LaSrVMoO6_6: A case study for AA-site covalency-driven local cationic order in double perovskites

An unusual atomic scale chemical fluctuation in LaSrVMoO$_6$, in terms of narrow patches of La,V and Sr,Mo-rich phases, has been probed in detail to understand the origin of such a chemical state. Exhaustive tuning of the equilibrium synthesis parameters showed that the extent of phase separation can never be melted down below an unit cell dimension making it impossible to achieve the conventional $B$-site ordered structure, which establishes that the observed `inhomogeneous' patch-like structure with minimum dimension of few angstroms is a reality in LaSrVMoO$_6$. Therefore, another type of local chemical order, hitherto unknown in double perovskites, gets introduced here. X-ray diffraction, electron microscopy elemental mapping, magnetic, and various spectroscopic studies have been carried out on samples, synthesized under different conditions. These experimental results in conjunction with {\it ab-initio} electronic structure calculation revealed that it is the energy stability, gained by typical La-O covalency as in LaVO$_3$, that leads to the preferential La,V and Sr,Mo ionic proximity, and the consequent patchy structure.Comment: 21 pages, 7 figure

Parameterized Complexity of Perfectly Matched Sets

For an undirected graph G, a pair of vertex disjoint subsets (A, B) is a pair of perfectly matched sets if each vertex in A (resp. B) has exactly one neighbor in B (resp. A). In the above, the size of the pair is |A| (= |B|). Given a graph G and a positive integer k, the Perfectly Matched Sets problem asks whether there exists a pair of perfectly matched sets of size at least k in G. This problem is known to be NP-hard on planar graphs and W[1]-hard on general graphs, when parameterized by k. However, little is known about the parameterized complexity of the problem in restricted graph classes. In this work, we study the problem parameterized by k, and design FPT algorithms for: i) apex-minor-free graphs running in time 2^O(?k)? n^O(1), and ii) K_{b,b}-free graphs. We obtain a linear kernel for planar graphs and k^?(d)-sized kernel for d-degenerate graphs. It is known that the problem is W[1]-hard on chordal graphs, in fact on split graphs, parameterized by k. We complement this hardness result by designing a polynomial-time algorithm for interval graphs

Fine Grained Dataflow Tracking with Proximal Gradients

Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel information leak detection. However, DTA is fundamentally limited by the Boolean nature of taint labels, which provide no information about the significance of detected dataflows and lead to false positives/negatives on complex real world programs. We introduce proximal gradient analysis (PGA), a novel, theoretically grounded approach that can track more accurate and fine-grained dataflow information. PGA uses proximal gradients, a generalization of gradients for non-differentiable functions, to precisely compose gradients over non-differentiable operations in programs. Composing gradients over programs eliminates many of the dataflow propagation errors that occur in DTA and provides richer information about how each measured dataflow effects a program. We compare our prototype PGA implementation to three state of the art DTA implementations on 7 real-world programs. Our results show that PGA can improve the F1 accuracy of data flow tracking by up to 33% over taint tracking (20% on average) without introducing any significant overhead (<5% on average). We further demonstrate the effectiveness of PGA by discovering 22 bugs (20 confirmed by developers) and 2 side-channel leaks, and identifying exploitable dataflows in 19 existing CVEs in the tested programs.Comment: To appear in USENIX Security 202

Regional Impacts of COVID-19 on Carbon Dioxide Detected Worldwide from Space

Activity reductions in early 2020 due to the Coronavirus Disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO2) emissions. Despite their record size, the resulting atmospheric signals are smaller than and obscured by climate variability in atmospheric transport and biospheric fluxes, notably that related to the 2019-2020 Indian Ocean Dipole. Monitoring CO2 anomalies and distinguishing human and climatic causes thus remains a new frontier in Earth system science. We show, for the first time, that the impact of short-term, regional changes in fossil fuel emissions on CO2 concentrations was observable from space. Starting in February and continuing through May, column CO2 over many of the World's largest emitting regions was 0.14 to 0.62 parts per million less than expected in a pandemic-free scenario, consistent with reductions of 3 to 13 percent in annual, global emissions. Current spaceborne technologies are therefore approaching levels of accuracy and precision needed to support climate mitigation strategies with future missions expected to meet those needs