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Large-scale extreme-ultraviolet (EUV) waves commonly exhibit as single wavefront and are believed to be caused by coronal mass ejections (CMEs). Utilizing high spatiotemporal resolution imaging observations from the Solar Dynamics Observatory, we present two sequentially generated wave trains originating from the same active region: a narrow quasiperiodic fast-propagating (QFP) wave train that propagates along the coronal loop system above the jet and a broad QFP wave train that travels along the solar surface beneath the jet. The measurements indicate that the narrow QFP wave train and the accompanying flare's quasiperiodic pulsations (QPPs) have nearly identical onsets and periods. This result suggests that the accompanying flare process excites the observed narrow QFP wave train. However, the broad QFP wave train starts approximately 2 minutes before the QPPs of the flare, but consistent with the interaction between the unwinding jet and the solar surface. Moreover, we find that the \zx{period of the broad QFP wave train, approximately 130\,s, closely matches that of the unwinding jet}. This period is significantly longer than the 30\,s period of the accompanying flare's QPPs. Based on these findings, we propose that the intermittent energy release of the accompanying flare excited the narrow QFP wave train confined propagating in the coronal loop system. The unwinding jet, rather than the intermittent energy release in the accompanying flare, triggered the broad QFP wave train propagating along the solar surface.

In this manuscript, we investigate a $(p, q)$-Schr\"{o}dinger-Kirchhoff equation involving a continuous positive potential that meets the del Pino-Felmer type conditions. Using Recceri's classical variational approach, we prove the existence of three weak solutions., Comment: 15 pages, 0 figures

Reinforcement Learning from Human Feedback (RLHF) has enabled significant advancements within language modeling for powerful, instruction-following models. However, the alignment of these models remains a pressing challenge as the policy tends to overfit the learned ``proxy" reward model past an inflection point of utility as measured by a ``gold" reward model that is more performant -- a phenomenon known as overoptimisation. Prior work has mitigated this issue by computing a pessimistic statistic over an ensemble of reward models, which is common in Offline Reinforcement Learning but incredibly costly for language models with high memory requirements, making such approaches infeasible for sufficiently large models. To this end, we propose using a shared encoder but separate linear heads. We find this leads to similar performance as the full ensemble while allowing tremendous savings in memory and time required for training for models of similar size.

This paper presents three distinct wave trains that occurred on 2023 April 21: a broad quasi-periodic fast-propagating (QFP) wave train and a bi-directional narrow QFP wave train. The broad QFP wave train expands outward in a circular wavefront, while bi-directional narrow QFP wave trains propagate in the northward and southward directions, respectively. The concurrent presence of the wave trains offers a remarkable opportunity to investigate their respective triggering mechanisms. Measurement shows that the broad QFP wave train's speed is 300- 1100 km/s in different propagating directions. There is a significant difference in the speed of the bi-directional narrow QFP wave trains: the southward propagation achieves 1400 km/s, while the northward propagation only reaches about 550 km/s accompanied by a deceleration of about 1- 2 kms-2. Using the wavelet analysis, we find that the periodicity of the propagating wave trains in the southward and northward directions closely matches the quasi-periodic pulsations (QPPs) exhibited by the flares. Based on these results, the narrow QFP wave trains were most likely excited by the intermittent energy release in the accompanying flare. In contrast, the broad QFP wave train had a tight relationship with the erupting filament, probably attributed to the unwinding motion of the erupting filament or the leakage of the fast sausage wave train inside the filament body., Comment: 11 pages, 5 figures, accepted by ApJ

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark.

Double-decker filaments and their eruptions have been widely observed in recent years, but their physical formation mechanism is still unclear. Using high spatiotemporal resolution, multi-wavelength observations taken by the New Vacuum Solar Telescope and the Solar Dynamics Observatory, we show the formation of a double-decker pair of flux rope system by two successive tether-cutting eruptions in a bipolar active region. Due to the combined effect of photospheric shearing and convergence motions around the active region's polarity inversion line (PIL), the arms of two overlapping inverse-S-shaped short filaments reconnected at their intersection, which created a simultaneous upward-moving magnetic flux rope (MFR) and a downward-moving post-flare-loop (PFL) system striding the PIL. Meanwhile, four bright flare ribbons appeared at the footpoints of the newly formed MFR and the PFL. As the MFR rose, two elongated flare ribbons connected by a relatively larger PFL appeared on either side of the PIL. After a few minutes, another MFR formed in the same way at the same location and then erupted in the same direction as the first one. Detailed observational results suggest that the eruption of the first MFR might experienced a short pause before its successful eruption, while the second MFR was a failed eruption. This implies that the two newly formed MFRs might reach a new equilibrium at relatively higher heights for a while, which can be regarded as a transient double-decker flux rope system. The observations can well be explained by the tether-cutting model, and we propose that two successive confined tether-cutting eruptions can naturally produce a double-decker flux rope system, especially when the background coronal magnetic field has a saddle-like distribution of magnetic decay index profile in height., Comment: 18 pages, 6 figures, accepted by the ApJ

The excitation mechanism of coronal quasi-period fast-propagating (QFP) wave trains remains unresolved. Using Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory observations, we study a narrow and a broad QFP wave train excited one after another during the successive eruptions of filaments hosted within a fan-spine magnetic system on 2013 October 20. The consecutive occurrence of these two types of QFP wave trains in the same event provides an excellent opportunity to explore their excitation mechanisms and compare their physical parameters. Our observational results reveal that narrow and broad QFP wave trains exhibit distinct speeds, periods, energy fluxes, and relative intensity amplitudes, although originating from the same active region and being associated with the same {\em GOES} C2.9 flare. Using wavelet analysis, we find that the narrow QFP wave train shares a similar period with the flare itself, suggesting its possible excitation through the pulsed energy release in the magnetic reconnection process that generated the accompanying flare. On the other hand, the broad QFP wave train appears to be associated with the energy pulses released by the successive expansion and unwinding of filament threads. Additionally, it is plausible that the broad QFP wave train was also excited by the sequential stretching of closed magnetic field lines driven by the erupting filament. These findings shed light on the different excitation mechanisms and origins of the QFP wave trains., Comment: 11 pages, 4 figures, and 1 table; accepted by the ScChG

In the domain of intelligent transportation systems (ITS), collaborative perception has emerged as a promising approach to overcome the limitations of individual perception by enabling multiple agents to exchange information, thus enhancing their situational awareness. Collaborative perception overcomes the limitations of individual sensors, allowing connected agents to perceive environments beyond their line-of-sight and field of view. However, the reliability of collaborative perception heavily depends on the data aggregation strategy and communication bandwidth, which must overcome the challenges posed by limited network resources. To improve the precision of object detection and alleviate limited network resources, we propose an intermediate collaborative perception solution in the form of a graph attention network (GAT). The proposed approach develops an attention-based aggregation strategy to fuse intermediate representations exchanged among multiple connected agents. This approach adaptively highlights important regions in the intermediate feature maps at both the channel and spatial levels, resulting in improved object detection precision. We propose a feature fusion scheme using attention-based architectures and evaluate the results quantitatively in comparison to other state-of-the-art collaborative perception approaches. Our proposed approach is validated using the V2XSim dataset. The results of this work demonstrate the efficacy of the proposed approach for intermediate collaborative perception in improving object detection average precision while reducing network resource usage.