Tootfinder

from my link log —
Optimizing with novel calendrical algorithms.
https://jhpratt.dev/blog/optimizing-with-novel-calendrical-algorithms/
saved 2025-05-15

So arguably the Pac-Man ghost logic is the exact opposite of “AI”.
It’s extremely well defined behavior based on very basic deterministic algorithms that are so simple a 8-year old player can understand them and anticipate them correctly for those high-scores.
The game actually is famous for the simple enemy logic and studied by games designers for it.
Pac-Man enemy behavior:
deterministic, well-defined algorithm that can easily be executed on the most primitive* computer hardware
“AI”:
black-box algorithm with indeterministic behavior that requires a metric shitload of compute
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*By “primitive computer hardware” I mean that the Pac-Man arcade machine runs on a Zilog Z80 CPU with 8,500 transistors.The CPU in the phone you are reading this on has (latest iPhone as example) 15 billion transistors or so (almost 2 million times more) and runs tens of thousands of times faster.

Prezes UOKiK stawia pierwsze zarzuty greenwashingu.
https://uokik.gov.pl/ekosciema-prezes-uokik-stawia-zarzuty-allegro-dhl-dpd-i-inpost

An em algorithm for quantum Boltzmann machines
Takeshi Kimura, Kohtaro Kato, Masahito Hayashi
https://arxiv.org/abs/2507.21569 https://arxiv.org/pdf/2507.2…

An em algorithm for quantum Boltzmann machines
We develop a quantum version of the em algorithm for training quantum Boltzmann machines. The em algorithm is an information-geometric extension of the well-known expectation-maximization (EM) algorithm, offering a structured alternative to gradient-based methods with potential advantages in stability and convergence. We implement the algorithm on a semi-quantum restricted Boltzmann machine, where quantum effects are confined to the hidden layer. This structure enables analytical update rules w…

Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation
Ronak Mehta, Mateus Piovezan Otto, Noah Stanis, Azadeh Yazdan-Shahmorad, Zaid Harchaoui
https://arxiv.org/abs/2508.20618

Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation
We develop a stochastic algorithm for independent component analysis that incorporates multi-trial supervision, which is available in many scientific contexts. The method blends a proximal gradient-type algorithm in the space of invertible matrices with joint learning of a prediction model through backpropagation. We illustrate the proposed algorithm on synthetic and real data experiments. In particular, owing to the additional supervision, we observe an increased success rate of the non-convex…

RM-Dijkstra: A surface optimal path planning algorithm based on Riemannian metric
Yu Zhang, Xiao-Song Yang
https://arxiv.org/abs/2506.22170 https://…

RM-Dijkstra: A surface optimal path planning algorithm based on Riemannian metric
The Dijkstra algorithm is a classic path planning method, which operates in a discrete graph space to determine the shortest path from a specified source point to a target node or all other nodes based on non-negative edge weights. Numerous studies have focused on the Dijkstra algorithm due to its potential application. However, its application in surface path planning for mobile robots remains largely unexplored. In this letter, a surface optimal path planning algorithm called RM-Dijkstra is p…

Evaluating Massively Parallel Algorithms for DFA Minimisation, Equivalence Checking and Inclusion Checking
Jan Heemstra, Jan Martens, Anton Wijs
https://arxiv.org/abs/2508.20735

Evaluating Massively Parallel Algorithms for DFA Minimisation, Equivalence Checking and Inclusion Checking
We study parallel algorithms for the minimisation and equivalence checking of Deterministic Finite Automata (DFAs). Regarding DFA minimisation, we implement four different massively parallel algorithms on Graphics Processing Units~(GPUs). Our results confirm the expectations that the algorithm with the theoretically best time complexity is not practically suitable to run on GPUs due to the large amount of resources needed. We empirically verify that parallel partition refinement algorithms from…

Faster exponential algorithms for cut problems via geometric data structures
L\'aszl\'o Kozma, Junqi Tan
https://arxiv.org/abs/2506.22281 https://

Faster exponential algorithms for cut problems via geometric data structures
For many hard computational problems, simple algorithms that run in time $2^n \cdot n^{O(1)}$ arise, say, from enumerating all subsets of a size-$n$ set. Finding (exponentially) faster algorithms is a natural goal that has driven much of the field of exact exponential algorithms (e.g., see Fomin and Kratsch, 2010). In this paper we obtain algorithms with running time $O(1.9999977^n)$ on input graphs with $n$ vertices, for the following well-studied problems: - $d$-Cut: find a proper cut in wh…

The lattice packing problem in dimension 9 by Voronoi's algorithm
Mathieu Dutour Sikiri\'c, Wessel van Woerden
https://arxiv.org/abs/2508.20719 https://

The lattice packing problem in dimension 9 by Voronoi's algorithm
In 1908, Voronoi introduced an algorithm that solves the lattice packing problem in any dimension in finite time. Voronoi showed that any lattice with optimal packing density must be a so-called perfect lattice, and his algorithm enumerates the finitely many perfect lattices up to similarity in a fixed dimension. However, due to the high complexity of the algorithm this enumeration had, until now, only been completed up to dimension 8. In this work we compute all 2237251040 perfect lattices i…

Efficient Data Retrieval and Comparative Bias Analysis of Recommendation Algorithms for YouTube Shorts and Long-Form Videos
Selimhan Dagtas, Mert Can Cakmak, Nitin Agarwal
https://arxiv.org/abs/2507.21467

Efficient Data Retrieval and Comparative Bias Analysis of Recommendation Algorithms for YouTube Shorts and Long-Form Videos
The growing popularity of short-form video content, such as YouTube Shorts, has transformed user engagement on digital platforms, raising critical questions about the role of recommendation algorithms in shaping user experiences. These algorithms significantly influence content consumption, yet concerns about biases, echo chambers, and content diversity persist. This study develops an efficient data collection framework to analyze YouTube's recommendation algorithms for both short-form and long…