Tootfinder

An interview with Runway CEO Cris Valenzuela on expediting the movie-making process, copyright cases, why using Runway is like using a camera, and more (Nilay Patel/The Verge)
https://www.theverge.com/decoder-podcast-w

Runway CEO Cris Valenzuela wants Hollywood to embrace AI video
The head of the AI video generation platform on copyright and the future of filmmaking.

On the Spherical Dirichlet Distribution: Corrections and Results
Jose H Guardiola
https://arxiv.org/abs/2506.04441 https://arxiv.org/…

On the Spherical Dirichlet Distribution: Corrections and Results
This note corrects a technical error in Guardiola (2020, Journal of Statistical Distributions and Applications), presents updated derivations, and offers an extended discussion of the properties of the spherical Dirichlet distribution. Today, data mining and gene expressions are at the forefront of modern data analysis. Here we introduce a novel probability distribution that is applicable in these fields. This paper develops the proposed Spherical-Dirichlet Distribution designed to fit vectors …

On the distribution of critical points of the Eisenstein series $E_6$ and monodromy interpretation
Zhijie Chen
https://arxiv.org/abs/2506.03475 https://

On the distribution of critical points of the Eisenstein series $E_6$ and monodromy interpretation
In previous works joint with Lin, we proved that the Eisenstein series $E_4$ (resp. $E_2$) has at most one critical point in every fundamental domain $γ(F_0)$ of $Γ_{0}(2)$, where $γ(F_0)$ are translates of the basic fundamental domain $F_0$ via the Möbius transformation of $γ\inΓ_{0}(2)$. But the method can not work for the Eisenstein series $E_6$. In this paper, we develop a new approach to show that $E_6'(τ)$ has exactly either $1$ or $2$ zeros in every fundamental domain $γ(F_0)$ …

This https://arxiv.org/abs/2506.00486 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

It Takes a Good Model to Train a Good Model: Generalized Gaussian Priors for Optimized LLMs
Despite rapid advancements in the research and deployment of large language models (LLMs), the statistical distribution of model parameters, as well as their influence on initialization, training dynamics, and downstream efficiency, has received surprisingly little attention. A recent work introduced BackSlash, a training-time compression algorithm. It first demonstrated that pre-trained LLM parameters follow generalized Gaussian distributions (GGDs) better. By optimizing GG priors during train…

DAS-MAE: A self-supervised pre-training framework for universal and high-performance representation learning of distributed fiber-optic acoustic sensing
Junyi Duan, Jiageng Chen, Zuyuan He
https://arxiv.org/abs/2506.04552

DAS-MAE: A self-supervised pre-training framework for universal and high-performance representation learning of distributed fiber-optic acoustic sensing
Distributed fiber-optic acoustic sensing (DAS) has emerged as a transformative approach for distributed vibration measurement with high spatial resolution and long measurement range while maintaining cost-efficiency. However, the two-dimensional spatial-temporal DAS signals present analytical challenges. The abstract signal morphology lacking intuitive physical correspondence complicates human interpretation, and its unique spatial-temporal coupling renders conventional image processing methods…

Maximizing the Value of Predictions in Control: Accuracy Is Not Enough
Yiheng Lin, Christopher Yeh, Zaiwei Chen, Adam Wierman
https://arxiv.org/abs/2506.04497

Maximizing the Value of Predictions in Control: Accuracy Is Not Enough
We study the value of stochastic predictions in online optimal control with random disturbances. Prior work provides performance guarantees based on prediction error but ignores the stochastic dependence between predictions and disturbances. We introduce a general framework modeling their joint distribution and define "prediction power" as the control cost improvement from the optimal use of predictions compared to ignoring the predictions. In the time-varying Linear Quadratic Regulator (LQR) s…

This https://arxiv.org/abs/2506.02139 has been replaced.
link: https://scholar.google.com/scholar?q=a

The Unified Cognitive Consciousness Theory for Language Models: Anchoring Semantics, Thresholds of Activation, and Emergent Reasoning
Few-shot learning in large language models (LLMs) reveals a core paradox: certain tasks generalize from just a few examples, while others demand extensive supervision. To explain this, we introduce the Unified Cognitive Consciousness Theory (UCCT), which reconceptualizes LLMs not as deficient agents, but as unconscious substrates: dense, distributed repositories of linguistic and conceptual patterns that operate without explicit semantics, intention, or goal-directed reasoning. Under this view,…

Linearly Distributive Fox Theorem
Rose Kudzman-Blais
https://arxiv.org/abs/2506.02180 https://arxiv.org/pdf/2506.02180

Linearly Distributive Fox Theorem
Linearly distributive categories (LDC), introduced by Cockett and Seely to model multiplicative linear logic, are categories equipped with two monoidal structures that interact via linear distributivities. A seminal result in monoidal category theory is the Fox theorem, which characterizes cartesian categories as symmetric monoidal categories whose objects are equipped with canonical comonoid structures. The aim of this work is to extend the Fox theorem to LDCs and characterize the subclass of …

Constraining the Baryon Fraction in the Intergalactic Medium with 92 localized Fast Radio Bursts
Yang Liu, Yuchen Zhang, Hongwei Yu, Puxun Wu
https://arxiv.org/abs/2506.03536

Constraining the Baryon Fraction in the Intergalactic Medium with 92 localized Fast Radio Bursts
Fast radio bursts (FRBs) are emerging as powerful cosmological probes for constraining the baryon fraction in the intergalactic medium (IGM), offering a promising avenue to address the missing baryon problem. In this paper, we analyze constraints on the IGM baryon fraction ($f_\mathrm{IGM}$) using 92 localized FRBs, incorporating a corrected probability distribution function for the IGM dispersion measure within three different cosmological models. We find that variations in the underlying cosm…

In May, Microsoft launched Signal, a 120-page print magazine focused on its tech and distributed to business leaders every few months, to counter digital noise (Tom Warren/The Verge)
https://www.theverge.com/notepad-microsoft

Why Microsoft has created its own print magazine
Microsoft has created its own print magazine, named Signal. It’s a reaction to the digital noise and ephemeral content.