technocracy

Below is my annual summary of some of the best books I read in the past year. There are a few themes that weave their way throughout: the brain, running, history, and biographies. Here I also try to rank them loosely – the first ones are my top choices. Enjoy!  The Strange Order of Things by Antonio Damasio I heard Damasio talk a few times and was unimpressed, feeling that his arrogance to substance ratio was too high […]

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Predicting the stress-strain behaviors of additively manufactured materials is crucial for part qualification in additive manufacturing (AM). Conventional physics-based constitutive models often oversimplify material properties, while data-driven machine learning (ML) models often lack physical consistency and interpretability. To address these issues, we propose a physics-informed machine learning (PIML) framework to improve the predictive performance and physical consistency for predicting the stress-strain curves of additively manufactured polymers and metals. A polynomial regression model is used to predict the yield […]

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arXiv:2602.15237v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in emergency first response (EFR) applications to support situational awareness (SA) and decision-making, yet most operate on text or 2D imagery and offer little support for core EFR SA competencies like spatial reasoning. We address this gap by evaluating a prototype that fuses robot-mounted depth sensing and YOLO detection with a vision language model (VLM) capable of verbalizing metrically-grounded distances of detected objects (e.g., the chair […]

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Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensional computing (HDC) is an emerging resource efficient and low-complexity ML method that allows hardware efficient implementations of (re-)training and inference procedures. In this paper, we propose a maximum-margin HDC classifier, which significantly outperforms baseline HDC methods on several benchmark datasets. Our method leverages a formal relation between HDC and support vector machines (SVMs) that we established for […]

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arXiv:2602.05799v1 Announce Type: cross Abstract: We study non-stationary single-item, periodic-review inventory control problems in which the demand distribution is unknown and may change over time. We analyze how demand non-stationarity affects learning performance across inventory models, including systems with demand backlogging or lost-sales, both with and without lead times. For each setting, we propose an adaptive online algorithm that optimizes over the class of base-stock policies and establish performance guarantees in terms of dynamic regret relative to the […]

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This is not an article delving into the details of AWS Secrets Manager or SSM Parameter Store, but a quick overview of how to use them to store secrets like API keys as I did in this project. https://github.com/avr2002/files-api/tree/main?embedable=true AWS Secrets Manager AWS Secrets Manager helps you to securely store, rotate, and manage sensitive information like API keys, database credentials, OAuth tokens, and other secrets. Instead of hardcoding credentials in your application code, you can store them in […]

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Predicting atomic-scale crack propagation in aluminum nitride (AlN) is critical for semiconductor reliability but remains prohibitively expensive via molecular dynamics (MD). We develop a diffusion-based generative machine learning model to predict atomic-scale crack propagation in AlN, a critical semiconductor material, by conditioning solely on initial microstructure embeddings. Trained on MD simulations of single-crack systems, the model achieves a significant speedup while accurately forecasting dynamic fracture processes, including stress-driven crack initiation, crack branching, and atomic-scale bridging ligaments. Crucially, it […]

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arXiv:2601.05415v1 Announce Type: cross Abstract: Multi-group classification arises in many prediction and decision-making problems, including applications in epidemiology, genomics, finance, and image recognition. Although classification methods have advanced considerably, much of the literature focuses on binary problems, and available extensions often provide limited flexibility for multi-group settings. Recent work has extended linear discriminant analysis to multiple groups, but more general methods are still needed to handle complex structures such as nonlinear decision boundaries and group-specific covariance patterns. We […]

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arXiv:2603.08733v1 Announce Type: new Abstract: Ancilla reuse in repeated syndrome extraction couples reset quality to logical-cycle latency. We evaluate blind reset — unitary-only recycling via scaled sequence replay — on IQM Garnet, Rigetti Ankaa-3, and IonQ under matched seeds, sequence lengths, and shot budgets. Using ancilla cleanliness F_clean=P(|0>), per-cycle latency, and a distance-3 repetition-code logical-error proxy, platform-calibrated simulation identifies candidate regions where blind reset cuts cycle latency by up to 38x under NVQLink-class feedback overhead while maintaining F_clean […]

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