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pro vyhledávání: '"Yousefzadeh P"'
Effective congestion management along signalized corridors is essential for improving productivity and reducing costs, with arterial travel time serving as a key performance metric. Traditional approaches, such as Coordinated Signal Timing and Adapti
Externí odkaz:
http://arxiv.org/abs/2412.11095
Autor:
Yousefzadeh, Roozbeh, Cao, Xuenan
Using AI to write formal proofs for mathematical problems is a challenging task that has seen some advancements in recent years. Automated systems such as Lean can verify the correctness of proofs written in formal language, yet writing the proofs in
Externí odkaz:
http://arxiv.org/abs/2411.18872
Autor:
Wu, Jiuda, Yousefzadeh, Behrooz
Materials and devices subject to spatiotemporal modulation of their effective properties have a demonstrated ability to support nonreciprocal transmission of waves. Most notably, spatiotemporally modulated systems can restrict wave transmission to on
Externí odkaz:
http://arxiv.org/abs/2411.18734
The "Fluid Mechanic Sewing Machine" creates periodic patterns through the coiling nature of a viscous fluid falling onto a moving surface. At relatively moderate heights, the reported patterns are translating coiling, alternating loops, W pattern, an
Externí odkaz:
http://arxiv.org/abs/2410.21503
Autor:
Huijbregts, Lucas, Hsiao-Hsuan, Liu, Detterer, Paul, Hamdioui, Said, Yousefzadeh, Amirreza, Bishnoi, Rajendra
Current Artificial Intelligence (AI) computation systems face challenges, primarily from the memory-wall issue, limiting overall system-level performance, especially for Edge devices with constrained battery budgets, such as smartphones, wearables, a
Externí odkaz:
http://arxiv.org/abs/2410.09130
Autor:
Wu, Jiuda, Yousefzadeh, Behrooz
Waveguides subject to spatiotemporal modulations are known to exhibit nonreciprocal vibration transmission, whereby interchanging the locations of the source and receiver change the end-to-end transmission characteristics. The scenario of typical int
Externí odkaz:
http://arxiv.org/abs/2410.08533
Currently, neural-network processing in machine learning applications relies on layer synchronization, whereby neurons in a layer aggregate incoming currents from all neurons in the preceding layer, before evaluating their activation function. This i
Externí odkaz:
http://arxiv.org/abs/2408.05098
Spiking neural networks (SNNs) for event-based optical flow are claimed to be computationally more efficient than their artificial neural networks (ANNs) counterparts, but a fair comparison is missing in the literature. In this work, we propose an ev
Externí odkaz:
http://arxiv.org/abs/2407.20421
Autor:
Arjmand, Cina, Xu, Yingfu, Shidqi, Kevin, Dobrita, Alexandra F., Vadivel, Kanishkan, Detterer, Paul, Sifalakis, Manolis, Yousefzadeh, Amirreza, Tang, Guangzhi
Neuromorphic processors are well-suited for efficiently handling sparse events from event-based cameras. However, they face significant challenges in the growth of computing demand and hardware costs as the input resolution increases. This paper prop
Externí odkaz:
http://arxiv.org/abs/2406.17483
Autor:
Dobrita, Alexandra, Yousefzadeh, Amirreza, Thorpe, Simon, Vadivel, Kanishkan, Detterer, Paul, Tang, Guangzhi, van Schaik, Gert-Jan, Konijnenburg, Mario, Gebregiorgis, Anteneh, Hamdioui, Said, Sifalakis, Manolis
For Edge AI applications, deploying online learning and adaptation on resource-constrained embedded devices can deal with fast sensor-generated streams of data in changing environments. However, since maintaining low-latency and power-efficient infer
Externí odkaz:
http://arxiv.org/abs/2406.17285