Tootfinder

Stop Lying to Me: New Visual Tools to Choose the Most Honest Nonlinear Dimension Reduction
Jayani P. Gamage, Dianne Cook, Paul Harrison, Michael Lydeamore
https://arxiv.org/abs/2506.22051

Stop Lying to Me: New Visual Tools to Choose the Most Honest Nonlinear Dimension Reduction
Nonlinear dimension reduction (NLDR) techniques such as tSNE, and UMAP provide a low-dimensional representation of high-dimensional data (\pD{}) by applying a nonlinear transformation. NLDR often exaggerates random patterns. But NLDR views have an important role in data analysis because, if done well, they provide a concise visual (and conceptual) summary of \pD{} distributions. The NLDR methods and hyper-parameter choices can create wildly different representations, making it difficult to deci…

Rosen sind rot, Maskendeals grau,
Spahn sagt: »Ich wusste von nix, ganz genau!«
Die Villa war günstig, ganz ohne Verstand –

Süßer Laptop: Das ist mal eine originelle #Verpackung für #Schokolade. Zum Glück ist selbige nicht so alt wie das gezeigte User Interface MacOS 9 mit zeitgenössischer Website🙈
#Geschenk

hBN alignment orientation controls moir\'e strength in rhombohedral graphene
Matan Uzan, Weifeng Zhi, Matan Bocarsly, Junkai Dong, Surajit Dutta, Nadav Auerbach, Niladri Sekhar Kander, Mikhail Labendik, Yuri Myasoedov, Martin E. Huber, Kenji Watanabe, Takashi Taniguchi, Daniel E. Parker, Eli Zeldov
https://arxiv.org/abs/2507.20647

hBN alignment orientation controls moiré strength in rhombohedral graphene
Rhombohedral multilayer graphene hosts a rich landscape of correlated symmetry-broken phases, driven by strong interactions from its flat band edges. Aligning to hexagonal boron nitride (hBN) creates a moiré pattern, leading to recent observations of exotic ground states such as integer and fractional quantum anomalous Hall effects. Here, we show that the moiré effects and resulting correlated phase diagrams are critically influenced by a previously underestimated structural choice: the hBN a…

Revolutionising Antibacterial Warfare: Machine Learning and Molecular Dynamics Unveiling Potential Gram-Negative Bacteria Inhibitors
Pritish Joshi, Niladri Patra
https://arxiv.org/abs/2505.23356

Revolutionising Antibacterial Warfare: Machine Learning and Molecular Dynamics Unveiling Potential Gram-Negative Bacteria Inhibitors
Diseases caused by bacteria have been a threat to human civilisation for centuries. Despite the availability of numerous antibacterial drugs today, bacterial diseases continue to pose life-threatening challenges. The credit for this goes to Gram-Negative bacteria, which have developed multi-drug resistant properties towards \b{eta}-lactams, chloramphenicols, fluoroquinolones, tetracyclines, carbapenems, and macrolide antibiotics. V arious mechanisms of bacterial defence contribute to drug resis…

#AntiAKW #UmweltFAIRändern #Grüne und #Linke in

Replaced article(s) found for cs.SI. https://arxiv.org/list/cs.SI/new
[1/1]:
- Evaluating link prediction: New perspectives and recommendations
Bhargavi Kalyani I, A Rama Prasad Mathi, Niladri Sett