LLM Observability with Self-Hosted Langfuse and vLLM
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SFO: Learning PDE Operators via Spectral Filtering
Partial differential equations (PDEs) govern complex systems, yet neural operators often struggle to efficiently capture the long-range, nonlocal interactions inherent in their solution maps. We introduce Spectral Filtering Operator (SFO), a neural operator that parameterizes integral kernels using the Universal Spectral Basis (USB), a fixed, global orthonormal basis derived from the eigenmodes of the Hilbert matrix in spectral filtering theory. Motivated by our theoretical finding that the discrete Green’s functions of shift-invariant PDE discretizations exhibit spatial Linear Dynamical […]
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SQL Part 2.
SQL is a very organized, functional but tricky language. It is tricky because you have to logically think about which things or variables need to be factored in in order to smartly obtain important data from a table/database. I learnt a lot in one of my projects about handling some of the functions used in SQL and how they are used so that we can query a dataset with relevant data. It does get tricky, lets see some […]
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Causal Learning Should Embrace the Wisdom of the Crowd
Learning causal structures typically represented by directed acyclic graphs (DAGs) from observational data is notoriously challenging due to the combinatorial explosion of possible graphs and inherent ambiguities in observations. This paper argues that causal learning is now ready for the emergence of a new paradigm supported by rapidly advancing technologies, fulfilling the long-standing vision of leveraging human causal knowledge. This paradigm integrates scalable crowdsourcing platforms for data collection, interactive knowledge elicitation for expert opinion modeling, robust aggregation techniques […]
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Facebook Echo Chambers Are Real But They May Not Be Driving Polarization
:::info Authors: Brendan Nyhan Jaime Settle Emily Thorson Magdalena Wojcieszak Pablo Barberá Annie Y. Chen Hunt Allcott Taylor Brown Adriana Crespo-Tenorio Drew Dimmery Deen Freelon Matthew Gentzkow Sandra González-Bailón Andrew M. Guess Edward Kennedy Young Mie Kim David Lazer Neil Malhotra Devra Moehler Jennifer Pan Daniel Robert Thomas Rebekah Tromble Carlos Velasco Rivera Arjun Wilkins Beixian Xiong Chad Kiewiet de Jonge Annie Franco Winter Mason Natalie Jomini Stroud Joshua A. Tucker ::: Abstract Many critics raise concerns about […]
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Quiz: Sending Emails With Python
In this quiz, you’ll test your understanding of Sending Emails With Python. By working through this quiz, you’ll revisit how to build messages with the EmailMessage class, secure your SMTP connection, attach files, send HTML alternatives, route replies to a different mailbox, and address multiple recipients at once. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn […]
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FACTO: Function-space Adaptive Constrained Trajectory Optimization for Robotic Manipulators
arXiv:2602.20225v1 Announce Type: new Abstract: This paper introduces Function-space Adaptive Constrained Trajectory Optimization (FACTO), a new trajectory optimization algorithm for both single- and multi-arm manipulators. Trajectory representations are parameterized as linear combinations of orthogonal basis functions, and optimization is performed directly in the coefficient space. The constrained problem formulation consists of both an objective functional and a finite-dimensional objective defined over truncated coefficients. To address nonlinearity, FACTO uses a Gauss-Newton approximation with exponential moving averaging, yielding a smoothed […]
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A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets
Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is often hindered by the limited availability of labelled experimental data and the high computational cost of generating large-scale high-fidelity simulation datasets. This study presents a multifidelity transfer learning framework that integrates lightweight physics-based simulations, convolutional autoencoder (CAE)-based deep feature learning, a feed-forward neural network, and limited experimental measurements […]
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Fast and Robust: Computationally Efficient Covariance Estimation for Sub-Weibull Vectors
arXiv:2512.17632v2 Announce Type: replace Abstract: High-dimensional covariance estimation is notoriously sensitive to outliers. While statistically optimal estimators exist for general heavy-tailed distributions, they often rely on computationally expensive techniques like semidefinite programming or iterative M-estimation ($O(d^3)$). In this work, we target the specific regime of textbf{Sub-Weibull distributions} (characterized by stretched exponential tails $exp(-t^alpha)$). We investigate a computationally efficient alternative: the textbf{Cross-Fitted Norm-Truncated Estimator}. Unlike element-wise truncation, our approach preserves the spectral geometry while requiring $O(Nd^2)$ operations, which represents […]
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SHIELD: A Segmented Hierarchical Memory Architecture for Energy-Efficient LLM Inference on Edge NPUs
arXiv:2604.07396v1 Announce Type: new Abstract: Large Language Model (LLM) inference on edge Neural Processing Units (NPUs) is fundamentally constrained by limited on-chip memory capacity. Although high-density embedded DRAM (eDRAM) is attractive for storing activation workspaces, its periodic refresh consumes substantial energy. Prior work has primarily focused on reducing off-chip traffic or optimizing refresh for persistent Key-Value (KV) caches, while transient and error-resilient Query and Attention Output (QO) activations are largely overlooked. We propose SHIELD, a lifecycle-aware segmented eDRAM […]
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