Tootfinder

“Our parents kept us carefully isolated - from birth - from even witnessing examples of other children being disrespectful, and even the faintest disobedience gets slapped out of you starting as a baby.”
https://open.substack.com/pub/aella/p/the-joy-is-not-optional…

POPQC: Parallel Optimization for Quantum Circuits (Extended Version)
Pengyu Liu, Jatin Arora, Mingkuan Xu, Umut A. Acar
https://arxiv.org/abs/2506.13720 ht…

POPQC: Parallel Optimization for Quantum Circuits (Extended Version)
Optimization of quantum programs or circuits is a fundamental problem in quantum computing and remains a major challenge. State-of-the-art quantum circuit optimizers rely on heuristics and typically require superlinear, and even exponential, time. Recent work proposed a new approach that pursues a weaker form of optimality called local optimality. Parameterized by a natural number $Ω$, local optimality insists that each and every $Ω$-segment of the circuit is optimal with respect to an extern…

Branch, or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models
Ziwei Liu, Borui Kang, Wei Li, Hangjie Yuan, Yanbing Yang, Wenbin Li, Jun Luo, Yifan Zhu, Tao Feng
https://arxiv.org/abs/2506.12409

Branch, or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models
Continual learning in vision-language models (VLMs) faces critical challenges in balancing parameter efficiency, memory consumption, and optimization stability. While First-Order (FO) optimization (e.g., SGD) dominate current approaches, their deterministic gradients often trap models in suboptimal local minima and incur substantial memory overhead. This paper pioneers a systematic exploration of Zeroth-Order (ZO) optimization for vision-language continual learning (VLCL). We first identify the…

Is the Fitness Dependent Optimizer Ready for the Future of Optimization?
Ardalan H. Awlla, Tarik A. Rashid, Ronak M. Abdullah
https://arxiv.org/abs/2506.10983

Is the Fitness Dependent Optimizer Ready for the Future of Optimization?
Metaheuristic algorithms are optimization methods that are inspired by real phenomena in nature or the behavior of living beings, e.g., animals, to be used for solving complex problems, as in engineering, energy optimization, health care, etc. One of them was the creation of the Fitness Dependent Optimizer (FDO) in 2019, which is based on bee-inspired swarm intelligence and provides efficient optimization. This paper aims to introduce a comprehensive review of FDO, including its basic concepts,…

Interesting piece on Ali Khamenei in The Guardian at the moment: https://www.theguardian.com/world/2025/jun/16/ali-khamenei-ruthless-defender-of-iran-revolution-with-few-good-options-left

Die spinnen, die TechBros.
"OpenAI executives have discussed filing an antitrust complaint with US regulators against Microsoft, the company's largest investor, The Wall Street Journal reported Monday, marking a dramatic escalation in tensions between the two long-term AI partners."

Filing: WBD says it plans to "significantly reduce" CEO David Zaslav's annual pay, including his cash bonus, after shareholders rejected his 2024 pay package (Todd Spangler/Variety)
https://variety.com/2025/tv/news/warne

Warner Bros Discovery CEO David Zaslav Pay to Drop After Company Split
WBD CEO David Zaslav will see his pay package take a haircut after the proposed separation of Warner Bros. Discovery in 2026. The company granted him $225 million in stock option awards.

Browns may keep four QBs in 2025: Building the perfect starter using best skills of each Cleveland option

https://www.cbssports.com/nfl/news/brow…

Optimal Experimental Design Criteria for Data-Consistent Inversion
Troy Butler, John Jakeman, Michael Pilosov, Scott Walsh, Timothy Wildey
https://arxiv.org/abs/2506.12157

Optimal Experimental Design Criteria for Data-Consistent Inversion
The ability to design effective experiments is crucial for obtaining data that can substantially reduce the uncertainty in the predictions made using computational models. An optimal experimental design (OED) refers to the choice of a particular experiment that optimizes a particular design criteria, e.g., maximizing a utility function, which measures the information content of the data. However, traditional approaches for optimal experimental design typically require solving a large number of …

Three-dimensional Deep Shape Optimization with a Limited Dataset
Yongmin Kwon, Namwoo Kang
https://arxiv.org/abs/2506.12326 https://a…

Three-dimensional Deep Shape Optimization with a Limited Dataset
Generative models have attracted considerable attention for their ability to produce novel shapes. However, their application in mechanical design remains constrained due to the limited size and variability of available datasets. This study proposes a deep learning-based optimization framework specifically tailored for shape optimization with limited datasets, leveraging positional encoding and a Lipschitz regularization term to robustly learn geometric characteristics and maintain a meaningful…