Tootfinder

HOLA: Enhancing Audio-visual Deepfake Detection via Hierarchical Contextual Aggregations and Efficient Pre-training
Xuecheng Wu, Danlei Huang, Heli Sun, Xinyi Yin, Yifan Wang, Hao Wang, Jia Zhang, Fei Wang, Peihao Guo, Suyu Xing, Junxiao Xue, Liang He
https://arxiv.org/abs/2507.22781

HOLA: Enhancing Audio-visual Deepfake Detection via Hierarchical Contextual Aggregations and Efficient Pre-training
Advances in Generative AI have made video-level deepfake detection increasingly challenging, exposing the limitations of current detection techniques. In this paper, we present HOLA, our solution to the Video-Level Deepfake Detection track of 2025 1M-Deepfakes Detection Challenge. Inspired by the success of large-scale pre-training in the general domain, we first scale audio-visual self-supervised pre-training in the multimodal video-level deepfake detection, which leverages our self-built data…

Ionic emissions and activity evolution in comet C/2020 F3 (NEOWISE): Insights from long-slit spectroscopy and photometry
K. Aravind, E. Jehin, S. Hmmidouch, M. Vander Donckt, S. Ganesh, P. Rousselot, P. Hardy, D. Sahu, J. Manfroid, Z. Benkhaldoun
https://arxiv.org/abs/2507.21707

Ionic emissions and activity evolution in comet C/2020 F3 (NEOWISE): Insights from long-slit spectroscopy and photometry
Comet C/2020 F3 (NEOWISE) was the brightest comet in the northern hemisphere since C/1995 O1 (Hale-Bopp), providing a unique opportunity to study its composition and spatial distribution of emissions. We conducted narrow-band photometry and long-slit low-resolution spectroscopy to monitor the comet's activity and compositional evolution over several weeks post-perihelion. Narrow-band images (OH, NH, CN, C$_2$, C$_3$, BC, GC, RC) and broad-band images (B, V, Rc, Ic) were acquired with TRAPPIST-N…

Molecular adhesion assay for biopolymer systems
Jeremy A. Cribb (Department of Physics and Astronomy, The University of North Carolina at Chapel Hill), Farnaz Fazelpour (Marsico Lung Institute, The University of North Carolina at Chapel Hill), David A. Wollensak (Department of Physics and Astronomy, The University of North Carolina at Chapel Hill), Danielle Rice (College of Engineering, North Carolina Agricultural and Technical State University), Max deJong (Department of Physics and A…

Molecular adhesion assay for biopolymer systems
Molecular adhesion plays a central role in many biological systems, yet existing methods to quantify adhesive strength often struggle to bridge the gap between single-molecule resolution and biologically relevant environments. Here, we present a scalable micromagnetic bead-based adhesion assay capable of quantifying detachment forces under physiologically meaningful conditions. Designed to probe mucoadhesion in the context of mucociliary clearance, our system applies controlled magnetic forces …

LLM coding is the opposite of DRY
An important principle in software engineering is DRY: Don't Repeat Yourself. We recognize that having the same code copied in more than one place is bad for several reasons:
1. It makes the entire codebase harder to read.
2. It increases maintenance burden, since any problems in the duplicated code need to be solved in more than one place.
3. Because it becomes possible for the copies to drift apart if changes to one aren't transferred to the other (maybe the person making the change has forgotten there was a copy) it makes the code more error-prone and harder to debug.
All modern programming languages make it almost entirely unnecessary to repeat code: we can move the repeated code into a "function" or "module" and then reference it from all the different places it's needed. At a larger scale, someone might write an open-source "library" of such functions or modules and instead of re-implementing that functionality ourselves, we can use their code, with an acknowledgement. Using another person's library this way is complicated, because now you're dependent on them: if they stop maintaining it or introduce bugs, you've inherited a problem, but still, you could always copy their project and maintain your own version, and it would be not much more work than if you had implemented stuff yourself from the start. It's a little more complicated than this, but the basic principle holds, and it's a foundational one for software development in general and the open-source movement in particular. The network of "citations" as open-source software builds on other open-source software and people contribute patches to each others' projects is a lot of what makes the movement into a community, and it can lead to collaborations that drive further development. So the DRY principle is important at both small and large scales.
Unfortunately, the current crop of hyped-up LLM coding systems from the big players are antithetical to DRY at all scales:
- At the library scale, they train on open source software but then (with some unknown frequency) replicate parts of it line-for-line *without* any citation [1]. The person who was using the LLM has no way of knowing that this happened, or even any way to check for it. In theory the LLM company could build a system for this, but it's not likely to be profitable unless the courts actually start punishing these license violations, which doesn't seem likely based on results so far and the difficulty of finding out that the violations are happening. By creating these copies (and also mash-ups, along with lots of less-problematic stuff), the LLM users (enabled and encouraged by the LLM-peddlers) are directly undermining the DRY principle. If we see what the big AI companies claim to want, which is a massive shift towards machine-authored code, DRY at the library scale will effectively be dead, with each new project simply re-implementing the functionality it needs instead of every using a library. This might seem to have some upside, since dependency hell is a thing, but the downside in terms of comprehensibility and therefore maintainability, correctness, and security will be massive. The eventual lack of new high-quality DRY-respecting code to train the models on will only make this problem worse.
- At the module & function level, AI is probably prone to re-writing rather than re-using the functions or needs, especially with a workflow where a human prompts it for many independent completions. This part I don't have direct evidence for, since I don't use LLM coding models myself except in very specific circumstances because it's not generally ethical to do so. I do know that when it tries to call existing functions, it often guesses incorrectly about the parameters they need, which I'm sure is a headache and source of bugs for the vibe coders out there. An AI could be designed to take more context into account and use existing lookup tools to get accurate function signatures and use them when generating function calls, but even though that would probably significantly improve output quality, I suspect it's the kind of thing that would be seen as too-baroque and thus not a priority. Would love to hear I'm wrong about any of this, but I suspect the consequences are that any medium-or-larger sized codebase written with LLM tools will have significant bloat from duplicate functionality, and will have places where better use of existing libraries would have made the code simpler. At a fundamental level, a principle like DRY is not something that current LLM training techniques are able to learn, and while they can imitate it from their training sets to some degree when asked for large amounts of code, when prompted for many smaller chunks, they're asymptotically likely to violate it.
I think this is an important critique in part because it cuts against the argument that "LLMs are the modern compliers, if you reject them you're just like the people who wanted to keep hand-writing assembly code, and you'll be just as obsolete." Compilers actually represented a great win for abstraction, encapsulation, and DRY in general, and they supported and are integral to open source development, whereas LLMs are set to do the opposite.
[1] to see what this looks like in action in prose, see the example on page 30 of the NYTimes copyright complaint against OpenAI (#AI #GenAI #LLMs #VibeCoding

Exploring the role of accretion disk geometry in shaping black hole shadows
Zi-Liang Wang
https://arxiv.org/abs/2506.21148 https://ar…

Exploring the role of accretion disk geometry in shaping black hole shadows
We study black hole imaging in the context of geometrically thick accretion disks in Schwarzschild spacetime. By decomposing the emitting region into a set of one-dimensional luminous segments, each characterized by its inclination angle and inner radius, we construct transfer functions that capture key image features-namely, the direct image, lensing ring, and photon ring. This approach allows a unified treatment of disk geometry and viewing angle. We explore three regimes: optically thin, opt…

H2EAL: Hybrid-Bonding Architecture with Hybrid Sparse Attention for Efficient Long-Context LLM Inference
Zizhuo Fu, Xiaotian Guo, Wenxuan Zeng, Shuzhang Zhong, Yadong Zhang, Peiyu Chen, Runsheng Wang, Le Ye, Meng Li
https://arxiv.org/abs/2508.16653

H2EAL: Hybrid-Bonding Architecture with Hybrid Sparse Attention for Efficient Long-Context LLM Inference
Large language models (LLMs) have demonstrated remarkable proficiency in a wide range of natural language processing applications. However, the high energy and latency overhead induced by the KV cache limits the edge deployment, especially for long contexts. Emerging hybrid bonding (HB) technology has been proposed as a promising alternative to conventional near-memory processing (NMP) architectures, offering improved bandwidth efficiency and lower power consumption while exhibiting characteris…

🌐 GÉANT sets a new benchmark for long-haul optical networking: 3,403 km of terrestrial transmission at 400G without transponders or regeneration!
This field trial, conducted in June on the GÉANT live production network using Cisco / Acacia 400G ULH QSFP-DD coherent pluggable optics, demonstrates the capabilities of new technologies to bridge extended distances without the need of traditional stand-alone DWDM transponders.
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The extinction of the contact process in a one-dimensional random environment with long-range interactions
Pablo A. Gomes, Marcelo R. Hil\'ario, Bernardo N. B. de Lima, Thomas Mountford
https://arxiv.org/abs/2506.17444

The extinction of the contact process in a one-dimensional random environment with long-range interactions
We study the contact process on the long-range percolation cluster on $\mathbb{Z}$ where each edge $\langle i,j \rangle$ is open with probability $|i-j|^{-s}$ for $s> 2$. Using a renormalization procedure we apply Peierls-type argument to prove that the contact process dies out if the transmission rate is smaller than a critical threshold. Our methods involve the control of crossing probabilities for percolation on randomly-stretched lattices as in https://doi.org/10.1214/22-AAP1887.

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I have a happy favour to ask.
Last year, I had a US intern working with us and she now is looking for help that you- good reader of this account- could give.
(She needs academic survey participants)
Check out her flyer below, click the link, and please RT
http…

Research Study on Data Privacy and Collective Redress
A researcher at University College Dublin wants to conduct anonymous interviews to learn about why you would or would not participate in a legal action to rectify a data protection violation. This information can be used to help nonprofit organizations better represent you when organizations violate your data protection rights. Provide your email to receive more information. All personally identifiable information will be deleted following completion of the study.

An Ejection Event Captured by VLBI During the Outburst of Swift J1727.8$-$1613
Hongmin Cao, Jun Yang, S\'andor Frey, Callan M. Wood, James C. A. Miller-Jones, Krisztina \'E. Gab\'anyi, Giulia Migliori, Marcello Giroletti, Lang Cui, Tao An, Xiaoyu Hong, Weihua Wang
https://arxiv.org/abs/2506.18817

An Ejection Event Captured by VLBI During the Outburst of Swift J1727.8$-$1613
We observed a newly-discovered Galactic black hole X-ray binary Swift J1727.8$-$1613 with the European Very Long Baseline Interferometry Network (EVN) at 5 GHz. The observation was conducted immediately following a radio quenching event detected by the Karl G. Jansky Very Large Array (VLA). The visibility amplitude evolution over time reveals a large-amplitude radio flare and is consistent with an ejection event. The data can be interpreted either as a stationary component (i.e., the radio core…