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@arXiv_condmatsuprcon_bot@mastoxiv.page
2024-06-06 10:15:32

This arxiv.org/abs/2111.15488 has been replaced.
link: scholar.google.com/scholar?q=a

@andres4ny@social.ridetrans.it
2024-05-31 03:06:23

Fuuuuck. Once again, the NYC government website is absolutely useless.

NYC Dept of Sanitation's website. The page (which is linked from their /howtogetridof/) has their normal links, and then "Find out how to get rid of..." and a search bar. In the search bar, I've entered "a body".
Another NYC page (main page, not Sanitation) which is where the search in the prior image leads you. It says "Your Search: a+body", and the search results are below. Along with random results to links of pages titled "Hiring Process" and "Leadership", the first result is a link to a page titled "Dead Animals". Below the Dead Animals link, it says "Dead Animals. On Public Property. You can report a dead animal for removal from public areas, such as: Sidewalk; Streets; Highways; Parks; Beaches; B…
Back to the Dept Of Sanitation page, with its garish green theme. This page is titled "Dead Animals", and says the following:
"You can report a dead animal for removal from public areas, such as:

    Sidewalk
    Streets
    Highways
    Parks
    Beaches"
And after that:
    "Bodies of water"

So it turns out it was bodies *of water*.
@arXiv_eessIV_bot@mastoxiv.page
2024-04-30 07:34:40

Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Lennart Maack, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer
arxiv.org/abs/2404.18599 arxiv.org/pdf/2404.18599
arXiv:2404.18599v1 Announce Type: new
Abstract: Purpose: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse anomaly morphology. Self-supervised learning (SSL) can be used to learn representations from unlabelled data. However, there are no SSL methods designed for the downstream task of classifying paranasal anomalies in the maxillary sinus (MS).
Methods: Our approach uses a 3D Convolutional Autoencoder (CAE) trained in an unsupervised anomaly detection (UAD) framework. Initially, we train the 3D CAE to reduce reconstruction errors when reconstructing normal maxillary sinus (MS) image. Then, this CAE is applied to an unlabelled dataset to generate coarse anomaly locations by creating residual MS images. Following this, a 3D Convolutional Neural Network (CNN) reconstructs these residual images, which forms our SSL task. Lastly, we fine-tune the encoder part of the 3D CNN on a labelled dataset of normal and anomalous MS images.
Results: The proposed SSL technique exhibits superior performance compared to existing generic self-supervised methods, especially in scenarios with limited annotated data. When trained on just 10% of the annotated dataset, our method achieves an Area Under the Precision-Recall Curve (AUPRC) of 0.79 for the downstream classification task. This performance surpasses other methods, with BYOL attaining an AUPRC of 0.75, SimSiam at 0.74, SimCLR at 0.73 and Masked Autoencoding using SparK at 0.75.
Conclusion: A self-supervised learning approach that inherently focuses on localizing paranasal anomalies proves to be advantageous, particularly when the subsequent task involves differentiating normal from anomalous maxillary sinuses. Access our code at github.com/mtec-tuhh/self-supe

@arXiv_condmatmeshall_bot@mastoxiv.page
2024-05-07 07:31:32

Higher-order topology protected by latent crystalline symmetries
L. Eek, M. R\"ontgen, A. Moustaj, C. Morais Smith
arxiv.org/abs/2405.02704

@georgiamuseum@glammr.us
2024-05-13 13:30:02

If you don't live in Georgia (or even in Athens), you may not know who Bill Paul was, but this obituary penned by his family gives some idea of how important he was to art in the state. The acquisitions he made for our collection and the exhibitions he organized truly opened the door for experimental and contemporary art. RIP. ❤️

@arXiv_csDL_bot@mastoxiv.page
2024-03-25 07:29:56

Closing the Information Gap in Unidentified Anomalous Phenomena (UAP) Studies
Gretchen R. Stahlman
arxiv.org/abs/2403.15368

@grumpybozo@toad.social
2024-04-13 14:20:58

As the Dad of a 21-week preemie who died, a 23-week preemie who lives, and as a 30-week preemie myself.
No: not if it means offering more traumatized parents more uncertain efforts to save ever smaller babies.
It is already soul-flaying to have an early preemie. Many die. How a 23-week preemie *or a 30-week preemie* fares is a dice toss.
(My attempt to elaborate on that was just too hard.)

@arXiv_condmatmeshall_bot@mastoxiv.page
2024-05-31 07:00:35

Propagation, dissipation and breakdown in quantum anomalous Hall edge states probed by microwave edge plasmons
Torsten R\"oper, Hugo Thomas, Daniel Rosenbach, Anjana Uday, Gertjan Lippertz, Anne Denis, Pascal Morfin, Alexey A. Taskin, Yoichi Ando, Erwann Bocquillon
arxiv.org/abs/2405.19983

@arXiv_condmatmtrlsci_bot@mastoxiv.page
2024-03-19 09:03:56

This arxiv.org/abs/2303.06204 has been replaced.
initial toot: mastoxiv.page/@a…

@arXiv_condmatmtrlsci_bot@mastoxiv.page
2024-03-19 09:03:56

This arxiv.org/abs/2303.06204 has been replaced.
initial toot: mastoxiv.page/@a…