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Chemo-mechanical waves on active deformable surfaces are a key component for many vital cellular functions. In particular, these waves play a major role in force generation and long-range signal transmission in cells that dynamically change shape, as encountered during cell division or morphogenesis. Reconstituting and controlling such chemically controlled cell deformations is a crucial but unsolved challenge for the development of synthetic cells. Here, we develop an optogenetic method to elucidate the mechanism responsible for coordinating surface contraction waves that occur in oocytes of the starfish Patiria miniata during meiotic cell division. Using spatiotemporally-patterned light stimuli as a control input, we create chemo-mechanical cortical excitations that are decoupled from meiotic cues and drive diverse shape deformations ranging from local pinching to surface contraction waves and cell lysis. We develop a quantitative model that entails the hierarchy of chemical and mechanical dynamics, which allows to relate the variety of mechanical responses to optogenetic stimuli. Our framework systematically predicts and explains transitions of programmed shape dynamics. Finally, we qualitatively map the observed shape dynamics to elucidate how the versatility of intracellular protein dynamics can give rise to a broad range of mechanical phenomenologies. More broadly, our results pave the way toward real-time control over dynamical deformations in living organisms and can advance the design of synthetic cells and life-like cellular functions., Comment: 36 pages, 16 figures, 11 movies

The Varying Speed of Light (VSL) model describes how the speed of light in a vacuum changes with cosmological redshift. Despite numerous models, there is little observational evidence for this variation. While the speed of light can be accurately measured by physical means, cosmological methods are rarely used. Previous studies quantified the speed of light at specific redshifts using Gaussian processes and reconstructed the redshift-dependent function $c(z)$. It is crucial to quantify the speed of light across varying redshifts. We use the latest data on angular diameter distances $D_A(z)$ and Hubble parameters $H(z)$ from baryon acoustic oscillation (BAO) and cosmic chronometer measurements in the redshift interval $z\in[0.07,1.965]$. The speed of light $c(z)$ is determined using Gaussian and deep Gaussian processes to reconstruct $H(z)$, $D_A(z)$, and $D^{\prime}_A(z)$. Furthermore, we conduct comparisons across three distinct models, encompassing two renowned VSL models. We get the result of the parameters constraints in the models (1) for the ``$c$-c" model, $c_0=29492.6 \pm^{6.2}_{5.3} \mathrm{~km} \mathrm{~s}^{-1}$. (2) For the ``$c$-cl" model, $c_0=29665.5 \pm^{11.2}_{11.4}\mathrm{~km} \mathrm{~s}^{-1}$ and $n=0.05535 \pm^{0.00008}_{0.00007}$. (3) For the ``$c$-CPL" model, $c_0=29555.7 \pm^{13.3}_{13.2} \mathrm{~km} \mathrm{~s}^{-1}$ and $n=-0.0607 \pm 0.0001$. Based on our findings, it may be inferred that Barrow's classical VSL model is not a suitable fit for our data. In contrast, the widely recognized Chevallier-Polarski-Linder (CPL) VSL model, under some circumstances, as well as the universal ``c is constant" model, demonstrate a satisfactory ability to account for our findings., Comment: 15 pages, 9 figures; Accepted for publication in MNRAS

In $\Lambda$CDM cosmology, galaxies form and evolve in their host dark matter (DM) halos. Halo mass is crucial for understanding the halo-galaxy connection. The abundance matching (AM) technique has been widely used to derive the halo masses of galaxy groups. However, quenching of the central galaxy can decouple the coevolution of its stellar mass and DM halo mass. Different halo assembly histories can also result in significantly different final stellar mass of the central galaxies. These processes can introduce substantial uncertainties in the halo masses derived from the AM method, particularly leading to a systematic bias between groups with star-forming centrals (blue groups) and passive centrals (red groups). To improve, we developed a new machine learning (ML) algorithm that accounts for these effects and is trained on simulations. Our results show that the ML method eliminates the systematic bias in the derived halo masses for blue and red groups and is, on average, $\sim1/3$ more accurate than the AM method. With careful calibration of observable quantities from simulations and observations from SDSS, we apply our ML model to the SDSS Yang et al. groups to derive their halo masses down to $10^{11.5}\mathrm{M_\odot}$ or even lower. The derived SDSS group halo mass function agrees well with the theoretical predictions, and the derived stellar-to-halo mass relations for both red and blue groups matches well with those obtained from direct weak lensing measurements. These new halo mass estimates enable more accurate investigation of the galaxy-halo connection and the role of the halos in galaxy evolution., Comment: 21 pages, 9 figures. Submitted to ApJ, comments welcome!

The spectral energy distribution (SED) of galaxies is essential for deriving fundamental properties like stellar mass and star formation history (SFH). However, conventional methods, including both parametric and non-parametric approaches, often fail to accurately recover the observed cosmic star formation rate (SFR) density due to oversimplified or unrealistic assumptions about SFH and their inability to account for the complex SFH variations across different galaxy populations. To address this issue, we introduce a novel approach that improves galaxy broad-band SED analysis by incorporating physical priors derived from hydrodynamical simulations. Tests using IllustrisTNG simulations demonstrate that our method can reliably determine galaxy physical properties from broad-band photometry, including stellar mass within 0.05 dex, current SFR within 0.3 dex, and fractional stellar formation time within 0.2 dex, with a negligible fraction of catastrophic failures. When applied to the SDSS main photometric galaxy sample with spectroscopic redshift, our estimates of stellar mass and SFR are consistent with the widely-used MPA-JHU and GSWLC catalogs. Notably, using the derived SFHs of individual SDSS galaxies, we estimate the cosmic SFR density and stellar mass density with remarkable consistency to direct observations up to $z \sim 6$. This marks the first time SFHs derived from SEDs can accurately match observations. Consequently, our method can reliably recover observed spectral indices such as $\rm D_{\rm n}(4000)$ and $\rm H\delta_{\rm A}$ by synthesizing the full spectra of galaxies using the estimated SFHs and metal enrichment histories, relying solely on broad-band photometry as input. Furthermore, this method is extremely computationally efficient compared to conventional approaches., Comment: 22 pages, 12 figures. Submitted to ApJ, comments welcome!

Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time, but also often leads to frequent probe and sample damage, poor image quality and reproducibility issues for new types of samples or inexperienced users. Despite wide use, optimization of tapping mode imaging is an extremely hard problem, ill-suited to either classical control methods or machine learning. Here we introduce a reward-driven workflow to automate the optimization of SPM in the tapping mode. The reward function is defined based on multiple channels with physical and empirical knowledge of good scans encoded, representing a sample-agnostic measure of image quality and imitating the decision-making logic employed by human operators. This automated workflow gives optimal scanning parameters for different probes and samples and gives high-quality SPM images consistently in the attractive mode. This study broadens the application and accessibility of SPM and opens the door for fully automated SPM., Comment: 20 pages, 6 figures

Pion condensation is a theoretical prediction, where pions form a special state of matter under certain extreme conditions in heavy ion collisions or neutron stars. However, there is currently no solid experimental evidence confirming the existence of pion condensation. We present a near-direct evidence for the existence of pion condensation. In actively changing galactic processes protons can be accelerated to very high energies and collide with the medium (protons or nuclei). The kinetic energy of the protons is mainly used to produce a large number of pions in the central region via gluons. When the collision energy exceeds a certain threshold, the huge amount of soft gluons condensed in protons pour into the central region, the number of pion increases abruptly to the saturation limit, and almost all the available collision energy is used to make pions, creating a dense, low-temperature pion condensation environment. Due to energy conservation and relativistic covariance, the gamma ray spectra produced by condensed pions exhibit recognizable broken power law with the gluon condensation characteristics. We find that they are already present in many recorded gamma ray spectra. Our findings reveal a novel mechanism for the generation of pion condensation, which is prevalent in the formation of high-energy cosmic rays, and deepen our understanding of related topics in a variety of disciplines, including particle physics, astrophysics, condensed matter physics and nuclear physics., Comment: 27 pages, 12 Figures

The large-scale deployment of quantum secret sharing (QSS) in quantum networks is currently challenging due to the requirements for the generation and distribution of multipartite entanglement states. Here we present an efficient source-independent QSS protocol utilizing entangled photon pairs in quantum networks. Through the post-matching method, which means the measurement events in the same basis are matched, the key rate is almost independent of the number of participants. In addition, the unconditional security of our QSS against internal and external eavesdroppers can be proved by introducing an equivalent virtual protocol. Our protocol has great performance and technical advantages in future quantum networks., Comment: 13 pages, 9 figures

Using large language models, this paper presents techniques to improve extremely low-resourced indigenous language translations. Our approaches are grounded in the use of (1) the presence of a datastore consisting of a limited number of parallel translation examples, (2) the inherent capabilities of LLMs like GPT-3.5, and (3) a word-level translation dictionary. We harness the potential of LLMs and in-context learning techniques in such a setting for using LLMs as universal translators for extremely low-resourced languages. Our methodology hinges on utilizing LLMs as language compilers for selected language pairs, hypothesizing that they could internalize syntactic structures to facilitate accurate translation. We introduce three techniques: KNNPrompting with Retrieved Prompting Context, Chain-of-Thought Prompting and Learningfrom-Mistakes Prompting, with the last method addressing past errors. The evaluation results suggest that, even with limited corpora, LLMs can effectively translate extremely low-resource languages when paired with proper prompting.

Stochastic diffusion processes are pervasive in nature, from the seemingly erratic Brownian motion to the complex interactions of synaptically-coupled spiking neurons. Recently, drawing inspiration from Langevin dynamics, neuromorphic diffusion models were proposed and have become one of the major breakthroughs in the field of generative artificial intelligence. Unlike discriminative models that have been well developed to tackle classification or regression tasks, diffusion models as well as other generative models such as ChatGPT aim at creating content based upon contexts learned. However, the more complex algorithms of these models result in high computational costs using today's technologies, creating a bottleneck in their efficiency, and impeding further development. Here, we develop a spintronic voltage-controlled magnetoelectric memory hardware for the neuromorphic diffusion process. The in-memory computing capability of our spintronic devices goes beyond current Von Neumann architecture, where memory and computing units are separated. Together with the non-volatility of magnetic memory, we can achieve high-speed and low-cost computing, which is desirable for the increasing scale of generative models in the current era. We experimentally demonstrate that the hardware-based true random diffusion process can be implemented for image generation and achieve comparable image quality to software-based training as measured by the Frechet inception distance (FID) score, achieving ~10^3 better energy-per-bit-per-area over traditional hardware.

Quantum conference key agreement (QCKA) enables the unconditional secure distribution of conference keys among multiple participants. Due to challenges in high-fidelity preparation and long-distance distribution of multi-photon entanglement, entanglement-based QCKA is facing severe limitations in both key rate and scalability. Here, we propose a source-independent QCKA scheme utilizing the post-matching method, feasible within the entangled photon pair distribution network. We introduce an equivalent distributing virtual multi-photon entanglement protocol for providing the unconditional security proof even in the case of coherent attacks. For the symmetry star-network, comparing with previous $n$-photon entanglement protocol, the conference key rate is improved from $O(\eta^{n})$ to $O(\eta^{2})$, where $\eta$ is the transmittance from the entanglement source to one participant. Simulation results show that the performance of our protocol has multiple orders of magnitude advantages in the intercity distance. We anticipate that our approach will demonstrate its potential in the implementation of quantum networks., Comment: 10 pages, 6 figures

In this paper, we tackle the task of distractor generation (DG) for multiple-choice questions. Our study introduces two key designs. First, we propose \textit{retrieval augmented pretraining}, which involves refining the language model pretraining to align it more closely with the downstream task of DG. Second, we explore the integration of knowledge graphs to enhance the performance of DG. Through experiments with benchmarking datasets, we show that our models significantly outperform the state-of-the-art results. Our best-performing model advances the F1@3 score from 14.80 to 16.47 in MCQ dataset and from 15.92 to 16.50 in Sciq dataset., Comment: Findings at ACL 2024

Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.

For spinful systems with spin 1/2, it is generally believed that P and T invariant strong and second-order topologies exist in four band and eight band system, respectively. Here, by using periodic driving, we find it is possible to have strong topological insulator, second-order topological insulator and hybrid-order topological insulator in a single four band system. Furthermore, we established a direct connection between topology and dynamics. More convenient experimental detection for these topological phases has also been proposed. This study provides the theoretical basis for novel topological insulator that possess hybrid-order boundary states beyond the conventional regimes., Comment: 7 pages

We develop a second-order accurate central scheme for the two-dimensional hyperbolic system of in-homogeneous conservation laws. The main idea behind the scheme is that we combine the well-balanced deviation method with the Kurganov-Tadmor (KT) scheme. The approach satisfies the well-balanced property and retains the advantages of KT scheme: Riemann-solver-free and the avoidance of oversampling on the regions between Riemann-fans. The scheme is implemented and applied to a number of numerical experiments for the Euler equations with gravitational source term and the results are non-oscillatory. Based on the same idea, we construct a semi-discrete scheme where we combine the above two methods and illustrate the maximum principle.

Real-space imaging of three-dimensional atomic structures is a critical yet challenging task in materials science. Although scanning transmission electron microscopy has achieved sub-angstrom lateral resolution through techniques like electron ptychography1,2, depth resolution remains limited to only 2 to 3 nanometers with a single projection setup3,4. Attaining better depth resolution typically necessitates large sample tilt angles and many projections, as seen in atomic electron tomography5,6. Here, we develop a new algorithm based on multislice electron ptychography which couples only a few projections at small tilt angles, but is sufficient to improve the depth resolution by more than threefold to the sub-nanometer scale, and potentially to the atomic level. This technique maintains high resolving power for both light and heavy atoms, and significantly improves the visibility of single dopants. We are thus able to experimentally detect dilute substitutional praseodymium dopants in a brownmillerite oxide, Ca2Co2O5, in three dimensions and observe the accompanying lattice distortion. This technique requires only a moderate level of data acquisition or processing, and can be seamlessly integrated into electron microscopes equipped with conventional components., Comment: 27 pages, 5 figures, 10 supplementary figures

The intermediate-mass-ratio inspirals (IMRIs) may be surrounded by dark matter (DM) minispikes. The dynamical friction from these DM minispike structures can affect the dynamics and the gravitational wave (GW) emission of the IMRIs. We analyze the effects of general dynamical friction, with a particular contribution from DM particles moving faster than the stellar-mass black hole in an eccentric IMRI. The results show that the dynamical friction caused by these DM particles tends to eccentricify the orbit, and the general dynamical friction is able to increase the eccentricity. We also analyze the effects of general dynamical friction on the GW characteristic strain. The results indicate that the peak value of the characteristic strain occurs at higher frequencies as the power law index of DM minispike $\gamma_\mathrm{sp}$ increases. For the first time, a general analytical relation between the frequency peak value of characteristic strain of GWs and $\gamma_\mathrm{sp}$ is established. Using the analytical relation, the presence of DM and its halo density may be determined potentially from future GW data., Comment: 7 pages, 4 figures

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $ n \rightarrow { inv} $ and $ nn \rightarrow { inv} $. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$., Comment: 28 pages, 7 figures, 4 tables

Utilizing large language models (LLMs) for transforming natural language questions into SQL queries (text-to-SQL) is a promising yet challenging approach, particularly when applied to real-world databases with complex and extensive schemas. In particular, effectively incorporating data catalogs and database values for SQL generation remains an obstacle, leading to suboptimal solutions. We address this problem by proposing a new pipeline that effectively retrieves relevant data and context, selects an efficient schema, and synthesizes correct and efficient SQL queries. To increase retrieval precision, our pipeline introduces a hierarchical retrieval method leveraging model-generated keywords, locality-sensitive hashing indexing, and vector databases. Additionally, we have developed an adaptive schema pruning technique that adjusts based on the complexity of the problem and the model's context size. Our approach generalizes to both frontier proprietary models like GPT-4 and open-source models such as Llama-3-70B. Through a series of ablation studies, we demonstrate the effectiveness of each component of our pipeline and its impact on the end-to-end performance. Our method achieves new state-of-the-art performance on the cross-domain challenging BIRD dataset.

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 rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements towards operationalization of automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer (HPC), which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning., Comment: 16 pages, 7 figures

In this paper, we propose a new MUSCL scheme by combining the ideas of the Kurganov and Tadmor scheme and the so-called Deviation method which results in a well-balanced finite volume method for the hyperbolic balance laws, by evolving the difference between the exact solution and a given stationary solution. After that, we derive a semi-discrete scheme from this new scheme and it can be shown to be essentially TVD when applied to a scalar conservation law. In the end, we apply and validate the developed methods by numerical experiments and solve classical problems featuring Euler equations with gravitational source term.

Recently, Beloborodov suggested that there exists a resonance phenomenon between an extremely intense electromagnetic wave and internal magnetized particles. The particles exchange energy with the wave at frequent resonance events and then reach the radiation reaction limit immediately. This process greatly enhances the scattering cross section of the particles. Note that these results only involve an extraordinary (X) mode wave. In this paper, we focus on an intense ordinary (O) mode wave propagating through magnetized particles and compare it with the case of the X-mode wave. Our result shows that the scattering cross section of the particles in the O-mode wave is significantly smaller than that in the X-mode wave. This has important implications for the transparency of a fast radio burst (FRB) inside the magnetosphere of a magnetar. We argue that there is a strong scattering region in the stellar magnetosphere, within which an O-mode wave is more transparent than an X-mode wave for an FRB., Comment: 8 pages, 5 figures, accepted by ApJ

Quantum communication and quantum metrology are widely compelling applications in the field of quantum information science, and quantum remote sensing is an intersection of both. Despite their differences, there are notable commonalities between quantum communication and quantum remote sensing, as they achieve their functionalities through the transmission of quantum states. Here we propose a novel quantum integrated sensing and communication (QISAC) protocol, which achieves quantum sensing under the Heisenberg limit while simultaneously enabling quantum secure communication through the transmission of entanglements. We have theoretically proven its security against eavesdroppers. The security of QISAC is characterized by the secrecy capacity for information bit as well as asymmetric Fisher information gain for sensing. Through simulations conducted under the constraints of limited entanglement resources, we illustrate that QISAC maintains high accuracy in the estimation of phase. Hence our QISAC offers a fresh perspective for the applications of future quantum networks.

The domain of computer vision has experienced significant advancements in facial-landmark detection, becoming increasingly essential across various applications such as augmented reality, facial recognition, and emotion analysis. Unlike object detection or semantic segmentation, which focus on identifying objects and outlining boundaries, faciallandmark detection aims to precisely locate and track critical facial features. However, deploying deep learning-based facial-landmark detection models on embedded systems with limited computational resources poses challenges due to the complexity of facial features, especially in dynamic settings. Additionally, ensuring robustness across diverse ethnicities and expressions presents further obstacles. Existing datasets often lack comprehensive representation of facial nuances, particularly within populations like those in Taiwan. This paper introduces a novel approach to address these challenges through the development of a knowledge distillation method. By transferring knowledge from larger models to smaller ones, we aim to create lightweight yet powerful deep learning models tailored specifically for facial-landmark detection tasks. Our goal is to design models capable of accurately locating facial landmarks under varying conditions, including diverse expressions, orientations, and lighting environments. The ultimate objective is to achieve high accuracy and real-time performance suitable for deployment on embedded systems. This method was successfully implemented and achieved a top 6th place finish out of 165 participants in the IEEE ICME 2024 PAIR competition., Comment: technical report. 6th/165 in IEEE ICME 2024 PAIR competition

Handling graph data is one of the most difficult tasks. Traditional techniques, such as those based on geometry and matrix factorization, rely on assumptions about the data relations that become inadequate when handling large and complex graph data. On the other hand, deep learning approaches demonstrate promising results in handling large graph data, but they often fall short of providing interpretable explanations. To equip the graph processing with both high accuracy and explainability, we introduce a novel approach that harnesses the power of a large language model (LLM), enhanced by an uncertainty-aware module to provide a confidence score on the generated answer. We experiment with our approach on two graph processing tasks: few-shot knowledge graph completion and graph classification. Our results demonstrate that through parameter efficient fine-tuning, the LLM surpasses state-of-the-art algorithms by a substantial margin across ten diverse benchmark datasets. Moreover, to address the challenge of explainability, we propose an uncertainty estimation based on perturbation, along with a calibration scheme to quantify the confidence scores of the generated answers. Our confidence measure achieves an AUC of 0.8 or higher on seven out of the ten datasets in predicting the correctness of the answer generated by LLM., Comment: Because my organization does not allow members to privately upload papers to arXiv, I am requesting a withdrawal of my submission

Phase singularities are phase-indeterminate points where wave amplitudes are zero, which manifest as phase vertices or wavefront dislocations. In the realm of optical and electron beams, the phase singularity has been extensively explored, demonstrating a profound connection to orbital angular momentum. Direct local imaging of the impact of orbital angular momentum on phase singularities at the nanoscale, however, remains a challenge and has yet to be achieved. Here, we study the role of orbital angular momentum in phase singularities in graphene, particularly at the atomic level, through scanning tunneling microscopy and spectroscopy. Our experiments demonstrate that the scatterings between different orbital angular momentum states, which are induced by local rotational symmetry-breaking potentials, can generate additional phase singularity, and result in robust single wavefront dislocation in real space. Our results pave the way for exploring the effects of orbital degree of freedom on quantum phases in quasiparticle interference processes., Comment: 28 pages, 3 figures, 10 extended figures

In-plane anisotropic layered materials (ALMs), such as black phosphorus (BP), exhibit unique angle-resolved polarized Raman (ARPR) spectroscopy characteristics, as attributed to birefringence, linear dichroism and complex Raman tensor. Moreover, the ARPR intensity profiles of BP flakes deposited on multilayer dielectrics are notably sensitive to their thickness, owing to interference effects. The intricate anisotropic effects present challenges in accurately predicting the ARPR intensity of BP flakes. In this study, we propose a comprehensive strategy for predicting the ARPR intensity of BP flakes by explicitly considering optical anisotropy, encompassing birefringence, linear dichroism, and anisotropic cavity interference effects within multilayered structures. Through this approach, we have identified the intrinsic complex Raman tensors for phonon modes, independent of the BP flake thickness. By leveraging this methodology, we have elucidated the flake thickness-dependent effective complex Raman tensor elements, allowing for precise prediction of the observed ARPR intensity profile for the BP flake. This work provides a profound understanding of ARPR behaviors for ALM flakes., Comment: 6 pages, 4 figures

In this study, we introduce the DriveEnv-NeRF framework, which leverages Neural Radiance Fields (NeRF) to enable the validation and faithful forecasting of the efficacy of autonomous driving agents in a targeted real-world scene. Standard simulator-based rendering often fails to accurately reflect real-world performance due to the sim-to-real gap, which represents the disparity between virtual simulations and real-world conditions. To mitigate this gap, we propose a workflow for building a high-fidelity simulation environment of the targeted real-world scene using NeRF. This approach is capable of rendering realistic images from novel viewpoints and constructing 3D meshes for emulating collisions. The validation of these capabilities through the comparison of success rates in both simulated and real environments demonstrates the benefits of using DriveEnv-NeRF as a real-world performance indicator. Furthermore, the DriveEnv-NeRF framework can serve as a training environment for autonomous driving agents under various lighting conditions. This approach enhances the robustness of the agents and reduces performance degradation when deployed to the target real scene, compared to agents fully trained using the standard simulator rendering pipeline., Comment: Project page: https://github.com/muyishen2040/DriveEnvNeRF

In this paper, we consider the standard quantum information decoupling, in which Alice aims to decouple her system from the environment by local operations and discarding some of her systems. To achieve an $\varepsilon$-decoupling with trace distance as the error criterion, we establish a near-optimal one-shot characterization for the largest dimension of the remainder system in terms of the conditional $(1-\varepsilon)$-hypothesis-testing entropy. When the underlying system is independent and identically prepared, our result leads to the matched second-order rate as well as the matched moderate deviation rate. As an application, we find an achievability bound in entanglement distillation protocol, where the objective is for Alice and Bob to transform their quantum state to maximally entangled state with largest possible dimension using only local operations and one-way classical communications.

We use a newly recognized gluon distribution in the nucleon, which was predicted by a QCD evolution equation to consistently explain several intriguing phenomena associated with gamma-ray bursts. They are the GeV-TeV spectra of GRB 221009A, the remarkably symmetrical explosion cloud in kilonova AT2017gfo, and the absence of a very high-energy gamma-ray signature in GRB 170817A. We find that these occurrences can be attributed to the gluon condensation within nucleons, i.e., a significant number of soft gluons within nucleons are condensed at a critical momentum, resulting in the emergence of a steep and high peak in the gluon distributions. Through this profound connection between microscopic and macroscopic phenomena, we have not only expanded the applications of the hadronic scenario in cosmic gamma-ray emissions but also presented new evidence for the existence of gluon condensation., Comment: 30 pages, 6 figures, submitted to JCAP

For dark matter (DM) direct detections, the kinematic effects such as those of the inelastic scattering can play important role in light DM searches. The light DM detection is generally difficult because of its small recoil energy. But the recoil energy of the exothermic inelastic DM scattering could exceed the detection threshold due to the contribution from the DM mass-splitting, making the direct detection of sub-GeV DM feasible. In this work, we systematically study signatures of the light exothermic inelastic DM from the recoil spectra including both the DM-electron scattering and Migdal effect. Such inelastic DM has mass around (sub-)GeV scale with DM mass-splitting of $O(1-10^2)$keV. We analyze the direct detection sensitivities to such light inelastic DM. For different inelastic DM masses and mass-splittings, we find that the DM-electron recoil and Migdal effect can contribute significantly and differently to the direct detection signatures. The DM-lepton and/or DM-quark interactions may vary for different DM models, and their interplay leads to a diversity in the recoil spectra. Hence, it is important to perform a combined analysis to include both the DM-electron recoil and Migdal effect. We further demonstrate that this analysis has strong impacts on the cosmological and laboratory bounds for the inelastic DM., Comment: Comments: 33 pages, Journal version. Only minor refinements, all results and conclusions unchanged

Vibrational strong coupling can modify chemical reaction pathways in unconventional ways. Thus far, Fabry-Perot cavities formed by pairs of facing mirrors have been mostly utilized to achieve vibrational strong coupling. In this study, we demonstrate the application of plasmonic microparticle arrays defining non-local metasurfaces that can sustain surface lattice resonances as a novel tool to enable chemical reactions under vibrational strong coupling. We show that the solvolysis kinetics of \textit{para}-nitrophenyl acetate can be accelerated by a factor of 2.7 by strong coupling to the carbonyl bond of the solvent and the solute with a surface lattice resonance. Our work introduces a new platform to investigate and control polaritonic chemical reactions. In contrast to Fabry-Perot cavities, metasurfaces define open optical cavities with single surfaces, which removes alignment hurdles, facilitating polaritonic chemistry across large areas.