April 2026

Earlier in the week I looked at how upgrading hardware can expand the PostgreSQL performance envelope. Now I’m going to show another solution: TimescaleDB. TimescaleDB is an open-source PostgreSQL extension that adds time-series capabilities to PostgreSQL. It was first released in 2017 by Tiger Data (originally Timescale) and is available for free as a self-hosted extension or as a managed service (Tiger Cloud). Under the hood, TimescaleDB automatically partitions tables into time-based chunks (called hypertables), adds native compression with hypercore, continuous aggregates for pre-computed […]

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With over 12 years architecting secure, scalable systems for millions of users, the ACM Fremont Program Chair is channeling deep engineering expertise into a community platform the Bay Area didn’t know it was missing **Fremont, California | April 2026 There is a particular kind of engineer who treats knowledge as something to be shared, not hoarded. Deepak Kole is that kind of engineer. A seasoned software professional with over twelve years of experience architecting and delivering secure, scalable, […]

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Engineering teams often think about infrastructure only when something breaks. A remote connection fails. A server becomes unstable. A workstation loses access to a critical system. A field team cannot synchronize data. An industrial environment starts showing communication issues that no one can explain clearly. At that point, infrastructure suddenly becomes visible. But by then, the real problem has usually been present for a long time. In my experience working with networks, servers, secure connectivity, technical support, and […]

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arXiv:2604.25655v1 Announce Type: new Abstract: Nonlinear dynamical systems with regime transitions are typically described by ordinary differential equations with jumping parameters parameters. Traditional methods often treat change-point detection and parameter estimation as separate tasks, ignoring the inherent coupling between them. To address this, we propose residual-loss anomaly analysis of physics-informed neural networks, a unified framework that leverages dynamical consistency within the physics-informed learning paradigm. This approach jointly infers piecewise parameters and transition points under a single set of […]

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arXiv:2604.25272v1 Announce Type: new Abstract: Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this work, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each item we can recommend is a node of an undirected graph and its expected rating is similar to the one of its neighbors. […]

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arXiv:2604.25271v1 Announce Type: new Abstract: We consider adversarial multi-armed bandit problems where the learner is allowed to observe losses of a number of arms beside the arm that it actually chose. We study the case where all non-chosen arms reveal their loss with a fixed but unknown probability $r$, independently of each other and the action of the learner. We propose two algorithms that work for different ranges of $r$. We show that after $T$ rounds in a […]

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arXiv:2604.25158v1 Announce Type: new Abstract: Support vector machines (SVMs) are a standard tool for binary classification, but their classical formulations are purely data-driven and offer no direct way to encode trusted benchmark models or structured preferences on selected subsets of the data. We propose Elite-Driven Support Vector Machines (EDSVM), a general framework that augments regularized empirical risk minimization by guiding the slack variables for a curated set of elite observations (typically the union of support vectors from one […]

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arXiv:2604.25025v1 Announce Type: new Abstract: Preference feedback, in the form of pairwise comparisons rather than scalar scores, has seen increasing use in applications such as human-, laboratory-, and expert-in-the-loop design, as well as scientific discovery. We propose a Thompson Sampling (TS) approach to Bayesian optimization with preferential feedback that models comparisons using a monotone link on latent utility differences and leverages the dueling kernel induced by a base kernel. We provide a finite-time analysis showing that the performance […]

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