242 research outputs found
An Emperical Analysis of the Corperate Ownership Concentration on the Operation Performance after IPOs of Chinese listed SMEs
AbstractThe purpose of this paper is to investigates whether the large shareholders of small and medium firms (SMEs) take advantage of the inside information and decrease their shares before their operation performance begins to decline after the Initial Public Offerings (IPOs). By using the data from annual reports of SMEs listed on Shenzhen Stock Exchange in China from 2004 to 2006, this study explores both the relationship and the interaction effects between the change of operation performance and the ownership concentration of SMEs around their IPOs. The statistic analysis indicates that there is a significantly positive relationship between the ownership concentration and their operation performance after IPOs during the sample period. Moreover, the companies with higher ownership decreasing encounter more severe operation performance decline, which sugests that the listed companies intend to package their book profits before IPOs for the sake of increasing their issuing prices and enlarge their financing scales
Game among Interdependent Networks: The Impact of Rationality on System Robustness
Many real-world systems are composed of interdependent networks that rely on
one another. Such networks are typically designed and operated by different
entities, who aim at maximizing their own payoffs. There exists a game among
these entities when designing their own networks. In this paper, we study the
game investigating how the rational behaviors of entities impact the system
robustness. We first introduce a mathematical model to quantify the interacting
payoffs among varying entities. Then we study the Nash equilibrium of the game
and compare it with the optimal social welfare. We reveal that the cooperation
among different entities can be reached to maximize the social welfare in
continuous game only when the average degree of each network is constant.
Therefore, the huge gap between Nash equilibrium and optimal social welfare
generally exists. The rationality of entities makes the system inherently
deficient and even renders it extremely vulnerable in some cases. We analyze
our model for two concrete systems with continuous strategy space and discrete
strategy space, respectively. Furthermore, we uncover some factors (such as
weakening coupled strength of interdependent networks, designing suitable
topology dependency of the system) that help reduce the gap and the system
vulnerability
Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes
Learning network dynamics from the empirical structure and spatio-temporal
observation data is crucial to revealing the interaction mechanisms of complex
networks in a wide range of domains. However, most existing methods only aim at
learning network dynamic behaviors generated by a specific ordinary
differential equation instance, resulting in ineffectiveness for new ones, and
generally require dense observations. The observed data, especially from
network emerging dynamics, are usually difficult to obtain, which brings
trouble to model learning. Therefore, how to learn accurate network dynamics
with sparse, irregularly-sampled, partial, and noisy observations remains a
fundamental challenge. We introduce Neural ODE Processes for Network Dynamics
(NDP4ND), a new class of stochastic processes governed by stochastic
data-adaptive network dynamics, to overcome the challenge and learn continuous
network dynamics from scarce observations. Intensive experiments conducted on
various network dynamics in ecological population evolution, phototaxis
movement, brain activity, epidemic spreading, and real-world empirical systems,
demonstrate that the proposed method has excellent data adaptability and
computational efficiency, and can adapt to unseen network emerging dynamics,
producing accurate interpolation and extrapolation with reducing the ratio of
required observation data to only about 6\% and improving the learning speed
for new dynamics by three orders of magnitude.Comment: preprin
Embedded Firmware Solutions: Development Best Practices for the Internet of Things
Embedded Firmware Solutions is the perfect introduction and daily-use field guide--for the thousands of firmware designers, hardware engineers, architects, managers, and developers--to Intel’s new firmware direction (including Quark coverage), showing how to integrate Intel® Architecture designs into their plans. Featuring hands-on examples and exercises using Open Source codebases, like Coreboot and EFI Development Kit (tianocore) and Chromebook, this is the first book that combines a timely and thorough overview of firmware solutions for the rapidly evolving embedded ecosystem with in-depth coverage of requirements and optimization
A Tensor-Based Framework for Studying Eigenvector Multicentrality in Multilayer Networks
Centrality is widely recognized as one of the most critical measures to
provide insight in the structure and function of complex networks. While
various centrality measures have been proposed for single-layer networks, a
general framework for studying centrality in multilayer networks (i.e.,
multicentrality) is still lacking. In this study, a tensor-based framework is
introduced to study eigenvector multicentrality, which enables the
quantification of the impact of interlayer influence on multicentrality,
providing a systematic way to describe how multicentrality propagates across
different layers. This framework can leverage prior knowledge about the
interplay among layers to better characterize multicentrality for varying
scenarios. Two interesting cases are presented to illustrate how to model
multilayer influence by choosing appropriate functions of interlayer influence
and design algorithms to calculate eigenvector multicentrality. This framework
is applied to analyze several empirical multilayer networks, and the results
corroborate that it can quantify the influence among layers and multicentrality
of nodes effectively.Comment: 57 pages, 10 figure
Wireless Transmission of Images With The Assistance of Multi-level Semantic Information
Semantic-oriented communication has been considered as a promising to boost
the bandwidth efficiency by only transmitting the semantics of the data. In
this paper, we propose a multi-level semantic aware communication system for
wireless image transmission, named MLSC-image, which is based on the deep
learning techniques and trained in an end to end manner. In particular, the
proposed model includes a multilevel semantic feature extractor, that extracts
both the highlevel semantic information, such as the text semantics and the
segmentation semantics, and the low-level semantic information, such as local
spatial details of the images. We employ a pretrained image caption to capture
the text semantics and a pretrained image segmentation model to obtain the
segmentation semantics. These high-level and low-level semantic features are
then combined and encoded by a joint semantic and channel encoder into symbols
to transmit over the physical channel. The numerical results validate the
effectiveness and efficiency of the proposed semantic communication system,
especially under the limited bandwidth condition, which indicates the
advantages of the high-level semantics in the compression of images
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