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Graph Neural Networks (GNNs) have been widely used in various graph-based applications. Recent studies have shown that GNNs are vulnerable to link-level membership inference attacks (LMIA) which can infer whether a given link was included in the training graph of a GNN model. While most of the studies focus on the privacy vulnerability of the links in the entire graph, none have inspected the privacy risk of specific subgroups of links (e.g., links between LGBT users). In this paper, we present the first study of disparity in subgroup vulnerability (DSV) of GNNs against LMIA. First, with extensive empirical evaluation, we demonstrate the existence of non-negligible DSV under various settings of GNN models and input graphs. Second, by both statistical and causal analysis, we identify the difference between three specific graph structural properties of subgroups as one of the underlying reasons for DSV. Among the three properties, the difference between subgroup density has the largest causal effect on DSV. Third, inspired by the causal analysis, we design a new defense mechanism named FairDefense to mitigate DSV while providing protection against LMIA. At a high level, at each iteration of target model training, FairDefense randomizes the membership of edges in the training graph with a given probability, aiming to reduce the gap between the density of different subgroups for DSV mitigation. Our empirical results demonstrate that FairDefense outperforms the existing defense methods in the trade-off between defense and target model accuracy. More importantly, it offers better DSV mitigation.

 

Artificially Expanded Genetic Information Systems (AEGIS) add independently replicable unnatural nucleotide pairs to the natural G:C and A:T/U pairs found in native DNA, joining the unnatural pairs through alternative modes of hydrogen bonding. Whether and how AEGIS pairs are recognized and processed by multi-subunit cellular RNA polymerases (RNAPs) remains unknown. Here, we show thatE. coliRNAP selectively recognizes unnatural nucleobases in a six-letter expanded genetic system. High-resolution cryo-EM structures of three RNAP elongation complexes containing template-substrate UBPs reveal the shared principles behind the recognition of AEGIS and natural base pairs. In these structures, RNAPs are captured in an active state, poised to perform the chemistry step. At this point, the unnatural base pair adopts a Watson-Crick geometry, and the trigger loop is folded into an active conformation, indicating that the mechanistic principles underlying recognition and incorporation of natural base pairs also apply to AEGIS unnatural base pairs. These data validate the design philosophy of AEGIS unnatural basepairs. Further, we provide structural evidence supporting a long-standing hypothesis that pair mismatch during transcription occurs via tautomerization. Together, our work highlights the importance of Watson-Crick complementarity underlying the design principles of AEGIS base pair recognition.

 

Despite extensive research efforts in developing aqueous rechargeable zinc metal batteries (RZMBs) as high-energy-density alternatives to both lithium ion and lithium metal batteries, the commercial prospects for RZMBs are still obfuscated by fundamental scientific questions. In particular, the electrode–electrolyte interphase properties and behaviors are still intensely debated topics in this field. In this review, we provide a comprehensive and thorough overview of the solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI) in aqueous RZMBs, with an emphasis on the formation mechanisms and characteristics of the SEI and CEI. We then summarize state-of-the-art techniques for characterizing the SEI/CEI to reveal the intrinsic correlation between the functionalities of the interphases and the electrochemical performances. Finally, future directions are proposed, including studies on aqueous SEI/CEI evolution as a function of pH and temperature, as well as SEI/CEI studies for high-energy-density and long-lifetime RZMBs. 

Rust is a young systems programming language, but it has gained tremendous popularity thanks to its assurance of memory safety. However, the performance of Rust has been less systematically understood, although many people are claiming that Rust is comparable to C/C++ regarding efficiency. In this paper, we aim to understand the performance of Rust, using C as the baseline. First, we collect a set of micro benchmarks where each program is implemented with both Rust and C. To ensure fairness, we manually validate that the Rust version and the C version implement the identical functionality using the same algorithm. Our measurement based on the micro benchmarks shows that Rust is in general slower than C, but the extent of the slowdown varies across different programs. On average, Rust brings a 1.77x “performance overhead” compared to C. Second, we dissect the root causes of the overhead and unveil that it is primarily incurred by run-time checks inserted by the compiler and restrictions enforced by the language design. With the run-time checks disabled and the restrictions loosened, Rust presents a performance indistinguishable from C. 

In this work, we formulate and solve a new type of approximate nearest neighbor search (ANNS) problems called ANNS after linear transformation (ALT). In ANNS-ALT, we search for the vector (in a dataset) that, after being linearly transformed by a user-specified query matrix, is closest to a query vector. It is a very general mother problem in the sense that a wide range of baby ANNS problems that have important applications in databases and machine learning can be reduced to and solved as ANNS-ALT, or its dual that we call ANNS-ALTD. We propose a novel and computationally efficient solution, called ONe Index for All Kernels (ONIAK), to ANNS-ALT and all its baby problems when the data dimension d is not too large (say d ≤ 200). In ONIAK, a universal index is built, once and for all, for answering all future ANNS-ALT queries that can have distinct query matrices. We show by experiments that, when d is not too large, ONIAK has better query performance than linear scan on the mother problem (of ANNS-ALT), and has query performances comparable to those of the state-of-the-art solutions on the baby problems. However, the algorithmic technique behind this universal index approach suffers from a so-called dimension blowup problem that can make the indexing time prohibitively long for a large dataset. We propose a novel algorithmic technique, called fast GOE quadratic form (FGoeQF), that completely solves the (prohibitively long indexing time) fallout of the dimension blowup problem. We also propose a Johnson-Lindenstrauss transform (JLT) based ANNS-ALT (and ANNS-ALTD) solution that significantly outperforms any competitor when d is large. 

The purpose of this study is to diagnose mesoscale factors responsible for the formation and development of an extreme rainstorm that occurred on 20 July 2021 in Zhengzhou, China. The rainstorm produced 201.9 mm of rainfall in 1 h, breaking the record of mainland China for 1-h rainfall accumulation in the past 73 years. Using 2-km continuously cycled analyses with 6-min updates that were produced by assimilating observations from radar and dense surface networks with a four-dimensional variational (4DVar) data assimilation system, we illustrate that the modification of environmental easterlies by three mesoscale disturbances played a critical role in the development of the rainstorm. Among the three systems, a mesobeta-scale low pressure system (mesolow) that developed from an inverted trough southwest of Zhengzhou was key to the formation and intensification of the rainstorm. We show that the rainstorm formed via sequential merging of three convective cells, which initiated along the convergence bands in the mesolow. Further, we present evidence to suggest that the mesolow and two terrain-influenced flows near the Taihang Mountains north of Zhengzhou, including a barrier jet and a downslope flow, contributed to the local intensification of the rainstorm and the intense 1-h rainfall. The three mesoscale features coexisted near Zhengzhou in the several hours before the extreme 1-h rainfall and enhanced local wind convergence and moisture transport synergistically. Our analysis also indicated that the strong midlevel south/southwesterly winds from the mesolow along with the gravity-current-modified low-level northeasterly barrier jet enhanced the vertical wind shear, which provided favorable local environment supporting the severe rainstorm.

 

Switching of perpendicular magnetization via spin–orbit torque (SOT) is of particular interest in the development of non-volatile magnetic random access memory (MRAM) devices. We studied current-induced magnetization switching of Ir/GdFeCo/Cu/Pt heterostructures in a Hall cross geometry as a function of the in-plane applied magnetic field. Remarkably, magnetization switching is observed at zero applied field. This is shown to result from the competition between SOT, the Oersted field generated by the charge current, and the material's coercivity. Our results show a means of achieving zero-field switching that can impact the design of future spintronics devices, such as SOT-MRAM.