Tootfinder

Been working on some additions/adjustments for #Umbra, one of my recent algorithmic/generative art pieces, currently shown exclusively on Layer.
Here's a 3 minute segment (of an infinite realtime animation). Give it time to develop/change! Fullscreen playback probably best...
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An Exact Branch and Bound Algorithm for the generalized Qubit Mapping Problem
Bj{\o}rnar Luteberget, Kjell Fredrik Pettersen, Giorgio Sartor, Franz G. Fuchs, Dominik Leib, Tobias Seidel, Raoul Heese
https://arxiv.org/abs/2508.21718

An Exact Branch and Bound Algorithm for the generalized Qubit Mapping Problem
Quantum circuits are typically represented by a (ordered) sequence of gates over a set of virtual qubits. During compilation, the virtual qubits of the gates are assigned to the physical qubits of the underlying quantum hardware, a step often referred to as the qubit assignment problem. To ensure that the resulting circuit respects hardware connectivity constraints, additional SWAP gates are inserted as needed, which is known as the qubit routing problem. Together, they are called the Qubit Map…

Is it talent?
https://www.instagram.com/reel/DQM0-w2jEOd/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

Duaa Izzidien - Visual Storyteller & Artist on Instagram: "I had far too much fun creating this reel and couldn’t bring myself to delete any of it to make it shorter and more algorithm friendly 🙈 If you watched it all the way to the end - well done! You’ve just demonstrated the very thing the reel is about - showing up is the only talent that matters. My arrows don’t always hit the target. My paintings don’t always turn out how I planned. And honestly? Life rarely goes the way I intend. But really that isn’t what matters. In Islam we say that actions are by intentions and that sometimes means letting go of controlling our outcomes. We can control our intentions, our effort, showing up - but we have to remember that the result doesn’t actually come from those actions. Sometimes the arrow misses because there’s a better lesson waiting or perhaps it’s to remind you to stay humble and remember that ‘you’ are not the architect of your success. Sometimes the painting goes “wrong” because it’s becoming something more beautiful than you imagined. Sometimes life doesn’t work out the way you planned because there’s something different, better, round the corner for you. A huge thank you to @thabitoon_archers and @mamluk.academy for teaching me. You’ve taught me far more than just archery - you’ve taught me a rich history and life lessons that bring peace. (any mistakes in my form are entirely mine!). Want to learn how to use art as a tool for trusting and letting go of control? DM me ‘CREATE’ and I’ll show you these techniques. #showingisenough #trusttheprocess #archery #archerygirl #traditionalarchery #archerylife #overwhelm #personalgrowth #innerstrength #growthmindset #breakthrough #findingmyself #resilience #transformation #letgoofcontrol"
24K likes, 763 comments - duaaizzidien on October 24, 2025: "I had far too much fun creating this reel and couldn’t bring myself to delete any of it to make it shorter and more algorithm friendly 🙈 If you watched it all the way to the end - well done! You’ve just demonstrated the very thing the reel is about - showing up is the only talent that matters. My arrows don’t always hit the target. My paintings don’t always turn out how I planned. And honestly? Life rarely goes the way I …

A look at the potential future of personalized, AI-generated entertainment, and how it could both submerge human originality and enable new forms of expression (Joshua Rothman/New Yorker)
https://www.newyorker.com/magazine/2025/09/01/ai-is-coming-for-cu…

A.I. Is Coming for Culture
We’re used to algorithms guiding our choices. But when machines can effortlessly generate the content we consume, Joshua Rothman asks, what’s left for the human imagination?

Reconstruction of acoustic sources from the initial arrival time of waves
Qiuyi Li, Bo Chen, Peng Gao, Yu Sun, Yao Sun
https://arxiv.org/abs/2509.23819 https://

Reconstruction of acoustic sources from the initial arrival time of waves
In this paper, a novel time domain sampling method based on the initial arrival time of waves is proposed to reconstruct acoustic sources, including point sources, curve sources, surface sources and block sources. The uniqueness of reconstructing sources whose spatial support is a convex region is proved. Theoretical analyses are provided to demonstrate the validity of the proposed sampling method in reconstructing various types of sound sources. The proposed algorithm does not involve the time…

Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors
Ezra Johnson, Adam Lechowicz, Mohammad Hajiesmaili
https://arxiv.org/abs/2509.26511 https://

Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors
A wide range of sustainability and grid-integration strategies depend on workload shifting, which aligns the timing of energy consumption with external signals such as grid curtailment events, carbon intensity, or time-of-use electricity prices. The main challenge lies in the online nature of the problem: operators must make real-time decisions (e.g., whether to consume energy now) without knowledge of the future. While forecasts of signal values are typically available, prior work on learning-…

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems
Mrinalkanti Ghosh
https://arxiv.org/abs/2508.21327 https:/…

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems
This thesis explores algorithmic applications and limitations of convex relaxation hierarchies for approximating some discrete and continuous optimization problems. - We show a dichotomy of approximability of constraint satisfaction problems (CSPs) by linear programming (LP) relaxations: for every CSP, the approximation obtained by a basic LP relaxation, is no weaker than the approximation obtained using relaxations given by super-constant levels of the Sherali-Adams hierarchy on instances of…

EZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting
Yujin Park, Haejun Chung, Ikbeom Jang
https://arxiv.org/abs/2508.21550

EZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting
Pairwise comparison is often favored over absolute rating or ordinal classification in subjective or difficult annotation tasks due to its improved reliability. However, exhaustive comparisons require a massive number of annotations (O(n^2)). Recent work has greatly reduced the annotation burden (O(n log n)) by actively sampling pairwise comparisons using a sorting algorithm. We further improve annotation efficiency by (1) roughly pre-ordering items using the Contrastive Language-Image Pre-trai…

Scaling Equilibrium Propagation to Deeper Neural Network Architectures
Sankar Vinayak. E. P, Gopalakrishnan Srinivasan
https://arxiv.org/abs/2509.26003 https://

Scaling Equilibrium Propagation to Deeper Neural Network Architectures
Equilibrium propagation has been proposed as a biologically plausible alternative to the backpropagation algorithm. The local nature of gradient computations, combined with the use of convergent RNNs to reach equilibrium states, make this approach well-suited for implementation on neuromorphic hardware. However, previous studies on equilibrium propagation have been restricted to networks containing only dense layers or relatively small architectures with a few convolutional layers followed by a…

Fast Feature Field ($\text{F}^3$): A Predictive Representation of Events
Richeek Das, Kostas Daniilidis, Pratik Chaudhari
https://arxiv.org/abs/2509.25146 https://

Fast Feature Field ($\text{F}^3$): A Predictive Representation of Events
This paper develops a mathematical argument and algorithms for building representations of data from event-based cameras, that we call Fast Feature Field ($\text{F}^3$). We learn this representation by predicting future events from past events and show that it preserves scene structure and motion information. $\text{F}^3$ exploits the sparsity of event data and is robust to noise and variations in event rates. It can be computed efficiently using ideas from multi-resolution hash encoding and de…