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We investigate the growth of two-dimensional (2D) crystals on fluctuating surfaces using a phase field crystal model that is relevant on atomic length and diffusive time scales. Motivated by recent experiments which achieved unprecedented fast growth of large-size high-quality 2D crystals on liquid substrates, we uncover novel effects of liquid surfaces on microstructural ordering. We find that substrate fluctuations generate short-ranged noise that speeds up crystallization and grain growth of the overlayer, surpassing that of free-standing system. Coupling to the liquid substrate fluctuations can also modulate local randomness, leading to intriguing disordered structures with hidden spatial order, i.e., disordered hyperuniformity. These results reveal the physical mechanisms underlying the fast growth of 2D crystals on liquid surfaces and demonstrate a novel strategy for synthesizing disordered hyperuniform thin film structures., Comment: 9 pages, 4 figures

In this work the influence of $5|7$ dislocations in multiplayer graphene stacks (up to six layers) is examined. The study is conducted through a recently developed Phase Field Crystal (PFC) model for multilayer systems incorporating out-of-plane deformations and parameterized to match to density functional theory calculations for graphene bilayers and other systems. The specific configuration considered consists of one monolayer containing four $5|7$ dislocations (i.e., two dislocation dipoles) sandwiched in between perfect graphene layers. The study reveals how the strain field from the dislocations in the defected layer leads to out-of-plane deformations that in turn cause deformations of neighboring layers. Quantitative predictions are made for the defect free energy of the multilayer stacks as compared to a defect-free system, which is shown to increase with the number of layers and system size. Furthermore it is predicted that system defect energy saturates by roughly ten sheets in the stack, indicating the range of defect influence across the multilayer. Variations of stress field distribution and layer height profiles in different layer of the stack are also quantitatively identified., Comment: 11 pages, 18 figures

In this paper, we introduced the Unified Three-Form Dark Sector (UTFDS) model, a unified dark sector model that combines dark energy and dark matter through a three-form field. In this framework, the potential of the three-form field acts as dark matter, while the kinetic term represents dark energy. The interaction between dark matter and dark energy is driven by the energy exchange between these two terms. Given the dynamical equations of UTFDS, we provide an autonomous system of evolution equations for UTFDS and perform a stability analysis of its fixed points. The result aligns with our expectations for a unified dark sector. Furthermore, we discover that the dual Lagrangian of the UTFDS Lagrangian is equivalent to a Dirac-Born-Infeld (DBI) Lagrangian. By fixing the parameter $\kappa X_0$ to 250, 500, 750, we refer to the resulting models as the $\overline{\rm UTFDS}$ model with $\kappa X_0$=250, 500, 750, respectively. We then place constraints on these three $\overline{\rm UTFDS}$ models and the $\Lambda$CDM model in light of the Planck 2018 Cosmic Microwave Background (CMB) anisotropies, Redshift Space Distortions (RSD) observations, Baryon Acoustic Oscillation (BAO) measurements, and the $S_8$ prior chosen according to the KiDS1000 Weak gravitational Lensing (WL) measuement. We find that the $\overline{\rm UTFDS}$ model with $\kappa X_0$=500 is the only one among the four models where both $\sigma_8$ and $S_8$ tensions, between CMB and RSD+BAO+WL datasets, are below 2.0$\sigma$. Furthermore, the tensions are relieved without exacerbating the $H_0$ tension. Although both the CMB and RSD+BAO+WL datasets provide definite/positive evidence favoring $\Lambda$CDM over the $\overline{\rm UTFDS}$ model with $\kappa X_0$=500, the evidence is not strong enough to rule out further study of this model., Comment: 16 pages, 6 tables, 5 figures

AI is undergoing a paradigm shift, with breakthroughs achieved by systems orchestrating multiple large language models (LLMs) and other complex components. As a result, developing principled and automated optimization methods for compound AI systems is one of the most important new challenges. Neural networks faced a similar challenge in its early days until backpropagation and automatic differentiation transformed the field by making optimization turn-key. Inspired by this, we introduce TextGrad, a powerful framework performing automatic ``differentiation'' via text. TextGrad backpropagates textual feedback provided by LLMs to improve individual components of a compound AI system. In our framework, LLMs provide rich, general, natural language suggestions to optimize variables in computation graphs, ranging from code snippets to molecular structures. TextGrad follows PyTorch's syntax and abstraction and is flexible and easy-to-use. It works out-of-the-box for a variety of tasks, where the users only provide the objective function without tuning components or prompts of the framework. We showcase TextGrad's effectiveness and generality across a diverse range of applications, from question answering and molecule optimization to radiotherapy treatment planning. Without modifying the framework, TextGrad improves the zero-shot accuracy of GPT-4o in Google-Proof Question Answering from $51\%$ to $55\%$, yields $20\%$ relative performance gain in optimizing LeetCode-Hard coding problem solutions, improves prompts for reasoning, designs new druglike small molecules with desirable in silico binding, and designs radiation oncology treatment plans with high specificity. TextGrad lays a foundation to accelerate the development of the next-generation of AI systems., Comment: 41 pages, 6 figures

Nonreciprocal forces violating Newton's third law are common in a plethora of nonequilibrium situations ranging from predator-prey systems to the swarming of birds and effective colloidal interactions under flow. While many recent studies have focused on two species with nonreciprocal coupling, much less is examined for the basic single-component system breaking the actio and reactio equality of force within the same species. Here, we systematically derive the fundamental field theory of single-species nonreciprocal interactions from microscopic dynamics, leading to a generic framework termed Active Model N (N denoting nonreciprocity). We explore the rich dynamics of pattern formation in this intrinsic nonreciprocal system and the emergence of self-traveling states with persistent variation and flowing of active branched patterns. One particular new characteristic pattern is an interwoven self-knitting "yarn" structure with a unique feature of simultaneous development of micro- and bulk phase separations. The growth dynamics of a "ball-of-wool" active droplet towards these self-knitted yarn or branched states exhibits a crossover between different scaling behaviors. The mechanism underlying this distinct class of active phase separation is attributed to the interplay between force nonreciprocity and competition. Our predictions can be applied to various biological and artificial active matter systems controlled by single-species nonreciprocity., Comment: 25 pages, 7 figures

Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledge is accessible, verifiable, and built upon over time. Recently, there has been immense speculation about how many people are using large language models (LLMs) like ChatGPT in their academic writing, and to what extent this tool might have an effect on global scientific practices. However, we lack a precise measure of the proportion of academic writing substantially modified or produced by LLMs. To address this gap, we conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time. Our statistical estimation operates on the corpus level and is more robust than inference on individual instances. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers (up to 17.5%). In comparison, Mathematics papers and the Nature portfolio showed the least LLM modification (up to 6.3%). Moreover, at an aggregate level, our analysis reveals that higher levels of LLM-modification are associated with papers whose first authors post preprints more frequently, papers in more crowded research areas, and papers of shorter lengths. Our findings suggests that LLMs are being broadly used in scientific writings.

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices., Comment: 46 pages, 31 figures, ICML '24

Three-dimensional 3rd-order topological insulators (TOTIs) and superconductors (TOTSCs), as the highestorder topological phases hosting zero corner modes in physical dimension, has sparked extensive research interest. However, such topological states have not been discovered in reality due to the lack of experimental schemes of realization. Here, we propose a novel surface Chern-Simons (CS) theory for 3rd-order topological phases, and show that the theory enables a feasible and systematic design of TOTIs and TOTSCs. We show that the emergence of zero Dirac (Majorana) corner modes is entirely captured by an emergent $\mathbb{Z}_{2}$ CS term that can be further characterized by a novel two-particle Wess-Zumino (WZ) term uncovered here in the surfaces of three-dimensional topological materials. Importantly, our proposed CS term characterization and two-particle WZ term mechanism provide a unique perspective to design TOTIs (TOTSCs) in terms of minimal ingredients, feasibly guiding the search for underlying materials, with promising candidates being discussed. This work shall advance both the theoretical and experimental research for highest-order topological matters., Comment: 5+11 pages, 4+5 figures

We report the test results of several independent foreground-cleaning pipelines used in the Ali CMB Polarization Telescope experiment (AliCPT-1), a high-altitude CMB imager in the Northern hemisphere with thousands of detectors dedicated to the search for a primordial CMB polarization $B$-mode signature. Based on simulated data from 4 detector modules and a single season of observation, which we refer to as Data Challenge 1 (DC1), we employ different and independent pipelines to examine the robustness and effectiveness of the estimates on foreground parameters and the primordial $B$-mode detection. The foreground-cleaning strategies used in the pipelines include the parametric method of template fitting (TF) and the non-parametric methods of the constrained internal linear combination (cILC), the analytical blind separation (ABS), and the generalized least squares (GLS). We examine the impact of possible foreground residuals on the estimate of the CMB tensor-to-scalar ratio ($r$) for each pipeline by changing the contamination components in the simulated maps and varying the foreground models and sky patches for various tests. According to the DC1 data with the simulation input value $r_{\rm true}=0.023$, the foreground residual contamination levels in the TF/ABS/cILC/GLS pipelines are well within the corresponding statistical errors at the $2\sigma$ level. Furthermore, by utilizing the tension estimator, which helps identify significant residual foreground contamination in the detection of the primordial $B$-mode signal by quantifying the discrepancy between various $r$ measurements, we conclude that the presence of small foreground residuals does not lead to any significant inconsistency in the estimation of $r$., Comment: 38 pages, 22 figures; accepted for publication in ApJS

Surface effect of low-surface-tension contaminants accumulating at the evaporation surface can easily induce membrane wetting in the application of membrane distillation, especially in hypersaline scenarios. In this work, we propose a novel strategy to eliminate the surface effect and redistribute contaminants at the evaporation interface with simply incorporating a layer of hydrogel. The as-fabricated composite membrane exhibits remarkable stability, even when exposed to extreme conditions, such as a salt concentration of 5M and surfactant concentration of 8 mM. The breakthrough pressure of the membrane is as high as 20 bars in the presence of surfactants, surpassing commercial hydrophobic membranes by one to two magnitudes. Combined study of density functional theory and molecular dynamics simulations reveals the important role of hydrogel-surfactant interaction in suppressing the surface effect. As a proof of concept, we also demonstrate the stable performance of the membrane in processing synthetic wastewater containing surfactants of 144 mg L-1, mineral oils of 1g L-1 and NaCl of 192 g L-1, showing potential of the membrane in addressing challenges of hypersaline water treatment and zero liquid discharge processes., Comment: 24 pages, 12 figures

The dynamics of active smectic liquid crystals confined on a spherical surface is explored through an active phase field crystal model. Starting from an initially randomly perturbed isotropic phase, several types of topological defects are spontaneously formed, and then annihilate during a coarsening process until a steady state is achieved. The coarsening process is highly complex involving several scaling laws of defect densities as a function of time where different dynamical exponents can be identified. In general the exponent for the final stage towards the steady state is significantly larger than that in the passive and in the planar case, i.e., the coarsening is getting accelerated both by activity and by the topological and geometrical properties of the sphere. A defect type characteristic for this active system is a rotating spiral of evolving smectic layering lines. On a sphere this defect type also determines the steady state. Our results can in principle be confirmed by dense systems of synthetic or biological active particles., Comment: 22 pages, 6 figures

An eye-movement-based predicted trajectory guidance control (ePTGC) is proposed to mitigate the maneuverability degradation of a teleoperated ground vehicle caused by communication delays. Human sensitivity to delays is the main reason for the performance degradation of a ground vehicle teleoperation system. The proposed framework extracts human intention from eye-movement. Then, it combines it with contextual constraints to generate an intention-compliant guidance trajectory, which is then employed to control the vehicle directly. The advantage of this approach is that the teleoperator is removed from the direct control loop by using the generated trajectories to guide vehicle, thus reducing the adverse sensitivity to delay. The delay can be compensated as long as the prediction horizon exceeds the delay. A human-in-loop simulation platform is designed to evaluate the teleoperation performance of the proposed method at different delay levels. The results are analyzed by repeated measures ANOVA, which shows that the proposed method significantly improves maneuverability and cognitive burden at large delay levels (>200 ms). The overall performance is also much better than the PTGC which does not employ the eye-movement feature., Comment: 9 pages, 11 figures

Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such as haze, rain, and snow since the visual appearances of crowds in such scenes are drastically different from those images in clear weather of typical datasets. In this paper, we propose a method for robust crowd counting in adverse weather scenarios. Instead of using a two-stage approach that involves image restoration and crowd counting modules, our model learns effective features and adaptive queries to account for large appearance variations. With these weather queries, the proposed model can learn the weather information according to the degradation of the input image and optimize with the crowd counting module simultaneously. Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets. The source code and trained models will be made available to the public., Comment: including supplemental material

We study the acceleration of charged particles by ultra-relativistic shocks using test-particle Monte-Carlo simulations. Two field configurations are considered: (i) shocks with uniform upstream magnetic field in the plane of the shock, and (ii) shocks in which the upstream magnetic field has a cylindrical geometry. Particles are assumed to diffuse in angle due to frequent non-resonant scattering on small-scale fields. The steady-state distribution of particles' Lorentz factors is shown to approximately satisfy $dN/d\gamma \propto \gamma^{-2.2}$ provided the particle motion is scattering dominated on at least one side of the shock. For scattering dominated transport, the acceleration rate scales as $t_{\rm acc}\propto t^{1/2}$, though recovers Bohm scaling $t_{\rm acc}\propto t$ if particles become magnetised on one side of the shock. For uniform field configurations, a limiting energy is reached when particles are magnetised on both sides of the shock. For the cylindrical field configuration, this limit does not apply, and particles of one sign of charge will experience a curvature drift that redirects particles upstream. For the non-resonant scattering model considered, these particles preferentially escape only when they reach the confinement limit determined by the finite system size, and the distribution approaches the escapeless limit $dN/d\gamma \propto \gamma^{-1}$. The cylindrical field configuration resembles that expected for jets launched by the Blandford $\&$ Znajek mechanism, the luminous jets of AGN and GRBs thus provide favourable sites for the production of ultra-high energy cosmic rays.

In this paper a systematic examination of graphene/hexagonal boron nitride (g/hBN) bilayers is presented, through a recently developed two-dimensional phase field crystal model that incorporates out-of-plane deformations. The system parameters are determined by closely matching the stacking energies and heights of graphene/hBN bilayers to those obtained from existing quantum-mechanical density functional theory calculations. Out-of-plane deformations are shown to reduce the energies of inversion domain boundaries in hBN, and the coupling between graphene and hBN layers leads to a bilayer defect configuration consisting of an inversion boundary in hBN and a domain wall in graphene. Simulations of twisted bilayers reveal the structure, energy, and elastic properties of the corresponding Moir\'e patterns, and show a crossover, as the misorientation angle between the layers increases, from a well-defined hexagonal network of domain boundaries and junctions to smeared-out patterns. The transition occurs when the thickness of domain walls approaches the size of the Moir\'e patterns, and coincides with the peaks in the average von Mises and volumetric stresses of the bilayer., Comment: 11 pages, 16 figures

Maneuverability and drivability of the teleoperated ground vehicle could be seriously degraded by large communication delays if the delays are not properly compensated. This paper proposes a predicted trajectory guidance control (PTGC) framework to compensate for such delays, thereby improving the performance of the teleoperation system. The novelty of this PTGC framework is that teleoperators intended trajectory is predicted at the vehicle side with their delayed historical control commands and the LiDAR 3D point cloud of the environment, and then the vehicle is guided by the predicted trajectory. By removing the teleoperator from the direct control loop, the presented method is less sensitive to delays, and delays are compensated as long as the prediction horizon exceeds the delays. Human-in-the-loop simulation experiments are designed to evaluate the teleoperation performance with the proposed method under five delay levels. Based on the repeated measurement analysis of variance, it is concluded that the PTGC method can significantly improve the performance of the teleoperated ground vehicles under large delays(>200ms), such as the task completion time (TCT), deviation to centerline (D2C) and steering effort (SE). In addition, the results also show that teleoperators can adapt to smaller delays, and the presented method is ineffective in such cases., Comment: 10 pages, 11 figures

Accurate and reliable prediction of driving intentions and future trajectories contributes to cooperation between human drivers and ADAS in complex traffic environments. This paper proposes a visual AOI (Area of Interest) based multimodal trajectory prediction model for probabilistic risk assessment at intersections. In this study, we find that the visual AOI implies the driving intention and is about 0.6-2.1 s ahead of the operation. Therefore, we designed a trajectory prediction model that integrates the driving intention (DI) and the multimodal trajectory (MT) predictions. The DI model was pre-trained independently to extract the driving intention using features including the visual AOI, historical vehicle states, and environmental context. The intention prediction experiments verify that the visual AOI-based DI model predicts steering intention 0.925 s ahead of the actual steering operation. The trained DI model is then integrated into the trajectory prediction model to filter multimodal trajectories. The trajectory prediction experiments show that the proposed model outperforms the state-of-the-art models. Risk assessment for traffics at intersections verifies that the proposed method achieves high accuracy and a low false alarm rate, and identifies the potential risk about 3 s before a conflict occurs., Comment: 11 pages, 13 figures

Using an eigenfunction expansion to solve the transport equation, complemented by Monte-Carlo simulations, we show that ultrarelativistic shocks can be effective particle accelerators even when they fail to produce large amplitude turbulence in the downstream plasma. This finding contradicts the widely held belief that a uniform downstream magnetic field perpendicular to the shock normal inhibits acceleration by the first order Fermi process. In the ultrarelativistic limit, we find a stationary power-law particle spectrum of index s=4.17 for these shocks, close to that predicted for a strictly parallel shock., Comment: 9 pages, 6 figures, accepted for publication in Monthly Notices of the Royal Astronomical Society

The growth and microstructural properties of ternary monolayers of two-dimensional hexagonal materials are examined, including both individual two-dimensional crystalline grains and in-plane heterostructures, multijunctions, or superlattices. The study is conducted through the development of a ternary phase field crystal model incorporating sublattice ordering and the coupling among the three atomic components. The results demonstrate that a transition of compositional pattern or modulation in this type of two-dimensional ternary crystals, from phase separation to geometrically frustrated lattice atomic ordering, can be controlled via the varying degree of energetic preference of heteroelemental neighboring over the homoelemental ones. Effects of growth and system conditions are quantitatively identified through numerical calculations and analyses of interspecies spatial correlations and the degree of alloy intermixing or disordering. These findings are applied to simulating the growth of monolayer lateral heterostructures with atomically sharp heterointerface, and via the sequential process of edge-epitaxy, the formation of the corresponding superlattices or structures with multiple heterojunctions, with outcomes consistent with recent experiments of in-plane multi-heterostructures of transition metal dichalcogenides. Also explored is a distinct type of alloy-based lateral heterostructures and multijunctions which integrate ternary ordered alloy domains with the adjoining blocks of binary compounds, providing a more extensive variety of two-dimensional heterostructural materials., Comment: 12 pages, 8 figures

AliCPT is the first Chinese cosmic microwave background (CMB) experiment which will make the most precise measurements of the CMB polarization in the northern hemisphere. The key science goal for AliCPT is the detection of primordial gravitational waves (PGWs). It is well known that an epoch of cosmic inflation, in the very early universe, can produce PGWs, which leave an imprint on the CMB in form of odd parity $B$-mode polarization. In this work, we study the performance of the component separation and parameter estimation pipelines in context of constraining the value of the tensor-to-scalar ratio. Based on the simulated data for one observation season, we compare five different pipelines with different working principles. Three pipelines perform component separation at map or spectra level before estimating $r$ from the cleaned spectra, while the other two pipelines performs a global fit for both foreground parameters and $r$. We also test different methods to account for the effects of time stream filtering systematics. This work shows that our pipelines provide consistent and robust constraints on the tensor-to-scalar ratio and a consistent sensitivity $\sigma(r) \sim 0.02$. This showcases the potential of precise $B$-mode polarization measurement with AliCPT-1. AliCPT will provide a powerful opportunity to detect PGWs, which is complementary with various ground-based CMB experiments in the southern hemisphere., Comment: 34 pages, 10 figs, 5 tables, JCAP in press

AliCPT-1 is the first Chinese CMB experiment aiming for high precision measurement of Cosmic Microwave Background B-mode polarization. The telescope, currently under deployment in Tibet, will observe in two frequency bands centered at 90 and 150 GHz. We forecast the CMB lensing reconstruction, lensing-galaxy as well as lensing-CIB (Cosmic Infrared Background) cross correlation signal-to-noise ratio (SNR) for AliCPT-1. We consider two stages with different integrated observation time, namely "4 module*yr" (first stage) and "48 module*yr" (final stage). For lensing reconstruction, we use three different quadratic estimators, namely temperature-only, polarization-only and minimum-variance estimators, using curved sky geometry. We take into account the impact of inhomogeneous hit counts as well as of the mean-field bias due to incomplete sky coverage. In the first stage, our results show that the 150 GHz channel is able to measure the lensing signal at $15\sigma$ significance with the minimum-variance estimator. In the final stage, the measurement significance will increase to $31\sigma$. We also combine the two frequency data in the harmonic domain to optimize the SNR. Our result show that the coadding procedure can significantly reduce the reconstruction bias in the multiple range l>800. Thanks to the high quality of the polarization data in the final stage of AliCPT-1, the EB estimator will dominate the lensing reconstruction in this stage. We also estimate the SNR of cross-correlations between AliCPT-1 CMB lensing and other tracers of the large scale structure of the universe. For its cross-correlation with DESI galaxies/quasars, we report the cross-correlation SNR = 10-20 for the 4 redshift bins at 0.05

The persistent dynamics in systems out of equilibrium, particularly those characterized by annihilation and creation of topological defects, is known to involve complicated spatiotemporal processes and is deemed difficult to control. Here the complex dynamics of defects in active smectic layers exposed to strong confinements is explored, through self-propulsion of active particles and a variety of confining geometries with different topology, ranging from circular, flower-shaped epicycloid, to hypocycloid cavities, channels, and rings. We identify a wealth of dynamical behaviors during the evolution of complex spatiotemporal defect patterns as induced by the confining shape and topology, particularly a perpetual creation-annihilation dynamical state at intermediate activity with large fluctuations of topological defects and a controllable transition from oscillatory to damped time correlation of defect number density via mechanisms governed by boundary cusps. Our results are obtained by using an active phase field crystal approach. Possible experimental realizations are also discussed., Comment: 30 pages, 9 figures, 1 page of supplementary information. Commun. Phys., in press