570 research outputs found
Anisotropic Rabi model
We define the anisotropic Rabi model as the generalization of the spin-boson
Rabi model: The Hamiltonian system breaks the parity symmetry; the rotating and
counter-rotating interactions are governed by two different coupling constants;
a further parameter introduces a phase factor in the counter-rotating terms.
The exact energy spectrum and eigenstates of the generalized model is worked
out. The solution is obtained as an elaboration of a recent proposed method for
the isotropic limit of the model. In this way, we provide a long sought
solution of a cascade of models with immediate relevance in different physical
fields, including i) quantum optics: two-level atom in single mode cross
electric and magnetic fields; ii) solid state physics: electrons in
semiconductors with Rashba and Dresselhaus spin-orbit coupling; iii) mesoscopic
physics: Josephson junctions flux-qubit quantum circuits.Comment: 5 pages+ 6 pages supplementary, 7 figures, accepted by Phys. Rev.
Asynchronous Federated Learning for Edge-assisted Vehicular Networks
Vehicular networks enable vehicles support real-time vehicular applications
through training data. Due to the limited computing capability, vehicles
usually transmit data to a road side unit (RSU) at the network edge to process
data. However, vehicles are usually reluctant to share data with each other due
to the privacy issue. For the traditional federated learning (FL), vehicles
train the data locally to obtain a local model and then upload the local model
to the RSU to update the global model, thus the data privacy can be protected
through sharing model parameters instead of data. The traditional FL updates
the global model synchronously, i.e., the RSU needs to wait for all vehicles to
upload their models for the global model updating. However, vehicles may
usually drive out of the coverage of the RSU before they obtain their local
models through training, which reduces the accuracy of the global model. It is
necessary to propose an asynchronous federated learning (AFL) to solve this
problem, where the RSU updates the global model once it receives a local model
from a vehicle. However, the amount of data, computing capability and vehicle
mobility may affect the accuracy of the global model. In this paper, we jointly
consider the amount of data, computing capability and vehicle mobility to
design an AFL scheme to improve the accuracy of the global model. Extensive
simulation experiments have demonstrated that our scheme outperforms the FL
schemeComment: This paper has been submitted to WCS
Power and type I error rate of false discovery rate approaches in genome-wide association studies
In genome-wide genetic studies with a large number of markers, balancing the type I error rate and power is a challenging issue. Recently proposed false discovery rate (FDR) approaches are promising solutions to this problem. Using the 100 simulated datasets of a genome-wide marker map spaced about 3 cM and phenotypes from the Genetic Analysis Workshop 14, we studied the type I error rate and power of Storey's FDR approach, and compared it to the traditional Bonferroni procedure. We confirmed that Storey's FDR approach had a strong control of FDR. We found that Storey's FDR approach only provided weak control of family-wise error rate (FWER). For these simulated datasets, Storey's FDR approach only had slightly higher power than the Bonferroni procedure. In conclusion, Storey's FDR approach is more powerful than the Bonferroni procedure if strong control of FDR or weak control of FWER is desired. Storey's FDR approach has little power advantage over the Bonferroni procedure if there is low linkage disequilibrium among the markers. Further evaluation of the type I error rate and power of the FDR approaches for higher linkage disequilibrium and for haplotype analyses is warranted
Experimental study on the swelling behaviour of bentonite/claystone mixture
International audienceA mixture of the MX80 bentonite and the Callovo-Oxfordian (COx) claystone were investigated by carrying out a series of experiments including determination of the swelling pressure of compacted samples by constant-volume method, pre-swell method, zero-swell method and swell-consolidation method. Distilled water, synthetic water and humidity controlled vapour were employed for hydration. Results show that upon wetting the swelling pressure increases with decreasing suction; however, there are no obvious effects of synthetic water chemistry and hydration procedure on the swelling behaviour in both short and long terms. For the same initial dry density, the swelling pressure decreases with increasing pre-swell strain; whereas there is a well defined logarithmic relation between the swelling pressure and final dry density of the sample regardless of the initial dry densities and the experimental methods. It was also found that swelling pressure depends on the loading-wetting conditions as a consequence of the different microstructure changes occurred in different conditions. Furthermore, it was attempted to elaborate a general relationship between the swelling pressure and the final dry density for various reference bentonites
Characteristics of transient pressure performance of horizontal wells in fractured-vuggy tight fractal reservoirs considering nonlinear seepage
International audienceSince the classical seepage theory has limitations in characterizing the heterogeneity of fractured-vuggy tight reservoirs, well test interpretation results are not consistent with actual production by far. Based on the nonlinear percolation theory, a new nonlinear seepage equation considering the boundary layer and yield stress was derived to describe the seepage characteristics of dense matrix blocks and the stress sensitivity and fractal features of fracture systems were characterized by applying the fractal theory. Thus, the nonlinear model of a horizontal well in a fractured-vuggy tight fractal reservoir was established naturally. Then the finite element method was applied to solve the bottom hole pressure based on the processing of internal boundary conditions. After solving the model, the seepage characteristics of different models were summarized by analyzing the bottom hole pressure dynamic curves and the sensitivity analysis of multiple parameters such the nonlinear parameter and fractal index were conducted. Finally, the practicality of the model was proved through a field application. The results show that the pressure dynamic curves can be divided into nine flow stages and the increase of the nonlinear parameter will cause the intensity of the cross flow from matrix blocks to the fracture system to decrease. The fractal index is irrelevant to the intensity of the cross flow while it decides the upwarping degree of the curve at the middle and late flow stages. On the basis of the results of the field application, it can be concluded that the model fits well with actual production and the application of this model can improve the accuracy of well test interpretation
Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing
Vehicular edge computing (VEC) is a promising technology to support real-time
applications through caching the contents in the roadside units (RSUs), thus
vehicles can fetch the contents requested by vehicular users (VUs) from the RSU
within short time. The capacity of the RSU is limited and the contents
requested by VUs change frequently due to the high-mobility characteristics of
vehicles, thus it is essential to predict the most popular contents and cache
them in the RSU in advance. The RSU can train model based on the VUs' data to
effectively predict the popular contents. However, VUs are often reluctant to
share their data with others due to the personal privacy. Federated learning
(FL) allows each vehicle to train the local model based on VUs' data, and
upload the local model to the RSU instead of data to update the global model,
and thus VUs' privacy information can be protected. The traditional synchronous
FL must wait all vehicles to complete training and upload their local models
for global model updating, which would cause a long time to train global model.
The asynchronous FL updates the global model in time once a vehicle's local
model is received. However, the vehicles with different staying time have
different impacts to achieve the accurate global model. In this paper, we
consider the vehicle mobility and propose an Asynchronous FL based
Mobility-aware Edge Caching (AFMC) scheme to obtain an accurate global model,
and then propose an algorithm to predict the popular contents based on the
global model. Experimental results show that AFMC outperforms other baseline
caching schemes.Comment: This paper has been submitted to The 14th International Conference on
Wireless Communications and Signal Processing (WCSP 2022
High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks
Federated edge learning (FEEL) technology for vehicular networks is
considered as a promising technology to reduce the computation workload while
keep the privacy of users. In the FEEL system, vehicles upload data to the edge
servers, which train the vehicles' data to update local models and then return
the result to vehicles to avoid sharing the original data. However, the cache
queue in the edge is limited and the channel between edge server and each
vehicle is a time varying wireless channel, which makes a challenge to select a
suitable number of vehicles to upload data to keep a stable cache queue in edge
server and maximize the learning accuracy. Moreover, selecting vehicles with
different resource statuses to update data will affect the total amount of data
involved in training, which further affects the model accuracy. In this paper,
we propose a vehicle selection scheme, which maximizes the learning accuracy
while ensuring the stability of the cache queue, where the statuses of all the
vehicles in the coverage of edge server are taken into account. The performance
of this scheme is evaluated through simulation experiments, which indicates
that our proposed scheme can perform better than the known benchmark scheme.Comment: This paper has been submitted to China Communication
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning
The vehicular edge computing (VEC) can cache contents in different RSUs at
the network edge to support the real-time vehicular applications. In VEC, owing
to the high-mobility characteristics of vehicles, it is necessary to cache the
user data in advance and learn the most popular and interesting contents for
vehicular users. Since user data usually contains privacy information, users
are reluctant to share their data with others. To solve this problem,
traditional federated learning (FL) needs to update the global model
synchronously through aggregating all users' local models to protect users'
privacy. However, vehicles may frequently drive out of the coverage area of the
VEC before they achieve their local model trainings and thus the local models
cannot be uploaded as expected, which would reduce the accuracy of the global
model. In addition, the caching capacity of the local RSU is limited and the
popular contents are diverse, thus the size of the predicted popular contents
usually exceeds the cache capacity of the local RSU. Hence, the VEC should
cache the predicted popular contents in different RSUs while considering the
content transmission delay. In this paper, we consider the mobility of vehicles
and propose a cooperative Caching scheme in the VEC based on Asynchronous
Federated and deep Reinforcement learning (CAFR). We first consider the
mobility of vehicles and propose an asynchronous FL algorithm to obtain an
accurate global model, and then propose an algorithm to predict the popular
contents based on the global model. In addition, we consider the mobility of
vehicles and propose a deep reinforcement learning algorithm to obtain the
optimal cooperative caching location for the predicted popular contents in
order to optimize the content transmission delay. Extensive experimental
results have demonstrated that the CAFR scheme outperforms other baseline
caching schemes.Comment: This paper has been submitted to IEEE Journal of Selected Topics in
Signal Processin
Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method
Vehicles on the road exchange data with base station (BS) frequently through
vehicle to infrastructure (V2I) communications to ensure the normal use of
vehicular applications, where the IEEE 802.11 distributed coordination function
(DCF) is employed to allocate a minimum contention window (MCW) for channel
access. Each vehicle may change its MCW to achieve more access opportunities at
the expense of others, which results in unfair communication performance.
Moreover, the key access parameters MCW is the privacy information and each
vehicle are not willing to share it with other vehicles. In this uncertain
setting, age of information (AoI) is an important communication metric to
measure the freshness of data, we design an intelligent vehicular node to learn
the dynamic environment and predict the optimal MCW which can make it achieve
age fairness. In order to allocate the optimal MCW for the vehicular node, we
employ a learning algorithm to make a desirable decision by learning from
replay history data. In particular, the algorithm is proposed by extending the
traditional DQN training and testing method. Finally, by comparing with other
methods, it is proved that the proposed DQN method can significantly improve
the age fairness of the intelligent node.Comment: This paper has been accepted by Chinese Journal of Electronics.
Simulation codes have been provided at:
https://github.com/qiongwu86/Age-Fairnes
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