957 research outputs found
Thermophysical Properties of Lignocellulose: A Cell-scale Study down to 41K
Thermal energy transport is of great importance in lignocellulose pyrolysis
for bio-fuels. The thermophysical properties of lignocellulose significantly
affect the overall properties of bio-composites and the related thermal
transport. In this work, cell-scale lignocellulose (mono-layer plant cells) is
prepared to characterize their thermal properties from room temperature down to
41 K. The thermal conductivities of cell-scale lignocellulose along different
directions show a little anisotropy due to the cell structure anisotropy. It is
found that with temperature going down, the volumetric specific heat of the
lignocellulose shows a slower decreasing trend against temperature than that of
microcrystalline cellulose, and its value is always higher than that of
microcrystalline cellulose. The thermal conductivity of lignocellulose
decreases with temperature from 243 K to 317 K due to increasing phonon-phonon
scatterings. From 41 K to 243 K, the thermal conductivity rises with
temperature and its change mainly depends on the heat capacity's change
Uplink Performance of Cell-Free Extremely Large-Scale MIMO Systems
In this paper, we investigate the uplink performance of cell-free (CF)
extremely large-scale multiple-input-multipleoutput (XL-MIMO) systems, which is
a promising technique for future wireless communications. More specifically, we
consider the practical scenario with multiple base stations (BSs) and multiple
user equipments (UEs). To this end, we derive exact achievable spectral
efficiency (SE) expressions for any combining scheme. It is worth noting that
we derive the closed-form SE expressions for the CF XL-MIMO with maximum ratio
(MR) combining. Numerical results show that the SE performance of the CF
XL-MIMO can be hugely improved compared with the small-cell XL-MIMO. It is
interesting that a smaller antenna spacing leads to a higher correlation level
among patch antennas. Finally, we prove that increasing the number of UE
antennas may decrease the SE performance with MR combining
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
Most current studies on survey analysis and risk tolerance modelling lack
professional knowledge and domain-specific models. Given the effectiveness of
generative adversarial learning in cross-domain information, we design an
Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale
inequality. ADGAN utilizes the information-sufficient domain to provide extra
information to improve the representation learning on the
information-insufficient domain via domain alignment. We provide data analysis
and user model on two data sources: Consumer Consumption Information and Survey
Information. We further test ADGAN on a real-world dataset with view embedding
structures and show ADGAN can better deal with the class imbalance and
unqualified data space than state-of-the-art, demonstrating the effectiveness
of leveraging asymmetrical domain information
Quantum Microscopy of Cancer Cells at the Heisenberg Limit
Entangled biphoton sources exhibit nonclassical characteristics and have been
applied to novel imaging techniques such as ghost imaging, quantum holography,
and quantum optical coherence tomography. The development of wide-field quantum
imaging to date has been hindered by low spatial resolutions, speeds, and
contrast-to-noise ratios (CNRs). Here, we present quantum microscopy by
coincidence (QMC) with balanced pathlengths, which enables super-resolution
imaging at the Heisenberg limit with substantially higher speeds and CNRs than
existing wide-field quantum imaging methods. QMC benefits from a configuration
with balanced pathlengths, where a pair of entangled photons traversing
symmetric paths with balanced optical pathlengths in two arms behave like a
single photon with half the wavelength, leading to 2-fold resolution
improvement. Concurrently, QMC resists stray light up to 155 times stronger
than classical signals. The low intensity and entanglement features of
biphotons in QMC promise nondestructive bioimaging. QMC advances quantum
imaging to the microscopic level with significant improvements in speed and CNR
toward bioimaging of cancer cells. We experimentally and theoretically prove
that the configuration with balanced pathlengths illuminates an avenue for
quantum-enhanced coincidence imaging at the Heisenberg limit.Comment: 20 pages, 4 figures; Supplementary Information 15 pages, 9 figure
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs
Distilling high-accuracy Graph Neural Networks~(GNNs) to low-latency
multilayer perceptrons~(MLPs) on graph tasks has become a hot research topic.
However, MLPs rely exclusively on the node features and fail to capture the
graph structural information. Previous methods address this issue by processing
graph edges into extra inputs for MLPs, but such graph structures may be
unavailable for various scenarios. To this end, we propose a Prototype-Guided
Knowledge Distillation~(PGKD) method, which does not require graph
edges~(edge-free) yet learns structure-aware MLPs. Specifically, we analyze the
graph structural information in GNN teachers, and distill such information from
GNNs to MLPs via prototypes in an edge-free setting. Experimental results on
popular graph benchmarks demonstrate the effectiveness and robustness of the
proposed PGKD.Comment: 8 pages, 4 figures, 9 table
ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization
Due to its simplicity and outstanding ability to generalize, stochastic
gradient descent (SGD) is still the most widely used optimization method
despite its slow convergence. Meanwhile, adaptive methods have attracted rising
attention of optimization and machine learning communities, both for the
leverage of life-long information and for the profound and fundamental
mathematical theory. Taking the best of both worlds is the most exciting and
challenging question in the field of optimization for machine learning. Along
this line, we revisited existing adaptive gradient methods from a novel
perspective, refreshing understanding of second moments. Our new perspective
empowers us to attach the properties of second moments to the first moment
iteration, and to propose a novel first moment optimizer,
\emph{Angle-Calibrated Moment method} (\method). Our theoretical results show
that \method is able to achieve the same convergence rate as mainstream
adaptive methods. Furthermore, extensive experiments on CV and NLP tasks
demonstrate that \method has a comparable convergence to SOTA Adam-type
optimizers, and gains a better generalization performance in most cases.Comment: 25 pages, 4 figure
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