OPR

The ability to reproduce a parallel execution is desirable for debugging and program reliability purposes. In debugging (13), the programmer needs to manually step back in time, while for resilience (6) this is automatically performed by the the application upon failure. To be useful, replay has to faithfully reproduce the original execution. For parallel programs the main challenge is inferring and maintaining the order of conflicting operations (data races). Deterministic record and replay (R&R) techniques have been developed for multithreaded shared memory programs (5), as well as distributed memory programs (14). Our main interest is techniques for large scale scientific (3; 4) programming models

Time dependent diffusion in a disordered medium with partially absorbing walls: A perturbative approach

We present an analytical study of the time dependent diffusion coefficient in a dilute suspension of spheres with partially absorbing boundary condition. Following Kirkpatrick (J. Chem. Phys. 76, 4255) we obtain a perturbative expansion for the time dependent particle density using volume fraction $f$ of spheres as an expansion parameter. The exact single particle $t$-operator for partially absorbing boundary condition is used to obtain a closed form time-dependent diffusion coefficient $D(t)$ accurate to first order in the volume fraction $f$. Short and long time limits of $D(t)$ are checked against the known short-time results for partially or fully absorbing boundary conditions and long-time results for reflecting boundary conditions. For fully absorbing boundary condition the long time diffusion coefficient is found to be $D(t)=5 a^2/(12 f D_{0} t) +O((D_0t/a^2)^{-2})$, to the first order of perturbation theory. Here $f$ is small but non-zero, $D_0$ the diffusion coefficient in the absence of spheres, and $a$ the radius of the spheres. The validity of this perturbative result is discussed

An observational study of aerosols and tropical cyclones over the eastern atlantic ocean basin for recent hurricane seasons

The aerosol vertical distribution in the tropical cyclone (TC) main development region (MDR) during the recent active hurricane seasons (2015–2018) was investigated using observations from NASA’s Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Satellite. The Total Attenuated Backscatter (TAB) at 532 nm was measured by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP Lidar) onboard CALIPSO which is a polar orbiting satellite that evaluates the role clouds and atmospheric aerosols play in Earth’s weather, climate and air quality. The TAB was used to illustrate the dispersion and magnitude of the aerosol vertical distribution in the TC-genesis region. A combination of extinction quality flag, cloud fraction, and cloud-aerosol discrimination (CAD) scores were used to filter out the impact of clouds. To better describe the qualitative and quantitative difference of aerosol along the paths of African Easterly Waves (AEWs), the MDR was further divided into two domains from 18◦ W to 30◦ W (Domain 1) and 30◦ W to 45◦ W (Domain 2), respectively. The distribution of average aerosol concentration from the time of active cyclogenesis was compared and quantified between each case. The resulting observations suggest that there are two distinct layers of aerosols in the vertical profile, a near surface layer from 0.5–1.75 km and an upper layer at 1.75–5 km in altitude. A quantification of the total aerosol concentration values indicate domain 2 cases were associated with higher aerosol concentrations than domain 1 cases. The environmental variables such as sea surface temperature (SST), vertical windshear (VWS), and relative humidity (RH) tended to be favorable for genesis to occur. Among all cases in this study, the results suggested tropical cyclone genesis and further development occurred under dust-loaded conditions while the environmental variables were favorable, indicating that dust aerosols may not play a significant role in inhibiting the genesis process of TCs

Similarity-Based Classification in Partially Labeled Networks

We propose a similarity-based method, using the similarity between nodes, to address the problem of classification in partially labeled networks. The basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices, including five local ones and five global ones. Empirical results on the co-purchase network of political books show that the similarity-based method can give high accurate classification even when the labeled nodes are sparse which is one of the difficulties in classification. Furthermore, we find that when the target network has many labeled nodes, the local indices can perform as good as those global indices do, while when the data is sparce the global indices perform better. Besides, the similarity-based method can to some extent overcome the unconsistency problem which is another difficulty in classification.Comment: 13 pages,3 figures,1 tabl