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Vision-based autonomous driving shows great potential due to its satisfactory performance and low costs. Most existing methods adopt dense representations (e.g., bird's eye view) or sparse representations (e.g., instance boxes) for decision-making, which suffer from the trade-off between comprehensiveness and efficiency. This paper explores a Gaussian-centric end-to-end autonomous driving (GaussianAD) framework and exploits 3D semantic Gaussians to extensively yet sparsely describe the scene. We initialize the scene with uniform 3D Gaussians and use surrounding-view images to progressively refine them to obtain the 3D Gaussian scene representation. We then use sparse convolutions to efficiently perform 3D perception (e.g., 3D detection, semantic map construction). We predict 3D flows for the Gaussians with dynamic semantics and plan the ego trajectory accordingly with an objective of future scene forecasting. Our GaussianAD can be trained in an end-to-end manner with optional perception labels when available. Extensive experiments on the widely used nuScenes dataset verify the effectiveness of our end-to-end GaussianAD on various tasks including motion planning, 3D occupancy prediction, and 4D occupancy forecasting. Code: https://github.com/wzzheng/GaussianAD., Comment: Code is available at: https://github.com/wzzheng/GaussianAD

We search for the stochastic gravitational-wave background (SGWB) predicted by pre-big-bang (PBB) cosmology using data from the first three observing runs of Advanced LIGO and Advanced Virgo. PBB cosmology proposes an alternative to cosmic inflation where the Universe evolves from a weak-coupling, low-curvature state to the hot Big Bang through a high-curvature bounce phase, predicting a distinctive SGWB spectrum. We perform a Bayesian analysis of the cross-correlation data to constrain the model parameters characterizing the PBB spectrum. We find no evidence for a PBB-induced SGWB, with a Bayes factor of $0.03$ between the PBB and noise-only model, strongly favoring the noise-only hypothesis. Our analysis establishes a lower bound $\beta \gtrsim -0.19$ at $95\%$ confidence level, which is compatible with the theoretical requirement $\beta \geq 0$ for a smooth bounce transition. While we do not detect a signal, our constraints remain consistent with the basic theoretical framework of PBB cosmology, demonstrating the potential of gravitational-wave observations to test early Universe theories., Comment: 13 pages, 1 figures

Fine-grained remote sensing image segmentation is essential for accurately identifying detailed objects in remote sensing images. Recently, vision transformer models (VTMs) pre-trained on large-scale datasets have demonstrated strong zero-shot generalization. However, directly applying them to specific tasks may lead to domain shift. We introduce a novel end-to-end learning paradigm combining knowledge guidance with domain refinement to enhance performance. We present two key components: the Feature Alignment Module (FAM) and the Feature Modulation Module (FMM). FAM aligns features from a CNN-based backbone with those from the pretrained VTM's encoder using channel transformation and spatial interpolation, and transfers knowledge via KL divergence and L2 normalization constraint. FMM further adapts the knowledge to the specific domain to address domain shift. We also introduce a fine-grained grass segmentation dataset and demonstrate, through experiments on two datasets, that our method achieves a significant improvement of 2.57 mIoU on the grass dataset and 3.73 mIoU on the cloud dataset. The results highlight the potential of combining knowledge transfer and domain adaptation to overcome domain-related challenges and data limitations. The project page is available at https://xavierjiezou.github.io/KTDA/., Comment: 6 pages, 3 figures, 6 tables

In this paper, we examine Lie group actions on moduli spaces (sets themselves built as quotients by group actions) and their fixed points. We show that when the Lie group is compact and connected, we obtain a linear constraint. This constraint makes the problem of finding fixed points one of representation theory, greatly simplifying the search for such points. We obtain a similar result when the Lie group is one-dimensional. For compact and disconnected Lie groups, we show that we need only additionally check a finite number of points. Finally, we show that the subgroup fixing an equivalence class in the moduli space is a compact Lie subgroup., Comment: 14 pages

Understanding and controlling non-equilibrium processes at ultralow temperatures are central to quantum physics and technology. In such extreme environments, quantum coherence and dissipation can interact intimately to give rise to intriguing thermalization phenomena. Here, we experimentally and theoretically demonstrate a novel scenario of thermalization in an ultracold atomic system, distinct from various quantum thermalization scenarios currently under intense investigations. We observe that after a sudden quench, an atomic Bose-Einstein condensate (BEC) behaves as a rigid body and undergoes a random walk in momentum space due to atom loss and interactions with the surrounding thermal component. Consequently its center of mass degree of freedom gets heated up at a constant rate. This heating mechanism, rooted in random momentum scattering, falls into the paradigm of stochastic heating initiated by Fermi and thoroughly explored in plasma physics, which differs conceptually from the traditional thermal conduction. At longer times, the stochastic heating of the BEC is balanced by forced evaporative cooling, and a Maxwell-Boltzmann distribution is achieved. Our findings offer new perspectives on the non-equilibrium dynamics of open Bose systems at ultralow temperature and quantum thermalization., Comment: 9 pages, 6 figures

Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT experiment is equipped with a novel gadolinium-doped water Cherenkov detector, which encloses the xenon dual-phase time projection chamber (TPC). The neutron veto (NV) tags neutrons via their capture on gadolinium or hydrogen, which release $\gamma$-rays that are subsequently detected as Cherenkov light. In this work, we present the key features and the first results of the XENONnT NV when operated with demineralized water in the initial phase of the experiment. Its efficiency for detecting neutrons is $(82\pm 1)\,\%$, the highest neutron detection efficiency achieved in a water Cherenkov detector. This enables a high efficiency of $(53\pm 3)\,\%$ for the tagging of WIMP-like neutron signals, inside a tagging time window of $250\,\mathrm{\mu s}$ between TPC and NV, leading to a livetime loss of $1.6\,\%$ during the first science run of XENONnT.

Ferromagnetism and electrical insulation are often at odds, signifying an inherent trade off. The simultaneous optimization of both in one material, essential for advancing spintronics and topological electronics, necessitates the individual manipulation over various degrees of freedom of strongly correlated electrons. Here, by selective control of the spin exchange and Coulomb interactions, we report the achievement of SrFeO2 thin films with resistivity above 106 Ohm.cm and strong magnetization with Curie temperature extrapolated to be 1200 K. Robust ferromagnetism is obtained down to 1.0 nm thickness on substrate and 2.0 nm for freestanding films. Featuring an out of plane oriented ripple infinite layer structure, this ferromagnetic insulating phase is obtained through extensive reduction of as grown brownmillerite SrFeO2.5 films at high compressive strains. Pronounced spin Hall magnetoresistance signals up to 0.0026 is further demonstrated with a Pt Hall bar device. Our findings promise emerging spintronic and topological electronic functionalities harnessing spin dynamics with minimized power dissipations.

The construction of loss functions presents a major challenge in data-driven modeling involving weak-form operators in PDEs and gradient flows, particularly due to the need to select test functions appropriately. We address this challenge by introducing self-test loss functions, which employ test functions that depend on the unknown parameters, specifically for cases where the operator depends linearly on the unknowns. The proposed self-test loss function conserves energy for gradient flows and coincides with the expected log-likelihood ratio for stochastic differential equations. Importantly, it is quadratic, facilitating theoretical analysis of identifiability and well-posedness of the inverse problem, while also leading to efficient parametric or nonparametric regression algorithms. It is computationally simple, requiring only low-order derivatives or even being entirely derivative-free, and numerical experiments demonstrate its robustness against noisy and discrete data.

The end-to-end autonomous driving paradigm has recently attracted lots of attention due to its scalability. However, existing methods are constrained by the limited scale of real-world data, which hinders a comprehensive exploration of the scaling laws associated with end-to-end autonomous driving. To address this issue, we collected substantial data from various driving scenarios and behaviors and conducted an extensive study on the scaling laws of existing imitation learning-based end-to-end autonomous driving paradigms. Specifically, approximately 4 million demonstrations from 23 different scenario types were gathered, amounting to over 30,000 hours of driving demonstrations. We performed open-loop evaluations and closed-loop simulation evaluations in 1,400 diverse driving demonstrations (1,300 for open-loop and 100 for closed-loop) under stringent assessment conditions. Through experimental analysis, we discovered that (1) the performance of the driving model exhibits a power-law relationship with the amount of training data; (2) a small increase in the quantity of long-tailed data can significantly improve the performance for the corresponding scenarios; (3) appropriate scaling of data enables the model to achieve combinatorial generalization in novel scenes and actions. Our results highlight the critical role of data scaling in improving the generalizability of models across diverse autonomous driving scenarios, assuring safe deployment in the real world. Project repository: https://github.com/ucaszyp/Driving-Scaling-Law

Causality plays an important role in understanding intelligent behavior, and there is a wealth of literature on mathematical models for causality, most of which is focused on causal graphs. Causal graphs are a powerful tool for a wide range of applications, in particular when the relevant variables are known and at the same level of abstraction. However, the given variables can also be unstructured data, like pixels of an image. Meanwhile, the causal variables, such as the positions of objects in the image, can be arbitrary deterministic functions of the given variables. Moreover, the causal variables may form a hierarchy of abstractions, in which the macro-level variables are deterministic functions of the micro-level variables. Causal graphs are limited when it comes to modeling this kind of situation. In the presence of deterministic relationships there is generally no causal graph that satisfies both the Markov condition and the faithfulness condition. We introduce factored space models as an alternative to causal graphs which naturally represent both probabilistic and deterministic relationships at all levels of abstraction. Moreover, we introduce structural independence and establish that it is equivalent to statistical independence in every distribution that factorizes over the factored space. This theorem generalizes the classical soundness and completeness theorem for d-separation., Comment: 29 pages

Nodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of node patterns or to make predictions based on distinct node patterns, resulting in unsatisfactory classification performance. In this paper, we reveal that different node predictors are good at handling nodes with specific patterns and only apply one node predictor uniformly could lead to suboptimal result. To mitigate this gap, we propose a mixture of experts framework, MoE-NP, for node classification. Specifically, MoE-NP combines a mixture of node predictors and strategically selects models based on node patterns. Experimental results from a range of real-world datasets demonstrate significant performance improvements from MoE-NP.

Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that closely mirror training data distributions. However, these methods struggle with rendering novel trajectories, such as lane changes. Recent works have demonstrated that integrating world model knowledge alleviates these issues. Despite their efficiency, these approaches still encounter difficulties in the accurate representation of more complex maneuvers, with multi-lane shifts being a notable example. Therefore, we introduce ReconDreamer, which enhances driving scene reconstruction through incremental integration of world model knowledge. Specifically, DriveRestorer is proposed to mitigate artifacts via online restoration. This is complemented by a progressive data update strategy designed to ensure high-quality rendering for more complex maneuvers. To the best of our knowledge, ReconDreamer is the first method to effectively render in large maneuvers. Experimental results demonstrate that ReconDreamer outperforms Street Gaussians in the NTA-IoU, NTL-IoU, and FID, with relative improvements by 24.87%, 6.72%, and 29.97%. Furthermore, ReconDreamer surpasses DriveDreamer4D with PVG during large maneuver rendering, as verified by a relative improvement of 195.87% in the NTA-IoU metric and a comprehensive user study., Comment: Project Page: https://recondreamer.github.io

Optical metasurface has brought a revolution in label-free molecular sensing, attracting extensive attention. Currently, such sensing approaches are being designed to respond to peak wavelengths with a higher Q factor in the visible and near-infrared regions.Nevertheless, a higher Q factor that enhances light confinement will inevitably deteriorate the wavelength sensitivity and complicate the sensing system. We propose a Q-switched sensing mechanism, which enables the real part of the refractive index to effectively perturbate the damping loss of the oscillator, resulting in a boost of peak intensity.Consequently, a higher Q factor in Q-switched sensor can further enhance the peak sensitivity while remaining compatible with broadband light sources, simultaneously meeting the requirements of high performance and a compact system.This is achieved in a unique 3D bound-state-in-continuum (BIC) metasurface which can be mass-produced by wafer-scale aluminum-nanoimprinting technology and provides a peak intensity sensitivity up to 928 %/RIU.Therefore, a miniaturized BIC biosensing system is realized, with a limit of detection to 10E-5 refractive index units and 129 aM extracellular vesicles in clinical lung cancer diagnosis, both of which are magnitudes lower than those of current state-of-the-art biosensors. It further demonstrates significant potential for home cancer self-testing equipment for post-operative follow-up. This Q-switched sensing mechanism offers a new perspective for the commercialization of advanced and practical BIC optical biosensing systems in real-setting scenarios.

We investigate whether the Pre-Big Bang (PBB) scenario from string cosmology can explain the stochastic gravitational wave background signal reported in the NANOGrav 15-year dataset. Using Bayesian analysis techniques, we constrain the key parameters of the PBB model by comparing its theoretical predictions with the observed data. Our analysis yields $\beta = -0.12^{+0.06}_{-0.21}$ ($90\%$ credible interval) for the dilaton-dynamics parameter, which lies outside the theoretically allowed range $0 \leq \beta < 3$ with more than $5\sigma$ confidence. Additionally, model comparison strongly favors a simple power-law spectrum over the PBB scenario, with a Bayes factor of approximately $468$. These results demonstrate that the PBB scenario, in its current formulation, cannot adequately explain the NANOGrav observations, highlighting the need for either significant modifications to the model or alternative explanations for the observed signal., Comment: V3, 8 pages, 3 figures, references added, typo corrected

System identification involves constructing mathematical models of dynamic systems using input-output data, enabling analysis and prediction of system behaviour in both time and frequency domains. This approach can model the entire system or capture specific dynamics within it. For meaningful analysis, it is essential for the model to accurately reflect the underlying system's behaviour. This paper introduces NonSysId, an open-sourced MATLAB software package designed for nonlinear system identification, specifically focusing on NARMAX models. The software incorporates an advanced term selection methodology that prioritises on simulation (free-run) accuracy while preserving model parsimony. A key feature is the integration of iterative Orthogonal Forward Regression (iOFR) with Predicted Residual Sum of Squares (PRESS) statistic-based term selection, facilitating robust model generalisation without the need for a separate validation dataset. Furthermore, techniques for reducing computational overheads are implemented. These features make NonSysId particularly suitable for real-time applications such as structural health monitoring, fault diagnosis, and biomedical signal processing, where it is a challenge to capture the signals under consistent conditions, resulting in limited or no validation data., Comment: 14 pages, 7 figures

Magnetars are slowly rotating, highly magnetized young neutron stars that can show transient radio phenomena for radio pulses and fast radio bursts. We conducted radio observations of from two magnetars SGR J1935+2154 and 3XMM J185246.6+003317 and a high-magnetic field pulsar PSR J1846$-$0258 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We performed single pulse and periodicity searches and did not detect radio signals from them. From the piggyback data recorded by other FAST telescope beams when we observed the magnetar SGR 1935+2154, we serendipitously discovered a new radio pulsar, PSR J1935+2200. We carried out the follow-up observations and obtained the timing solution based on these new observations and the archive FAST data. PSR J1935+2200 is an isolated old pulsar, with a spin period of $0.91$s, a spin-period derivative of $9.19 \times 10^{-15}$~s~s$^{-1}$, and a characteristic age of $1.57$ Myr. It is a weak pulsar with a flux density of 9.8 $\mu$Jy at 1.25 GHz. Discovery of a new pulsar from the long FAST observations of 30 minutes implies that there may be more weak older pulsars in the Galactic disk to be discovered., Comment: 6 pages, 3 figures and 3 tables. Submitted to RAA

Scoliosis is one of the most common diseases in adolescents. Traditional screening methods for the scoliosis usually use radiographic examination, which requires certified experts with medical instruments and brings the radiation risk. Considering such requirement and inconvenience, we propose to use natural images of the human back for wide-range scoliosis screening, which is a challenging problem. In this paper, we notice that the human back has a certain degree of symmetry, and asymmetrical human backs are usually caused by spinal lesions. Besides, scoliosis severity levels have ordinal relationships. Taking inspiration from this, we propose a dual-path scoliosis detection network with two main modules: symmetric feature matching module (SFMM) and ordinal regression head (ORH). Specifically, we first adopt a backbone to extract features from both the input image and its horizontally flipped image. Then, we feed the two extracted features into the SFMM to capture symmetric relationships. Finally, we use the ORH to transform the ordinal regression problem into a series of binary classification sub-problems. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods as well as human performance, which provides a promising and economic solution to wide-range scoliosis screening. In particular, our method achieves accuracies of 95.11% and 81.46% in estimation of general severity level and fine-grained severity level of the scoliosis, respectively., Comment: This paper has been accepted by Applied Intelligence

We report on a blinded search for dark matter with single- and few-electron signals in the first science run of XENONnT relying on a novel detector response framework that is physics-model-dependent. We derive 90\% confidence upper limits for dark matter-electron interactions. Heavy and light mediator cases are considered for the standard halo model and dark matter up-scattered in the Sun. We set stringent new limits on dark matter-electron scattering via a heavy mediator with a mass within 10-20\,MeV/$c^2$ and electron absorption of axion-like particles and dark photons for $m_\chi$ below 0.186\,keV/$c^2$., Comment: 9 pages, 5 figures

Vibronic interactions between nuclear motion and electronic states are critical for the accurate modeling of photochemistry. However, accurate simulations of fully quantum non-adiabatic dynamics are often prohibitively expensive for classical methods beyond small systems. In this work, we present a quantum algorithm based on product formulas for simulating time evolution under a general vibronic Hamiltonian in real space, capable of handling an arbitrary number of electronic states and vibrational modes. We develop the first trotterization scheme for vibronic Hamiltonians beyond two electronic states and introduce an array of optimization techniques for the exponentiation of each fragment in the product formula, resulting in a remarkably low cost of implementation. To demonstrate practical relevance, we outline a proof-of-principle integration of our algorithm into a materials discovery pipeline for designing more efficient singlet fission-based organic solar cells. Based on commutator bounds, we estimate that a $100$ femtosecond evolution using a second-order Trotter product formula of a $4$-state model of an anthracene-fullerene interface requires $117$ qubits and $1.5 \times 10^7$ Toffoli gates in a reduced dimensionality of $11$ modes. In its full dimensionality of $246$ modes, it requires $1065$ qubits and $2.7 \times 10^9$ Toffoli gates.

We report on an air-spaced virtually imaged phased array (VIPA) spectrometer that resolves the modes of a mid-infrared frequency comb with a repetition rate of 250 MHz, without an optical filter cavity. With a record spectral resolution of 94 MHz for VIPA, the spectrometer enables precision molecular spectroscopy with high resolution, broad spectral coverage and fast data acquisition. We demonstrate the capabilities of the spectrometer by measuring the absorption spectra of molecular species generated in plasmas. Using plasmas of a mixture of nitrogen, hydrogen, and methane at a low pressure of 1.5 mbar, we obtained high-resolution spectra of methane, around 3017 cm$^{-1}$, as well as hydrogen cyanide and ammonia, around 3240 cm$^{-1}$, demonstrating a wide spectral coverage over 290 cm$^{-1}$ (equivalent to 8.7 THz). The spectrometer performance in terms of Allan-Werle deviation and noise-equivalent absorption is discussed. The air-spaced VIPA concept offers a compact and practical spectrometer that harnesses the full potential of a stabilized frequency comb, making it suitable for a wide range of high-precision spectroscopic applications., Comment: 11 pages, 4 figures

Cloud segmentation is a critical challenge in remote sensing image interpretation, as its accuracy directly impacts the effectiveness of subsequent data processing and analysis. Recently, vision foundation models (VFM) have demonstrated powerful generalization capabilities across various visual tasks. In this paper, we present a parameter-efficient adaptive approach, termed Cloud-Adapter, designed to enhance the accuracy and robustness of cloud segmentation. Our method leverages a VFM pretrained on general domain data, which remains frozen, eliminating the need for additional training. Cloud-Adapter incorporates a lightweight spatial perception module that initially utilizes a convolutional neural network (ConvNet) to extract dense spatial representations. These multi-scale features are then aggregated and serve as contextual inputs to an adapting module, which modulates the frozen transformer layers within the VFM. Experimental results demonstrate that the Cloud-Adapter approach, utilizing only 0.6% of the trainable parameters of the frozen backbone, achieves substantial performance gains. Cloud-Adapter consistently achieves state-of-the-art performance across various cloud segmentation datasets from multiple satellite sources, sensor series, data processing levels, land cover scenarios, and annotation granularities. We have released the code and model checkpoints at https://xavierjiezou.github.io/Cloud-Adapter/ to support further research., Comment: 13 pages, 9 figures

We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/)

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1

We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends.

Distillation is a quark-smearing method for the construction of a broad class of hadron operators useful in lattice QCD computations and defined via a projection operator into a vector space of smooth gauge-covariant fields. A new orthonormal basis for this space is constructed which builds in locality. This basis is useful for the construction of stochastic methods to estimate the correlation functions computed in Monte Carlo calculations relevant for hadronic physics., Comment: 16 pages, 10 figures

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La$_{1-x}$Sr$_x$FeO$_3$. We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision., Comment: 30 pages, 5 figures, 2 tables

Many cyber-physical systems can naturally be formulated as switched systems with constrained switching. This includes systems where one of the signals in the feedback loop may be lost. Possible sources for losses are shared or unreliable communication media in networked control systems, or signals which are discarded, e.g., when using a shared computation device such as a processor in real-time control applications. The use of switched systems with constrained switching is not limited to cyber-physical systems but, includes many other relevant applications such as power systems and modeling virus mutations. In this chapter, we introduce a framework for analyzing and designing controllers which guarantee robust quadratic performance for switched systems with constrained switching. The possible switching sequences are described by the language of a labeled graph where the labels are linked to the different subsystems. The subsystems are allowed to have different input and output dimensions, and their state-space representations can be affected by a broad class of uncertainties in a rational way. The proposed framework exploits ideas from dissipativity-based linear control theory to derive analysis and synthesis inequalities given by linear matrix inequalities. We demonstrate how the proposed framework can be applied to the design of controllers for uncertain weakly hard real-time control systems - a system class naturally appearing in networked and real-time control.

Lennard-Jones (LJ) systems exhibit strikingly invariant structure and dynamics when energy polydispersity is introduced [Ingebrigtsen and Dyre, J. Phys. Chem. B 127, 2837 (2023)]. For instance, at a given state point the radial distribution function and the mean-square displacement as a function of time are virtually unaffected by energy polydispersity, which is in contrast to what happens when size polydispersity is introduced. We here argue - and validate by simulations of up to 30% polydispersity - that this invariance reflects an approximate invariance of the constant-potential-energy surface. $NVU$ dynamics is defined as geodesic motion at constant potential energy; because this dynamics is equivalent to Newtonian dynamics in the thermodynamic limit, the approximate invariance of the constant-potential-energy surface implies virtually the same structure and dynamics of energy polydisperse LJ systems as for the single-component version. In contrast, the constant-potential-energy surface is shown to be significantly affected by the introduction of size polydispersity.

Owing to their rapid cooling rate and hence loss-limited propagation distance, cosmic-ray electrons and positrons (CRe) at very high energies probe local cosmic-ray accelerators and provide constraints on exotic production mechanisms such as annihilation of dark matter particles. We present a high-statistics measurement of the spectrum of CRe candidate events from 0.3 to 40 TeV with the High Energy Stereoscopic System (H.E.S.S.), covering two orders of magnitude in energy and reaching a proton rejection power of better than $10^{4}$. The measured spectrum is well described by a broken power law, with a break around 1 TeV, where the spectral index increases from $\Gamma_1 = 3.25$ $\pm$ 0.02 (stat) $\pm$ 0.2 (sys) to $\Gamma_2 = 4.49$ $\pm$ 0.04 (stat) $\pm$ 0.2 (sys). Apart from the break, the spectrum is featureless. The absence of distinct signatures at multi-TeV energies imposes constraints on the presence of nearby CRe accelerators and the local CRe propagation mechanisms., Comment: main paper: 8 pages, 4 figures, supplemental material: 12 pages, 14 figures, accepted for publication in Physical Review Letters https://journals.aps.org/prl/

Dual-comb ranging has emerged as an effective technology for long-distance metrology, providing absolute distance measurements with high speed, precision, and accuracy. Here, we demonstrate a dual-comb ranging method that utilizes a free-space transceiver unit, enabling dead-zone-free measurements and simultaneous ranging with interchanged comb roles to allow for long-distance measurements even when the target is moving. It includes a GPU-accelerated algorithm for real-time signal processing and a free-running single-cavity solid-state dual-comb laser with a carrier wavelength $\lambda_c \approx$ 1055 nm, a pulse repetition rate of 1 GHz and a repetition rate difference of 5.06 kHz. This combination offers a fast update rate and sufficient signal strength to reach a single-shot time-of-flight precision of around 0.1 $\mu$m (i.e. $< \lambda_c/4$) on a cooperative target placed at a distance of more than 40 m. The free-running laser is sufficiently stable to use the phase information for interferometric distance measurements, which improves the single-shot precision to $<$20 nm. To assess the ranging accuracy, we track the motion of the cooperative target when moved over 40 m and compare it to a reference interferometer. The residuals between the two measurements are below 3 $\mu$m. These results highlight the potential of this approach for accurate and dead-zone-free long-distance ranging, supporting real-time tracking with nm-level precision., Comment: Supplementary Information included

We discuss how the two existing approximate N$^3$LO (aN$^3$LO) sets of parton distributions (PDFs) from the MSHT20 and NNPDF4.0 series can be combined for LHC phenomenology, both in the pure QCD case and for the QCD$\otimes$QED sets that include the photon PDF. Using the resulting combinations, we present predictions for the total inclusive cross-section for Higgs production in gluon fusion, vector boson fusion, and associated production at the LHC Run-3. For the gluon fusion and vector boson fusion channels, the corrections that arise when using correctly matched aN$^3$LO PDFs with N$^3$LO cross section calculations, compared to using NNLO PDFs, are significant, in many cases larger than the PDF uncertainty, and generally larger than the differences between the two aN$^3$LO PDF sets entering the combination. The combined aN$^3$LO PDF sets, MSHT20xNNPDF40_an3lo and MSHT20xNNPDF40_an3lo_qed, are made publicly available in the LHAPDF format and can be readily used for LHC phenomenology., Comment: 22 pages, 11 figures, LHAPDF grids available from https://zenodo.org/records/13843626

We study a generalisation of Vizing's theorem, where the goal is to simultaneously colour the edges of graphs $G_1,\dots,G_k$ with few colours. We obtain asymptotically optimal bounds for the required number of colours in terms of the maximum degree $\Delta$, for small values of $k$ and for an infinite sequence of values of $k$. This asymptotically settles a conjecture of Cabello for $k=2$. Moreover, we show that $\sqrt k \Delta + o(\Delta)$ colours always suffice, which tends to the optimal value as $k$ grows. We also show that $\ell \Delta + o(\Delta)$ colours are enough when every edge appears in at most $\ell$ of the graphs, which asymptotically confirms a conjecture of Cambie. Finally, our results extend to the list setting. We also find a close connection to a conjecture of F\"uredi, Kahn, and Seymour from the 1990s and an old problem about fractional matchings., Comment: 14 pages

In materials and pharmaceutical development, rapidly and accurately determining the similarity between X-ray powder diffraction (XRPD) measurements is crucial for efficient solid form screening and analysis. We present SMolNet, a classifier based on a Siamese network architecture, designed to automate the comparison of XRPD patterns. Our results show that training SMolNet on loss functions from the self-supervised learning domain yields a substantial boost in performance with respect to class separability and precision, specifically when classifying phases of previously unseen compounds. The application of SMolNet demonstrates significant improvements in screening efficiency across multiple active pharmaceutical ingredients, providing a powerful tool for scientists to discover and categorize measurements with reliable accuracy.

This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains. Practitioners commonly address early saturation with periodic reclustering, which we demonstrate to be insufficient to address performance plateaus. We call this phenomenon the "reclustering barrier" and empirically show when the reclustering barrier occurs, what its underlying mechanisms are, and how it is possible to Break the Reclustering Barrier with our algorithm BRB. BRB avoids early over-commitment to initial clusterings and enables continuous adaptation to reinitialized clustering targets while remaining conceptually simple. Applying our algorithm to widely-used centroid-based DC algorithms, we show that (1) BRB consistently improves performance across a wide range of clustering benchmarks, (2) BRB enables training from scratch, and (3) BRB performs competitively against state-of-the-art DC algorithms when combined with a contrastive loss. We release our code and pre-trained models at https://github.com/Probabilistic-and-Interactive-ML/breaking-the-reclustering-barrier .

We demonstrate that automatic differentiation, which has become commonly available in machine learning frameworks, is an efficient way to explore ideas that lead to algorithmic improvement in multi-scale affine image registration and affine super-resolution problems. In our first experiment on multi-scale registration, we implement an ODE predictor-corrector method involving a derivative with respect to the scale parameter and the Hessian of an image registration objective function, both of which would be difficult to compute without AD. Our findings indicate that exact Hessians are necessary for the method to provide any benefits over a traditional multi-scale method; a Gauss-Newton Hessian approximation fails to provide such benefits. In our second experiment, we implement a variable projected Gauss-Newton method for super-resolution and use AD to differentiate through the iteratively computed projection, a method previously unaddressed in the literature. We show that Jacobians obtained without differentiating through the projection are poor approximations to the true Jacobians of the variable projected forward map and explore the performance of some other approximations. By addressing these problems, this work contributes to the application of AD in image registration and sets a precedent for further use of machine learning tools in this field., Comment: 15 pages, 11 figures, to be submitted to SIAM Undergraduate Research Online

Data assimilation plays a crucial role in numerical modeling, enabling the integration of real-world observations into mathematical models to enhance the accuracy and predictive capabilities of simulations. This approach is widely applied in fields such as meteorology, oceanography, and environmental science, where the dynamic nature of systems demands continuous updates to model states. However, the calibration of models in these high-dimensional, nonlinear systems poses significant challenges. In this paper, we explore a novel calibration methodology using diffusion generative models. We generate synthetic data that statistically aligns with a given set of observations (in this case the increments of the numerical approximation of a solution of a partial differential equation). This allows us to efficiently implement a model reduction and assimilate data from a reference system state modeled by a highly resolved numerical solution of the rotating shallow water equation of order 104 degrees of freedom into a stochastic system having two orders of magnitude less degrees of freedom. To do so, the new samples are incorporated into a particle filtering methodology augmented with tempering and jittering for dynamic state estimation, a method particularly suited for handling complex and multimodal distributions. This work demonstrates how generative models can be used to improve the predictive accuracy for particle filters, providing a more computationally efficient solution for data assimilation and model calibration.

The double neutron star PSR J1846-0513 is discovered by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) in Commensal Radio Astronomy FAST Survey. The pulsar is revealed to be harbored in an eccentric orbit with $e=0.208$ and orbital period of 0.613 days. The total mass of the system is constrained to be $2.6287(35)\rm{M}_{\odot}$, with a mass upper limit of $1.3455{\rm~M}_{\odot}$ for the pulsar and a mass lower limit of $1.2845{\rm~M}_{\odot}$ for the companion star. To reproduce its evolution history, we perform a 1D model for the formation of PSR J1846-0513 whose progenitor is assumed to be neutron star - helium (He) star system via MESA code. Since the large eccentricity is widely believed to originate from an asymmetric supernova explosion, we also investigate the dynamical effects of the supernova explosion. Our simulated results show that the progenitor of PSR J1846-0513 could be a binary system consisting of a He star of $3.3-4.0{\rm~M}_\odot$ and a neutron star in a circular orbit with an initial period of $\sim0.5$ days., Comment: 15 pages, 7 figures, Accepted to be published on RAA

Large Language Models (LLMs) have been extensively researched and used in both academia and industry since the rise in popularity of the Transformer model, which demonstrates excellent performance in AI. However, the computational demands of LLMs are immense, and the energy resources required to run them are often limited. For instance, popular models like GPT-3, with 175 billion parameters and a storage requirement of 350 GB, present significant challenges for deployment on resource-constrained IoT devices and embedded systems. These systems often lack the computational capacity to handle such large models. Quantization, a technique that reduces the precision of model values to a smaller set of discrete values, offers a promising solution by reducing the size of LLMs and accelerating inference. In this research, we provide a comprehensive analysis of quantization techniques within the machine learning field, with a particular focus on their application to LLMs. We begin by exploring the mathematical theory of quantization, followed by a review of common quantization methods and how they are implemented. Furthermore, we examine several prominent quantization methods applied to LLMs, detailing their algorithms and performance outcomes.

Precise measurements of the electromagnetic dipole moments of charged leptons are powerful probes of physics beyond the Standard Model of particle physics. It is essential to study avenues for optimizing the measurement strategies for the anomalous magnetic dipole moment (AMDM) and the electric dipole moment (EDM) of the $\tau$-lepton which have recently become accessible at the Large Hadron Collider (LHC). In this work, we study the prospects of employing optimal observables for measuring the AMDM of the $\tau$-lepton in PbPb collisions at the LHC. The performance of optimal observables is investigated in a statistical analysis, assuming an integrated luminosity of 2.0 nb$^{-1}$, approximately corresponding to the amount of data collected during PbPb collisions in Run 2 of the LHC. Improvements of sensitivity to the $\tau$ AMDM with optimal observables are obtained only for a larger phase space than studied so far, more integrated luminosity or including the determination of the helicities of the $\tau$-leptons. Both, chances and limitations, of optimal observables for the $\tau$ AMDM determination will be important to consider in future experiments.

As an extension of isotropic Gaussian random fields and Q-Wiener processes on d-dimensional spheres, isotropic Q-fractional Brownian motion is introduced and sample H\"older regularity in space-time is shown depending on the regularity of the spatial covariance operator Q and the Hurst parameter H. The processes are approximated by a spectral method in space for which strong and almost sure convergence are shown. The underlying sample paths of fractional Brownian motion are simulated by circulant embedding or conditionalized random midpoint displacement. Temporal convergence and computational complexity are numerically tested, the latter matching the complexity of simulating a Q-Wiener process if allowing for a temporal error., Comment: 14 pages, 3 figures

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

Probabilistic Bit (P-Bit) device serves as the core hardware for implementing Ising computation. However, the severe intrinsic variations of stochastic P-Bit devices hinder the large-scale expansion of the P-Bit array, significantly limiting the practical usage of Ising computation. In this work, a behavioral model which attributes P-Bit variations to two parameters {\alpha} and {\Delta}V is proposed. Then the weight compensation method is introduced, which can mitigate {\alpha} and {\Delta}V of P-Bits device variations by rederiving the weight matrix, enabling them to compute as ideal identical PBits without the need for weights retraining. Accurately extracting the {\alpha} and {\Delta}V simultaneously from a large P-Bit array which is prerequisite for the weight compensation method is a crucial and challenging task. To solve this obstacle, we present the novel automatic variation extraction algorithm which can extract device variations of each P-Bit in a large array based on Boltzmann machine learning. In order for the accurate extraction of variations from an extendable P-Bit array, an Ising Hamiltonian based on 3D ferromagnetic model is constructed, achieving precise and scalable array variation extraction. The proposed Automatic Extraction and Compensation algorithm is utilized to solve both 16-city traveling salesman problem(TSP) and 21-bit integer factorization on a large P-Bit array with variation, demonstrating its accuracy, transferability, and scalability., Comment: 15 pages, 17 figures

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, model architecture, reward design, training strategies, and planner pipelines. These planners promise better generalizations on complicated and few-shot cases than previous methods. However, experiment results show that many of these approaches produce limited generalization abilities in planning performance due to overly complex designs or training paradigms. In this paper, we review and benchmark previous methods focusing on generalizations. The experimental results indicate that as models are appropriately scaled, many design elements become redundant. We introduce StateTransformer-2 (STR2), a scalable, decoder-only motion planner that uses a Vision Transformer (ViT) encoder and a mixture-of-experts (MoE) causal Transformer architecture. The MoE backbone addresses modality collapse and reward balancing by expert routing during training. Extensive experiments on the NuPlan dataset show that our method generalizes better than previous approaches across different test sets and closed-loop simulations. Furthermore, we assess its scalability on billions of real-world urban driving scenarios, demonstrating consistent accuracy improvements as both data and model size grow., Comment: 7 pages, 3 figures

This paper introduces the Minimal Biorobotic Stealth Distance (MBSD), a novel quantitative metric to evaluate the bionic resemblance of biorobotic aircraft. Current technological limitations prevent dragonfly-inspired aircrafts from achieving optimal performance at biological scales. To address these challenges, we use the DDD-1 dragonfly-inspired aircraft, a hover-capable direct-drive aircraft, to explore the impact of the MBSD on aircraft design. Key contributions of this research include: (1) the establishment of the MBSD as a quantifiable and operable evaluation metric that influences aircraft design, integrating seamlessly with the overall design process and providing a new dimension for optimizing bionic aircraft, balancing mechanical attributes and bionic characteristics; (2) the creation and analysis of a typical aircraft in four directions: essential characteristics of the MBSD, its coupling relationship with existing performance metrics (Longest Hover Duration and Maximum Instantaneous Forward Flight Speed), multi-objective optimization, and application in a typical mission scenario; (3) the construction and validation of a full-system model for the direct-drive dragonfly-inspired aircraft, demonstrating the design model's effectiveness against existing aircraft data. Detailed calculations of the MBSD consider appearance similarity, dynamic similarity, and environmental similarity., Comment: 61 pages, 32 figures

We propose and demonstrate experimentally continuous phased dynamical decoupling (CPDD), where we apply a continuous field with discrete phase changes for quantum sensing and robust compensation of environmental and amplitude noise. CPDD does not use short pulses, making it particularly suitable for experiments with limited driving power or nuclear magnetic resonance at high magnetic fields. It requires control of the timing of the phase changes, offering much greater precision than the Rabi frequency control needed in standard continuous sensing schemes. We successfully apply our method to nanoscale nuclear magnetic resonance and combine it with quantum heterodyne detection, achieving $\mu$Hz uncertainty in the estimated signal frequency for a 120 s measurement. Our work expands significantly the applicability of dynamical decoupling and opens the door for a wide range of experiments, e.g., in Nitrogen-Vacancy centers, trapped ions or trapped atoms., Comment: The main text includes 4 figures, 6 pages. The supplemental material includes 6 more figures and provides more details on the theory, numerical simulation, as well as additional experimental results

The k-means clustering algorithm is a popular algorithm that partitions data into k clusters. There are many improvements to accelerate the standard algorithm. Most current research employs upper and lower bounds on point-to-cluster distances and the triangle inequality to reduce the number of distance computations, with only arrays as underlying data structures. These approaches cannot exploit that nearby points are likely assigned to the same cluster. We propose a new k-means algorithm based on the cover tree index, that has relatively low overhead and performs well, for a wider parameter range, than previous approaches based on the k-d tree. By combining this with upper and lower bounds, as in state-of-the-art approaches, we obtain a hybrid algorithm that combines the benefits of tree aggregation and bounds-based filtering.

Multimodal Large Language Models (MLLMs) have demonstrated impressive abilities across various tasks, including visual question answering and chart comprehension, yet existing benchmarks for chart-related tasks fall short in capturing the complexity of real-world multi-chart scenarios. Current benchmarks primarily focus on single-chart tasks, neglecting the multi-hop reasoning required to extract and integrate information from multiple charts, which is essential in practical applications. To fill this gap, we introduce MultiChartQA, a benchmark that evaluates MLLMs' capabilities in four key areas: direct question answering, parallel question answering, comparative reasoning, and sequential reasoning. Our evaluation of a wide range of MLLMs reveals significant performance gaps compared to humans. These results highlight the challenges in multi-chart comprehension and the potential of MultiChartQA to drive advancements in this field. Our code and data are available at https://github.com/Zivenzhu/Multi-chart-QA, Comment: 18 pages, 9 figures