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Data-driven scientific discovery requires the iterative integration of scientific domain knowledge, statistical expertise, and an understanding of data semantics to make nuanced analytical decisions, e.g., about which variables, transformations, and statistical models to consider. LM-based agents equipped with planning, memory, and code execution capabilities have the potential to support data-driven science. However, evaluating agents on such open-ended tasks is challenging due to multiple valid approaches, partially correct steps, and different ways to express the same decisions. To address these challenges, we present BLADE, a benchmark to automatically evaluate agents' multifaceted approaches to open-ended research questions. BLADE consists of 12 datasets and research questions drawn from existing scientific literature, with ground truth collected from independent analyses by expert data scientists and researchers. To automatically evaluate agent responses, we developed corresponding computational methods to match different representations of analyses to this ground truth. Though language models possess considerable world knowledge, our evaluation shows that they are often limited to basic analyses. However, agents capable of interacting with the underlying data demonstrate improved, but still non-optimal, diversity in their analytical decision making. Our work enables the evaluation of agents for data-driven science and provides researchers deeper insights into agents' analysis approaches.

Dense matching is crucial for 3D scene reconstruction since it enables the recovery of scene 3D geometry from image acquisition. Deep Learning (DL)-based methods have shown effectiveness in the special case of epipolar stereo disparity estimation in the computer vision community. DL-based methods depend heavily on the quality and quantity of training datasets. However, generating ground-truth disparity maps for real scenes remains a challenging task in the photogrammetry community. To address this challenge, we propose a method for generating ground-truth disparity maps directly from Light Detection and Ranging (LiDAR) and images to produce a large and diverse dataset for six aerial datasets across four different areas and two areas with different resolution images. We also introduce a LiDAR-to-image co-registration refinement to the framework that takes special precautions regarding occlusions and refrains from disparity interpolation to avoid precision loss. Evaluating 11 dense matching methods across datasets with diverse scene types, image resolutions, and geometric configurations, which are deeply investigated in dataset shift, GANet performs best with identical training and testing data, and PSMNet shows robustness across different datasets, and we proposed the best strategy for training with a limit dataset. We will also provide the dataset and training models; more information can be found at https://github.com/whuwuteng/Aerial_Stereo_Dataset.

Searching for exotic spin-dependent interactions that beyond the standard model has been of interest for past decades and is crucial for unraveling the mysteries of the universe. Previous laboratory searches primarily focus on searching for either static or modulated energy-level shifts caused by exotic spin-dependent interactions. Here, we introduce a theoretical model based on thermal motion of particles, providing another efficient way to search for exotic spin-dependent interactions. The theoretical model indicates that as the exotic spin-dependent interactions are related with the relative displacements and velocities of atoms, atoms undergoing thermal motion would experience a fluctuating energy-level shift induced by the exotic interactions. Moreover, the resulting exotic energy-level shift noise could be sensed by high-sensitivity instruments. By using the model and taking the high-sensitivity atomic magnetometer as an example, we set the most stringent laboratory experiment constraints on eight different kinds of exotic spin- and velocity-dependent interactions, with five of which at the force range below 1 cm have not been covered previously. Furthermore, this theoretical model can be easily applied in other fields of quantum sensing, such as atomic clocks, atom interferometers and NV-diamond sensors, to further improve the laboratory constraints on exotic spin-dependent interactions.

We propose a bootstrap-based test to detect a mean shift in a sequence of high-dimensional observations with unknown time-varying heteroscedasticity. The proposed test builds on the U-statistic based approach in Wang et al. (2022), targets a dense alternative, and adopts a wild bootstrap procedure to generate critical values. The bootstrap-based test is free of tuning parameters and is capable of accommodating unconditional time varying heteroscedasticity in the high-dimensional observations, as demonstrated in our theory and simulations. Theoretically, we justify the bootstrap consistency by using the recently proposed unconditional approach in Bucher and Kojadinovic (2019). Extensions to testing for multiple change-points and estimation using wild binary segmentation are also presented. Numerical simulations demonstrate the robustness of the proposed testing and estimation procedures with respect to different kinds of time-varying heteroscedasticity., Comment: Accepted by Electronic Journal of Statistics

Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications: visualization, simulation, navigation, etc. Because of the highly dynamic nature of these urban scenes, long term mapping should rely on frequent map updates. A trivial solution is to simply replace old data with newer data each time a new acquisition is made. However it has two drawbacks: 1) the old data may be of higher quality (resolution, precision) than the new and 2) the coverage of the scene might be different in various acquisitions, including varying occlusions. In this paper, we propose a fully automatic pipeline to address these two issues by formulating the problem of merging meshes with different quality, coverage and acquisition time. Our method is based on a combined distance and visibility based change detection, a time series analysis to assess the sustainability of changes, a mesh mosaicking based on a global boolean optimization and finally a stitching of the resulting mesh pieces boundaries with triangle strips. Finally, our method is demonstrated on Robotcar and Stereopolis datasets., Comment: 6 pages without reference

Ultralight bosons such as axion-like particles are viable candidates for dark matter. They can form stable, macroscopic field configurations in the form of topological defects that could concentrate the dark matter density into many distinct, compact spatial regions that are small compared to the galaxy but much larger than the Earth. Here, we report the results of a search for transient signals from axion-like particle domain walls with the Global Network of Optical Magnetometers for Exotic physics searches (GNOME). We search the data, consisting of correlated measurements from optical atomic magnetometers located in laboratories all over the world, for patterns of signals propagating through the network consistent with domain walls. The analysis of data from a continuous month-long operation of the GNOME finds no statistically significant signals, thus placing experimental constraints on such dark matter scenarios., Comment: 25 pages, 6 figures, 2 tables

In this paper, we propose a class of monitoring statistics for a mean shift in a sequence of high-dimensional observations. Inspired by the recent U-statistic based retrospective tests developed by Wang et al.(2019) and Zhang et al.(2020), we advance the U-statistic based approach to the sequential monitoring problem by developing a new adaptive monitoring procedure that can detect both dense and sparse changes in real-time. Unlike Wang et al.(2019) and Zhang et al.(2020), where self-normalization was used in their tests, we instead introduce a class of estimators for $q$-norm of the covariance matrix and prove their ratio consistency. To facilitate fast computation, we further develop recursive algorithms to improve the computational efficiency of the monitoring procedure. The advantage of the proposed methodology is demonstrated via simulation studies and real data illustrations.

We report the results of an experimental search for ultralight axion-like dark matter in the mass range 162 neV to 166 neV. The detection scheme of our Cosmic Axion Spin Precession Experiment (CASPEr) is based on a precision measurement of $^{207}$Pb solid-state nuclear magnetic resonance in a polarized ferroelectric crystal. Axion-like dark matter can exert an oscillating torque on $^{207}$Pb nuclear spins via the electric-dipole moment coupling $g_d$, or via the gradient coupling $g_{\text{aNN}}$. We calibrated the detector and characterized the excitation spectrum and relaxation parameters of the nuclear spin ensemble with pulsed magnetic resonance measurements in a 4.4 T magnetic field. We swept the magnetic field near this value and searched for axion-like dark matter with Compton frequency within a 1 MHz band centered at 39.65 MHz. Our measurements place the upper bounds $|g_d|<9.5\times10^{-4}\,\text{GeV}^{-2}$ and $|g_{\text{aNN}}|<2.8\times10^{-1}\,\text{GeV}^{-1}$ (95% confidence level) in this frequency range. The constraint on $g_d$ corresponds to an upper bound of $1.0\times 10^{-21}\,\text{e}\cdot\text{cm}$ on the amplitude of oscillations of the neutron electric dipole moment, and $4.3\times 10^{-6}$ on the amplitude of oscillations of CP-violating $\theta$ parameter of quantum chromodynamics. Our results demonstrate the feasibility of using solid-state nuclear magnetic resonance to search for axion-like dark matter in the nano-electronvolt mass range.

To support building permit issuing with automatic digital tools, the reuse of models produced by designers would make the process quicker and more objective. However, current studies and pilots often leave a gap with respect to the models as actually provided by architects, having varying quality and content. In this study, rather than taking a top down approach, we started from the available data and made the necessary inferences, which gave the opportunity to tackle basic and common issues often preventing smooth automatic processing. Specific characteristics of the IFC models were outlined and a tool was developed to extract the necessary information from them to check representative regulations. While the case study is specific in location, regulations and input models, the type of issues encountered are a generally applicable example for automated code compliance checking. This represents a solid base for future works towards the automation of building permits issuing., Comment: Author version of the manuscript, published by Automation in construction

Atomic comagnetometers, which measure the spin precession frequencies of overlapped species simultaneously, are widely applied to search for exotic spin-dependent interactions. Here we propose and implement an all-optical single-species Cs atomic comagnetometer based on the optical free induction decay (FID) signal of Cs atoms in hyperfine levels $F_g=3~\&~4$ within the same atomic ensemble. We experimentally show that systematic errors induced by magnetic field gradients and laser fields are highly suppressed in the comagnetometer, but those induced by asynchronous optical pumping and drift of residual magnetic field in the shield dominate the uncertainty of the comagnetometer. With this comagnetometer system, we set the constraint on the strength of spin-gravity coupling of the proton at a level of $10^{-18}$ eV, comparable to the most stringent one. With further optimization in magnetic field stabilization and spin polarization, the systematic errors can be effectively suppressed, and signal-to-noise ratio (SNR) can be improved, promising to set more stringent constraints on spin-gravity interactions.

Zero- to ultralow-field nuclear magnetic resonance (ZULF NMR) is an alternative spectroscopic method to high-field NMR, in which samples are studied in the absence of a large magnetic field. Unfortunately, there is a large barrier to entry for many groups, because operating the optical magnetometers needed for signal detection requires some expertise in atomic physics and optics. Commercially available magnetometers offer a solution to this problem. Here we describe a simple ZULF NMR configuration employing commercial magnetometers, and demonstrate sufficient functionality to measure samples with nuclear spins prepolarized in a permanent magnet or initialized using parahydrogen. This opens the possibility for other groups to use ZULF NMR, which provides a means to study complex materials without magnetic susceptibility-induced line broadening, and to observe samples through conductive materials., Comment: 6 pages, 5 figures

The nature of dark matter, the invisible substance making up over $80\%$ of the matter in the Universe, is one of the most fundamental mysteries of modern physics. Ultralight bosons such as axions, axion-like particles or dark photons could make up most of the dark matter. Couplings between such bosons and nuclear spins may enable their direct detection via nuclear magnetic resonance (NMR) spectroscopy: as nuclear spins move through the galactic dark-matter halo, they couple to dark-matter and behave as if they were in an oscillating magnetic field, generating a dark-matter-driven NMR signal. As part of the Cosmic Axion Spin Precession Experiment (CASPEr), an NMR-based dark-matter search, we use ultralow-field NMR to probe the axion-fermion "wind" coupling and dark-photon couplings to nuclear spins. No dark matter signal was detected above background, establishing new experimental bounds for dark-matter bosons with masses ranging from $1.8\times 10^{-16}$ to $7.8\times 10^{-14}$ eV., Comment: 10 pages, 5 figures

We report the results of a search for axionlike dark matter using nuclear magnetic resonance (NMR) techniques. This search is part of the multi-faceted Cosmic Axion Spin Precession Experiment (CASPEr) program. In order to distinguish axionlike dark matter from magnetic fields, we employ a comagnetometry scheme measuring ultralow-field NMR signals involving two different nuclei ($^{13}$C and $^{1}$H) in a liquid-state sample of acetonitrile-2-$^{13}$C ($^{13}$CH$_{3}$CN). No axionlike dark matter signal was detected above background. This result constrains the parameter space describing the coupling of the gradient of the axionlike dark matter field to nucleons to be $g_{aNN}<6\times 10^{-5}$ GeV$^{-1}$ (95$\%$ confidence level) for particle masses ranging from $10^{-22}$ eV to $1.3\times10^{-17}$ eV, improving over previous laboratory limits for masses below $10^{-21}$ eV. The result also constrains the coupling of nuclear spins to the gradient of the square of the axionlike dark matter field, improving over astrophysical limits by orders of magnitude over the entire range of particle masses probed.