Magnetic Domain Structure in Ferromagnetic Kagome Metal DyMn6Sn6

Two types of magnetic domains, that is, type-I domain belt domain and type-II new stripe domain, are observed in a kagome metal DyMn6Sn6 by microscopic magneto-optic Kerr imaging technique. From 255 to 235 K, the spin reorientation is observed directly in DyMn6Sn6. We analyze the structure of two types of domains through brightness distribution of the images. The type-II domain exists from 235 to 160 K by zero-field cooling (ZFC). At the same time, type-I domain and type-II domain coexist and transform into each other with variation of temperature. Type-II domains can easily transform into type-I domains when the temperature and magnetic field changes, and this process is irreversible. These results demonstrate that the type-I domain is more stable than the type-II domain. The phase diagram of magnetic domains in DyMn6Sn6 is obtained

PaLM: Scaling Language Modeling with Pathways

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies

Optimal Planning of Active Distribution Network Based on Soft Open Point and Shunt Capacitors

As the penetration of distributed wind power in the distribution network continues to increase, the uncertainty of its output has a serious impact on the stable operation of the distribution network. It is difficult to meet the voltage regulation requirements when the wind power fluctuates frequently only by relying on shunt capacitors. Therefore, a coordinated optimization planning method based on soft open point (SOP) and shunt capacitors is proposed. Firstly, the bidirectional generative adversarial network (BIGAN) is used to characterize the uncertainty of wind power output and generate typical scenarios of wind power output. Secondly, a multi-objective optimization planning model of SOP and shunt capacitors is proposed based on the scene analysis method; Then, a solution strategy based on the improved elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed. Finally, the proposed planning model and solution are verified and analyzed in the improved IEEE 33-bus system

Corrosion behavior of high-performance crystalline CuCrZr/amorphous CuZrAl composites in NaCl solution

The corrosion mechanism of Cu-based crystalline/amorphous composites in NaCl solution is investigated. xCuCrZr/(1–x)CuZrAl (x = 10, 50, 90 wt%) crystalline/amorphous composites with excellent interfacial bonding are fabricated by ball milling and spark plasma sintering (SPS) processes. According to the findings of electrochemical testing and immersion experiments, composites containing 50% amorphous CuZrAl exhibit the lowest corrosion rate. In-depth behavioral analysis reveals that uniform corrosion dominates the crystalline CuCrZr phase, while pitting corrosion dominates the amorphous CuZrAl phase. The corrosion model of the composites is constructed to disclose corrosion processes at various phases as well as the kinetic mechanisms that govern individuals

IMF: Interpretable Multi-Hop Forecasting on Temporal Knowledge Graphs

Temporal knowledge graphs (KGs) have recently attracted increasing attention. The temporal KG forecasting task, which plays a crucial role in such applications as event prediction, predicts future links based on historical facts. However, current studies pay scant attention to the following two aspects. First, the interpretability of current models is manifested in providing reasoning paths, which is an essential property of path-based models. However, the comparison of reasoning paths in these models is operated in a black-box fashion. Moreover, contemporary models utilize separate networks to evaluate paths at different hops. Although the network for each hop has the same architecture, each network achieves different parameters for better performance. Different parameters cause identical semantics to have different scores, so models cannot measure identical semantics at different hops equally. Inspired by the observation that reasoning based on multi-hop paths is akin to answering questions step by step, this paper designs an Interpretable Multi-Hop Reasoning (IMR) framework based on consistent basic models for temporal KG forecasting. IMR transforms reasoning based on path searching into stepwise question answering. In addition, IMR develops three indicators according to the characteristics of temporal KGs and reasoning paths: the question matching degree, answer completion level, and path confidence. IMR can uniformly integrate paths of different hops according to the same criteria; IMR can provide the reasoning paths similarly to other interpretable models and further explain the basis for path comparison. We instantiate the framework based on common embedding models such as TransE, RotatE, and ComplEx. While being more explainable, these instantiated models achieve state-of-the-art performance against previous models on four baseline datasets

Genetic features of livestock-associated <i>Staphylococcus aureus</i> ST9 isolates from Chinese pigs that carry the lsa(E) gene for quinupristin/dalfopristin resistance

Whole-genome sequencing (WGS) was used to investigate the genetic features of the recently identified lsa(E) gene in porcine S. aureus ST9 isolates. Three quinupristin/dalfopristin-resistant isolates harboring the lsa(E) gene (two MRSA and one MSSA) were sequenced. Phylogenetic analysis of 184S. aureus genomes showed that ST9 porcine isolates belong to a distinct sequence cluster. Further analysis showed that all isolates were deficient in the recently described type IV restriction-modification system and SCCmec type XII was identified in the two MRSA isolates, which included a rare class C2 mec gene complex. A 24kb ΨSCC fragment was found in the MRSA and MSSA isolates sharing 99% nucleotide sequence homology with the ΨSCCJCSC6690 (O-2) element of a ST9 MRSA isolate from Thailand (accession number AB705453). Comparison of these ST9 isolates with 181 publically available S. aureus genomes identified 24 genes present in all (100%) ST9 isolates, that were absent from the most closely related human isolate. Our analysis suggests that the sequenced quinupristin/dalfopristin-resistant ST9 lineage represent a reservoir of mobile genetic elements associated with resistance and virulence features.</p