1,253 research outputs found
Optimal Linear Network Coding When 3 Nodes Communicate Over Broadcast Erasure Channels with ACK
This work considers the following scenario: Three nodes {1, 2, 3} would like to communicate with each other by sending packets through unreliable wireless medium.We consider the most general unicast traffic demands. Namely, there are six co-existing unicast flows with rates (R1--\u3e2,R1--\u3e3,R2--\u3e1,R2--\u3e3, R3--\u3e1,R3--\u3e2). When a node broadcasts a packet, a random subset of the other two nodes will receive the packet. After each transmission, causal ACKnowledgment is sent so that all nodes know whether the other nodes have received the packet or not. Such a setting has many unique features. For example, each node, say node 1, can assume many different roles: Being the transmitter of the information R1--\u3e2 and R1--\u3e3; being the receiver of the information R2--\u3e1 and R3--\u3e1; and being the relay for the information R2--\u3e3 and R3--\u3e2. This fully captures the fundamental behaviors of 3-node network communications. Allowing network coding (NC) to capitalize the diversity gain (i.e., overhearing packets transmitted by other nodes), this work characterizes the 6-dimensional linear network coding (LNC) capacity of the above erasure network. The results show that for any channel parameters, the LNC capacity can be achieved by a simple strategy that involves only a few LNC choices
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Complex computational models are often designed to simulate real-world
physical phenomena in many scientific disciplines. However, these simulation
models tend to be computationally very expensive and involve a large number of
simulation input parameters which need to be analyzed and properly calibrated
before the models can be applied for real scientific studies. We propose a
visual analysis system to facilitate interactive exploratory analysis of
high-dimensional input parameter space for a complex yeast cell polarization
simulation. The proposed system can assist the computational biologists, who
designed the simulation model, to visually calibrate the input parameters by
modifying the parameter values and immediately visualizing the predicted
simulation outcome without having the need to run the original expensive
simulation for every instance. Our proposed visual analysis system is driven by
a trained neural network-based surrogate model as the backend analysis
framework. Surrogate models are widely used in the field of simulation sciences
to efficiently analyze computationally expensive simulation models. In this
work, we demonstrate the advantage of using neural networks as surrogate models
for visual analysis by incorporating some of the recent advances in the field
of uncertainty quantification, interpretability and explainability of neural
network-based models. We utilize the trained network to perform interactive
parameter sensitivity analysis of the original simulation at multiple
levels-of-detail as well as recommend optimal parameter configurations using
the activation maximization framework of neural networks. We also facilitate
detail analysis of the trained network to extract useful insights about the
simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic
Experiments and simulations of MEMS thermal sensors for wall shear-stress measurements in aerodynamic control applications
MEMS thermal shear-stress sensors exploit heat-transfer effects to measure the shear stress exerted by an air flow on its solid boundary, and have promising applications in aerodynamic control. Classical theory for conventional, macroscale thermal shear-stress sensors states that the rate of heat removed by the flow from the sensor is proportional to the 1/3-power of the shear stress. However, we have observed that this theory is inconsistent with experimental data from MEMS sensors. This paper seeks to develop an understanding of MEMS thermal shear-stress sensors through a study including both experimental and theoretical investigations. We first obtain experimental data that confirm the inadequacy of the classical theory by wind-tunnel testing of prototype MEMS shear-stress sensors with different dimensions and materials. A theoretical analysis is performed to identify that this inadequacy is due to the lack of a thin thermal boundary layer in the fluid flow at the sensor surface, and then a two-dimensional MEMS shear-stress sensor theory is presented. This theory incorporates important heat-transfer effects that are ignored by the classical theory, and consistently explains the experimental data obtained from prototype MEMS sensors. Moreover, the prototype MEMS sensors are studied with three-dimensional simulations, yielding results that quantitatively agree with experimental data. This work demonstrates that classical assumptions made for conventional thermal devices should be carefully examined for miniature MEMS devices
Disordered Fe vacancies and superconductivity in potassium-intercalated iron selenide (K2-xFe4+ySe5)
The parent compound of an unconventional superconductor must contain unusual
correlated electronic and magnetic properties of its own. In the high-Tc
potassium intercalated FeSe, there has been significant debate regarding what
the exact parent compound is. Our studies unambiguously show that the
Fe-vacancy ordered K2Fe4Se5 is the magnetic, Mott insulating parent compound of
the superconducting state. Non-superconducting K2Fe4Se5 becomes a
superconductor after high temperature annealing, and the overall picture
indicates that superconductivity in K2-xFe4+ySe5 originates from the Fe-vacancy
order to disorder transition. Thus, the long pending question whether magnetic
and superconducting state are competing or cooperating for cuprate
superconductors may also apply to the Fe-chalcogenide superconductors. It is
believed that the iron selenides and related compounds will provide essential
information to understand the origin of superconductivity in the iron-based
superconductors, and possibly to the superconducting cuprates
Tailoring excitonic states of van der Waals bilayers through stacking configuration, band alignment and valley-spin
Excitons in monolayer semiconductors have large optical transition dipole for
strong coupling with light field. Interlayer excitons in heterobilayers, with
layer separation of electron and hole components, feature large electric dipole
that enables strong coupling with electric field and exciton-exciton
interaction, at the cost that the optical dipole is substantially quenched (by
several orders of magnitude). In this letter, we demonstrate the ability to
create a new class of excitons in transition metal dichalcogenide (TMD) hetero-
and homo-bilayers that combines the advantages of monolayer- and
interlayer-excitons, i.e. featuring both large optical dipole and large
electric dipole. These excitons consist of an electron that is well confined in
an individual layer, and a hole that is well extended in both layers, realized
here through the carrier-species specific layer-hybridization controlled
through the interplay of rotational, translational, band offset, and
valley-spin degrees of freedom. We observe different species of such
layer-hybridized valley excitons in different heterobilayer and homobilayer
systems, which can be utilized for realizing strongly interacting
excitonic/polaritonic gases, as well as optical quantum coherent controls of
bidirectional interlayer carrier transfer either with upper conversion or down
conversion in energy
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