Tootfinder

Signature of climate change: Nearly half of harmful wildfire smoke exposure linked to human-caused warming https://phys.org/news/2025-12-signature-climate-wildfire-exposure-linked.html

Signature of climate change: Nearly half of harmful wildfire smoke exposure linked to human-caused warming
Across the western U.S., wildfires and the dangerous smoke that results have increased in frequency and intensity since the 1990s—that much is clear. Surprisingly less clear are the exact reasons why: While greenhouse gas-related global warming is often cited as a culprit, to what extent can this claim be quantified?

Super Micro's shares drop 5% after the server maker reports it expects $5B in Q1 revenue, down from its $6B to $7B guidance, due to "design win upgrades" (Ashley Capoot/CNBC)
https://www.cnbc.com/2025/10/23/super-micro-stock-preliminary-earnings.htm…

Super Micro shares fall 6% on weak preliminary results
Super Micro said "design win upgrades" pushed some expected first-quarter revenue to the second quarter.

Day 29: Leanne Betasamosake Simpson
I've been sitting on Simpson for a while because there's some overlap in her writing with Robin Wall Kimmerer, and I've had a lot of different genres/styles/subjects/media I've wanted to post at least one author from. But I've now hit repeats on at least YA romance and manga, and Simpson's writing is actually quite different from Kimmerer's in a lot of ways. While Kimmerer is a biologist by training and literally braids that knowledge together with her knowledge of Potawatomi cosmology and ethics, Simpson is an Anishinaabe philosopher and anarchist, and her position as a scholar of Indigenous philosophy adds a different depth to her work: she talks in more depth about knowledge relationships and her connections with specific elders, and she has more citations to other Indigenous theorists, which is the one criticism I've ever seen of Kimmerer's work. Rather than being Indigenous and a scientist, she's Indigenous and a scholar of indigenous studies.
I've only read "Theory of Water" by Simpson, but it was excellent, and especially inspiring to read as an anarchist. Simpson's explicit politics are another difference from Kimmerer's work, which is more implicitly than explicitly political. This allows Simpson to draw extremely interesting connections to other anarchist theorists and movements. "Theory of Water" is probably a bit less accessible than "Braiding Sweetgrass," but it's richer from a theory perspective as a result.
In any case, Simpson is a magnificent writer, sharing personal insights and stories along with (and inseparable from) her theoretical ideas.
#30AuthorsNoMen

A survey of moment bounds for $\zeta(s)$: from Heath Brown's work to the present
Alexandra Florea
https://arxiv.org/abs/2509.20335 https://arxiv.org/pd…

A survey of moment bounds for $ζ(s)$: from Heath Brown's work to the present
In this expository article, we review some of the ideas behind the work of Heath-Brown (D.~R. Heath-Brown, {\em Fractional moments of the Riemann Zeta Function}, J. Lond. Math. Soc., (2), {\bf 24}, no.1, (1981), 65--78) on upper and lower bounds for moments of the Riemann zeta-function, as well as the impact this work had on subsequent developments in the field. We survey recent results on the topic, which essentially recover the expected rate of growth for all moments - unconditionally for sma…

Some Simplifications for the Expectation-Maximization (EM) Algorithm: The Linear Regression Model Case
Daniel A. Griffith
https://arxiv.org/abs/2509.19461 https://

Some Simplifications for the Expectation-Maximization (EM) Algorithm: The Linear Regression Model Case
The EM algorithm is a generic tool that offers maximum likelihood solutions when datasets are incomplete with data values missing at random or completely at random. At least for its simplest form, the algorithm can be rewritten in terms of an ANCOVA regression specification. This formulation allows several analytical results to be derived that permit the EM algorithm solution to be expressed in terms of new observation predictions and their variances. Implementations can be made with a linear r…

Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy
Manuel Perez-Carrasco, Maya Nasr, Sebastien Roche, Chris Chan Miller, Zhan Zhang, Core Francisco Park, Eleanor Walker, Cecilia Garraffo, Douglas Finkbeiner, Ritesh Gautam, Steven Wofsy
https://arxiv.org/abs/2509.19665

Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy
Effective cloud and cloud shadow detection is a critical prerequisite for accurate retrieval of concentrations of atmospheric methane or other trace gases in hyperspectral remote sensing. This challenge is especially pertinent for MethaneSAT and for its airborne companion mission, MethaneAIR. In this study, we use machine learning methods to address the cloud and cloud shadow detection problem for sensors with these high spatial resolutions instruments. Cloud and cloud shadows in remote sensing…

Graphene and thin graphite films for ultrafast optical Kerr gating at 1 GHz repetition rate under focused illumination
Amr Farrag, Assegid M. Flatae, Mario Agio
https://arxiv.org/abs/2511.17713 https://arxiv.org/pdf/2511.17713 https://arxiv.org/html/2511.17713
arXiv:2511.17713v1 Announce Type: new
Abstract: The ability to address sub-picosecond events of weak optical signals is essential for progress in quantum science, nonlinear optics, and ultrafast spectroscopy. While up-conversion and optical Kerr gating (OKG) offer femtosecond resolution, they are generally limited to ensemble measurements, making ultrafast detection in nano-optics challenging. OKG, with its broadband response and high throughput without phase-matching, is especially promising when used at high repetition rates under focused illumination.
Here, we demonstrate an ultrafast detection scheme using the third-order nonlinearity of graphene and thin graphite films, operating at 1 GHz with sub-nanojoule pulses and achieving 141 fs temporal resolution. Their exceptionally large nonlinear refractive index, orders of magnitude higher than conventional Kerr media, enhances detection efficiency at smaller thicknesses, enables sub-picosecond response, and supports broadband operation. Their atomic-scale thickness minimizes dispersion and simplifies integration with microscopy platforms, optical fibers, and nanophotonic circuits, making them a compact, practical material platform for nano-optical and on-chip ultrafast Kerr gating.
toXiv_bot_toot

Short-Segment Speaker Verification with Pre-trained Models and Multi-Resolution Encoder
Jisoo Myoung, Sangwook Han, Kihyuk Kim, Jong Won Shin
https://arxiv.org/abs/2509.19721 ht…

Short-Segment Speaker Verification with Pre-trained Models and Multi-Resolution Encoder
Speaker verification (SV) utilizing features obtained from models pre-trained via self-supervised learning has recently demonstrated impressive performances. However, these pre-trained models (PTMs) usually have a temporal resolution of 20 ms, which is lower than typical filterbank features. It may be problematic especially for short-segment SV with an input segment shorter than 2 s, in which we need to extract as much information as possible from the input with a limited length. Although there…

DSA, AIA, and LLMs: Approaches to conceptualizing and auditing moderation in LLM-based chatbots across languages and interfaces in the electoral contexts
Natalia Stanusch, Raziye Buse Cetin, Salvatore Romano, Miazia Schueler, Meret Baumgartner, Bastian August, Alexandra Rosca
https://arxiv.org/abs/2509.19890

DSA, AIA, and LLMs: Approaches to conceptualizing and auditing moderation in LLM-based chatbots across languages and interfaces in the electoral contexts
The integration of Large Language Models (LLMs) into chatbot-like search engines poses new challenges for governing, assessing, and scrutinizing the content output by these online entities, especially in light of the Digital Service Act (DSA). In what follows, we first survey the regulation landscape in which we can situate LLM-based chatbots and the notion of moderation. Second, we outline the methodological approaches to our study: a mixed-methods audit across chatbots, languages, and electio…

Gyges: Dynamic Cross-Instance Parallelism Transformation for Efficient LLM Inference
Haoyu Chen, Xue Li, Kun Qian, Yu Guan, Jin Zhao, Xin Wang
https://arxiv.org/abs/2509.19729 h…

Gyges: Dynamic Cross-Instance Parallelism Transformation for Efficient LLM Inference
Efficiently processing the dynamics of requests, especially the context length variance, is important in Large Language Model (LLM) serving scenarios. However, there is an intrinsic trade-off: while leveraging parallelism strategies, such as Tensor Parallelism (TP), can coordinate multiple GPUs to accommodate larger context lengths, it inevitably results in degraded overall throughput. In this paper, we propose Cross-Instance Parallelism Transformation (Gyges), which adaptively adjusts the para…