Tootfinder

MTS-DMAE: Dual-Masked Autoencoder for Unsupervised Multivariate Time Series Representation Learning
Yi Xu, Yitian Zhang, Yun Fu
https://arxiv.org/abs/2509.16078 https://

MTS-DMAE: Dual-Masked Autoencoder for Unsupervised Multivariate Time Series Representation Learning
Unsupervised multivariate time series (MTS) representation learning aims to extract compact and informative representations from raw sequences without relying on labels, enabling efficient transfer to diverse downstream tasks. In this paper, we propose Dual-Masked Autoencoder (DMAE), a novel masked time-series modeling framework for unsupervised MTS representation learning. DMAE formulates two complementary pretext tasks: (1) reconstructing masked values based on visible attributes, and (2) est…

Because I’m seeing this in “serious” articles, Dyson spheres are—literally—a joke.
The paper from Freeman Dyson is literally a fully purpose-written joke making fun of early SETI efforts, with a cascade of ever more implausible assertions leading to “we should be surrounded with these spheres so let’s look for them”.
They’re a joke making fun of gullible people who don’t think things through.
Now gullible people who don’t think things through (like Sam Altman) are using the term like it’s a real thing that could actually work.

I’ve worked over the past year to reduce the amount of noise in my consciousness on a daily basis.
By that I mean - information noise, not literal sounds “noise”. (That problem was solved long ago by some good earplugs and noise canceling earphones.)
I’ve gotten used to spending less time on social media, regularly blocking most apps on my devices (anything with a feed news, most work communication apps, etc.), putting my phone and other devices aside for extended periods of time. Often go to work places with my iPad explicitly having its WiFi turned off and selecting cafes that don’t offer WiFi at all.
Negotiated better boundaries at work and in personal life where I exchange messages with people less often but try to make those interactions more meaningful, and people rarely expect me to respond to requests in less than 24 hours. Spent a lot of time setting up custom notification settings on all apps that would allow it, so I get fewer pings. With software, choosing fewer cloud-based options and using tools that are simple and require as few interruptions as possible.
Accustomed myself to lower-tech versions of doing things I like to do: reading on paper, writing by hand, drawing in physical sketchbooks, got a typewriter for typing without a screen. Choosing to call people on audio more, trying to make more of an effort to see people in person. Going to museums to look at art instead of browsing Pinterest. Defaulting to the library when looking for information.
I’m commenting on this now for two reasons:
1. I am pretty proud of myself for how much I’ve actually managed to reduce the constant stream of modern life esp. as a remote worker in tech!
2. Now that I’ve reached a breaking point of reducing enough noise that it’s NOTICEABLE - I am struck by the silence. I don’t know what to do with it. I don’t know how to navigate it and fill it. I made this space to be able to read and write and think more deeply - for now I feel stuck in limbo where I’m just reacquainting myself with the concept of having any space in my mind at all.

I think I need to clarify some shit for (white) liberals.
How many times have you wondered if someone you're talking to in an informant sent to entrap you? How many times have you or a friend of yours been hit by a car, intentionally? How many friends have been hit, or almost hit? Ever been stabbed? Know anyone who has? Has the FBI ever knocked on a friend's door? Have police ever kicked down your door? Have you ever been arrested? Pepper sprayed? Does the sound or smell or blast balls give you flashbacks? Do you ever wonder what all the CS exposure is doing to your body? How many times have you been shot or shot at? Do you wonder every day if this is the day they'll come to kill you? Would anyone in your social circle answer these questions differently?
When you vote, you risk nothing (big asterisk, but if I'm talking to you then it doesn't apply to you). What you get out of voting is exactly what you put into it. Direct action is the same.
If you aren't worried about someone murdering you, then you probably aren't actually threatening the system. That's the difference between voting, and doing something useful. If they had to murder all the liberals in order to keep going, fascism would end. If they're only murdering radicals and marginalized people, then you're just like all the "good Germans" who hated Hitler but did essentially nothing.
It's already that bad for some people. How much are you willing to risk? How many people are you willing to sacrifice for your comfort? These are the questions we're all thinking about every time you tell us to vote.
(I'm tagging this #USPol so it's easy for folks to filter out if they're already well acquaintaned with the horror. I'm not CW, because USPol is just expected to be triggering.)

Regular homomorphisms and mixed motives
Ivan Hernandez, Pablo Pelaez
https://arxiv.org/abs/2509.15920 https://arxiv.org/pdf/2509.15920

Regular homomorphisms and mixed motives
Let $X$ be a smooth projective variety of dimension $d$ over an algebraically closed field $k$. The main goal of this paper is to study, in the context of Voevodsky's triangulated category of motives $DM_k$, the group $CH^n_{\mathrm{alg}}(X)$ of codimension $n$ algebraic cycles of $X$, algebraically equivalent to zero, modulo rational equivalence, $1\leq n \leq d$. Namely, for any regular homomorphism $ψ$ (in the sense of Samuel) defined on $CH^n_{\mathrm{alg}}(X)$, we construct $M^n_ψ(X)\in …

An Empirical Study on How Video-LLMs Answer Video Questions
Chenhui Gou, Ziyu Ma, Zicheng Duan, Haoyu He, Feng Chen, Akide Liu, Bohan Zhuang, Jianfei Cai, Hamid Rezatofighi
https://arxiv.org/abs/2508.15360

An Empirical Study on How Video-LLMs Answer Video Questions
Taking advantage of large-scale data and pretrained language models, Video Large Language Models (Video-LLMs) have shown strong capabilities in answering video questions. However, most existing efforts focus on improving performance, with limited attention to understanding their internal mechanisms. This paper aims to bridge this gap through a systematic empirical study. To interpret existing VideoLLMs, we adopt attention knockouts as our primary analytical tool and design three variants: Video…

Die #Schifffahrt hätte als erste Branche globale #Klimaregeln einführen können.
Doch ein geplanter #Kraftstoffstandard und

Klimapakt für Schifffahrt gescheitert: Kein Fahrplan mit Trump
Offenbar ist es den USA hinter den Kulissen gelungen, eine Einigung auf mehr Klimaschutz in der Schifffahrt zu verhindern. Dabei ist die amerikanische Flotte verhältnismäßig klein, kommentiert Hermannus Pfeiffer.

Measuring the environmental impact of delivering AI at Google Scale
Cooper Elsworth, Keguo Huang, David Patterson, Ian Schneider, Robert Sedivy, Savannah Goodman, Ben Townsend, Parthasarathy Ranganathan, Jeff Dean, Amin Vahdat, Ben Gomes, James Manyika
https://arxiv.org/abs/2508.15734

Measuring the environmental impact of delivering AI at Google Scale
The transformative power of AI is undeniable - but as user adoption accelerates, so does the need to understand and mitigate the environmental impact of AI serving. However, no studies have measured AI serving environmental metrics in a production environment. This paper addresses this gap by proposing and executing a comprehensive methodology for measuring the energy usage, carbon emissions, and water consumption of AI inference workloads in a large-scale, AI production environment. Our approa…

Universal Error Correction for Distributed Quantum Computing
Daowen Qiu, Ligang Xiao, Le Luo, Paulo Mateus
https://arxiv.org/abs/2508.15580 https://arxiv.o…

Universal Error Correction for Distributed Quantum Computing
In distributed quantum computing, the final solution of a problem is usually achieved by catenating these partial solutions resulted from different computing nodes, but intolerable errors likely yield in this catenation process. In this paper, we propose a universal error correction scheme to reduce errors and obtain effective solutions. Then, we apply this error correction scheme to designing a distributed phase estimation algorithm that presents a basic tool for studying distributed Shor's al…

CFP> Intrinsic Perfection (UHM, 12–13 March 2026) Update of the links
https://ift.tt/OI7mxfF
H-Diplo Roundtable XXV-12 on Fall, _Dien Bien Phu: Un coin d’Enfer_ H-Diplo Roundtable…
via Input 4 RELCFP