The previous post discussed two ways to compute the nth Fibonacci number. The first is to compute all the Fibonacci numbers up to the nth iteratively using the defining property of Fibonacci numbers Fn + 2 = Fn + Fn + 1 with extended integer arithmetic. The second approach is to use Binet’s formula Fn = round( φn / √ 5 ) where φ is the golden ratio. It’s not clear which approach is more efficient. You could say […]
arXiv:2602.21332v1 Announce Type: new Abstract: The Spearman footrule is a voting rule that takes as input voter preferences expressed as rankings. It outputs a ranking that minimizes the sum of the absolute differences between the position of each candidate in the ranking and in the voters’ preferences. In this paper, we study the computational complexity of two extensions of the Spearman footrule when the number of voters is a small constant. The first extension, introduced by Pascual et […]
Exito Media Concepts, a globally recognized leader in B2B technology events, is proud to announce the 34th Edition of the BFSI IT Summit Indonesia 2026, the country’s foremost gathering of banking, financial services, and insurance (BFSI) technology leaders. The summit will take place on 16th April 2026 at the JW Marriott Mega Kuningan, Jakarta, bringing together over 150+ CIOs, CTOs, and digital transformation leaders to chart the future of financial services technology in Indonesia. Indonesia’s BFSI sector is […]
arXiv:2603.06872v1 Announce Type: cross Abstract: We present a unified theoretical and computational framework for constructing reproducing kernels tailored to transport equations and adapted to Koopman eigenfunctions of nonlinear dynamical systems. These eigenfunctions satisfy a transport-type partial differential equation (PDE) that we invert using three analytically grounded methods: (i) A Lions-type variational principle in a reproducing kernel Hilbert space (RKHS), (ii) convolution with a Green’s function, and (iii) a resolvent operator constructed via Laplace transforms along characteristic flows. We […]
arXiv:2601.02537v1 Announce Type: new Abstract: Oblivious load-balancing in networks involves routing traffic from sources to destinations using predetermined routes independent of the traffic, so that the maximum load on any link in the network is minimized. We investigate oblivious load-balancing schemes for a $Ntimes N$ torus network under sparse traffic where there are at most $k$ active source-destination pairs. We are motivated by the problem of load-balancing in large-scale LEO satellite networks, which can be modelled as a […]
Here we go again: you see the notification, and you feel that familiar resistance. Code Review often feels cognitively heavy and non-deterministic: too many paths to follow, too much ambiguity, too many opinions. That uncertainty breeds procrastination and bikeshedding, or, at the other extreme, drive-by approvals. But how do you maximize the value of a review without exhausting the reviewers or trapping the team in endless debates? I’ve distilled a healthy, sustainable review process into an acronym: PERFECT. […]
The US Department of Transportation apparently thinks it’s a good idea to use artificial intelligence to draft rules impacting the safety of airplanes, cars, and pipelines, a ProPublica investigation revealed Monday. It could be a problem if DOT becomes the first agency to use AI to draft rules, ProPublica pointed out, since AI is known to confidently get things wrong and hallucinate fabricated information. Staffers fear that any failure to catch AI errors could result in flawed laws, […]
Autonomous vehicles (AVs) rely on accurate trajectory prediction for safe navigation in diverse traffic environments, yet existing models struggle with long-tail scenarios-rare but safety-critical events characterized by abrupt maneuvers, high collision risks, and complex interactions. These challenges stem from data imbalance, inadequate definitions of long-tail trajectories, and suboptimal learning strategies that prioritize common behaviors over infrequent ones. To address this, we propose SAIL, a novel framework that systematically tackles the long-tail problem by first defining and modeling trajectories […]
arXiv:2602.16742v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has been shown effective in enhancing the visual reflection and reasoning capabilities of Large Multimodal Models (LMMs). However, existing datasets are predominantly derived from either small-scale manual construction or recombination of prior resources, which limits data diversity and coverage, thereby constraining further gains in model performance. To this end, we introduce textbf{DeepVision-103K}, a comprehensive dataset for RLVR training that covers diverse K12 mathematical topics, extensive knowledge points, […]
arXiv:2603.10055v1 Announce Type: new Abstract: Pre-training is crucial for large language models (LLMs), as it is when most representations and capabilities are acquired. However, natural language pre-training has problems: high-quality text is finite, it contains human biases, and it entangles knowledge with reasoning. This raises a fundamental question: is natural language the only path to intelligence? We propose using neural cellular automata (NCA) to generate synthetic, non-linguistic data for pre-pre-training LLMs–training on synthetic-then-natural language. NCA data exhibits rich […]