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Motivated by the old idea of using the moon as a resonant gravitational-wave (GW) detector, as well as the recent updates in modeling the lunar response to GWs, we re-evaluate the feasibility of using a network of lunar seismometers to constrain the stochastic GW background (SGWB). In particular, using the updated model of the lunar response, we derive the pattern functions for the two polarizations of GW. With these pattern functions, we further calculate the overlap reduction functions for a network of lunar seismometers, where we have relaxed the conventional assumption that lunar seismometers are perfectly leveled to measure only the vertical acceleration. We apply our calculation to two future lunar projects, namely, Chang'e and the Lunar Gravitational-Wave Antenna (LGWA). We find that the two projects could constrain the SGWB to a level of $\Omega_{\text{GW}}^{\text{Chang'e}} < 2.4 \times 10^{2}$ and $\Omega_{\text{GW}}^{\text{LGWA}} < 2.0 \times 10^{-10}$, respectively, which corresponds to a signal-to-noise ratio of SNR $=3$. These results are better than the constraints placed previously on the SGWB in the mid-frequency band (around $10^{-3}- 10~\text{Hz}$) by various types of experiments., Comment: Accepted for publication in Physical Review D

The recent increasing interest in detecting gravitational waves (GWs) by lunar seismic measurement urges us to have a clear understanding of the response of the moon to passing GWs. In this paper, we clarify the relationship between two seemly different response functions which have been derived previously using two different methods, one taking the field-theory approach and the other using the tidal force induced by GWs. We revisit their derivation and prove, by both analytical arguments and numerical calculations, that the two response functions are equivalent. Their apparent difference can be attributed to the choice of different coordinates. Using the correct response function, we calculate the sensitivities (to GWs) of several designed lunar seismometers, and find that the sensitivity curves between $10^{-3}$ and $0.1$ Hz are much flatter than the previous calculations based on normal-mode model. Our results will help clarifying the scientific objectives of lunar GW observation, as well as provide important constraints on the design of lunar GW detectors., Comment: Accepted for publication in Physical Review D

The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) across numerous downstream NLP tasks. In this era of open-ended language modeling, where task boundaries are gradually fading, an urgent question emerges: have we made significant advances in text classification under the full benefit of LLMs? To answer this question, we propose RGPT, an adaptive boosting framework tailored to produce a specialized text classification LLM by recurrently ensembling a pool of strong base learners. The base learners are constructed by adaptively adjusting the distribution of training samples and iteratively fine-tuning LLMs with them. Such base learners are then ensembled to be a specialized text classification LLM, by recurrently incorporating the historical predictions from the previous learners. Through a comprehensive empirical comparison, we show that RGPT significantly outperforms 8 SOTA PLMs and 7 SOTA LLMs on four benchmarks by 1.36% on average. Further evaluation experiments show a clear surpassing of RGPT over human classification.

Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new vision for the development of NLP. Despite their remarkable performance in natural language generating (NLG), LLMs lack a distinct focus on the emotion understanding domain. As a result, using LLMs for emotion recognition may lead to suboptimal and inadequate precision. Another limitation of LLMs is that they are typical trained without leveraging multi-modal information. To overcome these limitations, we propose DialogueLLM, a context and emotion knowledge tuned LLM that is obtained by fine-tuning LLaMA models with 13,638 multi-modal (i.e., texts and videos) emotional dialogues. The visual information is considered as the supplementary knowledge to construct high-quality instructions. We offer a comprehensive evaluation of our proposed model on three benchmarking emotion recognition in conversations (ERC) datasets and compare the results against the SOTA baselines and other SOTA LLMs. Additionally, DialogueLLM-7B can be easily trained using LoRA on a 40GB A100 GPU in 5 hours, facilitating reproducibility for other researchers.

Acquiring precise information about the mode content of a laser is critical for multiplexed optical communications, optical imaging with active wave-front control, and quantum-limited interferometric measurements. Hologram-based mode decomposition devices, such as spatial light modulators, allow a fast, direct measurement of the mode content, but they have limited precision due to cross-coupling between modes. Here we report the first proof-of-principle demonstration of mode decomposition with a metasurface, resulting in significantly enhanced precision. A mode-weight fluctuation of $6\times 10^{-7}$ was be measured with 1 second of averaging at a Fourier frequency of 80 Hz, an improvement of more than three orders of magnitude compared to the state-of-the-art spatial light modulator decomposition. The improvement is attributable to the reduction in cross-coupling enabled by the exceptional small pixel size of the metasurface. We show a systematic study of the limiting sources of noise, and we show that there is a promising path towards complete mode decomposition with similar precision., Comment: Peer-reviewed version. Accepted for publication in Physical Review A. 6 pages, 8 figures