155 research outputs found
NSME: a framework for network worm modeling and simulation
Various worms have a devastating impact on Internet. Packet level network modeling and simulation has become an approach to find effective countermeasures against worm threat. However, current alternatives are not fit enough for this purpose. For instance, they mostly focus on the details of lower layers of the network so that the abstraction of application layer is very coarse.
In our work, we propose a formal description of network and worm models, and define network virtualization levels to differentiate the expression capability of current alternatives. We then implement a framework, called NSME, based on NS2 for dedicated worm modeling and simulation with more details of application layer. We also analyze and compare the consequential overheads. The additional real-time characteristics and a worm simulation model are further discussed.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI
Social Network Effect on Bidding Strategy Adoption in Online P2P Lending Market
Bidding strategy in online auctions, as a sort of strategic behavior, can help bidders to get what they want more efficiently and effectively. It receives much attention in many researches. However, the determinants of bidding strategy adoption still remain unclear. In this study, we investigate the role of social network in bidding strategy adoption using real transaction data from an online P2P lending market. The analyses reveal that 1) bidding strategy tends to be homogeneous in different online social networks. 2) Joining an online social network does not change the bidding strategy adoption behavior significantly. 3) The size of social network will affect bidding strategy adoption and smaller ones are more homogeneous than bigger ones. 4) In a social network, bidders with different roles have different preferences on bidding strategies. Our findings can be considered as important empirical evidences for theories about social influence and human behavior
Deep Sufficient Representation Learning via Mutual Information
We propose a mutual information-based sufficient representation learning
(MSRL) approach, which uses the variational formulation of the mutual
information and leverages the approximation power of deep neural networks. MSRL
learns a sufficient representation with the maximum mutual information with the
response and a user-selected distribution. It can easily handle
multi-dimensional continuous or categorical response variables. MSRL is shown
to be consistent in the sense that the conditional probability density function
of the response variable given the learned representation converges to the
conditional probability density function of the response variable given the
predictor. Non-asymptotic error bounds for MSRL are also established under
suitable conditions. To establish the error bounds, we derive a generalized
Dudley's inequality for an order-two U-process indexed by deep neural networks,
which may be of independent interest. We discuss how to determine the intrinsic
dimension of the underlying data distribution. Moreover, we evaluate the
performance of MSRL via extensive numerical experiments and real data analysis
and demonstrate that MSRL outperforms some existing nonlinear sufficient
dimension reduction methods.Comment: 43 pages, 6 figures and 5 table
NSME: a framework for network worm modeling and simulation
Various worms have a devastating impact on Internet. Packet level network modeling and simulation has become an approach to find effective countermeasures against worm threat. However, current alternatives are not fit enough for this purpose. For instance, they mostly focus on the details of lower layers of the network so that the abstraction of application layer is very coarse.
In our work, we propose a formal description of network and worm models, and define network virtualization levels to differentiate the expression capability of current alternatives. We then implement a framework, called NSME, based on NS2 for dedicated worm modeling and simulation with more details of application layer. We also analyze and compare the consequential overheads. The additional real-time characteristics and a worm simulation model are further discussed.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI
Transcriptional coactivator PGC-1a integrates the mammalian clock and energy metabolism
The mammalian clock regulates major aspects of energy metabolism, including glucose and lipid homeostasis and mitochondrial oxidative metabolism(1,2). The biochemical basis for coordinated control of the circadian clock and diverse metabolic pathways is not well understood. Here we show that PGC-1 alpha (Ppargc1a), a transcriptional coactivator that regulates energy metabolism(3-9), is rhythmically expressed in the liver and skeletal muscle of mice. PGC-1 alpha stimulates the expression of clock genes, notably Bmal1 (Arntl) and Rev-erba (Nr1d1), through coactivation of the ROR family of orphan nuclear receptors. Mice lacking PGC-1 alpha show abnormal diurnal rhythms of activity, body temperature and metabolic rate. The disruption of physiological rhythms in these animals is correlated with aberrant expression of clock genes and those involved in energy metabolism. Analyses of PGC-1 alpha-deficient fibroblasts and mice with liver-specific knockdown of PGC-1 alpha indicate that it is required for cell-autonomous clock function. We have thus identified PGC-1 alpha as a key component of the circadian oscillator that integrates the mammalian clock and energy metabolism.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62918/1/nature05767.pd
A framework of network connectivity management in multi-clouds infrastructure
The network function virtualization (NFV) transformation
is gaining an incredible momentum from mobile
operators as one of the significant solutions to improve the
resource allocation and system scalability in fifth-generation
(5G) networks. However, the ultra-dense deployments in 5G
create high volumes of traffic that pushes the physical and
virtual resources of cloud-based networks to edge limits. Consider
a distributed cloud, replacing the core network with virtual
entities in the form of virtual network functions (VNFs) still
requires efficient integration with various underlying hardware
technologies. Therefore, orchestrating the distribution of load
between cloud geo-datacenters starts by instantiating a virtual
and physical network typologies that connect involved front haul with relevant VNFs. In this article, we provide a framework to manage calls within 5G network clusters for efficient utilization of computational resources and also to prevent unnecessary signaling. We also propose a new scheme to instantiate virtual tunnels for call forwarding between network clusters leading to new logic networks that combine geo-datacenters and fronthaul. To facilitate service chaining in cloud, we propose a new enhanced management and orchestration (E-MANO) architecture that brings network traffic policies from the application layer tothe fronthaul for instant monitoring of available resources. We provide analysis and testbed results in support of our proposals.
the fronthaul for instant monitoring of available resources. We
provide analysis and testbed results in support of our proposals
Gravitational losses for the binary systems induced by the next-to-leading spin-orbit coupling effects
The orbital energy and momentum of the compact binary systems will loss due
to gravitational radiation. Based on the mass and mass-current multipole
moments of the binary system with the spin vector defined by Boh\'{e} et al.
[Class. Quantum Grav. 30, 075017 (2013)], we calculate the loss rates of
energy, angular and linear momentum induced by the next-to-leading spin-orbit
effects. For the case of circular orbit, the formulations for these losses are
given in terms of the orbital frequency.Comment: 18 page
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