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Dust associated with various stellar sources in galaxies at all cosmic epochs remains a controversial topic, particularly whether supernovae (SNe) play an important role in dust production. We report evidence of dust formation in the cold, dense shell behind the ejecta-circumstellar medium (CSM) interaction in the Type Ia-CSM SN 2018evt three years after the explosion, characterized by a rise in the mid-infrared (MIR) emission accompanied by an accelerated decline in the optical radiation of the SN. Such a dust-formation picture is also corroborated by the concurrent evolution of the profiles of the Ha emission line. Our model suggests enhanced CSM dust concentration at increasing distances from the SN as compared to what can be expected from the density profile of the mass loss from a steady stellar wind. By the time of the last MIR observations at day +1041, a total amount of 1.2+-0.2x10^{-2} Msun of new dust has been formed by SN 2018evt, making SN 2018evt one of the most prolific dust factories among SNe with evidence of dust formation. The unprecedented witness of the intense production procedure of dust may shed light on the perceptions of dust formation in cosmic history., Comment: Accepted by Nature Astronomy, 6 main figures, 7 extended figures, and 2 extended tables

Precision measurement of magnetic fields is an important goal for fundamental science and practical sensing technology. Sensitive detection of a vector magnetic field is a crucial issue in quantum magnetometry, it remains a challenge to estimate a vector DC magnetic field with high efficiency and high precision. Here, we propose a general protocol for quantum vector DC magnetometry via selective phase accumulation of both non-entangled and entangled quantum probes. Based upon the Ramsey interferometry, our protocol may achieve selective phase accumulation of only one magnetic field component by inserting well-designed pulse sequence. In the parallel scheme, three parallel quantum interferometries are utilized to estimate three magnetic field components independently.In the sequential scheme, by applying a pulse sequence along different directions, three magnetic field components can be estimated simultaneously via only one quantum interferometry. In particular, if the input state is an entangled state such as the Greenberger-Horne-Zeilinger state, the measurement precisions of all three components may approach the Heisenberg limit. Our study not only develops a general protocol for measuring vector magnetic fields via quantum probes, but also provides a feasible way to achieve Heisenberg-limited multi-parameter estimation via many-body quantum entanglement.

The digitalization of energy sectors has expanded the coding responsibilities for power engineers and researchers. This research article explores the potential of leveraging Large Language Models (LLMs) to alleviate this burden. Here, we propose LLM-based frameworks for different programming tasks in power systems. For well-defined and routine tasks like the classic unit commitment (UC) problem, we deploy an end-to-end framework to systematically assesses four leading LLMs-ChatGPT 3.5, ChatGPT 4.0, Claude and Google Bard in terms of success rate, consistency, and robustness. For complex tasks with limited prior knowledge, we propose a human-in-the-loop framework to enable engineers and LLMs to collaboratively solve the problem through interactive-learning of method recommendation, problem de-composition, subtask programming and synthesis. Through a comparative study between two frameworks, we find that human-in-the-loop features like web access, problem decomposition with field knowledge and human-assisted code synthesis are essential as LLMs currently still fall short in acquiring cutting-edge and domain-specific knowledge to complete a holistic problem-solving project.

Quantum lock-in amplifier aims to extract an alternating signal within strong noise background by using quantum strategy. However, as the target signal usually has an unknown initial phase, we can't obtain the complete information of its amplitude, frequency and phase in a single lock-in measurement. Here, to overcome this challenge, we give a general protocol for achieving a quantum double lock-in amplifier and illustrate its realization. In analog to a classical double lock-in amplifier, our protocol is accomplished via two quantum mixers under orthogonal pulse sequences. The two orthogonal pulse sequences act the roles of two orthogonal reference signals in a classical double lock-in amplifier. Combining the output signals, the complete characteristics of the target signal can be obtained. As an example, we illustrate the realization of our quantum double lock-in amplifier via a five-level double-$\Lambda$ coherent population trapping system with $^{87}$Rb atoms, in which each $\Lambda$ structure acts as a quantum mixer and the two applied dynamical decoupling sequences take the roles of two orthogonal reference signals. Our numerical calculations show that the quantum double lock-in amplifier is robust against experimental imperfections, such as finite pulse length and stochastic noise. Our study opens an avenue for extracting complete characteristics of an alternating signal within strong noise background, which is beneficial for developing practical quantum sensing technologies.

Despite the prevalence and many successes of deep learning applications in de novo molecular design, the problem of peptide generation targeting specific proteins remains unsolved. A main barrier for this is the scarcity of the high-quality training data. To tackle the issue, we propose a novel machine learning based peptide design architecture, called Latent Space Approximate Trajectory Collector (LSATC). It consists of a series of samplers on an optimization trajectory on a highly non-convex energy landscape that approximates the distributions of peptides with desired properties in a latent space. The process involves little human intervention and can be implemented in an end-to-end manner. We demonstrate the model by the design of peptide extensions targeting Beta-catenin, a key nuclear effector protein involved in canonical Wnt signalling. When compared with a random sampler, LSATC can sample peptides with $36\%$ lower binding scores in a $16$ times smaller interquartile range (IQR) and $284\%$ less hydrophobicity with a $1.4$ times smaller IQR. LSATC also largely outperforms other common generative models. Finally, we utilized a clustering algorithm to select 4 peptides from the 100 LSATC designed peptides for experimental validation. The result confirms that all the four peptides extended by LSATC show improved Beta-catenin binding by at least $20.0\%$, and two of the peptides show a $3$ fold increase in binding affinity as compared to the base peptide.

Atomic spectroscopy, an essential tool for frequency estimation, is widely used in quantum sensing. Atom-atom interaction can be used to generate entanglement for achieving quantum enhanced sensing. However, atom-atom interaction always induces collision shift, which brings systematic error in determining the resonance frequency. Contradiction between utilizing atom-atom interaction and suppressing collision shift generally exists in atomic spectroscopy. Here, we propose an antisymmetric Rabi spectroscopy protocol without collision shift in the presence of atom-atom interactions. We analytically find that the antisymmetric point can be used for determining the resonance frequency. For small Rabi frequency, our antisymmetric Rabi spectroscopy with slight atom-atom interaction can provide better measurement precision than the conventional Rabi spectroscopy. With stronger atom-atom interaction and Rabi frequency, the spectrum resolution can be dramatically improved and the measurement precision may even beat the standard quantum limit. Moreover, unlike the quantum-enhanced Ramsey interferometry via spin squeezing, our scheme is robust against detection noises. Our antisymmetric Rabi spectroscopy protocol has promising applications in various practical quantum sensors such as atomic clocks and atomic magnetometers., Comment: 6 pages, 4 figures

Power suppliers can exercise market power to gain higher profit. However, this becomes difficult when external information is extremely rare. To get a promising performance in an extremely incomplete information market environment, a novel model-free reinforcement learning algorithm based on the Learning Automata (LA) is proposed in this paper. Besides, this paper analyses the rationality and convergence of the algorithm in case studies based on the Cournot market model.

Q learning is widely used to simulate the behaviors of generation companies (GenCos) in an electricity market. However, existing Q learning method usually requires numerous iterations to converge, which is time-consuming and inefficient in practice. To enhance the calculation efficiency, a novel Q learning algorithm improved by dichotomy is proposed in this paper. This method modifies the update process of the Q table by dichotomizing the state space and the action space step by step. Simulation results in a repeated Cournot game show the effectiveness of the proposed algorithm.