23,976 research outputs found
Weyl-Heisenberg Frame Wavelets with Basic Supports
Let , be two fixed non-zero constants. A measurable set is called a Weyl-Heisenberg frame set for if the function
generates a Weyl-Heisenberg frame for under
modulates by and translates by , i.e.,
is a frame for . It is
an open question on how to characterize all frame sets for a given pair
in general. In the case that and , a result due to Casazza and
Kalton shows that the condition that the set
(where
are integers) is a Weyl-Heisenberg frame set for is equivalent to
the condition that the polynomial does not have
any unit roots in the complex plane. In this paper, we show that this result
can be generalized to a class of more general measurable sets (called basic
support sets) and to set theoretical functions and continuous functions defined
on such sets.Comment: 11 pages, 2 figure
From Weyl-Heisenberg Frames to Infinite Quadratic Forms
Let , be two fixed positive constants. A function is called a \textit{mother Weyl-Heisenberg frame wavelet} for if
generates a frame for under modulates by and
translates by , i.e., is a frame for
. In this paper, we establish a connection between mother
Weyl-Heisenberg frame wavelets of certain special forms and certain strongly
positive definite quadratic forms of infinite dimension. Some examples of
application in matrix algebra are provided
Stability and electronic properties of small BN nanotubes
We report the stability and electronic structures of the boron nitride
nanotubes (BNNTs) with diameters below 4 A by semi-empirical quantum mechanical
molecular dynamics simulations and ab initio calculations. Among them (3,0),
(3,1), (2,2), (4,0), (4,1) and (3,2) BNNTs can be stable well over room
temperature. These small BNNTs become globally stable when encapsulated in a
larger BNNT. It is found that the energy gaps and work functions of these small
BNNTs are strongly dependent on their chirality and diameters. The small zigzag
BNNTs become desirable semiconductors and have peculiar distribution of nearly
free electron states due to strong hybridization effect. When such a small BNNT
is inserted in a larger one, the energy gap of the formed double-walled BNNT
can even be much reduced due to the coupled effect of wall buckling difference
and NFE-pi hybridization.Comment: 28 pages, 9 figures, 1 tabl
Projected Performance Advantage of Multilayer Graphene Nanoribbon as Transistor Channel Material
The performance limits of the multilayer graphene nanoribbon (GNR)
field-effect transistor (FET) are assessed and compared to those of monolayer
GNR FET and carbon nanotube (CNT) FET. The results show that with a thin high-k
gate insulator and reduced interlayer coupling, multilayer GNR FET can
significantly outperform its CNT counterpart with a similar gate and bandgap in
terms of the ballistic on-current. In the presence of optical phonon
scattering, which has a short mean free path in the graphene-derived
nanostructures, the advantage of the multilayer GNRFET is even more
significant. The simulation results indicate multilayer GNRs with
incommensurate non-AB stacking and weak interlayer coupling are the best
candidate for high performance GNR FETs.Comment: 22 pages, 6 figure
Production of the Extreme-Ultraviolet Late Phase of an X Class Flare in a Three-Stage Magnetic Reconnection Process
We report observations of an X class flare on 2011 September 6 by the
instruments onboard the Solar Dynamics Observatory (SDO). The flare occurs in a
complex active region with multiple polarities. The Extreme-Ultraviolet (EUV)
Variability Experiment (EVE) observations in the warm coronal emission reveal
three enhancements, of which the third one corresponds to an EUV late phase.
The three enhancements have a one-to-one correspondence to the three stages in
flare evolution identified by the spatially-resolved Atmospheric Imaging
Assembly (AIA) observations, which are characterized by a flux rope eruption, a
moderate filament ejection, and the appearance of EUV late phase loops,
respectively. The EUV late phase loops are spatially and morphologically
distinct from the main flare loops. Multi-channel analysis suggests the
presence of a continuous but fragmented energy injection during the EUV late
phase resulting in the warm corona nature of the late phase loops. Based on
these observational facts, We propose a three-stage magnetic reconnection
scenario to explain the flare evolution. Reconnections in different stages
involve different magnetic fields but show a casual relationship between them.
The EUV late phase loops are mainly produced by the least energetic magnetic
reconnection in the last stage.Comment: 6 pages, 4 figures, 1 table. Accepted for Publication in ApJ
Deep Neural Network Embeddings with Gating Mechanisms for Text-Independent Speaker Verification
In this paper, gating mechanisms are applied in deep neural network (DNN)
training for x-vector-based text-independent speaker verification. First, a
gated convolution neural network (GCNN) is employed for modeling the
frame-level embedding layers. Compared with the time-delay DNN (TDNN), the GCNN
can obtain more expressive frame-level representations through carefully
designed memory cell and gating mechanisms. Moreover, we propose a novel
gated-attention statistics pooling strategy in which the attention scores are
shared with the output gate. The gated-attention statistics pooling combines
both gating and attention mechanisms into one framework; therefore, we can
capture more useful information in the temporal pooling layer. Experiments are
carried out using the NIST SRE16 and SRE18 evaluation datasets. The results
demonstrate the effectiveness of the GCNN and show that the proposed
gated-attention statistics pooling can further improve the performance.Comment: 5 pages, 3 figures, submitted to INTERSPEECH 201
Multi-Task Learning with High-Order Statistics for X-vector based Text-Independent Speaker Verification
The x-vector based deep neural network (DNN) embedding systems have
demonstrated effectiveness for text-independent speaker verification. This
paper presents a multi-task learning architecture for training the speaker
embedding DNN with the primary task of classifying the target speakers, and the
auxiliary task of reconstructing the first- and higher-order statistics of the
original input utterance. The proposed training strategy aggregates both the
supervised and unsupervised learning into one framework to make the speaker
embeddings more discriminative and robust. Experiments are carried out using
the NIST SRE16 evaluation dataset and the VOiCES dataset. The results
demonstrate that our proposed method outperforms the original x-vector approach
with very low additional complexity added.Comment: 5 pages,2 figures, submitted to INTERSPEECH 201
Statistical Learning Based Congestion Control for Real-time Video Communication
With the increasing demands on interactive video applications, how to adapt
video bit rate to avoid network congestion has become critical, since
congestion results in self-inflicted delay and packet loss which deteriorate
the quality of real-time video service. The existing congestion control is hard
to simultaneously achieve low latency, high throughput, good adaptability and
fair bandwidth allocation, mainly because of the hardwired control strategy and
egocentric convergence objective. To address these issues, we propose an
end-to-end statistical learning based congestion control, named Iris. By
exploring the underlying principles of self-inflicted delay, we reveal that
congestion delay is determined by sending rate, receiving rate and network
status, which inspires us to control video bit rate using a
statistical-learning congestion control model. The key idea of Iris is to force
all flows to converge to the same queue load, and adjust the bit rate by the
model. All flows keep a small and fixed number of packets queuing in the
network, thus the fair bandwidth allocation and low latency are both achieved.
Besides, the adjustment step size of sending rate is updated by online
learning, to better adapt to dynamically changing networks. We carried out
extensive experiments to evaluate the performance of Iris, with the
implementations of transport layer (UDP) and application layer (QUIC)
respectively. The testing environment includes emulated network, real-world
Internet and commercial LTE networks. Compared against TCP flavors and
state-of-the-art protocols, Iris is able to achieve high bandwidth utilization,
low latency and good fairness concurrently. Especially over QUIC, Iris is able
to increase the video bitrate up to 25%, and PSNR up to 1dB
On the Nature of the Extreme-Ultraviolet Late Phase of Solar Flares
The extreme-ultraviolet (EUV) late phase of solar flares is a second peak of
warm coronal emissions (e.g., Fe XVI) for many minutes to a few hours after the
GOES soft X-ray peak. It was first observed by the EUV Variability Experiment
(EVE) on board the Solar Dynamics Observatory (SDO). The late phase emission
originates from a second set of longer loops (late phase loops) that are higher
than the main flaring loops. It is suggested as being caused by either
additional heating or long-lasting cooling. In this paper, we study the role of
long-lasting cooling and additional heating in producing the EUV late phase
using the "enthalpy-based thermal evolution of loops" (EBTEL) model. We find
that a long cooling process in late phase loops can well explain the presence
of the EUV late phase emission, but we cannot exclude the possibility of
additional heating in the decay phase. Moreover, we provide two preliminary
methods based on the UV and EUV emissions from the Atmospheric Imaging Assembly
(AIA) on board SDO to determine whether an additional heating plays some role
or not in the late phase emission. Using nonlinear force-free field modeling,
we study the magnetic configuration of the EUV late phase. It is found that the
late phase can be generated either in hot spine field lines associated with a
magnetic null point or in large-scale magnetic loops of multipolar magnetic
fields. In this paper, we also discuss why the EUV late phase is usually
observed in warm coronal emissions and why the majority of flares do not
exhibit an EUV late phase.Comment: Accepted for publication in Ap
Efficient and Accurate Path Cost Estimation Using Trajectory Data
Using the growing volumes of vehicle trajectory data, it becomes increasingly
possible to capture time-varying and uncertain travel costs in a road network,
including travel time and fuel consumption. The current paradigm represents a
road network as a graph, assigns weights to the graph's edges by fragmenting
trajectories into small pieces that fit the underlying edges, and then applies
a routing algorithm to the resulting graph. We propose a new paradigm that
targets more accurate and more efficient estimation of the costs of paths by
associating weights with sub-paths in the road network. The paper provides a
solution to a foundational problem in this paradigm, namely that of computing
the time-varying cost distribution of a path.
The solution consists of several steps. We first learn a set of random
variables that capture the joint distributions of sub-paths that are covered by
sufficient trajectories. Then, given a departure time and a path, we select an
optimal subset of learned random variables such that the random variables'
corresponding paths together cover the path. This enables accurate joint
distribution estimation of the path, and by transferring the joint distribution
into a marginal distribution, the travel cost distribution of the path is
obtained. The use of multiple learned random variables contends with data
sparseness, the use of multi-dimensional histograms enables compact
representation of arbitrary joint distributions that fully capture the travel
cost dependencies among the edges in paths. Empirical studies with substantial
trajectory data from two different cities offer insight into the design
properties of the proposed solution and suggest that the solution is effective
in real-world settings.Comment: 16pages, 42 figure
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