Tootfinder

My interests in sealife & medieval history converge with the exhibition of woodcut prints by @… in the beautiful space of #StBenetsCam, founded before the Norman conquest:

AquaClaire (@AquaClaire@ecoevo.social)
Attached: 1 image Two powerful artworks by Pangrok Sulap are now on display in the beautiful space of St Bene't's in Cambridge: https://www.stbenetschurch.org/laut 📷 Revd Devin McLachlan 🧵 1/2 #sealife #StBenetsCam #BottomTrawling #ocean #pollution #MarineConservation #PangrokSulap #IconicFishes #Cambridge

‘They love their team’: Cowboys fans converge in Las Vegas for ‘Monday Night Football’ https://www.reviewjournal.com/sports/raiders/they-love-their-team-cowboys-fans-converge-in-las-vegas-for-monday-night-footb…

More than 1,400 dead across Asia after ‘rare’ cyclone & typhoon converge https://news.mongabay.com/short-article/2025/12/more-than-1400-dead-across-asia-after-rare-cyclone-typhoons-converge/

More than 1,400 dead across Asia after ‘rare’ cyclone & typhoon converge
At least 1,400 people have died as a result of flooding and landslides across Sri Lanka, Indonesia, Thailand and Malaysia, with many more still missing. The unusual combination of a tropical typhoon and two tropical cyclones is behind the mounting humanitarian disaster. Scientists and meteorologists note that Cyclone Senyar formed just north of the equator, […]

“If our world survives, the next great challenge to watch out for will come — you heard it here first — when the curves of research and development in artificial intelligence, molecular biology and robotics all converge.”
– Thomas Pynchon [writing in 1984]

Did you know it's possible for two lines to appear to diverge in a 2D perspective view, but actually converge in 3D? Kind of the opposite of parallel lines in 3D converging in 2D.
I discovered this while trying to implement a clipping algorithm. At first I didn't believe it was possible, it felt so foreign to everyday experience. It actually happens whenever two lines converge behind the center of projection, but we rarely see that IRL.
(Image rendered from pov of came…

#OTD 2012 Polder.

Emergent Mixed States for Baby Universes and Black Holes
Jonah Kudler-Flam, Edward Witten
https://arxiv.org/abs/2510.06376 https://arxiv.org/pdf/2510.06376…

Emergent Mixed States for Baby Universes and Black Holes
We examine the behavior of sequences of states in the large $N$ limit of AdS/CFT duality in cases in which the bulk duals involve baby universes or black holes. Such sequences generally fail to converge as pure states. Under suitable conditions, such as diverging coarse-grained entropy, they can converge to mixed states for the large $N$ algebra, as in the case of black holes. For Euclidean preparations that produce baby universes, the sequences do not converge, due to wormhole contributions, a…

Quantum Relative Entropy Decay Composition Yields Shallow, Unstructured k-Designs
Nicholas Laracuente
https://arxiv.org/abs/2510.08537 https://arxiv.org/pd…

Quantum Relative Entropy Decay Composition Yields Shallow, Unstructured k-Designs
A major line of questions in quantum information and computing asks how quickly locally random circuits converge to resemble global randomness. In particular, approximate k-designs are random unitary ensembles that resemble random circuits up to their first k moments. It was recently shown that on n qudits, random circuits with slightly structured architectures converge to k-designs in depth O(log n), even on one-dimensional connectivity. It has however remained open whether the same shallow de…

Drop-Muon: Update Less, Converge Faster
Kaja Gruntkowska, Yassine Maziane, Zheng Qu, Peter Richt\'arik
https://arxiv.org/abs/2510.02239 https://arxiv.o…

Drop-Muon: Update Less, Converge Faster
Conventional wisdom in deep learning optimization dictates updating all layers at every step-a principle followed by all recent state-of-the-art optimizers such as Muon. In this work, we challenge this assumption, showing that full-network updates can be fundamentally suboptimal, both in theory and in practice. We introduce a non-Euclidean Randomized Progressive Training method-Drop-Muon-a simple yet powerful framework that updates only a subset of layers per step according to a randomized sche…

On weak convergence of Gaussian conditional distributions
Sarah Lumpp, Mathias Drton
https://arxiv.org/abs/2510.12412 https://arxiv.org/pdf/2510.12412

On weak convergence of Gaussian conditional distributions
Weak convergence of joint distributions generally does not imply convergence of conditional distributions. In particular, conditional distributions need not converge when joint Gaussian distributions converge to a singular Gaussian limit. Algebraically, this is due to the fact that at singular covariance matrices, Schur complements are not continuous functions of the matrix entries. Our results lay out special conditions under which convergence of Gaussian conditional distributions nevertheless…

The AI boom is driving memory and storage shortages that may last a decade; OpenAI's Stargate has deals for 900K DRAM wafers per month, or ~40% of global output (Luke James/Tom's Hardware)
https://www.tomshardware.com/pc-components

AI data centers are swallowing the world's memory and storage supply, setting the stage for a pricing apocalypse that could last a decade
Once-cheap SSDs, DRAM, and HDD prices are climbing fast as AI demand and constrained supply converge to create the tightest market in years.

Great bike ride today! We do a group ride the last Sunday of each month. Three different groups start from different places around the city and converge on a spot and we hang out for a bit then all ride back from whence we came...
#BikeTooter #ScrappyHour

Strong convergence: a short survey
Ramon van Handel
https://arxiv.org/abs/2510.12520 https://arxiv.org/pdf/2510.12520…

Strong convergence: a short survey
A family of random matrices is said to converge strongly to a limiting family of operators if the operator norm of every noncommutative polynomial of the matrices converges to that of the limiting operators. Recent developments surrounding the strong convergence phenomenon have led to new progress on important problems in random graphs, geometry, operator algebras, and applied mathematics. We review classical and recent results in this area, and their applications to various areas of mathematic…

Accelerated Price Adjustment for Fisher Markets with Exact Recovery of Competitive Equilibrium
He Chen, Chonghe Jiang, Anthony Man-Cho So
https://arxiv.org/abs/2510.07759 https:…

Accelerated Price Adjustment for Fisher Markets with Exact Recovery of Competitive Equilibrium
The canonical price-adjustment process, tâtonnement, typically fails to converge to the exact competitive equilibrium (CE) and requires a high iteration complexity of $\tilde{\mathcal{O}}(1/ε)$ to compute $ε$-CE prices in widely studied linear and quasi-linear Fisher markets. This paper proposes refined price-adjustment processes to overcome these limitations. By formulating the task of finding CE of a (quasi-)linear Fisher market as a strongly convex nonsmooth minimization problem, we devel…

Building Gradient by Gradient: Decentralised Energy Functions for Bimanual Robot Assembly
Alexander L. Mitchell, Joe Watson, Ingmar Posner
https://arxiv.org/abs/2510.04696 https…

Building Gradient by Gradient: Decentralised Energy Functions for Bimanual Robot Assembly
There are many challenges in bimanual assembly, including high-level sequencing, multi-robot coordination, and low-level, contact-rich operations such as component mating. Task and motion planning (TAMP) methods, while effective in this domain, may be prohibitively slow to converge when adapting to disturbances that require new task sequencing and optimisation. These events are common during tight-tolerance assembly, where difficult-to-model dynamics such as friction or deformation require rapi…

Autonomous vehicles need social awareness to find optima in multi-agent reinforcement learning routing games
Anastasia Psarou, {\L}ukasz Gorczyca, Dominik Gawe{\l}, Rafa{\l} Kucharski
https://arxiv.org/abs/2510.11410

Autonomous vehicles need social awareness to find optima in multi-agent reinforcement learning routing games
Previous work has shown that when multiple selfish Autonomous Vehicles (AVs) are introduced to future cities and start learning optimal routing strategies using Multi-Agent Reinforcement Learning (MARL), they may destabilize traffic systems, as they would require a significant amount of time to converge to the optimal solution, equivalent to years of real-world commuting. We demonstrate that moving beyond the selfish component in the reward significantly relieves this issue. If each AV, apart…

Residual-Informed Learning of Solutions to Algebraic Loops
Felix Brandt, Andreas Heuermann, Philip Hannebohm, Bernhard Bachmann
https://arxiv.org/abs/2510.09317 https://

Residual-Informed Learning of Solutions to Algebraic Loops
This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also resolves the issue of ambiguous solutions, allowing the surrogate to converge to a consistent solution rather than averaging multiple valid on…

Sharp Lower Bounds for Linearized ReLU^k Approximation on the Sphere
Tong Mao, Jinchao Xu
https://arxiv.org/abs/2510.04060 https://arxiv.org/pdf/2510.04060…

Sharp Lower Bounds for Linearized ReLU^k Approximation on the Sphere
We prove a saturation theorem for linearized shallow ReLU$^k$ neural networks on the unit sphere $\mathbb S^d$. For any antipodally quasi-uniform set of centers, if the target function has smoothness $r>\tfrac{d+2k+1}{2}$, then the best $\mathcal{L}^2(\mathbb S^d)$ approximation cannot converge faster than order $n^{-\frac{d+2k+1}{2d}}$. This lower bound matches existing upper bounds, thereby establishing the exact saturation order $\tfrac{d+2k+1}{2d}$ for such networks. Our results place linea…

Multi-agent learning under uncertainty: Recurrence vs. concentration
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose Blanchet
https://arxiv.org/abs/2512.08132 https://arxiv.org/pdf/2512.08132 https://arxiv.org/html/2512.08132
arXiv:2512.08132v1 Announce Type: new
Abstract: In this paper, we examine the convergence landscape of multi-agent learning under uncertainty. Specifically, we analyze two stochastic models of regularized learning in continuous games -- one in continuous and one in discrete time with the aim of characterizing the long-run behavior of the induced sequence of play. In stark contrast to deterministic, full-information models of learning (or models with a vanishing learning rate), we show that the resulting dynamics do not converge in general. In lieu of this, we ask instead which actions are played more often in the long run, and by how much. We show that, in strongly monotone games, the dynamics of regularized learning may wander away from equilibrium infinitely often, but they always return to its vicinity in finite time (which we estimate), and their long-run distribution is sharply concentrated around a neighborhood thereof. We quantify the degree of this concentration, and we show that these favorable properties may all break down if the underlying game is not strongly monotone -- underscoring in this way the limits of regularized learning in the presence of persistent randomness and uncertainty.
toXiv_bot_toot

Debiased Front-Door Learners for Heterogeneous Effects
Yonghan Jung
https://arxiv.org/abs/2509.22531 https://arxiv.org/pdf/2509.22531

Debiased Front-Door Learners for Heterogeneous Effects
In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-door (FD) adjustment identifies causal effects through the mediator. We study the heterogeneous treatment effect (HTE) under FD identification and introduce two debiased learners: FD-DR-Learner and FD-R-Learner. Both attain fast, quasi-oracle rates (i.e., performance comparable to an oracle that knows the nuisances) even when nuisance functions converge as …

Predictively Oriented Posteriors
Yann McLatchie, Badr-Eddine Cherief-Abdellatif, David T. Frazier, Jeremias Knoblauch
https://arxiv.org/abs/2510.01915 https://

Predictively Oriented Posteriors
We advocate for a new statistical principle that combines the most desirable aspects of both parameter inference and density estimation. This leads us to the predictively oriented (PrO) posterior, which expresses uncertainty as a consequence of predictive ability. Doing so leads to inferences which predictively dominate both classical and generalised Bayes posterior predictive distributions: up to logarithmic factors, PrO posteriors converge to the predictively optimal model average at rate $n^…

Liquid-gas analog multicriticality in a frustrated Ising bilayer
Yuchen Fan
https://arxiv.org/abs/2510.05655 https://arxiv.org/pdf/2510.05655

Liquid-gas analog multicriticality in a frustrated Ising bilayer
We report the discovery of a multicritical point that extends the liquid-gas paradigm to systems with competing symmetry-breaking orders. Using large-scale Monte Carlo simulations of a frustrated bilayer Ising antiferromagnet with tunable couplings, we map out a rich finite-temperature phase diagram hosting three ordered phases separated by both continuous and first-order transitions. By tuning the couplings, a tricritical line and a critical end-point line converge into a single multicritical …

On the distribution of charges in a conducting needle
Orion Ciftja, Adrien Gu\'enard, Nefton Pali
https://arxiv.org/abs/2509.24029 https://arxiv.org/pd…

On the distribution of charges in a conducting needle
We study the distribution of point charges in a straight conductive needle and the electric field created by them. Starting from the bead model with $n$ point charges on the needle, we show the existence and uniqueness of an equilibrium state. We also study the differential system pertaining to the system and show that the system of point charges does not converge towards an equilibrium state. In order to move from a discrete to a continuous model we increase the number of the point charges and…

Uniqueness of the asymptotic limits for Ricci-flat manifolds with linear volume growth
Zetian Yan, Xingyu Zhu
https://arxiv.org/abs/2510.00420 https://arxi…

Uniqueness of the asymptotic limits for Ricci-flat manifolds with linear volume growth
Under natural assumptions on curvature and cross section, we establish the uniqueness of asymptotic limits and the exponential convergence rate for complete noncollapsed Ricci-flat manifolds with linear volume growth, which are known to only admit cylindrical asymptotic limits. In dimension four, these assumptions hold automatically, yielding unconditional uniqueness and convergence. In particular, our results show that all asymptotically cylindrical Calabi--Yau manifolds converge exponentially…

The hat polykite as an Iterated Function System
Corey de Wit
https://arxiv.org/abs/2510.00409 https://arxiv.org/pdf/2510.00409

The hat polykite as an Iterated Function System
This paper describes the celebrated aperiodic hat tiling by Smith et al. [Comb. Theory 8 (2024), 6] as generated by an overlapping iterated function system. We briefly introduce and study infinite sequences of iterated function systems that converge uniformly in each component, and use this theory to model the hat tiling's associated imperfect substitution system.

Global weak solutions and incompressible limit of two-dimensional isentropic compressible magnetohydrodynamic equations with ripped density and large initial data
Shuai Wang, Guochun Wu, Xin Zhong
https://arxiv.org/abs/2510.00812

Global weak solutions and incompressible limit of two-dimensional isentropic compressible magnetohydrodynamic equations with ripped density and large initial data
We establish the global existence of weak solutions to isentropic compressible magnetohydrodynamic equations with ripped density in the whole plane provided the bulk viscosity coefficient is properly large. Moreover, we show that such solutions converge globally in time to a weak solution of the inhomogeneous incompressible magnetohydrodynamic equations when the bulk viscosity coefficient tends to infinity. In particular, the initial data can be arbitrarily large and vacuum states are allowed i…

Graph-Based Learning of Free Surface Dynamics in Generalized Newtonian Fluids using Smoothed Particle Hydrodynamics
Hyo-Jin Kim, Jaekwang Kim, Hyung-Jun Park
https://arxiv.org/abs/2509.24264

Graph-Based Learning of Free Surface Dynamics in Generalized Newtonian Fluids using Smoothed Particle Hydrodynamics
In this study, we propose a graph neural network (GNN) model for efficiently predicting the flow behavior of non-Newtonian fluids with free surface dynamics. The numerical analysis of non-Newtonian fluids presents significant challenges, as traditional algorithms designed for Newtonian fluids with constant viscosity often struggle to converge when applied to non-Newtonian cases, where rheological properties vary dynamically with flow conditions. Among these, power-law fluids exhibit viscosity t…

$\log$-H\"older regularity of currents and equidistribution towards Green currents
Marco Vergamini
https://arxiv.org/abs/2510.00789 https://arxiv.org/…

$\log$-Hölder regularity of currents and equidistribution towards Green currents
Let $f$ be an endomorphism of a projective space or an automorphism of a compact Kähler manifold. We prove that the pull-backs of currents under the iterates of $f$ converge exponentially fast to the Green currents when tested at $\log$-Hölder-continuous observables whose $\mathrm{dd^c}$'s have bounded mass.

Approximate Bregman proximal gradient algorithm with variable metric Armijo--Wolfe line search
Kiwamu Fujiki, Shota Takahashi, Akiko Takeda
https://arxiv.org/abs/2510.06615 http…

Approximate Bregman proximal gradient algorithm with variable metric Armijo--Wolfe line search
We propose a variant of the approximate Bregman proximal gradient (ABPG) algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function. Although ABPG is known to converge globally to a stationary point even when the smooth part of the objective function lacks globally Lipschitz continuous gradients, and its iterates can often be expressed in closed form, ABPG relies on an Armijo line search to guarantee global convergence. Such reliance can slow down performanc…

Path--Averaged Contractions: A New Generalization of the Banach Contraction Principle
Nicola Fabiano
https://arxiv.org/abs/2510.01496 https://arxiv.org/pdf…

Path--Averaged Contractions: A New Generalization of the Banach Contraction Principle
We introduce a novel class of self-mappings on metric spaces, called \textbf{PA-contractions} (Path-Averaged Contractions), defined by an averaging condition over iterated distances. We prove that every continuous PA-contraction on a complete metric space has a unique fixed point, and the Picard iterates converge to it. This condition strictly generalizes the classical Banach contraction principle. We provide examples showing that PA-contractions are independent of F-contractions, Kannan, Chatt…

A note on the Maxwell's eigenvalues on thin sets
Francesco Ferraresso, Luigi Provenzano
https://arxiv.org/abs/2510.01846 https://arxiv.org/pdf/2510.018…

A note on the Maxwell's eigenvalues on thin sets
We analyse the Maxwell's spectrum on thin tubular neighborhoods of embedded surfaces of $\mathbb R^3$. We show that the Maxwell eigenvalues converge to the Laplacian eigenvalues of the surface as the thin parameter tends to zero. To achieve this, we reformulate the problem in terms of the spectrum of the Hodge Laplacian with relative conditions acting on co-closed differential $1$-forms. The result leads to new examples of domains where the Faber-Krahn inequality for Maxwell's eigenvalues fails…

Multi-objective Bayesian optimization for blocking in extreme value analysis and its application in additive manufacturing
Shehzaib Irfan, Nabeel Ahmad, Alexander Vinel, Daniel F. Silva, Shuai Shao, Nima Shamsaei, Jia Liu
https://arxiv.org/abs/2510.11960

Multi-objective Bayesian optimization for blocking in extreme value analysis and its application in additive manufacturing
Extreme value theory (EVT) is well suited to model extreme events, such as floods, heatwaves, or mechanical failures, which is required for reliability assessment of systems across multiple domains for risk management and loss prevention. The block maxima (BM) method, a particular approach within EVT, starts by dividing the historical observations into blocks. Then the sample of the maxima for each block can be shown, under some assumptions, to converge to a known class of distributions, which …

Neural Network Convergence for Variational Inequalities
Yun Zhao, Harry Zheng
https://arxiv.org/abs/2509.26535 https://arxiv.org/pdf/2509.26535

Neural Network Convergence for Variational Inequalities
We propose an approach to applying neural networks on linear parabolic variational inequalities. We use loss functions that directly incorporate the variational inequality on the whole domain to bypass the need to determine the stopping region in advance and prove the existence of neural networks whose losses converge to zero. We also prove the functional convergence in the Sobolev space. We then apply our approach to solving an optimal investment and stopping problem in finance. By leveraging …

An Adaptive ICP LiDAR Odometry Based on Reliable Initial Pose
Qifeng Wang, Weigang Li, Lei Nie, Xin Xu, Wenping Liu, Zhe Xu
https://arxiv.org/abs/2509.22058 https://

An Adaptive ICP LiDAR Odometry Based on Reliable Initial Pose
As a key technology for autonomous navigation and positioning in mobile robots, light detection and ranging (LiDAR) odometry is widely used in autonomous driving applications. The Iterative Closest Point (ICP)-based methods have become the core technique in LiDAR odometry due to their efficient and accurate point cloud registration capability. However, some existing ICP-based methods do not consider the reliability of the initial pose, which may cause the method to converge to a local optimum. …

Learning What To Hear: Boosting Sound-Source Association For Robust Audiovisual Instance Segmentation
Jinbae Seo, Hyeongjun Kwon, Kwonyoung Kim, Jiyoung Lee, Kwanghoon Sohn
https://arxiv.org/abs/2509.22740

Learning What To Hear: Boosting Sound-Source Association For Robust Audiovisual Instance Segmentation
Audiovisual instance segmentation (AVIS) requires accurately localizing and tracking sounding objects throughout video sequences. Existing methods suffer from visual bias stemming from two fundamental issues: uniform additive fusion prevents queries from specializing to different sound sources, while visual-only training objectives allow queries to converge to arbitrary salient objects. We propose Audio-Centric Query Generation using cross-attention, enabling each query to selectively attend to…

Non-collapsed eGH convergence and dimension
Jes\'us N\'u\~nez-Zimbr\'on, Jaime Santos-Rodr\'iguez, Sergio Zamora
https://arxiv.org/abs/2509.22821 https://…

Non-collapsed eGH convergence and dimension
Let $(X_i,p_i)$ be a non-collapsing sequence of pointed $n$-dimensional Riemannian manifolds with a uniform lower Ricci curvature bound, and $G_i \leq \text{Iso} (X_i)$ a sequence of closed subgroups of isometries. We show that if the triples $(X_i, G_i, p_i)$ converge in the equivariant Gromov--Hausdorff sense to a triple $(X,G,p)$, then $\text{dim} (G) \geq \limsup _{i \to \infty} \text{dim} (G_i)$, generalizing a result of Harvey to the non-compact setting. The argument also applies in the n…

On Non-Monotone Variational Inequalities
Sina Arefizadeh, Angelia Nedi\'c
https://arxiv.org/abs/2510.02724 https://arxiv.org/pdf/2510.02724

On Non-Monotone Variational Inequalities
In this paper, we provide some sufficient conditions for the existence of solutions to non-monotone Variational Inequalities (VIs) based on inverse mapping theory and degree theory. We have obtained several applicable sufficient conditions for this problem and have introduced a sufficient condition for the existence of a Minty solution. We have shown that the Korpelevich and Popov methods converge to a solution of a non-monotone VI, provided that a Minty solution exists.

RG theory of spontaneous stochasticity for Sabra model of turbulence
Alexei A. Mailybaev
https://arxiv.org/abs/2510.01204 https://arxiv.org/pdf/2510.01204

RG theory of spontaneous stochasticity for Sabra model of turbulence
We consider fluctuating Sabra models of turbulence, which exhibit the phenomenon of spontaneous stochasticity: their solutions converge to a stochastic process in the ideal limit, when both viscosity and small-scale noise vanish. In this paper, we develop a renormalization group (RG) approach to explain this phenomenon. Here, RG is understood as an exact relation between the stochastic properties of systems with different dissipative and noise terms, in contrast to the Kadanoff-Wilson coarse-gr…

On the Ricci flow on Trees
Shuliang Bai, Bobo Hua, Yong Lin, Shuliang Liu
https://arxiv.org/abs/2509.22140 https://arxiv.org/pdf/2509.22140

On the Ricci flow on Trees
In this paper, we study the evolution of metrics on finite trees under continuous-time Ricci flows based on the Lin-Lu-Yau version of Ollivier Ricci curvature. We analyze long-time dynamics of edge weights and curvatures, providing precise characterizations of their limiting behaviors. We prove that the Ricci flow converges to metric with zero curvature on edges whose normalized weights converge to positive values only if the tree is a caterpillar tree.

huh. TIL: there is a “four corners” place in Canada, like there is more famously in the USA, where Utah, Colorado, Arizona and New Mexico meet.
But ours is automatically cooler, figuratively and literally*, because it is a: at 60° Latitude, and b: involves two Territories and two Provinces.
It is where the Northwest Territories, Nunavut, Saskatchewan, and Manitoba meet.
It is also a LOT harder to get to. Though arguably, as it is amongst the thousands of lakes in the area, during the summer, a float plane would get you there.
Also, technically. It is not a perfect “joining” on the map which is just bad planning on Canada’s part, but at least it is hiking distance. Or that might be just a projection issue. They seem to converge at 60N 102W. I guess I could consult an official boundary document or something :)
Apple Map location: #agw not withstanding
#geography #uselessKnowledge #nerd #maps #canada #sk #mb #nwt #yt #climatechange