This work presents a preliminary study of a LMR-based gas sensor. Results of the device subjected to annealing process show a stable and repetitive response that is required for the utilization in gas sensing applicat...
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This article describes a fast transrating solution for HEVC based on classification and machine learning techniques. Two classifiers are trained to predict the range of CTU quadtree depths that will be searched to fin...
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ISBN:
(纸本)9781538644591;9781538644584
This article describes a fast transrating solution for HEVC based on classification and machine learning techniques. Two classifiers are trained to predict the range of CTU quadtree depths that will be searched to find the best CTU partitioning. Three approaches are proposed for reducing the number of features used by the classifiers, two based on feature selection, and one based on feature transformation using autoencoders. A full transrating framework based on x265 is built for model training and evaluation. Experimental results using the x265 encoder show that an average 41.81% computational complexity reduction can be achieved at the cost of a tolerable 0.29% Bjontegaard-Delta bitrate, outperforming competing methods.
This article describes a fast transrating solution for HEVC based on classification and machine learning techniques. Two classifiers are trained to predict the range of CTU quadtree depths that will be searched to fin...
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Topological insulating phases are usually found in periodic lattices stemming from collective resonant effects, and it may thus be expected that similar features may be prohibited in thermal diffusion, given its purel...
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We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, u...
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We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.
Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond th...
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Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmitta...
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Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmittance due to path-dependent losses. Here, we present a paradigm to encircle EPs that includes fast Hamiltonian variations on the parameter boundaries, termed Hamiltonian hopping, enabling ultrahigh-efficiency chiral mode switching. This protocol avoids path-dependent loss and allows us to experimentally demonstrate nearly 90% efficiency at 1550 nm in the clockwise direction, overcoming a long-standing challenge of non-Hermitian optical systems and powering up new opportunities for EP physics.
Molecular and atomic imaging required the development of electron and scanning probe microscopies to surpass the physical limits dictated by diffraction [1]. Nano-infrared experiments [2] and pico-cavity tip-enhanced ...
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