Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD algorithms either rely on restricted assumptions or fail to exploit environment information in training data. In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs. We further develop an adversarial training strategy to jointly optimize these two properties for casual subgraph discovery with theoretical guarantees. Extensive experiments and analysis show that LECI significantly outperforms prior methods on both synthetic and real-world datasets, establishing LECI as a practical and effective solution for graph OOD generalization

Characterization of PM2.5 Mass Concentration in the Onshore of Sanya, China

Numbers of real-time data (E-BAM) of PM2.5 were collected in the period from Jan 8th 2012 to Jan 1st 2013 at the laboratory of Tropical Ocean University (Sanya, China). The average mass concentration was 19.7 μg/m³. The highest 40.5 μg/m³ in October compared to the lowest 14.1 μg/m³ in July. From a seasonal perspective, the average PM2.5 mass concentration in fall and winter are relatively higher than that in both spring and summer. On the basis of satellite map of fire points and backward trajectories of the air masses, we primarily deduced that the PM2.5 in Sanya may be caused by the biomass burning and industrial pollutants from the area of Pearl River Delta of China and the Indo-China peninsula (e.g. Vietnam, Laos)

Formation Selection Criteria for Volume Fracturing in Chang 7 Tight Reservoir in the Ordos Basin

The Ordos basin possesses abundance of tight oil and it has huge commercial potential. Volume fracturing is an effective means for the exploitation of tight oil which is a significant impact by geological conditions. So far, the formation selection criteria targeting on volume fracturing of Chang 7 tight reservoir in the Ordos basin have not been established. This paper combined the experiments of rock mechanics and the fracturing simulating software Meyer, and built the selection criteria of Chang 7 tight reservoir in terms of the horizontal stress difference, the brittle index and the natural fractures. It is shown that the horizontal stress difference in Chang 7 tight reservoir is 6-12 MPa, the brittle index based on elastic parameters is 30-48, with strikingly regional natural fracture growth. The fracture geometry induced by volume fracturing is affected by the horizontal stress difference, the brittle index and the natural fracture numbers together. Complex fracture networks are likely to form in a highly naturally fractured section with the in-situ stress difference below 8 MPa and the brittle index over 40. The selection criteria is adequate for optimum formation selection and it has guide function in fieldwork which proved by field practice

Offline Equilibrium Finding

Offline reinforcement learning (Offline RL) is an emerging field that has recently begun gaining attention across various application domains due to its ability to learn behavior from earlier collected datasets. Using logged data is imperative when further interaction with the environment is expensive (computationally or otherwise), unsafe, or entirely unfeasible. Offline RL proved very successful, paving a path to solving previously intractable real-world problems, and we aim to generalize this paradigm to a multi-agent or multiplayer-game setting. Very little research has been done in this area, as the progress is hindered by the lack of standardized datasets and meaningful benchmarks. In this work, we coin the term offline equilibrium finding (OEF) to describe this area and construct multiple datasets consisting of strategies collected across a wide range of games using several established methods. We also propose a benchmark method -- an amalgamation of a behavior-cloning and a model-based algorithm. Our two model-based algorithms -- OEF-PSRO and OEF-CFR -- are adaptations of the widely-used equilibrium finding algorithms Deep CFR and PSRO in the context of offline learning. In the empirical part, we evaluate the performance of the benchmark algorithms on the constructed datasets. We hope that our efforts may help to accelerate research in large-scale equilibrium finding. Datasets and code are available at https://github.com/SecurityGames/oef

Pulsed ultrasound-modulated optical tomography using spectral hole-burning

We present a novel optical quantum sensor using spectral hole-burning for detecting signals in ultrasound-modulated optical tomography. In this technique, we utilize the capability of sub-MHz spectral filtering afforded by a spectral hole burning crystal to select the desired spectral component from the ultrasound-modulated diffuse light. This technique is capable of providing a large etendue, processing a large number of speckles in parallel, tolerating speckle decorrelation, and imaging in real-time. Experimental results are presented

Three odorant-binding proteins are involved in the behavioral response of Sogatella furcifera to rice plant volatiles

Plant volatiles play an important role in regulating insect behavior. Odorant binding proteins (OBPs) are involved in the first step of the olfactory signal transduction pathway and plant volatiles recognition. Sogatella furcifera is one of the most destructive pests of rice crops. Understanding the functions of S. furcifera OBPs (SfurOBPs) in the host plant location and the behavioral responses of S. furcifera to rice plant volatiles could lead to improved, more environmentally-friendly, methods for controlling this pest. We found that SfurOBP1 displayed only weak binding with all the tested volatiles. SfurOBP2, SfurOBP3 and SfurOBP11 had different binding affinities to β-ionone. SfurOBP2 and SfurOBP11 had strong binding affinities to β-caryophyllene (Ki = 2.23 µM) and plant alcohol (Ki = 2.98 µM), respectively. The results of Y-olfactometer experiments indicate that S. furcifera was significantly repelled by octanal and n-octane but strongly attracted by (+)-limonene, acetophenone, 2-heptanone, n-hendecane, α-farnesene and β-ionone. Furthermore, the dsRNA-mediated gene silencing of SfurOBP2, SfurOBP3 and SfurOBP11 shifted the olfactory behavior of S. furcifera for β-ionone, α-farnesene and plant alcohol, respectively. These results suggest that the SfurOBPs are involved in the recognition of rice plant volatiles, and several potential repellants and lures for controlling this pest

1-Methyl­hydrazinium picrate

In the title salt, CH7N2 +·C6H2N3O7 −, the dihedral angles between the three nitro groups and the plane of the benzene ring are 22.4 (2), 35.3 (2) and 2.8 (2)°. In the crystal, the components are linked by N—H⋯O and N—H⋯N hydrogen bonds into a two-dimensional network parallel to (10)