993 research outputs found
A Temporal-Rule Based Verification System for Business Collaboration Reliability
Based on the temporal rules defined for the business processe participating in a business collaboration, we present an implementation for a system called TiCoBTS to verify the reliability of the business collaboration
Influencing Lenders’ Repeat Investment Intention in P2P Lending Platforms in China through Signaling
Repeated investments by lenders are critical for the survival and success of an online P2P lending platform. Lenders’ trust in a platform influences their investment decisions. This study explores the impact of trust and its antecedents on lender investment intention in online P2P lending platforms. An online survey of 358 active P2P lenders on several leading online P2P lending platforms in China was conducted. Empirical results suggest that lender trust in a platform has a strong influence upon both perceived risk and investment intention. Furthermore, the results show that top management team heterogeneity, reputation, and quality of the platform have significant effects on lender trust. Both theoretical and practical implications of these findings are discussed
Ab initio investigation of the crystallization mechanism of cadmium selenide
Cadmium selenide (CdSe) is an inorganic semiconductor with unique optical and
electronic properties that made it useful in various applications, including
solar cells, light-emitting diodes, and biofluorescent tagging. In order to
synthesize high-quality crystals and subsequently integrate them into devices,
it is crucial to understand the atomic scale crystallization mechanism of CdSe.
Unfortunately, such studies are still absent in the literature.To overcome this
limitation, we employed an enhanced sampling-accelerated active learning
approach to construct a deep neural potential with ab initio accuracy for
studying the crystallization of CdSe.Our brute-force molecular dynamics
simulations revealed that a spherical-like nucleus formed spontaneously and
stochastically, resulting in a stacking disordered structure where the
competition between hexagonal wurtzite and cubic zinc blende polymorphs is
temperature-dependent. We found that pure hexagonal crystal can only be
obtained approximately above 1430 K, which is 35 K below its melting
temperature. We observed that the solidification dynamics of Cd and Se atoms
were distinct due to their different diffusion coefficients. The solidification
process was initiated by lower mobile Se atoms forming tetrahedral frameworks,
followed by Cd atoms occupying these tetrahedral centers and settling down
until the third-shell neighbor of Se atoms sited on their lattice positions.
Therefore, the medium-range ordering of Se atoms governs the crystallization
process of CdSe. Our findings indicate that understanding the complex dynamical
process is the key to comprehending the crystallization mechanism of compounds
like CdSe, and can shed lights in the synthesis of high-quality crystals.Comment: 25 pages, 7 figure
Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach
Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising
technology for future wireless communications. The deployment of XL-MIMO,
especially at high-frequency bands, leads to users being located in the
near-field region instead of the conventional far-field. This letter proposes
efficient model-based deep learning algorithms for estimating the near-field
wireless channel of XL-MIMO communications. In particular, we first formulate
the XL-MIMO near-field channel estimation task as a compressed sensing problem
using the spatial gridding-based sparsifying dictionary, and then solve the
resulting problem by applying the Learning Iterative Shrinkage and Thresholding
Algorithm (LISTA). Due to the near-field characteristic, the spatial
gridding-based sparsifying dictionary may result in low channel estimation
accuracy and a heavy computational burden. To address this issue, we further
propose a new sparsifying dictionary learning-LISTA (SDL-LISTA) algorithm that
formulates the sparsifying dictionary as a neural network layer and embeds it
into LISTA neural network. The numerical results show that our proposed
algorithms outperform non-learning benchmark schemes, and SDL-LISTA achieves
better performance than LISTA with ten times atoms reduction.Comment: 4 pages, 5 figure
Channel Estimation with Dynamic Metasurface Antennas via Model-Based Learning
Dynamic Metasurface Antenna (DMA) is a cutting-edge antenna technology
offering scalable and sustainable solutions for large antenna arrays. The
effectiveness of DMAs stems from their inherent configurable analog signal
processing capabilities, which facilitate cost-limited implementations.
However, when DMAs are used in multiple input multiple output (MIMO)
communication systems, they pose challenges in channel estimation due to their
analog compression. In this paper, we propose two model-based learning methods
to overcome this challenge. Our approach starts by casting channel estimation
as a compressed sensing problem. Here, the sensing matrix is formed using a
random DMA weighting matrix combined with a spatial gridding dictionary. We
then employ the learned iterative shrinkage and thresholding algorithm (LISTA)
to recover the sparse channel parameters. LISTA unfolds the iterative shrinkage
and thresholding algorithm into a neural network and trains the neural network
into a highly efficient channel estimator fitting with the previous channel. As
the sensing matrix is crucial to the accuracy of LISTA recovery, we introduce
another data-aided method, LISTA-sensing matrix optimization (LISTA-SMO), to
jointly optimize the sensing matrix. LISTA-SMO takes LISTA as a backbone and
embeds the sensing matrix optimization layers in LISTA's neural network,
allowing for the optimization of the sensing matrix along with the training of
LISTA. Furthermore, we propose a self-supervised learning technique to tackle
the difficulty of acquiring noise-free data. Our numerical results demonstrate
that LISTA outperforms traditional sparse recovery methods regarding channel
estimation accuracy and efficiency. Besides, LISTA-SMO achieves better channel
accuracy than LISTA, demonstrating the effectiveness in optimizing the sensing
matrix
- …