Zobrazeno 1 - 10
of 41 443
pro vyhledávání: '"Peer SO"'
Autor:
Cho, Min Sang, Grabowski, Paul E., Thopalli, Kowshik, Jayram, Thathachar S., Barrow, Michael J., Thiagarajan, Jayaraman J., Anirudh, Rushil, Le, Hai P., Scott, Howard A., Kallman, Joshua B., Stephens, Branson C., Foord, Mark E., Gaffney, Jim A., Bremer, Peer-Timo
The integration of machine learning techniques into Inertial Confinement Fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly Non-Local Thermodynamic Equilibrium (NLTE) model with
Externí odkaz:
http://arxiv.org/abs/2411.08789
Variation in $\alpha$ trace norm of a digraph by deletion of a vertex or an arc and its applications
Autor:
Bhat, Mushtaq A., Manan, Peer Abdul
Let $D$ be a digraph of order $n$ with adjacency matrix $A(D)$. For $\alpha\in[0,1)$, the $A_{\alpha}$ matrix of $D$ is defined as $A_{\alpha}(D)=\alpha {\Delta}^{+}(D)+(1-\alpha)A(D)$, where ${\Delta}^{+}(D)=\mbox{diag}~(d_1^{+},d_2^{+},\dots,d_n^{+
Externí odkaz:
http://arxiv.org/abs/2411.07935
Finite state machines (FSMs) regulate sequential circuits, including access to sensitive information and privileged CPU states. Courtesy of contemporary research on laser attacks, laser-based fault injection (LFI) is becoming even more precise where
Externí odkaz:
http://arxiv.org/abs/2411.02798
Autor:
Tapani, Tlek, Krahne, Roman, Caligiuri, Vincenzo, Griesi, Andrea, Ivanov, Yurii P., Cuscunà, Massimo, Balestra, Gianluca, Lin, Haifeng, Sapunova, Anastasiia, Franceschini, Paolo, Tognazzi, Andrea, De Angelis, Costantino, Divitini, Giorgio, Kwon, Hyunah, Fischer, Peer, Maccaferri, Nicolò, Garoli, Denis
Dry synthesis is a highly versatile method for the fabrication of nanoporous metal films, since it enables easy and reproducible deposition of single or multi-layer(s) of nanostructured materials that can find intriguing applications in plasmonics, p
Externí odkaz:
http://arxiv.org/abs/2411.01206
Autor:
Gomez, Nicolas Moreno, Athanassiadis, Athanasios G., Reuter, Fabian, Reese, Hendrik, Jade, Helen M., Poortinga, Albert, Ohl, Claus-Dieter, Fischer, Peer
Ultrasound offers promising applications in biology and chemistry, but quantifying local ultrasound conditions remains challenging due to the lack of non-invasive measurement tools. We introduce antibubbles as novel optical reporters of local ultraso
Externí odkaz:
http://arxiv.org/abs/2410.11477
Autor:
Brouillard, Philippe, Lachapelle, Sébastien, Kaltenborn, Julia, Gurwicz, Yaniv, Sridhar, Dhanya, Drouin, Alexandre, Nowack, Peer, Runge, Jakob, Rolnick, David
Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scientists study how phenomena, such as El Ni\~no, affect other climate processes at remote locations across the glob
Externí odkaz:
http://arxiv.org/abs/2410.07013
Autor:
Beth, Christian, Fleischmann, Pamela, Huch, Annika, Kazempour, Daniyal, Kröger, Peer, Kulow, Andrea, Renz, Matthias
In 2017 Day et al. introduced the notion of locality as a structural complexity-measure for patterns in the field of pattern matching established by Angluin in 1980. In 2019 Casel et al. showed that determining the locality of an arbitrary pattern is
Externí odkaz:
http://arxiv.org/abs/2410.00601
Accurate camera calibration is a well-known and widely used task in computer vision that has been researched for decades. However, the standard approach based on checkerboard calibration patterns has some drawbacks that limit its applicability. For e
Externí odkaz:
http://arxiv.org/abs/2409.20127
Semantic segmentation is an essential step for many vision applications in order to understand a scene and the objects within. Recent progress in hyperspectral imaging technology enables the application in driving scenarios and the hope is that the d
Externí odkaz:
http://arxiv.org/abs/2409.11205
Autor:
Bhat, Mushtaq A., Manan, Peer Abdul
Let $D$ be a digraph of order $n$ with adjacency matrix $A(D)$. For $\alpha\in[0,1)$, the $A_{\alpha}$ matrix of $D$ is defined as $A_{\alpha}(D)=\alpha {\Delta}^{+}(D)+(1-\alpha)A(D)$, where ${\Delta}^{+}(D)=\mbox{diag}~(d_1^{+},d_2^{+},\dots,d_n^{+
Externí odkaz:
http://arxiv.org/abs/2409.02602