GraLSP: Graph Neural Networks with Local Structural Patterns

It is not until recently that graph neural networks (GNNs) are adopted to perform graph representation learning, among which, those based on the aggregation of features within the neighborhood of a node achieved great success. However, despite such achievements, GNNs illustrate defects in identifying some common structural patterns which, unfortunately, play significant roles in various network phenomena. In this paper, we propose GraLSP, a GNN framework which explicitly incorporates local structural patterns into the neighborhood aggregation through random anonymous walks. Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns. The walks are then fed into the feature aggregation, where we design various mechanisms to address the impact of structural features, including adaptive receptive radius, attention and amplification. In addition, we design objectives that capture similarities between structures and are optimized jointly with node proximity objectives. With the adequate leverage of structural patterns, our model is able to outperform competitive counterparts in various prediction tasks in multiple datasets

VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing

Vertical Federated Learning (VFL) has emerged as one of the most predominant approaches for secure collaborative machine learning where the training data is partitioned by features among multiple parties. Most VFL algorithms primarily rely on two fundamental privacy-preserving techniques: Homomorphic Encryption (HE) and secure Multi-Party Computation (MPC). Though generally considered with stronger privacy guarantees, existing general-purpose MPC frameworks suffer from expensive computation and communication overhead and are inefficient especially under VFL settings. This study centers around MPC-based VFL algorithms and presents a novel approach for two-party vertical federated linear models via an efficient secret sharing (SS) scheme with a trusted coordinator. Our approach can achieve significant acceleration of the training procedure in vertical federated linear models of between 2.5x and 6.6x than other existing MPC frameworks under the same security setting

Palladium, platinum, selenium and tellurium enrichment in the Jiguanzui-Taohuazui Cu-Au Deposit, Edong Ore District: Distribution and comparison with Cu-Mo deposits

The Jiguanzui-Taohuazui Cu-Au deposit is located in the Edong ore district, Middle–Lower Yangtze River metallogenic belt, eastern China. The deposit is palladium, platinum, selenium and tellurium enriched; however, the distribution of these metals is unclear. Three mineral assemblages of ore in the deposit have been identified, namely: a magnetite-bornite-chalcopyrite-(hematite) assemblage (Mt-Bn-Cp-Hm), a chalcopyrite-pyrite assemblage (Cp-Py), and a pyrite-chalcopyrite-(sphalerite) assemblage (Py-Cp-Sph). Forty-eight bulk ore assay results show high concentrations of up to 66.9 ppb for Pd, 5.9 ppb for Pt, 150 ppm for Se and 249 ppm for Te. The high temperature Mt-Bn-Cp-Hm assemblage (530–380 °C) is enriched in Pt and Pd, whereas the Py-Cp-Sph assemblage in the marble-replacement ore (300–220 °C) hosts the major Se and Te mineralization. Palladium, Pt, and Se are mostly hosted in sulfide minerals, whereas Te is hosted in tellurides and Bi-Te-S sulfosalt minerals. Building on previous experimental and thermodynamic calculations, we propose the major controls on the Pd and Pt distribution in the deposit are temperature and salinity, whereas the Se and Te mineralization is promoted by the precipitation of major sulfide phases such as pyrite, chalcopyrite and sphalerite. A comparison of the ores from the Jiguanzui-Taohuazui Cu-Au and Tongshankou Cu-Mo deposits in the Edong ore district shows that the Cu-Au deposit has higher PGE and Te, but similar Se concentrations. This scenario is consistent with the average grades and bulk ore contents of these elements from global (oxidized) porphyry (±skarn) Cu deposits. This suggests that the saturation of magmatic sulfides in the magma chamber as a result of higher proportion of crustal S-rich and/or reduced material contamination can be detrimental for PGE and Te enrichment processes, and thus, Cu-Au porphyry (±skarn) deposits have more potential for higher Pd and Te concentrations than the Cu-Mo deposits