33,221 research outputs found
Potential thermoelectric materials (M=Sn and Pb) in perovskite structures from the first-principles calculations
The thermoelectric properties of halide perovskites (M=Sn
and Pb) are investigated from a combination of first-principles calculations
and semiclassical Boltzmann transport theory by considering both the electron
and phonon transport. The electronic part is performed using a modified Becke
and Johnson (mBJ) exchange potential, including spin-orbit coupling (SOC),
while the phonon part is computed using generalized gradient approximation
(GGA). It is found that SOC has remarkable detrimental effect on n-type power
factor, while has a negligible influence in p-type doping, which can be
explained by considering SOC effect on conduction and valence bands. Calculated
results show exceptionally low lattice thermal conductivities in
and , and the corresponding
room-temperature lattice thermal conductivity is 0.54 and 0.25 . At 1000 K, the maximal figure of
merit is up to 0.63 and 0.64 for and
with scattering time = s, and the peak is 0.49 and 0.41
with = s. These results make us believe that
(M=Sn and Pb) in perovskite structures may be potential thermoelectric
materials.Comment: 6 pages, 6 figure
Application-Level Resilience Modeling for HPC Fault Tolerance
Understanding the application resilience in the presence of faults is
critical to address the HPC resilience challenge. Currently, we largely rely on
random fault injection (RFI) to quantify the application resilience. However,
RFI provides little information on how fault tolerance happens, and RFI results
are often not deterministic due to its random nature. In this paper, we
introduce a new methodology to quantify the application resilience. Our
methodology is based on the observation that at the application level, the
application resilience to faults is due to the application-level fault masking.
The application-level fault masking happens because of application-inherent
semantics and program constructs. Based on this observation, we analyze
application execution information and use a data-oriented approach to model the
application resilience. We use our model to study how and why HPC applications
can (or cannot) tolerate faults. We demonstrate tangible benefits of using the
model to direct fault tolerance mechanisms.Comment: 11 pages, 9 figures, the manuscript has been submitted to the
International Conference for High Performance Computing, Networking, Storage
and Analysis (SC '17) conferenc
A Double AR Model Without Intercept: an Alternative to Modeling Nonstationarity and Heteroscedasticity
This paper presents a double AR model without intercept (DARWIN model) and
provides us a new way to study the non-stationary heteroskedastic time series.
It is shown that the DARWIN model is always non-stationary and heteroskedastic,
and its sample properties depends on the Lyapunov exponent. An
easy-to-implement estimator is proposed for the Lyapunov exponent, and it is
unbiased, strongly consistent and asymptotically normal. Based on this
estimator, a powerful test is constructed for testing the stability of the
model. Moreover, this paper proposes the quasi-maximum likelihood estimator
(QMLE) for the DARWIN model, which has an explicit form. The strong consistency
and asymptotical normality of the QMLE are established regardless of the sign
of the Lyapunov exponent. Simulation studies are conducted to assess the
performance of the estimation and testing and an empirical example is given for
illustrating the usefulness of the DARWIN model.Comment: 18 pages, 7 figure
PARIS: Predicting Application Resilience Using Machine Learning
Extreme-scale scientific applications can be more vulnerable to soft errors
(transient faults) as high-performance computing systems increase in scale. The
common practice to evaluate the resilience to faults of an application is
random fault injection, a method that can be highly time consuming. While
resilience prediction modeling has been recently proposed to predict
application resilience in a faster way than fault injection, it can only
predict a single class of fault manifestation (SDC) and there is no evidence
demonstrating that it can work on previously unseen programs, which greatly
limits its re-usability. We present PARIS, a resilience prediction method that
addresses the problems of existing prediction methods using machine learning.
Using carefully-selected features and a machine learning model, our method is
able to make resilience predictions of three classes of fault manifestations
(success, SDC, and interruption) as opposed to one class like in current
resilience prediction modeling. The generality of our approach allows us to
make prediction on new applications, i.e., previously unseen applications,
providing large applicability to our model. Our evaluation on 125 programs
shows that PARIS provides high prediction accuracy, 82% and 77% on average for
predicting the rate of success and interruption, respectively, while the
state-of-the-art resilience prediction model cannot predict them. When
predicting the rate of SDC, PARIS provides much better accuracy than the
state-of-the-art (38% vs. -273%). PARIS is much faster (up to 450x speedup)
than the traditional method (random fault injection)
Pressure enhanced thermoelectric properties in Mg2Sn
Pressure dependence of electronic structures and thermoelectric properties of
are investigated by using a modified Becke and Johnson (mBJ)
exchange potential, including spin-orbit coupling (SOC). The corresponding
value of spin-orbit splitting at point is 0.47 eV, which is in good
agreement with the experimental value 0.48 eV. With the pressure increasing,
the energy band gap first increases, and then decreases. In certain doping
range, the power factor for n-type has the same trend with energy band gap,
when the pressure increases. Calculated results show that the pressure can lead
to significantly enhanced power factor in n-type doping below the critical
pressure, and the corresponding lattice thermal conductivity near the critical
pressure shows the relatively small value. These results make us believe that
thermoelectric properties of can be improved in n-type doping
by pressure.Comment: 4 pages, 6 figure
Spin-orbital coupling effect on power factor in semiconducting transition-metal dichalcogenide monolayers
The electronic structures and thermoelectric properties of semiconducting
transition-metal dichalcogenide monolayers (M=Zr, Hf, Mo, W and
Pt; X=S, Se and Te) are investigated by combining first-principles and
Boltzmann transport theory, including spin-orbital coupling (SOC). It is found
that the gap decrease increases from S to Te in each cation group, when the SOC
is opened. The spin-orbital splitting has the same trend with gap reducing.
Calculated results show that SOC has noteworthy detrimental effect on p-type
power factor, while has a negligible influence in n-type doping except W cation
group, which can be understood by considering the effects of SOC on the valence
and conduction bands. For (X=S, Se and Te), the SOC leads to
observably enhanced power factor in n-type doping, which can be explained by
SOC-induced band degeneracy, namely bands converge. Among all cation groups, Pt
cation group shows the highest Seebeck coefficient, which leads to best power
factor, if we assume scattering time is fixed. Calculated results show that
(M=Zr, Hf, Mo, W and Pt) have best p-type power factor for all
cation groups, and that (M=Zr and Hf), and
(M=Mo and Pt) have more wonderful n-type power factor in
respective cation group. Therefore, these results may be useful for further
theoretical prediction or experimental search of excellent thermoelectric
materials from semiconducting transition-metal dichalcogenide monolayers.Comment: 8 pages, 8 figure
Systematic study of decay for isomer related nuclei within a two-potential approach
decay occurs in both ground states and isomers of nuclei. In this
work, we use the two-potential approach to systematically study whether
isomeric states play a key role on particle clustering or not. The
results indicate the ratios of decay preformation probabilities of
isomers to ground states are found to be around 1
Systematic study of decay half-lives for even-even nuclei within a two-potential approach
decay is a common and important process for natural radioactivity of
heavy and superheavy nuclei. The decay half-lives for even-even nuclei
from Z=62 to Z=118 are systematically researched based on the two-potential
approach with a quasi-stationary state approximation. To describe the
deviations between experimental half-lives and calculated results due to the
nuclear shell structure, a hindrance factor related with particle
preformation probability is introduced. Our results can well reproduce the
experimental data equally to the density-dependent cluster model and the
generalized liquid drop model. We also study the isospin effect of nuclear
potential in this work. Considering the isospin effect the calculated results
improved about 7.3
Noncoherent Multiantenna Receivers for Cognitive Backscatter System with Multiple RF Sources
Cognitive backscattering, an integration of cognitive radio and backsatter
modulation, is emerging as a potential candidate for green Internet of Things
(IoT). In cognitive backscatter systems, the backscatter device (BD) shares not
only the same spectrum, but also the same radio-frequency (RF) source with the
legacy system. In this paper, we investigate the signal transmission problem,
in which a basic transmission model is considered which consists of K RF
sources, one BD and one reader equipped with M antennas. A non-cooperative
scenario is considered, where there is no cooperation between the legacy
systems and the backscatter system, and no pilots are transmitted from the RF
sources or BD to the reader. The on-off keying differential modulation is
adopted to achieve noncoherent transmission. Firstly, through the capacity
analyses, we point out that high-throughput backscatter transmission can be
achieved when the number of the receive antennas satisfies M>K. The Chernoff
Information (CI) is also derived to predict the detection performance. Next, we
address the signal detection problem at the reader. The optimal soft decision
(SD) and suboptimal hard decision (HD) detectors are designed based on the
maximum likelihood criterion. To tackle the non-cooperation challenge, a fully
blind channel estimation method is proposed to learn the detection-required
parameters based on clustering. Extensive numerical results verify the
effectiveness of the proposed detectors and the channel estimation method. In
addition, it is illustrated that the increase of K may not necessarily lead to
performance degradation when multiple receive antennas are exploited.Comment: 12 pages, 10 figure
Further investigation of the relativistic symmetry by similarity renormalization group
Following a recent rapid communications[Phys.Rev.C85,021302(R) (2012)], we
present more details on the investigation of the relativistic symmetry by use
of the similarity renormalization group. By comparing the contributions of the
different components in the diagonal Dirac Hamiltonian to the pseudospin
splitting, we have found that two components of the dynamical term make similar
influence on the pseudospin symmetry. The same case also appears in the
spin-orbit interactions. Further, we have checked the influences of every term
on the pseudospin splitting and their correlations with the potential
parameters for all the available pseudospin partners. The result shows that the
spin-orbit interactions always play a role in favor of the pseudospin symmetry,
and whether the pseudospin symmetry is improved or destroyed by the dynamical
term relating the shape of the potential as well as the quantum numbers of the
state. The cause why the pseudospin symmetry becomes better for the levels
closer to the continuum is disclosed.Comment: 10pages,12figures, accepted by Physical Review
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