optimization problems while preserving accuracy. In particular, sketches that are based on structured matrices, such as the Hadamard matrix, can be applied efficiently and often yield solutions that approximate those of the original problem at much lower computational cost. In differential privacy (DP), Gaussian sketching has been used to solve DP linear regression, beginning with...
Modulation-Reaction Networks
arXiv:2606.01193v1 Announce Type: new Abstract: Biochemical systems involve both the flow of matter, in which entities transform into one another via reactions, and the flow of information, in which entities regulate which reactions may occur. Boolean networks capture the latter; reaction networks capture the former. Yet no unified qualitative formalism treats regulated reactions as its principal objects of study, despite their prominence in standards such as the Systems Biology Graphical...
Inference of Online Newton Methods with Nesterov's Accelerated Sketching
Announce Type: replace-cross Abstract: Reliable decision-making with streaming data requires principled uncertainty quantification of online methods. While first-order methods enable efficient iterate updates, their inference procedures still require updating proper (covariance) matrices, incurring $O(d^2)$ time and memory complexity, and are sensitive to ill-conditioning and noise heterogeneity of the problem. This costly inference task offers an opportunity for more robust second-order...
Low-Variance Randomised Numerical Linear Algebra for Finite Element Simulation
arXiv:2606.08817v1 Announce Type: new Abstract: We present a low-variance randomised numerical linear algebra approach for multi-query finite element systems arising from parametric elliptic partial differential equations with applications to digital twins and online model calibration. The method relies on Galerkin subspace projection for reducing the dimensionality, and then combines parameter-oblivious leverage-score Bernoulli sampling with a control variates scheme to yield a...
Stable Deep Reinforcement Learning via Isotropic Gaussian Representations
arXiv:2602.19373v3 Announce Type: replace Abstract: Deep reinforcement learning systems often suffer from unstable training dynamics due to non-stationarity, where learning objectives and data distributions evolve over time. We show that under non-stationary targets, isotropic Gaussian embeddings are provably advantageous. In particular, they induce stable tracking of time-varying targets for linear readouts, achieve maximal entropy under a fixed variance budget, and encourage a balanced use...
Negative and Fractional Types in the Fidelity Framework
arXiv:2606.04352v1 Announce Type: new Abstract: Our Native Type Universe (NTU) has been detailed through five previous papers establishing the substrate our framework's compilation pipeline targets across multiple hardware platforms. We have found in the course of that work a deeper reach this foundation makes available: negative and fractional types as native first-class constructs.
UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD
arXiv:2606.05058v1 Announce Type: new Abstract: Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models. However, CAD research typically studies tasks in isolation, and multi-modal, multi-task learning for CAD is hindered by the absence of a unified benchmark. To address this gap, we introduce UniCAD, a comprehensive benchmark for multi-modal CAD learning that covers point-to-CAD reconstruction, text/image-to-CAD...
Crystal Nights by Greg Egan
Publication history - Interzone #215, April 2008. - Free podcast at Transmissions From Beyond. [Site no longer active] - Oceanic (collection, Orion) -
Human-Like Neural Nets by Catapulting
Human-like Neural Nets by Catapulting Speculative proposal to create artificial neural nets with human-like performance by high-learning-rate/regularization training of overparameterized NNs to trigger catapulting/grokking. Over-parameterization as a route to true generalization would resolve many outstanding mysteries of artificial versus natural intelligence. There are many mysteries about deep learning and human intelligence, but we could describe the biggest anomaly this way: why are...