Tensor methods have gained increasingly a ention from various applications, including machine learning, quantum chemistry, healthcare analytics, social network analysis, data mining, and signal processing, to name a few. Sparse tensors and their algorithms become critical to further improve the performance of these methods and enhance the interpretability of their output. is work presents a sparse tensor algorithm benchmark suite (PASTA) for single-and multi-core CPUs. To the best of our knowledge, this is the rst benchmark suite for sparse tensor world. PASTA targets on: 1) helping application users to evaluate di erent computer systems using its representative computational workloads; 2) providing insights to be er utilize existed computer architecture and systems and inspiration for the future design. is benchmark suite will be publicly released.
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Mask-based lensless cameras break the constraints of traditional lens-based cameras, introducing highly flexible imaging systems. However, the inherent restrictions of imaging devices lead to low reconstruction quality. To overcome this challenge, we propose an explicit-restriction convolutional framework for lensless imaging, whose forward model effectively incorporates multiple restrictions by introducing the linear and noise-like nonlinear terms. As examples, numerical and experimental reconstructions based on the limitation of sensor size, pixel pitch, and bit depth are analyzed. By tailoring our framework for specific factors, better perceptual image quality or reconstructions with 4× pixel density can be achieved. This proposed framework can be extended to lensless imaging systems with different masks or structures.
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