Zobrazeno 1 - 10
of 8 029
pro vyhledávání: '"Haghighi P"'
Autor:
Haghighi, Yasaman, Demonsant, Celine, Chalimourdas, Panagiotis, Naeini, Maryam Tavasoli, Munoz, Jhon Kevin, Bacca, Bladimir, Suter, Silvan, Gani, Matthieu, Alahi, Alexandre
In this paper, we introduce HEADS-UP, the first egocentric dataset collected from head-mounted cameras, designed specifically for trajectory prediction in blind assistance systems. With the growing population of blind and visually impaired individual
Externí odkaz:
http://arxiv.org/abs/2409.20324
Autor:
Skalli, Anas, Goldmann, Mirko, Haghighi, Nasibeh, Reitzenstein, Stephan, Lott, James A., Brunner, Daniel
Artificial neural networks (ANNs) represent a fundamentally connectionnist and distributed approach to computing, and as such they differ from classical computers that utilize the von Neumann architecture. This has revived research interest in new un
Externí odkaz:
http://arxiv.org/abs/2409.01042
Autor:
Herten, Andreas, Achilles, Sebastian, Alvarez, Damian, Badwaik, Jayesh, Behle, Eric, Bode, Mathis, Breuer, Thomas, Caviedes-Voullième, Daniel, Cherti, Mehdi, Dabah, Adel, Sayed, Salem El, Frings, Wolfgang, Gonzalez-Nicolas, Ana, Gregory, Eric B., Mood, Kaveh Haghighi, Hater, Thorsten, Jitsev, Jenia, John, Chelsea Maria, Meinke, Jan H., Meyer, Catrin I., Mezentsev, Pavel, Mirus, Jan-Oliver, Nassyr, Stepan, Penke, Carolin, Römmer, Manoel, Sinha, Ujjwal, Vieth, Benedikt von St., Stein, Olaf, Suarez, Estela, Willsch, Dennis, Zhukov, Ilya
Publikováno v:
2024 SC24: International Conference for High Performance Computing, Networking, Storage and Analysis SC
Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real applications that represent user requirements into benchmark suites, to gua
Externí odkaz:
http://arxiv.org/abs/2408.17211
Additive manufacturing (AM) technologies have undergone significant advancements through the integration of cooperative robotics additive manufacturing (C-RAM) platforms. By deploying AM processes on the end effectors of multiple robotic arms, not on
Externí odkaz:
http://arxiv.org/abs/2408.04827
Autor:
Lourenço, Maicon Pierre, Zadeh-Haghighi, Hadi, Hostaš, Jiří, Naseri, Mosayeb, Gaur, Daya, Simon, Christoph, Salahub, Dennis R.
The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery using classica
Externí odkaz:
http://arxiv.org/abs/2407.18731
Autor:
Zadeh-Haghighi, Hadi, Golami, Omid, Kavatamane, Vinaya Kumar, Barclay, Paul E., Simon, Christoph
Our study aims to increase the spatial resolution of high-sensitivity magnetometry based on singlet-transition infrared (IR) absorption using nitrogen-vacancy (NV) centers in diamonds in monolithic cavities, with potential applications in bio-magneti
Externí odkaz:
http://arxiv.org/abs/2407.05569
In order to investigate whether the radical pair mechanism (RPM) can explain the effects of telecommunication frequency radiation on reactive oxygen species production, we modelled the effects of oscillating magnetic fields on radical pair systems. O
Externí odkaz:
http://arxiv.org/abs/2407.03358
Recent studies in vitro and in vivo suggest that flavin adenine dinucleotide (FAD) on its own might be able to act as a biological magnetic field sensor. Motivated by these observations, in this study, we develop a detailed quantum theoretical model
Externí odkaz:
http://arxiv.org/abs/2406.14580
Autor:
Haghighi, Hassan, Mosakhani, Mohammad
The purpose of this paper is to construct some special kind of subschemes in $\mathbb{P}^N$ with $ N\ge 3$, which we call them "fat flat subschemes" and compute their Waldschmidt constants. These subschemes are constructed by adding, in a particular
Externí odkaz:
http://arxiv.org/abs/2406.01051
Publikováno v:
IEEE International Requirements Engineering Conference, 2024
Language models that can learn a task at inference time, called in-context learning (ICL), show increasing promise in natural language inference tasks. In ICL, a model user constructs a prompt to describe a task with a natural language instruction an
Externí odkaz:
http://arxiv.org/abs/2404.12576