arXiv:2601.20496v1 Announce Type: new Abstract: Generating dense physical fields from sparse measurements is a fundamental question in sampling, signal processing, and many other applications. State-of-the-art methods either use spatial statistics or rely on examples of dense fields in the training phase, which often are not available, and thus rely on synthetic data. Here, we present a reconstruction method that generates dense fields from sparse measurements, without assuming availability of the spatial statistics, nor of examples of the dense […]

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arXiv:2601.17023v1 Announce Type: new Abstract: Career decision-making is a socio-technical problem: individuals exercise bounded agency while navigating labor market institutions, organizational incentive structures, and information asymmetries that shape feasible trajectories. Existing frameworks optimize along single dimensions – financial returns, work-life balance, or mission alignment – without explicit models for inter-dimensional tradeoffs or temporal dynamics. We propose The Three Axes of Success, a normative decision framework decomposing career trajectories into Wealth (career capital accumulation and economic optionality), Autonomy (control […]

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The compliance industrial complex is selling a new product. Having peddled GDPR checklists and SOC2 reports for years, the same vendors are offering AI governance frameworks with the promises of making your neural networks ethical, safe, and compliant. While the policies sound reasonable, with impressive documentation and an expensive consultant, none of it is stopping breaches. As a cybersecurity ghostwriter who has collaborated with several Silicon Valley Startups and tech founders in the APAC region, I’ve watched hundreds […]

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arXiv:2411.03384v2 Announce Type: replace Abstract: In this paper, we solve stochastic partial differential equations (SPDEs) numerically by using (possibly random) neural networks in the truncated Wiener chaos expansion of their corresponding solution. Moreover, we provide some approximation rates for learning the solution of SPDEs with additive and/or multiplicative noise. Finally, we apply our results in numerical examples to approximate the solution of three SPDEs: the stochastic heat equation, the Heath-Jarrow-Morton equation, and the Zakai equation.

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Traditionally, supply chains operated within reasonably stable and predictable demand environments. Businesses utilized the historical sales pattern to plan for production, inventory, and logistics. These were usually based on historical data and monthly forecasts. This has changed significantly. Due to the pace of change in consumer buying patterns; increased spikes in demand due to promotional activity; and the conditions occurring outside of the control of businesses (e.g., weather-related disruptions, economic indicators), the way in which consumers purchase goods […]

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arXiv:2601.05292v1 Announce Type: new Abstract: With the increasing threat posed by modulation classification to wireless security, this paper proposes a secure communication framework based on modulation order confusion (MOC), which intentionally disguises the original modulation as a higher- or lower-order one to mislead eavesdroppers. For single-antenna systems, two schemes are developed: symbol random mapping and symbol time diversity, enabling modulation order confusion with customized receivers. For multi-antenna systems, receiver-transparent MOC schemes are proposed, including series-expansion-based and constellation-path-based signal […]

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arXiv:2604.09849v1 Announce Type: new Abstract: We study whether in-domain pretraining of Bidirectional Encoder Representations from Transformer (BERT) model improves subdomain-level detection of exfiltration at low false positive rates. While previous work mostly examines fine-tuned generic Transformers, it does not aim to isolate the effect of pretraining on the downstream task of classification. To address this gap, we develop a controlled pipeline where we freeze operating points on validation and transfer them to the test set, thus enabling clean […]

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Ultrafast online learning is essential for high-frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub-microsecond timescales. Meeting these requirements demands low-latency, fixed-precision computation under strict memory constraints, a regime in which conventional Multi-Layer Perceptrons (MLPs) are both inefficient and numerically unstable. We identify key properties of Kolmogorov-Arnold Networks (KANs) that align with these constraints. Specifically, we show that: (i) KAN updates exploiting B-spline locality are sparse, enabling superior on-chip resource […]

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How are you, hacker? 🪐 What’s happening in tech today, January 5, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Construction on San Francisco’s iconic Golden Gate Bridge began in 1933, All U.S. offensive action in North Vietnam got suspended in 1972, AOL acquired Time Warner & became the largest media company in the world in 2000, and we present you with these top quality stories. From Code Smell 12 – […]

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