82 research outputs found
Mechanism design for distributed task and resource allocation among self-interested agents in virtual organizations
The aggregate power of all resources on the Internet is enormous. The Internet can
be viewed as a massive virtual organization that holds tremendous amounts of information
and resources with different ownerships. However, little is known about how to run this
organization efficiently.
This dissertation studies the problems of distributed task and resource allocation
among self-interested agents in virtual organizations. The developed solutions are not
allocation mechanisms that can be imposed by a centralized designer, but decentralized
interaction mechanisms that provide incentives to self-interested agents to behave
cooperatively. These mechanisms also take computational tractability into consideration
due to the inherent complexity of distributed task and resource allocation problems.
Targeted allocation mechanisms can achieve global task allocation efficiency in a
virtual organization and establish stable resource-sharing communities based on agentsâÃÂÃÂ
own decisions about whether or not to behave cooperatively. This high level goal requires
solving the following problems: synthetic task allocation, decentralized coalition formation
and automated multiparty negotiation. For synthetic task allocation, in which each task needs to be accomplished by a
virtual team composed of self-interested agents from different real organizations, my
approach is to formalize the synthetic task allocation problem as an algorithmic mechanism
design optimization problem. I have developed two approximation mechanisms that I prove
are incentive compatible for a synthetic task allocation problem.
This dissertation also develops a decentralized coalition formation mechanism,
which is based on explicit negotiation among self-interested agents. Each agent makes its
own decisions about whether or not to join a candidate coalition. The resulting coalitions
are stable in the core in terms of coalition rationality. I have applied this mechanism to
form resource sharing coalitions in computational grids and buyer coalitions in electronic
markets.
The developed negotiation mechanism in the decentralized coalition formation
mechanism realizes automated multilateral negotiation among self-interested agents who
have symmetric authority (i.e., no mediator exists and agents are peers).
In combination, the decentralized allocation mechanisms presented in this
dissertation lay a foundation for realizing automated resource management in open and
scalable virtual organizations
Mechanism design for distributed task and resource allocation among self-interested agents in virtual organizations
The aggregate power of all resources on the Internet is enormous. The Internet can
be viewed as a massive virtual organization that holds tremendous amounts of information
and resources with different ownerships. However, little is known about how to run this
organization efficiently.
This dissertation studies the problems of distributed task and resource allocation
among self-interested agents in virtual organizations. The developed solutions are not
allocation mechanisms that can be imposed by a centralized designer, but decentralized
interaction mechanisms that provide incentives to self-interested agents to behave
cooperatively. These mechanisms also take computational tractability into consideration
due to the inherent complexity of distributed task and resource allocation problems.
Targeted allocation mechanisms can achieve global task allocation efficiency in a
virtual organization and establish stable resource-sharing communities based on agentsâÃÂÃÂ
own decisions about whether or not to behave cooperatively. This high level goal requires
solving the following problems: synthetic task allocation, decentralized coalition formation
and automated multiparty negotiation. For synthetic task allocation, in which each task needs to be accomplished by a
virtual team composed of self-interested agents from different real organizations, my
approach is to formalize the synthetic task allocation problem as an algorithmic mechanism
design optimization problem. I have developed two approximation mechanisms that I prove
are incentive compatible for a synthetic task allocation problem.
This dissertation also develops a decentralized coalition formation mechanism,
which is based on explicit negotiation among self-interested agents. Each agent makes its
own decisions about whether or not to join a candidate coalition. The resulting coalitions
are stable in the core in terms of coalition rationality. I have applied this mechanism to
form resource sharing coalitions in computational grids and buyer coalitions in electronic
markets.
The developed negotiation mechanism in the decentralized coalition formation
mechanism realizes automated multilateral negotiation among self-interested agents who
have symmetric authority (i.e., no mediator exists and agents are peers).
In combination, the decentralized allocation mechanisms presented in this
dissertation lay a foundation for realizing automated resource management in open and
scalable virtual organizations
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input
Non-autoregressive translation (NAT) models, which remove the dependence on
previous target tokens from the inputs of the decoder, achieve significantly
inference speedup but at the cost of inferior accuracy compared to
autoregressive translation (AT) models. Previous work shows that the quality of
the inputs of the decoder is important and largely impacts the model accuracy.
In this paper, we propose two methods to enhance the decoder inputs so as to
improve NAT models. The first one directly leverages a phrase table generated
by conventional SMT approaches to translate source tokens to target tokens,
which are then fed into the decoder as inputs. The second one transforms
source-side word embeddings to target-side word embeddings through
sentence-level alignment and word-level adversary learning, and then feeds the
transformed word embeddings into the decoder as inputs. Experimental results
show our method largely outperforms the NAT baseline~\citep{gu2017non} by
BLEU scores on WMT14 English-German task and BLEU scores on WMT16
English-Romanian task.Comment: AAAI 201
Ground state properties of a multi-component bosonic mixture: a Gutzwiller mean-field study
Using the single-site Gutzwiller method, we theoretically study the ground
state and the interspecies entanglement properties of interexchange symmetric
multi-component (two- and three-) bosonic mixtures in an optical lattice, and
the results are generalized to an -component () system. We
compute the mean-field phase diagram, the interspecies entanglement entropy,
and the ground state spectral decomposition. Three phases namely the
-component Superfluid state (nSF), the -component Mott insulator state
(nMI), and the Super-counter-fluid state (SCF) are observed. Interestingly, we
find that there are SCF lobes to separate every two neighboring nMI lobes
in the phase diagram. More importantly, we derive the exact general expression
of the interspecies entanglement entropy for the SCF phase. In addition, we
also investigate the demixing effect of an n-component mixture and demonstrate
that the mixing-demixing critical point is independent of n.Comment: 12 pages, 6 figure
What motivates farmers to adopt low-carbon agricultural technologies? Empirical evidence from thousands of rice farmers in Hubei province, central China
Low-carbon agriculture is essential for protecting the global climate and sustainable agricultural economics. Since China is a predominantly agricultural country, the adoption of low-carbon agricultural technologies by local farmers is crucial. The past literature on low-carbon technologies has highlighted the influence of demographic, economic, and environmental factors, while the psychological factors have been underexplored. A questionnaire-based approach was used to assess the psychological process underlying the adoption of low-carbon agricultural technologies by 1,114 Chinese rice farmers in this paper, and structural equation modeling (SEM) was empirically employed to test our theoretical model
Generation of integration-free neural progenitor cells from cells in human urine
Human neural stem cells hold great promise for research and therapy in neural disease. We describe the generation of integration-free and expandable human neural progenitor cells (NPCs). We combined an episomal system to deliver reprogramming factors with a chemically defined culture medium to reprogram epithelial-like cells from human urine into NPCs (hUiNPCs). These transgene-free hUiNPCs can self-renew and can differentiate into multiple functional neuronal subtypes and glial cells in vitro. Although functional in vivo analysis is still needed, we report that the cells survive and differentiate upon transplant into newborn rat brain.postprin
Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst
The recently discovered neutron star transient Swift J0243.6+6124 has been
monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT).
Based on the obtained data, we investigate the broadband spectrum of the source
throughout the outburst. We estimate the broadband flux of the source and
search for possible cyclotron line in the broadband spectrum. No evidence of
line-like features is, however, found up to . In the absence of
any cyclotron line in its energy spectrum, we estimate the magnetic field of
the source based on the observed spin evolution of the neutron star by applying
two accretion torque models. In both cases, we get consistent results with
, and peak luminosity of which makes the source the first Galactic ultraluminous
X-ray source hosting a neutron star.Comment: publishe
Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite
As China's first X-ray astronomical satellite, the Hard X-ray Modulation
Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15,
2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy
satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was
designed to perform pointing, scanning and gamma-ray burst (GRB) observations
and, based on the Direct Demodulation Method (DDM), the image of the scanned
sky region can be reconstructed. Here we give an overview of the mission and
its progresses, including payload, core sciences, ground calibration/facility,
ground segment, data archive, software, in-orbit performance, calibration,
background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech.
Astron. arXiv admin note: text overlap with arXiv:1910.0443
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