10,282 research outputs found
Differential Recurrent Neural Networks for Action Recognition
The long short-term memory (LSTM) neural network is capable of processing
complex sequential information since it utilizes special gating schemes for
learning representations from long input sequences. It has the potential to
model any sequential time-series data, where the current hidden state has to be
considered in the context of the past hidden states. This property makes LSTM
an ideal choice to learn the complex dynamics of various actions.
Unfortunately, the conventional LSTMs do not consider the impact of
spatio-temporal dynamics corresponding to the given salient motion patterns,
when they gate the information that ought to be memorized through time. To
address this problem, we propose a differential gating scheme for the LSTM
neural network, which emphasizes on the change in information gain caused by
the salient motions between the successive frames. This change in information
gain is quantified by Derivative of States (DoS), and thus the proposed LSTM
model is termed as differential Recurrent Neural Network (dRNN). We demonstrate
the effectiveness of the proposed model by automatically recognizing actions
from the real-world 2D and 3D human action datasets. Our study is one of the
first works towards demonstrating the potential of learning complex time-series
representations via high-order derivatives of states
Traffic Driven Resource Allocation in Heterogenous Wireless Networks
Most work on wireless network resource allocation use physical layer
performance such as sum rate and outage probability as the figure of merit.
These metrics may not reflect the true user QoS in future heterogenous networks
(HetNets) with many small cells, due to large traffic variations in overlapping
cells with complicated interference conditions. This paper studies the spectrum
allocation problem in HetNets using the average packet sojourn time as the
performance metric. To be specific, in a HetNet with base terminal stations
(BTS's), we determine the optimal partition of the spectrum into possible
spectrum sharing combinations. We use an interactive queueing model to
characterize the flow level performance, where the service rates are decided by
the spectrum partition. The spectrum allocation problem is formulated using a
conservative approximation, which makes the optimization problem convex. We
prove that in the optimal solution the spectrum is divided into at most
pieces. A numerical algorithm is provided to solve the spectrum allocation
problem on a slow timescale with aggregate traffic and service information.
Simulation results show that the proposed solution achieves significant gains
compared to both orthogonal and full spectrum reuse allocations with moderate
to heavy traffic.Comment: 6 pages, 5 figures IEEE GLOBECOM 2014 (accepted for publication
Traffic-Driven Spectrum Allocation in Heterogeneous Networks
Next generation cellular networks will be heterogeneous with dense deployment
of small cells in order to deliver high data rate per unit area. Traffic
variations are more pronounced in a small cell, which in turn lead to more
dynamic interference to other cells. It is crucial to adapt radio resource
management to traffic conditions in such a heterogeneous network (HetNet). This
paper studies the optimization of spectrum allocation in HetNets on a
relatively slow timescale based on average traffic and channel conditions
(typically over seconds or minutes). Specifically, in a cluster with base
transceiver stations (BTSs), the optimal partition of the spectrum into
segments is determined, corresponding to all possible spectrum reuse patterns
in the downlink. Each BTS's traffic is modeled using a queue with Poisson
arrivals, the service rate of which is a linear function of the combined
bandwidth of all assigned spectrum segments. With the system average packet
sojourn time as the objective, a convex optimization problem is first
formulated, where it is shown that the optimal allocation divides the spectrum
into at most segments. A second, refined model is then proposed to address
queue interactions due to interference, where the corresponding optimal
allocation problem admits an efficient suboptimal solution. Both allocation
schemes attain the entire throughput region of a given network. Simulation
results show the two schemes perform similarly in the heavy-traffic regime, in
which case they significantly outperform both the orthogonal allocation and the
full-frequency-reuse allocation. The refined allocation shows the best
performance under all traffic conditions.Comment: 13 pages, 11 figures, accepted for publication by JSAC-HC
Cold and Hot Nuclear Matter Effects on Charmonium Production in p+Pb Collisions at LHC Energy
We study cold and hot nuclear matter effects on charmonium production in p+Pb
collisions at TeV in a transport approach. At the
forward rapidity, the cold medium effect on all the states and the
hot medium effect on the excited states only can explain well the
and yield and transverse momentum distribution measured by the
ALICE collaboration, and we predict a significantly larger
broadening in comparison with . However, we can not reproduce the
and data at the backward rapidity with reasonable cold and hot
medium effects.Comment: 6 pages, 5 figure
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