Potential thermoelectric materials CsMI3\mathrm{CsMI_3} (M=Sn and Pb) in perovskite structures from the first-principles calculations

The thermoelectric properties of halide perovskites $\mathrm{CsMI_3}$ (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 $\mathrm{CsSnI_3}$ and $\mathrm{CsPbI_3}$, and the corresponding room-temperature lattice thermal conductivity is 0.54 $\mathrm{W m^{-1} K^{-1}}$ and 0.25 $\mathrm{W m^{-1} K^{-1}}$. At 1000 K, the maximal figure of merit $ZT$ is up to 0.63 and 0.64 for $\mathrm{CsSnI_3}$ and $\mathrm{CsPbI_3}$ with scattering time $\tau$=$10^{-14}$ s, and the peak $ZT$ is 0.49 and 0.41 with $\tau$=$10^{-15}$ s. These results make us believe that $\mathrm{CsMI_3}$ (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 $\mathrm{Mg_2Sn}$ 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 $\Gamma$ 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 $\mathrm{Mg_2Sn}$ 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 $\mathrm{MX_2}$ (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 $\mathrm{WX_2}$ (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 $\mathrm{MS_2}$ (M=Zr, Hf, Mo, W and Pt) have best p-type power factor for all cation groups, and that $\mathrm{MSe_2}$ (M=Zr and Hf), $\mathrm{WS_2}$ and $\mathrm{MTe_2}$ (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 α\alpha decay for isomer related nuclei within a two-potential approach

$\alpha$ 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 $\alpha$ particle clustering or not. The results indicate the ratios of $\alpha$ decay preformation probabilities of isomers to ground states are found to be around 1

Systematic study of α\alpha decay half-lives for even-even nuclei within a two-potential approach

$\alpha$ decay is a common and important process for natural radioactivity of heavy and superheavy nuclei. The $\alpha$ 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 $\alpha$ 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