Miracle-dz/UniFAR-FTUs huggingface dataset:
Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives

Agent-based modeling and simulation has evolved as a powerful tool for modeling complex systems, offering insights into emergent behaviors and interactions among diverse agents. Integrating large language models into agent-based modeling and simulation presents a promising avenue for enhancing simulation capabilities. This paper surveys the landscape of utilizing large language models in agent-based modeling and simulation, examining their challenges and promising future directions. In this survey, since this is an interdisciplinary field, we first introduce the background of agent-based modeling and simulation and large language model-empowered agents. We then discuss the motivation for applying large language models to agent-based simulation and systematically analyze the challenges in environment perception, human alignment, action generation, and evaluation. Most importantly, we provide a comprehensive overview of the recent works of large language model-empowered agent-based modeling and simulation in multiple scenarios, which can be divided into four domains: cyber, physical, social, and hybrid, covering simulation of both real-world and virtual environments. Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
Neural operator-based super-fidelity: A warm-start approach for accelerating steady-state simulations

In recent years, using neural networks to speed up the solving of partial differential equations (PDEs) has gained significant traction in both academic and industrial settings. However, the use of neural networks as standalone surrogate models raises concerns about the reliability of solutions due to their dependence on data volume, quality, and training algorithms, especially in precision-critical scientific tasks. This study introduces a novel"super-fidelity"method, which uses neural networks for initial warm-starting in solving steady-state PDEs, ensuring both speed and accuracy. Drawing from super-resolution concepts in computer vision, our approach maps low-fidelity model solutions to high-fidelity targets using a vector-cloud neural network with equivariance (VCNN-e), maintaining all necessary invariance and equivariance properties for scalar and vector solutions. This method adapts well to different spatial resolutions. We tested this approach in two scientific computing scenarios: one with weak nonlinearity, using low Reynolds number flows around elliptical cylinders, and another with strong nonlinearity, using high Reynolds number flows over airfoils. In both cases, our neural operator-based initialization significantly accelerated convergence by at least two-fold, without sacrificing accuracy, compared to traditional methods. Its robustness is confirmed across various iterative algorithms with different linear equation solvers. The approach also demonstrated time savings in multiple simulations, even including model development time. Additionally, we propose an efficient training data generation strategy. Overall, our method offers an efficient way to accelerate steady-state PDE solutions using neural operators without loss of accuracy, especially relevant in precision-focused scientific applications.
Singular Control of (Reflected) Brownian Motion: A Computational Method Suitable for Queueing Applications

Motivated by applications in queueing theory, we consider a class of singular stochastic control problems whose state space is the $d$%-dimensional positive orthant. The original problem is approximated by a drift control problem, to which we apply a recently developed computational method that is feasible for dimensions up to $d=30$ or more. To show that nearly optimal solutions are obtainable using this method, we present computational results for a variety of examples, including queueing network examples that have appeared previously in the literature.
An Exhumed Late Paleozoic Canyon in the Rocky Mountains

Landscapes are thought to be youthful, particularly those of active orogenic belts. Unaweep Canyon in the Colorado Rocky Mountains, a large gorge drained by two opposite‐flowing creeks, is an exception. Its origin has long been enigmatic, but new data indicate that it is an exhumed late Paleozoic landform. Its survival within a region of profound late Paleozoic orogenesis demands a reassessment of tectonic models for the Ancestral Rocky Mountains, and its form and genesis have significant implications for understanding late Paleozoic equatorial climate. This discovery highlights the utility of paleogeomorphology as a tectonic and climatic indicator.
On the Kodaira-Spencer's problem on almost Hermitian $4$-manifolds

In 1954, Hirzebruch reported a problem posed by Kodaira and Spencer: on compact almost complex manifolds, is the dimension $h^{p,q}_{\bar \partial}$ of the kernel of the Dolbeault Laplacian independent of the choice of almost Hermitian metric? In this paper, we review recent progresses on the original problem and we introduce a similar one: on compact almost complex manifolds, find a generalization of Bott-Chern and Aeppli numbers which is metric-independent. We find a solution to our problem valid on almost K\"ahler $4$-manifolds.
Jet Classification Using High-Level Features from Anatomy of Top Jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) in top jet tagging and simulated jet comparison at the hadronic calorimeter angular resolution scale while requiring fewer computational resources. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM.
Pauli Hamiltonians with an Aharonov-Bohm Flux

We study a two-dimensional Pauli operator describing a charged quantum particle with spin $1/2$ moving on a plane in presence of an orthogonal Aharonov-Bohm magnetic flux. We classify all the admissible self-adjont realizations and give a complete picture of their spectral and scattering properties. Symmetries of the resulting Hamiltonians are also discussed, as well as their connection with the Dirac operator perturbed by an Aharonov-Bohm singularity.
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material's synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale.
Prediction and Control in Continual Reinforcement Learning

Temporal difference (TD) learning is often used to update the estimate of the value function which is used by RL agents to extract useful policies. In this paper, we focus on value function estimation in continual reinforcement learning. We propose to decompose the value function into two components which update at different timescales: a permanent value function, which holds general knowledge that persists over time, and a transient value function, which allows quick adaptation to new situations. We establish theoretical results showing that our approach is well suited for continual learning and draw connections to the complementary learning systems (CLS) theory from neuroscience. Empirically, this approach improves performance significantly on both prediction and control problems.
Human-Machine Teaming for UAVs: An Experimentation Platform

Full automation is often not achievable or desirable in critical systems with high-stakes decisions. Instead, human-AI teams can achieve better results. To research, develop, evaluate, and validate algorithms suited for such teaming, lightweight experimentation platforms that enable interactions between humans and multiple AI agents are necessary. However, there are limited examples of such platforms for defense environments. To address this gap, we present the Cogment human-machine teaming experimentation platform, which implements human-machine teaming (HMT) use cases that features heterogeneous multi-agent systems and can involve learning AI agents, static AI agents, and humans. It is built on the Cogment platform and has been used for academic research, including work presented at the ALA workshop at AAMAS this year [1]. With this platform, we hope to facilitate further research on human-machine teaming in critical systems and defense environments.
Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation

We study the clustering problem for mixtures of bounded covariance distributions, under a fine-grained separation assumption. Specifically, given samples from a $k$-component mixture distribution $D = \sum_{i =1}^k w_i P_i$, where each $w_i \ge \alpha$ for some known parameter $\alpha$, and each $P_i$ has unknown covariance $\Sigma_i \preceq \sigma^2_i \cdot I_d$ for some unknown $\sigma_i$, the goal is to cluster the samples assuming a pairwise mean separation in the order of $(\sigma_i+\sigma_j)/\sqrt{\alpha}$ between every pair of components $P_i$ and $P_j$. Our contributions are as follows: For the special case of nearly uniform mixtures, we give the first poly-time algorithm for this clustering task. Prior work either required separation scaling with the maximum cluster standard deviation (i.e. $\max_i \sigma_i$) [DKK+22b] or required both additional structural assumptions and mean separation scaling as a large degree polynomial in $1/\alpha$ [BKK22]. For general-weight mixtures, we point out that accurate clustering is information-theoretically impossible under our fine-grained mean separation assumptions. We introduce the notion of a clustering refinement -- a list of not-too-small subsets satisfying a similar separation, and which can be merged into a clustering approximating the ground truth -- and show that it is possible to efficiently compute an accurate clustering refinement of the samples. Furthermore, under a variant of the"no large sub-cluster'' condition from in prior work [BKK22], we show that our algorithm outputs an accurate clustering, not just a refinement, even for general-weight mixtures. As a corollary, we obtain efficient clustering algorithms for mixtures of well-conditioned high-dimensional log-concave distributions. Moreover, our algorithm is robust to $\Omega(\alpha)$-fraction of adversarial outliers.
Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay

The 21-cm signal provides a novel avenue to measure the thermal state of the universe during cosmic dawn and reionization (redshifts $z\sim 5-30$), and thus to probe energy injection from decaying or annihilating dark matter (DM). These DM processes are inherently inhomogeneous: both decay and annihilation are density dependent, and furthermore the fraction of injected energy that is deposited at each point depends on the gas ionization and density, leading to further anisotropies in absorption and propagation. In this work, we develop a new framework for modeling the impact of spatially inhomogeneous energy injection and deposition during cosmic dawn, accounting for ionization and baryon density dependence, as well as the attenuation of propagating photons. We showcase how this first completely inhomogeneous treatment affects the predicted 21-cm power spectrum in the presence of exotic sources of energy injection, and forecast the constraints that upcoming HERA measurements of the 21-cm power spectrum will set on DM decays to photons and to electron/positron pairs. These projected constraints considerably surpass those derived from CMB and Lyman-$\alpha$ measurements, and for decays to electron/positron pairs they exceed all existing constraints in the sub-GeV mass range, reaching lifetimes of $\sim 10^{28}\,\mathrm{s}$. Our analysis demonstrates the unprecedented sensitivity of 21-cm cosmology to exotic sources of energy injection during the cosmic dark ages. Our code, $\mathtt{DM21cm}$, includes all these effects and is publicly available in an accompanying release.
On partial rigidity of $\mathcal{S}$-adic subshifts

We develop combinatorial tools to study partial rigidity within the class of minimal $\mathcal{S}$-adic subshifts. By leveraging the combinatorial data of well-chosen Kakutani-Rokhlin partitions, we establish a necessary and sufficient condition for partial rigidity. Additionally, we provide an explicit expression to compute the partial rigidity rate and an associated partial rigidity sequence. As applications, we compute the partial rigidity rate for a variety of constant length substitution subshifts, such as the Thue-Morse subshift, where we determine a partial rigidity rate of 2/3. We also exhibit non-rigid substitution subshifts with partial rigidity rates arbitrarily close to 1 and as a consequence, using products of the aforementioned substitutions, we obtain that any number in $[0, 1]$ is the partial rigidity rate of a system.
Floating active carpets drive transport and aggregation in aquatic ecosystems

Communities of swimming microorganisms often thrive near liquid-air interfaces. We study how such `active carpets' shape their aquatic environment by driving biogenic transport in the water column beneath the active carpet. The collective flows that these organisms generate lead to diffusive upward fluxes of nutrients from deeper water layers, and downward fluxes of oxygen and carbon. Combining analytical theory and simulations, we examine the key transport metrics including the single and pair diffusivity, the first passage time for particle pair encounters, and the rate of particle aggregation. Our findings reveal that the hydrodynamic fluctuations driven by active carpets have a region of influence that reaches orders of magnitude further in distance than the size of the organisms. These non-equilibrium fluctuations lead to a strongly enhanced diffusion of particles, which is anisotropic and space-dependent. The fluctuations also facilitate encounters of particle pairs, which we quantify by analyzing their velocity pair correlation functions as a function of distance between the particles. We found that the size of the particles plays a crucial role in their encounter rates, with larger particles situated near the active carpet being more favourable for aggregation. Overall, this research has broadened our comprehension of aquatic systems out of equilibrium, particularly how biologically driven fluctuations contribute to the transport of fundamental elements in biogeochemical cycles.
Direct observation of a magnetic field-induced Wigner crystal

Eugene Wigner predicted long ago that when the Coulomb interactions between electrons become much stronger than their kinetic energy, electrons crystallize into a closely packed lattice. A variety of two-dimensional systems have shown evidence for Wigner crystals; however, a spontaneously formed classical or quantum Wigner crystal (WC) has never been directly visualized. Neither the identification of the WC symmetry nor direct investigation of its melting has been accomplished. Here we use high-resolution scanning tunneling microscopy (STM) measurements to directly image a magnetic field-induced electron WC in Bernal-stacked bilayer graphene (BLG), and examine its structural properties as a function of electron density, magnetic field, and temperature. At high fields and the lowest temperature, we observe a triangular lattice electron WC in the lowest Landau Level (LLL) of BLG. The WC possesses the expected lattice constant and is robust in a range of filling factors between $\nu\sim$ 0.13 and $\nu\sim$ 0.38 except near fillings where it competes with fractional quantum Hall (FQH) states. Increasing the density or temperature results in the melting of the WC into a liquid phase that is isotropic but has a modulated structure characterized by the WC's Bragg wavevector. At low magnetic fields, the WC unexpectedly transitions into an anisotropic stripe phase, which has been commonly anticipated to form in higher LLs. Analysis of individual lattice sites reveals signatures that may be related to the quantum zero-point motion of electrons in the WC lattice.
Separable algebras in multitensor C$ ^* $-categories are unitarizable

S. Carpi et al. (Comm. Math. Phys., 402 (2023), 169–212) proved that every connected (i.e., haploid) Frobenius algebra in a tensor C$ ^* $-category is unitarizable (i.e., isomorphic to a special C$ ^* $-Frobenius algebra). Building on this result, we extend it to the non-connected case by showing that an algebra in a multitensor C$ ^* $-category is unitarizable if and only if it is separable.
Spread complexity for measurement-induced non-unitary dynamics and Zeno effect

Using spread complexity and spread entropy, we study non-unitary quantum dynamics. For non-hermitian Hamiltonians, we extend the bi-Lanczos construction for the Krylov basis to the Schr\"odinger picture. Moreover, we implement an algorithm adapted to complex symmetric Hamiltonians. This reduces the computational memory requirements by half compared to the bi-Lanczos construction. We apply this construction to the one-dimensional tight-binding Hamiltonian subject to repeated measurements at fixed small time intervals, resulting in effective non-unitary dynamics. We find that the spread complexity initially grows with time, followed by an extended decay period and saturation. The choice of initial state determines the saturation value of complexity and entropy. In analogy to measurement-induced phase transitions, we consider a quench between hermitian and non-hermitian Hamiltonian evolution induced by turning on regular measurements at different frequencies. We find that as a function of the measurement frequency, the time at which the spread complexity starts growing increases. This time asymptotes to infinity when the time gap between measurements is taken to zero, indicating the onset of the quantum Zeno effect, according to which measurements impede time evolution.
Prompting Hard or Hardly Prompting: Prompt Inversion for Text-to-Image Diffusion Models

The quality of the prompts provided to text-to-image diffusion models determines how faithful the generated content is to the user's intent, often requiring `prompt engineering'. To harness visual concepts from target images without prompt engineering, current approaches largely rely on embedding inversion by optimizing and then mapping them to pseudo-tokens. However, working with such high-dimensional vector representations is challenging because they lack semantics and interpretability, and only allow simple vector operations when using them. Instead, this work focuses on inverting the diffusion model to obtain interpretable language prompts directly. The challenge of doing this lies in the fact that the resulting optimization problem is fundamentally discrete and the space of prompts is exponentially large; this makes using standard optimization techniques, such as stochastic gradient descent, difficult. To this end, we utilize a delayed projection scheme to optimize for prompts representative of the vocabulary space in the model. Further, we leverage the findings that different timesteps of the diffusion process cater to different levels of detail in an image. The later, noisy, timesteps of the forward diffusion process correspond to the semantic information, and therefore, prompt inversion in this range provides tokens representative of the image semantics. We show that our approach can identify semantically interpretable and meaningful prompts for a target image which can be used to synthesize diverse images with similar content. We further illustrate the application of the optimized prompts in evolutionary image generation and concept removal.
Reconstruction of rough surfaces from a single receiver at grazing angle

The paper develops a method for recovering a one-dimensional rough surface profile from scattered wave field, using a single receiver and repeated measurements when the surface is moving with respect to source and receiver. This extends a previously introduced marching method utilizing low grazing angles, and addresses the key issue of the requirement for many simultaneous receivers. The algorithm recovers the surface height below the receiver point step-by-step as the surface is moved, using the parabolic wave integral equations. Numerical examples of reconstructed surfaces demonstrate that the method is robust in both Dirichlet and Neumann boundary conditions, and with respect to different roughness characteristics and to some degree of measurement noise.
Complex Time Evolution in Tensor Networks

Real-time calculations in tensor networks are strongly limited in time by entanglement growth, restricting the achievable frequency resolution of Green's functions, spectral functions, self-energies, and other related quantities. By extending the time evolution to contours in the complex plane, entanglement growth is curtailed, enabling numerically efficient high-precision calculations of time-dependent correlators and Green's functions with detailed frequency resolution. Various approaches to time evolution in the complex plane and the required post-processing for extracting the pure real-time and frequency information are compared. We benchmark our results on the examples of the single-impurity Anderson model using matrix-product states and of the three-band Hubbard-Kanamori and Dworin-Narath models using a tree tensor network. Our findings indicate that the proposed methods are also applicable to challenging realistic calculations of materials.