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This thesis examines COVID-19-related black humour on Chinese and Australian social media. Informed by Bakhtin’s carnivalesque, combining Content Analysis with Critical Discourse Analysis, this study compares themes, types, and power dynamics of black humour between Weibo and Twitter. While the Australian “Larrikin” spirit serves as a cultural heritage and symbol, the Chinese carnivalesque has ceased to be an exclusive attribute of the populace and has been co-opted by the official authorities as a tool to advance their ideological agenda.

Astrometry, the precise measurement of star motions, offers an alternative avenue to investigate low-frequency gravitational waves through the spatial deflection of photons, complementing pulsar timing arrays reliant on timing residuals. Upcoming data from Gaia, Theia, and Roman can not only cross-check pulsar timing array findings but also explore the uncharted frequency range bridging pulsar timing arrays and LISA. We present an analytical framework to evaluate the feasibility of detecting a gravitational wave background, considering measurement noise and the intrinsic variability of the stochastic background. Furthermore, we highlight astrometry's crucial role in uncovering key properties of the gravitational wave background, such as spectral index and chirality, employing information-matrix analysis. Finally, we simulate the emergence of quadrupolar correlations, commonly referred to as the generalized Hellings-Downs curves.

Large vertical cylindrical structure is an important type of structure in polarimetric synthetic aperture radar (PolSAR) images. Objects with this structure, such as oil tanks, granaries and rotor sails, attract much attention in multiple fields. Before detecting these targets with these structures, scattering mechanism analysis and feature extraction are necessary prerequisite steps. However, due to the lack of scattering mechanism analysis and characterization for such structures, traditional feature extraction methods, like polarimetric decomposition methods, usually fail to distinguish these targets. In this letter, according to high frequency approximation theory, the scattering mechanism of large vertical cylindrical structure with finite radius and height is analyzed. The coherency matrix of this structure with multiple pixels is derived further for scattering characterization. The model is called vertical cylindrical scattering model (VCDM), which is further introduced into a polarimetric decomposition method for feature extraction. Experiments prove that the model can describe the scattering mechanism of large vertical cylindrical structure accurately. Besides, the feature extraction method can effectively extract features for large vertical cylindrical structures in PolSAR images, which helps detect targets with this kind of structures., This work was supported in part by the National Natural Science Foundation of China under Grant 62271172 and Grant 61871158 and in part by the Aeronautical Science Foundation of China under Grant 20182077008., Peer Reviewed, Postprint (author's final draft)

Dihedral is a common structure in polarimetric SAR images and can be found on many man-made targets. Many researchers have proposed different dihedral models, but the accuracy of these models is limited. In this case, the feature extraction methods based on these models are also not effective enough, which affects the subsequent applications such as target detection. Therefore, it is necessary to propose a new and accurate scattering model, which can be applied to dihedral with different orientation angles for feature extraction and target detection. In this paper, a general scattering model called modified oriented dihedral scattering model (MODM) is proposed based on physical optics (PO) and geometric optics (GO) of high-frequency approximation techniques. By analyzing the propagation and reflection of electromagnetic wave, MODM can accurately describe the scattering characteristic of dihedral for all observation conditions. In order to apply the model to real PolSAR images, MODM is introduced into a new feature extraction method, which is called five-scattering component polarimetric decomposition method (MODM-5SD). Feature extraction and target detection experiments of buildings with various oriented dihedral structures are performed using different data sets, which show that dihedral scattering components from oriented dihedral structures can be more effectively extracted by MODM-5SD. In addition, more buildings with oriented dihedral structures can be detected with the features from MODM-5SD. The experimental results show that MODM can more accurately describe the scattering characteristic of dihedral, which can be further applied for scattering characterization and feature extraction of targets with typical dihedral structures., This work was supported in part by the National Natural Science Foundation of China under Grant 62271172 and Grant 61871158, and in part by Aeronautical Science Foundation of China under Grant 20182077008., Peer Reviewed, Postprint (published version)

Dark photons, aside from constituting non-relativistic dark matter, can also be generated relativistically through the decay or annihilation of other dark matter candidates, contributing to a galactic dark photon background. The production of dark photons tends to favor specific polarization modes, determined by the microscopic coupling between dark matter and dark photons. We leverage data obtained from previous searches for dark photon dark matter using a superconducting radio-frequency cavity to explore galactic dark photon fluxes. The interplay of anisotropic directions and Earth's rotation introduces a diurnal modulation of signals within the cavities, manifesting distinct variation patterns for longitudinal and transverse modes. Our findings highlight the efficacy of superconducting radio-frequency cavities, characterized by significantly high-quality factors, as powerful telescopes for detecting galactic dark photons, unveiling a novel avenue in the indirect search for dark matter through multi-messenger astronomy., Comment: 16 pages, 3 figures

Dark photons, aside from constituting non-relativistic dark matter, can also be generated relativistically through the decay or annihilation of other dark matter candidates, contributing to a galactic dark photon background. The production of dark photons tends to favor specific polarization modes, determined by the microscopic coupling between dark matter and dark photons. We leverage data obtained from previous searches for dark photon dark matter using a superconducting radio-frequency cavity to explore galactic dark photon fluxes. The interplay of anisotropic directions and Earth's rotation introduces a diurnal modulation of signals within the cavities, manifesting distinct variation patterns for longitudinal and transverse modes. Our findings highlight the efficacy of superconducting radio-frequency cavities, characterized by significantly high-quality factors, as powerful telescopes for detecting galactic dark photons, unveiling a novel avenue in the indirect search for dark matter through multi-messenger astronomy., Comment: 16 pages, 3 figures

We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasting problem as sampling from the conditional distribution of the future system state given its current state. To this end, we leverage the framework of stochastic interpolants, which facilitates the construction of a generative model between an arbitrary base distribution and the target. We design a fictitious, non-physical stochastic dynamics that takes as initial condition the current system state and produces as output a sample from the target conditional distribution in finite time and without bias. This process therefore maps a point mass centered at the current state onto a probabilistic ensemble of forecasts. We prove that the drift coefficient entering the stochastic differential equation (SDE) achieving this task is non-singular, and that it can be learned efficiently by square loss regression over the time-series data. We show that the drift and the diffusion coefficients of this SDE can be adjusted after training, and that a specific choice that minimizes the impact of the estimation error gives a F\"ollmer process. We highlight the utility of our approach on several complex, high-dimensional forecasting problems, including stochastically forced Navier-Stokes and video prediction on the KTH and CLEVRER datasets.

Multiobjective optimization (MOO) is prevalent in numerous applications, in which a Pareto front (PF) is constructed to display optima under various preferences. Previous methods commonly utilize the set of Pareto objectives (particles on the PF) to represent the entire PF. However, the empirical distribution of the Pareto objectives on the PF is rarely studied, which implicitly impedes the generation of diverse and representative Pareto objectives in previous methods. To bridge the gap, we suggest in this paper constructing \emph{uniformly distributed} Pareto objectives on the PF, so as to alleviate the limited diversity found in previous MOO approaches. We are the first to formally define the concept of ``uniformity" for an MOO problem. We optimize the maximal minimal distances on the Pareto front using a neural network, resulting in both asymptotically and non-asymptotically uniform Pareto objectives. Our proposed method is validated through experiments on real-world and synthetic problems, which demonstrates the efficacy in generating high-quality uniform Pareto objectives and the encouraging performance exceeding existing state-of-the-art methods. The detailed model implementation and the code are scheduled to be open-sourced upon publication.

Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Language Models (LMs). Our experiments reveal that this example-level performance inconsistency exists not only between retrieval-augmented and retrieval-free LM but also among different retrievers. To understand this phenomenon, we investigate the degeneration behavior of RALMs and theoretically decompose it into four categories. Further analysis based on our decomposition reveals that the innate difference in knowledge sources and the unpredictable degeneration of the reader model contribute most to the inconsistency. Drawing from our analysis, we introduce Ensemble of Retrievers (EoR), a trainable framework that can adaptively retrieve from different knowledge sources and effectively decrease unpredictable reader errors. Our experiments on Open Domain Question Answering show that EoR substantially improves performance over the RALM with a single retriever by considerably reducing inconsistent behaviors., Comment: ACL 2024 (findings)

Diffusion models (DMs) have recently shown outstanding capability in modeling complex image distributions, making them expressive image priors for solving Bayesian inverse problems. However, most existing DM-based methods rely on approximations in the generative process to be generic to different inverse problems, leading to inaccurate sample distributions that deviate from the target posterior defined within the Bayesian framework. To harness the generative power of DMs while avoiding such approximations, we propose a Markov chain Monte Carlo algorithm that performs posterior sampling for general inverse problems by reducing it to sampling the posterior of a Gaussian denoising problem. Crucially, we leverage a general DM formulation as a unified interface that allows for rigorously solving the denoising problem with a range of state-of-the-art DMs. We demonstrate the effectiveness of the proposed method on six inverse problems (three linear and three nonlinear), including a real-world black hole imaging problem. Experimental results indicate that our proposed method offers more accurate reconstructions and posterior estimation compared to existing DM-based imaging inverse methods.

The article presents a systematic study of the problem of conditioning a Gaussian random variable $\xi$ on nonlinear observations of the form $F \circ \phi(\xi)$ where $\phi: \mathcal{X} \to \mathbb{R}^N$ is a bounded linear operator and $F$ is nonlinear. Such problems arise in the context of Bayesian inference and recent machine learning-inspired PDE solvers. We give a representer theorem for the conditioned random variable $\xi \mid F\circ \phi(\xi)$, stating that it decomposes as the sum of an infinite-dimensional Gaussian (which is identified analytically) as well as a finite-dimensional non-Gaussian measure. We also introduce a novel notion of the mode of a conditional measure by taking the limit of the natural relaxation of the problem, to which we can apply the existing notion of maximum a posteriori estimators of posterior measures. Finally, we introduce a variant of the Laplace approximation for the efficient simulation of the aforementioned conditioned Gaussian random variables towards uncertainty quantification.

Large garages are ubiquitous yet intricate scenes that present unique challenges due to their monotonous colors, repetitive patterns, reflective surfaces, and transparent vehicle glass. Conventional Structure from Motion (SfM) methods for camera pose estimation and 3D reconstruction often fail in these environments due to poor correspondence construction. To address these challenges, we introduce LetsGo, a LiDAR-assisted Gaussian splatting framework for large-scale garage modeling and rendering. We develop a handheld scanner, Polar, equipped with IMU, LiDAR, and a fisheye camera, to facilitate accurate data acquisition. Using this Polar device, we present the GarageWorld dataset, consisting of eight expansive garage scenes with diverse geometric structures, which will be made publicly available for further research. Our approach demonstrates that LiDAR point clouds collected by the Polar device significantly enhance a suite of 3D Gaussian splatting algorithms for garage scene modeling and rendering. We introduce a novel depth regularizer that effectively eliminates floating artifacts in rendered images. Additionally, we propose a multi-resolution 3D Gaussian representation designed for Level-of-Detail (LOD) rendering. This includes adapted scaling factors for individual levels and a random-resolution-level training scheme to optimize the Gaussians across different resolutions. This representation enables efficient rendering of large-scale garage scenes on lightweight devices via a web-based renderer. Experimental results on our GarageWorld dataset, as well as on ScanNet++ and KITTI-360, demonstrate the superiority of our method in terms of rendering quality and resource efficiency., Comment: Project Page: https://zhaofuq.github.io/LetsGo

Sequential-in-time methods solve a sequence of training problems to fit nonlinear parametrizations such as neural networks to approximate solution trajectories of partial differential equations over time. This work shows that sequential-in-time training methods can be understood broadly as either optimize-then-discretize (OtD) or discretize-then-optimize (DtO) schemes, which are well known concepts in numerical analysis. The unifying perspective leads to novel stability and a posteriori error analysis results that provide insights into theoretical and numerical aspects that are inherent to either OtD or DtO schemes such as the tangent space collapse phenomenon, which is a form of over-fitting. Additionally, the unified perspective facilitates establishing connections between variants of sequential-in-time training methods, which is demonstrated by identifying natural gradient descent methods on energy functionals as OtD schemes applied to the corresponding gradient flows.

This thesis focuses on numerical methods for scientific computing and scientific machine learning, specifically on solving partial differential equations and inverse problems. The design of numerical algorithms usually encompasses a spectrum that ranges from specialization to generality. Classical approaches, such as finite element methods, and contemporary scientific machine learning approaches, like neural nets, can be viewed as lying at relatively opposite ends of this spectrum. Throughout this thesis, we tackle mathematical challenges associated with both ends by advancing rigorous multiscale and statistical numerical methods. Regarding the multiscale numerical methods, we present an exponentially convergent multiscale finite element method for solving high-frequency Helmholtz's equation with rough coefficients. To achieve this, we first identify the local low-complexity structure of Helmholtz's equations when the resolution is smaller than the wavelength. Then, we construct local basis functions by solving local spectral problems and couple them globally through non-overlapped domain decomposition and Galerkin's method. This results in a numerical method that achieves nearly exponentially convergent accuracy regarding the number of local basis functions, even when the solution is highly non-smooth. We also analyze the role of a subsampled lengthscale in variational multiscale methods, characterizing the tradeoff between accuracy and efficiency in the numerical upscaling of heterogeneous PDEs and scattered data approximation. As for the statistical numerical methods, we discuss using Gaussian processes and kernel methods to solve nonlinear PDEs and inverse problems. This framework incorporates the flavor of scientific machine learning automation and extends classical meshless solvers. It transforms general PDE problems into quadratic optimization with nonlinear constraints. We present the theoretical underpinning of the methodology. For the sc

This thesis focuses on numerical methods for scientific computing and scientific machine learning, specifically on solving partial differential equations and inverse problems. The design of numerical algorithms usually encompasses a spectrum that ranges from specialization to generality. Classical approaches, such as finite element methods, and contemporary scientific machine learning approaches, like neural nets, can be viewed as lying at relatively opposite ends of this spectrum. Throughout this thesis, we tackle mathematical challenges associated with both ends by advancing rigorous multiscale and statistical numerical methods. Regarding the multiscale numerical methods, we present an exponentially convergent multiscale finite element method for solving high-frequency Helmholtz's equation with rough coefficients. To achieve this, we first identify the local low-complexity structure of Helmholtz's equations when the resolution is smaller than the wavelength. Then, we construct local basis functions by solving local spectral problems and couple them globally through non-overlapped domain decomposition and Galerkin's method. This results in a numerical method that achieves nearly exponentially convergent accuracy regarding the number of local basis functions, even when the solution is highly non-smooth. We also analyze the role of a subsampled lengthscale in variational multiscale methods, characterizing the tradeoff between accuracy and efficiency in the numerical upscaling of heterogeneous PDEs and scattered data approximation. As for the statistical numerical methods, we discuss using Gaussian processes and kernel methods to solve nonlinear PDEs and inverse problems. This framework incorporates the flavor of scientific machine learning automation and extends classical meshless solvers. It transforms general PDE problems into quadratic optimization with nonlinear constraints. We present the theoretical underpinning of the methodology. For the sc

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics.

The Event Horizon Telescope (EHT) Collaboration recently published the first images of the supermassive black holes in the cores of the Messier 87 and Milky Way galaxies. These observations have provided a new means to study supermassive black holes and probe physical processes occurring in the strong-field regime. We review the prospects of future observations and theoretical studies of supermassive black hole systems with the next-generation Event Horizon Telescope (ngEHT), which will greatly enhance the capabilities of the existing EHT array. These enhancements will open up several previously inaccessible avenues of investigation, thereby providing important new insights into the properties of supermassive black holes and their environments. This review describes the current state of knowledge for five key science cases, summarising the unique challenges and opportunities for fundamental physics investigations that the ngEHT will enable., Comment: To be submitted to journal. Comments are welcome

Spinning black holes can transfer a significant fraction of their energy to ultralight bosonic fields via superradiance, condensing them in a co-rotating structure or "cloud". This mechanism turns black holes into powerful particle detectors for bosons with extremely feeble interactions. To explore its full potential, the couplings between such particles and the Maxwell field in the presence of plasma need to be understood. In this work, we study these couplings using numerical relativity. We first focus on the coupled axion-Maxwell system evolving on a black hole background. By taking into account the axionic coupling concurrently with the growth of the cloud, we observe for the first time that a new stage emerges: that of a stationary state where a constant flux of electromagnetic waves is fed by superradiance, for which we find accurate analytical estimates. Moreover, we show that the existence of electromagnetic instabilities in the presence of plasma is entirely controlled by the axionic coupling; even for dense plasmas, an instability is triggered for high enough couplings., Comment: 33 pages, 24 figures; references and figure added

The use of mathematical models in the field of tumor-immune interactions offers an analytical framework, which allows the study of specific questions related to tumor-immune dynamics and potential approaches for tumor treatment. This thesis deals with various mathematical approaches to describe tumor-immune interactions using systems of ordinary and delay differential equations.

According to World Health Organization, 13.1 million people will die in the world just because of cancer by 2030. Early tumor detection is very crucial to saving the world from this alarming mortality rate. However, it is an insurmountable challenge for the existing medical imaging techniques with limited imaging resolution to detect microscopic tumors. Hence, the need of the hour is to explore novel cross-disciplinary strategies to solve this problem. The rise of nanotechnologies provides a strong belief to solve complex medical problems such as early tumor detection. Nanoparticles with sizes ranging between 1-100 nanometers can be used as contrast agents. Their small sizes enable them to leak out of blood vessels and accumulate within tumors. Moreover, their chemical, optical, magnetic and electronic properties also change at nanoscale, which make them an ideal probing agent to spatially highlight the tumor site. Though, using nanoparticles to target malignant tumors is a promising concept, only 0.7% of the injected nanoparticles reach the tumor according to the statistical results of last 10 years. In PhD work, we proposed novel in vivo computational frameworks for fast, accurate and robust nanobiosensing. Specifically, the peritumoral region corresponds to the “objective function”; the tumor is the “global optimum”; the region of interest is the “domain” of the objective function; and the nanoswimmers are the “computational agents” (i.e., guesses or optimization variables). First, in externally manipulable in vivo computation, nanoswimmers are used as contrast agents to probe the region of interest. The observable characteristics of these nanoswimmers, under the influence of tumor-induced biological gradients, are utilized by the external tracking system to steer nanoswimmers towards the possible tumor direction. To take it one step ahead, we provide solutions to the real-life constraints of in vivo natural computation such as uniformity of the external steering

We present the first use of a tunable superconducting radio frequency cavity to perform a scan search for dark photon dark matter with novel data analysis strategies. We mechanically tuned the resonant frequency of a cavity embedded in the liquid helium with a temperature of $2$ K, scanning the dark photon mass over a frequency range of $1.37$ MHz centered at $1.3$ GHz. By exploiting the superconducting radio frequency cavity's considerably high quality factors of approximately $10^{10}$, our results demonstrate the most stringent constraints to date on a substantial portion of the exclusion parameter space, particularly concerning the kinetic mixing coefficient between dark photons and electromagnetic photons $\epsilon$, yielding a value of $\epsilon < 2.2 \times 10^{-16}$., Comment: 11 pages, 7 figures

Ultralight bosons can grow substantially in the vicinity of a black hole, through superradiant energy extraction. Consequently, such bosons can potentially reach field values close to the Planck scale, making black holes powerful transducers of such fields. If a scalar field couples to neutrino, it can trigger parametric production of neutrinos, and potentially quench their superradiant growth. During this saturation phase, scalar clouds can accelerate neutrinos to the TeV energy scale, generating fluxes that surpass those produced by atmospheric neutrinos., Comment: 13 pages, 3 figures

Spinning black holes can transfer a significant fraction of their energy to ultralight bosonic fields via superradiance, condensing them in a co-rotating structure or "cloud". This mechanism turns black holes into powerful particle detectors for bosons with extremely feeble interactions. To explore its full potential, the couplings between such particles and the Maxwell field in the presence of plasma need to be understood. In this work, we study these couplings using numerical relativity. We first focus on the coupled axion-Maxwell system evolving on a black hole background. By taking into account the axionic coupling concurrently with the growth of the cloud, we observe for the first time that a new stage emerges: that of a stationary state where a constant flux of electromagnetic waves is fed by superradiance, for which we find accurate analytical estimates. Moreover, we show that the existence of electromagnetic instabilities in the presence of plasma is entirely controlled by the axionic coupling; even for dense plasmas, an instability is triggered for high enough couplings., Comment: 33 pages, 24 figures; references and figure added

We provide a concise review of the exponentially convergent multiscale finite element method (ExpMsFEM) for efficient model reduction of PDEs in heterogeneous media without scale separation and in high-frequency wave propagation. The ExpMsFEM is built on the non-overlapped domain decomposition in the classical MsFEM while enriching the approximation space systematically to achieve a nearly exponential convergence rate regarding the number of basis functions. Unlike most generalizations of the MsFEM in the literature, the ExpMsFEM does not rely on any partition of unity functions. In general, it is necessary to use function representations dependent on the right-hand side to break the algebraic Kolmogorov n-width barrier to achieve exponential convergence. Indeed, there are online and offline parts in the function representation provided by the ExpMsFEM. The online part depends on the right-hand side locally and can be computed in parallel efficiently. The offline part contains basis functions that are used in the Galerkin method to assemble the stiffness matrix; they are all independent of the right-hand side, so the stiffness matrix can be used repeatedly in multi-query scenarios.

The shear strength of rock fractures serves as a crucial control on the strength and deformation behavior of engineering rock masses. To reduce the uncertainties in the shear strength evaluation, a hybrid machine learning model (GS-SVR model) of the support vector regression (SVR) underpinned by the grid search optimization algorithm (GS) was proposed. It achieves the prediction of shear strength by generalization and deduction of a large amount of data on rock fracture parameters, which avoids the complex derivation of theoretical equations. For practical application, a dataset comprising more than 134 shear tests on various rocks was compiled to collect the relevant three-dimensional morphological and mechanical parameters for training and prediction. Three classical shear strength models and the original SVR model were introduced for further comparison. Finally, sensitivity analysis was carried out to explore the relative importance of input variables to the shear strength. The results showed that the GS-SVR model (correlation coefficient R2 = 0.984, root mean squared error RMSE=0.383) outperformed the original SVR model (R2 = 0.936, RMSE=0.568). Moreover, compared with three classical shear strength models, the prediction results of the GS-SVR model were also most consistent with the experimental results (with the lowest RMSE and the highest R2). This machine learning model enhanced by GS can be used as a reliable and accurate shear strength prediction tool to partially replace laboratory tests to save costs., QC 20230922

The shear constitutive model is important for the analysis of the interface shear deformation mechanism. In this study, five statistical distributions, including four that have never been applied in interface shear deformation, are introduced to describe the damage evolution of the interface. The corresponding statistical damage constitutive models are developed, and they have been validated using a series of experimental data (both laboratory and in-situ tests) at the rock-concrete interface. The comparison results with laboratory data show that the Mitscherlich model has the lowest prediction accuracy. By comparing with in-situ data, the Weibull model shows the best-predicted performance. Based on the Akaike information criterion metric, the Morgan-Mercer-Flodin (MMF) model performs much better than the other models in the laboratory tests, which indicates the MMF model can be introduced for a comprehensive analysis of interface shear deformation. In addition, the similarities between the MMF model and the Weibull model are found through the canonical transformation of the MMF model. And the analysis of the two unknown parameters of the MMF model shows that they are related to the yield characteristics and strength characteristics of the interface, respectively. Based on the damage variable evolution, the shear deformation of the interface can be divided into three phases, i.e., damage initiation phase, damage acceleration phase and damage slowing phase. And the parameters of the MMF model have an important influence on the damage variable curves., QC 20230707

Ultralight bosons can grow substantially in the vicinity of a black hole, through superradiant energy extraction. Consequently, such bosons can potentially reach field values close to the Planck scale, making black holes powerful transducers of such fields. If a scalar field couples to neutrino, it can trigger parametric production of neutrinos, and potentially quench their superradiant growth. During this saturation phase, scalar clouds can accelerate neutrinos to the TeV energy scale, generating fluxes that surpass those produced by atmospheric neutrinos., Comment: 13 pages, 3 figures

We report on results of the on-ground X-ray calibration of the Lobster Eye Imager for Astronomy (LEIA), an experimental space wide-field (18.6*18.6 square degrees) X-ray telescope built from novel lobster eye mirco-pore optics. LEIA was successfully launched on July 27, 2022 onboard the SATech-01 satellite. To achieve full characterisation of its performance before launch, a series of tests and calibrations have been carried out at different levels of devices, assemblies and the complete module. In this paper, we present the results of the end-to-end calibration campaign of the complete module carried out at the 100-m X-ray Test Facility at IHEP. The PSF, effective area and energy response of the detectors were measured in a wide range of incident directions at several X-ray line energies. The distributions of the PSF and effective areas are roughly uniform across the FoV, in large agreement with the prediction of lobster-eye optics. The mild variations and deviations from the prediction of idealized lobster-eye optics can be understood to be caused by the imperfect shapes and alignment of the micro-pores as well as the obscuration by the supporting frames, which can be well reproduced by MC simulations. The spatial resolution of LEIA defined by the FWHM of the focal spot ranges from 4-8 arcmin with a median of 5.7. The measured effective areas are in range of 2-3 $cm^2$ at ~1.25 keV across the entire FoV, and its dependence on photon energy is in large agreement with simulations. The gains of the CMOS sensors are in range of 6.5-6.9 eV/DN, and the energy resolutions in the range of ~120-140 eV at 1.25 keV and ~170-190 eV at 4.5 keV. These results have been ingested into the calibration database and applied to the analysis of the scientific data acquired by LEIA. This work paves the way for the calibration of the Wide-field X-Ray Telescope modules of the Einstein Probe mission., Comment: 24 pages, 13 figures. Submitted to Experimental Astronomy

Electromagnetic resonant systems, such as cavities or LC circuits, have emerged as powerful detectors for probing ultralight boson dark matter and high-frequency gravitational waves. However, the limited resonant bandwidth of conventional single-mode resonators, imposed by quantum fluctuations, necessitates numerous scan steps to cover broad unexplored frequency regions. The incorporation of multiple auxiliary modes can realize a broadband detector while maintaining a substantial signal response. The broadened sensitive width can be on the same order as the resonant frequency, encompassing several orders of the source frequency for heterodyne detection, where a background cavity mode transitions into another. Consequently, our approach enables significantly deeper exploration of the parameter space within the same integration time compared to single-mode detection., Comment: 18 pages, 6 figures

Liquid argon time projection chambers (LArTPCs) are widely used in particle detection for their tracking and calorimetric capabilities. The particle physics community actively builds and improves high-quality simulators for such detectors in order to develop physics analyses in a realistic setting. The fidelity of these simulators relative to real, measured data is limited by the modeling of the physical detectors used for data collection. This modeling can be improved by performing dedicated calibration measurements. Conventional approaches calibrate individual detector parameters or processes one at a time. However, the impact of detector processes is entangled, making this a poor description of the underlying physics. We introduce a differentiable simulator that enables a gradient-based optimization, allowing for the first time a simultaneous calibration of all detector parameters. We describe the procedure of making a differentiable simulator, highlighting the challenges of retaining the physics quality of the standard, non-differentiable version while providing meaningful gradient information. We further discuss the advantages and drawbacks of using our differentiable simulator for calibration. Finally, we provide a starting point for extensions to our approach, including applications of the differentiable simulator to physics analysis pipelines.

Boosted dark particles of astrophysical origin can lead to nonstandard nuclear or electron recoil signals in direct detection experiments. We conduct an investigation of the daily modulation feature of a potential future signal of this type. In particular, we perform simulations of the dark particle interactions with electrons in atoms building up the Earth on its path to the detector and provide in-depth predictions for the expected daily changes in the signal for various direct detection experiments, including XENONnT, PandaX, and LUX-ZEPLIN.

The current prospective risk assessment study evaluated the application of the Chinese translation of the Historical-Clinical-Risk Management-20 Version 3 (HCR-20V3) in a sample of 152 offenders with mental disorders and civil psychiatric patients. The ratings of the presence and relevance of risk factors were compared, as well as summary risk ratings (SRRs), both across offenders and civil psychiatric patients, and across male and female sub-samples. Interrater reliability was consistently “excellent” for the presence and relevance of risk factors and for SRRs. Concurrent validity analyses indicated that HCR-20V3 was strongly correlated with Violence Risk Scale (from r = 0.53 to 0.71). The results of predictive validity analyses provided strong support for the bivariate associations between the main indices of HCR-20V3 and violence within 6 weeks, 7–24 weeks, and 6 months; SRRs added incrementally to both relevance and presence ratings across three follow-up lengths.

Ultralight bosons near rotating black holes can grow significantly via superradiant energy extraction, potentially reaching field values close to the Planck scale, thereby turning black holes into effective transducers for these fields. The interaction between a boson field and fermions can initiate a parametric production of fermions, potentially halting the exponential growth and leading to a saturated state of the boson cloud. This dynamic offers a framework for establishing limits on boson-neutrino interactions, which have traditionally been restricted by neutrino self-interaction considerations. At the saturation phase, boson clouds have the capacity to propel neutrinos to TeV-scale energies, generating fluxes that surpass atmospheric neutrino fluxes from nearby black holes. This mechanism extends to dark sector fermions, leading to the generation of boosted dark matter. These fluxes open novel avenues for observation through high-energy neutrino detectors like IceCube, as well as through dark matter direct detection, by directing observational efforts towards targeted black holes., Comment: 17 pages, 3 figures

Spinning black holes can transfer a significant fraction of their energy to ultralight bosonic fields via superradiance, condensing them in a co-rotating structure or "cloud". This mechanism turns black holes into powerful particle detectors for bosons with extremely feeble interactions. To explore its full potential, the couplings between such particles and the Maxwell field in the presence of plasma need to be understood. In this work, we study these couplings using numerical relativity. We first focus on the coupled axion-Maxwell system evolving on a black hole background. By taking into account the axionic coupling concurrently with the growth of the cloud, we observe for the first time that a new stage emerges: that of a stationary state where a constant flux of electromagnetic waves is fed by superradiance, for which we find accurate analytical estimates. Moreover, we show that the existence of electromagnetic instabilities in the presence of plasma is entirely controlled by the axionic coupling; even for dense plasmas, an instability is triggered for high enough couplings., Comment: 33 pages, 24 figures; references and figure added

We present the first use of a tunable superconducting radio frequency cavity to perform a scan search for dark photon dark matter with novel data analysis strategies. We mechanically tuned the resonant frequency of a cavity embedded in the liquid helium with a temperature of $2$ K, scanning the dark photon mass over a frequency range of $1.37$ MHz centered at $1.3$ GHz. By exploiting the superconducting radio frequency cavity's considerably high quality factors of approximately $10^{10}$, our results demonstrate the most stringent constraints to date on a substantial portion of the exclusion parameter space, particularly concerning the kinetic mixing coefficient between dark photons and electromagnetic photons $\epsilon$, yielding a value of $\epsilon < 2.2 \times 10^{-16}$., Comment: 11 pages, 7 figures

Sampling a probability distribution with an unknown normalization constant is a fundamental problem in computational science and engineering. This task may be cast as an optimization problem over all probability measures, and an initial distribution can be evolved to the desired minimizer dynamically via gradient flows. Mean-field models, whose law is governed by the gradient flow in the space of probability measures, may also be identified; particle approximations of these mean-field models form the basis of algorithms. The gradient flow approach is also the basis of algorithms for variational inference, in which the optimization is performed over a parameterized family of probability distributions such as Gaussians, and the underlying gradient flow is restricted to the parameterized family. By choosing different energy functionals and metrics for the gradient flow, different algorithms with different convergence properties arise. In this paper, we concentrate on the Kullback-Leibler divergence after showing that, up to scaling, it has the unique property that the gradient flows resulting from this choice of energy do not depend on the normalization constant. For the metrics, we focus on variants of the Fisher-Rao, Wasserstein, and Stein metrics; we introduce the affine invariance property for gradient flows, and their corresponding mean-field models, determine whether a given metric leads to affine invariance, and modify it to make it affine invariant if it does not. We study the resulting gradient flows in both probability density space and Gaussian space. The flow in the Gaussian space may be understood as a Gaussian approximation of the flow. We demonstrate that the Gaussian approximation based on the metric and through moment closure coincide, establish connections between them, and study their long-time convergence properties showing the advantages of affine invariance., Comment: 82 pages, 8 figures (Welcome any feedback!)

Multi-view imaging systems enable uniform coverage of 3D space and reduce the impact of occlusion, which is beneficial for 3D object detection and tracking accuracy. However, existing imaging systems built with multi-view cameras or depth sensors are limited by the small applicable scene and complicated composition. In this paper, we propose a wireless multi-view multi-modal 3D imaging system generally applicable to large outdoor scenes, which consists of a master node and several slave nodes. Multiple spatially distributed slave nodes equipped with cameras and LiDARs are connected to form a wireless sensor network. While providing flexibility and scalability, the system applies automatic spatio-temporal calibration techniques to obtain accurate 3D multi-view multi-modal data. This system is the first imaging system that integrates mutli-view RGB cameras and LiDARs in large outdoor scenes among existing 3D imaging systems. We perform point clouds based 3D object detection and long-term tracking using the 3D imaging dataset collected by this system. The experimental results show that multi-view point clouds greatly improve 3D object detection and tracking accuracy regardless of complex and various outdoor environments.

We present the first use of a tunable superconducting radio frequency cavity to perform a scan search for dark photon dark matter with novel data analysis strategies. We mechanically tuned the resonant frequency of a cavity embedded in the liquid helium with a temperature of $2$ K, scanning the dark photon mass over a frequency range of $1.37$ MHz centered at $1.3$ GHz. By exploiting the superconducting radio frequency cavity's considerably high quality factors of approximately $10^{10}$, our results demonstrate the most stringent constraints to date on a substantial portion of the exclusion parameter space, particularly concerning the kinetic mixing coefficient between dark photons and electromagnetic photons $\epsilon$, yielding a value of $\epsilon < 2.2 \times 10^{-16}$., Comment: 11 pages, 7 figures

Gravitational atoms produced from the superradiant extraction of rotational energy of spinning black holes can reach energy densities significantly higher than that of dark matter, turning black holes into powerful potential detectors for ultralight bosons. These structures are formed by coherently oscillating bosons, which induce oscillating metric perturbations deflecting photon geodesics passing through their interior. The deviation of nearby geodesics can be further amplified near critical bound photon orbits. We discuss the prospect of detecting this deflection using photon ring autocorrelations with the Event Horizon Telescope and its next-generation upgrade, which can probe a large unexplored region of the cloud mass parameter space when compared with previous constraints.

We introduce a priori Sobolev-space error estimates for the solution of nonlinear, and possibly parametric, PDEs using Gaussian process and kernel based methods. The primary assumptions are: (1) a continuous embedding of the reproducing kernel Hilbert space of the kernel into a Sobolev space of sufficient regularity; and (2) the stability of the differential operator and the solution map of the PDE between corresponding Sobolev spaces. The proof is articulated around Sobolev norm error estimates for kernel interpolants and relies on the minimizing norm property of the solution. The error estimates demonstrate dimension-benign convergence rates if the solution space of the PDE is smooth enough. We illustrate these points with applications to high-dimensional nonlinear elliptic PDEs and parametric PDEs. Although some recent machine learning methods have been presented as breaking the curse of dimensionality in solving high-dimensional PDEs, our analysis suggests a more nuanced picture: there is a trade-off between the regularity of the solution and the presence of the curse of dimensionality. Therefore, our results are in line with the understanding that the curse is absent when the solution is regular enough.

Conversational recommender systems (CRSs) aim to understand the information needs and preferences expressed in a dialogue to recommend suitable items to the user. Most of the existing conversational recommendation datasets are synthesized or simulated with crowdsourcing, which has a large gap with real-world scenarios. To bridge the gap, previous work contributes a dataset E-ConvRec, based on pre-sales dialogues between users and customer service staff in E-commerce scenarios. However, E-ConvRec only supplies coarse-grained annotations and general tasks for making recommendations in pre-sales dialogues. Different from that, we use real user needs as a clue to explore the E-commerce conversational recommendation in complex pre-sales dialogues, namely user needs-centric E-commerce conversational recommendation (UNECR). In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios. U-NEED consists of 3 types of resources: (i) 7,698 fine-grained annotated pre-sales dialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148 product knowledge tuples. To facilitate the research of UNECR, we propose 5 critical tasks: (i) pre-sales dialogue understanding (ii) user needs elicitation (iii) user needs-based recommendation (iv) pre-sales dialogue generation and (v) pre-sales dialogue evaluation. We establish baseline methods and evaluation metrics for each task. We report experimental results of 5 tasks on U-NEED. We also report results in 3 typical categories. Experimental results indicate that the challenges of UNECR in various categories are different., Comment: SIGIR23 Resource Track

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its \textit{partial derivatives}, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute $\epsilon$-approximate inverse Cholesky factors of the kernel matrices with complexity $O(N\log^d(N/\epsilon))$ in space and $O(N\log^{2d}(N/\epsilon))$ in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm's near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Amp\`ere equations., Comment: typo corrected

Astrometry, the precise measurement of star motions, offers an alternative avenue to investigate low-frequency gravitational waves through the spatial deflection of photons, complementing pulsar timing arrays reliant on timing residuals. Upcoming data from Gaia, Theia, and Roman can not only cross-check pulsar timing array findings but also explore the uncharted frequency range bridging pulsar timing arrays and LISA. We present an analytical framework to evaluate the feasibility of detecting a gravitational wave background, considering measurement noise and the intrinsic variability of the stochastic background. Furthermore, we highlight astrometry's crucial role in uncovering key properties of the gravitational wave background, such as spectral index and chirality, employing information-matrix analysis. Finally, we simulate the emergence of quadrupolar correlations, commonly referred to as the generalized Hellings-Downs curves., Comment: 34 pages, 8 figures

We report on results of the on-ground X-ray calibration of the Lobster Eye Imager for Astronomy (LEIA), an experimental space wide-field (18.6*18.6 square degrees) X-ray telescope built from novel lobster eye mirco-pore optics. LEIA was successfully launched on July 27, 2022 onboard the SATech-01 satellite. To achieve full characterisation of its performance before launch, a series of tests and calibrations have been carried out at different levels of devices, assemblies and the complete module. In this paper, we present the results of the end-to-end calibration campaign of the complete module carried out at the 100-m X-ray Test Facility at IHEP. The PSF, effective area and energy response of the detectors were measured in a wide range of incident directions at several X-ray line energies. The distributions of the PSF and effective areas are roughly uniform across the FoV, in large agreement with the prediction of lobster-eye optics. The mild variations and deviations from the prediction of idealized lobster-eye optics can be understood to be caused by the imperfect shapes and alignment of the micro-pores as well as the obscuration by the supporting frames, which can be well reproduced by MC simulations. The spatial resolution of LEIA defined by the FWHM of the focal spot ranges from 4-8 arcmin with a median of 5.7. The measured effective areas are in range of 2-3 $cm^2$ at ~1.25 keV across the entire FoV, and its dependence on photon energy is in large agreement with simulations. The gains of the CMOS sensors are in range of 6.5-6.9 eV/DN, and the energy resolutions in the range of ~120-140 eV at 1.25 keV and ~170-190 eV at 4.5 keV. These results have been ingested into the calibration database and applied to the analysis of the scientific data acquired by LEIA. This work paves the way for the calibration of the Wide-field X-Ray Telescope modules of the Einstein Probe mission., Comment: 24 pages, 13 figures. Submitted to Experimental Astronomy

The Event Horizon Telescope (EHT) Collaboration recently published the first images of the supermassive black holes in the cores of the Messier 87 and Milky Way galaxies. These observations have provided a new means to study supermassive black holes and probe physical processes occurring in the strong-field regime. We review the prospects of future observations and theoretical studies of supermassive black hole systems with the next-generation Event Horizon Telescope (ngEHT), which will greatly enhance the capabilities of the existing EHT array. These enhancements will open up several previously inaccessible avenues of investigation, thereby providing important new insights into the properties of supermassive black holes and their environments. This review describes the current state of knowledge for five key science cases, summarising the unique challenges and opportunities for fundamental physics investigations that the ngEHT will enable., Comment: To be submitted to journal. Comments are welcome

Boosted dark particles of astrophysical origin can lead to nonstandard nuclear or electron recoil signals in direct detection experiments. We conduct an investigation of the daily modulation feature of a potential future signal of this type. In particular, we perform simulations of the dark particle interactions with electrons in atoms building up the Earth on its path to the detector and provide in-depth predictions for the expected daily changes in the signal for various direct detection experiments, including XENONnT, PandaX, and LUX-ZEPLIN.