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We explore near-field thermal radiation transport in nanoparticles embedded within a multilayer slab structure, focusing on dynamic modulation of heat flux via cavity interactions. Our findings reveal that by tuning the distance between reflectors and nanoparticles, thermal transport can be significantly suppressed or enhanced, driven by selective excitation of surface modes within the cavity. By precisely adjusting inter-slab gaps, we achieve multi-order control over thermal flux while maintaining stability across a broad range of configurations. Notably, internal slab arrangement plays a pivotal role, with compact designs yielding the most pronounced effects. This work unveils a novel mechanism for manipulating near-field heat transfer, with exciting potential for nanoscale thermal management and thermal sensing technologies.

Autoregressive models are a class of generative model that probabilistically predict the next output of a sequence based on previous inputs. The autoregressive sequence is by definition one-dimensional (1D), which is natural for language tasks and hence an important component of modern architectures like recurrent neural networks (RNNs) and transformers. However, when language models are used to predict outputs on physical systems that are not intrinsically 1D, the question arises of which choice of autoregressive sequence -- if any -- is optimal. In this paper, we study the reconstruction of critical correlations in the two-dimensional (2D) Ising model, using RNNs and transformers trained on binary spin data obtained near the thermal phase transition. We compare the training performance for a number of different 1D autoregressive sequences imposed on finite-size 2D lattices. We find that paths with long 1D segments are more efficient at training the autoregressive models compared to space-filling curves that better preserve the 2D locality. Our results illustrate the potential importance in choosing the optimal autoregressive sequence ordering when training modern language models for tasks in physics., Comment: 12 pages, 4 figures

This paper is devoted to the study of blow-up phenomenon for a fouth-order nonlocal parabolic equation with Neumann boundary condition, \begin{equation*} \left\{\begin{array}{ll}\ds u_{t}+u_{xxxx}=|u|^{p-1}u-\frac{1}{a}\int_{0}^a|u|^{p-1}u\ dx, & u_x(0)=u_x(a)=u_{xxx}(0)=u_{xxx}(a)=0, & u(x,0)=u_0(x)\in H^2(0, a),\ \ \int_0^au_0(x)\ dx=0, &\end{array}\right. \end{equation*} where $a$ is a positive constant and $p>1$. The existing results on the problem suggest that the weak solution will blow up in finite time if $I(u_0)<0$ and the initial energy satisfies some appropriate assumptions, here $I(u_0)$ is the initial Nehari functional. In this paper, we extend the previous blow-up conditions with proving that those assumptions on the energy functional are superfluous and only $I(u_0)<0$ is sufficient to ensure the weak solution blowing up in finite time. Our conclusion depicts the significant influence of mass conservation on the dynamic behavior of solution., Comment: Comments are welcome

Charge-order states of broken symmetry, such as charge density wave (CDW), are able to induce exceptional physical properties, however, the precise understanding of the underlying physics is still elusive. Here, we combine fluctuational electrodynamics and density functional theory to reveal an unconventional thermophotonic effect in CDW-bearing TiSe$_2$, referred to as thermophotonic-CDW ($tp$-CDW). The interplay of plasmon polariton and CDW electron excitations give rise to an anomalous negative temperature dependency in thermal photons transport, offering an intuitive fingerprint for a transformation of the electron order. Additionally, the demonstrated nontrivial features of $tp$-CDW transition hold promise for a controllable manipulation of heat flow, which could be extensively utilized in various fields such as thermal science and electron dynamics, as well as in next-generation energy devices., Comment: 7 pages, 4 figures

We introduce a generative pretained transformer (GPT) designed to learn the measurement outcomes of a neutral atom array quantum computer. Based on a vanilla transformer, our encoder-decoder architecture takes as input the interacting Hamiltonian, and outputs an autoregressive sequence of qubit measurement probabilities. Its performance is studied in the vicinity of a quantum phase transition in Rydberg atoms in a square lattice array. We explore the ability of the architecture to generalize, by producing groundstate measurements for Hamiltonian parameters not seen in the training set. We focus on examples of physical observables obtained from inference on three different models, trained in fixed compute time on a single NVIDIA A100 GPU. These can act as benchmarks for the scaling of larger RydbergGPT models in the future. Finally, we provide RydbergGPT open source, to aid in the development of foundation models based off of a wide variety of quantum computer interactions and data sets in the future., Comment: 9 pages, 4 figures

We explore a novel approach to achieving anisotropic thermal photon tunneling, inspired by the concept of parity-time symmetry in quantum physics. Our method leverages the modulation of constitutive optical parameters, oscillating between loss and gain regimes. This modulation reveals a variety of distinct effects in thermal photon behavior and dispersion. Specifically, we identify complex tunneling modes through gain-loss engineering, which include thermal photonic defect states and Fermi-arc-like phenomena, which surpass those achievable through traditional polariton engineering. Our research also elucidates the laws governing the evolution of radiative energy in the presence of gain and loss interactions, and highlights the unexpected inefficacy of gain in enhancing thermal photon energy transport compared to systems characterized solely by loss. This study not only broadens our understanding of thermal photon tunneling but also establishes a versatile platform for manipulating photon energy transport, with potential applications in thermal management, heat science, and the development of advanced energy devices., Comment: 8 pages, 5 figures

The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. The technologies of interacting with data particularly have an important entanglement with LLMs as efficient and intuitive data interactions are paramount. In this paper, we present DB-GPT, a revolutionary and product-ready Python library that integrates LLMs into traditional data interaction tasks to enhance user experience and accessibility. DB-GPT is designed to understand data interaction tasks described by natural language and provide context-aware responses powered by LLMs, making it an indispensable tool for users ranging from novice to expert. Its system design supports deployment across local, distributed, and cloud environments. Beyond handling basic data interaction tasks like Text-to-SQL with LLMs, it can handle complex tasks like generative data analysis through a Multi-Agents framework and the Agentic Workflow Expression Language (AWEL). The Service-oriented Multi-model Management Framework (SMMF) ensures data privacy and security, enabling users to employ DB-GPT with private LLMs. Additionally, DB-GPT offers a series of product-ready features designed to enable users to integrate DB-GPT within their product environments easily. The code of DB-GPT is available at Github(https://github.com/eosphoros-ai/DB-GPT) which already has over 10.7k stars. Please install DB-GPT for your own usage with the instructions(https://github.com/eosphoros-ai/DB-GPT#install) and watch a 5-minute introduction video on Youtube(https://youtu.be/n_8RI1ENyl4) to further investigate DB-GPT.

In 1992, Gundersen (Complex Var. Elliptic Equ.20 (1992), no. 1-4, 99-106.) proposed the following famous open question: if two non-constant meromorphic functions share three values IM and share a fourth value CM, then do the functions necessarily share all four values CM? The open question is a long-standing question in the studies of the Nevanlinna$'$s value distribution theory of meromorphic functions, and has not been completely resolved by now. In this paper, we prove that if two distinct non-constant meromorphic functions $f$ and $g$ of finite order share $0,$ $1,$ $c$ IM and $\infty$ CM, where $c$ is a finite complex value such that $c\not\in\{0,1\},$ then $f$ and $g$ share $0,$ $1,$ $c,$ $\infty$ CM. Applying the main result obtained in this paper, we completely resolve a question proposed by Gary G. Gundersen on Page 458 of his paper (J. London Math. Soc. 20(1979), no. 2, 457-466.)concerning the nonexistence of two distinct non-constant meromorphic functions sharing three distinct values DM and a fourth value CM. The obtained result also improves the corresponding result on Pages 109-117 in (E. Mues, Bemerkungen zum vier-punkte-satz, Complex Methods on Partial Diferential Equations, 109-117, Math. Res. 53, Akademie-Verlag, Berlin, 1989.) concerning the nonexistence of two distinct non-constant entire functions that share three distinct finite values DM. Examples are provided to show that the main results obtained in this paper, in a sense, are best possible., Comment: The total pages of the present paper is 39. Neither figures nor conferences are related to this present paper

The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. Database technologies particularly have an important entanglement with LLMs as efficient and intuitive database interactions are paramount. In this paper, we present DB-GPT, a revolutionary and production-ready project that integrates LLMs with traditional database systems to enhance user experience and accessibility. DB-GPT is designed to understand natural language queries, provide context-aware responses, and generate complex SQL queries with high accuracy, making it an indispensable tool for users ranging from novice to expert. The core innovation in DB-GPT lies in its private LLM technology, which is fine-tuned on domain-specific corpora to maintain user privacy and ensure data security while offering the benefits of state-of-the-art LLMs. We detail the architecture of DB-GPT, which includes a novel retrieval augmented generation (RAG) knowledge system, an adaptive learning mechanism to continuously improve performance based on user feedback and a service-oriented multi-model framework (SMMF) with powerful data-driven agents. Our extensive experiments and user studies confirm that DB-GPT represents a paradigm shift in database interactions, offering a more natural, efficient, and secure way to engage with data repositories. The paper concludes with a discussion of the implications of DB-GPT framework on the future of human-database interaction and outlines potential avenues for further enhancements and applications in the field. The project code is available at https://github.com/eosphoros-ai/DB-GPT. Experience DB-GPT for yourself by installing it with the instructions https://github.com/eosphoros-ai/DB-GPT#install and view a concise 10-minute video at https://www.youtube.com/watch?v=KYs4nTDzEhk.

The Kitaev chiral spin liquid has captured widespread interest in recent decades because of its intrinsic non-Abelian excitations, yet the experimental realization is challenging. Here we propose to realize and detect Kitaev chiral spin liquid in a deformed honeycomb array of Rydberg atoms. Through a novel laser-assisted dipole-dipole interaction mechanism to generate both effective hopping and pairing terms for hard-core bosons, together with van der Waals interaction, we achieve the Kitaev spin liquid model with high precision. The gapped non-Abelian spin liquid phase is then obtained. Further, we propose to probe the chiral Majorana edge modes by light Bragg scattering and by imagining their chiral motion, in which only spin degree of freedom needs to be measured. Our work broadens the range of exotic quantum many-body phases that can be realized and detected in atomic systems, and makes an important step toward manipulating non-Abelian anyons.

We numerically investigate a hybrid trapping architecture for 2D ion crystals using static electrode voltages and optical cavity fields for in-plane and out-of-plane confinements, respectively. By studying the stability of 2D crystals against 2D-3D structural phase transitions, we identify the necessary trapping parameters for ytterbium ions. Multiple equilibrium configurations for 2D crystals are possible, and we analyze their stability by estimating potential barriers between them. We find that scattering to anti-trapping states limits the trapping lifetime, which is consistent with recent experiments employing other optical trapping architectures. These 2D ion crystals offer an excellent platform for quantum simulation of frustrated spin systems, benefiting from their 2D triangular lattice structure and phonon-mediated spin-spin interactions. Quantum information processing with tens of ions is feasible in this scheme with current technologies., Comment: 10 pages, 7 figures, 1 table

Recently, the application of an external magnetic field to actively control the near-field heat transfer has emerged as an appealing and promising technique. Existing studies have shown that an external static magnetic field tends to reduce the subwavelength radiative flux exchanged between two planar structures containing magneto-optical (MO) materials, but so far the nearfield thermomagnetic effects in systems with more such structures at different temperatures have not been reported. Here, we are focused on examining how the presence of an external magnetic field modifies the radiative energy transfer in a many-body configuration consisting of three MO n-doped semiconductors slabs, separated by subwavelength vacuum gaps. To exactly calculate the radiative flux transferred in such an anisotropic planar system, a general Green-function-based approach is offered, which allows one to investigate the radiative heat transfer in arbitrary manybody systems with planar geometry. We demonstrate that, under specific choices of the geometrical and thermal parameters, the applied magnetic field is able to either reduce or enhance the near-field energy transfer in three-element MO planar systems, depending on the interplay between the damped evanescent fields of the zero-field surface waves and the propagating hyperbolic modes induced by magnetic fields. Our study broadens the understanding concerning to the use of external fields to actively control the heat transfer in subwavelength regimes, and may be leveraged for potential applications in the realm of nanoscale thermal management., Comment: 18 pages, 7 figures