Tootfinder

Tja, zo lang het om anderen gaat hoef je niet zo kritisch te zijn, maar zodra het om jezelf gaat ...
Nu hij parkinson heeft, denkt oud-Kamerlid en boerenzoon Rendert Algra anders over pesticiden https://www.volkskrant.nl/binnenlan…

Nu hij parkinson heeft, denkt oud-Kamerlid en boerenzoon Rendert Algra anders over pesticiden
Voormalig CDA-Tweede Kamerlid Rendert Algra (61) heeft de ziekte van Parkinson. De Fries weet het zeker: dat is te wijten aan zijn jeugd op de boerderij, toen hij met bestrijdingsmiddelen spoot. Die moeten veel kritischer worden beoordeeld, vindt hij nu.

In the before times we used to run a festival called 'algomech' in Sheffield UK. The pandemic put paid to that but we're now coming back with a new small festival called Alpaca (festival of Algorithmic Patterns in the Creative Arts). If you are curious about strange patterns in different forms then this one is for you!
There'll be a top notch algorave with people writing code to make electronic dance music, also a concert with pattern-based instrumental music and closin…

Willkommen auf der Peertube-Instanz von heise medien. 🐙📺
Wenn ihr unsere Videos ohne undurchsichtige Algorithmen schauen möchtet, seid ihr hier genau richtig: https://peertube.heise.de

Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
Shuangning Li, Chonghuan Wang, Jingyan Wang
https://arxiv.org/abs/2507.11891

Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has shown that the standard difference-in-means estimator is biased in estimating the global treatment effect (GTE) due to a particular form of interference between experimental units. Specifically, units under the treatment and control algorithms contribute to a shared pool of data that subsequently train both algorithms, resulting in interference between the two groups. The bias …

A polynomial delay algorithm generating all potential maximal cliques in triconnected planar graphs
Alexander Grigoriev, Yasuaki Kobayashi, Hisao Tamaki, Tom C. van der Zanden
https://arxiv.org/abs/2506.12635

A polynomial delay algorithm generating all potential maximal cliques in triconnected planar graphs
We develop a new characterization of potential maximal cliques of a triconnected planar graph and, using this characterization, give a polynomial delay algorithm generating all potential maximal cliques of a given triconnected planar graph. Combined with the dynamic programming algorithms due to Bouchitt{é} and Todinca, this algorithm leads to a treewidth algorithm for general planar graphs that runs in time linear in the number of potential maximal cliques and polynomial in the number of vert…

Performance Enhancement of the Recursive Least Squares Algorithms with Rank Two Updates
Alexander Stotsky
https://arxiv.org/abs/2507.11095 https://

Performance Enhancement of the Recursive Least Squares Algorithms with Rank Two Updates
New recursive least squares algorithms with rank two updates (RLSR2) that include both exponential and instantaneous forgetting (implemented via a proper choice of the forgetting factor and the window size) are introduced and systematically associated in this report with well-known RLS algorithms with rank one updates. Moreover, new properties (which can be used for further performance improvement) of the recursive algorithms associated with the convergence of the inverse of information matrix …

Quantum algorithm for solving generalized eigenvalue problems with application to the Schr\"odinger equation
Grzegorz Rajchel-Mieldzio\'c, Szymon Pli\'s, Emil Zak
https://arxiv.org/abs/2506.13534

Quantum algorithm for solving generalized eigenvalue problems with application to the Schrödinger equation
Accurate computation of multiple eigenvalues of quantum Hamiltonians is essential in quantum chemistry, materials science, and molecular spectroscopy. Estimating excited-state energies is challenging for classical algorithms due to exponential scaling with system size, posing an even harder problem than ground-state calculations. We present a quantum algorithm for estimating eigenvalues and singular values of parameterized matrix families, including solving generalized eigenvalue problems that …

Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors
Tobias Kern, Leon Tolksdorf, Christian Birkner
https://arxiv.org/abs/2507.11241

Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors
Physically reduced-scale vehicles are emerging to accelerate the development of advanced automated driving functions. In this paper, we investigate the effects of scaling on self-localization accuracy with visual and visual-inertial algorithms using cameras and an inertial measurement unit (IMU). For this purpose, ROS2-compatible visual and visual-inertial algorithms are selected, and datasets are chosen as a baseline for real-sized vehicles. A test drive is conducted to record data of reduced-…

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
Azhar Ikhtiarudin, Aditi Das, Param Thakkar, Akash Kundu
https://arxiv.org/abs/2507.12189

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
We introduce BenchRL-QAS, a unified benchmarking framework for systematically evaluating reinforcement learning (RL) algorithms in quantum architecture search (QAS) across diverse variational quantum algorithm tasks and system sizes ranging from 2- to 8-qubit. Our study benchmarks nine RL agents including both value-based and policy-gradient methods on representative quantum problems such as variational quantum eigensolver, variational quantum state diagonalization, quantum classification, and …

A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
Kwassi Joseph Dzahini, Jeffrey M. Larson, Matt Menickelly, Stefan M. Wild
https://arxiv.org/abs/2507.10992

A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
We introduce ANASTAARS, a noise-aware scalable classical optimizer for variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA). ANASTAARS leverages adaptive random subspace strategies to efficiently optimize the ansatz parameters of a QAOA circuit, in an effort to address challenges posed by a potentially large number of QAOA layers. ANASTAARS iteratively constructs random interpolation models within low-dimensional affine subspaces defined via Johnson--Lind…