Zobrazeno 1 - 10
of 1 603
pro vyhledávání: '"Langhammer P"'
Autor:
Lepsien, Arvid, Pegoraro, Marco, Fonger, Frederik, Langhammer, Dominic, Aleknonytė-Resch, Milda, Koschmider, Agnes
Various kinds of uncertainty can occur in event logs, e.g., due to flawed recording, data quality issues, or the use of probabilistic models for activity recognition. Stochastically known event logs make these uncertainties transparent by encoding mu
Externí odkaz:
http://arxiv.org/abs/2410.00067
eGPU, a recently-reported soft GPGPU for FPGAs, has demonstrated very high clock frequencies (more than 750 MHz) and small footprint. This means that for the first time, commercial soft processors may be competitive for the kind of heavy numerical co
Externí odkaz:
http://arxiv.org/abs/2406.03227
We study a spatial Markovian particle system with pairwise coagulation, a spatial version of the Marcus--Lushnikov process: according to a coagulation kernel $K$, particle pairs merge into a single particle, and their masses are united. We introduce
Externí odkaz:
http://arxiv.org/abs/2401.06668
Current soft processor architectures for FPGAs do not utilize the potential of the massive parallelism available. FPGAs now support many thousands of embedded floating point operators, and have similar computational densities to GPGPUs. Several soft
Externí odkaz:
http://arxiv.org/abs/2401.04261
Autor:
Martvall, Viktor, Moberg, Henrik Klein, Theodoridis, Athanasios, Tomeček, David, Ekborg-Tanner, Pernilla, Nilsson, Sara, Volpe, Giovanni, Erhart, Paul, Langhammer, Christoph
The ability to rapidly detect hydrogen gas upon occurrence of a leak is critical for the safe large-scale implementation of hydrogen (energy) technologies. However, to date, no technically viable sensor solution exists that meets the corresponding re
Externí odkaz:
http://arxiv.org/abs/2312.15372
Autor:
Koschmider, Agnes, Aleknonytė-Resch, Milda, Fonger, Frederik, Imenkamp, Christian, Lepsien, Arvid, Apaydin, Kaan, Harms, Maximilian, Janssen, Dominik, Langhammer, Dominic, Ziolkowski, Tobias, Zisgen, Yorck
The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence i
Externí odkaz:
http://arxiv.org/abs/2401.13677
Autor:
Altenburger, Björn, Andersson, Carl, Levin, Sune, Westerlund, Fredrik, Fritzsche, Joachim, Langhammer, Christoph
Single particle catalysis aims at determining factors that dictate nanoparticle activity and selectivity. Existing methods often use fluorescent model reactions at low reactant concentrations, operate at low pressures, or rely on plasmonic enhancemen
Externí odkaz:
http://arxiv.org/abs/2310.15562
Autor:
Rouhani, Bita Darvish, Zhao, Ritchie, More, Ankit, Hall, Mathew, Khodamoradi, Alireza, Deng, Summer, Choudhary, Dhruv, Cornea, Marius, Dellinger, Eric, Denolf, Kristof, Dusan, Stosic, Elango, Venmugil, Golub, Maximilian, Heinecke, Alexander, James-Roxby, Phil, Jani, Dharmesh, Kolhe, Gaurav, Langhammer, Martin, Li, Ada, Melnick, Levi, Mesmakhosroshahi, Maral, Rodriguez, Andres, Schulte, Michael, Shafipour, Rasoul, Shao, Lei, Siu, Michael, Dubey, Pradeep, Micikevicius, Paulius, Naumov, Maxim, Verrilli, Colin, Wittig, Ralph, Burger, Doug, Chung, Eric
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and int
Externí odkaz:
http://arxiv.org/abs/2310.10537
This paper introduces the eGPU, a SIMT soft processor designed for FPGAs. Soft processors typically achieve modest operating frequencies, a fraction of the headline performance claimed by modern FPGA families, and obtain correspondingly modest perfor
Externí odkaz:
http://arxiv.org/abs/2307.08378
Autor:
Volpe, Giovanni, Wählby, Carolina, Tian, Lei, Hecht, Michael, Yakimovich, Artur, Monakhova, Kristina, Waller, Laura, Sbalzarini, Ivo F., Metzler, Christopher A., Xie, Mingyang, Zhang, Kevin, Lenton, Isaac C. D., Rubinsztein-Dunlop, Halina, Brunner, Daniel, Bai, Bijie, Ozcan, Aydogan, Midtvedt, Daniel, Wang, Hao, Sladoje, Nataša, Lindblad, Joakim, Smith, Jason T., Ochoa, Marien, Barroso, Margarida, Intes, Xavier, Qiu, Tong, Yu, Li-Yu, You, Sixian, Liu, Yongtao, Ziatdinov, Maxim A., Kalinin, Sergei V., Sheridan, Arlo, Manor, Uri, Nehme, Elias, Goldenberg, Ofri, Shechtman, Yoav, Moberg, Henrik K., Langhammer, Christoph, Špačková, Barbora, Helgadottir, Saga, Midtvedt, Benjamin, Argun, Aykut, Thalheim, Tobias, Cichos, Frank, Bo, Stefano, Hubatsch, Lars, Pineda, Jesus, Manzo, Carlo, Bachimanchi, Harshith, Selander, Erik, Homs-Corbera, Antoni, Fränzl, Martin, de Haan, Kevin, Rivenson, Yair, Korczak, Zofia, Adiels, Caroline Beck, Mijalkov, Mite, Veréb, Dániel, Chang, Yu-Wei, Pereira, Joana B., Matuszewski, Damian, Kylberg, Gustaf, Sintorn, Ida-Maria, Caicedo, Juan C., Cimini, Beth A, Bell, Muyinatu A. Lediju, Saraiva, Bruno M., Jacquemet, Guillaume, Henriques, Ricardo, Ouyang, Wei, Le, Trang, Gómez-de-Mariscal, Estibaliz, Sage, Daniel, Muñoz-Barrutia, Arrate, Lindqvist, Ebba Josefson, Bergman, Johanna
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural netw
Externí odkaz:
http://arxiv.org/abs/2303.03793